diff --git a/README.md b/README.md index f2ca12a..a4874ba 100644 --- a/README.md +++ b/README.md @@ -45,6 +45,7 @@ Atlas Librarian is a modular system designed to process, organize, and make sear ``` atlas/ +|-- lexikon/ # Frontend app ├── librarian/ │ ├── atlas-librarian/ # Main application │ ├── librarian-core/ # Core functionality and storage diff --git a/lexikon/.env.local.example b/lexikon/.env.local.example new file mode 100644 index 0000000..00611c8 --- /dev/null +++ b/lexikon/.env.local.example @@ -0,0 +1,3 @@ +NEXT_PUBLIC_SUPABASE_URL=your-project-url +NEXT_PUBLIC_SUPABASE_ANON_KEY=your-anon-key +SUPABASE_SERVICE_ROLE_KEY=your-service-role-key diff --git a/lexikon/.gitignore b/lexikon/.gitignore new file mode 100644 index 0000000..a93f45c --- /dev/null +++ b/lexikon/.gitignore @@ -0,0 +1,44 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.js + +# lockfiles +package-lock.json + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# local env files +.env*.local +.env + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts + + +# other +.github +# _legacy diff --git a/lexikon/.husky/pre-commit b/lexikon/.husky/pre-commit new file mode 100644 index 0000000..e69de29 diff --git a/lexikon/.npmrc b/lexikon/.npmrc new file mode 100644 index 0000000..92ebc2c --- /dev/null +++ b/lexikon/.npmrc @@ -0,0 +1,2 @@ +public-hoist-pattern[]=*@heroui/* +package-lock=true diff --git a/lexikon/LICENSE b/lexikon/LICENSE new file mode 100644 index 0000000..7f91f84 --- /dev/null +++ b/lexikon/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Next UI + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/lexikon/eslint.config.mjs b/lexikon/eslint.config.mjs new file mode 100644 index 0000000..739e7b1 --- /dev/null +++ b/lexikon/eslint.config.mjs @@ -0,0 +1,156 @@ +import path from "node:path"; +import { fileURLToPath } from "node:url"; + +import { fixupConfigRules, fixupPluginRules } from "@eslint/compat"; +import typescriptEslint from "@typescript-eslint/eslint-plugin"; +import tsParser from "@typescript-eslint/parser"; +import _import from "eslint-plugin-import"; +import jsxA11Y from "eslint-plugin-jsx-a11y"; +import prettier from "eslint-plugin-prettier"; +import react from "eslint-plugin-react"; +import unusedImports from "eslint-plugin-unused-imports"; +import { defineConfig, globalIgnores } from "eslint/config"; +import globals from "globals"; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = path.dirname(__filename); +const compat = new FlatCompat({ + baseDirectory: __dirname, + recommendedConfig: js.configs.recommended, + allConfig: js.configs.all, +}); + +export default defineConfig([ + globalIgnores([ + ".next/**", + "node_modules/**", + "out/**", + "dist/**", + "**/index.ts", + "**/index.d.ts", + ]), + { + extends: fixupConfigRules( + compat.extends( + "plugin:react/recommended", + "plugin:prettier/recommended", + "plugin:react-hooks/recommended", + "plugin:jsx-a11y/recommended", + "plugin:@next/next/recommended" + ) + ), + + plugins: { + react: fixupPluginRules(react), + "unused-imports": unusedImports, + import: fixupPluginRules(_import), + "@typescript-eslint": typescriptEslint, + "jsx-a11y": fixupPluginRules(jsxA11Y), + prettier: fixupPluginRules(prettier), + }, + + languageOptions: { + globals: { + ...Object.fromEntries( + Object.entries(globals.browser).map(([key]) => [key, "off"]) + ), + ...globals.node, + }, + + parser: tsParser, + ecmaVersion: 12, + sourceType: "module", + + parserOptions: { + ecmaFeatures: { + jsx: true, + }, + }, + }, + + settings: { + react: { + version: "detect", + }, + }, + + rules: { + "no-console": "warn", + "react/prop-types": "off", + "react/jsx-uses-react": "off", + "react/react-in-jsx-scope": "off", + "react-hooks/exhaustive-deps": "off", + "@next/next/no-assign-module-variable": "off", + "jsx-a11y/click-events-have-key-events": "warn", + "jsx-a11y/interactive-supports-focus": "warn", + "prettier/prettier": "warn", + + // Disable the built‑in unused vars rules: + "no-unused-vars": "off", + "@typescript-eslint/no-unused-vars": "off", + + // Enable unused imports rule: + "unused-imports/no-unused-imports": "error", + + // (Optional) If you want to also flag unused variables (excluding imports) + // you can use the plugin’s no-unused-vars rule: + // "unused-imports/no-unused-vars": [ + // "warn", + // { vars: "all", varsIgnorePattern: "^_", args: "after-used", argsIgnorePattern: "^_" } + // ], + + "import/order": [ + "warn", + { + groups: [ + "type", + "builtin", + "object", + "external", + "internal", + "parent", + "sibling", + "index", + ], + pathGroups: [ + { + pattern: "~/**", + group: "external", + position: "after", + }, + ], + "newlines-between": "always", + }, + ], + "react/self-closing-comp": "warn", + "react/jsx-sort-props": [ + "warn", + { + callbacksLast: true, + shorthandFirst: true, + noSortAlphabetically: false, + reservedFirst: true, + }, + ], + "padding-line-between-statements": [ + "warn", + { + blankLine: "always", + prev: "*", + next: "return", + }, + { + blankLine: "always", + prev: ["const", "let", "var"], + next: "*", + }, + { + blankLine: "any", + prev: ["const", "let", "var"], + next: ["const", "let", "var"], + }, + ], + "react/no-unknown-property": "warn", + }, + }, +]); diff --git a/lexikon/next.config.mjs b/lexikon/next.config.mjs new file mode 100644 index 0000000..093a0b7 --- /dev/null +++ b/lexikon/next.config.mjs @@ -0,0 +1,33 @@ +import createMDX from "@next/mdx"; + +/** @type {import('next').NextConfig} */ +const nextConfig = { + // Configure `pageExtensions` to include markdown and MDX files + pageExtensions: ["js", "jsx", "md", "mdx", "ts", "tsx"], + // Optionally, add any other Next.js config below + reactStrictMode: true, + allowedDevOrigins: ["*", "os-alpha.local"], + images: { + remotePatterns: [ + { + protocol: "https", + hostname: "moodle.fhgr.ch", + port: "", + pathname: "/**", + }, + ], + }, +}; + +const withMDX = createMDX({ + options: { + remarkPlugins: [ + ["remark-gfm", { strict: true, singleTilde: true }], + ["remark-math", { strict: true }], + ], + rehypePlugins: [["rehype-katex", { strict: true, throwOnError: true }]], + }, +}); + +// Merge MDX config with Next.js config +export default withMDX(nextConfig); diff --git a/lexikon/package.json b/lexikon/package.json new file mode 100644 index 0000000..8a3f4f5 --- /dev/null +++ b/lexikon/package.json @@ -0,0 +1,123 @@ +{ + "name": "lexikon", + "version": "0.0.1", + "private": true, + "type": "module", + "scripts": { + "dev": "next dev --turbopack -H 0.0.0.0 -p 3000", + "build": "next build --turbopack", + "start": "next start", + "lint": "next lint", + "prepare": "husky" + }, + "dependencies": { + "@codemirror/autocomplete": "^6.18.6", + "@codemirror/lang-javascript": "^6.2.3", + "@codemirror/lang-markdown": "^6.3.2", + "@codemirror/lang-python": "^6.1.7", + "@codemirror/theme-one-dark": "^6.1.2", + "@codemirror/view": "^6.36.5", + "@eslint/eslintrc": "^3.3.1", + "@heroui/button": "2.2.18-beta.2", + "@heroui/code": "2.2.14-beta.2", + "@heroui/input": "2.4.18-beta.2", + "@heroui/kbd": "2.2.14-beta.2", + "@heroui/link": "2.2.15-beta.2", + "@heroui/listbox": "2.3.17-beta.2", + "@heroui/navbar": "2.2.16-beta.2", + "@heroui/react": "2.8.0-beta.2", + "@heroui/snippet": "2.2.19-beta.2", + "@heroui/switch": "2.2.16-beta.2", + "@heroui/system": "2.4.14-beta.2", + "@heroui/theme": "2.4.14-beta.2", + "@mdx-js/loader": "^3.1.0", + "@mdx-js/react": "^3.1.0", + "@monaco-editor/react": "^4.7.0", + "@next/mdx": "^15.3.1", + "@react-aria/ssr": "3.9.8", + "@react-aria/visually-hidden": "3.8.22", + "@react-three/drei": "^10.0.6", + "@react-three/fiber": "^9.1.2", + "@react-types/shared": "3.29.0", + "@supabase/ssr": "^0.6.1", + "@supabase/supabase-js": "^2.49.4", + "@tailwindcss/postcss": "^4.1.4", + "@tanstack/react-query": "^5.75.5", + "@types/codemirror": "^5.60.15", + "@types/three": "^0.175.0", + "@typescript-eslint/typescript-estree": "^8.30.1", + "@uiw/codemirror-themes-all": "^4.23.10", + "@uiw/react-codemirror": "^4.23.10", + "clsx": "2.1.1", + "framer-motion": "^12.9.2", + "globals": "^16.0.0", + "intl-messageformat": "^10.7.16", + "katex": "^0.16.22", + "lucide-react": "^0.501.0", + "markdown-to-jsx": "^7.7.4", + "ml-kmeans": "^6.0.0", + "monaco-editor": "^0.52.2", + "motion": "^12.7.4", + "next": "^15.3.1", + "next-themes": "^0.4.6", + "react": "19.1.0", + "react-dom": "19.1.0", + "react-markdown": "^10.1.0", + "react-monaco-editor": "^0.58.0", + "react-swipeable": "^7.0.2", + "react-syntax-highlighter": "^15.6.1", + "react-three-fiber": "^6.0.13", + "react-use-measure": "^2.1.7", + "rehype-katex": "^7.0.1", + "rehype-sanitize": "^6.0.0", + "rehype-slug": "^6.0.0", + "remark-gfm": "^4.0.1", + "remark-math": "^6.0.0", + "swr": "^2.3.3", + "tailwind-merge": "^3.2.0", + "tailwind-variants": "1.0.0", + "tailwindcss": "4.1.4", + "three": "^0.175.0", + "three-stdlib": "^2.36.0", + "zustand": "^5.0.3" + }, + "devDependencies": { + "@eslint/compat": "^1.2.8", + "@next/eslint-plugin-next": "^15.3.1", + "@react-types/shared": "^3.28.0", + "@tailwindcss/postcss": "^4.0.14", + "@types/mdx": "^2.0.13", + "@types/node": "22.14.1", + "@types/react": "^19.1.2", + "@types/react-dom": "^19.1.2", + "@types/react-syntax-highlighter": "^15.5.13", + "@typescript-eslint/eslint-plugin": "8.30.1", + "@typescript-eslint/parser": "8.30.1", + "eslint": "^9.25.0", + "eslint-config-prettier": "10.1.2", + "eslint-plugin-import": "^2.31.0", + "eslint-plugin-jsx-a11y": "^6.10.2", + "eslint-plugin-node": "^11.1.0", + "eslint-plugin-prettier": "5.2.6", + "eslint-plugin-react": "^7.37.5", + "eslint-plugin-react-hooks": "^5.2.0", + "eslint-plugin-unused-imports": "4.1.4", + "husky": "^9.1.7", + "lint-staged": "^15.5.1", + "postcss": "^8.5.3", + "prettier": "^3.5.3", + "raw-loader": "^4.0.2", + "tailwindcss": "4.0.14", + "typescript": "5.8.3" + }, + "pnpm": { + "onlyBuiltDependencies": [ + "core-js" + ], + "overrides": { + "highlight.js@>=9.0.0 <10.4.1": ">=10.4.1", + "highlight.js@<9.18.2": ">=9.18.2" + } + }, + "packageManager": "pnpm@10.12.1+sha512.f0dda8580f0ee9481c5c79a1d927b9164f2c478e90992ad268bbb2465a736984391d6333d2c327913578b2804af33474ca554ba29c04a8b13060a717675ae3ac" +} diff --git a/lexikon/pnpm-lock.yaml b/lexikon/pnpm-lock.yaml new file mode 100644 index 0000000..22ed833 --- /dev/null +++ b/lexikon/pnpm-lock.yaml @@ -0,0 +1,11440 @@ +lockfileVersion: '9.0' + +settings: + autoInstallPeers: true + excludeLinksFromLockfile: false + +overrides: + highlight.js@>=9.0.0 <10.4.1: '>=10.4.1' + highlight.js@<9.18.2: '>=9.18.2' + +importers: + + .: + dependencies: + '@codemirror/autocomplete': + specifier: ^6.18.6 + version: 6.18.6 + '@codemirror/lang-javascript': + specifier: ^6.2.3 + version: 6.2.3 + '@codemirror/lang-markdown': + specifier: ^6.3.2 + version: 6.3.2 + '@codemirror/lang-python': + specifier: ^6.1.7 + version: 6.1.7 + '@codemirror/theme-one-dark': + specifier: ^6.1.2 + version: 6.1.2 + '@codemirror/view': + specifier: ^6.36.5 + version: 6.36.5 + '@eslint/eslintrc': + specifier: ^3.3.1 + version: 3.3.1 + '@heroui/button': + specifier: 2.2.18-beta.2 + version: 2.2.18-beta.2(@heroui/system@2.4.14-beta.2(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@heroui/code': + specifier: 2.2.14-beta.2 + version: 2.2.14-beta.2(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@heroui/input': + specifier: 2.4.18-beta.2 + version: 2.4.18-beta.2(@heroui/system@2.4.14-beta.2(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(@types/react@19.1.2)(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@heroui/kbd': + specifier: 2.2.14-beta.2 + version: 2.2.14-beta.2(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@heroui/link': + specifier: 2.2.15-beta.2 + version: 2.2.15-beta.2(@heroui/system@2.4.14-beta.2(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@heroui/listbox': + specifier: 2.3.17-beta.2 + version: 2.3.17-beta.2(@heroui/system@2.4.14-beta.2(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@heroui/navbar': + specifier: 2.2.16-beta.2 + version: 2.2.16-beta.2(@heroui/system@2.4.14-beta.2(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@heroui/react': + specifier: 2.8.0-beta.2 + version: 2.8.0-beta.2(@types/react@19.1.2)(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0)(tailwindcss@4.1.4) + '@heroui/snippet': + specifier: 2.2.19-beta.2 + version: 2.2.19-beta.2(@heroui/system@2.4.14-beta.2(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@heroui/switch': + specifier: 2.2.16-beta.2 + version: 2.2.16-beta.2(@heroui/system@2.4.14-beta.2(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@heroui/system': + specifier: 2.4.14-beta.2 + version: 2.4.14-beta.2(@heroui/theme@2.4.14-beta.2(tailwindcss@4.1.4))(framer-motion@12.9.2(react-dom@19.1.0(react@19.1.0))(react@19.1.0))(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@heroui/theme': + specifier: 2.4.14-beta.2 + version: 2.4.14-beta.2(tailwindcss@4.1.4) + '@mdx-js/loader': + specifier: ^3.1.0 + version: 3.1.0(acorn@8.14.1)(webpack@5.98.0) + '@mdx-js/react': + specifier: ^3.1.0 + version: 3.1.0(@types/react@19.1.2)(react@19.1.0) + '@monaco-editor/react': + specifier: ^4.7.0 + version: 4.7.0(monaco-editor@0.52.2)(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@next/mdx': + specifier: ^15.3.1 + version: 15.3.1(@mdx-js/loader@3.1.0(acorn@8.14.1)(webpack@5.98.0))(@mdx-js/react@3.1.0(@types/react@19.1.2)(react@19.1.0)) + '@react-aria/ssr': + specifier: 3.9.8 + version: 3.9.8(react@19.1.0) + '@react-aria/visually-hidden': + specifier: 3.8.22 + version: 3.8.22(react-dom@19.1.0(react@19.1.0))(react@19.1.0) + '@react-three/drei': + specifier: ^10.0.6 + version: 10.0.6(@react-three/fiber@9.1.2(@types/react@19.1.2)(react-dom@19.1.0(react@19.1.0))(react@19.1.0)(three@0.175.0))(@types/react@19.1.2)(@types/three@0.175.0)(react-dom@19.1.0(react@19.1.0))(react@19.1.0)(three@0.175.0) + 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+ scheduler@0.26.0: {} + + schema-utils@3.3.0: + dependencies: + '@types/json-schema': 7.0.15 + ajv: 6.12.6 + ajv-keywords: 3.5.2(ajv@6.12.6) + + schema-utils@4.3.2: + dependencies: + '@types/json-schema': 7.0.15 + ajv: 8.17.1 + ajv-formats: 2.1.1(ajv@8.17.1) + ajv-keywords: 5.1.0(ajv@8.17.1) + + scroll-into-view-if-needed@3.0.10: + dependencies: + compute-scroll-into-view: 3.1.1 + + semver@6.3.1: {} + + semver@7.7.1: {} + + serialize-javascript@6.0.2: + dependencies: + randombytes: 2.1.0 + + set-function-length@1.2.2: + dependencies: + define-data-property: 1.1.4 + es-errors: 1.3.0 + function-bind: 1.1.2 + get-intrinsic: 1.3.0 + gopd: 1.2.0 + has-property-descriptors: 1.0.2 + + set-function-name@2.0.2: + dependencies: + define-data-property: 1.1.4 + es-errors: 1.3.0 + functions-have-names: 1.2.3 + has-property-descriptors: 1.0.2 + + set-proto@1.0.0: + dependencies: + dunder-proto: 1.0.1 + es-errors: 1.3.0 + es-object-atoms: 1.1.1 + + sharp@0.34.1: + dependencies: + color: 4.2.3 + detect-libc: 2.0.3 + semver: 7.7.1 + optionalDependencies: + '@img/sharp-darwin-arm64': 0.34.1 + '@img/sharp-darwin-x64': 0.34.1 + '@img/sharp-libvips-darwin-arm64': 1.1.0 + '@img/sharp-libvips-darwin-x64': 1.1.0 + '@img/sharp-libvips-linux-arm': 1.1.0 + '@img/sharp-libvips-linux-arm64': 1.1.0 + '@img/sharp-libvips-linux-ppc64': 1.1.0 + '@img/sharp-libvips-linux-s390x': 1.1.0 + '@img/sharp-libvips-linux-x64': 1.1.0 + '@img/sharp-libvips-linuxmusl-arm64': 1.1.0 + '@img/sharp-libvips-linuxmusl-x64': 1.1.0 + '@img/sharp-linux-arm': 0.34.1 + '@img/sharp-linux-arm64': 0.34.1 + '@img/sharp-linux-s390x': 0.34.1 + '@img/sharp-linux-x64': 0.34.1 + '@img/sharp-linuxmusl-arm64': 0.34.1 + '@img/sharp-linuxmusl-x64': 0.34.1 + '@img/sharp-wasm32': 0.34.1 + '@img/sharp-win32-ia32': 0.34.1 + '@img/sharp-win32-x64': 0.34.1 + optional: true + + shebang-command@2.0.0: + dependencies: + shebang-regex: 3.0.0 + + shebang-regex@3.0.0: {} + + side-channel-list@1.0.0: + dependencies: + es-errors: 1.3.0 + 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state-local@1.0.7: {} + + stats-gl@2.4.2(@types/three@0.175.0)(three@0.175.0): + dependencies: + '@types/three': 0.175.0 + three: 0.175.0 + + stats.js@0.17.0: {} + + streamsearch@1.1.0: {} + + string-argv@0.3.2: {} + + string-width@7.2.0: + dependencies: + emoji-regex: 10.4.0 + get-east-asian-width: 1.3.0 + strip-ansi: 7.1.0 + + string.prototype.includes@2.0.1: + dependencies: + call-bind: 1.0.8 + define-properties: 1.2.1 + es-abstract: 1.23.9 + + string.prototype.matchall@4.0.12: + dependencies: + call-bind: 1.0.8 + call-bound: 1.0.4 + define-properties: 1.2.1 + es-abstract: 1.23.9 + es-errors: 1.3.0 + es-object-atoms: 1.1.1 + get-intrinsic: 1.3.0 + gopd: 1.2.0 + has-symbols: 1.1.0 + internal-slot: 1.1.0 + regexp.prototype.flags: 1.5.4 + set-function-name: 2.0.2 + side-channel: 1.1.0 + + string.prototype.repeat@1.0.0: + dependencies: + define-properties: 1.2.1 + es-abstract: 1.23.9 + + string.prototype.trim@1.2.10: + dependencies: + call-bind: 1.0.8 + call-bound: 1.0.4 + define-data-property: 1.1.4 + define-properties: 1.2.1 + es-abstract: 1.23.9 + es-object-atoms: 1.1.1 + has-property-descriptors: 1.0.2 + + string.prototype.trimend@1.0.9: + dependencies: + call-bind: 1.0.8 + call-bound: 1.0.4 + define-properties: 1.2.1 + es-object-atoms: 1.1.1 + + string.prototype.trimstart@1.0.8: + dependencies: + call-bind: 1.0.8 + define-properties: 1.2.1 + es-object-atoms: 1.1.1 + + stringify-entities@4.0.4: + dependencies: + character-entities-html4: 2.1.0 + character-entities-legacy: 3.0.0 + + strip-ansi@7.1.0: + dependencies: + ansi-regex: 6.1.0 + + strip-bom@3.0.0: {} + + strip-final-newline@3.0.0: {} + + strip-json-comments@3.1.1: {} + + style-mod@4.1.2: {} + + style-to-js@1.1.16: + dependencies: + style-to-object: 1.0.8 + + style-to-object@1.0.8: + dependencies: + inline-style-parser: 0.2.4 + + styled-jsx@5.1.6(react@19.1.0): + dependencies: + client-only: 0.0.1 + react: 19.1.0 + + supports-color@7.2.0: + dependencies: + has-flag: 4.0.0 + + supports-color@8.1.1: + dependencies: + has-flag: 4.0.0 + + supports-preserve-symlinks-flag@1.0.0: {} + + suspend-react@0.1.3(react@19.1.0): + dependencies: + react: 19.1.0 + + swr@2.3.3(react@19.1.0): + dependencies: + dequal: 2.0.3 + react: 19.1.0 + use-sync-external-store: 1.5.0(react@19.1.0) + + synckit@0.11.4: + dependencies: + '@pkgr/core': 0.2.4 + tslib: 2.8.1 + + tailwind-merge@3.0.2: {} + + tailwind-merge@3.2.0: {} + + tailwind-variants@1.0.0(tailwindcss@4.1.4): + dependencies: + tailwind-merge: 3.0.2 + tailwindcss: 4.1.4 + + tailwindcss@4.1.4: {} + + tapable@2.2.1: {} + + terser-webpack-plugin@5.3.14(webpack@5.98.0): + dependencies: + '@jridgewell/trace-mapping': 0.3.25 + jest-worker: 27.5.1 + schema-utils: 4.3.2 + serialize-javascript: 6.0.2 + terser: 5.39.0 + webpack: 5.98.0 + + terser@5.39.0: + dependencies: + '@jridgewell/source-map': 0.3.6 + acorn: 8.14.1 + commander: 2.20.3 + source-map-support: 0.5.21 + + three-mesh-bvh@0.8.3(three@0.175.0): + dependencies: + three: 0.175.0 + + three-stdlib@2.36.0(three@0.175.0): + dependencies: + '@types/draco3d': 1.4.10 + '@types/offscreencanvas': 2019.7.3 + '@types/webxr': 0.5.22 + draco3d: 1.5.7 + fflate: 0.6.10 + potpack: 1.0.2 + three: 0.175.0 + + three@0.175.0: {} + + to-regex-range@5.0.1: + dependencies: + is-number: 7.0.0 + + tr46@0.0.3: {} + + trim-lines@3.0.1: {} + + troika-three-text@0.52.4(three@0.175.0): + dependencies: + bidi-js: 1.0.3 + three: 0.175.0 + troika-three-utils: 0.52.4(three@0.175.0) + troika-worker-utils: 0.52.0 + webgl-sdf-generator: 1.1.1 + + troika-three-utils@0.52.4(three@0.175.0): + dependencies: + three: 0.175.0 + + troika-worker-utils@0.52.0: {} + + trough@2.2.0: {} + + ts-api-utils@2.1.0(typescript@5.8.3): + dependencies: + typescript: 5.8.3 + + tsconfig-paths@3.15.0: + dependencies: + '@types/json5': 0.0.29 + json5: 1.0.2 + minimist: 1.2.8 + strip-bom: 3.0.0 + + tslib@2.8.1: {} + + tunnel-rat@0.1.2(@types/react@19.1.2)(react@19.1.0): + dependencies: + zustand: 4.5.6(@types/react@19.1.2)(react@19.1.0) + transitivePeerDependencies: + - '@types/react' + - immer + - react + + type-check@0.4.0: + dependencies: + prelude-ls: 1.2.1 + + typed-array-buffer@1.0.3: + dependencies: + call-bound: 1.0.4 + es-errors: 1.3.0 + is-typed-array: 1.1.15 + + typed-array-byte-length@1.0.3: + dependencies: + call-bind: 1.0.8 + for-each: 0.3.5 + gopd: 1.2.0 + has-proto: 1.2.0 + is-typed-array: 1.1.15 + + typed-array-byte-offset@1.0.4: + dependencies: + available-typed-arrays: 1.0.7 + call-bind: 1.0.8 + for-each: 0.3.5 + gopd: 1.2.0 + has-proto: 1.2.0 + is-typed-array: 1.1.15 + reflect.getprototypeof: 1.0.10 + + typed-array-length@1.0.7: + dependencies: + call-bind: 1.0.8 + for-each: 0.3.5 + gopd: 1.2.0 + is-typed-array: 1.1.15 + possible-typed-array-names: 1.1.0 + reflect.getprototypeof: 1.0.10 + + typescript@5.8.3: {} + + unbox-primitive@1.1.0: + dependencies: + call-bound: 1.0.4 + has-bigints: 1.1.0 + has-symbols: 1.1.0 + which-boxed-primitive: 1.1.1 + + undici-types@6.21.0: {} + + unified@11.0.5: + dependencies: + '@types/unist': 3.0.3 + bail: 2.0.2 + devlop: 1.1.0 + extend: 3.0.2 + is-plain-obj: 4.1.0 + trough: 2.2.0 + vfile: 6.0.3 + + unist-util-find-after@5.0.0: + dependencies: + '@types/unist': 3.0.3 + unist-util-is: 6.0.0 + + unist-util-is@6.0.0: + dependencies: + '@types/unist': 3.0.3 + + unist-util-position-from-estree@2.0.0: + dependencies: + '@types/unist': 3.0.3 + + unist-util-position@5.0.0: + dependencies: + '@types/unist': 3.0.3 + + unist-util-remove-position@5.0.0: + dependencies: + '@types/unist': 3.0.3 + unist-util-visit: 5.0.0 + + unist-util-stringify-position@4.0.0: + dependencies: + '@types/unist': 3.0.3 + + unist-util-visit-parents@6.0.1: + dependencies: + '@types/unist': 3.0.3 + unist-util-is: 6.0.0 + + unist-util-visit@5.0.0: + dependencies: + '@types/unist': 3.0.3 + unist-util-is: 6.0.0 + unist-util-visit-parents: 6.0.1 + + update-browserslist-db@1.1.3(browserslist@4.24.4): + dependencies: + browserslist: 4.24.4 + escalade: 3.2.0 + picocolors: 1.1.1 + + uri-js@4.4.1: + dependencies: + punycode: 2.3.1 + + use-composed-ref@1.4.0(@types/react@19.1.2)(react@19.1.0): + dependencies: + react: 19.1.0 + optionalDependencies: + '@types/react': 19.1.2 + + use-isomorphic-layout-effect@1.2.0(@types/react@19.1.2)(react@19.1.0): + dependencies: + react: 19.1.0 + optionalDependencies: + '@types/react': 19.1.2 + + use-latest@1.3.0(@types/react@19.1.2)(react@19.1.0): + dependencies: + react: 19.1.0 + use-isomorphic-layout-effect: 1.2.0(@types/react@19.1.2)(react@19.1.0) + optionalDependencies: + '@types/react': 19.1.2 + + use-sync-external-store@1.5.0(react@19.1.0): + dependencies: + react: 19.1.0 + + utility-types@3.11.0: {} + + vfile-location@5.0.3: + dependencies: + '@types/unist': 3.0.3 + vfile: 6.0.3 + + vfile-message@4.0.2: + dependencies: + '@types/unist': 3.0.3 + unist-util-stringify-position: 4.0.0 + + vfile@6.0.3: + dependencies: + '@types/unist': 3.0.3 + vfile-message: 4.0.2 + + w3c-keyname@2.2.8: {} + + watchpack@2.4.2: + dependencies: + glob-to-regexp: 0.4.1 + graceful-fs: 4.2.11 + + web-namespaces@2.0.1: {} + + webgl-constants@1.1.1: {} + + webgl-sdf-generator@1.1.1: {} + + webidl-conversions@3.0.1: {} + + webpack-sources@3.2.3: {} + + webpack@5.98.0: + dependencies: + '@types/eslint-scope': 3.7.7 + '@types/estree': 1.0.7 + '@webassemblyjs/ast': 1.14.1 + '@webassemblyjs/wasm-edit': 1.14.1 + '@webassemblyjs/wasm-parser': 1.14.1 + acorn: 8.14.1 + browserslist: 4.24.4 + chrome-trace-event: 1.0.4 + enhanced-resolve: 5.18.1 + es-module-lexer: 1.6.0 + eslint-scope: 5.1.1 + events: 3.3.0 + glob-to-regexp: 0.4.1 + graceful-fs: 4.2.11 + json-parse-even-better-errors: 2.3.1 + loader-runner: 4.3.0 + mime-types: 2.1.35 + neo-async: 2.6.2 + schema-utils: 4.3.2 + tapable: 2.2.1 + terser-webpack-plugin: 5.3.14(webpack@5.98.0) + watchpack: 2.4.2 + webpack-sources: 3.2.3 + transitivePeerDependencies: + - '@swc/core' + - esbuild + - uglify-js + + whatwg-url@5.0.0: + dependencies: + tr46: 0.0.3 + webidl-conversions: 3.0.1 + + which-boxed-primitive@1.1.1: + dependencies: + is-bigint: 1.1.0 + is-boolean-object: 1.2.2 + is-number-object: 1.1.1 + is-string: 1.1.1 + is-symbol: 1.1.1 + + which-builtin-type@1.2.1: + dependencies: + call-bound: 1.0.4 + function.prototype.name: 1.1.8 + has-tostringtag: 1.0.2 + is-async-function: 2.1.1 + is-date-object: 1.1.0 + is-finalizationregistry: 1.1.1 + is-generator-function: 1.1.0 + is-regex: 1.2.1 + is-weakref: 1.1.1 + isarray: 2.0.5 + which-boxed-primitive: 1.1.1 + which-collection: 1.0.2 + which-typed-array: 1.1.19 + + which-collection@1.0.2: + dependencies: + is-map: 2.0.3 + is-set: 2.0.3 + is-weakmap: 2.0.2 + is-weakset: 2.0.4 + + which-typed-array@1.1.19: + dependencies: + available-typed-arrays: 1.0.7 + call-bind: 1.0.8 + call-bound: 1.0.4 + for-each: 0.3.5 + get-proto: 1.0.1 + gopd: 1.2.0 + has-tostringtag: 1.0.2 + + which@2.0.2: + dependencies: + isexe: 2.0.0 + + word-wrap@1.2.5: {} + + wrap-ansi@9.0.0: + dependencies: + ansi-styles: 6.2.1 + string-width: 7.2.0 + strip-ansi: 7.1.0 + + ws@8.18.1: {} + + xtend@4.0.2: {} + + yaml@2.7.1: {} + + yocto-queue@0.1.0: {} + + zustand@4.5.6(@types/react@19.1.2)(react@19.1.0): + dependencies: + use-sync-external-store: 1.5.0(react@19.1.0) + optionalDependencies: + '@types/react': 19.1.2 + react: 19.1.0 + + zustand@5.0.3(@types/react@19.1.2)(react@19.1.0)(use-sync-external-store@1.5.0(react@19.1.0)): + optionalDependencies: + '@types/react': 19.1.2 + react: 19.1.0 + use-sync-external-store: 1.5.0(react@19.1.0) + + zwitch@2.0.4: {} diff --git a/lexikon/postcss.config.mjs b/lexikon/postcss.config.mjs new file mode 100644 index 0000000..c4964ca --- /dev/null +++ b/lexikon/postcss.config.mjs @@ -0,0 +1,5 @@ +const config = { + plugins: ["@tailwindcss/postcss"], +}; + +export default config; diff --git a/lexikon/public/assets/examples/hero.jpg b/lexikon/public/assets/examples/hero.jpg new file mode 100644 index 0000000..184d714 Binary files /dev/null and b/lexikon/public/assets/examples/hero.jpg differ diff --git a/lexikon/public/assets/examples/user.png b/lexikon/public/assets/examples/user.png new file mode 100644 index 0000000..e28ad0d Binary files /dev/null and b/lexikon/public/assets/examples/user.png differ diff --git a/lexikon/public/favicon.ico b/lexikon/public/favicon.ico new file mode 100644 index 0000000..718d6fe Binary files /dev/null and b/lexikon/public/favicon.ico differ diff --git a/lexikon/src/app/(auth)/confirm/route.ts b/lexikon/src/app/(auth)/confirm/route.ts new file mode 100644 index 0000000..14a92fc --- /dev/null +++ b/lexikon/src/app/(auth)/confirm/route.ts @@ -0,0 +1,28 @@ +import { type EmailOtpType } from "@supabase/supabase-js"; +import { type NextRequest } from "next/server"; +import { redirect } from "next/navigation"; +import { supaServer } from "shared/api/supabase/server"; + +export async function GET(request: NextRequest) { + const { searchParams } = new URL(request.url); + const token_hash = searchParams.get("token_hash"); + const type = searchParams.get("type") as EmailOtpType | null; + const next = searchParams.get("next") ?? "/"; + + if (token_hash && type) { + const supabase = await supaServer(); + + const { error } = await supabase.auth.verifyOtp({ + type, + token_hash, + }); + + if (!error) { + // redirect user to specified redirect URL or root of app + redirect(next); + } + } + + // redirect the user to an error page with some instructions + redirect("/error"); +} diff --git a/lexikon/src/app/SideNavContent.tsx b/lexikon/src/app/SideNavContent.tsx new file mode 100644 index 0000000..1082433 --- /dev/null +++ b/lexikon/src/app/SideNavContent.tsx @@ -0,0 +1,189 @@ +"use client"; +import { Divider, Select, SelectItem, Skeleton } from "@heroui/react"; +import { AnimatePresence } from "framer-motion"; +import LibrarianLink from "features/librarian/LibrarianLink"; +import { + CourseListGroupHeader, + CourseListItem, +} from "features/study/components/CourseList"; +import { useState, useCallback, useEffect } from "react"; +import SearchField from "shared/ui/SearchField"; +import { container } from "shared/styles/variants"; +import useCoursesModulesStore from "features/study/stores/coursesStore"; +import { CourseModule } from "shared/domain/course"; +import useTermStore from "features/study/stores/termStore"; +import { Term } from "shared/domain/term"; +import { ChevronsUpDown } from "lucide-react"; + +export default function SidenavContent() { + const { terms, selectedTerm, setSelectedTerm, fetchTerms } = useTermStore(); + + // error is now string | null + const { coursesModules, isLoading, error, loadCoursesModules } = + useCoursesModulesStore(); + + useEffect(() => { + // Fetch terms on mount + fetchTerms(); + + // Set the first term as selected if available + if (terms.length > 0) { + const firstTerm = terms[0]; + if (!selectedTerm) { + setSelectedTerm(firstTerm); + } + } + }, [fetchTerms]); + + useEffect(() => { + if (!selectedTerm) return; + loadCoursesModules(selectedTerm); + }, [selectedTerm, loadCoursesModules]); + + // Debounced search value + const [searchValue, setSearchValue] = useState(""); + const [debouncedSearch, setDebouncedSearch] = useState(""); + useEffect(() => { + const handler = setTimeout(() => { + setDebouncedSearch(searchValue); + }, 100); // 100ms debounce + return () => clearTimeout(handler); + }, [searchValue]); + + const handleSearchChange = useCallback((value: string) => { + setSearchValue(value); + }, []); + const handleSearchClear = useCallback(() => { + setSearchValue(""); + }, []); + // Filter modules by debounced search value (case-insensitive, matches name or code) + const filteredCoursesModules = + coursesModules?.filter((courseModule) => { + if (!debouncedSearch.trim()) return true; + const q = debouncedSearch.trim().toLowerCase(); + return ( + courseModule.module_name.toLowerCase().includes(q) || + (courseModule.module_code?.toLowerCase?.().includes(q) ?? false) + ); + }) || []; + + // Separate filtered modules into favorites and others + const favoriteCoursesModules: CourseModule[] = + filteredCoursesModules.filter( + (courseModule) => courseModule.is_user_favorite + ); + const otherCoursesModules: CourseModule[] = filteredCoursesModules.filter( + (courseModule) => !courseModule.is_user_favorite + ); + + return ( +
+ + + {/* Use theme-aware background color for divider */} + + + {/* Search Field */} + + + {terms.length > 0 ? ( + + ) : ( + + )} + + {/* Course List */} +
+ {isLoading && ( + // Show skeletons while loading +
+ + + + + + + +
+ )} + {error && ( + // Display the error message string +
Error: {error}
+ )} + {!isLoading && !error && coursesModules && ( + // Render favorites then all Modules with toggle + <> + {favoriteCoursesModules.length > 0 && ( + <> + + + {favoriteCoursesModules.map((cm, i) => ( + + ))} + + + )} + + {otherCoursesModules.map((cm, i) => ( + + ))} + + )} +
+
+ ); +} + +const TermSelector = ({ + terms, + selectedTerm, + onSelectTerm, +}: { + terms: Term[]; + selectedTerm: Term | undefined; + onSelectTerm: (term: Term) => void; +}) => { + const defaultKey = selectedTerm?.semester_id ?? terms[0]?.semester_id; + + return ( + + ); +}; diff --git a/lexikon/src/app/api/(study)/course/route.ts b/lexikon/src/app/api/(study)/course/route.ts new file mode 100644 index 0000000..6642f2b --- /dev/null +++ b/lexikon/src/app/api/(study)/course/route.ts @@ -0,0 +1,94 @@ +import { deleteSummary, createSummary, updateSummary } from "@/src/features/study/queries/CRUD-Summaries"; +import { CourseSummary } from "@/src/shared/domain/summary"; + +import { NextResponse } from "next/server"; + + + +interface RequestBody { + moduleid?: number; + termid?: number; + newChapterName?: string; + summary?: CourseSummary; + newContent?: string; +} + +/** + * DELETE /api/summaries + * Deletes the given chapter summary. + */ +export async function DELETE(request: Request) { + try { + const body: RequestBody = await request.json(); + + if (!body.summary) { + return NextResponse.json({ error: "Missing summary" }, { status: 400 }); + } + const result = await deleteSummary(body.summary); + + return NextResponse.json(result); + } catch (error) { + console.error("Error in DELETE /api/summaries:", error); + + return NextResponse.json( + { error: "Failed to delete summary" }, + { status: 500 }, + ); + } +} + +/** + * POST /api/summaries + * Creates a new chapter summary. + */ +export async function POST(request: Request) { + try { + const body: RequestBody = await request.json(); + const { moduleid, termid, newChapterName } = body; + + if (!moduleid || !termid || !newChapterName) { + return NextResponse.json( + { error: "Missing parameters" }, + { status: 400 }, + ); + } + const chapter = await createSummary(moduleid, String(termid), newChapterName); + + return NextResponse.json(chapter); + } catch (error) { + console.error("Error in POST /api/summaries:", error); + + return NextResponse.json( + { error: "Failed to create summary" }, + { status: 500 }, + ); + } +} + +/** + * PATCH /api/summaries + * Updates summary content or renames a chapter. + */ +export async function PATCH(request: Request) { + try { + const body: RequestBody = await request.json(); + + if (body.summary && body.newContent) { + const result = await updateSummary(body.summary, { + newName: body.newChapterName, + content: body.newContent, + }); + + return NextResponse.json(result); + } + + return NextResponse.json({ error: "Invalid parameters" }, { status: 400 }); + } catch (error) { + console.error("Error in PATCH /api/summaries:", error); + + return NextResponse.json( + { error: "Failed to update summary" }, + { status: 500 }, + ); + } +} diff --git a/lexikon/src/app/api/(study)/terms/route.ts b/lexikon/src/app/api/(study)/terms/route.ts new file mode 100644 index 0000000..7391c2f --- /dev/null +++ b/lexikon/src/app/api/(study)/terms/route.ts @@ -0,0 +1,35 @@ +import { fetchTerms } from "features/study/queries/CRUD-Terms"; +import { NextResponse } from "next/server"; + + +export async function GET(request: Request) { + const url = new URL(request.url); + const moduleidParam = url.searchParams.get("moduleid"); + + if (!moduleidParam) { + return NextResponse.json( + { error: "Missing moduleid parameter" }, + { status: 400 }, + ); + } + const moduleid = parseInt(moduleidParam, 10); + + if (isNaN(moduleid)) { + return NextResponse.json( + { error: "Invalid moduleid parameter" }, + { status: 400 }, + ); + } + try { + const terms = await fetchTerms(moduleid); + + return NextResponse.json(terms); + } catch (error) { + console.error("Error in GET /api/terms:", error); + + return NextResponse.json( + { error: "Failed to fetch terms" }, + { status: 500 }, + ); + } +} diff --git a/lexikon/src/app/api/librarian/crawler/download/route.ts b/lexikon/src/app/api/librarian/crawler/download/route.ts new file mode 100644 index 0000000..e69de29 diff --git a/lexikon/src/app/api/librarian/crawler/index/route.ts b/lexikon/src/app/api/librarian/crawler/index/route.ts new file mode 100644 index 0000000..7459120 --- /dev/null +++ b/lexikon/src/app/api/librarian/crawler/index/route.ts @@ -0,0 +1,17 @@ +import { getMockedMoodleIndex } from "features/librarian/queries/crawlerQueries"; +import { NextResponse } from "next/server"; + +export async function GET() { + try { + const moodleIndex = await getMockedMoodleIndex(); + + return NextResponse.json(moodleIndex ?? []); + } catch (error) { + console.error("Error in GET /api/librarian:", error); + + return NextResponse.json( + { error: "Failed to Get MoodleIndex" }, + { status: 500 }, + ); + } +} diff --git a/lexikon/src/app/dashboard/page.tsx b/lexikon/src/app/dashboard/page.tsx new file mode 100644 index 0000000..fc246cf --- /dev/null +++ b/lexikon/src/app/dashboard/page.tsx @@ -0,0 +1,14 @@ +"use client"; + +import { Button } from "@heroui/react"; +import { emptyBucket } from "shared/api/supabase"; + +export default function Dashboard() { + return ( +
+ +
+ ); +} diff --git a/lexikon/src/app/error.tsx b/lexikon/src/app/error.tsx new file mode 100644 index 0000000..9ed5104 --- /dev/null +++ b/lexikon/src/app/error.tsx @@ -0,0 +1,31 @@ +"use client"; + +import { useEffect } from "react"; + +export default function Error({ + error, + reset, +}: { + error: Error; + reset: () => void; +}) { + useEffect(() => { + // Log the error to an error reporting service + /* eslint-disable no-console */ + console.error(error); + }, [error]); + + return ( +
+

Something went wrong!

+ +
+ ); +} diff --git a/lexikon/src/app/layout.tsx b/lexikon/src/app/layout.tsx new file mode 100644 index 0000000..14e71f5 --- /dev/null +++ b/lexikon/src/app/layout.tsx @@ -0,0 +1,58 @@ +import "shared/styles/globals.css"; +import clsx from "clsx"; +import { Metadata, Viewport } from "next"; +import { fontSans } from "shared/config/fonts"; +import { siteConfig } from "shared/config/site"; +import { Providers } from "./providers"; +import { SideNav } from "../features/navigation/sidenav/SideNav"; +import SidenavContent from "./SideNavContent"; + +export const metadata: Metadata = { + title: { + default: siteConfig.name, + template: `%s - ${siteConfig.name}`, + }, + description: siteConfig.description, + icons: { + icon: "/favicon.ico", + }, +}; + +export const viewport: Viewport = { + themeColor: [ + { media: "(prefers-color-scheme: light)", color: "white" }, + { media: "(prefers-color-scheme: dark)", color: "black" }, + ], +}; + +export default function RootLayout({ + children, +}: { + children: React.ReactNode; +}) { + return ( + + + + +
+ + + +
+ {/* Main content area */} +
{children}
+
+
+ + + + ); +} diff --git a/lexikon/src/app/librarian/aisanitizer/page.tsx b/lexikon/src/app/librarian/aisanitizer/page.tsx new file mode 100644 index 0000000..48b4b77 --- /dev/null +++ b/lexikon/src/app/librarian/aisanitizer/page.tsx @@ -0,0 +1,44 @@ +'use client'; +import { Spinner } from '@heroui/react'; +import { useEffect, useState } from 'react'; +import ExtractorDataTable from '../components/output/ExtractorDataTable'; +export default function Page() { + const [latestResult, setLatestResult] = useState(null); + + useEffect(() => { + const loadLatestResult = async () => { + const result = await fetch( + `http://localhost:8000/api/runs/AISanitizer/latest` + ); + setLatestResult(await result.json()); + }; + loadLatestResult(); + }, []); + + if (!latestResult) { + return ; + } + + // When no data is found, show a message + if ( + latestResult.data === null || + latestResult.data === undefined || + (Array.isArray(latestResult.data) && latestResult.data.length === 0) + ) { + return ( +
+

No data found.

+
+ ); + } + + return ( +
+
+ {latestResult && ( + + )} +
+
+ ); +} diff --git a/lexikon/src/app/librarian/chunker/page.tsx b/lexikon/src/app/librarian/chunker/page.tsx new file mode 100644 index 0000000..f76638c --- /dev/null +++ b/lexikon/src/app/librarian/chunker/page.tsx @@ -0,0 +1,44 @@ +'use client'; +import { Spinner } from '@heroui/react'; +import { useEffect, useState } from 'react'; +import ChunkerDataTable from '../components/output/ChunkerDataTable'; +export default function Page() { + const [latestResult, setLatestResult] = useState(null); + + useEffect(() => { + const loadLatestResult = async () => { + const result = await fetch( + `http://localhost:8000/api/runs/Chunker/latest` + ); + setLatestResult(await result.json()); + }; + loadLatestResult(); + }, []); + + if (!latestResult) { + return ; + } + + // When no data is found, show a message + if ( + latestResult.data === null || + latestResult.data === undefined || + (Array.isArray(latestResult.data) && latestResult.data.length === 0) + ) { + return ( +
+

No data found.

+
+ ); + } + + return ( +
+
+ {latestResult && ( + + )} +
+
+ ); +} diff --git a/lexikon/src/app/librarian/components/ReuseOutputButton.tsx b/lexikon/src/app/librarian/components/ReuseOutputButton.tsx new file mode 100644 index 0000000..65ac1e8 --- /dev/null +++ b/lexikon/src/app/librarian/components/ReuseOutputButton.tsx @@ -0,0 +1,41 @@ +'use client'; +import { useState } from 'react'; +import { Button } from '@heroui/button'; + +export default function ReuseOutputButton({ + prevRunId, + nextWorker, + flowId, + onStarted, +}: { + prevRunId: string; + nextWorker: string; + flowId: string; + onStarted?: (runId: string) => void; +}) { + const [loading, setLoading] = useState(false); + + const handleClick = async () => { + setLoading(true); + const res = await fetch( + `/api/worker/${nextWorker}/submit/${prevRunId}/chain`, + { method: 'POST' } + ); + const data = await res.json(); + setLoading(false); + if (onStarted && data?.run_id) onStarted(data.run_id); + }; + + return ( + + ); +} diff --git a/lexikon/src/app/librarian/components/WorkerOutputMarkdown.tsx b/lexikon/src/app/librarian/components/WorkerOutputMarkdown.tsx new file mode 100644 index 0000000..e4bcb94 --- /dev/null +++ b/lexikon/src/app/librarian/components/WorkerOutputMarkdown.tsx @@ -0,0 +1,40 @@ +'use client'; +import { useEffect, useState } from 'react'; +import ReactMarkdown from 'react-markdown'; +import MDXPreview from 'shared/ui/markdown/MDXPreview'; + +const API_BASE_URL = process.env.NEXT_PUBLIC_API_BASE_URL || 'http://localhost:8000'; + +export default function WorkerOutputMarkdown({ runId }: { runId: string }) { + const [markdown, setMarkdown] = useState(''); + const [error, setError] = useState(null); + + useEffect(() => { + const fetchMarkdown = async () => { + try { + const res = await fetch(`${API_BASE_URL}/api/runs/${runId}/artifact`); + if (!res.ok) { + throw new Error('Failed to fetch markdown'); + } + let md_raw = await res.json(); + md_raw = md_raw.replace(/\\n/g, '\n'); + md_raw = md_raw.replace('"', ''); + setMarkdown(md_raw); + } catch (err) { + setError(err.message); + } + }; + + fetchMarkdown(); + }, [runId]); + + if (error) { + return
Error: {error}
; + } + + return ( +
+ +
+ ); +} diff --git a/lexikon/src/app/librarian/components/WorkerStateChip.tsx b/lexikon/src/app/librarian/components/WorkerStateChip.tsx new file mode 100644 index 0000000..8a79f16 --- /dev/null +++ b/lexikon/src/app/librarian/components/WorkerStateChip.tsx @@ -0,0 +1,27 @@ +'use client'; +import { Chip } from '@heroui/react'; + +const STATE_STYLES: Record = { + PENDING: 'bg-gray-400 text-gray-900', + RUNNING: 'bg-blue-500 text-white animate-pulse', + COMPLETED: 'bg-green-500 text-white', + FAILED: 'bg-red-500 text-white', +}; + +const STATE_LABELS: Record = { + PENDING: 'Pending', + RUNNING: 'Running', + COMPLETED: 'Completed', + FAILED: 'Failed', +}; + +export default function WorkerStateChip({ state }: { state: string }) { + return ( + + {STATE_LABELS[state] || state} + + ); +} diff --git a/lexikon/src/app/librarian/components/output/ChunkerDataTable.tsx b/lexikon/src/app/librarian/components/output/ChunkerDataTable.tsx new file mode 100644 index 0000000..1e651f0 --- /dev/null +++ b/lexikon/src/app/librarian/components/output/ChunkerDataTable.tsx @@ -0,0 +1,164 @@ +import { + Select, + SelectItem, + Table, + TableBody, + TableCell, + TableColumn, + TableHeader, + TableRow, +} from '@heroui/react'; +import { useEffect, useState } from 'react'; + +interface ChunkerData { + terms: { + id: string; + name: string; + courses: { + id: string; + name: string; + chunks: { + id: string; + name: string; + tokens: number; + }[]; + images: string[]; + }[]; + }[]; +} + +export default function ChunkerDataTable({ + chunkerData, +}: { + chunkerData: ChunkerData; + }) { + if (!chunkerData || !chunkerData.terms || chunkerData.terms.length === 0) { + return ( +
+

No data

+
+ ); + } + // Initialize selected term to the first term in the program + const [selectedTerm, setSelectedTerm] = useState( + chunkerData.terms[0] || null + ); + + const [selectedCourse, setSelectedCourse] = + useState(null); // Initialize to null + + const [flattenedChunks, setFlattenedChunks] = useState( + [] + ); + + const getFlattenedChunks = (course: ChunkedCourse | null) => { + return course?.chunks.map((chunk) => ({ + id: chunk.id, + name: chunk.name, + tokens: chunk.tokens, + })); + }; + + useEffect(() => { + if (selectedTerm) { + setSelectedCourse(selectedTerm.courses[0] || null); + } + }, [selectedTerm]); + + useEffect(() => { + if (selectedTerm && selectedCourse) { + setFlattenedChunks(getFlattenedChunks(selectedCourse)); + } + }, [selectedCourse]); + + return ( +
+ {/* Term Selector */} +
+ + + {/* Course Selector */} + +
+ + + + {/* Courses Table */} + {selectedTerm && selectedCourse && flattenedChunks && ( +
+
+ + + + Name + + + Tokens + + + + {flattenedChunks.map((chunk, i) => { + return ( + + + {chunk.name} + + + {chunk.tokens} + + + ); + })} + +
+
+
+ )} +
+ ); +} diff --git a/lexikon/src/app/librarian/components/output/DegreeProgramTable.tsx b/lexikon/src/app/librarian/components/output/DegreeProgramTable.tsx new file mode 100644 index 0000000..0da93c7 --- /dev/null +++ b/lexikon/src/app/librarian/components/output/DegreeProgramTable.tsx @@ -0,0 +1,127 @@ +import { + Select, + SelectItem, + Table, + TableBody, + TableCell, + TableColumn, + TableHeader, + TableRow, +} from '@heroui/react'; +import { useState } from 'react'; + +interface DegreeProgram { + terms: { + id: string; + name: string; + courses: { + id: string; + name: string; + hero_image: string; + content_ressource_id: string; + files: string[]; + }[]; + }[]; +} + +export default function DegreeProgramTable({ program }: { program: DegreeProgram }) { + if (!program || !program.terms || program.terms.length === 0) { + return ( +
+

+ No data +

+
+ ); + } + // Initialize selected term to the first term in the program + const [selectedTermId, setSelectedTermId] = useState( + program.terms[0]?.id || '' + ); + // Find the term object corresponding to the selected ID + const selectedTerm = + program.terms.find((term) => term.id === selectedTermId) || + program.terms[0]; + + return ( +
+ {/* Term Selector */} + + + + {/* Courses Table */} + {selectedTerm && ( +
+ + + + Course Name + + + Hero Image + + + Content ID + + + Files + + + + {selectedTerm.courses.map((course) => ( + + + {course.name} + + + {course.hero_image ? ( + {course.name} + ) : ( + + No image + + )} + + + {course.content_ressource_id} + + + {course.files && + course.files.length > 0 ? ( + course.files.join(', ') + ) : ( + + None + + )} + + + ))} + +
+
+ )} +
+ ); +} diff --git a/lexikon/src/app/librarian/components/output/DownloadDataTable.tsx b/lexikon/src/app/librarian/components/output/DownloadDataTable.tsx new file mode 100644 index 0000000..52796ec --- /dev/null +++ b/lexikon/src/app/librarian/components/output/DownloadDataTable.tsx @@ -0,0 +1,93 @@ +import { + Select, + SelectItem, + Table, + TableBody, + TableCell, + TableColumn, + TableHeader, + TableRow, +} from '@heroui/react'; +import { useState } from 'react'; + +interface DownloadData { + terms: { + id: string; + name: string; + courses: { + id: string; + name: string; + }[]; + }[]; +} + +export default function DownloadDataTable({ + downloadData, +}: { + downloadData: DownloadData; +}) { + if (!downloadData || !downloadData.terms) { + return
No data available
; + } + // Initialize selected term to the first term in the program + const [selectedTermId, setSelectedTermId] = useState( + downloadData.terms[0]?.id || '' + ); + // Find the term object corresponding to the selected ID + const selectedTerm = + downloadData.terms.find((term) => term.id === selectedTermId) || + downloadData.terms[0]; + + return ( +
+ {/* Term Selector */} + + + + {/* Courses Table */} + {selectedTerm && ( +
+ + + + Course Name + + + ID + + + + {selectedTerm.courses.map((course) => ( + + + {course.name} + + + {course.id} + + + ))} + +
+
+ )} +
+ ); +} diff --git a/lexikon/src/app/librarian/components/output/ExtractorDataTable.tsx b/lexikon/src/app/librarian/components/output/ExtractorDataTable.tsx new file mode 100644 index 0000000..a3caccd --- /dev/null +++ b/lexikon/src/app/librarian/components/output/ExtractorDataTable.tsx @@ -0,0 +1,238 @@ +import { + Select, + SelectItem, + Table, + TableBody, + TableCell, + TableColumn, + TableHeader, + TableRow, +} from '@heroui/react'; +import { FileText, Image } from 'lucide-react'; +import { useEffect, useState } from 'react'; + +type FlattenedFiles = { + id: string; + name: string; + chapter_name: string; + type: 'content' | 'media'; +}[]; + +type ExtractedCourse = { + id: string; + name: string; + chapters: { + id: string; + name: string; + content_files: { + id: string; + name: string; + }[]; + media_files: { + id: string; + name: string; + }[]; + }[]; +}; +interface ExtractData { + terms: { + id: string; + name: string; + courses: ExtractedCourse[]; + }[]; +} + +export default function ExtractorDataTable({ data }: { data: ExtractData }) { + if (!data || !data.terms || data.terms.length === 0) { + return ( +
+

No data

+
+ ); + } + + // Initialize selected term to the first term in the program + const [selectedTermId, setSelectedTermId] = useState( + data.terms[0]?.id || '' + ); + // Find the term object corresponding to the selected ID + const selectedTerm = + data.terms.find((term) => term.id === selectedTermId) || + (data.terms.length > 0 ? data.terms[0] : null); + + const [selectedCourse, setSelectedCourse] = + useState(null); // Initialize to null + + const [flattenedFiles, setFlattenedFiles] = useState([]); + + // Effect 1: Ensure selectedCourse is valid for the current selectedTerm, + // and updates it if the selectedTerm changes and the course becomes invalid, + // or to set an initial course. + useEffect(() => { + if (selectedTerm) { + const courseIsInCurrentTerm = selectedTerm.courses.some( + (c) => c.id === selectedCourse?.id + ); + if (!courseIsInCurrentTerm) { + // If selectedCourse is not in the current selectedTerm (e.g., term changed, initial load, or course became invalid) + // then set to the first course of the current selectedTerm. + setSelectedCourse(selectedTerm.courses[0] || null); + } + // If courseIsInCurrentTerm is true, it means: + // 1. User selected a course within the current term - their selection is preserved. + // 2. Term changed, and the previous selectedCourse happens to be valid in the new term - selection preserved. + } else { + // No term selected (e.g., data.terms is empty) + setSelectedCourse(null); + } + }, [selectedTerm, selectedCourse?.id]); // Re-run if selectedTerm object changes or selectedCourse.id changes + + // Effect 2: Update flattenedFiles when selectedCourse changes + useEffect(() => { + setFlattenedFiles(getFlattenedFiles(selectedCourse)); + }, [selectedCourse]); // Only depends on selectedCourse + + // Instead of having a list of chapters, with a list of content and media files, we want to have a single list of files, but keep the chapter name + const getFlattenedFiles = ( + course: ExtractedCourse | null + ): FlattenedFiles => { + const flattenedFiles: FlattenedFiles = []; + if (!course || !course.chapters) { + return flattenedFiles; + } + course.chapters.forEach((chapter) => { + chapter.content_files.forEach((file) => { + flattenedFiles.push({ + id: file.id, + name: file.name, + chapter_name: chapter.name, + type: 'content', + }); + }); + chapter.media_files.forEach((file) => { + flattenedFiles.push({ + id: file.id, + name: file.name, + chapter_name: chapter.name, + type: 'media', + }); + }); + }); + return flattenedFiles; + }; + + return ( +
+ {/* Term Selector */} +
+ + + {/* Course Selector */} + +
+ + {/* Courses Table */} + {selectedTerm && selectedCourse && ( +
+
+ + + + Name + + + Chapter + + + Type + + + + {flattenedFiles.map((file, i) => { + const isNewChapter = + file.chapter_name != + flattenedFiles[i - 1] + ?.chapter_name && i; + const nextChapterNew = + flattenedFiles[i + 1]?.chapter_name != + file.chapter_name && i; + const pd = isNewChapter + ? 'py-4 pt-6' + : nextChapterNew + ? 'py-4 pb-6' + : 'py-4'; + return ( + + + {file.name} + + + {file.chapter_name} + + + {file.type === 'content' ? ( + + ) : ( + + )} + + + ); + })} + +
+
+
+ )} +
+ ); +} diff --git a/lexikon/src/app/librarian/crawler/page.tsx b/lexikon/src/app/librarian/crawler/page.tsx new file mode 100644 index 0000000..fa48f36 --- /dev/null +++ b/lexikon/src/app/librarian/crawler/page.tsx @@ -0,0 +1,82 @@ +'use client'; +import { Button, Spinner } from '@heroui/react'; +import { useEffect, useState } from 'react'; +import DegreeProgramTable from '../components/output/DegreeProgramTable'; +export default function Page() { + const [latestResult, setLatestResult] = useState(null); + const [isCrawling, setIsCrawling] = useState(false); + + useEffect(() => { + const loadLatestResult = async () => { + const result = await fetch( + `http://localhost:8000/api/runs/Crawler/latest` + ); + setLatestResult(await result.json()); + }; + loadLatestResult(); + }, []); + + const submitCrawler = async () => { + setIsCrawling(true); + const body = { + data: { + name: 'Computational and Data Science', + id: '1157', + }, + }; + const result = await fetch( + `http://localhost:8000/api/worker/Crawler/submit`, + { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify(body), + } + ); + setLatestResult(await result.json()); + setIsCrawling(false); + }; + + if (!latestResult) { + return ; + } + + // When no degree program is found, show a message with a button to start a new crawl + if ( + latestResult.data?.degree_program === null || + latestResult.data?.degree_program === undefined + ) { + return isCrawling ? ( +
+ <> + +

+ Crawling in progress... +

+ +
+ ) : ( +
+

+ No data found. Please start a new crawl. +

+ +
+ ); + } + + return ( +
+
+ {latestResult && ( + + )} +
+
+ ); +} diff --git a/lexikon/src/app/librarian/downloader/page.tsx b/lexikon/src/app/librarian/downloader/page.tsx new file mode 100644 index 0000000..50b0df1 --- /dev/null +++ b/lexikon/src/app/librarian/downloader/page.tsx @@ -0,0 +1,44 @@ +'use client'; +import { Spinner } from '@heroui/react'; +import { useEffect, useState } from 'react'; +import DownloadDataTable from '../components/output/DownloadDataTable'; +export default function Page() { + const [latestResult, setLatestResult] = useState(null); + + useEffect(() => { + const loadLatestResult = async () => { + const result = await fetch( + `http://localhost:8000/api/runs/Downloader/latest` + ); + setLatestResult(await result.json()); + }; + loadLatestResult(); + }, []); + + if (!latestResult) { + return ; + } + + // When no data is found, show a message + if ( + latestResult.data === null || + latestResult.data === undefined || + (Array.isArray(latestResult.data) && latestResult.data.length === 0) + ) { + return ( +
+

No data found.

+
+ ); + } + + return ( +
+
+ {latestResult && ( + + )} +
+
+ ); +} diff --git a/lexikon/src/app/librarian/extractor/page.tsx b/lexikon/src/app/librarian/extractor/page.tsx new file mode 100644 index 0000000..c17c6ce --- /dev/null +++ b/lexikon/src/app/librarian/extractor/page.tsx @@ -0,0 +1,44 @@ +'use client'; +import { Spinner } from '@heroui/react'; +import { useEffect, useState } from 'react'; +import ExtractorDataTable from '../components/output/ExtractorDataTable'; +export default function Page() { + const [latestResult, setLatestResult] = useState(null); + + useEffect(() => { + const loadLatestResult = async () => { + const result = await fetch( + `http://localhost:8000/api/runs/Extractor/latest` + ); + setLatestResult(await result.json()); + }; + loadLatestResult(); + }, []); + + if (!latestResult) { + return ; + } + + // When no data is found, show a message + if ( + latestResult.data === null || + latestResult.data === undefined || + (Array.isArray(latestResult.data) && latestResult.data.length === 0) + ) { + return ( +
+

No data found.

+
+ ); + } + + return ( +
+
+ {latestResult && ( + + )} +
+
+ ); +} diff --git a/lexikon/src/app/librarian/layout.tsx b/lexikon/src/app/librarian/layout.tsx new file mode 100644 index 0000000..07b6615 --- /dev/null +++ b/lexikon/src/app/librarian/layout.tsx @@ -0,0 +1,286 @@ +'use client'; + +import { Button } from '@heroui/button'; +import { Spinner } from '@heroui/react'; +import { TopToolbar } from 'features/navigation/TopToolBar'; +import { ArrowDown, ArrowRight, LinkIcon, Rotate3D } from 'lucide-react'; // Import LinkIcon, ArrowDown, ArrowRight +import { usePathname, useRouter } from 'next/navigation'; +import { useEffect, useState } from 'react'; +import { + SequenceValidationResult, + validateWorkerSequence, +} from 'shared/utils/sequenceValidator'; + +interface WorkerMeta { + name: string; + input: string; // The Input Type of the Worker + output: string; // The Output Type of the Worker +} + +export default function Layout({ children }: { children: React.ReactNode }) { + const [url, setUrl] = useState(''); + const router = useRouter(); + const pathname = usePathname(); + const [workers, setWorkers] = useState([]); + const [selectedWorkerName, setSelectedWorkerName] = useState( + null + ); + const [selectedWorkerIndex, setSelectedWorkerIndex] = useState(-1); // New state for index + const [inputOutputColors, setInputOutputColors] = useState< + Record + >({}); + const [isChaining, setIsChaining] = useState(false); // State for chain button loading + + // Infer selected worker from the route (case-insensitive, match by lowercased name) + useEffect(() => { + const match = pathname.match(/^\/librarian\/?([^\/?#]*)/i); + if (match && match[1] && workers.length > 0) { + const routeName = match[1].toLowerCase(); + const foundIndex = workers.findIndex( + (w) => w.name.toLowerCase() === routeName + ); + if (foundIndex !== -1) { + setSelectedWorkerName(workers[foundIndex].name); + setSelectedWorkerIndex(foundIndex); + } else { + setSelectedWorkerName(null); + setSelectedWorkerIndex(-1); + } + } else if (workers.length > 0) { + setSelectedWorkerName(null); // Or select none if URL is just /librarian + setSelectedWorkerIndex(-1); + } else { + setSelectedWorkerName(null); + setSelectedWorkerIndex(-1); + } + }, [pathname, workers, router]); + + useEffect(() => { + const fetchWorkers = async () => { + try { + const res = await fetch(`http://localhost:8000/api/worker/`); + if (!res.ok) { + console.error(`Error fetching workers: ${res.status}`); + setWorkers([]); + setInputOutputColors({}); + return; + } + const data: WorkerMeta[] = await res.json(); + if (data && data.length > 0) { + const validationResult: SequenceValidationResult = + validateWorkerSequence(data); + if (validationResult.isValid) { + setWorkers(validationResult.sequence); + gen_input_output_colors(validationResult.sequence); + } else { + console.error( + 'Failed to build worker sequence:', + validationResult.error + ); + setWorkers([]); + setInputOutputColors({}); + } + } else { + setWorkers([]); + setInputOutputColors({}); + } + } catch (error) { + console.error('Exception while fetching workers:', error); + setWorkers([]); + setInputOutputColors({}); + } + }; + fetchWorkers(); + }, []); + + const gen_input_output_colors = (currentWorkers: WorkerMeta[]) => { + const new_input_output_colors: Record = {}; + const uniqueTypesSet = new Set(); + + currentWorkers.forEach((worker) => { + uniqueTypesSet.add(worker.input); + uniqueTypesSet.add(worker.output); + }); + + // Sort unique types for deterministic color assignment + const sortedUniqueTypes = Array.from(uniqueTypesSet).sort(); + + const goldenRatioConjugate = 0.618033988749895; // (Math.sqrt(5) - 1) / 2 + const saturation = 0.7; // Adjusted for better visibility + const lightness = 0.55; // Adjusted for better visibility + + sortedUniqueTypes.forEach((type, index) => { + // Generate hue systematically + const hue = (index * goldenRatioConjugate) % 1; + new_input_output_colors[type] = `hsl(${hue * 360}, ${ + saturation * 100 + }%, ${lightness * 100}%)`; + }); + setInputOutputColors(new_input_output_colors); + }; + + const handleChainOutput = async ( + currentWorkerName: string, + previousWorkerName: string + ) => { + setIsChaining(true); + try { + const res = await fetch( + `http://localhost:8000/api/worker/${currentWorkerName}/submit/${previousWorkerName}/chain/latest`, + { + method: 'POST', + } + ); + if (!res.ok) { + const errorText = await res.text(); + console.error( + `Error chaining output: ${res.status} - ${errorText}` + ); + alert(`Error chaining output: ${res.status} - ${errorText}`); + return; + } + // Output chained successfully, now refresh the current route + router.refresh(); + } catch (error) { + console.error('Exception while chaining output:', error); + alert(`An exception occurred while chaining output: ${error}`); + } finally { + setIsChaining(false); + } + }; + + return ( +
+ +
+
+ {workers.length === 0 ? ( +
+ +
+ ) : ( +
    +
  • + +
    • + + {workers.map((worker, i) => { + const isActive = i <= selectedWorkerIndex; + const isCurrent = i === selectedWorkerIndex; + + return ( +
  • +
    +
    + {i + 1} +
    + {i < workers.length - 1 && ( +
    + )} +
    +
    + + {isCurrent && + selectedWorkerIndex > 0 && + workers[ + selectedWorkerIndex - 1 + ] && ( + + )} +
    +
    • + ); + })} +
+ )} +
+
+ {children} +
+
+
+ ); +} diff --git a/lexikon/src/app/librarian/page.tsx b/lexikon/src/app/librarian/page.tsx new file mode 100644 index 0000000..bec4a4f --- /dev/null +++ b/lexikon/src/app/librarian/page.tsx @@ -0,0 +1,3 @@ +export default function Page() { + return
Please select a worker
; +} diff --git a/lexikon/src/app/librarian/vspace/data.ts b/lexikon/src/app/librarian/vspace/data.ts new file mode 100644 index 0000000..2b0a3a1 --- /dev/null +++ b/lexikon/src/app/librarian/vspace/data.ts @@ -0,0 +1,24 @@ +// src/types.ts +export interface DataPoint { + chunk_1024_embeddings_id: number; + chunk: string; + course_id: number; + file_id: number; + source_file: string | null; + file_type: string; + object_id: string | null; + // embedding: number[]; // Für den Plot nicht direkt benötigt + created_at: string; + updated_at: string; + x: number; + y: number; + z: number; + cluster: string; // Als String für kategoriale Farben + hover_text: string; +} + +export interface LoadedData { + columns: string[]; + index: number[]; + data: any[][]; +} diff --git a/lexikon/src/app/librarian/vspace/page.tsx b/lexikon/src/app/librarian/vspace/page.tsx new file mode 100644 index 0000000..1d979cf --- /dev/null +++ b/lexikon/src/app/librarian/vspace/page.tsx @@ -0,0 +1,14 @@ +'use client'; +import React from 'react'; +import TsnePlot from '../../../features/tsnePlot/TsnePlot'; + +const HomePage: React.FC = () => { + return ( +
+ {/* Optional: Header oder andere UI-Elemente */} + +
+ ); +}; + +export default HomePage; diff --git a/lexikon/src/app/librarian/vspace/page_old.tsx b/lexikon/src/app/librarian/vspace/page_old.tsx new file mode 100644 index 0000000..492be23 --- /dev/null +++ b/lexikon/src/app/librarian/vspace/page_old.tsx @@ -0,0 +1,204 @@ +'use client'; // Ensure this is at the top if not already present +import TsnePlotView, { + DataPoint, +} from 'features/tsnePlot/tsnePlotView'; // Corrected import path +import { kmeans } from 'ml-kmeans'; // Import kmeans +import { useEffect, useState } from 'react'; + +// Define a more specific type for your t-SNE data structure if possible +interface TsneDataType { + columns: string[]; + index: number[]; + data: (string | number | number[] | null)[][]; +} + +// Pre-defined color palette for clusters +const clusterColors: { [key: string]: string } = {}; + +// Function to generate a color if not predefined, or get a consistent random color based on clusterId +const getClusterColor = (clusterId: string): string => { + if (clusterColors[clusterId]) { + return clusterColors[clusterId]; + } + let hash = 0; + for (let i = 0; i < clusterId.length; i++) { + hash = clusterId.charCodeAt(i) + ((hash << 5) - hash); + hash = hash & hash; // Convert to 32bit integer + } + const color = `#${`00000${(hash & 0xffffff).toString(16)}`.slice(-6)}`; + clusterColors[clusterId] = color; // Cache for future use + return color; +}; + +// Function to calculate Sum of Squared Errors (SSE) +const calculateSSE = ( + points: number[][], + assignments: number[], + centroids: number[][] +): number => { + let sse = 0; + points.forEach((point, i) => { + const centroid = centroids[assignments[i]]; + if (point && centroid) { + const distance = point.reduce( + (sum, val, dim) => sum + (val - centroid[dim]) ** 2, + 0 + ); + sse += distance; + } + }); + return sse; +}; + +// Helper function to transform data and apply k-means +const processDataWithKMeans = (rawData: TsneDataType): DataPoint[] => { + console.log( + 'Raw data for clustering (first 5 items, showing elements 10, 11, 12):', + rawData.data.slice(0, 5).map((item) => [item[10], item[11], item[12]]) + ); // Log first 5 position data candidates + const positions = rawData.data + .map((item) => [item[10], item[11], item[12]] as number[]) + .filter( + (pos) => + Array.isArray(pos) && + pos.length === 3 && + pos.every((p) => typeof p === 'number' && !isNaN(p)) // Ensure they are valid numbers + ); + + if (positions.length === 0) { + console.error('No valid position data found for clustering.'); + return []; + } + + // Elbow method to find optimal k + const sseValues: { k: number; sse: number }[] = []; + const maxK = Math.min(10, positions.length); // Test up to 10 clusters or number of points + console.log(`Running Elbow method for k from 2 to ${maxK}`); + + for (let k = 2; k <= maxK; k++) { + try { + const ans = kmeans(positions, k, { initialization: 'kmeans++' }); + const sse = calculateSSE( + positions, + ans.clusters, + ans.centroids // Corrected: ans.centroids is already number[][] + ); + sseValues.push({ k, sse }); + console.log(`SSE for k=${k}: ${sse}`); + } catch (error) { + console.error(`Error running kmeans for k=${k}:`, error); + // If k is too large for the number of unique points, kmeans might fail. + break; + } + } + + let optimalK = maxK > 1 ? 2 : 1; // Default to 2 clusters if possible + if (sseValues.length > 1) { + // Find the elbow: a simple approach is to look for a significant drop in SSE decrease rate + // This is a heuristic. A more robust method might involve analyzing the second derivative or a threshold. + let maxDiff = 0; + let bestKIndex = 0; + + for (let i = 0; i < sseValues.length - 1; i++) { + const diff = sseValues[i].sse - sseValues[i + 1].sse; + if (i > 0) { + const prevDiff = sseValues[i - 1].sse - sseValues[i].sse; + // If the current drop is much smaller than the previous one, consider the previous k as elbow + if (prevDiff > diff * 2 && prevDiff > maxDiff) { + maxDiff = prevDiff; + bestKIndex = i; + } + } + } + // If no clear elbow by the above heuristic, pick k where SSE reduction starts to diminish + // Or, more simply, pick the k that had the largest single drop from k-1 to k, after the first one. + if (bestKIndex === 0 && sseValues.length > 1) { + // Fallback if the above heuristic didn't find a clear elbow + optimalK = sseValues[0].k; // Start with the first k + let largestDrop = 0; + for (let i = 0; i < sseValues.length - 1; i++) { + const drop = sseValues[i].sse - sseValues[i + 1].sse; + if (drop > largestDrop) { + largestDrop = drop; + optimalK = sseValues[i + 1].k; + } + } + } else if (sseValues[bestKIndex]) { + optimalK = sseValues[bestKIndex].k; + } + } else if (sseValues.length === 1) { + optimalK = sseValues[0].k; + } + console.log(`Optimal k determined: ${optimalK}`); + + // Run k-means with optimal k + const finalResult = kmeans(positions, optimalK, { + initialization: 'kmeans++', + }); + + let dataPointIndex = 0; + return rawData.data.map((item, originalIndex) => { + const posArray = [item[10], item[11], item[12]]; + let clusterId = `cluster-undefined`; + let finalPosition: [number, number, number]; + + // Check if this point was included in clustering (it might have been filtered out if invalid) + if ( + Array.isArray(posArray) && + posArray.length === 3 && + posArray.every((p) => typeof p === 'number' && !isNaN(p)) + ) { + finalPosition = posArray as [number, number, number]; + if (dataPointIndex < finalResult.clusters.length) { + clusterId = `cluster-${finalResult.clusters[dataPointIndex]}`; + } + dataPointIndex++; + } else { + // Fallback for invalid or filtered out points + finalPosition = [ + (Math.random() - 0.5) * 10, + (Math.random() - 0.5) * 10, + (Math.random() - 0.5) * 10, + ]; + } + + return { + id: String(item[3] || `point-${originalIndex}`), // item[3] is 'id' + position: finalPosition, + color: getClusterColor(clusterId), + clusterId: clusterId, + }; + }); +}; + +export default function VSpacePage() { + const [plotData, setPlotData] = useState([]); + + useEffect(() => { + const typedTsneData: TsneDataType = tsneDataJson as TsneDataType; + const processedData = processDataWithKMeans(typedTsneData); + setPlotData(processedData); + }, []); + + if (plotData.length === 0) { + return
Loading and processing data...
; // Or some other loading indicator + } + + return ( +
+

+ VSpace Page with K-Means Clustering +

+
+ +
+
+ ); +} diff --git a/lexikon/src/app/librarian/vspace/tsne_data.ts b/lexikon/src/app/librarian/vspace/tsne_data.ts new file mode 100644 index 0000000..1eacbe1 --- /dev/null +++ b/lexikon/src/app/librarian/vspace/tsne_data.ts @@ -0,0 +1,56347 @@ + +const tsneData = { + "columns": [ + "chunk_1024_embeddings_id", + "chunk", + "course_id", + "file_id", + "source_file", + "file_type", + "object_id", + "embedding", + "created_at", + "updated_at", + "x", + "y", + "z", + "cluster", + "hover_text" + ], + "index": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53 + ], + "data": [ + [ + 11680, + "Fallbeispiel\n26", + 18240, + 15497727, + null, + "md", + null, + [ + 0.26656806, + 0.5253133, + 0.48931304, + -0.7356992, + -0.74937826, + 0.11828868, + -0.17567016, + 1.0539167, + -0.6556967, + -1.1363449, + 0.59633434, + 0.7216692, + 0.5009073, + 0.22413087, + 0.051205747, + -0.27063003, + -0.6022064, + -0.28698733, + -0.30200198, + -0.37739652, + -0.46618682, + 0.06087881, + -0.32322696, + 0.6103993, + -0.39389443, + -0.36408284, + -0.06465165, + -0.25247136, + 0.29849216, + -0.329827, + 0.15293445, + 0.4391276, + -0.31882104, + -1.1607982, + 0.29291245, + 0.15577278, + 0.48590055, + -0.3544446, + -1.0439099, + 0.31719062, + -0.24835658, + 0.90581405, + -0.41005722, + -0.48515812, + -0.39847326, + -0.50715244, + 0.56266606, + -0.6822293, + -0.26057157, + 0.45712548, + -0.39637214, + 0.1647966, + 0.56844103, + -0.1755153, + 0.014567126, + 0.14899859, + -0.6519034, + 0.055350937, + 0.56038535, + 0.024977146, + -0.2422539, + -0.03229313, + -0.4092406, + -0.4774099, + 0.21014415, + -0.3916191, + 0.5014661, + -0.2616044, + 0.32053307, + -0.0119501315, + 0.0020810738, + -0.20608665, + 1.5120302, + 0.27151453, + -0.56938565, + -0.5938383, + -0.15028925, + 0.5248443, + -0.07836835, + 0.5862071, + 1.9518499, + -0.29352084, + 0.16167137, + 0.6656772, + -0.64406455, + 0.14786011, + 0.29126352, + 0.40705577, + 0.35676283, + -0.70462024, + -0.2845476, + -0.20026281, + 0.16994011, + 0.56802464, + 0.102048956, + 0.06729406, + -0.8646794, + -0.02687789, + 0.9970468, + 0.5349855, + -0.016164992, + 0.12714043, + 0.19145852, + -0.4511863, + -0.13893025, + -0.21061097, + 0.017473783, + 0.50224406, + -0.39983827, + 0.3322587, + 0.8150785, + -0.20400569, + 0.30269042, + -0.050447002, + 0.12337914, + -0.1522372, + -0.08428399, + 0.6042814, + 0.263124, + 0.53562266, + -0.40173313, + -0.760457, + 0.4933507, + -0.5390687, + -0.7922663, + -0.32986876, + 0.5690958, + 0.0022670329, + -0.5344524, + 0.12269394, + 0.062258597, + 0.035294395, + 0.38164723, + 0.031857587, + -0.6859449, + -0.5394271, + 0.61993945, + -0.09601092, + 0.5261599, + -0.8607084, + -0.338204, + 0.34294045, + 0.17505255, + 0.10175794, + 0.2897252, + -0.3890931, + -0.107151136, + -0.59475565, + 0.41035452, + -0.43786067, + 0.36351028, + -0.068275586, + 1.1509488, + -0.49508047, + -0.26332015, + -0.24175367, + -0.49321502, + 0.15217349, + 0.17802197, + -0.16390277, + 0.20737249, + 0.31161678, + -0.59115326, + -1.9579428, + 0.5080412, + 0.35225263, + -0.46993, + -0.1142135, + -0.3094358, + -0.40894303, + 0.17217349, + -0.131535, + 0.66721207, + -0.39847577, + -0.07186281, + 1.1062379, + 0.5459901, + 0.19205517, + -0.05452239, + -0.3005535, + 0.18785034, + -0.019584786, + -0.15299694, + 0.802385, + -0.09650222, + 0.6198228, + 0.33676514, + 0.26600185, + -0.15315658, + 0.23925877, + 0.44982696, + -0.5339282, + -0.2574655, + 0.051052533, + 0.68586266, + 0.44841826, + -0.1668159, + 0.3570672, + 0.055192806, + 0.23274027, + 0.03289677, + -0.6316706, + -0.96117246, + -0.8100191, + -0.021939987, + 0.20976685, + -0.83067364, + -0.06156082, + 0.20507357, + -0.3007136, + 0.019340632, + 0.5069736, + 0.4016804, + -0.15571849, + -0.06319993, + 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Cluster: 2
Chunk: Fallbeispiel\n26" + ], + [ + 11681, + "A1 A2 A3 A4 A5 A6 Sum Note\n10 8 8 6 10 6 48\nStudiengang ::: Bachelor of Science FHGR in Computational and Data Science\nModul ::: Effiziente Algorithmen im FS 2025\nDozent ::: Prof.\n\nDr.\n\nMartin B\u00fcnner Martin\nVorname, Nachname: ________________________________________________________\nBearbeitungszeit ::: 90 Minuten\nErlaubte Hilfsmittel ::: Eigener Taschenrechner\nDisclaimer :::\nL\u00f6sungsweg muss klar ersichtlich sein, unbelegte Resultate k\u00f6nnen nicht ber\u00fccksichtigt werden.\n\no\nKeine Kommunikation unter den Studierenden oder mit Dritten.\n\no\nW\u00e4hrend der Pr\u00fcfung sind Smartphones und Smartwatches auszuschalten.\n\no\nWiederholungsrecht f\u00fcr die Pr\u00fcfung ist bei Betrug verwirkt.\n\no\nL\u00f6sungen m\u00fcssen schriftlich abgegeben werden.\n\no\nHinweise f\u00fcr die Studierenden :::\nPr\u00fcfungsbl\u00e4tter nur einseitig beschriften.\n\no\nLesen Sie zuerst alle Aufgaben sorgf\u00e4ltig durch, bevor Sie zu l\u00f6sen beginnen.\n\nBearbeiten Sie alle\no\nFragestellungen.\n\nDie Aufgaben sind nicht nach Schwierigkeitsgrad geordnet.\n\no\nFachhochschule Graub\u00fcndenAUFGABE 1: (10 Punkte)\n(a) Eine verkettete Liste mit 4 Elementen A[1\u20264] sei mit den jeweiligen Zeigern im Speicher wie folgt abgelegt\nmit A[1] \uf0e0 2001.\n\nErg\u00e4nzen Sie die fehlenden Eintr\u00e4ge so wie in der Vorlesung und den \u00dcbungen. (\n\n4P)\n(b) Zeichnen Sie den Graphen mit der Adjazenzliste: (2P)\nFachhochschule Graub\u00fcnden(c ) Ein bin\u00e4rer Baum ist mit Zeigern im Speicher abgelegt.\n\nTragen Sie die fehlenden Zeiger in die Tabelle ein (wie in\nder Vorlesung und den \u00dcbungen): (4P)\nFachhochschule Graub\u00fcndenAUFGABE 2: (8 Punkte)\n(a) Wir suchen das Feld in der Skipliste mit dem Wert 2.\n\nWelche Werte der Skipliste werden gepr\u00fcft? (\n\n2P)\n(b) Zeichnen Sie einen bin\u00e4ren Suchbaum der Tiefe 3. (\n\n2P)\nFachhochschule Graub\u00fcnden(c) F\u00fcgen Sie nacheinander die folgenden Elemente in einen bin\u00e4ren Suchbaum ein:\nMarlen, Adrian, Peter, Urs, Mike, Bernd, Carl, Karsten, Sabine, Susanne, Christine, Kirsten, Alexander. (\n\n4P)\nFachhochschule Graub\u00fcndenAUFGABE 3: (8 Punkte)\n(a) F\u00fcgen Sie in dem bin\u00e4ren Suchbaum das Element mit dem Schl\u00fcssel-Wert 11 ein.\n\nWie sieht der\nSuchbaum danach aus? (\n\n2P)\n(b) Suchen Sie den Schl\u00fcssel mit dem Wert 5.\n\nWelche Schl\u00fcssel m\u00fcssen f\u00fcr diese Suche abgefragt werden.\n\n(2P)\nFachhochschule Graub\u00fcnden(c) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 5.\n\nWie sieht danach der Suchbaum aus? (\n\n2P)\n(d) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 7.\n\nWie sieht danach der Suchbaum aus? 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Cluster: 2
Chunk: A1 A2 A3 A4 A5 A6 Sum Note\n10 8 8 6 10 6 48\nStudiengang ::: Bachelor of Science FHGR in Computational and Data Science\nModul ::: Effiziente Algorithmen im FS 2025\nDozent ::: Prof.\n\nDr.\n\nMartin B\u00fcnner ..." + ], + [ + 11682, + ")\n(b) Suchen Sie den Schl\u00fcssel mit dem Wert 5.\n\n\n\nWelche Schl\u00fcssel m\u00fcssen f\u00fcr diese Suche abgefragt werden.\n\n\n\n(2P)\nFachhochschule Graub\u00fcnden(c) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 5.\n\n\n\nWie sieht danach der Suchbaum aus? (\n\n\n\n2P)\n(d) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 7.\n\n\n\nWie sieht danach der Suchbaum aus? (\n\n2P)\nFachhochschule Graub\u00fcndenAUFGABE 4: (6 Punkte)\nF\u00fchren Sie Shellsort mit den Schrittweiten 7, 3, 1 aus: (6P)\n1 2 3 4 5 6 7 8 9 10 11 12 13 14\n15 30 17 10 11 4 6 9 2 1 7 32 5 3\nDokumentieren Sie die Schritte so wie in der Vorlesung und den \u00dcbungen.\n\nFachhochschule Graub\u00fcndenAUFGABE 5: (10 Punkte)\n(a) Ordnen Sie die Folge in einem Max-Heap und speichern diesen in einem Feld A=[\u2026.]\n\nab.\n\nDokumentieren Sie\ndie Zwischenergebnisse so wie in der Vorlesung und \u00dcbung. (\n\n4P)\n9\n4 5\n7 10 6 8\n(b) Nehmen Sie das Ergebnis von (a) und f\u00fchren drei Basis-Schritte von Heapsort durch.\n\nDokumentieren Sie die\nZwischenergebnisse so wie in der Vorlesung und \u00dcbung. (\n\n6P)\nFachhochschule Graub\u00fcndenFachhochschule Graub\u00fcndenAUFGABE 6: (6 Punkte)\n(a) Bestimmen Sie die Balancefaktoren des bin\u00e4ren Suchbaums.\n\nHandelt es sich um ein AVL-Baum? (\n\n2P)\n(b) L\u00f6schen Sie aus dem AVL-Baum das Element \u00ab17\u00bb.\n\nIst es danach noch ein AVL-Baum? (\n\n2P)\n(c) F\u00fcgen Sie danach das Element \u00ab15\u00bb hinzu.\n\nIst es danach noch ein AVL-Baum? 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Cluster: 2
Chunk: )\n(b) Suchen Sie den Schl\u00fcssel mit dem Wert 5.\n\n\n\nWelche Schl\u00fcssel m\u00fcssen f\u00fcr diese Suche abgefragt werden.\n\n\n\n(2P)\nFachhochschule Graub\u00fcnden(c) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 5.\n\n\n\nWie..." + ], + [ + 11683, + "prung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n\n\n(a) Stellen Sie ein Gleichungs-System mit 3 nichtlinearen Gleichungen f\u00fcr die Unbekannten (x, y, t) auf.\n\nx2 + y2 \u2013 1 = 0,\nx \u2013 sin(t) = 0,\ny \u2013 sin(2t + \u03c0\/4) = 0\n(b) Finden Sie f\u00fcr jeden Schnittpunkt jeweils einen guten Startwert f\u00fcr den Downhill-Simplex-Algorithmus.\n\nz.B. nur f\u00fcr x>0, da symmetrisch:\n(x ,y , t ) = [0.7, 0.75, arcsin(0.7) ]\n0 0 0\n[0.25, 1, arcsin(0.25) ]\n[1, -0.25, arcsin(1) ]\n[0.7, -0.75, arcsin(0.7) ]\n(b) L\u00f6sen Sie das Gleichungs-System indem Sie ein unrestringiertes Optimierungsproblem mit 3 Design-\nVariablen mit dem Downhill-Simplex-Algorithmus l\u00f6sen.\n\nBestimmen Sie alle L\u00f6sungen mit den Startwerten\nvon (b)\nHier reicht es alle Schnittpunkte mit x > 0 zu bestimmen, da beide Kurven achsensymmetrisch sind.\n\nStartwert [0.7, 0.75, arcsin(0.7) ] \uf0e0 L\u00f6sung [0.70674446 0.7078306 0.78488616]\nStartwert [0.25, 1, arcsin(0.25) ] \uf0e0 L\u00f6sung [0.26014633 0.96664301 0.2631811 ]\nStartwert [1, -0.25, arcsin(1) ] \uf0e0 L\u00f6sung [1, -0.25, arcsin(1) ]\nStartwert [0.6, -0.85, np.arcsin(0.6)] \uf0e0 L\u00f6sung [0.70732644 0.70666723 0.78570889], hier musste\nder Startwert noch variiert\/probiert werden, bis die Konvergenz auf den gesuchten Schnittpunkt erreicht\nwurde.\n\n51\u00dcbung 7.2.5\nEs seien folgende Optimierungsprobleme zu l\u00f6sen.\n\nGehen Sie jeweils wie folgt vor:\n(a) Graphische L\u00f6sung.\n\n(b) Aufstellen eines unrestringierten 2-dim Optimierungsproblems.\n\n(c) L\u00f6sen des unrestringierten 2-dim Optimierungsproblems mittels Downhill-Simplex-Algorithmus.\n\n(I) Min [ x + x ]\n1 2\nx \u2265 x 2\n2 1\nL\u00f6sung (-0.5; 0.25)\n(II) Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\nL\u00f6sung (0.5; -0.5)\n52\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .", + 18240, + 8452030, + null, + "md", + null, + [ + 0.3179646, + -0.103203535, + -0.03015862, + -0.33203667, + 0.21279645, + 0.25659063, + 0.264594, + 0.5064091, + -0.5108686, + 0.1827112, + -0.2057376, + 0.41959545, + 0.16683517, + 0.05212103, + 0.4227515, + 0.40559927, + 0.49333963, + -0.033038758, + 0.5204243, + -0.29663774, + -0.56361336, + 0.06465066, + 0.3913966, + 0.48964748, + -0.6096554, + 0.39608756, + -0.13872966, + 0.1466504, + 0.10674975, + 1.1831636, + -0.17645934, + -0.07092726, + -0.3881887, + -0.3163711, + -0.27526757, + -0.6076026, + 0.8258395, + 0.20650324, + -0.659403, + -0.299567, + -0.42918032, + 1.034678, 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Cluster: 7
Chunk: prung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n\n\n(a) Stellen Sie ein Gleichungs-System mit 3 nic..." + ], + [ + 11684, + "von nichtlinearen Gleichungen M. B\u00fcnner B3.02\n4.2 Dowhill-Simplex, Restriktionen, grafische L\u00f6sung, M. B\u00fcnner B3.02\n18 02.05.2025 13:30-15:00 Fr Straffunktionen\n19 08.05.2025 13:30-15:00 Do 4.3 Newton: In 2-dim mit inv.\n\nHesse-Matrix l\u00f6sen M. B\u00fcnner B3.02\n19 09.05.2025 13:30-15:00 Fr 4.4 Steepest Descent M. B\u00fcnner B3.02\n20 15.05.2025 13:30-15:00 Do 5.\n\nRestringierte Optimierung in 2D linear M. B\u00fcnner B3.02\n20 16.05.2025 13:30-15:00 Fr in 2D nichtlinear M. B\u00fcnner B3.02\n21 22.05.2025 13:30-15:00 Do Linear Programming M. B\u00fcnner B3.02\n21 23.05.2025 13:30-15:00 Fr Nonlinear Programming M. B\u00fcnner B3.02\n22 30.05.2025 13:30-15:00 Fr \u00dcbungen M. B\u00fcnner B3.02\nM. B\u00fcnner B3.02\nHinweis:\n- Zwischen den Pr\u00e4senzterminen werden Coachings angeboten, die Termine dazu werden in der Pr\u00e4senzveranstaltung vereinbart.\n\nUnterschrift DozentLeistungsnachweis\nK\u00fcrzel cds204\nName Effiziente Algorithmen\nTyp Modul\nECTS 4\nProzent Art Form Hilfsmittel Bemerkungen Dauer (minuten)\n100%abgesetzte Modulschlusspr\u00fcfung schriftlich closed book eigener Taschenrechner nicht programmierbar 90\nFormelsammlung\nkeine\nUnterschrift Dozierende", + 18240, + 1108348, + null, + "md", + null, + [ + -0.5218154, + 0.13426243, + 0.46825144, + 0.22017159, + -0.27285972, + -0.035619803, + -0.3280491, + -0.19852506, + -0.15948859, + -0.4725128, + -0.87682056, + 0.38066867, + 0.68204814, + -0.77832884, + -0.29271296, + 0.8834769, + 0.22653359, + 0.1016869, + -0.29083347, + -0.7487563, + -0.9182125, + 0.06628891, + 0.4982129, + 0.79736966, + -0.7450637, + 0.021702912, + -0.49623805, + 0.32254717, + 0.39165622, + 0.6728145, + -0.20694828, + 0.38492647, + 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Cluster: 0
Chunk: von nichtlinearen Gleichungen M. B\u00fcnner B3.02\n4.2 Dowhill-Simplex, Restriktionen, grafische L\u00f6sung, M. B\u00fcnner B3.02\n18 02.05.2025 13:30-15:00 Fr Straffunktionen\n19 08.05.2025 13:30-15:00 Do 4.3 Newton..." + ], + [ + 11690, + "17.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nExpansion\nf\nf\nn+1\nf\nn\nf\n2\nf\n1\nf\nr7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nContraction\nf\nf\nf n+1\nr\nf\nn\nf\n2\nf\n17.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nShrinkage7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nKonstruktion des Start-Simplex\n\uf0fc oft automatisiert (x% Abweichungen von einem Startpunkt)\n\uf0fc Gr\u00f6sse des Start-Simplex bestimmt die initiale Schrittweite\nRestringierte Probleme\n\uf0fc 1-3 Gleichheits- oder Ungleichheits-Restriktionen mittels Straf-Funktionen in die Zielfunktion integrieren.7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nAbbruchbedingungen7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nRosenbrock (Banana) Function\n2 2 2\n\ud835\udc53\ud835\udc53(\ud835\udc65\ud835\udc65, \ud835\udc66\ud835\udc66) = (1 \u2212 \ud835\udc65\ud835\udc65) + 100(\ud835\udc66\ud835\udc66 \u2212 \ud835\udc65\ud835\udc65 )7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex \u2013 Himmelblau Funktion7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-AlgorithmusIn Python \uf0e0 scipy.optimize.fmin\nscipy.optimize.fmin \u2014 SciPy v1.13.0 Manual\n\uf0e0 einfaches Bsp in der Vorlesung\n\uf0e0 \u00dcbung 7.2.1 u 7.2.2\n227.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nVorteile\n\uf0fe Sehr einfach anzuwenden\n\uf0fe Simplex passt sich der \u201elandscape\u201c der Funktion an\n\uf0fe Ausreichende Stabilit\u00e4t f\u00fcr die meisten Probleme mit N<5\n\uf0fe Effizienz in der Praxis gut genug f\u00fcr kleine N<5\n\uf0fe Zielfunktion muss nicht in analytisch geschlossener Form\nbekannt sein\n\uf0fe Ben\u00f6tigt keine stetig ableitbaren Zielfunktionen\n\uf0fe keine Ableitungen\n\uf0fe Findet lokale Minima\n\uf0fe Einige wenige Constraints k\u00f6nnen mit Penalties abgebildet\nwerden.7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nNachteile\n\uf0fe Ineffizient (im Vergleich zu Quasi-Newton-Methoden),\ndeswegen nur f\u00fcr kleine N<=5 anzuwenden.\n\n\uf0fe Constraints k\u00f6nnen \u201enicht wirklich\u201c abgebildet werden.", + 18240, + 8862084, + null, + "md", + null, + [ + -0.76435846, + 0.1580934, + -0.073087715, + -0.29936278, + 0.10853522, + -0.093156494, + 0.2814383, + 0.5353781, + 0.51500803, + -0.0007352047, + -0.3806599, + -0.15644482, + 0.41168827, + 0.22463825, + 0.088270195, + 0.6871004, + -0.45986772, + 0.57305783, + 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Cluster: 5
Chunk: 17.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nExpansion\nf\nf\nn+1\nf\nn\nf\n2\nf\n1\nf\nr7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nContraction\nf\nf\n..." + ], + [ + 11685, + "Semesterinformation\nK\u00fcrzel cds204\nName Effiziente Algorithmen\nTyp Modul\nECTS 4\nKW Datum Zeit Wochentag Thema Inhalt Dozent Raum Bemerkungen\n8 20.02.2025 13:30-15:00 Do 1.\n\nDynamische Speicherstrukturen einfach und doppelt verkettete Listen (evtl.\n\nFIFO u LIFO) M. B\u00fcnner B3.02\n8 21.02.2025 13:30-15:00 Fr (evtl.\n\nFIFO u LIFO) Bin\u00e4re B\u00e4ume und Graphen M. B\u00fcnner B3.02\n9 27.02.2025 13:30-15:00 Do \u00dcbungen M. B\u00fcnner B3.02\n9 28.02.2025 13:30-15:00 Fr 1.\n\nHeaps und Heapsort 1.1 Heaps M. B\u00fcnner B3.02\n10 06.03.2025 13:30-15:00 Do 1.1 \u00fcben und 1.2 Heapsort M. B\u00fcnner B3.02\n10 07.03.2025 13:30-15:00 Fr 1.2 Heapsort mit Python M. B\u00fcnner B3.02\n11 13.03.2025 13:30-15:00 Do \u00dcbungen M. B\u00fcnner B3.02\n11 14.03.2025 13:30-15:00 Fr 3.\n\nNicht-konvexe Optimierung in einer Dimension Parameterscan, Bisektionen M. B\u00fcnner B3.02\n12 20.03.2025 13:30-15:00 Do Modifizierter Newton M. B\u00fcnner B3.02\n12 21.03.2025 13:30-15:00 Fr \u00dcbungen M. B\u00fcnner B3.02\n13 27.03.2025 13:30-15:00 Do Schrittweitensteuerung M. B\u00fcnner B3.02\n13 28.03.2025 13:30-15:00 Fr Straffunktionen M. B\u00fcnner B3.02\n14 03.04.2025 13:30-15:00 Do \u00dcbungen M. B\u00fcnner B3.02\n4.\n\nReelwertige Optimierung in N Dimensionen 4.0 Definitionen M. B\u00fcnner B3.02\n14 04.04.2025 13:30-15:00 Fr 4.1 Parameter-Scan\n17 24.04.2025 13:30-15:00 Do 4.2 Downhill-Simplex-Algorithmus M. B\u00fcnner B3.02\n17 25.04.2025 13:30-15:00 Fr \u00dcbungen M. B\u00fcnner B3.02\n18 01.05.2025 13:30-15:00 Do 4.2 Dowhill-Simplex L\u00f6sung von nichtlinearen Gleichungen M. B\u00fcnner B3.02\n4.2 Dowhill-Simplex, Restriktionen, grafische L\u00f6sung, M. 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Cluster: 0
Chunk: Semesterinformation\nK\u00fcrzel cds204\nName Effiziente Algorithmen\nTyp Modul\nECTS 4\nKW Datum Zeit Wochentag Thema Inhalt Dozent Raum Bemerkungen\n8 20.02.2025 13:30-15:00 Do 1.\n\nDynamische Speicherstrukture..." + ], + [ + 11686, + "Effiziente Algorithmen\n7.\n\nReelwertige Optimierung in N Dimensionen\nProf.\n\nDr.\n\nMartin B\u00fcnner7.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nMotivation:\n20 Datenpunkte in DataSinus2.csv\n[xData,yData] = np.loadtxt('DataSinus2.csv',delimiter=';\u2019)\nModellierung der Daten mit einer Funktion aus der Modellklasse\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit 4 freien Parametern a = ( a , a , a , a ).\n\nWas sind die besten Werte f\u00fcr a?\n\n1 2 3 4\nAnsatz mit der Summe der kleinsten Quadrate (Gauss, 17xx):\n\uf0e0 Tafel\n\uf0e0 Optimierungs-Problem mit 4 Design-Variablen\nMin S( a , a , a , a )\n1 2 3 4\nWie wir so was l\u00f6sen ist der Inhalt dieses Kapitels.\n\n17.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nReellwertiges unrestringiertes Optimierungs-Problem mit N Design-Variablen (N Dimensionen)\nMin f(x), x in RN\nMin f(x x x ), (x x x ) in RN\n1 , 2 , ...., N 1 , 2 , ...., N\nBemerkungen:\n1.\n\nAbleitung f\u2019(x) \uf0e0 Gradient gradf(x)\n2.\n\nAbleitung f\u2019\u2019(x) \uf0e0 Hesse-Matrix H(x)\nf(x) ist konvex, falls H(x) positiv definit (alle Eigenwerte positiv) ist.\n\n\uf0e0 Es existiert kein oder genau ein lokales Minimum.\n\nSei f(x) 2-mal differenzierbar, dann gilt f\u00fcr lokale Minima an der Stelle x*:\ngradf(x*) = 0\nH(x*) positiv definit (alle Eigenwerte positiv).\n\nBemerkung: Die Definitheit der Hesse-Matrix m\u00fcssen Sie (noch) nicht selber berechnen k\u00f6nnen.\n\n27.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nBeispiel: 2D-Optimierungsproblem mit der Himmelblau-Funktion\nMin f(x x )\n1 , 2\nmit (x 2+ x -11) 2 + (x + x 2 -7) 2.\n\n1 2 1 2\n4 lokale Minima:\n\uf0e0 \u00dcbung\n37.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nBeispiel: 2D-Optimierungsproblem mit der Rosenbrock-Funktion\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\n1 , 2 1 2 1\nL\u00f6sung f(1,1) = 0.\n\n\uf0e0 \u00dcbung\n47.\n\nReelwertige Optimierung in N Dimensionen\n7.1 Parameterscan\nMin f(x), x in RN\nin Hypercube x \u2264 x \u2264 x und x - x = L.\nlow up up low\nEinfache Absch\u00e4tzung: Wir unterteilen jede Design-Variable in M = L\/T \u00e4quidistante Segmente.\n\nDann werden MN\ni i\nFunktionsauswertungen ben\u00f6tigt.\n\n\uf0e0 Tafel\nBeispiel:\n1.", + 18240, + 7444022, + null, + "md", + null, + [ + -0.8677129, + 0.38449863, + 0.5034417, + -0.103949405, + -0.37239298, + -0.27794132, + 0.391599, + -0.10016646, + -0.023383643, + -0.031077437, + -0.52423716, + -0.08872123, + 0.30995914, + 0.6822999, + -0.119418964, + 0.09067443, + 0.016116112, + 0.6925044, + 0.024169125, + -0.37904954, + -0.22198641, + -0.31751263, + 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Cluster: 5
Chunk: Effiziente Algorithmen\n7.\n\nReelwertige Optimierung in N Dimensionen\nProf.\n\nDr.\n\nMartin B\u00fcnner7.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nMotivation:\n20 Datenpunkte in DataSinus2.cs..." + ], + [ + 11687, + "ige Optimierung in N Dimensionen\n7.1 Parameterscan\nMin f(x), x in RN\nin Hypercube x \u2264 x \u2264 x und x - x = L.\nlow up up low\nEinfache Absch\u00e4tzung: Wir unterteilen jede Design-Variable in M = L\/T \u00e4quidistante Segmente.\n\n\n\nDann werden MN\ni i\nFunktionsauswertungen ben\u00f6tigt.\n\n\n\n\uf0e0 Tafel\nBeispiel:\n1.\n\nN=2, M=100, Zeit f\u00fcr eine Funktionsauswertung: 1 ms:\n\uf0e0 Rechenzeit: 1002 * 0.001 s = 105 s = 10 s\n2.N=4, M=100, Zeit f\u00fcr eine Funktionsauswertung: 1 ms:\n\uf0e0 Rechenzeit: 1004 * 0.001 s = 105 s = 1.2 Tage\n3.\n\nN=10, M=100, Zeit f\u00fcr eine Funktionsauswertung: 1 ms:\n\uf0e0 Rechenzeit: 10010 * 0.001 s = 1017 s = 3.2 Mrd.\n\nJahre (zum Vergleich: Alter des Sonnensystems ca.\n\n13 Mrd.\n\nJ.)\n57.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nheisst auch Downhill-Simplex-Algorithmus. \u00ab\n\nL\u00f6st\u00bb das unrestringierte N-dimensionale reelwertige Optimierungsproblem:\nMin f(x), x in RN\nNelder, Mead (1965)\nBemerkungen:\n\uf0fc f(x) muss stetig sein.\n\n\uf0fc Verwendung in der Praxis f\u00fcr 2 \u2264 N \u2264 10.\n\nF\u00fcr N gr\u00f6sser 10 zu in-effizient.\n\n\uf0fc Vorteil: gradienten-frei\n67.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nIdee des Downhill Simplex-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\n97.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nf\nGegeben: Simplex S mit n+1 Ecken, x x\n1, \u2026 n+1\nf\nn+1\n1.\n\nWerte f aus und sortiere f(x ) < f(x )< \u2026 < f(x )\nf\n1 2 n+1 n\n2.\n\nBerechne Zentrum der n besten Punkte\n1 n\n\u2211\nx = x\ni\nn\ni=1\nf\n2\nf\n1\n3.\n\nF\u00fchre genau eine Operation\nReflection, Expansion, Contraction oder Shrink\ndurch7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nReflection Spiegele x an Schwerpunkt\nn+1\nf\nf\nn+1\nf\nn\nf\nr\nf\n2\nf\n17.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nExpansion\nf\nf\nn+1\nf\nn\nf\n2\nf\n1\nf\nr7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nContraction\nf\nf\nf n+1\nr\nf\nn\nf\n2\nf\n17.", + 18240, + 7260651, + null, + "md", + null, + [ + -0.111103505, + 0.27513874, + -0.024644863, + 0.24044713, + -0.5872487, + 0.96111953, + 0.5122346, + -0.691302, + 0.4659692, + -0.34453544, + 0.18847531, + 0.4933645, + 0.34522104, + 0.5350193, + -0.28161824, + -0.20311373, + 0.117067285, + 0.32576823, + 0.8590197, + -1.1533606, + -0.5475748, + 0.09159811, + -0.13058528, + 0.8629452, + -0.4752931, + 1.0980564, + -0.7857408, + -0.7443192, + 0.06946849, + 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Cluster: 6
Chunk: ige Optimierung in N Dimensionen\n7.1 Parameterscan\nMin f(x), x in RN\nin Hypercube x \u2264 x \u2264 x und x - x = L.\nlow up up low\nEinfache Absch\u00e4tzung: Wir unterteilen jede Design-Variable in M = L\/T \u00e4quidista..." + ], + [ + 11688, + "Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\nL\u00f6sung (0.5; -0.5)\n52\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\ngradf(x x ) = (3x 2 - 4 , 2x )\n1 , 2 1 2\n(b) Bestimmen Sie H(x x ) .\n\n1 , 2\nH(x x ) = ( (6x , 0), (0, 2) )\n1 , 2 1\n(c) Bestimmen Sie H-1(x x ) .\n\n1 , 2\nH(x x ) -1 = ( (1\/(6x ), 0), (0, 1\/2) )\n1 , 2 1\n(d) F\u00fchren Sie eine Newton-Iteration durch und Bestimmen Sie (x x ) mit dem Startwert (x =1 x =1) .\n\n1,1 , 2,1 1,0 , 2,0\n(x x ) = (7\/6, 0)\n1,1 , 2,1\n53\u00dcbung 7.3.2\nWir suchen das Minimum der Rosenbrock-Funktion oder Banana-Funktion:\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n(a) Berechnen Sie den Gradienten, die Hesse-Matrix und die inverse Hesse-Matrix der Rosenbrock-Funktion.\n\ngrad f(x x ) = ( 2(1 \u2013 x ) + 200(x - x 2) * (-2x ), 200(x - x 2) ) = ( 2 \u2013 2x - 400x x + 400x 3 , 200x - 200x 2 )\n1 , 2 1 2 1 1 2 1 1 1 2 1 2 1\nf = \u2013 2 - 400x + 1200x 2\n11 2 1\nf = - 400x\n12 1\nf = 200\n22\nH(x x ) = ( (f , f ) , (f , f ) )\n1 , 2 11 12 12 22\nD(x x ) = f * f - f 2\n1 , 2 11 22 12\nH-1(x x ) = ( (f , -f ) , (-f , f ) ) \/ D(x x )\n1 , 2 22 12 12 11 1 , 2\n(b) Stellen Sie damit die Newton-Iterations-Formel auf.\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n54\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\n(b) Es sei a = 1.", + 18240, + 14859415, + 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Cluster: 4
Chunk: Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\nL\u00f6sung (0.5; -0.5)\n52\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\ngradf(x x..." + ], + [ + 11689, + "die Newton-Iterations-Formel auf.\n\n\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n\n\n54\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n\n\n1 , 2\n(b) Es sei a = 1.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\ngradf = (3x 2 \u2013 4, 2x )\n1 2\nIteration x x gradf |gradf |2\n1,n 2,n n n\nn = 0 1, 1 -1, 2 5\nn = 1 2,-1 8,-2 68\nn = 2 -6,1 104,2 10\u2019820\n(c) a=0.1: Iteration x x gradf 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Cluster: 4
Chunk: die Newton-Iterations-Formel auf.\n\n\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n\n\n54\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n\n\n1 , 2 1 1..." + ], + [ + 11703, + "Fallbeispiel\n26", + 18240, + 16380617, + null, + "md", + null, + [ + 0.26656806, + 0.5253133, + 0.48931304, + -0.7356992, + -0.74937826, + 0.11828868, + -0.17567016, + 1.0539167, + -0.6556967, + -1.1363449, + 0.59633434, + 0.7216692, + 0.5009073, + 0.22413087, + 0.051205747, + -0.27063003, + -0.6022064, + -0.28698733, + -0.30200198, + -0.37739652, + -0.46618682, + 0.06087881, + -0.32322696, + 0.6103993, + -0.39389443, + -0.36408284, + -0.06465165, + -0.25247136, + 0.29849216, + -0.329827, + 0.15293445, + 0.4391276, + -0.31882104, + -1.1607982, + 0.29291245, + 0.15577278, + 0.48590055, + -0.3544446, + -1.0439099, + 0.31719062, + -0.24835658, + 0.90581405, + -0.41005722, + -0.48515812, + -0.39847326, + -0.50715244, + 0.56266606, + 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Cluster: 2
Chunk: Fallbeispiel\n26" + ], + [ + 11691, + "Einige wenige Constraints k\u00f6nnen mit Penalties abgebildet\nwerden.7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nNachteile\n\uf0fe Ineffizient (im Vergleich zu Quasi-Newton-Methoden),\ndeswegen nur f\u00fcr kleine N<=5 anzuwenden.\n\n\n\n\uf0fe Constraints k\u00f6nnen \u201enicht wirklich\u201c abgebildet werden.\n\n\uf0fe Wahl des Startsimplex hat Einfluss welches lokale Minimum\ngefunden wird\n\uf0fe Wahl des Startsimplex beeinflusst Konvergenz\n\uf0fe Fehlender Konvergenzbeweis\n\uf0fe Z.B. Stagnation um nicht-optimalen Punkt\n\uf0fe rauschempfindlich7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill-Simplex mit Zielfunktion mit additivem Rauschen s:\nMin [ f(x) + s ], x in RN\nBeispiele\n\uf0fc Double Precision Numerik: \u03c3 \u2248 10-16,\ns\n\uf0fc FEM-Simulation: \u03c3 \u2248 10-3 - 10-6.\n\ns\nBest-Practice\n\uf0fc Anwender (Sie!)\n\nm\u00fcssen das Rauschlevel kennen.\n\n\uf0fc Toleranzen (ftol, xtol) an das Rauschlevel anpassen.\n\n257.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nNichtlineare Regression mit Downhill-Simplex \uf0e0 \u00dcbung 7.2.2\nNichtlineare Gleichungen l\u00f6sen mit Downhill-Simplex \uf0e0 \u00dcbung 7.2.3\nSchnittpunkte zweier Kreise:\n- M1: Kreis um (1,2) mit Radius 3,\n- M2: Kreis um (2,0) mit Radius 2.\n\n(a) Optimierungs-Problem aufstellen.\n\n(b) Gute Startpunkte bestimmen.\n\n(c) L\u00f6sen mit Downhill-Simplex.\n\n267.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nIhre Aufgaben ist es einen Satelliten am Ort (x,y) so zu\npositionieren, dass er sich im Gravitationsfeld der 3\nM3 M1 Planeten nicht bewegt.\n\n(a) Optimierungs-Problem aufstellen.\n\n(b) Gute Startpunkte bestimmen.\n\n(c) L\u00f6sen mit Downhill-Simplex.\n\nM2\n277.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\n2-Dim Optimierungsproblem mit 2 linearen\nUngleichheitsbedingungen\nMin [ x 2 + x 2 ]\n1 2\nx + x \u2265 1\n1 2\nx \u2265 2\n2\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\nBemerkung: Da die Zielfunktion f\u00fcr den Downhill-Simplex\nAlgorithmus nicht differenzierbar sein muss, k\u00f6nnen hier\nlineare Straffunktionen (genauso wie quadratische\nStraffunktionen) verwendet werden.\n\n287.", + 18240, + 13135318, + null, + "md", + null, + [ + -0.8539323, + -0.3307703, + -0.12917906, + -0.9902739, + -0.6676355, + 1.2118105, + 0.029592108, + 0.22852649, + -0.17469743, + 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Cluster: 3
Chunk: Einige wenige Constraints k\u00f6nnen mit Penalties abgebildet\nwerden.7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nNachteile\n\uf0fe Ineffizient (im Vergleich zu Qu..." + ], + [ + 11692, + "1\n1 2\nx \u2265 2\n2\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\nBemerkung: Da die Zielfunktion f\u00fcr den Downhill-Simplex\nAlgorithmus nicht differenzierbar sein muss, k\u00f6nnen hier\nlineare Straffunktionen (genauso wie quadratische\nStraffunktionen) verwendet werden.\n\n\n\n287.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\n2-Dim Optimierungsproblem mit 1 nichtlineare\nGleichheitsbedingung\nMin [ x 2 + x 2 ]\n1 2\nx = ( x \u2013 2 ) 2\n2 1\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\n\uf0e0 \u00dcbungen 7.2.3 \u2013 7.2.5\n297.\n\nReelwertige Optimierung in N Dimensionen\n7.3 Newton-Verfahren\nMin f(x), x in RN\nf\u00fcr N = 1: Newton-Algorithmus\nx = x \u2013 f\u2019 \/f\u2019\u2019 = x \u2013 (f\u2019\u2019 )-1*f\u2019\nn+1 n n n n n n\nf\u00fcr N > 1: Newton-Algorithmus\nx = x \u2013 H-1 (x ) * gradf (x )\nn+1 n n n\nx = x \u2013 H-1 * gradf\nn+1 n n n\nWie f\u00fcr N=1: exakt falls f(x) quadratisch.\n\n307.\n\nReelwertige Optimierung in N Dimensionen\n7.3 Newton-Verfahren\nFalls f(x) nicht quadratisch und wir haben einen guten Startwert in der N\u00e4he eines lokalen Minimus (konvexes Gebiet),\nf\u00fchren wir Iterationen durch\n\uf0e0 Beispiel \u00dcbung 7.3.1 in der Vorlesung\nWeiter: Um die Stabilit\u00e4t zu erh\u00f6hen f\u00fchren wir noch eine Schrittweiten-Steuerung ein (hilft, wenn der Startwert nicht so gut\nsein sollte).\n\nx = x \u2013 c*H-1 * gradf\nn+1 n i n n\nmit 0 < c \u2264 1 steuert die Schrittweite (z.B. Backtracking).\n\ni\n\uf0e0 Hausaufgabe 7.3.2\n317.\n\nReelwertige Optimierung in N Dimensionen\n7.4 Steepest-Descent\nOder auch: Methode des steilsten Abstiegs\nIn der Praxis erweist sich die Bestimmung von H-1 als schwierig und aufwendig.\n\nn\n\uf0e0 H-1 durch etwas \u00abeinfacheres\u00bb n\u00e4hern.\n\nn\n\uf0e0 brutal einfache Idee : H-1 \u2248 1.\n\nn\nDamit ergibt sich der Steepest-Descent-Algorithmus mit Schrittweitensteuerung:\nx = x \u2013 c * gradf\nn+1 n i n\nmit c > 0 steuert die Schrittweite.\n\ni\nDer Steepest-Descent-Alg ist nicht invariant unter der Transformation f \uf0e0 a*f und x \uf0e0 b*x.\n\nDamit liefert der Steepest-\nDescent-Alg NUR eine Richtung.\n\nEr liefert keine Information \u00fcber die L\u00e4nge des Schrittes.\n\n\uf0e0 \u00dcbung 7.7.1 ohne Schrittweitensteuerung.\n\n327.", + 18240, + 13504558, + null, + "md", + null, + [ + -1.1105303, + 0.06864798, + 0.71910334, + -0.18397726, + 0.40972143, + -0.7054399, + 0.120589584, + 0.06977714, + 0.17998672, + 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Cluster: 5
Chunk: 1\n1 2\nx \u2265 2\n2\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\nBemerkung: Da die Zielfunktion f\u00fcr den Downhill-Simplex\nAlgorithmus nicht differenzierbar sein m..." + ], + [ + 11693, + "mit c > 0 steuert die Schrittweite.\n\n\n\ni\nDer Steepest-Descent-Alg ist nicht invariant unter der Transformation f \uf0e0 a*f und x \uf0e0 b*x.\n\n\n\nDamit liefert der Steepest-\nDescent-Alg NUR eine Richtung.\n\n\n\nEr liefert keine Information \u00fcber die L\u00e4nge des Schrittes.\n\n\n\n\uf0e0 \u00dcbung 7.7.1 ohne Schrittweitensteuerung.\n\n\n\n327.\n\nReelwertige Optimierung in N Dimensionen\n7.4 Steepest-Descent\nAusserdem ist die Richtung des Gradienten bei nicht-isotropen Minima (dies ist die Regel) schlecht.\n\nisotrop: Gradientenrichtung gut nicht-isotrop: Gradientenrichtung schlecht\n.\n\nDies f\u00fchrt zu dem \u00abklassischen\u00bb Zig-Zag-Routen, die nicht erw\u00fcnscht sind.\n\n337.\n\nReelwertige Optimierung in N Dimensionen\n7.4 Steepest-Descent\nIdee einer SEHR einfachen Schrittweitensteuerung:\nFalls Schrittweite zu gross: Reduziere Schrittweise iterativ um die H\u00e4lfte, so lange bis f < f .\n\nn+1 n\nFalls Schrittweite evtl zu klein: Verdopple Schrittweite so lange bis f < f , dann reduziere Schrittweite noch 1-mal.\n\nn+1 n\n\uf0e0 test_Steepest_Descent.py\n\uf0e0 \u00dcbung 7.7.2\n34\u00dcbungen\n35\u00dcbung 7.1.1\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem Parameterscan.\n\nVariieren Sie dazu die Parameter in dem Bereich:\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8).\n\nF\u00fchren Sie dabei Parameterscans mit jeweils 4, 8, 16, 32, 64 Testpunkten pro Parameter durch\n(wenn ihr Computer 128 und mehr schafft, probieren Sie es).\n\nVisualisieren Sie Ihre Ergebnisse.\n\n36\u00dcbung 7.2.1\nFinden Sie jeweils die L\u00f6sung des Optimierungsproblems\nMin f(x)\nmit Hilfe des Downhill-Simplex-Algorithmus.\n\nVariieren Sie die Startwerte um ko-existierende lokale Minima zu entdecken.\n\n(a) f(x) = -1\/5 x 3 + 10x + x 2.\n\n1 1 2\n(b) f(x) = 10( x \u2013 1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.\n\n1 2 1 2\n37\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Downhill-Simplex-Algorithmus. ,", + 18240, + 6084764, + null, + "md", + null, + [ + 0.21610712, + -0.34170783, + -0.2582149, + -0.13907842, + -0.61066616, + 1.0078603, + -0.3240201, + 0.6873087, + -0.29100716, + 0.42602944, + -0.19834957, + 0.07046136, + 0.19968116, + 0.05360639, + 0.4098373, + 0.8089135, + 0.38811648, + 0.020720284, + -0.29712686, + 0.09276265, + 0.24396716, + 0.49379405, + 0.14850694, + 0.8405345, + -0.42497858, + 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Cluster: 3
Chunk: mit c > 0 steuert die Schrittweite.\n\n\n\ni\nDer Steepest-Descent-Alg ist nicht invariant unter der Transformation f \uf0e0 a*f und x \uf0e0 b*x.\n\n\n\nDamit liefert der Steepest-\nDescent-Alg NUR eine Richtung.\n\n\n\nEr ..." + ], + [ + 11694, + "05x 2.\n\n\n\n1 2 1 2\n37\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\n\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Downhill-Simplex-Algorithmus. ,\n\n(a) Extrahieren Sie aus den Daten (durch Analyse und \u00dcberlegung) einen geeigneten Startwert.\n\nBestimmen Sie die Parameter.\n\n(b) W\u00e4hlen Sie per Zufall verschiedene Startwerte im Bereich\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8)\nund bestimmen Sie die Parameter.\n\nVisualisieren Sie Ihre Ergebnisse.\n\n38\u00dcbung 7.2.3\nL\u00f6sen Sie die folgenden nichtlinearen Gleichungs-Systeme durch Formulieren eines Optimierungs-Problems und dem Downhill-\nSimplex-Verfahren.\n\nVersuchen Sie eventuell vorhanden mehrere L\u00f6sungen durch Variation des Anfangswertes zu finden:\n(a)\n(b)\n(c)\n39\u00dcbung 7.2.4\nWir suchen die Schnittpunkte der folgenden Linien:\n- ein Kreis um den Ursprung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n(a) Stellen Sie ein Gleichungs-System mit 3 nichtlinearen Gleichungen f\u00fcr die Unbekannten (x, y, t)\nauf.\n\n(b) Finden Sie f\u00fcr jeden Schnittpunkt jeweils einen guten Startwert f\u00fcr den Downhill-Simplex-\nAlgorithmus.\n\n(b) L\u00f6sen Sie das Gleichungs-System indem Sie ein unrestringiertes Optimierungsproblem mit 3\nDesign-Variablen mit dem Downhill-Simplex-Algorithmus l\u00f6sen.\n\nBestimmen Sie alle L\u00f6sungen mit\nden Startwerten von (b)\n40\u00dcbung 7.2.5\nEs seien folgende Optimierungsprobleme zu l\u00f6sen.\n\nGehen Sie jeweils wie folgt vor:\n(a) Graphische L\u00f6sung\n(b) Aufstellen eines unrestringierten 2-dim Optimierungsproblems\n(c) L\u00f6sen des unrestringierten 2-dim Optimierungsproblems mittels Downhill-Simplex-Algorithmus\n(I) Min [ x + x ]\n1 2\nx \u2265 x 2\n2 1\n(II) Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\n41\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\n(b) Bestimmen Sie H(x x ) .\n\n1 , 2\n(c) Bestimmen Sie H-1(x x ) .\n\n1 , 2\n(d) F\u00fchren Sie eine Newton-Iteration \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem Startwert (x =1 x =1) .", + 18240, + 16279813, + null, + "md", + null, + [ + 0.13278152, + -0.31135443, + -0.06157794, + -0.0959831, + -0.2155047, + 0.18859595, + -0.16959096, + -0.34411773, + -0.041826297, + -0.31756315, + -0.9122811, + -0.22444451, + 0.0037292335, + 0.19952506, + 0.5708837, + 0.2232977, + 0.6871961, + 0.60550606, + 0.30983904, + -0.8043447, + -0.27264884, + 0.05291005, 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Cluster: 1
Chunk: 05x 2.\n\n\n\n1 2 1 2\n37\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\n\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Dow..." + ], + [ + 11695, + ".\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n\n\n1 , 2\n(b) Bestimmen Sie H(x x ) .\n\n\n\n1 , 2\n(c) Bestimmen Sie H-1(x x ) .\n\n\n\n1 , 2\n(d) F\u00fchren Sie eine Newton-Iteration \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem Startwert (x =1 x =1) .\n\n1,1 , 2,1 1,0 , 2,0\n42\u00dcbung 7.3.2\nWir suchen das Minimum der Rosenbrock-Funktion oder Banana-Funktion:\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n(a) Berechnen Sie den Gradienten, die Hesse-Matrix und die inverse Hesse-Matrix der Rosenbrock-Funktion.\n\n(b) Stellen Sie damit die Newton-Iterations-Formel auf und programmieren diese in Python.\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n43\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\n(b) Es sei a = 1.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\n(c) Es sei a = 0.1.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\n(d) Es sei a = 10.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\n44\u00dcbung 7.4.2\nWir suchen das Minimum der Rosenbrock-Funktion oder Banana-Funktion:\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n(a) Berechnen Sie den Gradienten der Rosenbrock-Funktion.", + 18240, + 2278700, + null, + "md", + null, + [ + -0.3088199, + 0.3640352, + -0.31572556, + -0.5632599, + -0.8831732, + 0.1357588, + 0.2828112, + -0.073052764, + -0.14028428, + -0.4137379, + -0.3779284, + 0.22783622, + 0.6706066, + -0.056991823, + 0.40256593, + 0.05860323, + -0.27182338, + 0.73582864, + 0.04000853, + -0.95994973, + 0.3459747, + -0.008289742, + 0.791801, + 0.7151977, + -0.7154374, + 0.9411023, + -0.18865997, + 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Cluster: 3
Chunk: .\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n\n\n1 , 2\n(b) Bestimmen Sie H(x x ) .\n\n\n\n1 , 2\n(c) Bestimmen Sie H-1(x x ) .\n\n\n\n1 , 2\n(d) F\u00fchren Sie eine Newton-Iteration \u00abvon Hand\u00bb durch und Bestimme..." + ], + [ + 11696, + "(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\n\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n\n\n(a) Berechnen Sie den Gradienten der Rosenbrock-Funktion.\n\n(b) Verwenden Sie das Script test_steepest_descent.py .\n\nF\u00fchren Sie f\u00fcr verschiedene Startwerte und f\u00fcr verschiedene Toleranzen die Optimierung mit dem Steepest-\nDescent-Algorithmus durch.\n\nNotieren Sie jeweils die Anzahl der Funktionsaufrufe, die ben\u00f6tigt werden.\n\n(c) In dem Skript test_steepest_descent.py wird die Schrittweite mit dem Faktor a = 2 erh\u00f6ht bzw.\n\n1\/a = \u00bd erniedrigt.\n\nExperimentieren Sie mit verschiedenen a, z.B. a= 1.5\noder a = 3.\n\nWas beobachten Sie?\n\n45Musterl\u00f6sungen\n46\u00dcbung 7.1.1\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem Parameterscan.\n\nVariieren Sie dazu die Parameter in dem Bereich:\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8).\n\nF\u00fchren Sie dabei Parameterscans mit jeweils 4, 8, 16, 32, 64 Testpunkten pro Parameter durch\n(wenn ihr Computer 128 und mehr schafft, probieren Sie es).\n\nVisualisieren Sie Ihre Ergebnisse.\n\nM a* S*\n4 [4.\n\n1.667 6.283 6. ]\n\n38.7003\n8 [4.714 1.714 5.386 5.429] 1.835116\n16 [4.8 1.667 5.864 5.2 ] 2.0037\n32 [4.742 1.645 5.878 5.097] 0.075264\n64 [4.714 1.651 5.884 5.143] 0.233\n47\u00dcbung 7.2.1\nFinden Sie jeweils die L\u00f6sung des Optimierungsproblems\nMin f(x)\nmit Hilfe des Downhill-Simplex-Algorithmus.\n\nVariieren Sie die Startwerte um ko-existierende lokale Minima zu entdecken.\n\n(a) f(x) = -1\/5 x 3 + 10x + x 2.\n\n1 1 2\nL\u00f6sung: x* = (-4.0825, 0).\n\n(b) f(x) = 10( x \u2013 1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\nL\u00f6sung: x* = (1, 2, 3, 4).\n\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.", + 18240, + 2678476, + null, + "md", + null, + [ + -0.3678096, + 0.22411136, + 0.14555797, + -0.42699647, + -0.729299, + 0.24891435, + 0.4685517, + -0.28782007, + 0.0039182007, + 0.33779606, + -0.33120748, + 0.55808645, + 0.5444058, + 0.027608246, + 0.08851585, + 0.5265253, + 0.1125632, + -0.078657985, + -0.16471888, + -0.8651752, + 0.34232825, + 0.29238293, + 0.58000296, + 0.3738271, + -0.053281233, + 0.2833863, + 0.17771712, + 0.2546254, + 0.19173825, + 0.3640164, + -1.1249534, + -0.62808365, + -0.20911168, + -0.7429463, + -0.23612238, + 0.031128652, + 0.08355768, + 0.49920803, + -1.0714387, + 0.2685635, + -0.7407983, + 0.34027272, + 0.057589408, + -0.14611419, + -0.7766918, + 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Cluster: 3
Chunk: (x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\n\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n..." + ], + [ + 11697, + "1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\nL\u00f6sung: x* = (1, 2, 3, 4).\n\n\n\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.\n\n1 2 1 2\nEs existieren zahlreiche lokale Minima, z.B.\nlokale Minima f*\n[-1.38925764 -7.41822149] 1.3132611100220473\n[-1.38922156 3.91063 ] -0.9233486372581649\n[ 3.76097004 -1.79076623] -0.06792301515654611\n[ 3.76098637 -7.41815126] 2.836459612005422\n[-1.38926088 -1.79077318] -1.5911215163755672\nDavon ist das beste gefundene lokale Minima x* = [-1.38926088 -1.79077318] mit f* = -1.5911215163755672.\n\n48\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Downhill-Simplex-Algorithmus.\n\n(a) Extrahieren Sie aus den Daten (durch Analyse und \u00dcberlegung) einen geeigneten Startwert.\n\nBestimmen Sie die Parameter.\n\nz.B. Startwert [4.5, 1.5, 0, 5 ]\n(b) W\u00e4hlen Sie per Zufall verschiedene Startwerte im Bereich\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8)\nund bestimmen Sie die Parameter.\n\nVisualisieren Sie Ihre Ergebnisse.\n\nF\u00fcr die meisten Startwerte wird ein S(a*) < 10-8 realisiert bei ca.\n\n200 \u2013 400 Funktionsaufrufen.\n\nz.B.\nStartwert a0 = [3.69327012 1.467473 5.76522026 7.31055702]\na* = [4.735 1.658 5.795 5.119]\nS* =1.046512101321329e-08\n49\u00dcbung 7.2.3\nL\u00f6sen Sie die folgenden nichtlinearen Gleichungs-Systeme durch Formulieren eines Optimierungs-Problems und dem Downhill-\nSimplex-Verfahren:\n(a)\n2 L\u00f6sungen:\n(b)\n1 L\u00f6sung: x= 4, y = 3\n(c)\n2 L\u00f6sungen:\n50\u00dcbung 7.2.4\nWir suchen die Schnittpunkte der folgenden Linien:\n- ein Kreis um den Ursprung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n(a) Stellen Sie ein 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"ID: 14893838
Cluster: 5
Chunk: 1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\nL\u00f6sung: x* = (1, 2, 3, 4).\n\n\n\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.\n\n1 2 1 2\nEs existieren zahlreiche lokale Minima, z.B.\nlokale..." + ], + [ + 11698, + "Semesterinformation\nK\u00fcrzel cds204\nName Effiziente Algorithmen\nTyp Modul\nECTS 4\nKW Datum Zeit Wochentag Thema Inhalt Dozent Raum Bemerkungen\n8 20.02.2025 13:30-15:00 Do 1.\n\nDynamische Speicherstrukturen einfach und doppelt verkettete Listen (evtl.\n\nFIFO u LIFO) M. B\u00fcnner B3.02\n8 21.02.2025 13:30-15:00 Fr (evtl.\n\nFIFO u LIFO) Bin\u00e4re B\u00e4ume und Graphen M. B\u00fcnner B3.02\n9 27.02.2025 13:30-15:00 Do \u00dcbungen M. B\u00fcnner B3.02\n9 28.02.2025 13:30-15:00 Fr 1.\n\nHeaps und Heapsort 1.1 Heaps M. B\u00fcnner B3.02\n10 06.03.2025 13:30-15:00 Do 1.1 \u00fcben und 1.2 Heapsort M. B\u00fcnner B3.02\n10 07.03.2025 13:30-15:00 Fr 1.2 Heapsort mit Python M. B\u00fcnner B3.02\n11 13.03.2025 13:30-15:00 Do \u00dcbungen M. B\u00fcnner B3.02\n11 14.03.2025 13:30-15:00 Fr 3.\n\nNicht-konvexe Optimierung in einer Dimension Parameterscan, Bisektionen M. B\u00fcnner B3.02\n12 20.03.2025 13:30-15:00 Do Modifizierter Newton M. B\u00fcnner B3.02\n12 21.03.2025 13:30-15:00 Fr \u00dcbungen M. B\u00fcnner B3.02\n13 27.03.2025 13:30-15:00 Do Schrittweitensteuerung M. B\u00fcnner B3.02\n13 28.03.2025 13:30-15:00 Fr Straffunktionen M. B\u00fcnner B3.02\n14 03.04.2025 13:30-15:00 Do \u00dcbungen M. B\u00fcnner B3.02\n4.\n\nReelwertige Optimierung in N Dimensionen 4.0 Definitionen M. B\u00fcnner B3.02\n14 04.04.2025 13:30-15:00 Fr 4.1 Parameter-Scan\n17 24.04.2025 13:30-15:00 Do 4.2 Downhill-Simplex-Algorithmus M. B\u00fcnner B3.02\n17 25.04.2025 13:30-15:00 Fr \u00dcbungen M. B\u00fcnner B3.02\n18 01.05.2025 13:30-15:00 Do 4.2 Dowhill-Simplex L\u00f6sung von nichtlinearen Gleichungen M. B\u00fcnner B3.02\n4.2 Dowhill-Simplex, Restriktionen, grafische L\u00f6sung, M. 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Cluster: 0
Chunk: Semesterinformation\nK\u00fcrzel cds204\nName Effiziente Algorithmen\nTyp Modul\nECTS 4\nKW Datum Zeit Wochentag Thema Inhalt Dozent Raum Bemerkungen\n8 20.02.2025 13:30-15:00 Do 1.\n\nDynamische Speicherstrukture..." + ], + [ + 11699, + "von nichtlinearen Gleichungen M. B\u00fcnner B3.02\n4.2 Dowhill-Simplex, Restriktionen, grafische L\u00f6sung, M. B\u00fcnner B3.02\n18 02.05.2025 13:30-15:00 Fr Straffunktionen\n19 08.05.2025 13:30-15:00 Do 4.3 Newton: In 2-dim mit inv.\n\nHesse-Matrix l\u00f6sen M. B\u00fcnner B3.02\n19 09.05.2025 13:30-15:00 Fr 4.4 Steepest Descent M. B\u00fcnner B3.02\n20 15.05.2025 13:30-15:00 Do 5.\n\nRestringierte Optimierung in 2D linear M. B\u00fcnner B3.02\n20 16.05.2025 13:30-15:00 Fr in 2D nichtlinear M. B\u00fcnner B3.02\n21 22.05.2025 13:30-15:00 Do Linear Programming M. B\u00fcnner B3.02\n21 23.05.2025 13:30-15:00 Fr Nonlinear Programming M. B\u00fcnner B3.02\n22 30.05.2025 13:30-15:00 Fr \u00dcbungen M. B\u00fcnner B3.02\nM. 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Cluster: 0
Chunk: von nichtlinearen Gleichungen M. B\u00fcnner B3.02\n4.2 Dowhill-Simplex, Restriktionen, grafische L\u00f6sung, M. B\u00fcnner B3.02\n18 02.05.2025 13:30-15:00 Fr Straffunktionen\n19 08.05.2025 13:30-15:00 Do 4.3 Newton..." + ], + [ + 11700, + ")\n(b) Suchen Sie den Schl\u00fcssel mit dem Wert 5.\n\n\n\nWelche Schl\u00fcssel m\u00fcssen f\u00fcr diese Suche abgefragt werden.\n\n\n\n(2P)\nFachhochschule Graub\u00fcnden(c) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 5.\n\n\n\nWie sieht danach der Suchbaum aus? (\n\n\n\n2P)\n(d) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 7.\n\n\n\nWie sieht danach der Suchbaum aus? (\n\n2P)\nFachhochschule Graub\u00fcndenAUFGABE 4: (6 Punkte)\nF\u00fchren Sie Shellsort mit den Schrittweiten 7, 3, 1 aus: (6P)\n1 2 3 4 5 6 7 8 9 10 11 12 13 14\n15 30 17 10 11 4 6 9 2 1 7 32 5 3\nDokumentieren Sie die Schritte so wie in der Vorlesung und den \u00dcbungen.\n\nFachhochschule Graub\u00fcndenAUFGABE 5: (10 Punkte)\n(a) Ordnen Sie die Folge in einem Max-Heap und speichern diesen in einem Feld A=[\u2026.]\n\nab.\n\nDokumentieren Sie\ndie Zwischenergebnisse so wie in der Vorlesung und \u00dcbung. (\n\n4P)\n9\n4 5\n7 10 6 8\n(b) Nehmen Sie das Ergebnis von (a) und f\u00fchren drei Basis-Schritte von Heapsort durch.\n\nDokumentieren Sie die\nZwischenergebnisse so wie in der Vorlesung und \u00dcbung. (\n\n6P)\nFachhochschule Graub\u00fcndenFachhochschule Graub\u00fcndenAUFGABE 6: (6 Punkte)\n(a) Bestimmen Sie die Balancefaktoren des bin\u00e4ren Suchbaums.\n\nHandelt es sich um ein AVL-Baum? (\n\n2P)\n(b) L\u00f6schen Sie aus dem AVL-Baum das Element \u00ab17\u00bb.\n\nIst es danach noch ein AVL-Baum? (\n\n2P)\n(c) F\u00fcgen Sie danach das Element \u00ab15\u00bb hinzu.\n\nIst es danach noch ein AVL-Baum? 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Cluster: 2
Chunk: )\n(b) Suchen Sie den Schl\u00fcssel mit dem Wert 5.\n\n\n\nWelche Schl\u00fcssel m\u00fcssen f\u00fcr diese Suche abgefragt werden.\n\n\n\n(2P)\nFachhochschule Graub\u00fcnden(c) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 5.\n\n\n\nWie..." + ], + [ + 11701, + "A1 A2 A3 A4 A5 A6 Sum Note\n10 8 8 6 10 6 48\nStudiengang ::: Bachelor of Science FHGR in Computational and Data Science\nModul ::: Effiziente Algorithmen im FS 2025\nDozent ::: Prof.\n\nDr.\n\nMartin B\u00fcnner Martin\nVorname, Nachname: ________________________________________________________\nBearbeitungszeit ::: 90 Minuten\nErlaubte Hilfsmittel ::: Eigener Taschenrechner\nDisclaimer :::\nL\u00f6sungsweg muss klar ersichtlich sein, unbelegte Resultate k\u00f6nnen nicht ber\u00fccksichtigt werden.\n\no\nKeine Kommunikation unter den Studierenden oder mit Dritten.\n\no\nW\u00e4hrend der Pr\u00fcfung sind Smartphones und Smartwatches auszuschalten.\n\no\nWiederholungsrecht f\u00fcr die Pr\u00fcfung ist bei Betrug verwirkt.\n\no\nL\u00f6sungen m\u00fcssen schriftlich abgegeben werden.\n\no\nHinweise f\u00fcr die Studierenden :::\nPr\u00fcfungsbl\u00e4tter nur einseitig beschriften.\n\no\nLesen Sie zuerst alle Aufgaben sorgf\u00e4ltig durch, bevor Sie zu l\u00f6sen beginnen.\n\nBearbeiten Sie alle\no\nFragestellungen.\n\nDie Aufgaben sind nicht nach Schwierigkeitsgrad geordnet.\n\no\nFachhochschule Graub\u00fcndenAUFGABE 1: (10 Punkte)\n(a) Eine verkettete Liste mit 4 Elementen A[1\u20264] sei mit den jeweiligen Zeigern im Speicher wie folgt abgelegt\nmit A[1] \uf0e0 2001.\n\nErg\u00e4nzen Sie die fehlenden Eintr\u00e4ge so wie in der Vorlesung und den \u00dcbungen. (\n\n4P)\n(b) Zeichnen Sie den Graphen mit der Adjazenzliste: (2P)\nFachhochschule Graub\u00fcnden(c ) Ein bin\u00e4rer Baum ist mit Zeigern im Speicher abgelegt.\n\nTragen Sie die fehlenden Zeiger in die Tabelle ein (wie in\nder Vorlesung und den \u00dcbungen): (4P)\nFachhochschule Graub\u00fcndenAUFGABE 2: (8 Punkte)\n(a) Wir suchen das Feld in der Skipliste mit dem Wert 2.\n\nWelche Werte der Skipliste werden gepr\u00fcft? (\n\n2P)\n(b) Zeichnen Sie einen bin\u00e4ren Suchbaum der Tiefe 3. (\n\n2P)\nFachhochschule Graub\u00fcnden(c) F\u00fcgen Sie nacheinander die folgenden Elemente in einen bin\u00e4ren Suchbaum ein:\nMarlen, Adrian, Peter, Urs, Mike, Bernd, Carl, Karsten, Sabine, Susanne, Christine, Kirsten, Alexander. (\n\n4P)\nFachhochschule Graub\u00fcndenAUFGABE 3: (8 Punkte)\n(a) F\u00fcgen Sie in dem bin\u00e4ren Suchbaum das Element mit dem Schl\u00fcssel-Wert 11 ein.\n\nWie sieht der\nSuchbaum danach aus? (\n\n2P)\n(b) Suchen Sie den Schl\u00fcssel mit dem Wert 5.\n\nWelche Schl\u00fcssel m\u00fcssen f\u00fcr diese Suche abgefragt werden.\n\n(2P)\nFachhochschule Graub\u00fcnden(c) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 5.\n\nWie sieht danach der Suchbaum aus? (\n\n2P)\n(d) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 7.\n\nWie sieht danach der Suchbaum aus? 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Cluster: 2
Chunk: A1 A2 A3 A4 A5 A6 Sum Note\n10 8 8 6 10 6 48\nStudiengang ::: Bachelor of Science FHGR in Computational and Data Science\nModul ::: Effiziente Algorithmen im FS 2025\nDozent ::: Prof.\n\nDr.\n\nMartin B\u00fcnner ..." + ], + [ + 11702, + "prung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n\n\n(a) Stellen Sie ein Gleichungs-System mit 3 nichtlinearen Gleichungen f\u00fcr die Unbekannten (x, y, t) auf.\n\nx2 + y2 \u2013 1 = 0,\nx \u2013 sin(t) = 0,\ny \u2013 sin(2t + \u03c0\/4) = 0\n(b) Finden Sie f\u00fcr jeden Schnittpunkt jeweils einen guten Startwert f\u00fcr den Downhill-Simplex-Algorithmus.\n\nz.B. nur f\u00fcr x>0, da symmetrisch:\n(x ,y , t ) = [0.7, 0.75, arcsin(0.7) ]\n0 0 0\n[0.25, 1, arcsin(0.25) ]\n[1, -0.25, arcsin(1) ]\n[0.7, -0.75, arcsin(0.7) ]\n(b) L\u00f6sen Sie das Gleichungs-System indem Sie ein unrestringiertes Optimierungsproblem mit 3 Design-\nVariablen mit dem Downhill-Simplex-Algorithmus l\u00f6sen.\n\nBestimmen Sie alle L\u00f6sungen mit den Startwerten\nvon (b)\nHier reicht es alle Schnittpunkte mit x > 0 zu bestimmen, da beide Kurven achsensymmetrisch sind.\n\nStartwert [0.7, 0.75, arcsin(0.7) ] \uf0e0 L\u00f6sung [0.70674446 0.7078306 0.78488616]\nStartwert [0.25, 1, arcsin(0.25) ] \uf0e0 L\u00f6sung [0.26014633 0.96664301 0.2631811 ]\nStartwert [1, -0.25, arcsin(1) ] \uf0e0 L\u00f6sung [1, -0.25, arcsin(1) ]\nStartwert [0.6, -0.85, np.arcsin(0.6)] \uf0e0 L\u00f6sung [0.70732644 0.70666723 0.78570889], hier musste\nder Startwert noch variiert\/probiert werden, bis die Konvergenz auf den gesuchten Schnittpunkt erreicht\nwurde.\n\n51\u00dcbung 7.2.5\nEs seien folgende Optimierungsprobleme zu l\u00f6sen.\n\nGehen Sie jeweils wie folgt vor:\n(a) Graphische L\u00f6sung.\n\n(b) Aufstellen eines unrestringierten 2-dim Optimierungsproblems.\n\n(c) L\u00f6sen des unrestringierten 2-dim Optimierungsproblems mittels Downhill-Simplex-Algorithmus.\n\n(I) Min [ x + x ]\n1 2\nx \u2265 x 2\n2 1\nL\u00f6sung (-0.5; 0.25)\n(II) Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\nL\u00f6sung (0.5; -0.5)\n52\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .", + 18240, + 9006747, + null, + "md", + null, + [ + 0.3179646, + -0.103203535, + -0.03015862, + -0.33203667, + 0.21279645, + 0.25659063, + 0.264594, + 0.5064091, + -0.5108686, + 0.1827112, + -0.2057376, + 0.41959545, + 0.16683517, + 0.05212103, + 0.4227515, + 0.40559927, + 0.49333963, + -0.033038758, + 0.5204243, + -0.29663774, + -0.56361336, + 0.06465066, + 0.3913966, + 0.48964748, + -0.6096554, + 0.39608756, + -0.13872966, + 0.1466504, + 0.10674975, + 1.1831636, + -0.17645934, + -0.07092726, + -0.3881887, + -0.3163711, + -0.27526757, + -0.6076026, + 0.8258395, + 0.20650324, + -0.659403, + -0.299567, + -0.42918032, + 1.034678, + 0.58659273, + -0.05486596, + -0.8073823, + 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"2025-05-20T05:32:27.646986", + "2025-05-20T05:32:27.646986", + -47.8755645752, + 122.5512313843, + -46.6768035889, + "7", + "ID: 9006747
Cluster: 7
Chunk: prung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n\n\n(a) Stellen Sie ein Gleichungs-System mit 3 nic..." + ], + [ + 11704, + "ige Optimierung in N Dimensionen\n7.1 Parameterscan\nMin f(x), x in RN\nin Hypercube x \u2264 x \u2264 x und x - x = L.\nlow up up low\nEinfache Absch\u00e4tzung: Wir unterteilen jede Design-Variable in M = L\/T \u00e4quidistante Segmente.\n\n\n\nDann werden MN\ni i\nFunktionsauswertungen ben\u00f6tigt.\n\n\n\n\uf0e0 Tafel\nBeispiel:\n1.\n\nN=2, M=100, Zeit f\u00fcr eine Funktionsauswertung: 1 ms:\n\uf0e0 Rechenzeit: 1002 * 0.001 s = 105 s = 10 s\n2.N=4, M=100, Zeit f\u00fcr eine Funktionsauswertung: 1 ms:\n\uf0e0 Rechenzeit: 1004 * 0.001 s = 105 s = 1.2 Tage\n3.\n\nN=10, M=100, Zeit f\u00fcr eine Funktionsauswertung: 1 ms:\n\uf0e0 Rechenzeit: 10010 * 0.001 s = 1017 s = 3.2 Mrd.\n\nJahre (zum Vergleich: Alter des Sonnensystems ca.\n\n13 Mrd.\n\nJ.)\n57.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nheisst auch Downhill-Simplex-Algorithmus. \u00ab\n\nL\u00f6st\u00bb das unrestringierte N-dimensionale reelwertige Optimierungsproblem:\nMin f(x), x in RN\nNelder, Mead (1965)\nBemerkungen:\n\uf0fc f(x) muss stetig sein.\n\n\uf0fc Verwendung in der Praxis f\u00fcr 2 \u2264 N \u2264 10.\n\nF\u00fcr N gr\u00f6sser 10 zu in-effizient.\n\n\uf0fc Vorteil: gradienten-frei\n67.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nIdee des Downhill Simplex-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\n97.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nf\nGegeben: Simplex S mit n+1 Ecken, x x\n1, \u2026 n+1\nf\nn+1\n1.\n\nWerte f aus und sortiere f(x ) < f(x )< \u2026 < f(x )\nf\n1 2 n+1 n\n2.\n\nBerechne Zentrum der n besten Punkte\n1 n\n\u2211\nx = x\ni\nn\ni=1\nf\n2\nf\n1\n3.\n\nF\u00fchre genau eine Operation\nReflection, Expansion, Contraction oder Shrink\ndurch7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nReflection Spiegele x an Schwerpunkt\nn+1\nf\nf\nn+1\nf\nn\nf\nr\nf\n2\nf\n17.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nExpansion\nf\nf\nn+1\nf\nn\nf\n2\nf\n1\nf\nr7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nContraction\nf\nf\nf n+1\nr\nf\nn\nf\n2\nf\n17.", + 18240, + 4015301, + null, + "md", + null, + [ + -0.111103505, + 0.27513874, + -0.024644863, + 0.24044713, + -0.5872487, + 0.96111953, + 0.5122346, + -0.691302, + 0.4659692, + -0.34453544, + 0.18847531, + 0.4933645, + 0.34522104, + 0.5350193, + -0.28161824, + -0.20311373, + 0.117067285, + 0.32576823, + 0.8590197, + -1.1533606, + -0.5475748, + 0.09159811, + -0.13058528, + 0.8629452, + -0.4752931, + 1.0980564, + -0.7857408, + -0.7443192, + 0.06946849, + 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Cluster: 6
Chunk: ige Optimierung in N Dimensionen\n7.1 Parameterscan\nMin f(x), x in RN\nin Hypercube x \u2264 x \u2264 x und x - x = L.\nlow up up low\nEinfache Absch\u00e4tzung: Wir unterteilen jede Design-Variable in M = L\/T \u00e4quidista..." + ], + [ + 11706, + "die Newton-Iterations-Formel auf.\n\n\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n\n\n54\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n\n\n1 , 2\n(b) Es sei a = 1.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\ngradf = (3x 2 \u2013 4, 2x )\n1 2\nIteration x x gradf |gradf |2\n1,n 2,n n n\nn = 0 1, 1 -1, 2 5\nn = 1 2,-1 8,-2 68\nn = 2 -6,1 104,2 10\u2019820\n(c) a=0.1: Iteration x x gradf |gradf |2\n1,n 2,n n n\nn = 0 1, 1 -0.1, 0.2 0.05\nn = 1 1.1,0.8 -0.037, 0.16 0.027\nn = 2 1.137,0,64 -0.01, 0.128 0.017\n(d) Es sei a = 10.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1) .\n\n1,0 , 2,0\ngradf = (30x 2 \u2013 40, 20x )\n1 2\nIteration x x gradf |gradf |2\n1,n 2,n n n\nn = 0 1, 1 -10, 20 500\nn = 1 11, -19 3590,-380 13\u2019032\u2019500\nn = 2 -3579, 3611 384277190, 7220 1.5e17\n55", + 18240, + 8958903, + null, + "md", + null, + [ + 0.035227485, + 0.43120527, + 0.52239215, + -0.13663736, + -0.31758854, + 0.29873326, + -0.29649162, + -0.5112855, + -0.15712917, + -0.4734825, + 0.1259042, + 0.08540109, + 0.3401257, + -0.40163463, + 0.83632165, + 0.2960099, + -0.5661757, + 0.77227396, + 0.12360928, + -0.86863804, + -0.04766032, + -0.29687282, + 0.7867298, + 0.37844926, + -0.37913114, + -0.14191093, + -0.10414994, + -0.0042673647, + 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Cluster: 4
Chunk: die Newton-Iterations-Formel auf.\n\n\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n\n\n54\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n\n\n1 , 2 1 1..." + ], + [ + 11708, + "17.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nExpansion\nf\nf\nn+1\nf\nn\nf\n2\nf\n1\nf\nr7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nContraction\nf\nf\nf n+1\nr\nf\nn\nf\n2\nf\n17.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nShrinkage7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nKonstruktion des Start-Simplex\n\uf0fc oft automatisiert (x% Abweichungen von einem Startpunkt)\n\uf0fc Gr\u00f6sse des Start-Simplex bestimmt die initiale Schrittweite\nRestringierte Probleme\n\uf0fc 1-3 Gleichheits- oder Ungleichheits-Restriktionen mittels Straf-Funktionen in die Zielfunktion integrieren.7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nAbbruchbedingungen7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nRosenbrock (Banana) Function\n2 2 2\n\ud835\udc53\ud835\udc53(\ud835\udc65\ud835\udc65, \ud835\udc66\ud835\udc66) = (1 \u2212 \ud835\udc65\ud835\udc65) + 100(\ud835\udc66\ud835\udc66 \u2212 \ud835\udc65\ud835\udc65 )7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex \u2013 Himmelblau Funktion7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-AlgorithmusIn Python \uf0e0 scipy.optimize.fmin\nscipy.optimize.fmin \u2014 SciPy v1.13.0 Manual\n\uf0e0 einfaches Bsp in der Vorlesung\n\uf0e0 \u00dcbung 7.2.1 u 7.2.2\n227.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nVorteile\n\uf0fe Sehr einfach anzuwenden\n\uf0fe Simplex passt sich der \u201elandscape\u201c der Funktion an\n\uf0fe Ausreichende Stabilit\u00e4t f\u00fcr die meisten Probleme mit N<5\n\uf0fe Effizienz in der Praxis gut genug f\u00fcr kleine N<5\n\uf0fe Zielfunktion muss nicht in analytisch geschlossener Form\nbekannt sein\n\uf0fe Ben\u00f6tigt keine stetig ableitbaren Zielfunktionen\n\uf0fe keine Ableitungen\n\uf0fe Findet lokale Minima\n\uf0fe Einige wenige Constraints k\u00f6nnen mit Penalties abgebildet\nwerden.7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nNachteile\n\uf0fe Ineffizient (im Vergleich zu Quasi-Newton-Methoden),\ndeswegen nur f\u00fcr kleine N<=5 anzuwenden.\n\n\uf0fe Constraints k\u00f6nnen \u201enicht wirklich\u201c abgebildet werden.", + 18240, + 8952219, + null, + "md", + null, + [ + -0.76435846, + 0.1580934, + -0.073087715, + -0.29936278, + 0.10853522, + -0.093156494, + 0.2814383, + 0.5353781, + 0.51500803, + -0.0007352047, + -0.3806599, + 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Cluster: 5
Chunk: 17.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nExpansion\nf\nf\nn+1\nf\nn\nf\n2\nf\n1\nf\nr7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nContraction\nf\nf\n..." + ], + [ + 11710, + "1\n1 2\nx \u2265 2\n2\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\nBemerkung: Da die Zielfunktion f\u00fcr den Downhill-Simplex\nAlgorithmus nicht differenzierbar sein muss, k\u00f6nnen hier\nlineare Straffunktionen (genauso wie quadratische\nStraffunktionen) verwendet werden.\n\n\n\n287.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\n2-Dim Optimierungsproblem mit 1 nichtlineare\nGleichheitsbedingung\nMin [ x 2 + x 2 ]\n1 2\nx = ( x \u2013 2 ) 2\n2 1\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\n\uf0e0 \u00dcbungen 7.2.3 \u2013 7.2.5\n297.\n\nReelwertige Optimierung in N Dimensionen\n7.3 Newton-Verfahren\nMin f(x), x in RN\nf\u00fcr N = 1: Newton-Algorithmus\nx = x \u2013 f\u2019 \/f\u2019\u2019 = x \u2013 (f\u2019\u2019 )-1*f\u2019\nn+1 n n n n n n\nf\u00fcr N > 1: Newton-Algorithmus\nx = x \u2013 H-1 (x ) * gradf (x )\nn+1 n n n\nx = x \u2013 H-1 * gradf\nn+1 n n n\nWie f\u00fcr N=1: exakt falls f(x) quadratisch.\n\n307.\n\nReelwertige Optimierung in N Dimensionen\n7.3 Newton-Verfahren\nFalls f(x) nicht quadratisch und wir haben einen guten Startwert in der N\u00e4he eines lokalen Minimus (konvexes Gebiet),\nf\u00fchren wir Iterationen durch\n\uf0e0 Beispiel \u00dcbung 7.3.1 in der Vorlesung\nWeiter: Um die Stabilit\u00e4t zu erh\u00f6hen f\u00fchren wir noch eine Schrittweiten-Steuerung ein (hilft, wenn der Startwert nicht so gut\nsein sollte).\n\nx = x \u2013 c*H-1 * gradf\nn+1 n i n n\nmit 0 < c \u2264 1 steuert die Schrittweite (z.B. Backtracking).\n\ni\n\uf0e0 Hausaufgabe 7.3.2\n317.\n\nReelwertige Optimierung in N Dimensionen\n7.4 Steepest-Descent\nOder auch: Methode des steilsten Abstiegs\nIn der Praxis erweist sich die Bestimmung von H-1 als schwierig und aufwendig.\n\nn\n\uf0e0 H-1 durch etwas \u00abeinfacheres\u00bb n\u00e4hern.\n\nn\n\uf0e0 brutal einfache Idee : H-1 \u2248 1.\n\nn\nDamit ergibt sich der Steepest-Descent-Algorithmus mit Schrittweitensteuerung:\nx = x \u2013 c * gradf\nn+1 n i n\nmit c > 0 steuert die Schrittweite.\n\ni\nDer Steepest-Descent-Alg ist nicht invariant unter der Transformation f \uf0e0 a*f und x \uf0e0 b*x.\n\nDamit liefert der Steepest-\nDescent-Alg NUR eine Richtung.\n\nEr liefert keine Information \u00fcber die L\u00e4nge des Schrittes.\n\n\uf0e0 \u00dcbung 7.7.1 ohne Schrittweitensteuerung.\n\n327.", + 18240, + 4255267, + null, + "md", + null, + [ + -1.1105303, + 0.06864798, + 0.71910334, + -0.18397726, + 0.40972143, + -0.7054399, + 0.120589584, + 0.06977714, + 0.17998672, + 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Cluster: 5
Chunk: 1\n1 2\nx \u2265 2\n2\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\nBemerkung: Da die Zielfunktion f\u00fcr den Downhill-Simplex\nAlgorithmus nicht differenzierbar sein m..." + ], + [ + 11705, + "Effiziente Algorithmen\n7.\n\nReelwertige Optimierung in N Dimensionen\nProf.\n\nDr.\n\nMartin B\u00fcnner7.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nMotivation:\n20 Datenpunkte in DataSinus2.csv\n[xData,yData] = np.loadtxt('DataSinus2.csv',delimiter=';\u2019)\nModellierung der Daten mit einer Funktion aus der Modellklasse\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit 4 freien Parametern a = ( a , a , a , a ).\n\nWas sind die besten Werte f\u00fcr a?\n\n1 2 3 4\nAnsatz mit der Summe der kleinsten Quadrate (Gauss, 17xx):\n\uf0e0 Tafel\n\uf0e0 Optimierungs-Problem mit 4 Design-Variablen\nMin S( a , a , a , a )\n1 2 3 4\nWie wir so was l\u00f6sen ist der Inhalt dieses Kapitels.\n\n17.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nReellwertiges unrestringiertes Optimierungs-Problem mit N Design-Variablen (N Dimensionen)\nMin f(x), x in RN\nMin f(x x x ), (x x x ) in RN\n1 , 2 , ...., N 1 , 2 , ...., N\nBemerkungen:\n1.\n\nAbleitung f\u2019(x) \uf0e0 Gradient gradf(x)\n2.\n\nAbleitung f\u2019\u2019(x) \uf0e0 Hesse-Matrix H(x)\nf(x) ist konvex, falls H(x) positiv definit (alle Eigenwerte positiv) ist.\n\n\uf0e0 Es existiert kein oder genau ein lokales Minimum.\n\nSei f(x) 2-mal differenzierbar, dann gilt f\u00fcr lokale Minima an der Stelle x*:\ngradf(x*) = 0\nH(x*) positiv definit (alle Eigenwerte positiv).\n\nBemerkung: Die Definitheit der Hesse-Matrix m\u00fcssen Sie (noch) nicht selber berechnen k\u00f6nnen.\n\n27.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nBeispiel: 2D-Optimierungsproblem mit der Himmelblau-Funktion\nMin f(x x )\n1 , 2\nmit (x 2+ x -11) 2 + (x + x 2 -7) 2.\n\n1 2 1 2\n4 lokale Minima:\n\uf0e0 \u00dcbung\n37.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nBeispiel: 2D-Optimierungsproblem mit der Rosenbrock-Funktion\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\n1 , 2 1 2 1\nL\u00f6sung f(1,1) = 0.\n\n\uf0e0 \u00dcbung\n47.\n\nReelwertige Optimierung in N Dimensionen\n7.1 Parameterscan\nMin f(x), x in RN\nin Hypercube x \u2264 x \u2264 x und x - x = L.\nlow up up low\nEinfache Absch\u00e4tzung: Wir unterteilen jede Design-Variable in M = L\/T \u00e4quidistante Segmente.\n\nDann werden MN\ni i\nFunktionsauswertungen ben\u00f6tigt.\n\n\uf0e0 Tafel\nBeispiel:\n1.", + 18240, + 7115600, + null, + "md", + null, + [ + -0.8677129, + 0.38449863, + 0.5034417, + -0.103949405, + -0.37239298, + -0.27794132, + 0.391599, + -0.10016646, + -0.023383643, + -0.031077437, + -0.52423716, + -0.08872123, + 0.30995914, + 0.6822999, + -0.119418964, + 0.09067443, + 0.016116112, + 0.6925044, + 0.024169125, + -0.37904954, + -0.22198641, + 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Cluster: 5
Chunk: Effiziente Algorithmen\n7.\n\nReelwertige Optimierung in N Dimensionen\nProf.\n\nDr.\n\nMartin B\u00fcnner7.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nMotivation:\n20 Datenpunkte in DataSinus2.cs..." + ], + [ + 11707, + "Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\nL\u00f6sung (0.5; -0.5)\n52\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\ngradf(x x ) = (3x 2 - 4 , 2x )\n1 , 2 1 2\n(b) Bestimmen Sie H(x x ) .\n\n1 , 2\nH(x x ) = ( (6x , 0), (0, 2) )\n1 , 2 1\n(c) Bestimmen Sie H-1(x x ) .\n\n1 , 2\nH(x x ) -1 = ( (1\/(6x ), 0), (0, 1\/2) )\n1 , 2 1\n(d) F\u00fchren Sie eine Newton-Iteration durch und Bestimmen Sie (x x ) mit dem Startwert (x =1 x =1) .\n\n1,1 , 2,1 1,0 , 2,0\n(x x ) = (7\/6, 0)\n1,1 , 2,1\n53\u00dcbung 7.3.2\nWir suchen das Minimum der Rosenbrock-Funktion oder Banana-Funktion:\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n(a) Berechnen Sie den Gradienten, die Hesse-Matrix und die inverse Hesse-Matrix der Rosenbrock-Funktion.\n\ngrad f(x x ) = ( 2(1 \u2013 x ) + 200(x - x 2) * (-2x ), 200(x - x 2) ) = ( 2 \u2013 2x - 400x x + 400x 3 , 200x - 200x 2 )\n1 , 2 1 2 1 1 2 1 1 1 2 1 2 1\nf = \u2013 2 - 400x + 1200x 2\n11 2 1\nf = - 400x\n12 1\nf = 200\n22\nH(x x ) = ( (f , f ) , (f , f ) )\n1 , 2 11 12 12 22\nD(x x ) = f * f - f 2\n1 , 2 11 22 12\nH-1(x x ) = ( (f , -f ) , (-f , f ) ) \/ D(x x )\n1 , 2 22 12 12 11 1 , 2\n(b) Stellen Sie damit die Newton-Iterations-Formel auf.\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n54\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\n(b) Es sei a = 1.", + 18240, + 13438939, + null, + 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Cluster: 4
Chunk: Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\nL\u00f6sung (0.5; -0.5)\n52\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\ngradf(x x..." + ], + [ + 11709, + "Einige wenige Constraints k\u00f6nnen mit Penalties abgebildet\nwerden.7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nNachteile\n\uf0fe Ineffizient (im Vergleich zu Quasi-Newton-Methoden),\ndeswegen nur f\u00fcr kleine N<=5 anzuwenden.\n\n\n\n\uf0fe Constraints k\u00f6nnen \u201enicht wirklich\u201c abgebildet werden.\n\n\uf0fe Wahl des Startsimplex hat Einfluss welches lokale Minimum\ngefunden wird\n\uf0fe Wahl des Startsimplex beeinflusst Konvergenz\n\uf0fe Fehlender Konvergenzbeweis\n\uf0fe Z.B. Stagnation um nicht-optimalen Punkt\n\uf0fe rauschempfindlich7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill-Simplex mit Zielfunktion mit additivem Rauschen s:\nMin [ f(x) + s ], x in RN\nBeispiele\n\uf0fc Double Precision Numerik: \u03c3 \u2248 10-16,\ns\n\uf0fc FEM-Simulation: \u03c3 \u2248 10-3 - 10-6.\n\ns\nBest-Practice\n\uf0fc Anwender (Sie!)\n\nm\u00fcssen das Rauschlevel kennen.\n\n\uf0fc Toleranzen (ftol, xtol) an das Rauschlevel anpassen.\n\n257.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nNichtlineare Regression mit Downhill-Simplex \uf0e0 \u00dcbung 7.2.2\nNichtlineare Gleichungen l\u00f6sen mit Downhill-Simplex \uf0e0 \u00dcbung 7.2.3\nSchnittpunkte zweier Kreise:\n- M1: Kreis um (1,2) mit Radius 3,\n- M2: Kreis um (2,0) mit Radius 2.\n\n(a) Optimierungs-Problem aufstellen.\n\n(b) Gute Startpunkte bestimmen.\n\n(c) L\u00f6sen mit Downhill-Simplex.\n\n267.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nIhre Aufgaben ist es einen Satelliten am Ort (x,y) so zu\npositionieren, dass er sich im Gravitationsfeld der 3\nM3 M1 Planeten nicht bewegt.\n\n(a) Optimierungs-Problem aufstellen.\n\n(b) Gute Startpunkte bestimmen.\n\n(c) L\u00f6sen mit Downhill-Simplex.\n\nM2\n277.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\n2-Dim Optimierungsproblem mit 2 linearen\nUngleichheitsbedingungen\nMin [ x 2 + x 2 ]\n1 2\nx + x \u2265 1\n1 2\nx \u2265 2\n2\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\nBemerkung: Da die Zielfunktion f\u00fcr den Downhill-Simplex\nAlgorithmus nicht differenzierbar sein muss, k\u00f6nnen hier\nlineare Straffunktionen (genauso wie quadratische\nStraffunktionen) verwendet werden.\n\n287.", + 18240, + 10468137, + null, + "md", + null, + [ + -0.8539323, + -0.3307703, + -0.12917906, + -0.9902739, + -0.6676355, + 1.2118105, + 0.029592108, + 0.22852649, + -0.17469743, + 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Cluster: 3
Chunk: Einige wenige Constraints k\u00f6nnen mit Penalties abgebildet\nwerden.7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nNachteile\n\uf0fe Ineffizient (im Vergleich zu Qu..." + ], + [ + 11711, + "mit c > 0 steuert die Schrittweite.\n\n\n\ni\nDer Steepest-Descent-Alg ist nicht invariant unter der Transformation f \uf0e0 a*f und x \uf0e0 b*x.\n\n\n\nDamit liefert der Steepest-\nDescent-Alg NUR eine Richtung.\n\n\n\nEr liefert keine Information \u00fcber die L\u00e4nge des Schrittes.\n\n\n\n\uf0e0 \u00dcbung 7.7.1 ohne Schrittweitensteuerung.\n\n\n\n327.\n\nReelwertige Optimierung in N Dimensionen\n7.4 Steepest-Descent\nAusserdem ist die Richtung des Gradienten bei nicht-isotropen Minima (dies ist die Regel) schlecht.\n\nisotrop: Gradientenrichtung gut nicht-isotrop: Gradientenrichtung schlecht\n.\n\nDies f\u00fchrt zu dem \u00abklassischen\u00bb Zig-Zag-Routen, die nicht erw\u00fcnscht sind.\n\n337.\n\nReelwertige Optimierung in N Dimensionen\n7.4 Steepest-Descent\nIdee einer SEHR einfachen Schrittweitensteuerung:\nFalls Schrittweite zu gross: Reduziere Schrittweise iterativ um die H\u00e4lfte, so lange bis f < f .\n\nn+1 n\nFalls Schrittweite evtl zu klein: Verdopple Schrittweite so lange bis f < f , dann reduziere Schrittweite noch 1-mal.\n\nn+1 n\n\uf0e0 test_Steepest_Descent.py\n\uf0e0 \u00dcbung 7.7.2\n34\u00dcbungen\n35\u00dcbung 7.1.1\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem Parameterscan.\n\nVariieren Sie dazu die Parameter in dem Bereich:\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8).\n\nF\u00fchren Sie dabei Parameterscans mit jeweils 4, 8, 16, 32, 64 Testpunkten pro Parameter durch\n(wenn ihr Computer 128 und mehr schafft, probieren Sie es).\n\nVisualisieren Sie Ihre Ergebnisse.\n\n36\u00dcbung 7.2.1\nFinden Sie jeweils die L\u00f6sung des Optimierungsproblems\nMin f(x)\nmit Hilfe des Downhill-Simplex-Algorithmus.\n\nVariieren Sie die Startwerte um ko-existierende lokale Minima zu entdecken.\n\n(a) f(x) = -1\/5 x 3 + 10x + x 2.\n\n1 1 2\n(b) f(x) = 10( x \u2013 1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.\n\n1 2 1 2\n37\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Downhill-Simplex-Algorithmus. ,", + 18240, + 4051675, + null, + "md", + null, + [ + 0.21610712, + -0.34170783, + -0.2582149, + -0.13907842, + -0.61066616, + 1.0078603, + -0.3240201, + 0.6873087, + -0.29100716, + 0.42602944, + -0.19834957, + 0.07046136, + 0.19968116, + 0.05360639, + 0.4098373, + 0.8089135, + 0.38811648, + 0.020720284, + -0.29712686, + 0.09276265, + 0.24396716, + 0.49379405, + 0.14850694, + 0.8405345, + -0.42497858, + 0.54847485, + 0.5315522, + 0.2563539, + 0.3031277, + 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Cluster: 3
Chunk: mit c > 0 steuert die Schrittweite.\n\n\n\ni\nDer Steepest-Descent-Alg ist nicht invariant unter der Transformation f \uf0e0 a*f und x \uf0e0 b*x.\n\n\n\nDamit liefert der Steepest-\nDescent-Alg NUR eine Richtung.\n\n\n\nEr ..." + ], + [ + 11712, + "05x 2.\n\n\n\n1 2 1 2\n37\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\n\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Downhill-Simplex-Algorithmus. ,\n\n(a) Extrahieren Sie aus den Daten (durch Analyse und \u00dcberlegung) einen geeigneten Startwert.\n\nBestimmen Sie die Parameter.\n\n(b) W\u00e4hlen Sie per Zufall verschiedene Startwerte im Bereich\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8)\nund bestimmen Sie die Parameter.\n\nVisualisieren Sie Ihre Ergebnisse.\n\n38\u00dcbung 7.2.3\nL\u00f6sen Sie die folgenden nichtlinearen Gleichungs-Systeme durch Formulieren eines Optimierungs-Problems und dem Downhill-\nSimplex-Verfahren.\n\nVersuchen Sie eventuell vorhanden mehrere L\u00f6sungen durch Variation des Anfangswertes zu finden:\n(a)\n(b)\n(c)\n39\u00dcbung 7.2.4\nWir suchen die Schnittpunkte der folgenden Linien:\n- ein Kreis um den Ursprung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n(a) Stellen Sie ein Gleichungs-System mit 3 nichtlinearen Gleichungen f\u00fcr die Unbekannten (x, y, t)\nauf.\n\n(b) Finden Sie f\u00fcr jeden Schnittpunkt jeweils einen guten Startwert f\u00fcr den Downhill-Simplex-\nAlgorithmus.\n\n(b) L\u00f6sen Sie das Gleichungs-System indem Sie ein unrestringiertes Optimierungsproblem mit 3\nDesign-Variablen mit dem Downhill-Simplex-Algorithmus l\u00f6sen.\n\nBestimmen Sie alle L\u00f6sungen mit\nden Startwerten von (b)\n40\u00dcbung 7.2.5\nEs seien folgende Optimierungsprobleme zu l\u00f6sen.\n\nGehen Sie jeweils wie folgt vor:\n(a) Graphische L\u00f6sung\n(b) Aufstellen eines unrestringierten 2-dim Optimierungsproblems\n(c) L\u00f6sen des unrestringierten 2-dim Optimierungsproblems mittels Downhill-Simplex-Algorithmus\n(I) Min [ x + x ]\n1 2\nx \u2265 x 2\n2 1\n(II) Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\n41\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\n(b) Bestimmen Sie H(x x ) .\n\n1 , 2\n(c) Bestimmen Sie H-1(x x ) .\n\n1 , 2\n(d) F\u00fchren Sie eine Newton-Iteration \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem Startwert (x =1 x =1) .", + 18240, + 11066362, + null, + "md", + null, + [ + 0.13278152, + -0.31135443, + -0.06157794, + -0.0959831, + -0.2155047, + 0.18859595, + -0.16959096, + -0.34411773, + -0.041826297, + -0.31756315, + -0.9122811, + -0.22444451, + 0.0037292335, + 0.19952506, + 0.5708837, + 0.2232977, + 0.6871961, + 0.60550606, + 0.30983904, + -0.8043447, + -0.27264884, + 0.05291005, 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Cluster: 1
Chunk: 05x 2.\n\n\n\n1 2 1 2\n37\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\n\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Dow..." + ], + [ + 11713, + ".\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n\n\n1 , 2\n(b) Bestimmen Sie H(x x ) .\n\n\n\n1 , 2\n(c) Bestimmen Sie H-1(x x ) .\n\n\n\n1 , 2\n(d) F\u00fchren Sie eine Newton-Iteration \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem Startwert (x =1 x =1) .\n\n1,1 , 2,1 1,0 , 2,0\n42\u00dcbung 7.3.2\nWir suchen das Minimum der Rosenbrock-Funktion oder Banana-Funktion:\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n(a) Berechnen Sie den Gradienten, die Hesse-Matrix und die inverse Hesse-Matrix der Rosenbrock-Funktion.\n\n(b) Stellen Sie damit die Newton-Iterations-Formel auf und programmieren diese in Python.\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n43\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\n(b) Es sei a = 1.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\n(c) Es sei a = 0.1.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\n(d) Es sei a = 10.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\n44\u00dcbung 7.4.2\nWir suchen das Minimum der Rosenbrock-Funktion oder Banana-Funktion:\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n(a) Berechnen Sie den Gradienten der Rosenbrock-Funktion.", + 18240, + 12595295, + null, + "md", + null, + [ + -0.3088199, + 0.3640352, + -0.31572556, + -0.5632599, + -0.8831732, + 0.1357588, + 0.2828112, + -0.073052764, + -0.14028428, + -0.4137379, + -0.3779284, + 0.22783622, + 0.6706066, + -0.056991823, + 0.40256593, + 0.05860323, + -0.27182338, + 0.73582864, + 0.04000853, + -0.95994973, + 0.3459747, + -0.008289742, + 0.791801, + 0.7151977, + -0.7154374, + 0.9411023, + -0.18865997, + 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Cluster: 3
Chunk: .\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n\n\n1 , 2\n(b) Bestimmen Sie H(x x ) .\n\n\n\n1 , 2\n(c) Bestimmen Sie H-1(x x ) .\n\n\n\n1 , 2\n(d) F\u00fchren Sie eine Newton-Iteration \u00abvon Hand\u00bb durch und Bestimme..." + ], + [ + 11714, + "(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\n\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n\n\n(a) Berechnen Sie den Gradienten der Rosenbrock-Funktion.\n\n(b) Verwenden Sie das Script test_steepest_descent.py .\n\nF\u00fchren Sie f\u00fcr verschiedene Startwerte und f\u00fcr verschiedene Toleranzen die Optimierung mit dem Steepest-\nDescent-Algorithmus durch.\n\nNotieren Sie jeweils die Anzahl der Funktionsaufrufe, die ben\u00f6tigt werden.\n\n(c) In dem Skript test_steepest_descent.py wird die Schrittweite mit dem Faktor a = 2 erh\u00f6ht bzw.\n\n1\/a = \u00bd erniedrigt.\n\nExperimentieren Sie mit verschiedenen a, z.B. a= 1.5\noder a = 3.\n\nWas beobachten Sie?\n\n45Musterl\u00f6sungen\n46\u00dcbung 7.1.1\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem Parameterscan.\n\nVariieren Sie dazu die Parameter in dem Bereich:\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8).\n\nF\u00fchren Sie dabei Parameterscans mit jeweils 4, 8, 16, 32, 64 Testpunkten pro Parameter durch\n(wenn ihr Computer 128 und mehr schafft, probieren Sie es).\n\nVisualisieren Sie Ihre Ergebnisse.\n\nM a* S*\n4 [4.\n\n1.667 6.283 6. ]\n\n38.7003\n8 [4.714 1.714 5.386 5.429] 1.835116\n16 [4.8 1.667 5.864 5.2 ] 2.0037\n32 [4.742 1.645 5.878 5.097] 0.075264\n64 [4.714 1.651 5.884 5.143] 0.233\n47\u00dcbung 7.2.1\nFinden Sie jeweils die L\u00f6sung des Optimierungsproblems\nMin f(x)\nmit Hilfe des Downhill-Simplex-Algorithmus.\n\nVariieren Sie die Startwerte um ko-existierende lokale Minima zu entdecken.\n\n(a) f(x) = -1\/5 x 3 + 10x + x 2.\n\n1 1 2\nL\u00f6sung: x* = (-4.0825, 0).\n\n(b) f(x) = 10( x \u2013 1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\nL\u00f6sung: x* = (1, 2, 3, 4).\n\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.", + 18240, + 8746023, + null, + "md", + null, + [ + -0.3678096, + 0.22411136, + 0.14555797, + -0.42699647, + -0.729299, + 0.24891435, + 0.4685517, + -0.28782007, + 0.0039182007, + 0.33779606, + -0.33120748, + 0.55808645, + 0.5444058, + 0.027608246, + 0.08851585, + 0.5265253, + 0.1125632, + -0.078657985, + -0.16471888, + -0.8651752, + 0.34232825, + 0.29238293, + 0.58000296, + 0.3738271, + -0.053281233, + 0.2833863, + 0.17771712, + 0.2546254, + 0.19173825, + 0.3640164, + -1.1249534, + -0.62808365, + -0.20911168, + -0.7429463, + -0.23612238, + 0.031128652, + 0.08355768, + 0.49920803, + -1.0714387, + 0.2685635, + -0.7407983, + 0.34027272, + 0.057589408, + -0.14611419, + -0.7766918, + 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Cluster: 3
Chunk: (x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\n\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n..." + ], + [ + 11715, + "1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\nL\u00f6sung: x* = (1, 2, 3, 4).\n\n\n\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.\n\n1 2 1 2\nEs existieren zahlreiche lokale Minima, z.B.\nlokale Minima f*\n[-1.38925764 -7.41822149] 1.3132611100220473\n[-1.38922156 3.91063 ] -0.9233486372581649\n[ 3.76097004 -1.79076623] -0.06792301515654611\n[ 3.76098637 -7.41815126] 2.836459612005422\n[-1.38926088 -1.79077318] -1.5911215163755672\nDavon ist das beste gefundene lokale Minima x* = [-1.38926088 -1.79077318] mit f* = -1.5911215163755672.\n\n48\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Downhill-Simplex-Algorithmus.\n\n(a) Extrahieren Sie aus den Daten (durch Analyse und \u00dcberlegung) einen geeigneten Startwert.\n\nBestimmen Sie die Parameter.\n\nz.B. Startwert [4.5, 1.5, 0, 5 ]\n(b) W\u00e4hlen Sie per Zufall verschiedene Startwerte im Bereich\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8)\nund bestimmen Sie die Parameter.\n\nVisualisieren Sie Ihre Ergebnisse.\n\nF\u00fcr die meisten Startwerte wird ein S(a*) < 10-8 realisiert bei ca.\n\n200 \u2013 400 Funktionsaufrufen.\n\nz.B.\nStartwert a0 = [3.69327012 1.467473 5.76522026 7.31055702]\na* = [4.735 1.658 5.795 5.119]\nS* =1.046512101321329e-08\n49\u00dcbung 7.2.3\nL\u00f6sen Sie die folgenden nichtlinearen Gleichungs-Systeme durch Formulieren eines Optimierungs-Problems und dem Downhill-\nSimplex-Verfahren:\n(a)\n2 L\u00f6sungen:\n(b)\n1 L\u00f6sung: x= 4, y = 3\n(c)\n2 L\u00f6sungen:\n50\u00dcbung 7.2.4\nWir suchen die Schnittpunkte der folgenden Linien:\n- ein Kreis um den Ursprung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n(a) Stellen Sie ein 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"ID: 1458139
Cluster: 5
Chunk: 1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\nL\u00f6sung: x* = (1, 2, 3, 4).\n\n\n\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.\n\n1 2 1 2\nEs existieren zahlreiche lokale Minima, z.B.\nlokale..." + ], + [ + 11716, + "Semesterinformation\nK\u00fcrzel cds204\nName Effiziente Algorithmen\nTyp Modul\nECTS 4\nKW Datum Zeit Wochentag Thema Inhalt Dozent Raum Bemerkungen\n8 20.02.2025 13:30-15:00 Do 1.\n\nDynamische Speicherstrukturen einfach und doppelt verkettete Listen (evtl.\n\nFIFO u LIFO) M. B\u00fcnner B3.02\n8 21.02.2025 13:30-15:00 Fr (evtl.\n\nFIFO u LIFO) Bin\u00e4re B\u00e4ume und Graphen M. B\u00fcnner B3.02\n9 27.02.2025 13:30-15:00 Do \u00dcbungen M. B\u00fcnner B3.02\n9 28.02.2025 13:30-15:00 Fr 1.\n\nHeaps und Heapsort 1.1 Heaps M. B\u00fcnner B3.02\n10 06.03.2025 13:30-15:00 Do 1.1 \u00fcben und 1.2 Heapsort M. B\u00fcnner B3.02\n10 07.03.2025 13:30-15:00 Fr 1.2 Heapsort mit Python M. B\u00fcnner B3.02\n11 13.03.2025 13:30-15:00 Do \u00dcbungen M. B\u00fcnner B3.02\n11 14.03.2025 13:30-15:00 Fr 3.\n\nNicht-konvexe Optimierung in einer Dimension Parameterscan, Bisektionen M. B\u00fcnner B3.02\n12 20.03.2025 13:30-15:00 Do Modifizierter Newton M. B\u00fcnner B3.02\n12 21.03.2025 13:30-15:00 Fr \u00dcbungen M. B\u00fcnner B3.02\n13 27.03.2025 13:30-15:00 Do Schrittweitensteuerung M. B\u00fcnner B3.02\n13 28.03.2025 13:30-15:00 Fr Straffunktionen M. B\u00fcnner B3.02\n14 03.04.2025 13:30-15:00 Do \u00dcbungen M. B\u00fcnner B3.02\n4.\n\nReelwertige Optimierung in N Dimensionen 4.0 Definitionen M. B\u00fcnner B3.02\n14 04.04.2025 13:30-15:00 Fr 4.1 Parameter-Scan\n17 24.04.2025 13:30-15:00 Do 4.2 Downhill-Simplex-Algorithmus M. B\u00fcnner B3.02\n17 25.04.2025 13:30-15:00 Fr \u00dcbungen M. B\u00fcnner B3.02\n18 01.05.2025 13:30-15:00 Do 4.2 Dowhill-Simplex L\u00f6sung von nichtlinearen Gleichungen M. B\u00fcnner B3.02\n4.2 Dowhill-Simplex, Restriktionen, grafische L\u00f6sung, M. 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Cluster: 0
Chunk: Semesterinformation\nK\u00fcrzel cds204\nName Effiziente Algorithmen\nTyp Modul\nECTS 4\nKW Datum Zeit Wochentag Thema Inhalt Dozent Raum Bemerkungen\n8 20.02.2025 13:30-15:00 Do 1.\n\nDynamische Speicherstrukture..." + ], + [ + 11717, + "Fallbeispiel\n26", + 18240, + 4173286, + null, + "md", + null, + [ + 0.26656806, + 0.5253133, + 0.48931304, + -0.7356992, + -0.74937826, + 0.11828868, + -0.17567016, + 1.0539167, + -0.6556967, + -1.1363449, + 0.59633434, + 0.7216692, + 0.5009073, + 0.22413087, + 0.051205747, + -0.27063003, + -0.6022064, + -0.28698733, + -0.30200198, + -0.37739652, + -0.46618682, + 0.06087881, + -0.32322696, + 0.6103993, + -0.39389443, + -0.36408284, + -0.06465165, + -0.25247136, + 0.29849216, + -0.329827, + 0.15293445, + 0.4391276, + -0.31882104, + -1.1607982, + 0.29291245, + 0.15577278, + 0.48590055, + -0.3544446, + -1.0439099, + 0.31719062, + -0.24835658, + 0.90581405, + -0.41005722, + -0.48515812, + -0.39847326, + -0.50715244, + 0.56266606, + -0.6822293, + 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Cluster: 2
Chunk: Fallbeispiel\n26" + ], + [ + 11718, + "A1 A2 A3 A4 A5 A6 Sum Note\n10 8 8 6 10 6 48\nStudiengang ::: Bachelor of Science FHGR in Computational and Data Science\nModul ::: Effiziente Algorithmen im FS 2025\nDozent ::: Prof.\n\nDr.\n\nMartin B\u00fcnner Martin\nVorname, Nachname: ________________________________________________________\nBearbeitungszeit ::: 90 Minuten\nErlaubte Hilfsmittel ::: Eigener Taschenrechner\nDisclaimer :::\nL\u00f6sungsweg muss klar ersichtlich sein, unbelegte Resultate k\u00f6nnen nicht ber\u00fccksichtigt werden.\n\no\nKeine Kommunikation unter den Studierenden oder mit Dritten.\n\no\nW\u00e4hrend der Pr\u00fcfung sind Smartphones und Smartwatches auszuschalten.\n\no\nWiederholungsrecht f\u00fcr die Pr\u00fcfung ist bei Betrug verwirkt.\n\no\nL\u00f6sungen m\u00fcssen schriftlich abgegeben werden.\n\no\nHinweise f\u00fcr die Studierenden :::\nPr\u00fcfungsbl\u00e4tter nur einseitig beschriften.\n\no\nLesen Sie zuerst alle Aufgaben sorgf\u00e4ltig durch, bevor Sie zu l\u00f6sen beginnen.\n\nBearbeiten Sie alle\no\nFragestellungen.\n\nDie Aufgaben sind nicht nach Schwierigkeitsgrad geordnet.\n\no\nFachhochschule Graub\u00fcndenAUFGABE 1: (10 Punkte)\n(a) Eine verkettete Liste mit 4 Elementen A[1\u20264] sei mit den jeweiligen Zeigern im Speicher wie folgt abgelegt\nmit A[1] \uf0e0 2001.\n\nErg\u00e4nzen Sie die fehlenden Eintr\u00e4ge so wie in der Vorlesung und den \u00dcbungen. (\n\n4P)\n(b) Zeichnen Sie den Graphen mit der Adjazenzliste: (2P)\nFachhochschule Graub\u00fcnden(c ) Ein bin\u00e4rer Baum ist mit Zeigern im Speicher abgelegt.\n\nTragen Sie die fehlenden Zeiger in die Tabelle ein (wie in\nder Vorlesung und den \u00dcbungen): (4P)\nFachhochschule Graub\u00fcndenAUFGABE 2: (8 Punkte)\n(a) Wir suchen das Feld in der Skipliste mit dem Wert 2.\n\nWelche Werte der Skipliste werden gepr\u00fcft? (\n\n2P)\n(b) Zeichnen Sie einen bin\u00e4ren Suchbaum der Tiefe 3. (\n\n2P)\nFachhochschule Graub\u00fcnden(c) F\u00fcgen Sie nacheinander die folgenden Elemente in einen bin\u00e4ren Suchbaum ein:\nMarlen, Adrian, Peter, Urs, Mike, Bernd, Carl, Karsten, Sabine, Susanne, Christine, Kirsten, Alexander. (\n\n4P)\nFachhochschule Graub\u00fcndenAUFGABE 3: (8 Punkte)\n(a) F\u00fcgen Sie in dem bin\u00e4ren Suchbaum das Element mit dem Schl\u00fcssel-Wert 11 ein.\n\nWie sieht der\nSuchbaum danach aus? (\n\n2P)\n(b) Suchen Sie den Schl\u00fcssel mit dem Wert 5.\n\nWelche Schl\u00fcssel m\u00fcssen f\u00fcr diese Suche abgefragt werden.\n\n(2P)\nFachhochschule Graub\u00fcnden(c) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 5.\n\nWie sieht danach der Suchbaum aus? (\n\n2P)\n(d) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 7.\n\nWie sieht danach der Suchbaum aus? 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Cluster: 2
Chunk: A1 A2 A3 A4 A5 A6 Sum Note\n10 8 8 6 10 6 48\nStudiengang ::: Bachelor of Science FHGR in Computational and Data Science\nModul ::: Effiziente Algorithmen im FS 2025\nDozent ::: Prof.\n\nDr.\n\nMartin B\u00fcnner ..." + ], + [ + 11719, + ")\n(b) Suchen Sie den Schl\u00fcssel mit dem Wert 5.\n\n\n\nWelche Schl\u00fcssel m\u00fcssen f\u00fcr diese Suche abgefragt werden.\n\n\n\n(2P)\nFachhochschule Graub\u00fcnden(c) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 5.\n\n\n\nWie sieht danach der Suchbaum aus? (\n\n\n\n2P)\n(d) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 7.\n\n\n\nWie sieht danach der Suchbaum aus? (\n\n2P)\nFachhochschule Graub\u00fcndenAUFGABE 4: (6 Punkte)\nF\u00fchren Sie Shellsort mit den Schrittweiten 7, 3, 1 aus: (6P)\n1 2 3 4 5 6 7 8 9 10 11 12 13 14\n15 30 17 10 11 4 6 9 2 1 7 32 5 3\nDokumentieren Sie die Schritte so wie in der Vorlesung und den \u00dcbungen.\n\nFachhochschule Graub\u00fcndenAUFGABE 5: (10 Punkte)\n(a) Ordnen Sie die Folge in einem Max-Heap und speichern diesen in einem Feld A=[\u2026.]\n\nab.\n\nDokumentieren Sie\ndie Zwischenergebnisse so wie in der Vorlesung und \u00dcbung. (\n\n4P)\n9\n4 5\n7 10 6 8\n(b) Nehmen Sie das Ergebnis von (a) und f\u00fchren drei Basis-Schritte von Heapsort durch.\n\nDokumentieren Sie die\nZwischenergebnisse so wie in der Vorlesung und \u00dcbung. (\n\n6P)\nFachhochschule Graub\u00fcndenFachhochschule Graub\u00fcndenAUFGABE 6: (6 Punkte)\n(a) Bestimmen Sie die Balancefaktoren des bin\u00e4ren Suchbaums.\n\nHandelt es sich um ein AVL-Baum? (\n\n2P)\n(b) L\u00f6schen Sie aus dem AVL-Baum das Element \u00ab17\u00bb.\n\nIst es danach noch ein AVL-Baum? (\n\n2P)\n(c) F\u00fcgen Sie danach das Element \u00ab15\u00bb hinzu.\n\nIst es danach noch ein AVL-Baum? 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Cluster: 2
Chunk: )\n(b) Suchen Sie den Schl\u00fcssel mit dem Wert 5.\n\n\n\nWelche Schl\u00fcssel m\u00fcssen f\u00fcr diese Suche abgefragt werden.\n\n\n\n(2P)\nFachhochschule Graub\u00fcnden(c) L\u00f6schen Sie das Element mit dem Schl\u00fcsselwert 5.\n\n\n\nWie..." + ], + [ + 11720, + "von nichtlinearen Gleichungen M. B\u00fcnner B3.02\n4.2 Dowhill-Simplex, Restriktionen, grafische L\u00f6sung, M. B\u00fcnner B3.02\n18 02.05.2025 13:30-15:00 Fr Straffunktionen\n19 08.05.2025 13:30-15:00 Do 4.3 Newton: In 2-dim mit inv.\n\nHesse-Matrix l\u00f6sen M. B\u00fcnner B3.02\n19 09.05.2025 13:30-15:00 Fr 4.4 Steepest Descent M. B\u00fcnner B3.02\n20 15.05.2025 13:30-15:00 Do 5.\n\nRestringierte Optimierung in 2D linear M. B\u00fcnner B3.02\n20 16.05.2025 13:30-15:00 Fr in 2D nichtlinear M. B\u00fcnner B3.02\n21 22.05.2025 13:30-15:00 Do Linear Programming M. B\u00fcnner B3.02\n21 23.05.2025 13:30-15:00 Fr Nonlinear Programming M. B\u00fcnner B3.02\n22 30.05.2025 13:30-15:00 Fr \u00dcbungen M. B\u00fcnner B3.02\nM. B\u00fcnner B3.02\nHinweis:\n- Zwischen den Pr\u00e4senzterminen werden Coachings angeboten, die Termine dazu werden in der Pr\u00e4senzveranstaltung vereinbart.\n\nUnterschrift DozentLeistungsnachweis\nK\u00fcrzel cds204\nName Effiziente Algorithmen\nTyp Modul\nECTS 4\nProzent Art Form Hilfsmittel Bemerkungen Dauer (minuten)\n100%abgesetzte Modulschlusspr\u00fcfung schriftlich closed book eigener Taschenrechner nicht programmierbar 90\nFormelsammlung\nkeine\nUnterschrift Dozierende", + 18240, + 6201665, + null, + "md", + null, + [ + -0.5218154, + 0.13426243, + 0.46825144, + 0.22017159, + -0.27285972, + -0.035619803, + -0.3280491, + -0.19852506, + -0.15948859, + -0.4725128, + -0.87682056, + 0.38066867, + 0.68204814, + -0.77832884, + -0.29271296, + 0.8834769, + 0.22653359, + 0.1016869, + -0.29083347, + -0.7487563, + -0.9182125, + 0.06628891, + 0.4982129, + 0.79736966, + -0.7450637, + 0.021702912, + -0.49623805, + 0.32254717, + 0.39165622, + 0.6728145, + -0.20694828, + 0.38492647, + 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Cluster: 0
Chunk: von nichtlinearen Gleichungen M. B\u00fcnner B3.02\n4.2 Dowhill-Simplex, Restriktionen, grafische L\u00f6sung, M. B\u00fcnner B3.02\n18 02.05.2025 13:30-15:00 Fr Straffunktionen\n19 08.05.2025 13:30-15:00 Do 4.3 Newton..." + ], + [ + 11721, + "Effiziente Algorithmen\n7.\n\nReelwertige Optimierung in N Dimensionen\nProf.\n\nDr.\n\nMartin B\u00fcnner7.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nMotivation:\n20 Datenpunkte in DataSinus2.csv\n[xData,yData] = np.loadtxt('DataSinus2.csv',delimiter=';\u2019)\nModellierung der Daten mit einer Funktion aus der Modellklasse\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit 4 freien Parametern a = ( a , a , a , a ).\n\nWas sind die besten Werte f\u00fcr a?\n\n1 2 3 4\nAnsatz mit der Summe der kleinsten Quadrate (Gauss, 17xx):\n\uf0e0 Tafel\n\uf0e0 Optimierungs-Problem mit 4 Design-Variablen\nMin S( a , a , a , a )\n1 2 3 4\nWie wir so was l\u00f6sen ist der Inhalt dieses Kapitels.\n\n17.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nReellwertiges unrestringiertes Optimierungs-Problem mit N Design-Variablen (N Dimensionen)\nMin f(x), x in RN\nMin f(x x x ), (x x x ) in RN\n1 , 2 , ...., N 1 , 2 , ...., N\nBemerkungen:\n1.\n\nAbleitung f\u2019(x) \uf0e0 Gradient gradf(x)\n2.\n\nAbleitung f\u2019\u2019(x) \uf0e0 Hesse-Matrix H(x)\nf(x) ist konvex, falls H(x) positiv definit (alle Eigenwerte positiv) ist.\n\n\uf0e0 Es existiert kein oder genau ein lokales Minimum.\n\nSei f(x) 2-mal differenzierbar, dann gilt f\u00fcr lokale Minima an der Stelle x*:\ngradf(x*) = 0\nH(x*) positiv definit (alle Eigenwerte positiv).\n\nBemerkung: Die Definitheit der Hesse-Matrix m\u00fcssen Sie (noch) nicht selber berechnen k\u00f6nnen.\n\n27.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nBeispiel: 2D-Optimierungsproblem mit der Himmelblau-Funktion\nMin f(x x )\n1 , 2\nmit (x 2+ x -11) 2 + (x + x 2 -7) 2.\n\n1 2 1 2\n4 lokale Minima:\n\uf0e0 \u00dcbung\n37.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nBeispiel: 2D-Optimierungsproblem mit der Rosenbrock-Funktion\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\n1 , 2 1 2 1\nL\u00f6sung f(1,1) = 0.\n\n\uf0e0 \u00dcbung\n47.\n\nReelwertige Optimierung in N Dimensionen\n7.1 Parameterscan\nMin f(x), x in RN\nin Hypercube x \u2264 x \u2264 x und x - x = L.\nlow up up low\nEinfache Absch\u00e4tzung: Wir unterteilen jede Design-Variable in M = L\/T \u00e4quidistante Segmente.\n\nDann werden MN\ni i\nFunktionsauswertungen ben\u00f6tigt.\n\n\uf0e0 Tafel\nBeispiel:\n1.", + 18240, + 14884748, + null, + "md", + null, + [ + -0.8677129, + 0.38449863, + 0.5034417, + -0.103949405, + -0.37239298, + -0.27794132, + 0.391599, + -0.10016646, + -0.023383643, + -0.031077437, + -0.52423716, + -0.08872123, + 0.30995914, + 0.6822999, + -0.119418964, + 0.09067443, + 0.016116112, + 0.6925044, + 0.024169125, + -0.37904954, + -0.22198641, + -0.31751263, + 0.48463133, + 0.5453157, + 0.35345456, + 0.7434466, + 0.1971786, + 0.27662086, + 0.31977284, + 0.11300657, + 0.38406906, + 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Cluster: 5
Chunk: Effiziente Algorithmen\n7.\n\nReelwertige Optimierung in N Dimensionen\nProf.\n\nDr.\n\nMartin B\u00fcnner7.\n\nReelwertige Optimierung in N Dimensionen\n7.0.\n\nDefinitionen\nMotivation:\n20 Datenpunkte in DataSinus2.cs..." + ], + [ + 11722, + "ige Optimierung in N Dimensionen\n7.1 Parameterscan\nMin f(x), x in RN\nin Hypercube x \u2264 x \u2264 x und x - x = L.\nlow up up low\nEinfache Absch\u00e4tzung: Wir unterteilen jede Design-Variable in M = L\/T \u00e4quidistante Segmente.\n\n\n\nDann werden MN\ni i\nFunktionsauswertungen ben\u00f6tigt.\n\n\n\n\uf0e0 Tafel\nBeispiel:\n1.\n\nN=2, M=100, Zeit f\u00fcr eine Funktionsauswertung: 1 ms:\n\uf0e0 Rechenzeit: 1002 * 0.001 s = 105 s = 10 s\n2.N=4, M=100, Zeit f\u00fcr eine Funktionsauswertung: 1 ms:\n\uf0e0 Rechenzeit: 1004 * 0.001 s = 105 s = 1.2 Tage\n3.\n\nN=10, M=100, Zeit f\u00fcr eine Funktionsauswertung: 1 ms:\n\uf0e0 Rechenzeit: 10010 * 0.001 s = 1017 s = 3.2 Mrd.\n\nJahre (zum Vergleich: Alter des Sonnensystems ca.\n\n13 Mrd.\n\nJ.)\n57.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nheisst auch Downhill-Simplex-Algorithmus. \u00ab\n\nL\u00f6st\u00bb das unrestringierte N-dimensionale reelwertige Optimierungsproblem:\nMin f(x), x in RN\nNelder, Mead (1965)\nBemerkungen:\n\uf0fc f(x) muss stetig sein.\n\n\uf0fc Verwendung in der Praxis f\u00fcr 2 \u2264 N \u2264 10.\n\nF\u00fcr N gr\u00f6sser 10 zu in-effizient.\n\n\uf0fc Vorteil: gradienten-frei\n67.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nIdee des Downhill Simplex-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\n97.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nf\nGegeben: Simplex S mit n+1 Ecken, x x\n1, \u2026 n+1\nf\nn+1\n1.\n\nWerte f aus und sortiere f(x ) < f(x )< \u2026 < f(x )\nf\n1 2 n+1 n\n2.\n\nBerechne Zentrum der n besten Punkte\n1 n\n\u2211\nx = x\ni\nn\ni=1\nf\n2\nf\n1\n3.\n\nF\u00fchre genau eine Operation\nReflection, Expansion, Contraction oder Shrink\ndurch7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nReflection Spiegele x an Schwerpunkt\nn+1\nf\nf\nn+1\nf\nn\nf\nr\nf\n2\nf\n17.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nExpansion\nf\nf\nn+1\nf\nn\nf\n2\nf\n1\nf\nr7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nContraction\nf\nf\nf n+1\nr\nf\nn\nf\n2\nf\n17.", + 18240, + 10192364, + null, + "md", + null, + [ + -0.111103505, + 0.27513874, + -0.024644863, + 0.24044713, + -0.5872487, + 0.96111953, + 0.5122346, + -0.691302, + 0.4659692, + -0.34453544, + 0.18847531, + 0.4933645, + 0.34522104, + 0.5350193, + -0.28161824, + -0.20311373, + 0.117067285, + 0.32576823, + 0.8590197, + -1.1533606, + -0.5475748, + 0.09159811, + -0.13058528, + 0.8629452, + -0.4752931, + 1.0980564, + -0.7857408, + -0.7443192, + 0.06946849, + 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Cluster: 6
Chunk: ige Optimierung in N Dimensionen\n7.1 Parameterscan\nMin f(x), x in RN\nin Hypercube x \u2264 x \u2264 x und x - x = L.\nlow up up low\nEinfache Absch\u00e4tzung: Wir unterteilen jede Design-Variable in M = L\/T \u00e4quidista..." + ], + [ + 11723, + "prung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n\n\n(a) Stellen Sie ein Gleichungs-System mit 3 nichtlinearen Gleichungen f\u00fcr die Unbekannten (x, y, t) auf.\n\nx2 + y2 \u2013 1 = 0,\nx \u2013 sin(t) = 0,\ny \u2013 sin(2t + \u03c0\/4) = 0\n(b) Finden Sie f\u00fcr jeden Schnittpunkt jeweils einen guten Startwert f\u00fcr den Downhill-Simplex-Algorithmus.\n\nz.B. nur f\u00fcr x>0, da symmetrisch:\n(x ,y , t ) = [0.7, 0.75, arcsin(0.7) ]\n0 0 0\n[0.25, 1, arcsin(0.25) ]\n[1, -0.25, arcsin(1) ]\n[0.7, -0.75, arcsin(0.7) ]\n(b) L\u00f6sen Sie das Gleichungs-System indem Sie ein unrestringiertes Optimierungsproblem mit 3 Design-\nVariablen mit dem Downhill-Simplex-Algorithmus l\u00f6sen.\n\nBestimmen Sie alle L\u00f6sungen mit den Startwerten\nvon (b)\nHier reicht es alle Schnittpunkte mit x > 0 zu bestimmen, da beide Kurven achsensymmetrisch sind.\n\nStartwert [0.7, 0.75, arcsin(0.7) ] \uf0e0 L\u00f6sung [0.70674446 0.7078306 0.78488616]\nStartwert [0.25, 1, arcsin(0.25) ] \uf0e0 L\u00f6sung [0.26014633 0.96664301 0.2631811 ]\nStartwert [1, -0.25, arcsin(1) ] \uf0e0 L\u00f6sung [1, -0.25, arcsin(1) ]\nStartwert [0.6, -0.85, np.arcsin(0.6)] \uf0e0 L\u00f6sung [0.70732644 0.70666723 0.78570889], hier musste\nder Startwert noch variiert\/probiert werden, bis die Konvergenz auf den gesuchten Schnittpunkt erreicht\nwurde.\n\n51\u00dcbung 7.2.5\nEs seien folgende Optimierungsprobleme zu l\u00f6sen.\n\nGehen Sie jeweils wie folgt vor:\n(a) Graphische L\u00f6sung.\n\n(b) Aufstellen eines unrestringierten 2-dim Optimierungsproblems.\n\n(c) L\u00f6sen des unrestringierten 2-dim Optimierungsproblems mittels Downhill-Simplex-Algorithmus.\n\n(I) Min [ x + x ]\n1 2\nx \u2265 x 2\n2 1\nL\u00f6sung (-0.5; 0.25)\n(II) Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\nL\u00f6sung (0.5; -0.5)\n52\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .", + 18240, + 4960559, + null, + "md", + null, + [ + 0.3179646, + -0.103203535, + -0.03015862, + -0.33203667, + 0.21279645, + 0.25659063, + 0.264594, + 0.5064091, + -0.5108686, + 0.1827112, + -0.2057376, + 0.41959545, + 0.16683517, + 0.05212103, + 0.4227515, + 0.40559927, + 0.49333963, + -0.033038758, + 0.5204243, + -0.29663774, + -0.56361336, + 0.06465066, + 0.3913966, + 0.48964748, + -0.6096554, + 0.39608756, + -0.13872966, + 0.1466504, + 0.10674975, + 1.1831636, + -0.17645934, + -0.07092726, + -0.3881887, + -0.3163711, + -0.27526757, + -0.6076026, + 0.8258395, + 0.20650324, + -0.659403, + -0.299567, + -0.42918032, + 1.034678, + 0.58659273, + 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Cluster: 7
Chunk: prung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n\n\n(a) Stellen Sie ein Gleichungs-System mit 3 nic..." + ], + [ + 11725, + "die Newton-Iterations-Formel auf.\n\n\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n\n\n54\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n\n\n1 , 2\n(b) Es sei a = 1.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\ngradf = (3x 2 \u2013 4, 2x )\n1 2\nIteration x x gradf |gradf |2\n1,n 2,n n n\nn = 0 1, 1 -1, 2 5\nn = 1 2,-1 8,-2 68\nn = 2 -6,1 104,2 10\u2019820\n(c) a=0.1: Iteration x x gradf |gradf 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Cluster: 4
Chunk: die Newton-Iterations-Formel auf.\n\n\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n\n\n54\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n\n\n1 , 2 1 1..." + ], + [ + 11727, + "17.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nExpansion\nf\nf\nn+1\nf\nn\nf\n2\nf\n1\nf\nr7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nContraction\nf\nf\nf n+1\nr\nf\nn\nf\n2\nf\n17.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nShrinkage7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nKonstruktion des Start-Simplex\n\uf0fc oft automatisiert (x% Abweichungen von einem Startpunkt)\n\uf0fc Gr\u00f6sse des Start-Simplex bestimmt die initiale Schrittweite\nRestringierte Probleme\n\uf0fc 1-3 Gleichheits- oder Ungleichheits-Restriktionen mittels Straf-Funktionen in die Zielfunktion integrieren.7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nAbbruchbedingungen7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nRosenbrock (Banana) Function\n2 2 2\n\ud835\udc53\ud835\udc53(\ud835\udc65\ud835\udc65, \ud835\udc66\ud835\udc66) = (1 \u2212 \ud835\udc65\ud835\udc65) + 100(\ud835\udc66\ud835\udc66 \u2212 \ud835\udc65\ud835\udc65 )7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex \u2013 Himmelblau Funktion7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-AlgorithmusIn Python \uf0e0 scipy.optimize.fmin\nscipy.optimize.fmin \u2014 SciPy v1.13.0 Manual\n\uf0e0 einfaches Bsp in der Vorlesung\n\uf0e0 \u00dcbung 7.2.1 u 7.2.2\n227.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nVorteile\n\uf0fe Sehr einfach anzuwenden\n\uf0fe Simplex passt sich der \u201elandscape\u201c der Funktion an\n\uf0fe Ausreichende Stabilit\u00e4t f\u00fcr die meisten Probleme mit N<5\n\uf0fe Effizienz in der Praxis gut genug f\u00fcr kleine N<5\n\uf0fe Zielfunktion muss nicht in analytisch geschlossener Form\nbekannt sein\n\uf0fe Ben\u00f6tigt keine stetig ableitbaren Zielfunktionen\n\uf0fe keine Ableitungen\n\uf0fe Findet lokale Minima\n\uf0fe Einige wenige Constraints k\u00f6nnen mit Penalties abgebildet\nwerden.7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nNachteile\n\uf0fe Ineffizient (im Vergleich zu Quasi-Newton-Methoden),\ndeswegen nur f\u00fcr kleine N<=5 anzuwenden.\n\n\uf0fe Constraints k\u00f6nnen \u201enicht wirklich\u201c abgebildet werden.", + 18240, + 13776980, + null, + "md", + null, + [ + -0.76435846, + 0.1580934, + -0.073087715, + -0.29936278, + 0.10853522, + -0.093156494, + 0.2814383, + 0.5353781, + 0.51500803, + -0.0007352047, + -0.3806599, + 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Cluster: 5
Chunk: 17.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nExpansion\nf\nf\nn+1\nf\nn\nf\n2\nf\n1\nf\nr7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nContraction\nf\nf\n..." + ], + [ + 11729, + "mit c > 0 steuert die Schrittweite.\n\n\n\ni\nDer Steepest-Descent-Alg ist nicht invariant unter der Transformation f \uf0e0 a*f und x \uf0e0 b*x.\n\n\n\nDamit liefert der Steepest-\nDescent-Alg NUR eine Richtung.\n\n\n\nEr liefert keine Information \u00fcber die L\u00e4nge des Schrittes.\n\n\n\n\uf0e0 \u00dcbung 7.7.1 ohne Schrittweitensteuerung.\n\n\n\n327.\n\nReelwertige Optimierung in N Dimensionen\n7.4 Steepest-Descent\nAusserdem ist die Richtung des Gradienten bei nicht-isotropen Minima (dies ist die Regel) schlecht.\n\nisotrop: Gradientenrichtung gut nicht-isotrop: Gradientenrichtung schlecht\n.\n\nDies f\u00fchrt zu dem \u00abklassischen\u00bb Zig-Zag-Routen, die nicht erw\u00fcnscht sind.\n\n337.\n\nReelwertige Optimierung in N Dimensionen\n7.4 Steepest-Descent\nIdee einer SEHR einfachen Schrittweitensteuerung:\nFalls Schrittweite zu gross: Reduziere Schrittweise iterativ um die H\u00e4lfte, so lange bis f < f .\n\nn+1 n\nFalls Schrittweite evtl zu klein: Verdopple Schrittweite so lange bis f < f , dann reduziere Schrittweite noch 1-mal.\n\nn+1 n\n\uf0e0 test_Steepest_Descent.py\n\uf0e0 \u00dcbung 7.7.2\n34\u00dcbungen\n35\u00dcbung 7.1.1\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem Parameterscan.\n\nVariieren Sie dazu die Parameter in dem Bereich:\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8).\n\nF\u00fchren Sie dabei Parameterscans mit jeweils 4, 8, 16, 32, 64 Testpunkten pro Parameter durch\n(wenn ihr Computer 128 und mehr schafft, probieren Sie es).\n\nVisualisieren Sie Ihre Ergebnisse.\n\n36\u00dcbung 7.2.1\nFinden Sie jeweils die L\u00f6sung des Optimierungsproblems\nMin f(x)\nmit Hilfe des Downhill-Simplex-Algorithmus.\n\nVariieren Sie die Startwerte um ko-existierende lokale Minima zu entdecken.\n\n(a) f(x) = -1\/5 x 3 + 10x + x 2.\n\n1 1 2\n(b) f(x) = 10( x \u2013 1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.\n\n1 2 1 2\n37\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Downhill-Simplex-Algorithmus. ,", + 18240, + 16188844, + null, + "md", + null, + [ + 0.21610712, + -0.34170783, + -0.2582149, + -0.13907842, + -0.61066616, + 1.0078603, + -0.3240201, + 0.6873087, + -0.29100716, + 0.42602944, + -0.19834957, + 0.07046136, + 0.19968116, + 0.05360639, + 0.4098373, + 0.8089135, + 0.38811648, + 0.020720284, + -0.29712686, + 0.09276265, + 0.24396716, + 0.49379405, + 0.14850694, + 0.8405345, + -0.42497858, + 0.54847485, + 0.5315522, + 0.2563539, + 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Cluster: 3
Chunk: mit c > 0 steuert die Schrittweite.\n\n\n\ni\nDer Steepest-Descent-Alg ist nicht invariant unter der Transformation f \uf0e0 a*f und x \uf0e0 b*x.\n\n\n\nDamit liefert der Steepest-\nDescent-Alg NUR eine Richtung.\n\n\n\nEr ..." + ], + [ + 11731, + ".\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n\n\n1 , 2\n(b) Bestimmen Sie H(x x ) .\n\n\n\n1 , 2\n(c) Bestimmen Sie H-1(x x ) .\n\n\n\n1 , 2\n(d) F\u00fchren Sie eine Newton-Iteration \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem Startwert (x =1 x =1) .\n\n1,1 , 2,1 1,0 , 2,0\n42\u00dcbung 7.3.2\nWir suchen das Minimum der Rosenbrock-Funktion oder Banana-Funktion:\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n(a) Berechnen Sie den Gradienten, die Hesse-Matrix und die inverse Hesse-Matrix der Rosenbrock-Funktion.\n\n(b) Stellen Sie damit die Newton-Iterations-Formel auf und programmieren diese in Python.\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n43\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\n(b) Es sei a = 1.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\n(c) Es sei a = 0.1.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\n(d) Es sei a = 10.\n\nF\u00fchren Sie zweimal eine Steepest-Descent-Iteration ohne Schrittweitensteuerung (c=1) \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem\n1,2 , 2,2\nStartwert (x =1 x =1).\n\nBestimmen Sie jeweils das Quadrat der L\u00e4nge des Gradienten.\n\n1,0 , 2,0\n44\u00dcbung 7.4.2\nWir suchen das Minimum der Rosenbrock-Funktion oder Banana-Funktion:\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n(a) Berechnen Sie den Gradienten der Rosenbrock-Funktion.", + 18240, + 6062665, + null, + "md", + null, + [ + -0.3088199, + 0.3640352, + -0.31572556, + -0.5632599, + -0.8831732, + 0.1357588, + 0.2828112, + -0.073052764, + -0.14028428, + -0.4137379, + -0.3779284, + 0.22783622, + 0.6706066, + -0.056991823, + 0.40256593, + 0.05860323, + -0.27182338, + 0.73582864, + 0.04000853, + -0.95994973, + 0.3459747, + -0.008289742, + 0.791801, + 0.7151977, + -0.7154374, + 0.9411023, + -0.18865997, + 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Cluster: 3
Chunk: .\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n\n\n1 , 2\n(b) Bestimmen Sie H(x x ) .\n\n\n\n1 , 2\n(c) Bestimmen Sie H-1(x x ) .\n\n\n\n1 , 2\n(d) F\u00fchren Sie eine Newton-Iteration \u00abvon Hand\u00bb durch und Bestimme..." + ], + [ + 11724, + "Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\nL\u00f6sung (0.5; -0.5)\n52\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\ngradf(x x ) = (3x 2 - 4 , 2x )\n1 , 2 1 2\n(b) Bestimmen Sie H(x x ) .\n\n1 , 2\nH(x x ) = ( (6x , 0), (0, 2) )\n1 , 2 1\n(c) Bestimmen Sie H-1(x x ) .\n\n1 , 2\nH(x x ) -1 = ( (1\/(6x ), 0), (0, 1\/2) )\n1 , 2 1\n(d) F\u00fchren Sie eine Newton-Iteration durch und Bestimmen Sie (x x ) mit dem Startwert (x =1 x =1) .\n\n1,1 , 2,1 1,0 , 2,0\n(x x ) = (7\/6, 0)\n1,1 , 2,1\n53\u00dcbung 7.3.2\nWir suchen das Minimum der Rosenbrock-Funktion oder Banana-Funktion:\nMin f(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n(a) Berechnen Sie den Gradienten, die Hesse-Matrix und die inverse Hesse-Matrix der Rosenbrock-Funktion.\n\ngrad f(x x ) = ( 2(1 \u2013 x ) + 200(x - x 2) * (-2x ), 200(x - x 2) ) = ( 2 \u2013 2x - 400x x + 400x 3 , 200x - 200x 2 )\n1 , 2 1 2 1 1 2 1 1 1 2 1 2 1\nf = \u2013 2 - 400x + 1200x 2\n11 2 1\nf = - 400x\n12 1\nf = 200\n22\nH(x x ) = ( (f , f ) , (f , f ) )\n1 , 2 11 12 12 22\nD(x x ) = f * f - f 2\n1 , 2 11 22 12\nH-1(x x ) = ( (f , -f ) , (-f , f ) ) \/ D(x x )\n1 , 2 22 12 12 11 1 , 2\n(b) Stellen Sie damit die Newton-Iterations-Formel auf.\n\nBestimmen Sie kritische Punkte mit verschiedenen Startwerten.\n\n54\u00dcbung 7.4.1\nWir suchen die kritischen Punkte von f(x x ) = a * ( x 3 - 4 x + x 2 ).\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\n(b) Es sei a = 1.", + 18240, + 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Cluster: 4
Chunk: Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\nL\u00f6sung (0.5; -0.5)\n52\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\ngradf(x x..." + ], + [ + 11726, + "Einige wenige Constraints k\u00f6nnen mit Penalties abgebildet\nwerden.7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nNachteile\n\uf0fe Ineffizient (im Vergleich zu Quasi-Newton-Methoden),\ndeswegen nur f\u00fcr kleine N<=5 anzuwenden.\n\n\n\n\uf0fe Constraints k\u00f6nnen \u201enicht wirklich\u201c abgebildet werden.\n\n\uf0fe Wahl des Startsimplex hat Einfluss welches lokale Minimum\ngefunden wird\n\uf0fe Wahl des Startsimplex beeinflusst Konvergenz\n\uf0fe Fehlender Konvergenzbeweis\n\uf0fe Z.B. Stagnation um nicht-optimalen Punkt\n\uf0fe rauschempfindlich7.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill-Simplex mit Zielfunktion mit additivem Rauschen s:\nMin [ f(x) + s ], x in RN\nBeispiele\n\uf0fc Double Precision Numerik: \u03c3 \u2248 10-16,\ns\n\uf0fc FEM-Simulation: \u03c3 \u2248 10-3 - 10-6.\n\ns\nBest-Practice\n\uf0fc Anwender (Sie!)\n\nm\u00fcssen das Rauschlevel kennen.\n\n\uf0fc Toleranzen (ftol, xtol) an das Rauschlevel anpassen.\n\n257.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nNichtlineare Regression mit Downhill-Simplex \uf0e0 \u00dcbung 7.2.2\nNichtlineare Gleichungen l\u00f6sen mit Downhill-Simplex \uf0e0 \u00dcbung 7.2.3\nSchnittpunkte zweier Kreise:\n- M1: Kreis um (1,2) mit Radius 3,\n- M2: Kreis um (2,0) mit Radius 2.\n\n(a) Optimierungs-Problem aufstellen.\n\n(b) Gute Startpunkte bestimmen.\n\n(c) L\u00f6sen mit Downhill-Simplex.\n\n267.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nIhre Aufgaben ist es einen Satelliten am Ort (x,y) so zu\npositionieren, dass er sich im Gravitationsfeld der 3\nM3 M1 Planeten nicht bewegt.\n\n(a) Optimierungs-Problem aufstellen.\n\n(b) Gute Startpunkte bestimmen.\n\n(c) L\u00f6sen mit Downhill-Simplex.\n\nM2\n277.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\n2-Dim Optimierungsproblem mit 2 linearen\nUngleichheitsbedingungen\nMin [ x 2 + x 2 ]\n1 2\nx + x \u2265 1\n1 2\nx \u2265 2\n2\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\nBemerkung: Da die Zielfunktion f\u00fcr den Downhill-Simplex\nAlgorithmus nicht differenzierbar sein muss, k\u00f6nnen hier\nlineare Straffunktionen (genauso wie quadratische\nStraffunktionen) verwendet werden.\n\n287.", + 18240, + 16414826, + null, + "md", + null, + [ + -0.8539323, + -0.3307703, + -0.12917906, + -0.9902739, + -0.6676355, + 1.2118105, + 0.029592108, + 0.22852649, + -0.17469743, + 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Cluster: 3
Chunk: Einige wenige Constraints k\u00f6nnen mit Penalties abgebildet\nwerden.7.\n\n\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\nDownhill Simplex\nNachteile\n\uf0fe Ineffizient (im Vergleich zu Qu..." + ], + [ + 11728, + "1\n1 2\nx \u2265 2\n2\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\nBemerkung: Da die Zielfunktion f\u00fcr den Downhill-Simplex\nAlgorithmus nicht differenzierbar sein muss, k\u00f6nnen hier\nlineare Straffunktionen (genauso wie quadratische\nStraffunktionen) verwendet werden.\n\n\n\n287.\n\nReelwertige Optimierung in N Dimensionen\n7.2 Nelder-Mead-Algorithmus\n2-Dim Optimierungsproblem mit 1 nichtlineare\nGleichheitsbedingung\nMin [ x 2 + x 2 ]\n1 2\nx = ( x \u2013 2 ) 2\n2 1\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\n\uf0e0 \u00dcbungen 7.2.3 \u2013 7.2.5\n297.\n\nReelwertige Optimierung in N Dimensionen\n7.3 Newton-Verfahren\nMin f(x), x in RN\nf\u00fcr N = 1: Newton-Algorithmus\nx = x \u2013 f\u2019 \/f\u2019\u2019 = x \u2013 (f\u2019\u2019 )-1*f\u2019\nn+1 n n n n n n\nf\u00fcr N > 1: Newton-Algorithmus\nx = x \u2013 H-1 (x ) * gradf (x )\nn+1 n n n\nx = x \u2013 H-1 * gradf\nn+1 n n n\nWie f\u00fcr N=1: exakt falls f(x) quadratisch.\n\n307.\n\nReelwertige Optimierung in N Dimensionen\n7.3 Newton-Verfahren\nFalls f(x) nicht quadratisch und wir haben einen guten Startwert in der N\u00e4he eines lokalen Minimus (konvexes Gebiet),\nf\u00fchren wir Iterationen durch\n\uf0e0 Beispiel \u00dcbung 7.3.1 in der Vorlesung\nWeiter: Um die Stabilit\u00e4t zu erh\u00f6hen f\u00fchren wir noch eine Schrittweiten-Steuerung ein (hilft, wenn der Startwert nicht so gut\nsein sollte).\n\nx = x \u2013 c*H-1 * gradf\nn+1 n i n n\nmit 0 < c \u2264 1 steuert die Schrittweite (z.B. Backtracking).\n\ni\n\uf0e0 Hausaufgabe 7.3.2\n317.\n\nReelwertige Optimierung in N Dimensionen\n7.4 Steepest-Descent\nOder auch: Methode des steilsten Abstiegs\nIn der Praxis erweist sich die Bestimmung von H-1 als schwierig und aufwendig.\n\nn\n\uf0e0 H-1 durch etwas \u00abeinfacheres\u00bb n\u00e4hern.\n\nn\n\uf0e0 brutal einfache Idee : H-1 \u2248 1.\n\nn\nDamit ergibt sich der Steepest-Descent-Algorithmus mit Schrittweitensteuerung:\nx = x \u2013 c * gradf\nn+1 n i n\nmit c > 0 steuert die Schrittweite.\n\ni\nDer Steepest-Descent-Alg ist nicht invariant unter der Transformation f \uf0e0 a*f und x \uf0e0 b*x.\n\nDamit liefert der Steepest-\nDescent-Alg NUR eine Richtung.\n\nEr liefert keine Information \u00fcber die L\u00e4nge des Schrittes.\n\n\uf0e0 \u00dcbung 7.7.1 ohne Schrittweitensteuerung.\n\n327.", + 18240, + 10744175, + null, + "md", + null, + [ + -1.1105303, + 0.06864798, + 0.71910334, + -0.18397726, + 0.40972143, + -0.7054399, + 0.120589584, + 0.06977714, + 0.17998672, + 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Cluster: 5
Chunk: 1\n1 2\nx \u2265 2\n2\n(a) Graphische L\u00f6sung\n(b) L\u00f6sung mit Downhill-Simplex Algorithmus mittels\nStraffunktionen\nBemerkung: Da die Zielfunktion f\u00fcr den Downhill-Simplex\nAlgorithmus nicht differenzierbar sein m..." + ], + [ + 11730, + "05x 2.\n\n\n\n1 2 1 2\n37\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\n\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Downhill-Simplex-Algorithmus. ,\n\n(a) Extrahieren Sie aus den Daten (durch Analyse und \u00dcberlegung) einen geeigneten Startwert.\n\nBestimmen Sie die Parameter.\n\n(b) W\u00e4hlen Sie per Zufall verschiedene Startwerte im Bereich\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8)\nund bestimmen Sie die Parameter.\n\nVisualisieren Sie Ihre Ergebnisse.\n\n38\u00dcbung 7.2.3\nL\u00f6sen Sie die folgenden nichtlinearen Gleichungs-Systeme durch Formulieren eines Optimierungs-Problems und dem Downhill-\nSimplex-Verfahren.\n\nVersuchen Sie eventuell vorhanden mehrere L\u00f6sungen durch Variation des Anfangswertes zu finden:\n(a)\n(b)\n(c)\n39\u00dcbung 7.2.4\nWir suchen die Schnittpunkte der folgenden Linien:\n- ein Kreis um den Ursprung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n(a) Stellen Sie ein Gleichungs-System mit 3 nichtlinearen Gleichungen f\u00fcr die Unbekannten (x, y, t)\nauf.\n\n(b) Finden Sie f\u00fcr jeden Schnittpunkt jeweils einen guten Startwert f\u00fcr den Downhill-Simplex-\nAlgorithmus.\n\n(b) L\u00f6sen Sie das Gleichungs-System indem Sie ein unrestringiertes Optimierungsproblem mit 3\nDesign-Variablen mit dem Downhill-Simplex-Algorithmus l\u00f6sen.\n\nBestimmen Sie alle L\u00f6sungen mit\nden Startwerten von (b)\n40\u00dcbung 7.2.5\nEs seien folgende Optimierungsprobleme zu l\u00f6sen.\n\nGehen Sie jeweils wie folgt vor:\n(a) Graphische L\u00f6sung\n(b) Aufstellen eines unrestringierten 2-dim Optimierungsproblems\n(c) L\u00f6sen des unrestringierten 2-dim Optimierungsproblems mittels Downhill-Simplex-Algorithmus\n(I) Min [ x + x ]\n1 2\nx \u2265 x 2\n2 1\n(II) Min [ x 2 + x 2 ]\n1 2\nx - x \u2013 1 = 0\n1 2\n41\u00dcbung 7.3.1\nWir suchen die kritischen Punkte von f(x x ) = x 3 - 4 x + x 2.\n\n1 , 2 1 1 2\n(a) Bestimmen Sie gradf(x x ) .\n\n1 , 2\n(b) Bestimmen Sie H(x x ) .\n\n1 , 2\n(c) Bestimmen Sie H-1(x x ) .\n\n1 , 2\n(d) F\u00fchren Sie eine Newton-Iteration \u00abvon Hand\u00bb durch und Bestimmen Sie (x x ) mit dem Startwert (x =1 x =1) .", + 18240, + 2887496, + null, + "md", + null, + [ + 0.13278152, + -0.31135443, + -0.06157794, + -0.0959831, + -0.2155047, + 0.18859595, + -0.16959096, + -0.34411773, + -0.041826297, + -0.31756315, + -0.9122811, + -0.22444451, + 0.0037292335, + 0.19952506, + 0.5708837, + 0.2232977, + 0.6871961, + 0.60550606, + 0.30983904, + -0.8043447, + -0.27264884, + 0.05291005, 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Cluster: 1
Chunk: 05x 2.\n\n\n\n1 2 1 2\n37\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\n\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Dow..." + ], + [ + 11732, + "(x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\n\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n\n\n\n(a) Berechnen Sie den Gradienten der Rosenbrock-Funktion.\n\n(b) Verwenden Sie das Script test_steepest_descent.py .\n\nF\u00fchren Sie f\u00fcr verschiedene Startwerte und f\u00fcr verschiedene Toleranzen die Optimierung mit dem Steepest-\nDescent-Algorithmus durch.\n\nNotieren Sie jeweils die Anzahl der Funktionsaufrufe, die ben\u00f6tigt werden.\n\n(c) In dem Skript test_steepest_descent.py wird die Schrittweite mit dem Faktor a = 2 erh\u00f6ht bzw.\n\n1\/a = \u00bd erniedrigt.\n\nExperimentieren Sie mit verschiedenen a, z.B. a= 1.5\noder a = 3.\n\nWas beobachten Sie?\n\n45Musterl\u00f6sungen\n46\u00dcbung 7.1.1\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem Parameterscan.\n\nVariieren Sie dazu die Parameter in dem Bereich:\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8).\n\nF\u00fchren Sie dabei Parameterscans mit jeweils 4, 8, 16, 32, 64 Testpunkten pro Parameter durch\n(wenn ihr Computer 128 und mehr schafft, probieren Sie es).\n\nVisualisieren Sie Ihre Ergebnisse.\n\nM a* S*\n4 [4.\n\n1.667 6.283 6. ]\n\n38.7003\n8 [4.714 1.714 5.386 5.429] 1.835116\n16 [4.8 1.667 5.864 5.2 ] 2.0037\n32 [4.742 1.645 5.878 5.097] 0.075264\n64 [4.714 1.651 5.884 5.143] 0.233\n47\u00dcbung 7.2.1\nFinden Sie jeweils die L\u00f6sung des Optimierungsproblems\nMin f(x)\nmit Hilfe des Downhill-Simplex-Algorithmus.\n\nVariieren Sie die Startwerte um ko-existierende lokale Minima zu entdecken.\n\n(a) f(x) = -1\/5 x 3 + 10x + x 2.\n\n1 1 2\nL\u00f6sung: x* = (-4.0825, 0).\n\n(b) f(x) = 10( x \u2013 1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\nL\u00f6sung: x* = (1, 2, 3, 4).\n\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.", + 18240, + 5832792, + null, + "md", + null, + [ + -0.3678096, + 0.22411136, + 0.14555797, + -0.42699647, + -0.729299, + 0.24891435, + 0.4685517, + -0.28782007, + 0.0039182007, + 0.33779606, + -0.33120748, + 0.55808645, + 0.5444058, + 0.027608246, + 0.08851585, + 0.5265253, + 0.1125632, + -0.078657985, + -0.16471888, + -0.8651752, + 0.34232825, + 0.29238293, + 0.58000296, + 0.3738271, + -0.053281233, + 0.2833863, + 0.17771712, + 0.2546254, + 0.19173825, + 0.3640164, + -1.1249534, + -0.62808365, + -0.20911168, + -0.7429463, + -0.23612238, + 0.031128652, + 0.08355768, + 0.49920803, + -1.0714387, + 0.2685635, + -0.7407983, + 0.34027272, + 0.057589408, + -0.14611419, + -0.7766918, + 0.26893753, + -0.23267646, + 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Cluster: 3
Chunk: (x x )\n1 , 2\nmit f(x x ) = (1 \u2013 x )2 + 100(x - x 2) 2 .\n\n\n\nDie Rosenbrock-Funktion ist eine herausfordernde Testfunktion f\u00fcr Optimierungs-Algorithmen, da Sie ein \u00abgekr\u00fcmmtes Tal\u00bb\n1 , 2 1 2 1\nbesitzt.\n..." + ], + [ + 11733, + "1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\nL\u00f6sung: x* = (1, 2, 3, 4).\n\n\n\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.\n\n1 2 1 2\nEs existieren zahlreiche lokale Minima, z.B.\nlokale Minima f*\n[-1.38925764 -7.41822149] 1.3132611100220473\n[-1.38922156 3.91063 ] -0.9233486372581649\n[ 3.76097004 -1.79076623] -0.06792301515654611\n[ 3.76098637 -7.41815126] 2.836459612005422\n[-1.38926088 -1.79077318] -1.5911215163755672\nDavon ist das beste gefundene lokale Minima x* = [-1.38926088 -1.79077318] mit f* = -1.5911215163755672.\n\n48\u00dcbung 7.2.2\nLaden Sie die Daten von DataSinus2.csv.\n\nBestimmen Sie die Parameter in dem Modell:\nf(x; a , a , a , a ) = a *sin(a *x + a ) + a\n1 2 3 4 1 2 3 4\nmit einem mit dem Downhill-Simplex-Algorithmus.\n\n(a) Extrahieren Sie aus den Daten (durch Analyse und \u00dcberlegung) einen geeigneten Startwert.\n\nBestimmen Sie die Parameter.\n\nz.B. Startwert [4.5, 1.5, 0, 5 ]\n(b) W\u00e4hlen Sie per Zufall verschiedene Startwerte im Bereich\n(3, 1, 0, 2) \u2264 a \u2264 (6, 2, 2\u03c0, 8)\nund bestimmen Sie die Parameter.\n\nVisualisieren Sie Ihre Ergebnisse.\n\nF\u00fcr die meisten Startwerte wird ein S(a*) < 10-8 realisiert bei ca.\n\n200 \u2013 400 Funktionsaufrufen.\n\nz.B.\nStartwert a0 = [3.69327012 1.467473 5.76522026 7.31055702]\na* = [4.735 1.658 5.795 5.119]\nS* =1.046512101321329e-08\n49\u00dcbung 7.2.3\nL\u00f6sen Sie die folgenden nichtlinearen Gleichungs-Systeme durch Formulieren eines Optimierungs-Problems und dem Downhill-\nSimplex-Verfahren:\n(a)\n2 L\u00f6sungen:\n(b)\n1 L\u00f6sung: x= 4, y = 3\n(c)\n2 L\u00f6sungen:\n50\u00dcbung 7.2.4\nWir suchen die Schnittpunkte der folgenden Linien:\n- ein Kreis um den Ursprung mit Radius R = 1 (rote Kurve),\n- Lissajous-Figur (Lissajous-Figur \u2013 Wikipedia) mit\nx = sin(t)\ny = sin(2t + \u03c0\/4)\nmit t in [0; 2\u03c0] (blaue Kurve).\n\n(a) Stellen Sie ein 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"ID: 5967370
Cluster: 5
Chunk: 1)2 + ( x \u2013 2)2 + 0.1( x \u2013 3)2 + ( x \u2013 4)2\n1 2 3 4\nL\u00f6sung: x* = (1, 2, 3, 4).\n\n\n\n(c) f(x) = sin(x + 0.1) + sin(x + 0.4) + 0.1x 2 + 0.05x 2.\n\n1 2 1 2\nEs existieren zahlreiche lokale Minima, z.B.\nlokale..." + ] + ] +} +export default tsneData; diff --git a/lexikon/src/app/librarian/vspace/tsne_types.ts b/lexikon/src/app/librarian/vspace/tsne_types.ts new file mode 100644 index 0000000..2f714f9 --- /dev/null +++ b/lexikon/src/app/librarian/vspace/tsne_types.ts @@ -0,0 +1,76 @@ +// TypeScript-Typen für vspace tsne.json + +/** + * Definiert die exakten Spaltennamen und deren Reihenfolge. + */ +type VSpaceColumns = [ + 'chunk_1024_embeddings_id', + 'chunk', + 'course_id', + 'file_id', + 'source_file', + 'file_type', + 'object_id', + 'embedding', + 'created_at', + 'updated_at', + 'x', + 'y', + 'z', + 'cluster', + 'hover_text', +]; + +/** + * Repräsentiert eine einzelne Datenzeile als Tupel, + * wobei jeder Eintrag dem Typ der entsprechenden Spalte entspricht. + */ +type DataRowTuple = [ + number, // chunk_1024_embeddings_id + string, // chunk + number, // course_id + number, // file_id + string | null, // source_file + string, // file_type + string | null, // object_id + number[], // embedding + string, // created_at (ISO-Datumsstring, z.B. "2024-05-03T09:34:39.000Z") + string, // updated_at (ISO-Datumsstring) + number, // x + number, // y + number, // z + number, // cluster + string, // hover_text +]; + +/** + * Interface für die gesamte Struktur der JSON-Datei. + */ +export interface VSpaceTSNEData { + columns: VSpaceColumns; + index: number[]; + data: DataRowTuple[]; +} + +/** + * Interface für ein einzelnes Datenelement, wenn es in ein + * Objekt umgewandelt wird (Spaltennamen als Schlüssel). + * Dies ist oft nützlich für die Verarbeitung der Daten. + */ +export interface ProcessedDataItem { + chunk_1024_embeddings_id: number; + chunk: string; + course_id: number; + file_id: number; + source_file: string | null; + file_type: string; + object_id: string | null; + embedding: number[]; + created_at: string; // Erwäge die Verwendung von `Date`, falls du die Strings parst + updated_at: string; // Erwäge die Verwendung von `Date`, falls du die Strings parst + x: number; + y: number; + z: number; + cluster: number; + hover_text: string; +} diff --git a/lexikon/src/app/not-found.tsx b/lexikon/src/app/not-found.tsx new file mode 100644 index 0000000..95c8263 --- /dev/null +++ b/lexikon/src/app/not-found.tsx @@ -0,0 +1,26 @@ +import { Metadata } from "next"; +import Link from "next/link"; + +export const metadata: Metadata = { + title: "404: Page Not Found", + description: "The page you are looking for could not be found.", +}; + +export default function NotFound() { + return ( +
+
+

404 - Page Not Found

+

+ The page you are looking for does not exist. +

+ + Return Home + +
+
+ ); +} diff --git a/lexikon/src/app/page.tsx b/lexikon/src/app/page.tsx new file mode 100644 index 0000000..dd4b0c5 --- /dev/null +++ b/lexikon/src/app/page.tsx @@ -0,0 +1,9 @@ +import SidenavContent from "./SideNavContent"; + +export default function Home() { + return ( +
+ +
+ ); +} diff --git a/lexikon/src/app/providers.tsx b/lexikon/src/app/providers.tsx new file mode 100644 index 0000000..2e70ba9 --- /dev/null +++ b/lexikon/src/app/providers.tsx @@ -0,0 +1,35 @@ +"use client"; + +import type { ThemeProviderProps } from "next-themes"; + +import * as React from "react"; +import { HeroUIProvider } from "@heroui/system"; +import { useRouter } from "next/navigation"; +import { ThemeProvider as NextThemesProvider } from "next-themes"; +import { ToastProvider } from "@heroui/react"; +import ResponsiveProvider from "shared/provider/ResponsiveProvider"; + +export interface ProvidersProps { + children: React.ReactNode; + themeProps?: ThemeProviderProps; +} +declare module "@react-types/shared" { + interface RouterConfig { + routerOptions: NonNullable< + Parameters["push"]>[1] + >; + } +} + +export function Providers({ children, themeProps }: ProvidersProps) { + const router = useRouter(); + + return ( + + + {children} + + + + ); +} diff --git a/lexikon/src/app/study/[courseId]/layout.tsx b/lexikon/src/app/study/[courseId]/layout.tsx new file mode 100644 index 0000000..b5b57a5 --- /dev/null +++ b/lexikon/src/app/study/[courseId]/layout.tsx @@ -0,0 +1,41 @@ +"use client"; + +import { TopToolbar } from "features/navigation/TopToolBar"; +import CourseActions from "features/study/components/CourseActions"; +import CoursePath from "features/study/components/CoursePath"; +import StudyProvider from "features/study/provider/StudyProvider"; +import { useParams } from "next/navigation"; +import { container } from "shared/styles/variants"; +import { cn } from "shared/utils/tailwind"; + +interface CourseLayoutProps { + children: React.ReactNode; + // summary: React.ReactNode; +} + +export default function CourseLayout({ children }: CourseLayoutProps) { + const params = useParams<{ courseId: string }>(); + const courseId = params.courseId; + return ( +
+ + + + + +
+ {children} +
+ +
+ ); +} diff --git a/lexikon/src/app/study/[courseId]/page.tsx b/lexikon/src/app/study/[courseId]/page.tsx new file mode 100644 index 0000000..7cb8846 --- /dev/null +++ b/lexikon/src/app/study/[courseId]/page.tsx @@ -0,0 +1,40 @@ +"use client"; +import { Spinner } from "@heroui/react"; +import CourseHero from "features/study/components/CourseHero"; +import { StudyContext } from "features/study/provider/StudyProvider"; +import { useRouter } from "next/navigation"; +import { useContext, useEffect } from "react"; + +export default function CoursePage() { + const { course, summaries } = useContext(StudyContext)!; + const router = useRouter(); + + useEffect(() => { + if (course && summaries.length > 0) { + // Redirect to first summary + const firstSummary = summaries[0]; + router.push( + `/study/${course.course_id}/summary/${firstSummary.summary_id}` + ); + } + }, [course, summaries, router]); + + if (!course) return ; + + return ( +
+ {summaries.length === 0 ? ( + <> +
+ This course has no summaries yet. +
+ Create a new summary to get started. +
+ + + ) : ( + + )} +
+ ); +} diff --git a/lexikon/src/app/study/[courseId]/summary/[summaryId]/page.tsx b/lexikon/src/app/study/[courseId]/summary/[summaryId]/page.tsx new file mode 100644 index 0000000..daaf037 --- /dev/null +++ b/lexikon/src/app/study/[courseId]/summary/[summaryId]/page.tsx @@ -0,0 +1,62 @@ +"use client"; +import { CourseContent } from "features/study/components/CourseContent"; +import CourseEdit from "features/study/components/CourseEdit"; +import CourseHero from "features/study/components/CourseHero"; +import { useSummary } from "features/study/hooks/useSummary"; +import { StudyContext } from "features/study/provider/StudyProvider"; +import { useParams, useRouter } from "next/navigation"; +import { useContext, useEffect } from "react"; + +export default function Page() { + const ctx = useContext(StudyContext); + if (!ctx) + throw new Error("CourseActions must be used within StudyProvider"); + + const params = useParams<{ summaryId: string }>(); + const summaryId = params.summaryId; + const { selectedSummary, mode } = ctx; + const { content } = useSummary(selectedSummary); + const router = useRouter(); + + // Update the URL when the selected summary changes + useEffect(() => { + if (!selectedSummary) { + // Find the summary with the same ID as the one in the URL + const found = ctx.summaries.find( + (s) => s.summary_id === Number(summaryId) + ); + if (found) { + ctx.setSelectedSummary(found); + } else { + // If no summary is found, redirect to the first summary + const firstSummary = ctx.summaries[0]; + if (firstSummary) { + ctx.setSelectedSummary(firstSummary); + router.replace( + `/study/${ctx.course?.course_id}/summary/${firstSummary.summary_id}` + ); + } + } + + return; + } + + const id = selectedSummary.summary_id; + if (id === undefined || id === Number(summaryId)) return; + + router.replace(`/study/${ctx.course?.course_id}/summary/${id}`); + }, [selectedSummary, summaryId, ctx.course?.course_id, router]); + + return ( + <> + {mode === "edit" ? ( + + ) : ( + <> + + + + )} + + ); +} diff --git a/lexikon/src/features/librarian/LibrarianLink.tsx b/lexikon/src/features/librarian/LibrarianLink.tsx new file mode 100644 index 0000000..aa418f9 --- /dev/null +++ b/lexikon/src/features/librarian/LibrarianLink.tsx @@ -0,0 +1,171 @@ +"use client"; +import React, { useRef, useEffect } from "react"; +import { useRouter } from "next/navigation"; + +/** + * "Librarian" button — encyclopedia letters cycling in hover background + * Letters change only on hover via randomString fill, reactively masked by cursor position. + */ +export default function LibrarianLink({ href }: { href: string }) { + const buttonRef = useRef(null); + const lettersRef = useRef(null); + const router = useRouter(); + + // Character generators + const chars = + "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789αβγδεζηθικλμξπρστυφχψωΓΔΘΛΞΠΣΦΨΩ"; + const randomChar = () => chars[Math.floor(Math.random() * chars.length)]; + const randomString = (length: number) => + Array.from({ length }) + .map(() => randomChar()) + .join(" "); // space after each char for wrapping + + // Track pointer for CSS mask and update letters on move + useEffect(() => { + const btn = buttonRef.current; + const letters = lettersRef.current; + + if (!btn || !letters) return; + + // Accept either PointerEvent or Touch + const handleMove = (e: PointerEvent | Touch) => { + const rect = btn.getBoundingClientRect(); + // Access clientX/Y which are present on both types + const x = e.clientX - rect.left; + const y = e.clientY - rect.top; + + letters.style.setProperty("--x", `${x}px`); + letters.style.setProperty("--y", `${y}px`); + letters.innerText = randomString(1500); + }; + + // Handler specifically for touch events + const handleTouchMove = (e: TouchEvent) => { + // Prevent default scroll behavior if needed + // e.preventDefault(); + if (e.touches.length > 0) { + handleMove(e.touches[0]); // Pass the first touch point + } + }; + + + + // Use named functions for add/remove event listeners + const handlePointerEnter = () => { letters.style.opacity = "1"; }; + const handlePointerLeave = () => { letters.style.opacity = "0"; }; + + btn.addEventListener("pointerenter", handlePointerEnter); + btn.addEventListener("pointerleave", handlePointerLeave); + btn.addEventListener("pointermove", handleMove); + // Mark touchmove as passive for better performance + btn.addEventListener("touchmove", handleTouchMove, { passive: true }); + + return () => { + btn.removeEventListener("pointerenter", handlePointerEnter); + btn.removeEventListener("pointerleave", handlePointerLeave); + btn.removeEventListener("pointermove", handleMove); + btn.removeEventListener("touchmove", handleTouchMove); + }; + }, []); + + // 3D tilt feedback + useEffect(() => { + const btn = buttonRef.current; + + if (!btn) return; + const handle3D = (e: PointerEvent) => { + const rect = btn.getBoundingClientRect(); + const x = e.clientX - rect.left; + const y = e.clientY - rect.top; + const midX = rect.width / 2; + const midY = rect.height / 2; + const rotY = ((x - midX) / midX) * 12; + const rotX = ((midY - y) / midY) * 12; + + btn.style.transform = `perspective(800px) rotateX(${rotX}deg) rotateY(${rotY}deg)`; + }; + const reset3D = () => { + btn.style.transform = `perspective(800px) rotateX(0deg) rotateY(0deg)`; + }; + + btn.addEventListener("pointermove", handle3D); + btn.addEventListener("pointerleave", reset3D); + + return () => { + btn.removeEventListener("pointermove", handle3D); + btn.removeEventListener("pointerleave", reset3D); + }; + }, []); + + // Navigate to href on click + const handleNavigate = (e: React.MouseEvent) => { + e.preventDefault(); + router.push(href); + } + + + // Click ripple + const handleClick = (e: React.MouseEvent) => { + const btn = buttonRef.current; + + if (!btn) return; + const circle = document.createElement("span"); + const rect = btn.getBoundingClientRect(); + const size = Math.max(rect.width, rect.height); + const x = e.clientX - rect.left - size / 2; + const y = e.clientY - rect.top - size / 2; + + circle.className = "ripple"; + Object.assign(circle.style, { + width: `${size}px`, + height: `${size}px`, + left: `${x}px`, + top: `${y}px`, + }); + btn.appendChild(circle); + setTimeout(() => btn.removeChild(circle), 600); + + // Navigate to href + handleNavigate(e); + }; + + return ( + // TODO: Add Background color, because it is transparent and looks awful in light mode +
+ +
+ ); +} diff --git a/lexikon/src/features/librarian/components/IndexTable.tsx b/lexikon/src/features/librarian/components/IndexTable.tsx new file mode 100644 index 0000000..f13b672 --- /dev/null +++ b/lexikon/src/features/librarian/components/IndexTable.tsx @@ -0,0 +1,85 @@ +"use client"; + +import { + Table, + TableBody, + TableCell, + TableColumn, + TableHeader, + TableRow, + Select, + SelectItem, + Image, +} from "@heroui/react"; +import { useState } from "react"; +import { example_hero_image } from "shared/config/mockData"; +import { MoodleIndex, TermIndex } from "shared/domain/librarian"; +import { container } from "shared/styles/variants"; + +const tableHeaders = ["id", "name", "image"]; +const fallbackImage = example_hero_image; + +export default function IndexTable({ index }: { index: MoodleIndex }) { + const degProg = index.degree_program; + const [selectedSemesterId, setSelectedSemesterId] = useState( + degProg.semesters[0]?.id ?? "" + ); + + // Find the selected semester object + const selectedSemester: TermIndex | undefined = degProg.semesters.find( + (s) => s.id === selectedSemesterId + ); + + return ( +
+
+ + + + {tableHeaders.map((header) => ( + {header} + ))} + + + {/* Provide an empty array fallback if selectedSemester is undefined */} + {(selectedSemester?.courses ?? []).map((course) => ( + + {course.id} + {course.name} + +
+ +
+ + + ))} + +
+
+
+ ); +} diff --git a/lexikon/src/features/librarian/mocks.ts b/lexikon/src/features/librarian/mocks.ts new file mode 100644 index 0000000..2c05e29 --- /dev/null +++ b/lexikon/src/features/librarian/mocks.ts @@ -0,0 +1,137 @@ +import { MoodleIndex } from 'shared/domain/librarian/moodleIndex'; + +const MOODLE_INDEX_MOCK_DATA: MoodleIndex = { + degree_program: { + id: '1157', + name: 'Computational and Data Science', + semesters: [ + { + id: '1745', + name: 'FS25', + courses: [ + { + id: '18863', + name: 'Programmierung und Prompt Engineering II', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1159522/course/overviewfiles/PythonBooks.PNG', + content_ressource_id: '1159522', + files: [], + }, + { + id: '18240', + name: 'Effiziente Algorithmen', + activity_type: '', + hero_image: '', + content_ressource_id: '1125554', + files: [], + }, + { + id: '18237', + name: 'Mathematik II', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1125458/course/overviewfiles/Integration_Differential_b.png', + content_ressource_id: '1125458', + files: [], + }, + { + id: '18236', + name: '2025 FS FHGR CDS Numerische Methoden', + activity_type: '', + hero_image: '', + content_ressource_id: '1125426', + files: [], + }, + { + id: '18228', + name: 'Datenbanken und Datenverarbeitung', + activity_type: '', + hero_image: '', + content_ressource_id: '1125170', + files: [], + }, + ], + }, + { + id: '1746', + name: 'HS24', + courses: [ + { + id: '18030', + name: 'Bootcamp Wissenschaftliches Arbeiten', + activity_type: '', + hero_image: '', + content_ressource_id: '1090544', + files: [], + }, + { + id: '17527', + name: 'Einführung in Data Science', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1059194/course/overviewfiles/cds1010.jpg', + content_ressource_id: '1059194', + files: [], + }, + { + id: '17526', + name: 'Einführung in Computational Science', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1059162/course/overviewfiles/cds_intro_sim.jpg', + content_ressource_id: '1059162', + files: [], + }, + { + id: '17525', + name: 'Mathematik I', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1059130/course/overviewfiles/AdobeStock_452512134.png', + content_ressource_id: '1059130', + files: [], + }, + { + id: '17507', + name: 'Programmierung und Prompt Engineering', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1058554/course/overviewfiles/10714013_33861.jpg', + content_ressource_id: '1058554', + files: [], + }, + { + id: '17505', + name: 'Algorithmen und Datenstrukturen', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1058490/course/overviewfiles/Bild1.png', + content_ressource_id: '1058490', + files: [], + }, + { + id: '17503', + name: 'Computer Science', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1058426/course/overviewfiles/Titelbild.jpg', + content_ressource_id: '1058426', + files: [], + }, + ], + }, + ], + }, + timestamp: '2025-04-27T14:20:11.354825+00:00', +}; + +// TODO: Remove this function when the crawler is ready +export async function getMockedMoodleIndex(): Promise { + return new Promise((resolve) => { + setTimeout(() => { + // Return a copy to prevent accidental mutation of the mock data + return resolve(JSON.parse(JSON.stringify(MOODLE_INDEX_MOCK_DATA))); + }, 1000); + }); +} diff --git a/lexikon/src/features/librarian/queries.ts b/lexikon/src/features/librarian/queries.ts new file mode 100644 index 0000000..4257d3f --- /dev/null +++ b/lexikon/src/features/librarian/queries.ts @@ -0,0 +1,30 @@ +'use client'; + +import { MoodleIndex } from 'shared/domain/librarian/moodleIndex'; +import { useQuery } from '@tanstack/react-query'; + +const API_BASE_URL = process.env.NEXT_PUBLIC_API_BASE_URL || 'http://localhost:8000'; + +async function fetchMoodleIndexFromApi(): Promise { + try { + const response = await fetch(`${API_BASE_URL}/api/librarian/moodle-index`); + if (!response.ok) { + throw new Error('Failed to fetch moodle index'); + } + return response.json(); + } catch (error) { + console.error('Error fetching moodle index:', error); + throw error; + } +} + +async function getMoodleIndex(): Promise { + return fetchMoodleIndexFromApi(); +} + +export function useMoodleIndex() { + return useQuery({ + queryKey: ['moodleIndex'], + queryFn: getMoodleIndex, + }); +} diff --git a/lexikon/src/features/librarian/tasks/api.ts b/lexikon/src/features/librarian/tasks/api.ts new file mode 100644 index 0000000..b7d458f --- /dev/null +++ b/lexikon/src/features/librarian/tasks/api.ts @@ -0,0 +1,104 @@ +import { LOCAL_API_BASE } from 'shared/config/api'; +import { + TaskInfo, + DownloadRequestCourse, +} from 'shared/domain/librarian/task'; + +type SummaryResponse = any; + +async function startTaskApi( + endpoint: string, + payload: TRequest +): Promise { + try { + const response = await fetch(`${LOCAL_API_BASE}/${endpoint}`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(payload), + }); + if (!response.ok) { + const errorText = await response.text(); + console.error( + `API Error starting task ${endpoint}: ${response.status} ${response.statusText}`, + errorText + ); + throw new Error( + `Failed to start task ${endpoint}: ${response.statusText}` + ); + } + return response.json(); + } catch (error) { + console.error('Error starting task:', error); + throw error; + } +} + +async function getTaskStatusApi( + endpoint: string, + taskId: string +): Promise { + try { + const response = await fetch( + `${LOCAL_API_BASE}/${endpoint}/status/${taskId}` + ); + if (!response.ok) { + const errorText = await response.text(); + console.error( + `API Error getting status for ${endpoint} task ${taskId}: ${response.status} ${response.statusText}`, + errorText + ); + throw new Error( + `Failed to get status for task ${endpoint}/${taskId}: ${response.statusText}` + ); + } + return response.json(); + } catch (error) { + console.error('Error getting task status:', error); + throw error; + } +} + +export const taskApi = { + startCrawl: async (noCache: boolean = false): Promise => { + return startTaskApi('crawl', { no_cache: noCache }); + }, + + getCrawlStatus: async (taskId: string): Promise => { + return getTaskStatusApi('crawl', taskId); + }, + + startDownload: async ( + courses: DownloadRequestCourse[] + ): Promise => { + return startTaskApi('download', { courses }); + }, + + getDownloadStatus: async (taskId: string): Promise => { + return getTaskStatusApi('download', taskId); + }, + + getSummary: async ( + termId: string, + courseId: string + ): Promise => { + try { + const response = await fetch( + `${LOCAL_API_BASE}/summaries/${termId}/${courseId}` + ); + if (!response.ok) { + const errorText = await response.text(); + console.error( + `API Error getting summary for ${termId}/${courseId}: ${response.status} ${response.statusText}`, + errorText + ); + throw new Error( + `Failed to get summary for ${termId}/${courseId}: ${response.statusText}` + ); + } + return response.json(); + } catch (error) { + console.error('Error getting summary:', error); + throw error; + } + }, +}; diff --git a/lexikon/src/features/librarian/tasks/components/TaskProgress.tsx b/lexikon/src/features/librarian/tasks/components/TaskProgress.tsx new file mode 100644 index 0000000..f3cc42d --- /dev/null +++ b/lexikon/src/features/librarian/tasks/components/TaskProgress.tsx @@ -0,0 +1,77 @@ +import { useEffect, useState } from 'react'; +import { Progress } from '@heroui/react'; +import { useTaskStatus } from '../hooks'; +import { taskHelpers } from '../helpers'; + +interface TaskProgressProps { + taskId: string; + taskType: string; + progressAriaLabel?: string; + onComplete?: (status: 'success' | 'error', error?: string) => void; +} + +export default function TaskProgress({ + taskId, + taskType, + progressAriaLabel = 'Task progress', + onComplete, +}: TaskProgressProps) { + const { taskInfo, error } = useTaskStatus(taskId, taskType); + + useEffect(() => { + if (taskInfo && taskHelpers.isTaskComplete(taskInfo) && onComplete) { + onComplete(taskInfo.state, taskHelpers.formatTaskError(taskInfo)); + } + }, [taskInfo, onComplete]); + + if (error) { + return ( +
+

{`Error: ${error.message}`}

+
+ ); + } + + if (!taskInfo) { + return ( +
+

Loading task information...

+
+ ); + } + + if (taskInfo.state === 'running') { + return ( +
+ +
+ ); + } + + if (taskInfo.state === 'error') { + return ( +
+

{taskHelpers.formatTaskError(taskInfo)}

+
+ ); + } + + if (taskInfo.state === 'success') { + return ( +
+

Task completed successfully.

+
+ ); + } + + return ( +
+

Task status: {taskInfo.state}.

+
+ ); +} diff --git a/lexikon/src/features/librarian/tasks/components/TaskStart.tsx b/lexikon/src/features/librarian/tasks/components/TaskStart.tsx new file mode 100644 index 0000000..e69de29 diff --git a/lexikon/src/features/librarian/tasks/helpers.ts b/lexikon/src/features/librarian/tasks/helpers.ts new file mode 100644 index 0000000..49c6807 --- /dev/null +++ b/lexikon/src/features/librarian/tasks/helpers.ts @@ -0,0 +1,19 @@ +import { TaskInfo } from 'shared/domain/librarian/task'; + +export const taskHelpers = { + isTaskComplete: (taskInfo: TaskInfo): boolean => { + return taskInfo.state === 'success' || taskInfo.state === 'error'; + }, + + getProgressPercentage: (taskInfo: TaskInfo): number => { + return taskInfo.progress ? Math.round(taskInfo.progress * 100) : 0; + }, + + formatTaskError: (taskInfo: TaskInfo): string => { + return taskInfo.detail || 'An unknown error occurred'; + }, + + getDownloadLinks: (taskInfo: TaskInfo): string[] => { + return taskInfo.download_links || []; + }, +}; diff --git a/lexikon/src/features/librarian/tasks/hooks.ts b/lexikon/src/features/librarian/tasks/hooks.ts new file mode 100644 index 0000000..1b9178f --- /dev/null +++ b/lexikon/src/features/librarian/tasks/hooks.ts @@ -0,0 +1,44 @@ +import { useState, useEffect, useCallback } from 'react'; +import { TaskInfo } from 'shared/domain/librarian/task'; +import { useQuery } from '@tanstack/react-query'; +import { taskApi } from './api'; + +export function useTaskStatus(taskId: string | null, taskType: string) { + const [taskInfo, setTaskInfo] = useState(null); + const [error, setError] = useState(null); + + const handleUpdate = useCallback((info: TaskInfo) => { + setTaskInfo(info); + setError(null); + }, []); + + const handleError = useCallback((err: any) => { + console.error('TaskWebSocket Error:', err); + const newError = + err instanceof Error + ? err + : new Error( + String(err.message || 'WebSocket connection error') + ); + setError(newError); + setTaskInfo(null); + }, []); + + const { data, isError, error: queryError } = useQuery( + ['taskStatus', taskId, taskType], + () => taskApi.getTaskStatusApi(taskType, taskId!), + { + enabled: !!taskId, + onSuccess: handleUpdate, + onError: handleError, + } + ); + + useEffect(() => { + if (isError) { + handleError(queryError); + } + }, [isError, queryError, handleError]); + + return { taskInfo: data || taskInfo, error }; +} diff --git a/lexikon/src/features/librarian/tasks/websocket.ts b/lexikon/src/features/librarian/tasks/websocket.ts new file mode 100644 index 0000000..52eeca4 --- /dev/null +++ b/lexikon/src/features/librarian/tasks/websocket.ts @@ -0,0 +1,70 @@ +import { LOCAL_API_BASE } from 'shared/config/api'; +import { TaskInfo } from 'shared/domain/librarian/task'; + +export class TaskWebSocket { + private ws: WebSocket | null = null; + private taskId: string; + private taskType: string; + private onUpdate: (taskInfo: TaskInfo) => void; + private onError: (error: any) => void; + + constructor( + taskId: string, + taskType: string, + onUpdate: (taskInfo: TaskInfo) => void, + onError: (error: any) => void + ) { + this.taskId = taskId; + this.taskType = taskType; + this.onUpdate = onUpdate; + this.onError = onError; + } + + connect() { + this.ws = new WebSocket( + `ws://${LOCAL_API_BASE.replace(/^http/, 'ws')}/${this.taskType}/ws/${this.taskId}` + ); + + this.ws.onmessage = (event) => { + try { + const taskInfo = JSON.parse(event.data); + this.onUpdate(taskInfo); + + if ( + taskInfo.state === 'success' || + taskInfo.state === 'error' + ) { + this.disconnect(); + } + } catch (e) { + this.onError(new Error('Failed to parse WebSocket message')); + this.disconnect(); + } + }; + + this.ws.onerror = (errorEvent) => { + const error = new Error('WebSocket error'); + (error as any).event = errorEvent; + this.onError(error); + this.disconnect(); + }; + + this.ws.onclose = (event) => { + if (event.wasClean === false) { + this.onError( + new Error( + `WebSocket closed unexpectedly: ${event.code} ${event.reason || ''}`.trim() + ) + ); + } + this.ws = null; + }; + } + + disconnect() { + if (this.ws) { + this.ws.close(); + this.ws = null; + } + } +} diff --git a/lexikon/src/features/navigation/ToolBar.tsx b/lexikon/src/features/navigation/ToolBar.tsx new file mode 100644 index 0000000..9584155 --- /dev/null +++ b/lexikon/src/features/navigation/ToolBar.tsx @@ -0,0 +1,19 @@ +"use client"; + +import { type ReactNode } from "react"; +import { tv } from "tailwind-variants"; + + +const toolbar = tv({ + base: "bg-default-50 sticky flex items-center h-18 px-4 w-full shrink-0", +}); + +export default function Toolbar({ children }: { children: ReactNode }) { + return ( +
+
+ {children} +
+
+ ); +} diff --git a/lexikon/src/features/navigation/TopToolBar.tsx b/lexikon/src/features/navigation/TopToolBar.tsx new file mode 100644 index 0000000..484d041 --- /dev/null +++ b/lexikon/src/features/navigation/TopToolBar.tsx @@ -0,0 +1,27 @@ +"use client"; + +import { type ReactNode } from "react"; +import Toolbar from "./ToolBar"; +import { useSidenavStore } from "./sidenav/store"; +import { SidebarClose, SidebarOpen } from "lucide-react"; +import IconButton from "shared/ui/primitives/IconButton"; + +type TopToolbarProps = { + children?: ReactNode; +}; + +export function TopToolbar({ children }: TopToolbarProps) { + const { open, toggleSidenav } = useSidenavStore(); + + return ( + + {!open && ( + + )} +
{children}
+ + ); +} diff --git a/lexikon/src/features/navigation/UserArea.tsx b/lexikon/src/features/navigation/UserArea.tsx new file mode 100644 index 0000000..9aed66a --- /dev/null +++ b/lexikon/src/features/navigation/UserArea.tsx @@ -0,0 +1,113 @@ +"use client"; + +import { + Dropdown, + DropdownItem, + DropdownMenu, + DropdownTrigger, + User, + Button, +} from "@heroui/react"; +import { LogOut, Settings } from "lucide-react"; +import { useContext, useState } from "react"; +import { useAuth, signOut } from "shared/api/supabase"; +import { ResponsiveContext } from "shared/provider/ResponsiveProvider"; +import LoginModal from "shared/ui/auth/LoginModal"; +import ConfirmModal from "shared/ui/primitives/ConfirmModal"; + +export default function UserArea( + props: { + className?: string; + } & React.ComponentProps +) { + const { user } = useAuth(); + const isMobile = useContext(ResponsiveContext); + + // Modal state + const [isLoginOpen, setLoginOpen] = useState(false); + const [isLogoutOpen, setLogoutOpen] = useState(false); + const [isLoggingOut, setIsLoggingOut] = useState(false); + + const userData = { + image: user?.user_metadata?.avatar_url, + name: user?.user_metadata?.full_name, + email: user?.email, + }; + + // If not logged in, show login button + if (!user) { + return ( + <> + + setLoginOpen(false)} + /> + + ); + } + + // If logged in, show user dropdown + return ( + <> + + + + + + +

Signed in as

+

{user?.email}

+ + } + > + Settings + + } + onPress={() => setLogoutOpen(true)} + > + Log Out + + + + { + setIsLoggingOut(true); + try { + await signOut(); + } catch (e) { + // Optionally show error feedback + } finally { + setIsLoggingOut(false); + } + }} + isConfirmLoading={isLoggingOut} + > + Are you sure you want to log out? + + + ); +} diff --git a/lexikon/src/features/navigation/sidenav/SideNav.tsx b/lexikon/src/features/navigation/sidenav/SideNav.tsx new file mode 100644 index 0000000..102d81d --- /dev/null +++ b/lexikon/src/features/navigation/sidenav/SideNav.tsx @@ -0,0 +1,58 @@ +"use client"; + +import { Divider } from "@heroui/react"; +import { PanelLeftClose, PanelLeftOpen, Pin, PinOff } from "lucide-react"; +import React from "react"; +import UserArea from "../UserArea"; +import { useSidenavStore } from "./store"; +import { motion } from "framer-motion"; +import IconButton from "shared/ui/primitives/IconButton"; +import { ThemeSwitch } from "shared/ui/themeSwitch"; + +const sidenavVariants = { + open: { width: "20rem" }, + closed: { width: "0rem" }, +}; +const sidenavTransition = { duration: 0.5, ease: [0.25, 0.1, 0.25, 1] }; + +export const SideNav = ({ children }: { children: React.ReactNode }) => { + const { open, pinned, togglePinned, toggleSidenav } = useSidenavStore(); + + return ( + +
+
+ + +
+ + {/* Use theme-aware background color for divider */} + + + {/* Content area */} +
{children}
+ + {/* Use theme-aware background color for divider */} + + + {/* Footer with User Info and Theme Switch */} +
+ + +
+
+ + ); +}; diff --git a/lexikon/src/features/navigation/sidenav/store.ts b/lexikon/src/features/navigation/sidenav/store.ts new file mode 100644 index 0000000..091df0b --- /dev/null +++ b/lexikon/src/features/navigation/sidenav/store.ts @@ -0,0 +1,24 @@ +import { create } from "zustand"; +import { persist, createJSONStorage } from 'zustand/middleware'; + +export interface SidenavState { + open: boolean; + pinned: boolean; + toggleSidenav: () => void; + togglePinned: () => void; +} + +export const useSidenavStore = create()( + persist( + (set) => ({ + open: false, + pinned: false, + toggleSidenav: () => set((state) => ({ open: !state.open })), + togglePinned: () => set((state) => ({ pinned: !state.pinned })), + }), + { + name: 'sidenav-storage', // name of the item in the storage (must be unique) + storage: createJSONStorage(() => localStorage), // (optional) by default, 'localStorage' is used + } + ) +) diff --git a/lexikon/src/features/study/components/CourseActions.tsx b/lexikon/src/features/study/components/CourseActions.tsx new file mode 100644 index 0000000..48a9418 --- /dev/null +++ b/lexikon/src/features/study/components/CourseActions.tsx @@ -0,0 +1,176 @@ +"use client"; +import { addToast } from "@heroui/toast"; +// filepath: src/features/study/components/_course/SummaryActions.tsx +import React, { useContext } from "react"; +import { useRouter, useParams } from "next/navigation"; +import { Input } from "@heroui/react"; +import { + ChevronLeft, + ChevronRight, + Edit, + Eye, + Plus, + Save, + Trash2, +} from "lucide-react"; + +import ConfirmModal from "shared/ui/primitives/ConfirmModal"; +import IconButton from "shared/ui/primitives/IconButton"; +import { StudyContext } from "../provider/StudyProvider"; + +export default function CourseActions() { + const ctx = useContext(StudyContext); + if (!ctx) throw new Error("CourseActions must be used within StudyProvider"); + + const { + mode, + busy, + dirty, + onModeChange, + navigateSummary, + selectedSummary, + setSelectedSummary, + onSave, + onCreate, + onDelete, + summaryName, + summaries, + } = ctx; + const router = useRouter(); + const params = useParams<{ courseId: string }>(); + const courseId = params.courseId; + + // Local modal states + const [isNewOpen, setIsNewOpen] = React.useState(false); + const [isDeleteOpen, setIsDeleteOpen] = React.useState(false); + const [newName, setNewName] = React.useState(""); + + const handleNavigate = (direction: "next" | "prev") => { + if (!selectedSummary || !summaries.length || !courseId) return; + const currentIndex = summaries.findIndex( + (summary) => summary.summary_id === selectedSummary.summary_id + ); + let newIndex = currentIndex; + if (direction === "next" && currentIndex < summaries.length - 1) { + newIndex = currentIndex + 1; + } + if (direction === "prev" && currentIndex > 0) { + newIndex = currentIndex - 1; + } + if (newIndex !== currentIndex && summaries[newIndex]) { + setSelectedSummary(summaries[newIndex]); + } + }; + + // After creating a summary, navigate to the new summary + const handleCreate = async (name: string) => { + const before = summaries.length; + await onCreate(name); + // Wait for summaries to update (could use effect, but simple polling for now) + setTimeout(() => { + const after = ctx.summaries; + if (after.length > before && courseId) { + const newSummary = after[after.length - 1]; + router.push( + `/study/${courseId}/summary/${newSummary.summary_id}` + ); + } + }, 300); + }; + + return ( + <> + handleNavigate("prev")} /> + handleNavigate("next")} + /> + setIsNewOpen(true)} + /> + + {mode === "edit" ? ( + { + // Use the real handleSave for feedback, not the context's onSave (which is void) + const result = await ctx.handleSave?.(); + if (!result || !result.error) { + addToast({ + title: "Saved", + description: "Summary saved successfully.", + color: "success", + timeout: 2500, + shouldShowTimeoutProgress: true, + }); + } else { + addToast({ + title: "Save failed", + description: + result.error || + "An error occurred while saving.", + color: "danger", + timeout: 4000, + shouldShowTimeoutProgress: true, + }); + } + }} + /> + ) : ( + setIsDeleteOpen(true)} + /> + )} + + onModeChange(mode === "edit" ? "view" : "edit")} + disabled={!selectedSummary && mode === "edit"} + /> + + {/* Modal – Neues Kapitel */} + { + if (!newName.trim()) return; + handleCreate(newName); + }} + confirmText="Create" + confirmButtonColor="primary" + > + setNewName(e.target.value)} + /> + + + {/* Modal – Kapitel löschen */} + +

+ Are you sure you want to delete chapter " + {selectedSummary?.chapter}"? +

+ + + ); +} diff --git a/lexikon/src/features/study/components/CourseContent.tsx b/lexikon/src/features/study/components/CourseContent.tsx new file mode 100644 index 0000000..2c770e2 --- /dev/null +++ b/lexikon/src/features/study/components/CourseContent.tsx @@ -0,0 +1,19 @@ +"use client"; + +import { Spinner } from "@heroui/react"; +import MDXPreview from "shared/ui/markdown/MDXPreview"; + +export const CourseContent = ({ value }: { value: string }) => { + return ( + <> + {value === null ? ( + + ) : ( + + )} + + ); +}; diff --git a/lexikon/src/features/study/components/CourseEdit.tsx b/lexikon/src/features/study/components/CourseEdit.tsx new file mode 100644 index 0000000..d6121b8 --- /dev/null +++ b/lexikon/src/features/study/components/CourseEdit.tsx @@ -0,0 +1,42 @@ +import { useContext } from "react"; +import { StudyContext } from "../provider/StudyProvider"; +import { useSummary } from "../hooks/useSummary"; +import { Input, Spinner } from "@heroui/react"; +import Editor from "shared/ui/code/Editor"; + +export default function CourseEdit() { + const { selectedSummary, summaryName, setSummaryName } = + useContext(StudyContext)!; + + const { content, setContent } = useSummary(selectedSummary); + + return ( +
+ {content === null ? ( + + ) : ( + <> + { + setSummaryName(e.target.value); + }} + /> + + + )} +
+ ); +} diff --git a/lexikon/src/features/study/components/CourseHero.tsx b/lexikon/src/features/study/components/CourseHero.tsx new file mode 100644 index 0000000..4fb1614 --- /dev/null +++ b/lexikon/src/features/study/components/CourseHero.tsx @@ -0,0 +1,30 @@ +"use client"; + +import { Image } from "@heroui/react"; +import { useContext } from "react"; +import { example_hero_image } from "shared/config/mockData"; +import { StudyContext } from "../provider/StudyProvider"; + +const CourseHero = ({ full }: { full?: boolean }) => { + const { course } = useContext(StudyContext)!; + const imageSrc = course?.hero_image || example_hero_image; + + return ( +
+ {`Hero + {/* Transparent overlay */} +
+ + {/* Course title and description */} +
+ ); +}; + +export default CourseHero; diff --git a/lexikon/src/features/study/components/CourseList.tsx b/lexikon/src/features/study/components/CourseList.tsx new file mode 100644 index 0000000..0869697 --- /dev/null +++ b/lexikon/src/features/study/components/CourseList.tsx @@ -0,0 +1,278 @@ +"use client"; + +import { Button, Link, PressEvent } from "@heroui/react"; +import React, { useState, useRef, useEffect } from "react"; +import { useRouter } from "next/navigation"; +import { + AnimatePresence, + motion, + useMotionValue, + useTransform, + useAnimation, +} from "framer-motion"; +import { Star } from "lucide-react"; +import useCoursesModulesStore from "../stores/coursesStore"; +import { CourseModule } from "shared/domain/course"; +import { toggleCourseFavorite } from "../queries/CRUD-Courses"; + +// ──────────────────────────────────────────────────────────────────────────────── +// Shared helpers +// ──────────────────────────────────────────────────────────────────────────────── +/** Drag distance (in px) required to toggle favorite on mobile swipe item */ +const DRAG_THRESHOLD = 120; + +/** + * Small wrapper around the Lucide Star so we don’t have to repeat the + * `fill={filled ? "currentColor" : "none"}` boiler‑plate everywhere. + */ +const FavoriteStar: React.FC<{ filled: boolean; size?: number }> = ({ + filled, + size = 20, +}) => ( + +); + +export type CourseListItemProps = { + courseModule: CourseModule; + onClick?: () => void; +}; + +export const CourseListItem: React.FC = ({ + courseModule, +}: { + courseModule: CourseModule; +}) => { + const [fav, setFav] = useState(!!courseModule.is_user_favorite); + const { reloadCoursesModules: reload } = useCoursesModulesStore(); + const handlefavoriteToggle = async (e: PressEvent) => { + try { + const newState = await toggleCourseFavorite(courseModule, !fav); + setFav(newState); + reload(); + } catch (error) { + console.error("Failed to toggle favorite status:", error); + } + }; + + return ( + + + + {courseModule.module_name} + + + {courseModule.module_code} + + +
+ +
+ + ); +}; + +export type MobileCourseListItemProps = { + courseModule: CourseModule; + onClick?: () => void; + className?: string; +}; +export const MobileCourseListItem: React.FC = ({ + courseModule, + className = "", +}) => { + const router = useRouter(); + const handlePress = () => { + router.push( + // `/study/${courseModule.course_id}-${courseModule.module_code}` + `/study/${courseModule.course_id}` + ); + }; + + return ( + + ); +}; + +// Swipeable item for mobile list with drag-to-fav logic +export type SwipeableModuleItemProps = { + courseModule: CourseModule; + onSwipe: (courseModule: CourseModule) => void; + onClick?: () => void; + className?: string; +}; +export const SwipeableModuleItem: React.FC = ({ + courseModule, + onSwipe, + className = "", +}) => { + const router = useRouter(); + const isDraggingRef = useRef(false); + const suppressClickRef = useRef(false); + const x = useMotionValue(0); + const controls = useAnimation(); + const [favState, setFavState] = useState( + !!courseModule.is_user_favorite + ); + const [previewFav, setPreviewFav] = useState(favState); + const isOverRef = useRef(false); + + useEffect(() => { + const fav = !!courseModule.is_user_favorite; + setFavState(fav); + setPreviewFav(fav); + }, [courseModule.is_user_favorite]); + + useEffect(() => { + x.on("change", (latest: number) => { + // only animate icon scale during active drag + if (!isDraggingRef.current) return; + if (latest > DRAG_THRESHOLD && !isOverRef.current) { + isOverRef.current = true; + setPreviewFav(!favState); + controls.start({ + scale: [1.4, 1], + transition: { + type: "spring", + stiffness: 300, + damping: 20, + }, + }); + } else if (latest <= DRAG_THRESHOLD && isOverRef.current) { + isOverRef.current = false; + setPreviewFav(favState); + controls.start({ + scale: [1.4, 1], + transition: { + type: "spring", + stiffness: 300, + damping: 20, + }, + }); + } + }); + }, [x, controls, favState]); + const bgOpacity = useTransform(x, [0, DRAG_THRESHOLD], [0, 1]); + const handleClick = () => { + if (suppressClickRef.current) return; + router.push(`/study/${courseModule.course_id}`); + }; + const actionIcon = ; + + return ( + + + + + + {actionIcon} + + + { + suppressClickRef.current = true; + setTimeout(() => { + suppressClickRef.current = false; + }, 300); + if (previewFav !== favState) { + setFavState(previewFav); + onSwipe(courseModule); + } + isOverRef.current = false; + x.set(0); + isDraggingRef.current = false; + }} + onDragStart={() => { + isDraggingRef.current = true; + }} + > + + + + + ); +}; + +export type CourseListHeaderProps = { + title: string; +}; + +export const CourseListGroupHeader: React.FC = ({ + title, +}) => ( +
+

{title}

+
+); + +export type ModuleListGroupProps = { + title: string; + items: CourseListItemProps[]; +}; + +const ModuleListGroup: React.FC = ({ title, items }) => ( +
+ + {items.map((item) => ( + + ))} +
+); + +export default ModuleListGroup; diff --git a/lexikon/src/features/study/components/CoursePath.tsx b/lexikon/src/features/study/components/CoursePath.tsx new file mode 100644 index 0000000..80c88a7 --- /dev/null +++ b/lexikon/src/features/study/components/CoursePath.tsx @@ -0,0 +1,76 @@ +"use client"; +import { useContext } from "react"; +import { StudyContext } from "../provider/StudyProvider"; +import { Select, SelectItem } from "@heroui/react"; +import { ChevronsUpDown } from "lucide-react"; +import type { CourseSummary } from "shared/domain/summary"; + +type SummarySelectorProps = { + summaries: CourseSummary[]; + selectedSummary: CourseSummary | null; + summaryName: string; +}; + +const SummarySelector = ({ + summaries, + selectedSummary, + summaryName, +}: SummarySelectorProps) => { + const { setSelectedSummary } = useContext(StudyContext)!; + const defaultKey = selectedSummary?.summary_id ?? summaries[0]?.summary_id; + + return ( + + ); +}; + +export default function CoursePath() { + const { + course, + summaries, + selectedSummary, + summaryName, + setSelectedSummary, + } = useContext(StudyContext)!; + return ( +
+ + {course?.module_name} + + / + +
+ ); +} diff --git a/lexikon/src/features/study/hooks/useCourse.ts b/lexikon/src/features/study/hooks/useCourse.ts new file mode 100644 index 0000000..e2f8295 --- /dev/null +++ b/lexikon/src/features/study/hooks/useCourse.ts @@ -0,0 +1,30 @@ +import { useState, useEffect } from "react"; +import { CourseModule } from "shared/domain/course"; +import { fetchCourse } from "../queries/CRUD-Courses"; + +export function useCourse(courseid: number) { + const [course, setCourse] = useState(null); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + + useEffect(() => { + if (!courseid) return; + setLoading(true); + fetchCourse(courseid).then((course) => { + if (!course) { + setError("Course not found"); + setLoading(false); + return; + } + setCourse(course); + setLoading(false); + }).catch((error) => { + setError(error.message); + setLoading(false); + }); + }, [courseid]); + + return { course, loading, error }; +}; + +export default useCourse; diff --git a/lexikon/src/features/study/hooks/useSummaries.ts b/lexikon/src/features/study/hooks/useSummaries.ts new file mode 100644 index 0000000..d216fe6 --- /dev/null +++ b/lexikon/src/features/study/hooks/useSummaries.ts @@ -0,0 +1,53 @@ +"use client"; + +import { useState, useEffect } from "react"; +// Use the shared CourseSummary type +import { CourseSummary } from "shared/domain/summary"; +// Import the server action +import { fetchSummaries } from "../queries/CRUD-Summaries"; + +/** + * Client-side hook to fetch summaries for a given course. + * Disables fetching if courseId is falsy. + */ +export function useSummaries(courseId: number) { + const [summaries, setSummaries] = useState([]); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + + // Fetch summaries from the server + const fetch = async () => { + if (!courseId) return; + setLoading(true); + setError(null); + try { + const data = await fetchSummaries(courseId); + + // Sort by chapter name + data.sort((a, b) => { + const nameA = (a.chapter ?? "").toLowerCase(); + const nameB = (b.chapter ?? "").toLowerCase(); + if (nameA < nameB) return -1; + if (nameA > nameB) return 1; + return 0; + }); + + setSummaries(data); + } catch (error: any) { + setError(error.message ?? "An unexpected error occurred"); + } finally { + setLoading(false); + } + }; + + // Fetch on mount and when courseId changes + useEffect(() => { + fetch(); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [courseId]); + + // Expose a reload/refetch function + const refetch = fetch; + + return { summaries, loading, error, refetch }; +} diff --git a/lexikon/src/features/study/hooks/useSummary.ts b/lexikon/src/features/study/hooks/useSummary.ts new file mode 100644 index 0000000..4d6b911 --- /dev/null +++ b/lexikon/src/features/study/hooks/useSummary.ts @@ -0,0 +1,57 @@ +"use client"; + +import { useEffect, useCallback } from "react"; +import { CourseSummary } from "shared/domain/summary"; +import { useSummaryStore } from "../stores/summaryStore"; + + +interface useSummaryProps { + content: string; + setContent: (content: string) => void; + saveContent: () => Promise<{ error?: string } | undefined>; + loading: boolean; + error: string | null; + renameSummary: (name: string, termCode: string) => Promise<{ error?: string } | undefined>; +} + + +// Custom hook to manage the content of the current summary (singleton/global) +export function useSummary(summary: CourseSummary | null): useSummaryProps { + const summaryId = summary?.summary_id; + const content = useSummaryStore((s) => (summaryId ? s.content[summaryId] ?? "" : "")); + const loading = useSummaryStore((s) => (summaryId ? s.loading[summaryId] ?? false : false)); + const error = useSummaryStore((s) => (summaryId ? s.error[summaryId] ?? null : null)); + const setContent = useCallback((c: string) => { + if (summaryId) useSummaryStore.getState().setContent(summaryId, c); + }, [summaryId]); + const fetchContent = useSummaryStore((s) => s.fetchContent); + const saveContent = useCallback(async () => { + if (summary && summaryId) { + return await useSummaryStore.getState().saveContent(summary); + } + return { error: "No summary selected" }; + }, [summary, summaryId]); + + const renameSummary = useCallback(async (name: string, termCode: string) => { + if (summary && summaryId) { + return await useSummaryStore.getState().renameSummary(summary, name); + } + return { error: "No summary selected" }; + }, [summary, summaryId]); + + // Fetch content when summary changes + useEffect(() => { + if (summary && summaryId) { + fetchContent(summary); + } + }, [summary, summaryId, fetchContent]); + + return { + content, + setContent, + loading, + error, + saveContent, + renameSummary, + }; +} diff --git a/lexikon/src/features/study/provider/StudyProvider.tsx b/lexikon/src/features/study/provider/StudyProvider.tsx new file mode 100644 index 0000000..bb78c53 --- /dev/null +++ b/lexikon/src/features/study/provider/StudyProvider.tsx @@ -0,0 +1,151 @@ +"use client"; +import { createContext, useState, useEffect, ReactNode } from "react"; +import { CourseSummary } from "shared/domain/summary"; +import { + createSummary, + deleteSummary, + updateSummary, +} from "../queries/CRUD-Summaries"; +import { useSummaries } from "../hooks/useSummaries"; +import useCourse from "../hooks/useCourse"; +import { CourseModule } from "shared/domain/course"; +import useTermStore from "../stores/termStore"; +import { useSummary } from "../hooks/useSummary"; + +interface StudyContextProps { + course: CourseModule | null; + summaries: CourseSummary[]; + selectedSummary: CourseSummary | null; + setSelectedSummary: (summary: CourseSummary | null) => void; + navigateSummary: (direction: "next" | "prev") => void; + mode: "view" | "edit"; + onModeChange: (mode: "view" | "edit") => void; + dirty: boolean; + busy: boolean; + setBusy: (busy: boolean) => void; + onCreate: (name: string) => Promise; + onSave: () => Promise<{ success: boolean; error?: string }>; + onDelete: () => Promise; + handleSave?: () => Promise<{ success: boolean; error?: string }>; + summaryName: string; + setSummaryName: (name: string) => void; +} + +export const StudyContext = createContext( + undefined +); + +interface StudyProviderProps { + children: ReactNode; + courseId: number; +} + +export default function StudyProvider({ + children, + courseId, +}: StudyProviderProps) { + const { + summaries, + loading: summariesLoading, + error: summariesError, + refetch, + } = useSummaries(courseId); + const { course } = useCourse(courseId); + + const [selectedSummary, setSelectedSummary] = + useState(null); + + const [dirty, setDirty] = useState(false); + const [busy, setBusy] = useState(false); + const [mode, setMode] = useState<"view" | "edit">("view"); + const [summaryName, setSummaryName] = useState( + selectedSummary?.chapter || "" + ); + const chapterCount = summaries.length; + const { selectedTerm } = useTermStore(); + const { content } = useSummary(selectedSummary); + + // Keep summaryName in sync with selectedSummary + useEffect(() => { + setSummaryName(selectedSummary?.chapter || ""); + }, [selectedSummary]); + + // Only create and refetch, let the component handle navigation + const handleCreate = async (name: string) => { + await createSummary(courseId, name, selectedTerm?.semester_code ?? ""); + await refetch(); + }; + const handleDelete = async () => { + if (!selectedSummary) return; + await deleteSummary(selectedSummary); + await refetch(); + }; + + // Only update state, let the component handle navigation + const navigateSummary = (direction: "next" | "prev") => { + if (!selectedSummary) return; + const currentIndex = summaries.findIndex( + (summary) => summary.summary_id === selectedSummary.summary_id + ); + let newIndex = currentIndex; + if (direction === "next" && currentIndex < chapterCount - 1) { + newIndex = currentIndex + 1; + } + if (direction === "prev" && currentIndex > 0) { + newIndex = currentIndex - 1; + } + if (newIndex !== currentIndex && summaries[newIndex]) { + setSelectedSummary(summaries[newIndex]); + } + }; + + // Save handler that updates summary using updateSummary only + const handleSave = async (): Promise<{ + success: boolean; + error?: string; + }> => { + if (!selectedSummary) + return { success: false, error: "No summary selected" }; + const error = await updateSummary(selectedSummary, { + newName: summaryName, + content: content, + }); + if (!error) { + setDirty(false); + return { success: true }; + } + // Ensure error is always a string + return { + success: false, + error: + typeof error === "string" + ? error + : (error?.error ?? "Unknown error"), + }; + }; + + const contextValue = { + course, + selectedSummary, + setSelectedSummary, + navigateSummary, + mode, + onModeChange: setMode, + dirty, + busy: busy || summariesLoading, + setBusy, + onCreate: handleCreate, + onSave: handleSave, + onDelete: handleDelete, + handleSave, + summaryName, + setSummaryName, + summaries, + }; + + return ( + + {children} + + ); +} diff --git a/lexikon/src/features/study/queries/CRUD-Courses.ts b/lexikon/src/features/study/queries/CRUD-Courses.ts new file mode 100644 index 0000000..56f88bc --- /dev/null +++ b/lexikon/src/features/study/queries/CRUD-Courses.ts @@ -0,0 +1,64 @@ +import { supaBrowser as supabase } from "shared/api/supabase"; +import { CourseModule } from "shared/domain/course"; +import { Term } from "shared/domain/term"; + +export async function fetchCourse(courseid: number): Promise { + const { data, error } = await supabase + .schema("library") + .from("mv_course_with_module") // TODO: MAke a better view + .select("*") + .eq("course_id", courseid) + .single(); + + if (error) throw error; + + return data as CourseModule; +} + +export async function fetchCoursesModules(term: Term): Promise { + const { data, error } = await supabase + .schema("library") + .from("mv_course_with_module") // TODO: MAke a better view + .select("*") + .eq("semester_id", term.semester_id) + + const { data: favs, error: favsError } = await supabase + .schema("library") + .from("module_favorites") + .select("module_id, user_uuid") + .eq("user_uuid", (await supabase.auth.getUser()).data.user?.id); + + if (!favsError) { + return data!.map((courseModule) => { + const is_user_favorite = favs.some( + (fav) => fav.module_id === courseModule.module_id + ); + return { ...courseModule, is_user_favorite }; + }) as CourseModule[]; + } + + if (error) throw error; + + return data as CourseModule[]; +} + +/** + * Toggle the “favorite” flag for the current user. + * Returns true if the RPC says the row is now a favorite. + */ +export async function toggleCourseFavorite( + courseModule: CourseModule, + isfavorite: boolean, +): Promise { + + const { data, error } = await supabase + .schema("library") + .rpc("upsert_module_favorites", { + p_favorite_state: isfavorite, + p_module_id: courseModule.module_id, + }); + + if (error) throw error; + + return data === "true"; +} diff --git a/lexikon/src/features/study/queries/CRUD-Summaries.ts b/lexikon/src/features/study/queries/CRUD-Summaries.ts new file mode 100644 index 0000000..8e58b2c --- /dev/null +++ b/lexikon/src/features/study/queries/CRUD-Summaries.ts @@ -0,0 +1,221 @@ +import { supaBrowser } from 'shared/api/supabase/browser'; +import { getNewSummaryPath, getSummaryPath, storagePaths } from "shared/config/paths"; +import { CourseSummary } from "shared/domain/summary"; + + +export async function getSummaryContent( + summary: CourseSummary, +) { + + if (!summary.bucket_id || !summary.object_path) { + console.error("Invalid summary file reference"); + + return; + } + + const { data, error: fetchError } = await supaBrowser.storage + .from(summary.bucket_id) + .download(summary.object_path); + + if (fetchError || !data) { + console.error("Failed to download summary file", fetchError); + + return; + } + + const text = await data.text(); + + return text; +} + +/** + * Create a new chapter summary for a given course and name. + */ + +export async function createSummary( + courseId: number, + name: string, + termCode: string, +): Promise { + if (!courseId || !name.trim() || !termCode) { + console.error( + "Missing parameters or empty chapter name", + courseId, + name, + termCode, + ); + return; + } + const filePath = getSummaryPath( + termCode, + courseId, + name.trim().replaceAll(" ", "-"), + ); + const bucketId = storagePaths.moduleContent.bucket; + // 1. Upload a empty markdown file to Supabase, so we have an objectid + const { data: storageData, error: storageError } = await supaBrowser.storage + .from(bucketId) + .upload(filePath, `# ${name}`, { + cacheControl: "3600", + upsert: true, + }); + + if (storageError) { + console.error("Error uploading file:", storageError); + + return; + } + + if (!storageData) { + console.error("No data returned from Supabase"); + + return; + } + const createdAtPath = storageData.path; + const objectId = storageData.id; + + // 2. Add an entry of the chapter to the database + const { data: createdChapter, error: upsertError } = await supaBrowser + .schema("library") + .rpc("upsert_summary_chapter", { + p_chapter: name, + p_course_id: courseId, + p_filename: createdAtPath, + p_object_uuid: objectId, + }) + .single(); + + if (upsertError) { + console.error("Error upserting chapter:", upsertError); + + return; + } + + if (!createdChapter) { + console.error("No data returned from Supabase"); + + return; + } + + return createdChapter as CourseSummary; +} + +/** + * Deletes a chapter summary and entry from the database and storage. + */ +export async function deleteSummary(summary: CourseSummary): Promise { + if (!summary || !summary.summary_id) { + console.error("Missing summary or summary_id"); + + return null; + } + const { data, error } = await supaBrowser + .schema("library") + .rpc("delete_summary", { p_summary_id: summary.summary_id! }); + + if (error) throw error; + + return data; +} + +/** + * Updates a chapter summary: can rename and/or update content. + * If both are provided, rename happens first. + */ +export async function updateSummary( + summary: CourseSummary, + options: { + newName?: string; + content?: string; + } +): Promise<{ error?: string } | undefined> { + if (!summary) { + return { error: "Missing summary" }; + } + + const { newName, content } = options; + + // Handle rename if all required params are present + if ( + newName && newName.trim() !== "" + ) { + const currentPath = summary.object_path; + if (!currentPath) { + return { error: "summary.object_path is null or undefined" }; + } + // Extract directory and replace filename + const newFilePath = getNewSummaryPath(currentPath, newName); + const bucketId = storagePaths.moduleContent.bucket; + const { error: storageError } = await supaBrowser.storage + .from(bucketId) + .move(currentPath, newFilePath); + if (storageError) { + return { error: storageError.message || "Error moving file" }; + } + // Rename chapter via RPC; returns trimmed chapter name + const { data: trimmedChapter, error: renameError } = await supaBrowser + .schema("library") + .rpc( + "rename_chapter", + { p_new_chapter: newName.trim(), p_summary_id: summary.summary_id! } + ) + .single(); + + if (renameError) { + return { error: renameError.message || "Error renaming chapter" }; + } + if (trimmedChapter === null) { + return { error: "Error renaming chapter" }; + } + // Update summary with trimmed name and new path + summary.chapter = trimmedChapter; + summary.object_path = newFilePath; + } + + console.log("Updating summary content", { + + content, + }); + + // Handle content update if requested + if (typeof content === "string") { + const path = summary.object_path; + const bucketId = summary.bucket_id; + + + const { data: uploadResult, error } = await supaBrowser.storage + .from(bucketId!) + .update(path!, content, { + cacheControl: "3600", + upsert: true, + }); + + console.debug("Upload result", uploadResult); + + if (error) { + return { error: error.message || "Error updating file" }; + } + if (!uploadResult) { + return { error: "No data returned from Supabase" }; + } + } + + return undefined; +} + +/** + * Fetches all chapter summaries for a course. + * @param courseId The ID of the course to fetch summaries for. + */ +export async function fetchSummaries(courseId: number): Promise { + + const { data, error } = await supaBrowser + .schema("library") + .from("summary") + .select("*") + .eq("course_id", courseId ?? 0); + + if (error) throw error; + + return (data ?? []) as CourseSummary[]; +} diff --git a/lexikon/src/features/study/queries/CRUD-Terms.ts b/lexikon/src/features/study/queries/CRUD-Terms.ts new file mode 100644 index 0000000..8582440 --- /dev/null +++ b/lexikon/src/features/study/queries/CRUD-Terms.ts @@ -0,0 +1,57 @@ +import { supaServer } from "shared/api/supabase/server"; +import { Term } from "shared/domain/term"; +import { supaBrowser as supabase } from "shared/api/supabase/browser"; + +export async function fetchTerms(): Promise { + const { data, error } = await supabase + .schema("library") + .from("semester") + .select("*") + // Sorting by year, then by FS before HS + .order("semester_code", { ascending: false }); + + if (error) throw error; + if (!data) return []; + + // Custom sort: by year, then FS before HS + return (data as Term[]).sort((a: Term, b: Term) => { + const parse = (code: string) => { + // code: e.g. "FS24" or "HS23" + const match = code.match(/^(FS|HS)(\d{2})$/); + if (!match) return { sem: "", year: 0 }; + const [, sem, year] = match; + return { sem, year: Number(year) }; + }; + const aParsed = parse(a.semester_code); + const bParsed = parse(b.semester_code); + + // Sort by year descending + if (aParsed.year !== bParsed.year) { + return bParsed.year - aParsed.year; + } + // FS before HS + if (aParsed.sem !== bParsed.sem) { + return aParsed.sem === "FS" ? -1 : 1; + } + return 0; + }); +} + + +/** + * Return all terms that belong to a module + */ +export async function fetchModuleTerms(moduleId: number): Promise { + if (!Number.isFinite(moduleId)) throw new Error("Invalid moduleId"); + + const supabase = + await supaServer(); + const { data, error } = await supabase + .schema("library") + .rpc("get_terms_per_module", { p_module_id: moduleId }); + + if (error) throw error; + if (!data) return []; + + return data as Term[]; +} diff --git a/lexikon/src/features/study/stores/coursesStore.ts b/lexikon/src/features/study/stores/coursesStore.ts new file mode 100644 index 0000000..6892d37 --- /dev/null +++ b/lexikon/src/features/study/stores/coursesStore.ts @@ -0,0 +1,68 @@ +"use client"; +import { CourseModule } from "shared/domain/course"; +import { create } from "zustand"; +import { fetchCoursesModules } from "../queries/CRUD-Courses"; +import { Term } from "shared/domain/term"; + +interface CoursesModulesState { + coursesModules: CourseModule[]; + isLoading: boolean; + term: Term | null; + error: string | null; + loadCoursesModules: (term: Term) => Promise; + reloadCoursesModules: () => void; +} + +export const useCoursesModulesStore = create((set) => ({ + coursesModules: [], + isLoading: false, + error: null, + term: null, + loadCoursesModules: async (term) => { + set({ isLoading: true, error: null }); // Reset error on new load attempt + try { + const coursesModules = await fetchCoursesModules(term); + + set({ coursesModules: coursesModules, isLoading: false, term: term }); + } catch (err) { + // Extract the error message for serialization + let errorMessage = "An unknown error occurred"; + if (err instanceof Error) { + errorMessage = err.message; + } else if (typeof err === 'string') { + errorMessage = err; + } else if (err && typeof err === 'object' && 'message' in err && typeof err.message === 'string') { + // Handle cases where the error might be an object with a message property + errorMessage = err.message; + } + set({ error: errorMessage, isLoading: false }); + } + }, + reloadCoursesModules: async () => { + + const { term } = useCoursesModulesStore.getState(); + if (term) { + set({ isLoading: true, error: null }); // Reset error on new load attempt + try { + const coursesModules = await fetchCoursesModules(term); + + set({ coursesModules: coursesModules, isLoading: false }); + } + catch (err) { + // Extract the error message for serialization + let errorMessage = "An unknown error occurred"; + if (err instanceof Error) { + errorMessage = err.message; + } else if (typeof err === 'string') { + errorMessage = err; + } else if (err && typeof err === 'object' && 'message' in err && typeof err.message === 'string') { + // Handle cases where the error might be an object with a message property + errorMessage = err.message; + } + set({ error: errorMessage, isLoading: false }); + } + } + }, +})); + +export default useCoursesModulesStore; diff --git a/lexikon/src/features/study/stores/summaryStore.ts b/lexikon/src/features/study/stores/summaryStore.ts new file mode 100644 index 0000000..11123b9 --- /dev/null +++ b/lexikon/src/features/study/stores/summaryStore.ts @@ -0,0 +1,45 @@ +import { create } from "zustand"; +import { CourseSummary } from "shared/domain/summary"; +import { + getSummaryContent +} from "../queries/CRUD-Summaries"; + +interface SummaryState { + content: Record; + loading: Record; + error: Record; + setContent: (summaryId: number, content: string) => void; + fetchContent: (summary: CourseSummary) => Promise; +} + +export const useSummaryStore = create((set) => ({ + content: {}, + loading: {}, + error: {}, + setContent: (summaryId, content) => + set((state) => ({ + content: { ...state.content, [summaryId]: content }, + })), + fetchContent: async (summary) => { + if (!summary?.summary_id) return; + set((state) => ({ + loading: { ...state.loading, [summary.summary_id!]: true }, + error: { ...state.error, [summary.summary_id!]: null }, + })); + try { + const content = await getSummaryContent(summary); + set((state) => ({ + content: { ...state.content, [summary.summary_id!]: content ?? "" }, + })); + } catch (e: any) { + set((state) => ({ + error: { ...state.error, [summary.summary_id!]: e?.message || "Failed to load summary" }, + content: { ...state.content, [summary.summary_id!]: "" }, + })); + } finally { + set((state) => ({ + loading: { ...state.loading, [summary.summary_id!]: false }, + })); + } + } +})); diff --git a/lexikon/src/features/study/stores/termStore.ts b/lexikon/src/features/study/stores/termStore.ts new file mode 100644 index 0000000..ea560bb --- /dev/null +++ b/lexikon/src/features/study/stores/termStore.ts @@ -0,0 +1,35 @@ +import { Term } from "shared/domain/term"; +import { create } from "zustand"; +import { fetchTerms } from "../queries/CRUD-Terms"; + +interface TermsState { + terms: Term[]; + selectedTerm: Term | undefined; + isLoading: boolean; + error: Error | null; + fetchTerms: () => Promise; + setSelectedTerm: (term: Term) => void; +} + +export const useTermStore = create((set) => ({ + terms: [], + selectedTerm: undefined, + setSelectedTerm: (term: Term) => { + set({ selectedTerm: term }); + }, + isLoading: false, + error: null, + fetchTerms: async () => { + set({ isLoading: true, error: null }); // Reset error state on new fetch + try { + const terms = await fetchTerms(); + set({ terms, selectedTerm: terms[0] || undefined, isLoading: false }); + } catch (err: unknown) { + // Ensure error is always an Error object + const error = err instanceof Error ? err : new Error(String(err)); + set({ error: error, isLoading: false, terms: [] }); // Clear terms on error + } + }, +})); + +export default useTermStore; diff --git a/lexikon/src/features/tsnePlot/TsnePlot.tsx b/lexikon/src/features/tsnePlot/TsnePlot.tsx new file mode 100644 index 0000000..2e5a420 --- /dev/null +++ b/lexikon/src/features/tsnePlot/TsnePlot.tsx @@ -0,0 +1,268 @@ +// src/components/TsnePlot.tsx +import { OrbitControls } from '@react-three/drei'; +import { Canvas } from '@react-three/fiber'; +import React, { useEffect, useMemo, useRef, useState } from 'react'; +import * as THREE from 'three'; +import { DataPoint, LoadedData } from '../../app/librarian/vspace/data'; // Pfad anpassen + +interface ScatterPointProps { + position: [number, number, number]; + color: THREE.ColorRepresentation; + pointData: DataPoint; + onPointerOver: (data: DataPoint | null, event: any) => void; + onPointerOut: () => void; + size?: number; + hoveredCluster: string | null; // Added to know which cluster is active +} + +const ScatterPoint: React.FC = ({ + position, + color, + pointData, + onPointerOver, + onPointerOut, + size = 0.5, // Standardgröße der Punkte + hoveredCluster, +}) => { + const meshRef = useRef(null!); + + // Determine if the point is part of the currently hovered cluster + const isInHoveredCluster = pointData.cluster === hoveredCluster; + // Determine if any cluster is currently being hovered + const isAnyClusterHovered = hoveredCluster !== null; + + // Calculate opacity: + // - If a cluster is hovered and this point is NOT in it, dim the point. + // - Otherwise (point is in the hovered cluster OR no cluster is hovered), full opacity. + const opacity = isAnyClusterHovered && !isInHoveredCluster ? 0.1 : 1.0; // Dimmed to 0.1, was 0.05 + + return ( + { + event.stopPropagation(); // Wichtig für Hover-Effekte mit mehreren Objekten + onPointerOver(pointData, event.nativeEvent); + }} + onPointerOut={() => { + onPointerOut(); + }} + > + + + + ); +}; + +interface TooltipState { + visible: boolean; + content: string; + x: number; + y: number; +} + +const TsnePlot: React.FC = () => { + const [dataPoints, setDataPoints] = useState([]); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const [tooltip, setTooltip] = useState({ + visible: false, + content: '', + x: 0, + y: 0, + }); + const [hoveredCluster, setHoveredCluster] = useState(null); // State for hovered cluster + + // Farbpalette für Cluster + const clusterColors: { [key: string]: THREE.Color } = useMemo( + () => ({ + '0': new THREE.Color(0xff0000), // Rot + '1': new THREE.Color(0x00ff00), // Grün + '2': new THREE.Color(0x0000ff), // Blau + '3': new THREE.Color(0xffff00), // Gelb + '4': new THREE.Color(0xff00ff), // Magenta + '5': new THREE.Color(0x00ffff), // Cyan + '6': new THREE.Color(0xffa500), // Orange + '7': new THREE.Color(0x800080), // Lila + // Füge bei Bedarf mehr Farben für mehr Cluster hinzu + }), + [] + ); + const defaultColor = useMemo(() => new THREE.Color(0xaaaaaa), []); + + useEffect(() => { + const fetchData = async () => { + try { + setLoading(true); + setError(null); + const response = await fetch('/tsne_data.json'); + if (!response.ok) { + throw new Error(`HTTP error! status: ${response.status}`); + } + const jsonData: LoadedData = await response.json(); + + const columns = jsonData.columns; + const parsedPoints = jsonData.data.map((rowData) => { + const point: any = {}; + columns.forEach((col, index) => { + point[col] = rowData[index]; + }); + return { + ...point, + x: Number(point.x), + y: Number(point.y), + z: Number(point.z), + cluster: String(point.cluster), // Sicherstellen, dass Cluster ein String ist + } as DataPoint; + }); + setDataPoints(parsedPoints); + } catch (e: any) { + setError(e.message); + console.error('Fehler beim Laden der Daten:', e); + } finally { + setLoading(false); + } + }; + fetchData(); + }, []); + + // Log hoveredCluster changes + useEffect(() => { + console.log('hoveredCluster changed:', hoveredCluster); + }, [hoveredCluster]); + + const handlePointerOver = (data: DataPoint | null, event: MouseEvent) => { + if (data) { + console.log('Pointer Over:', data.cluster, data); // Log on pointer over + const formattedHoverText = data.hover_text.replace(/
/g, '\n'); + setTooltip({ + visible: true, + content: formattedHoverText, + x: event.clientX + 10, + y: event.clientY + 10, + }); + setHoveredCluster(data.cluster); // Set hovered cluster + } + }; + + const handlePointerOut = () => { + console.log('Pointer Out'); // Log on pointer out + setTooltip((prev) => ({ ...prev, visible: false })); + setHoveredCluster(null); // Reset hovered cluster + }; + + // Skalierungsfaktor für die Koordinaten. Muss ggf. angepasst werden. + const scaleFactor = 1; // Siehe Erklärung im vorherigen Codebeispiel + + if (loading) + return ( +
+ Lade Daten... +
+ ); + if (error) + return ( +
+ Fehler beim Laden der Daten: {error} +
+ ); + + return ( + <> + {tooltip.visible && ( +
+ {tooltip.content} +
+ )} + + + + + {/* + Statt InstancedMesh hier einzelne Meshes für jeden Punkt. + Für SEHR viele Punkte (>10k) wäre InstancedMesh oder Points besser. + Aber für einige Tausend ist das oft noch handhabbar und einfacher für Hover-Events. + Wenn die Performance ein Problem wird, muss man hier auf InstancedMesh umstellen. + Siehe Kommentar unten für einen alternativen InstancedMesh-Ansatz. + */} + {dataPoints.map((point, index) => ( + + ))} + + + + + ); +}; + +export default TsnePlot; diff --git a/lexikon/src/features/tsnePlot/hoverStore.ts b/lexikon/src/features/tsnePlot/hoverStore.ts new file mode 100644 index 0000000..93211bf --- /dev/null +++ b/lexikon/src/features/tsnePlot/hoverStore.ts @@ -0,0 +1,13 @@ +'use client'; + +import { create } from "zustand/react"; + +interface HoverState { + hoveredClusterId: string | null; + setHoveredClusterId: (clusterId: string | null) => void; +} + +export const useHoverStore = create((set) => ({ + hoveredClusterId: null, + setHoveredClusterId: (clusterId) => set({ hoveredClusterId: clusterId }), +})); diff --git a/lexikon/src/features/tsnePlot/tsnePlotView.tsx b/lexikon/src/features/tsnePlot/tsnePlotView.tsx new file mode 100644 index 0000000..0ba2d97 --- /dev/null +++ b/lexikon/src/features/tsnePlot/tsnePlotView.tsx @@ -0,0 +1,119 @@ +'use client'; + +import { OrbitControls, Sphere } from '@react-three/drei'; +import { Canvas, useFrame, useThree } from '@react-three/fiber'; +import React, { useEffect, useRef, useState } from 'react'; +import * as THREE from 'three'; +import { useHoverStore } from './hoverStore'; + +interface DataPoint { + id: string; + position: [number, number, number]; + color: string; + clusterId?: string; +} + +interface ClusterPointProps { + point: DataPoint; +} + +const ClusterPoint: React.FC = ({ point, ...props }) => { + const ref = useRef(null!); + const { hoveredClusterId, setHoveredClusterId } = useHoverStore(); + const [isHovered, setIsHovered] = useState(false); + + const isVisible = + hoveredClusterId === null || hoveredClusterId === point.clusterId; + + useFrame(() => { + if ( + ref.current && + ref.current.material instanceof THREE.MeshStandardMaterial + ) { + (ref.current.material as THREE.MeshStandardMaterial).opacity = + isVisible ? 1 : 0.2; + } + }); + + return ( + { + e.stopPropagation(); + setIsHovered(true); + if (point.clusterId) { + setHoveredClusterId(point.clusterId); + } + }} + onPointerOut={(e) => { + e.stopPropagation(); + setIsHovered(false); + setHoveredClusterId(null); + }} + > + + + ); +}; + +interface PlotPointsProps { + data: DataPoint[]; +} + +const PlotPoints: React.FC = ({ data }) => { + return ( + + {data.map((point) => ( + + ))} + + ); +}; + +const Controls = () => { + // Use OrbitControlsProps for the ref type if possible, or the specific implementation type + const controlsRef = useRef(null); // Using any temporarily to bypass the complex type issue + const { camera, gl } = useThree(); + + useEffect(() => { + const domElement = gl.domElement; + const currentControls = controlsRef.current; + + if (currentControls) { + // ... (existing comments and logic for OrbitControls) + } + + return () => { + // ... (cleanup logic) + }; + }, [gl.domElement, camera]); + + return ; +}; + +interface TsnePlotViewProps { + data: DataPoint[]; +} + +const TsnePlotView: React.FC = ({ data }) => { + return ( +
+ + + + + + +
+ ); +}; + +export default TsnePlotView; +export type { DataPoint }; diff --git a/lexikon/src/mdx-components.tsx b/lexikon/src/mdx-components.tsx new file mode 100644 index 0000000..bacd21a --- /dev/null +++ b/lexikon/src/mdx-components.tsx @@ -0,0 +1,9 @@ +import type { MDXComponents } from "mdx/types"; +import { mdxComponents } from "shared/ui/markdown/mdxComponents"; + +export function useMDXComponents(components: MDXComponents): MDXComponents { + return { + ...components, + ...mdxComponents, + }; +} diff --git a/lexikon/src/middleware.ts b/lexikon/src/middleware.ts new file mode 100644 index 0000000..5188118 --- /dev/null +++ b/lexikon/src/middleware.ts @@ -0,0 +1,14 @@ +// This file re-exports the authentication middleware for Next.js +import { updateSession } from "shared/api/supabase/server/middleware"; + +// Rename updateSession to middleware for Next.js to recognize +export { updateSession as middleware }; + +// Optional: configure which paths the middleware should apply to +// By default, middleware runs on all paths; adjust matcher to exclude static files or specific routes +export const config = { + matcher: [ + // All paths except Next.js internals and public routes + "/((?!_next/static|_next/image|favicon.ico|login|confirm|error|api).*)", + ], +}; diff --git a/lexikon/src/shared/api/librarian/client.ts b/lexikon/src/shared/api/librarian/client.ts new file mode 100644 index 0000000..ef03951 --- /dev/null +++ b/lexikon/src/shared/api/librarian/client.ts @@ -0,0 +1 @@ +// fetch wrapper to FastAPI gateway diff --git a/lexikon/src/shared/api/librarian/hooks.ts b/lexikon/src/shared/api/librarian/hooks.ts new file mode 100644 index 0000000..82f6dad --- /dev/null +++ b/lexikon/src/shared/api/librarian/hooks.ts @@ -0,0 +1 @@ +// useCrawlIndex, useDownloadTaskStatus diff --git a/lexikon/src/shared/api/supabase/browser.ts b/lexikon/src/shared/api/supabase/browser.ts new file mode 100644 index 0000000..89225f2 --- /dev/null +++ b/lexikon/src/shared/api/supabase/browser.ts @@ -0,0 +1,36 @@ +"use client"; // must run in the browser +import { createBrowserClient } from "@supabase/ssr"; + +export const supaBrowser = createBrowserClient( + process.env.NEXT_PUBLIC_SUPABASE_URL!, + process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY!, +); + +export async function signInWithGoogle( + redirectTo = `${window.location.origin}/auth/callback`, +) { + const { error, data } = await supaBrowser.auth.signInWithOAuth({ + provider: "google", + options: { redirectTo }, + }); + + if (error) throw error; + + // the page will redirect automatically; data is only for tests + return data; +} + +export async function signOut() { + const { error } = await supaBrowser.auth.signOut(); + if (error) throw error; +} + +/** + * Cleans the module-content bucket by removing all files. + * Only use during development. + */ +export async function emptyBucket() { + const { data, error } = await supaBrowser.storage.emptyBucket('module-content'); + if (error) throw error; + return data; +} diff --git a/lexikon/src/shared/api/supabase/hooks.ts b/lexikon/src/shared/api/supabase/hooks.ts new file mode 100644 index 0000000..394007a --- /dev/null +++ b/lexikon/src/shared/api/supabase/hooks.ts @@ -0,0 +1,41 @@ +"use client"; + +import { type Session, type User } from "@supabase/supabase-js"; + +import { useEffect, useState } from "react"; +import { supaBrowser } from "./browser"; + + + +/** + * React hook to subscribe to Supabase Auth state in browser. + * Returns the current user session, user object, and loading status. + */ +export function useAuth() { + const [user, setUser] = useState(null); + const [session, setSession] = useState(null); + const [isLoading, setIsLoading] = useState(true); + + useEffect(() => { + // Get initial session + supaBrowser.auth.getSession().then(({ data }: { data: { session: Session | null } }) => { + setSession(data.session); + setUser(data.session?.user ?? null); + setIsLoading(false); + }); + // Listen for auth changes + const { + data: { subscription }, + } = supaBrowser.auth.onAuthStateChange((_: unknown, session: Session | null) => { + setSession(session); + setUser(session?.user ?? null); + setIsLoading(false); + }); + + return () => { + subscription.unsubscribe(); + }; + }, [supaBrowser]); + + return { user, session, isLoading }; +} diff --git a/lexikon/src/shared/api/supabase/index.ts b/lexikon/src/shared/api/supabase/index.ts new file mode 100644 index 0000000..9b3a46b --- /dev/null +++ b/lexikon/src/shared/api/supabase/index.ts @@ -0,0 +1,2 @@ +export * from "./browser"; +export * from "./hooks"; diff --git a/lexikon/src/shared/api/supabase/server/db.ts b/lexikon/src/shared/api/supabase/server/db.ts new file mode 100644 index 0000000..1345c5e --- /dev/null +++ b/lexikon/src/shared/api/supabase/server/db.ts @@ -0,0 +1,25 @@ +"use server"; + +import { createServerClient } from "@supabase/ssr"; +import { cookies } from "next/headers"; + +export async function supaServer() { + const cookieStore = await cookies(); + + return createServerClient( + process.env.NEXT_PUBLIC_SUPABASE_URL!, + process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY!, + { + cookies: { + getAll: () => + cookieStore.getAll().map(({ name, value }) => ({ name, value })), + setAll: (cookiesToSet) => { + // Set cookies received from Supabase + cookiesToSet.forEach(({ name, value, options }) => + cookieStore.set(name, value, options), + ); + }, + }, + }, + ); +} diff --git a/lexikon/src/shared/api/supabase/server/index.ts b/lexikon/src/shared/api/supabase/server/index.ts new file mode 100644 index 0000000..66de36b --- /dev/null +++ b/lexikon/src/shared/api/supabase/server/index.ts @@ -0,0 +1,2 @@ +export * from "./db"; +export * from "./login"; diff --git a/lexikon/src/shared/api/supabase/server/login.ts b/lexikon/src/shared/api/supabase/server/login.ts new file mode 100644 index 0000000..9981498 --- /dev/null +++ b/lexikon/src/shared/api/supabase/server/login.ts @@ -0,0 +1,45 @@ +"use server"; + +import { revalidatePath } from "next/cache"; +import { redirect } from "next/navigation"; +import { supaServer } from "./db"; + +export async function login(formData: FormData) { + const supabase = await supaServer(); + + // type-casting here for convenience + // in practice, you should validate your inputs + const data = { + email: formData.get("email") as string, + password: formData.get("password") as string, + }; + + const { error } = await supabase.auth.signInWithPassword(data); + + if (error) { + redirect("/error"); + } + + revalidatePath("/", "layout"); + redirect("/"); +} + +export async function signup(formData: FormData) { + const supabase = await supaServer(); + + // type-casting here for convenience + // in practice, you should validate your inputs + const data = { + email: formData.get("email") as string, + password: formData.get("password") as string, + }; + + const { error } = await supabase.auth.signUp(data); + + if (error) { + redirect("/error"); + } + + revalidatePath("/", "layout"); + redirect("/"); + } diff --git a/lexikon/src/shared/api/supabase/server/middleware.ts b/lexikon/src/shared/api/supabase/server/middleware.ts new file mode 100644 index 0000000..0f0cd60 --- /dev/null +++ b/lexikon/src/shared/api/supabase/server/middleware.ts @@ -0,0 +1,74 @@ +import { createServerClient } from "@supabase/ssr"; +import { NextResponse, type NextRequest } from "next/server"; + +export async function updateSession(request: NextRequest) { + let supabaseResponse = NextResponse.next({ + request, + }); + + const supabase = createServerClient( + process.env.NEXT_PUBLIC_SUPABASE_URL!, + process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY!, + { + cookies: { + getAll() { + return request.cookies.getAll(); + }, + setAll(cookiesToSet) { + cookiesToSet.forEach(({ name, value, options }) => + request.cookies.set(name, value), + ); + supabaseResponse = NextResponse.next({ + request, + }); + cookiesToSet.forEach(({ name, value, options }) => + supabaseResponse.cookies.set(name, value, options), + ); + }, + }, + }, + ); + + // Do not run code between createServerClient and + // supabase.auth.getUser(). A simple mistake could make it very hard to debug + // issues with users being randomly logged out. + + // IMPORTANT: DO NOT REMOVE auth.getUser() + + const { + data: { user }, + } = await supabase.auth.getUser(); + + + // TODO: Fix this and add Login logic +// if ( +// !user && +// !request.nextUrl.pathname.startsWith("/login") && +// !request.nextUrl.pathname.startsWith("/confirm") && +// !request.nextUrl.pathname.startsWith("/error") && +// !request.nextUrl.pathname.startsWith("/auth") && +// !request.nextUrl.pathname.startsWith("/api") +// ) { +// // no user, potentially respond by redirecting the user to the login page +// const url = request.nextUrl.clone(); + +// url.pathname = "/login"; + +// return NextResponse.redirect(url); +// } + + // IMPORTANT: You *must* return the supabaseResponse object as it is. + // If you're creating a new response object with NextResponse.next() make sure to: + // 1. Pass the request in it, like so: + // const myNewResponse = NextResponse.next({ request }) + // 2. Copy over the cookies, like so: + // myNewResponse.cookies.setAll(supabaseResponse.cookies.getAll()) + // 3. Change the myNewResponse object to fit your needs, but avoid changing + // the cookies! + // 4. Finally: + // return myNewResponse + // If this is not done, you may be causing the browser and server to go out + // of sync and terminate the user's session prematurely! + + return supabaseResponse; +} diff --git a/lexikon/src/shared/config/api.ts b/lexikon/src/shared/config/api.ts new file mode 100644 index 0000000..b46001a --- /dev/null +++ b/lexikon/src/shared/config/api.ts @@ -0,0 +1,2 @@ +// export const LOCAL_API_BASE = 'http://localhost:8000/api'; +export const LOCAL_API_BASE = 'os-machina.local:8000/v1'; diff --git a/lexikon/src/shared/config/fonts.ts b/lexikon/src/shared/config/fonts.ts new file mode 100644 index 0000000..0e7d9c9 --- /dev/null +++ b/lexikon/src/shared/config/fonts.ts @@ -0,0 +1,11 @@ +import { Fira_Code as FontMono, Inter as FontSans } from "next/font/google"; + +export const fontSans = FontSans({ + subsets: ["latin"], + variable: "--font-sans", +}); + +export const fontMono = FontMono({ + subsets: ["latin"], + variable: "--font-mono", +}); diff --git a/lexikon/src/shared/config/mockData.ts b/lexikon/src/shared/config/mockData.ts new file mode 100644 index 0000000..bf7786e --- /dev/null +++ b/lexikon/src/shared/config/mockData.ts @@ -0,0 +1,4 @@ +export const example_summary = + "https://raw.githubusercontent.com/DotNaos/TGI13/refs/heads/main/Informatik/Man-in-the-middle.md"; + +export const example_hero_image = "https://moodle.fhgr.ch/pluginfile.php/1058426/course/overviewfiles/Titelbild.jpg"; diff --git a/lexikon/src/shared/config/paths.ts b/lexikon/src/shared/config/paths.ts new file mode 100644 index 0000000..aaf8e75 --- /dev/null +++ b/lexikon/src/shared/config/paths.ts @@ -0,0 +1,30 @@ +export type StoragePaths = typeof storagePaths; + +export const storagePaths = { + moduleContent: { + bucket: "module-content", + }, +}; + +/** + * Generate the storage path for a summary file. + * @param termCode The term code (e.g., 'FS25') + * @param courseId The course ID + * @param summaryName The summary name (should be sanitized) + */ +export const getSummaryPath = ( + termCode: string, + courseId: number, + summaryName: string, +): string => { + return `${termCode}/courses/${courseId}/summaries/summary_${summaryName}.mdx`; +}; + + +export const getNewSummaryPath = ( + currentPath: string, + newName: string, +): string => { + const dir = currentPath.substring(0, currentPath.lastIndexOf("/")); + return `${dir}/summary_${newName.trim().replaceAll(" ", "-")}.mdx`; +}; diff --git a/lexikon/src/shared/config/site.ts b/lexikon/src/shared/config/site.ts new file mode 100644 index 0000000..f4a59b8 --- /dev/null +++ b/lexikon/src/shared/config/site.ts @@ -0,0 +1,6 @@ +export type SiteConfig = typeof siteConfig; + +export const siteConfig = { + name: "Lexikon", + description: "A comprehensive resource for knowledge and learning.", +}; diff --git a/lexikon/src/shared/domain/course.ts b/lexikon/src/shared/domain/course.ts new file mode 100644 index 0000000..44295d1 --- /dev/null +++ b/lexikon/src/shared/domain/course.ts @@ -0,0 +1,13 @@ +import { Database } from "shared/types/db"; +import { StudyModule } from "./module"; + +/** + * Represents a course in the system using the generated database type + */ +export type Course = Database["library"]["Tables"]["courses"]["Row"] & { + is_user_enrolled: boolean; +}; + +export type CourseModule = Course & StudyModule & { + is_user_favorite: boolean; +} diff --git a/lexikon/src/shared/domain/librarian/index.d.ts b/lexikon/src/shared/domain/librarian/index.d.ts new file mode 100644 index 0000000..f7cbc84 --- /dev/null +++ b/lexikon/src/shared/domain/librarian/index.d.ts @@ -0,0 +1,13 @@ +export type { + TaskInfo, + DownloadRequestCourse, + SummaryResponse, + DownloadRequest, +} from './task'; +export type { + MoodleIndex, + CourseIndex, + TermIndex, + DegreeProgramIndex, + FileEntryIndex, +} from './moodleIndex'; diff --git a/lexikon/src/shared/domain/librarian/moodleIndex.ts b/lexikon/src/shared/domain/librarian/moodleIndex.ts new file mode 100644 index 0000000..7d098a9 --- /dev/null +++ b/lexikon/src/shared/domain/librarian/moodleIndex.ts @@ -0,0 +1,30 @@ +export interface FileEntryIndex { + id: string; + name: string; +} + +export interface CourseIndex { + id: string; + name: string; + activity_type?: string; + hero_image?: string; + content_ressource_id?: string; + files: FileEntryIndex[]; +} + +export interface TermIndex { + id: string; + name: string; + courses: CourseIndex[]; +} + +export interface DegreeProgramIndex { + id: string; + name: string; + semesters: TermIndex[]; +} + +export interface MoodleIndex { + degree_program: DegreeProgramIndex; + timestamp: string; +} diff --git a/lexikon/src/shared/domain/librarian/task.ts b/lexikon/src/shared/domain/librarian/task.ts new file mode 100644 index 0000000..7d5c590 --- /dev/null +++ b/lexikon/src/shared/domain/librarian/task.ts @@ -0,0 +1,17 @@ +import { MoodleIndex } from "./moodleIndex"; +import { TaskResult } from "./taskResult"; + +export interface TaskInfo { + task_id: string; + name?: string; + state: 'queued' | 'running' | 'success' | 'error'; + progress?: number; + detail?: string; + download_links?: string[]; + result?: TaskResult; +} + +export interface DownloadRequestCourse { + course_id: string; + term_id: string; +} diff --git a/lexikon/src/shared/domain/librarian/taskRequest.ts b/lexikon/src/shared/domain/librarian/taskRequest.ts new file mode 100644 index 0000000..e69de29 diff --git a/lexikon/src/shared/domain/librarian/taskResult.ts b/lexikon/src/shared/domain/librarian/taskResult.ts new file mode 100644 index 0000000..d2eeffa --- /dev/null +++ b/lexikon/src/shared/domain/librarian/taskResult.ts @@ -0,0 +1,16 @@ +import { MoodleIndex } from "./moodleIndex"; +import { DownloadRequestCourse } from "./task"; + +export interface DownloadResult { + courses: DownloadRequestCourse[]; +} + +export interface SummaryResult { + summary: string; +} + +export interface CrawlResult { + moodleIndex: MoodleIndex; +} + +export type TaskResult = DownloadResult | SummaryResult | CrawlResult; diff --git a/lexikon/src/shared/domain/module.ts b/lexikon/src/shared/domain/module.ts new file mode 100644 index 0000000..de88746 --- /dev/null +++ b/lexikon/src/shared/domain/module.ts @@ -0,0 +1,6 @@ +import { Database } from "shared/types/db"; + +/** + * Represents a module in the system using the generated database type + */ +export type StudyModule = Database["library"]["Tables"]["modules"]["Row"]; diff --git a/lexikon/src/shared/domain/summary.ts b/lexikon/src/shared/domain/summary.ts new file mode 100644 index 0000000..b17ae5f --- /dev/null +++ b/lexikon/src/shared/domain/summary.ts @@ -0,0 +1,6 @@ +import { Database } from "shared/types/db"; + +/** + * Represents a summary view using the generated database view type + */ +export type CourseSummary = Database["library"]["Views"]["summary"]["Row"]; diff --git a/lexikon/src/shared/domain/term.ts b/lexikon/src/shared/domain/term.ts new file mode 100644 index 0000000..85fc2f3 --- /dev/null +++ b/lexikon/src/shared/domain/term.ts @@ -0,0 +1,7 @@ +import { Database } from 'shared/types/db'; + + +/** + * Represents a term in the system using the generated database type + */ +export type Term = Database["library"]["Tables"]["semester"]["Row"]; diff --git a/lexikon/src/shared/provider/ResponsiveProvider.tsx b/lexikon/src/shared/provider/ResponsiveProvider.tsx new file mode 100644 index 0000000..eb37677 --- /dev/null +++ b/lexikon/src/shared/provider/ResponsiveProvider.tsx @@ -0,0 +1,31 @@ +"use client"; + +import type { ReactNode } from "react"; +import { createContext, useState } from "react"; +import { useEffect } from "react"; + +export const ResponsiveContext = createContext(undefined); + +export default function ResponsiveProvider({ + children, +}: { + children: ReactNode; +}) { + const [isMobile, setIsMobile] = useState(undefined); + + useEffect(() => { + if (typeof window === "undefined") return; + const handleResize = () => setIsMobile(window.innerWidth < 640); + + handleResize(); + window.addEventListener("resize", handleResize); + + return () => window.removeEventListener("resize", handleResize); + }, [setIsMobile]); + + return ( + + {children} + + ); +} diff --git a/lexikon/src/shared/sequenceValidator.ts b/lexikon/src/shared/sequenceValidator.ts new file mode 100644 index 0000000..4e1a1b9 --- /dev/null +++ b/lexikon/src/shared/sequenceValidator.ts @@ -0,0 +1,70 @@ +// src/shared/sequenceValidator.ts +// Validates and builds a sequence of workers based on input/output types + +export interface WorkerMeta { + name: string; + input: string; + output: string; +} + +export interface SequenceValidationResult { + isValid: boolean; + sequence: WorkerMeta[]; + error?: string; +} + +/** + * Validates and builds a sequence of workers such that each worker's output matches the next worker's input. + * Returns the ordered sequence if valid, or an error message if not. + */ +export function validateWorkerSequence(workers: WorkerMeta[]): SequenceValidationResult { + if (!workers.length) { + return { isValid: false, sequence: [], error: 'No workers provided.' }; + } + + // Map input/output for quick lookup + const inputMap = new Map(); + const outputMap = new Map(); + for (const worker of workers) { + inputMap.set(worker.input, worker); + outputMap.set(worker.output, worker); + } + + // Find the starting worker (whose input is not any other worker's output) + const startWorker = workers.find( + w => !outputMap.has(w.input) + ); + if (!startWorker) { + return { isValid: false, sequence: [], error: 'No valid starting worker found.' }; + } + + // Build the sequence + const sequence: WorkerMeta[] = [startWorker]; + let current = startWorker; + const used = new Set([current.name]); + + while (sequence.length < workers.length) { + const next = workers.find( + w => w.input === current.output && !used.has(w.name) + ); + if (!next) { + break; + } + sequence.push(next); + used.add(next.name); + current = next; + } + + if (sequence.length !== workers.length) { + return { isValid: false, sequence, error: 'Workers cannot be sequenced linearly.' }; + } + + // Validate the chain + for (let i = 0; i < sequence.length - 1; i++) { + if (sequence[i].output !== sequence[i + 1].input) { + return { isValid: false, sequence, error: 'Invalid sequence: output/input mismatch.' }; + } + } + + return { isValid: true, sequence }; +} diff --git a/lexikon/src/shared/styles/hero.ts b/lexikon/src/shared/styles/hero.ts new file mode 100644 index 0000000..be90355 --- /dev/null +++ b/lexikon/src/shared/styles/hero.ts @@ -0,0 +1,4 @@ +// hero.ts +import { heroui } from "@heroui/react"; +import { defaultTheme, lexikonTheme } from "./heroui-themes"; +export default heroui(lexikonTheme); diff --git a/lexikon/src/shared/styles/heroui-themes.ts b/lexikon/src/shared/styles/heroui-themes.ts new file mode 100644 index 0000000..3e8c0d1 --- /dev/null +++ b/lexikon/src/shared/styles/heroui-themes.ts @@ -0,0 +1,274 @@ +export const defaultTheme = { + themes: { + light: { + colors: { + default: { + 50: "#fafafa", + 100: "#f2f2f3", + 200: "#ebebec", + 300: "#e3e3e6", + 400: "#dcdcdf", + 500: "#d4d4d8", + 600: "#afafb2", + 700: "#8a8a8c", + 800: "#656567", + 900: "#404041", + foreground: "#000", + DEFAULT: "#d4d4d8", + }, + primary: { + 50: "#dfedfd", + 100: "#b3d4fa", + 200: "#86bbf7", + 300: "#59a1f4", + 400: "#2d88f1", + 500: "#006fee", + 600: "#005cc4", + 700: "#00489b", + 800: "#003571", + 900: "#002147", + foreground: "#fff", + DEFAULT: "#006fee", + }, + secondary: { + 50: "#eee4f8", + 100: "#d7bfef", + 200: "#bf99e5", + 300: "#a773db", + 400: "#904ed2", + 500: "#7828c8", + 600: "#6321a5", + 700: "#4e1a82", + 800: "#39135f", + 900: "#240c3c", + foreground: "#fff", + DEFAULT: "#7828c8", + }, + success: { + 50: "#e2f8ec", + 100: "#b9efd1", + 200: "#91e5b5", + 300: "#68dc9a", + 400: "#40d27f", + 500: "#17c964", + 600: "#13a653", + 700: "#0f8341", + 800: "#0b5f30", + 900: "#073c1e", + foreground: "#000", + DEFAULT: "#17c964", + }, + warning: { + 50: "#fef4e4", + 100: "#fce4bd", + 200: "#fad497", + 300: "#f9c571", + 400: "#f7b54a", + 500: "#f5a524", + 600: "#ca881e", + 700: "#9f6b17", + 800: "#744e11", + 900: "#4a320b", + foreground: "#000", + DEFAULT: "#f5a524", + }, + danger: { + 50: "#fee1eb", + 100: "#fbb8cf", + 200: "#f98eb3", + 300: "#f76598", + 400: "#f53b7c", + 500: "#f31260", + 600: "#c80f4f", + 700: "#9e0c3e", + 800: "#73092e", + 900: "#49051d", + foreground: "#000", + DEFAULT: "#f31260", + }, + background: "#ffffff", + foreground: "#000000", + content1: { + DEFAULT: "#ffffff", + foreground: "#000", + }, + content2: { + DEFAULT: "#f4f4f5", + foreground: "#000", + }, + content3: { + DEFAULT: "#e4e4e7", + foreground: "#000", + }, + content4: { + DEFAULT: "#d4d4d8", + foreground: "#000", + }, + focus: "#006FEE", + overlay: "#000000", + }, + }, + dark: { + colors: { + default: { + 50: "#0d0d0e", + 100: "#19191c", + 200: "#26262a", + 300: "#323238", + 400: "#3f3f46", + 500: "#65656b", + 600: "#8c8c90", + 700: "#b2b2b5", + 800: "#d9d9da", + 900: "#ffffff", + foreground: "#fff", + DEFAULT: "#3f3f46", + }, + primary: { + 50: "#002147", + 100: "#003571", + 200: "#00489b", + 300: "#005cc4", + 400: "#006fee", + 500: "#2d88f1", + 600: "#59a1f4", + 700: "#86bbf7", + 800: "#b3d4fa", + 900: "#dfedfd", + foreground: "#fff", + DEFAULT: "#006fee", + }, + secondary: { + 50: "#240c3c", + 100: "#39135f", + 200: "#4e1a82", + 300: "#6321a5", + 400: "#7828c8", + 500: "#904ed2", + 600: "#a773db", + 700: "#bf99e5", + 800: "#d7bfef", + 900: "#eee4f8", + foreground: "#fff", + DEFAULT: "#7828c8", + }, + success: { + 50: "#073c1e", + 100: "#0b5f30", + 200: "#0f8341", + 300: "#13a653", + 400: "#17c964", + 500: "#40d27f", + 600: "#68dc9a", + 700: "#91e5b5", + 800: "#b9efd1", + 900: "#e2f8ec", + foreground: "#000", + DEFAULT: "#17c964", + }, + warning: { + 50: "#4a320b", + 100: "#744e11", + 200: "#9f6b17", + 300: "#ca881e", + 400: "#f5a524", + 500: "#f7b54a", + 600: "#f9c571", + 700: "#fad497", + 800: "#fce4bd", + 900: "#fef4e4", + foreground: "#000", + DEFAULT: "#f5a524", + }, + danger: { + 50: "#49051d", + 100: "#73092e", + 200: "#9e0c3e", + 300: "#c80f4f", + 400: "#f31260", + 500: "#f53b7c", + 600: "#f76598", + 700: "#f98eb3", + 800: "#fbb8cf", + 900: "#fee1eb", + foreground: "#000", + DEFAULT: "#f31260", + }, + background: "#000000", + foreground: "#ffffff", + content1: { + DEFAULT: "#18181b", + foreground: "#fff", + }, + content2: { + DEFAULT: "#27272a", + foreground: "#fff", + }, + content3: { + DEFAULT: "#3f3f46", + foreground: "#fff", + }, + content4: { + DEFAULT: "#52525b", + foreground: "#fff", + }, + focus: "#006FEE", + overlay: "#ffffff", + }, + }, + }, + layout: { + disabledOpacity: "0.5", + }, +}; + +export const lexikonTheme = { + themes: { + light: { + colors: { + default: { + 50: "#F9F9F9", + 100: "#ECECEC", + 200: "#E3E3E3", + 300: "#B4B4B4", + 400: "#9B9B9B", + 500: "#676767", + 600: "#424242", + 700: "#2F2F2F", + 800: "#212121", + 900: "#171717", + 950: "#0D0D0D", + + DEFAULT: "#D4D4D8", + }, + background: "#FFFFFF", + foreground: "#000000", + }, + }, + dark: { + colors: { + default: { + 50: "#0D0D0D", + 100: "#171717", + 200: "#212121", + 300: "#2F2F2F", + 400: "#424242", + 500: "#676767", + 600: "#9B9B9B", + 700: "#B4B4B4", + 800: "#E3E3E3", + 900: "#ECECEC", + 950: "#F9F9F9", + + DEFAULT: "#2F2F2F", + }, + background: "#000000", + foreground: "#FFFFFF", + }, + }, + }, + + layout: { + disabledOpacity: "0.5", + }, +}; diff --git a/lexikon/src/shared/styles/variants/container.ts b/lexikon/src/shared/styles/variants/container.ts new file mode 100644 index 0000000..4817359 --- /dev/null +++ b/lexikon/src/shared/styles/variants/container.ts @@ -0,0 +1,60 @@ +import { tv } from 'tailwind-variants'; + +export const container = tv({ + base: "flex items-center justify-center shrink-0 overflow-hidden", + variants: { + size: { + full: "w-full h-full grow", + min: "w-min h-min", + }, + maxW: { + none: "max-w-none", + xs: "max-w-xs", + sm: "max-w-sm", + md: "max-w-md", + lg: "max-w-lg", + xl: "max-w-xl", + full: "max-w-full", + }, + dir: { + row: "flex-row", + col: "flex-col", + }, + bg: { + none: "bg-transparent", + overlay: "bg-foreground/10", + }, + p: { + none: "p-0", + xs: "p-1", + sm: "p-2", + md: "p-4", + lg: "p-6", + xl: "p-8", + }, + gap: { + none: "gap-0", + xs: "gap-1", + sm: "gap-2", + md: "gap-4", + lg: "gap-6", + xl: "gap-8", + }, + rounded: { + none: "rounded-none", + sm: "rounded-md", + md: "rounded-lg", + lg: "rounded-xl", + xl: "rounded-2xl", + } + }, + defaultVariants: { + size: 'full', + maxW: 'none', + dir: 'row', + bg: 'none', + p: 'none', + gap: 'md', + rounded: 'none', + } +}) diff --git a/lexikon/src/shared/styles/variants/index.ts b/lexikon/src/shared/styles/variants/index.ts new file mode 100644 index 0000000..c5b5e8f --- /dev/null +++ b/lexikon/src/shared/styles/variants/index.ts @@ -0,0 +1 @@ +export * from "./container"; diff --git a/lexikon/src/shared/styles/variants/title.ts b/lexikon/src/shared/styles/variants/title.ts new file mode 100644 index 0000000..04b1f22 --- /dev/null +++ b/lexikon/src/shared/styles/variants/title.ts @@ -0,0 +1,53 @@ +import { tv } from "tailwind-variants"; + +export const title = tv({ + base: "tracking-tight inline font-semibold", + variants: { + color: { + violet: "from-[#FF1CF7] to-[#b249f8]", + yellow: "from-[#FF705B] to-[#FFB457]", + blue: "from-[#5EA2EF] to-[#0072F5]", + cyan: "from-[#00b7fa] to-[#01cfea]", + green: "from-[#6FEE8D] to-[#17c964]", + pink: "from-[#FF72E1] to-[#F54C7A]", + foreground: "dark:from-[#FFFFFF] dark:to-[#4B4B4B]", + }, + size: { + sm: "text-3xl lg:text-4xl", + md: "text-[2.3rem] lg:text-5xl leading-9", + lg: "text-4xl lg:text-6xl", + }, + fullWidth: { + true: "w-full block", + }, + }, + defaultVariants: { + size: "md", + }, + compoundVariants: [ + { + color: [ + "violet", + "yellow", + "blue", + "cyan", + "green", + "pink", + "foreground", + ], + class: "bg-clip-text text-transparent bg-linear-to-b", + }, + ], +}); + +export const subtitle = tv({ + base: "w-full md:w-1/2 my-2 text-lg lg:text-xl text-default-600 block max-w-full", + variants: { + fullWidth: { + true: "w-full!", + }, + }, + defaultVariants: { + fullWidth: true, + }, +}); diff --git a/lexikon/src/shared/tests/.gitkeep b/lexikon/src/shared/tests/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/lexikon/src/shared/types/db/database.types.ts b/lexikon/src/shared/types/db/database.types.ts new file mode 100644 index 0000000..1e8e405 --- /dev/null +++ b/lexikon/src/shared/types/db/database.types.ts @@ -0,0 +1,1607 @@ +export type Json = + | string + | number + | boolean + | null + | { [key: string]: Json | undefined } + | Json[]; + +export type Database = { + library: { + Tables: { + academic_season: { + Row: { + created_at: string; + partitioning_id: number; + prefix: string; + season_id: number; + season_name: string; + }; + Insert: { + created_at?: string; + partitioning_id?: number; + prefix: string; + season_id: number; + season_name: string; + }; + Update: { + created_at?: string; + partitioning_id?: number; + prefix?: string; + season_id?: number; + season_name?: string; + }; + Relationships: []; + }; + academic_season_p0: { + Row: { + created_at: string; + partitioning_id: number; + prefix: string; + season_id: number; + season_name: string; + }; + Insert: { + created_at?: string; + partitioning_id?: number; + prefix: string; + season_id: number; + season_name: string; + }; + Update: { + created_at?: string; + partitioning_id?: number; + prefix?: string; + season_id?: number; + season_name?: string; + }; + Relationships: []; + }; + cohort: { + Row: { + cohort_code: string; + cohort_description: string | null; + cohort_id: number; + cohort_name: string; + cohort_uuid: string; + cohort_year: string; + created_at: string; + partitioning_id: number; + }; + Insert: { + cohort_code: string; + cohort_description?: string | null; + cohort_id?: number; + cohort_name: string; + cohort_uuid?: string; + cohort_year: string; + created_at?: string; + partitioning_id?: number; + }; + Update: { + cohort_code?: string; + cohort_description?: string | null; + cohort_id?: number; + cohort_name?: string; + cohort_uuid?: string; + cohort_year?: string; + created_at?: string; + partitioning_id?: number; + }; + Relationships: []; + }; + cohort_p0: { + Row: { + cohort_code: string; + cohort_description: string | null; + cohort_id: number; + cohort_name: string; + cohort_uuid: string; + cohort_year: string; + created_at: string; + partitioning_id: number; + }; + Insert: { + cohort_code: string; + cohort_description?: string | null; + cohort_id: number; + cohort_name: string; + cohort_uuid?: string; + cohort_year: string; + created_at?: string; + partitioning_id?: number; + }; + Update: { + cohort_code?: string; + cohort_description?: string | null; + cohort_id?: number; + cohort_name?: string; + cohort_uuid?: string; + cohort_year?: string; + created_at?: string; + partitioning_id?: number; + }; + Relationships: []; + }; + cohort_semesters: { + Row: { + cohort_id: number; + cohort_semester_id: number; + created_at: string; + partitioning_id: number; + semester_id: number; + }; + Insert: { + cohort_id?: number; + cohort_semester_id?: number; + created_at?: string; + partitioning_id?: number; + semester_id?: number; + }; + Update: { + cohort_id?: number; + cohort_semester_id?: number; + created_at?: string; + partitioning_id?: number; + semester_id?: number; + }; + Relationships: [ + { + foreignKeyName: "cohort_semesters_cohort_id_fkey"; + columns: ["cohort_id", "partitioning_id"]; + isOneToOne: false; + referencedRelation: "cohort"; + referencedColumns: ["cohort_id", "partitioning_id"]; + }, + { + foreignKeyName: "cohort_semesters_semester_id_fkey"; + columns: ["semester_id", "partitioning_id"]; + isOneToOne: false; + referencedRelation: "semester"; + referencedColumns: ["semester_id", "partitioning_id"]; + }, + ]; + }; + cohort_semesters_p0: { + Row: { + cohort_id: number; + cohort_semester_id: number; + created_at: string; + partitioning_id: number; + semester_id: number; + }; + Insert: { + cohort_id: number; + cohort_semester_id: number; + created_at?: string; + partitioning_id?: number; + semester_id: number; + }; + Update: { + cohort_id?: number; + cohort_semester_id?: number; + created_at?: string; + partitioning_id?: number; + semester_id?: number; + }; + Relationships: []; + }; + courses: { + Row: { + content_ressource_id: number | null; + content_ressource_valid: boolean; + course_id: number; + created_at: string; + hero_image: string | null; + module_id: number; + partitioning_id: number; + semester_id: number; + }; + Insert: { + content_ressource_id?: number | null; + content_ressource_valid?: boolean; + course_id: number; + created_at?: string; + hero_image?: string | null; + module_id?: number; + partitioning_id?: number; + semester_id?: number; + }; + Update: { + content_ressource_id?: number | null; + content_ressource_valid?: boolean; + course_id?: number; + created_at?: string; + hero_image?: string | null; + module_id?: number; + partitioning_id?: number; + semester_id?: number; + }; + Relationships: [ + { + foreignKeyName: "courses_module_id_fkey"; + columns: ["module_id", "partitioning_id"]; + isOneToOne: false; + referencedRelation: "modules"; + referencedColumns: ["module_id", "partitioning_id"]; + }, + { + foreignKeyName: "courses_semester_id_fkey"; + columns: ["semester_id", "partitioning_id"]; + isOneToOne: false; + referencedRelation: "semester"; + referencedColumns: ["semester_id", "partitioning_id"]; + }, + ]; + }; + courses_buckets: { + Row: { + bucket_id: string; + course_id: number; + courses_buckets_id: number; + created_at: string; + partitioning_id: number; + }; + Insert: { + bucket_id: string; + course_id?: number; + courses_buckets_id?: number; + created_at?: string; + partitioning_id?: number; + }; + Update: { + bucket_id?: string; + course_id?: number; + courses_buckets_id?: number; + created_at?: string; + partitioning_id?: number; + }; + Relationships: [ + { + foreignKeyName: "courses_buckets_bucket_id_fkey"; + columns: ["bucket_id"]; + isOneToOne: false; + referencedRelation: "objects_from_users"; + referencedColumns: ["bucket_id"]; + }, + { + foreignKeyName: "courses_buckets_course_id_fkey"; + columns: ["course_id", "partitioning_id"]; + isOneToOne: false; + referencedRelation: "courses"; + referencedColumns: ["course_id", "partitioning_id"]; + }, + ]; + }; + courses_buckets_p0: { + Row: { + bucket_id: string; + course_id: number; + courses_buckets_id: number; + created_at: string; + partitioning_id: number; + }; + Insert: { + bucket_id: string; + course_id: number; + courses_buckets_id: number; + created_at?: string; + partitioning_id?: number; + }; + Update: { + bucket_id?: string; + course_id?: number; + courses_buckets_id?: number; + created_at?: string; + partitioning_id?: number; + }; + Relationships: []; + }; + courses_files: { + Row: { + course_id: number; + courses_files_id: number; + created_at: string; + file_id: number; + partitioning_id: number; + }; + Insert: { + course_id: number; + courses_files_id?: number; + created_at?: string; + file_id: number; + partitioning_id: number; + }; + Update: { + course_id?: number; + courses_files_id?: number; + created_at?: string; + file_id?: number; + partitioning_id?: number; + }; + Relationships: [ + { + foreignKeyName: "courses_files_course_id_fkey"; + columns: ["course_id", "partitioning_id"]; + isOneToOne: true; + referencedRelation: "courses"; + referencedColumns: ["course_id", "partitioning_id"]; + }, + { + foreignKeyName: "courses_files_file_id_fkey"; + columns: ["file_id", "partitioning_id"]; + isOneToOne: false; + referencedRelation: "filelinks"; + referencedColumns: ["file_id", "partitioning_id"]; + }, + ]; + }; + courses_files_p0: { + Row: { + course_id: number; + courses_files_id: number; + created_at: string; + file_id: number; + partitioning_id: number; + }; + Insert: { + course_id: number; + courses_files_id: number; + created_at?: string; + file_id: number; + partitioning_id: number; + }; + Update: { + course_id?: number; + courses_files_id?: number; + created_at?: string; + file_id?: number; + partitioning_id?: number; + }; + Relationships: []; + }; + courses_p0: { + Row: { + content_ressource_id: number | null; + content_ressource_valid: boolean; + course_id: number; + created_at: string; + hero_image: string | null; + module_id: number; + partitioning_id: number; + semester_id: number; + }; + Insert: { + content_ressource_id?: number | null; + content_ressource_valid?: boolean; + course_id: number; + created_at?: string; + hero_image?: string | null; + module_id: number; + partitioning_id?: number; + semester_id: number; + }; + Update: { + content_ressource_id?: number | null; + content_ressource_valid?: boolean; + course_id?: number; + created_at?: string; + hero_image?: string | null; + module_id?: number; + partitioning_id?: number; + semester_id?: number; + }; + Relationships: []; + }; + courses_users: { + Row: { + course_id: number; + courses_users_id: number; + created_at: string; + partitioning_id: number; + user_uuid: string; + }; + Insert: { + course_id: number; + courses_users_id?: number; + created_at?: string; + partitioning_id?: number; + user_uuid: string; + }; + Update: { + course_id?: number; + courses_users_id?: number; + created_at?: string; + partitioning_id?: number; + user_uuid?: string; + }; + Relationships: [ + { + foreignKeyName: "courses_users_course_id_fkey"; + columns: ["course_id", "partitioning_id"]; + isOneToOne: false; + referencedRelation: "courses"; + referencedColumns: ["course_id", "partitioning_id"]; + }, + { + foreignKeyName: "courses_users_user_uuid_fkey"; + columns: ["user_uuid"]; + isOneToOne: false; + referencedRelation: "links_from_users"; + referencedColumns: ["user_uuid"]; + }, + { + foreignKeyName: "courses_users_user_uuid_fkey"; + columns: ["user_uuid"]; + isOneToOne: false; + referencedRelation: "objects_from_users"; + referencedColumns: ["user_uuid"]; + }, + ]; + }; + courses_users_p0: { + Row: { + course_id: number; + courses_users_id: number; + created_at: string; + partitioning_id: number; + user_uuid: string; + }; + Insert: { + course_id: number; + courses_users_id: number; + created_at?: string; + partitioning_id?: number; + user_uuid: string; + }; + Update: { + course_id?: number; + courses_users_id?: number; + created_at?: string; + partitioning_id?: number; + user_uuid?: string; + }; + Relationships: []; + }; + degree_programs: { + Row: { + created_at: string; + partitioning_id: number; + program_code: string | null; + program_description: string | null; + program_id: number; + program_name: string; + program_uuid: string; + }; + Insert: { + created_at?: string; + partitioning_id?: number; + program_code?: string | null; + program_description?: string | null; + program_id?: number; + program_name: string; + program_uuid?: string; + }; + Update: { + created_at?: string; + partitioning_id?: number; + program_code?: string | null; + program_description?: string | null; + program_id?: number; + program_name?: string; + program_uuid?: string; + }; + Relationships: []; + }; + degree_programs_p0: { + Row: { + created_at: string; + partitioning_id: number; + program_code: string | null; + program_description: string | null; + program_id: number; + program_name: string; + program_uuid: string; + }; + Insert: { + created_at?: string; + partitioning_id?: number; + program_code?: string | null; + program_description?: string | null; + program_id: number; + program_name: string; + program_uuid?: string; + }; + Update: { + created_at?: string; + partitioning_id?: number; + program_code?: string | null; + program_description?: string | null; + program_id?: number; + program_name?: string; + program_uuid?: string; + }; + Relationships: []; + }; + file_labels: { + Row: { + created_at: string; + file_id: number; + file_labels: Json; + file_labels_id: number; + partitioning_id: number; + }; + Insert: { + created_at?: string; + file_id: number; + file_labels: Json; + file_labels_id: number; + partitioning_id?: number; + }; + Update: { + created_at?: string; + file_id?: number; + file_labels?: Json; + file_labels_id?: number; + partitioning_id?: number; + }; + Relationships: [ + { + foreignKeyName: "file_labels_file_id_fkey"; + columns: ["file_id", "partitioning_id"]; + isOneToOne: true; + referencedRelation: "filelinks"; + referencedColumns: ["file_id", "partitioning_id"]; + }, + ]; + }; + file_labels_p0: { + Row: { + created_at: string; + file_id: number; + file_labels: Json; + file_labels_id: number; + partitioning_id: number; + }; + Insert: { + created_at?: string; + file_id: number; + file_labels: Json; + file_labels_id: number; + partitioning_id?: number; + }; + Update: { + created_at?: string; + file_id?: number; + file_labels?: Json; + file_labels_id?: number; + partitioning_id?: number; + }; + Relationships: []; + }; + filelinks: { + Row: { + created_at: string; + file_id: number; + filename: string; + last_modification: string; + location_id: number; + partitioning_id: number; + }; + Insert: { + created_at?: string; + file_id?: number; + filename: string; + last_modification: string; + location_id: number; + partitioning_id?: number; + }; + Update: { + created_at?: string; + file_id?: number; + filename?: string; + last_modification?: string; + location_id?: number; + partitioning_id?: number; + }; + Relationships: [ + { + foreignKeyName: "filelinks_location_id_fkey"; + columns: ["location_id"]; + isOneToOne: false; + referencedRelation: "links_from_users"; + referencedColumns: ["location_type"]; + }, + { + foreignKeyName: "filelinks_location_id_fkey"; + columns: ["location_id"]; + isOneToOne: false; + referencedRelation: "location_types"; + referencedColumns: ["location_id"]; + }, + ]; + }; + filelinks_p0: { + Row: { + created_at: string; + file_id: number; + filename: string; + last_modification: string; + location_id: number; + partitioning_id: number; + }; + Insert: { + created_at?: string; + file_id: number; + filename: string; + last_modification: string; + location_id: number; + partitioning_id?: number; + }; + Update: { + created_at?: string; + file_id?: number; + filename?: string; + last_modification?: string; + location_id?: number; + partitioning_id?: number; + }; + Relationships: []; + }; + location_types: { + Row: { + created_at: string; + location: string; + location_id: number; + }; + Insert: { + created_at?: string; + location: string; + location_id: number; + }; + Update: { + created_at?: string; + location?: string; + location_id?: number; + }; + Relationships: []; + }; + module_favorites: { + Row: { + created_at: string; + module_favorites_id: number; + module_id: number; + partitioning_id: number; + updated_at: string; + user_uuid: string; + }; + Insert: { + created_at?: string; + module_favorites_id?: number; + module_id: number; + partitioning_id: number; + updated_at?: string; + user_uuid: string; + }; + Update: { + created_at?: string; + module_favorites_id?: number; + module_id?: number; + partitioning_id?: number; + updated_at?: string; + user_uuid?: string; + }; + Relationships: [ + { + foreignKeyName: "module_favorites_module_id_partitioning_id_fkey"; + columns: ["module_id", "partitioning_id"]; + isOneToOne: false; + referencedRelation: "modules"; + referencedColumns: ["module_id", "partitioning_id"]; + }, + { + foreignKeyName: "module_favorites_user_uuid_fkey"; + columns: ["user_uuid"]; + isOneToOne: false; + referencedRelation: "links_from_users"; + referencedColumns: ["user_uuid"]; + }, + { + foreignKeyName: "module_favorites_user_uuid_fkey"; + columns: ["user_uuid"]; + isOneToOne: false; + referencedRelation: "objects_from_users"; + referencedColumns: ["user_uuid"]; + }, + ]; + }; + module_favorites_p0: { + Row: { + created_at: string; + module_favorites_id: number; + module_id: number; + partitioning_id: number; + updated_at: string; + user_uuid: string; + }; + Insert: { + created_at?: string; + module_favorites_id: number; + module_id: number; + partitioning_id: number; + updated_at?: string; + user_uuid: string; + }; + Update: { + created_at?: string; + module_favorites_id?: number; + module_id?: number; + partitioning_id?: number; + updated_at?: string; + user_uuid?: string; + }; + Relationships: []; + }; + module_name_escaping: { + Row: { + correct_name: string; + created_at: string; + incorrect_name: string; + module_name_escaping_id: number; + updated_at: string; + }; + Insert: { + correct_name: string; + created_at?: string; + incorrect_name: string; + module_name_escaping_id?: number; + updated_at?: string; + }; + Update: { + correct_name?: string; + created_at?: string; + incorrect_name?: string; + module_name_escaping_id?: number; + updated_at?: string; + }; + Relationships: []; + }; + modules: { + Row: { + created_at: string; + is_placeholder_module: boolean; + module_code: string; + module_description: string | null; + module_id: number; + module_name: string; + module_uuid: string; + partitioning_id: number; + }; + Insert: { + created_at?: string; + is_placeholder_module?: boolean; + module_code: string; + module_description?: string | null; + module_id?: number; + module_name: string; + module_uuid?: string; + partitioning_id?: number; + }; + Update: { + created_at?: string; + is_placeholder_module?: boolean; + module_code?: string; + module_description?: string | null; + module_id?: number; + module_name?: string; + module_uuid?: string; + partitioning_id?: number; + }; + Relationships: []; + }; + modules_p0: { + Row: { + created_at: string; + is_placeholder_module: boolean; + module_code: string; + module_description: string | null; + module_id: number; + module_name: string; + module_uuid: string; + partitioning_id: number; + }; + Insert: { + created_at?: string; + is_placeholder_module?: boolean; + module_code: string; + module_description?: string | null; + module_id: number; + module_name: string; + module_uuid?: string; + partitioning_id?: number; + }; + Update: { + created_at?: string; + is_placeholder_module?: boolean; + module_code?: string; + module_description?: string | null; + module_id?: number; + module_name?: string; + module_uuid?: string; + partitioning_id?: number; + }; + Relationships: []; + }; + object_labels: { + Row: { + created_at: string; + object_id: string | null; + object_label_id: number; + object_labels: Json; + partitioning_id: number; + }; + Insert: { + created_at?: string; + object_id?: string | null; + object_label_id?: number; + object_labels: Json; + partitioning_id?: number; + }; + Update: { + created_at?: string; + object_id?: string | null; + object_label_id?: number; + object_labels?: Json; + partitioning_id?: number; + }; + Relationships: [ + { + foreignKeyName: "object_labels_object_id_fkey"; + columns: ["object_id"]; + isOneToOne: false; + referencedRelation: "objects_from_users"; + referencedColumns: ["object_id"]; + }, + ]; + }; + object_labels_p0: { + Row: { + created_at: string; + object_id: string | null; + object_label_id: number; + object_labels: Json; + partitioning_id: number; + }; + Insert: { + created_at?: string; + object_id?: string | null; + object_label_id: number; + object_labels: Json; + partitioning_id?: number; + }; + Update: { + created_at?: string; + object_id?: string | null; + object_label_id?: number; + object_labels?: Json; + partitioning_id?: number; + }; + Relationships: []; + }; + partitioning_master: { + Row: { + created_at: string; + partitioning_description: string | null; + partitioning_id: number; + partitioning_name: string; + valid_from: string | null; + }; + Insert: { + created_at?: string; + partitioning_description?: string | null; + partitioning_id: number; + partitioning_name: string; + valid_from?: string | null; + }; + Update: { + created_at?: string; + partitioning_description?: string | null; + partitioning_id?: number; + partitioning_name?: string; + valid_from?: string | null; + }; + Relationships: []; + }; + programs_modules: { + Row: { + created_at: string; + module_id: number; + partitioning_id: number; + program_id: number; + programs_modules_id: number; + }; + Insert: { + created_at?: string; + module_id?: number; + partitioning_id?: number; + program_id?: number; + programs_modules_id?: number; + }; + Update: { + created_at?: string; + module_id?: number; + partitioning_id?: number; + program_id?: number; + programs_modules_id?: number; + }; + Relationships: [ + { + foreignKeyName: "programs_modules_module_id_fkey"; + columns: ["module_id", "partitioning_id"]; + isOneToOne: false; + referencedRelation: "modules"; + referencedColumns: ["module_id", "partitioning_id"]; + }, + { + foreignKeyName: "programs_modules_program_id_fkey"; + columns: ["program_id", "partitioning_id"]; + isOneToOne: false; + referencedRelation: "degree_programs"; + referencedColumns: ["program_id", "partitioning_id"]; + }, + ]; + }; + programs_modules_p0: { + Row: { + created_at: string; + module_id: number; + partitioning_id: number; + program_id: number; + programs_modules_id: number; + }; + Insert: { + created_at?: string; + module_id: number; + partitioning_id?: number; + program_id: number; + programs_modules_id: number; + }; + Update: { + created_at?: string; + module_id?: number; + partitioning_id?: number; + program_id?: number; + programs_modules_id?: number; + }; + Relationships: []; + }; + semester: { + Row: { + created_at: string; + end_date: string | null; + partitioning_id: number; + season_id: number; + semester_code: string; + semester_id: number; + semester_uuid: string; + start_date: string | null; + }; + Insert: { + created_at?: string; + end_date?: string | null; + partitioning_id?: number; + season_id: number; + semester_code: string; + semester_id?: number; + semester_uuid?: string; + start_date?: string | null; + }; + Update: { + created_at?: string; + end_date?: string | null; + partitioning_id?: number; + season_id?: number; + semester_code?: string; + semester_id?: number; + semester_uuid?: string; + start_date?: string | null; + }; + Relationships: [ + { + foreignKeyName: "semester_season_id_fkey"; + columns: ["season_id", "partitioning_id"]; + isOneToOne: false; + referencedRelation: "academic_season"; + referencedColumns: ["season_id", "partitioning_id"]; + }, + ]; + }; + semester_p0: { + Row: { + created_at: string; + end_date: string | null; + partitioning_id: number; + season_id: number; + semester_code: string; + semester_id: number; + semester_uuid: string; + start_date: string | null; + }; + Insert: { + created_at?: string; + end_date?: string | null; + partitioning_id?: number; + season_id: number; + semester_code: string; + semester_id: number; + semester_uuid?: string; + start_date?: string | null; + }; + Update: { + created_at?: string; + end_date?: string | null; + partitioning_id?: number; + season_id?: number; + semester_code?: string; + semester_id?: number; + semester_uuid?: string; + start_date?: string | null; + }; + Relationships: []; + }; + summaries: { + Row: { + chapter: string; + course_id: number; + created_at: string; + object_id: string; + partitioning_id: number; + summary_id: number; + summary_index: number; + updated_at: string; + }; + Insert: { + chapter: string; + course_id: number; + created_at?: string; + object_id: string; + partitioning_id?: number; + summary_id?: number; + summary_index?: number; + updated_at?: string; + }; + Update: { + chapter?: string; + course_id?: number; + created_at?: string; + object_id?: string; + partitioning_id?: number; + summary_id?: number; + summary_index?: number; + updated_at?: string; + }; + Relationships: [ + { + foreignKeyName: "summaries_object_id_fkey"; + columns: ["object_id"]; + isOneToOne: false; + referencedRelation: "objects_from_users"; + referencedColumns: ["object_id"]; + }, + ]; + }; + summaries_p0: { + Row: { + chapter: string; + course_id: number; + created_at: string; + object_id: string; + partitioning_id: number; + summary_id: number; + summary_index: number; + updated_at: string; + }; + Insert: { + chapter: string; + course_id: number; + created_at?: string; + object_id: string; + partitioning_id?: number; + summary_id: number; + summary_index?: number; + updated_at?: string; + }; + Update: { + chapter?: string; + course_id?: number; + created_at?: string; + object_id?: string; + partitioning_id?: number; + summary_id?: number; + summary_index?: number; + updated_at?: string; + }; + Relationships: []; + }; + user_settings: { + Row: { + created_at: string; + location_id: number; + user_id: string; + user_settings: Json | null; + }; + Insert: { + created_at?: string; + location_id?: number; + user_id: string; + user_settings?: Json | null; + }; + Update: { + created_at?: string; + location_id?: number; + user_id?: string; + user_settings?: Json | null; + }; + Relationships: [ + { + foreignKeyName: "user_settings_location_id_fkey"; + columns: ["location_id"]; + isOneToOne: false; + referencedRelation: "links_from_users"; + referencedColumns: ["location_type"]; + }, + { + foreignKeyName: "user_settings_location_id_fkey"; + columns: ["location_id"]; + isOneToOne: false; + referencedRelation: "location_types"; + referencedColumns: ["location_id"]; + }, + { + foreignKeyName: "user_settings_user_id_fkey"; + columns: ["user_id"]; + isOneToOne: true; + referencedRelation: "links_from_users"; + referencedColumns: ["user_uuid"]; + }, + { + foreignKeyName: "user_settings_user_id_fkey"; + columns: ["user_id"]; + isOneToOne: true; + referencedRelation: "objects_from_users"; + referencedColumns: ["user_uuid"]; + }, + ]; + }; + }; + Views: { + courses_modules: { + Row: { + content_ressource_id: number | null; + course_id: number | null; + hero_image: string | null; + is_user_favorite: boolean | null; + module_code: string | null; + module_description: string | null; + module_id: number | null; + module_name: string | null; + user_uuid: string | null; + }; + Relationships: [ + { + foreignKeyName: "module_favorites_user_uuid_fkey"; + columns: ["user_uuid"]; + isOneToOne: false; + referencedRelation: "links_from_users"; + referencedColumns: ["user_uuid"]; + }, + { + foreignKeyName: "module_favorites_user_uuid_fkey"; + columns: ["user_uuid"]; + isOneToOne: false; + referencedRelation: "objects_from_users"; + referencedColumns: ["user_uuid"]; + }, + ]; + }; + links_from_users: { + Row: { + course_id: number | null; + degree_program_id: number | null; + file_id: number | null; + filename: string | null; + location_type: number | null; + semester_code: string | null; + semester_id: number | null; + user_uuid: string | null; + }; + Relationships: []; + }; + modules_from_degree_programs: { + Row: { + module_code: string | null; + module_id: number | null; + module_name: string | null; + program_id: number | null; + }; + Relationships: []; + }; + objects_from_users: { + Row: { + bucket_id: string | null; + module_id: number | null; + object_id: string | null; + object_path: string | null; + semester_code: string | null; + semester_id: number | null; + user_uuid: string | null; + }; + Relationships: []; + }; + summary: { + Row: { + bucket_id: string | null; + chapter: string | null; + course_id: number | null; + object_id: string | null; + object_path: string | null; + summary_id: number | null; + summary_index: number | null; + }; + Relationships: [ + { + foreignKeyName: "objects_bucketId_fkey"; + columns: ["bucket_id"]; + isOneToOne: false; + referencedRelation: "objects_from_users"; + referencedColumns: ["bucket_id"]; + }, + { + foreignKeyName: "summaries_object_id_fkey"; + columns: ["object_id"]; + isOneToOne: false; + referencedRelation: "objects_from_users"; + referencedColumns: ["object_id"]; + }, + ]; + }; + }; + Functions: { + create_module_summary: { + Args: { + p_module_id: number; + p_semester_id: number; + p_bucket_id: string; + p_object_path: string; + }; + Returns: boolean; + }; + delete_summary: { + Args: { + p_summary_id: number; + }; + Returns: Json; + }; + get_courses_modules: { + Args: { + p_module_id: number; + }; + Returns: { + module_code: string; + module_name: string; + module_description: string; + content_ressource_id: string; + hero_image: string; + }[]; + }; + get_module_id: { + Args: { + course_string: string; + }; + Returns: number; + }; + get_modules: { + Args: { + p_program_id?: number; + }; + Returns: { + module_id: number; + module_code: string; + module_name: string; + }[]; + }; + get_modules_with_semesters: { + Args: Record; + Returns: { + module_id: number; + module_code: string; + module_name: string; + module_description: string; + is_user_favorite: boolean; + }[]; + }; + get_moodle_index_by_user: { + Args: { + p_program_id: number; + p_user_uuid: string; + }; + Returns: string; + }; + get_object_id: { + Args: { + p_bucket: string; + p_destination_filepath: string; + }; + Returns: string; + }; + get_semesters_per_module: { + Args: { + p_module_id: number; + }; + Returns: { + semester_id: number; + semester_code: string; + }[]; + }; + get_summaries: { + Args: { + p_course_id: number; + }; + Returns: { + summary_id: number; + summary_index: number; + chapter: string; + object_path: string; + bucket_id: string; + }[]; + }; + get_summaries_by_module_semester: { + Args: { + p_module_id: number; + p_semester_id: number; + }; + Returns: { + summary_id: number; + summary_index: number; + chapter: string; + object_path: string; + bucket_id: string; + }[]; + }; + get_summary_object: { + Args: { + p_semester_id?: number; + p_module_id?: number; + }; + Returns: { + bucket_id: string; + object_path: string; + }[]; + }; + get_zombie_summary_objects: { + Args: { + p_summary_directores: string; + p_summary_filename_prefix: string; + p_summary_bucket_id: string; + }; + Returns: { + object_id: string; + object_path: string; + bucket_id: string; + }[]; + }; + refresh_courses_modules: { + Args: Record; + Returns: undefined; + }; + refresh_links_from_users: { + Args: Record; + Returns: undefined; + }; + refresh_modules_from_degree_programs: { + Args: Record; + Returns: undefined; + }; + refresh_objects_from_users: { + Args: Record; + Returns: undefined; + }; + refresh_summary: { + Args: Record; + Returns: undefined; + }; + rename_chapter: { + Args: { + p_new_chapter: string; + p_summary_id: number; + }; + Returns: string; + }; + upsert_course: { + Args: { + p_course_id: number; + p_course_name: string; + p_semester_id: number; + p_semester_name: string; + p_program_id: number; + p_hero_image?: string; + p_content_ressource_id?: number; + p_user_uuid?: string; + }; + Returns: string; + }; + upsert_courses_user: { + Args: { + p_course_id: number; + p_user_uuid: string; + }; + Returns: string; + }; + upsert_degree_program: { + Args: { + p_program_id: number; + p_program_name: string; + }; + Returns: undefined; + }; + upsert_file_and_course_file: { + Args: { + p_file_id: number; + p_filename: string; + p_last_modification: string; + p_uuid: string; + p_course_id: number; + }; + Returns: string; + }; + upsert_module: { + Args: { + p_module_uuid: string; + p_module_code: string; + p_module_name: string; + p_module_description?: string; + }; + Returns: undefined; + }; + upsert_module_favorites: { + Args: { + p_module_id: number; + p_favorite_state: boolean; + }; + Returns: string; + }; + upsert_programs_modules: { + Args: { + p_program_id: number; + p_module_id: number; + }; + Returns: string; + }; + upsert_semester: { + Args: { + p_semester_id: number; + p_semester_name: string; + }; + Returns: undefined; + }; + upsert_summary_chapter: { + Args: { + p_filename: string; + p_chapter: string; + p_object_uuid: string; + p_course_id: number; + }; + Returns: { + summary_id: number; + summary_index: number; + chapter: string; + object_path: string; + bucket_id: string; + }[]; + }; + }; + Enums: { + [_ in never]: never; + }; + CompositeTypes: { + [_ in never]: never; + }; + }; +}; + +type PublicSchema = Database[Extract]; + +export type Tables< + PublicTableNameOrOptions extends + | keyof (PublicSchema["Tables"] & PublicSchema["Views"]) + | { schema: keyof Database }, + TableName extends PublicTableNameOrOptions extends { schema: keyof Database } + ? keyof (Database[PublicTableNameOrOptions["schema"]]["Tables"] & + Database[PublicTableNameOrOptions["schema"]]["Views"]) + : never = never, +> = PublicTableNameOrOptions extends { schema: keyof Database } + ? (Database[PublicTableNameOrOptions["schema"]]["Tables"] & + Database[PublicTableNameOrOptions["schema"]]["Views"])[TableName] extends { + Row: infer R; + } + ? R + : never + : PublicTableNameOrOptions extends keyof (PublicSchema["Tables"] & + PublicSchema["Views"]) + ? (PublicSchema["Tables"] & + PublicSchema["Views"])[PublicTableNameOrOptions] extends { + Row: infer R; + } + ? R + : never + : never; + +export type TablesInsert< + PublicTableNameOrOptions extends + | keyof PublicSchema["Tables"] + | { schema: keyof Database }, + TableName extends PublicTableNameOrOptions extends { schema: keyof Database } + ? keyof Database[PublicTableNameOrOptions["schema"]]["Tables"] + : never = never, +> = PublicTableNameOrOptions extends { schema: keyof Database } + ? Database[PublicTableNameOrOptions["schema"]]["Tables"][TableName] extends { + Insert: infer I; + } + ? I + : never + : PublicTableNameOrOptions extends keyof PublicSchema["Tables"] + ? PublicSchema["Tables"][PublicTableNameOrOptions] extends { + Insert: infer I; + } + ? I + : never + : never; + +export type TablesUpdate< + PublicTableNameOrOptions extends + | keyof PublicSchema["Tables"] + | { schema: keyof Database }, + TableName extends PublicTableNameOrOptions extends { schema: keyof Database } + ? keyof Database[PublicTableNameOrOptions["schema"]]["Tables"] + : never = never, +> = PublicTableNameOrOptions extends { schema: keyof Database } + ? Database[PublicTableNameOrOptions["schema"]]["Tables"][TableName] extends { + Update: infer U; + } + ? U + : never + : PublicTableNameOrOptions extends keyof PublicSchema["Tables"] + ? PublicSchema["Tables"][PublicTableNameOrOptions] extends { + Update: infer U; + } + ? U + : never + : never; + +export type Enums< + PublicEnumNameOrOptions extends + | keyof PublicSchema["Enums"] + | { schema: keyof Database }, + EnumName extends PublicEnumNameOrOptions extends { schema: keyof Database } + ? keyof Database[PublicEnumNameOrOptions["schema"]]["Enums"] + : never = never, +> = PublicEnumNameOrOptions extends { schema: keyof Database } + ? Database[PublicEnumNameOrOptions["schema"]]["Enums"][EnumName] + : PublicEnumNameOrOptions extends keyof PublicSchema["Enums"] + ? PublicSchema["Enums"][PublicEnumNameOrOptions] + : never; + +export type CompositeTypes< + PublicCompositeTypeNameOrOptions extends + | keyof PublicSchema["CompositeTypes"] + | { schema: keyof Database }, + CompositeTypeName extends PublicCompositeTypeNameOrOptions extends { + schema: keyof Database; + } + ? keyof Database[PublicCompositeTypeNameOrOptions["schema"]]["CompositeTypes"] + : never = never, +> = PublicCompositeTypeNameOrOptions extends { schema: keyof Database } + ? Database[PublicCompositeTypeNameOrOptions["schema"]]["CompositeTypes"][CompositeTypeName] + : PublicCompositeTypeNameOrOptions extends keyof PublicSchema["CompositeTypes"] + ? PublicSchema["CompositeTypes"][PublicCompositeTypeNameOrOptions] + : never; diff --git a/lexikon/src/shared/types/db/index.d.ts b/lexikon/src/shared/types/db/index.d.ts new file mode 100644 index 0000000..73d5b90 --- /dev/null +++ b/lexikon/src/shared/types/db/index.d.ts @@ -0,0 +1 @@ +export { Database } from "./database.types"; diff --git a/lexikon/src/shared/types/icons/index.ts b/lexikon/src/shared/types/icons/index.ts new file mode 100644 index 0000000..cece4a4 --- /dev/null +++ b/lexikon/src/shared/types/icons/index.ts @@ -0,0 +1,5 @@ +import { SVGProps } from "react"; + +export type IconSvgProps = SVGProps & { + size?: number; +}; diff --git a/lexikon/src/shared/ui/SearchField.tsx b/lexikon/src/shared/ui/SearchField.tsx new file mode 100644 index 0000000..af5d925 --- /dev/null +++ b/lexikon/src/shared/ui/SearchField.tsx @@ -0,0 +1,36 @@ +import { Input } from "@heroui/react"; +import { SearchIcon } from "lucide-react"; + +const SearchField = ({ + placeholder, + value, + onChange, + onClear, +}: { + placeholder?: string; + value: string; + onChange: (value: string) => void; + onClear?: () => void; +}) => { + return ( + onChange(e.target.value)} + onClear={onClear} + size="md" + startContent={} + type="search" + /> + ); +}; + +export default SearchField; diff --git a/lexikon/src/shared/ui/auth/LoginModal.tsx b/lexikon/src/shared/ui/auth/LoginModal.tsx new file mode 100644 index 0000000..cb8a477 --- /dev/null +++ b/lexikon/src/shared/ui/auth/LoginModal.tsx @@ -0,0 +1,62 @@ +import { + Button, + Modal, + ModalBody, + ModalContent, + ModalHeader, +} from "@heroui/react"; +import { useState } from "react"; +import { signInWithGoogle } from "shared/api/supabase"; + +interface LoginModalProps { + isOpen: boolean; + onClose: () => void; +} + +export default function LoginModal({ isOpen, onClose }: LoginModalProps) { + const [isLoading, setIsLoading] = useState(false); + + const handleGoogleLogin = async () => { + try { + setIsLoading(true); + await signInWithGoogle("http://localhost:3000/auth/callback"); + // Don't close modal immediately - OAuth will redirect the page + } catch (error) { + setIsLoading(false); + onClose(); + } + }; + + const GoogleIcon = () => ( + Google Logo + ); + + return ( + + + + Sign in to Lexikon + + + + + + + ); +} diff --git a/lexikon/src/shared/ui/code/CodeMirror.tsx b/lexikon/src/shared/ui/code/CodeMirror.tsx new file mode 100644 index 0000000..406e0e6 --- /dev/null +++ b/lexikon/src/shared/ui/code/CodeMirror.tsx @@ -0,0 +1,155 @@ +// CodeMirrorComponent.tsx +import React, { FC } from "react"; +import CodeMirror from "@uiw/react-codemirror"; +import { javascript } from "@codemirror/lang-javascript"; +import { markdown } from "@codemirror/lang-markdown"; +import { autocompletion } from "@codemirror/autocomplete"; +import { EditorView } from "@codemirror/view"; +import { oneDark } from "@codemirror/theme-one-dark"; + +interface CodeMirrorComponentProps { + /** Current code as a string */ + value: string; + /** Callback fired on code change */ + onChange: (value: string) => void; + /** Programming language for syntax highlighting (default: 'markdown') */ + language?: string; + /** CodeMirror theme (default: 'dark') */ + theme?: string; +} + +/** + * A responsive CodeMirror component. + * This editor fills its parent container – for example, if the parent container + * is set to 100vw and 100vh, the editor will occupy the remaining space. + */ +const CodeMirrorComponent: FC = ({ + value, + onChange, + language = "markdown", + theme = "dark", +}) => { + // Set up language extension based on the language prop + const getLanguageExtension = () => { + switch (language) { + case "javascript": + case "js": + case "jsx": + return javascript(); + case "markdown": + case "md": + return markdown(); + default: + return markdown(); // Fallback to markdown for unsupported languages + } + }; + + // Create an extension that only overrides the background color. + const customBackground = EditorView.theme( + { + "&": { + backgroundColor: "transparent", + }, + ".cm-gutter": { + backgroundColor: "transparent", + }, + ".cm-gutters": { + backgroundColor: "transparent", + }, + ".cm-gutterElement.cm-activeLineGutter": { + backgroundColor: "transparent", + }, + ".cm-editor": { + borderColor: "transparent", + outline: "none !important", + boxShadow: "none !important", + }, + ".cm-editor *": { + // Target all children elements of the editor + outline: "none !important", + }, + ".cm-editor.cm-focused": { + outline: "none !important", + border: "none !important", + boxShadow: "none !important", + }, + ".cm-focused": { + outline: "none !important", + border: "none !important", + boxShadow: "none !important", + }, + ".cm-scroller": { + outline: "none !important", + }, + ".cm-content": { + outline: "none !important", + }, + ".cm-activeLine": { + backgroundColor: "rgba(0, 0, 0, 0.05) !important", + }, + "*:focus, *:focus-visible": { + outline: "none !important", + border: "none !important", + boxShadow: "none !important", + }, + }, + { dark: true } + ); + + // Compose the two extensions together. + const editorExtensions = [ + customBackground, + oneDark, + // ...other extensions as needed + ]; + + React.useEffect(() => { + // Add a global style to remove outlines + const style = document.createElement("style"); + + style.innerHTML = ` + .cm-editor, .cm-editor *, .cm-scroller, .cm-content { + outline: none !important; + box-shadow: none !important; + } + `; + document.head.appendChild(style); + + return () => { + document.head.removeChild(style); + }; + }, []); + + return ( +
+ +
+ ); +}; + +export default CodeMirrorComponent; diff --git a/lexikon/src/shared/ui/code/Editor.tsx b/lexikon/src/shared/ui/code/Editor.tsx new file mode 100644 index 0000000..51bc85f --- /dev/null +++ b/lexikon/src/shared/ui/code/Editor.tsx @@ -0,0 +1,68 @@ +"use client"; + +import { FC } from "react"; +import { AnimatePresence, motion } from "framer-motion"; +import CodeMirrorComponent from "./CodeMirror"; + +interface EditorProps { + value: string; + onChange: (value: string) => void; + language?: string; + theme?: string; +} + +/** + * Editor wrapper with animation. Wraps the code editor (e.g., CodeMirror). + */ +const Editor: FC = ({ + value, + onChange, + language = "markdown", + theme = "dark", +}) => { + return ( + + + + + + ); +}; + +export default Editor; diff --git a/lexikon/src/shared/ui/code/Monaco.tsx b/lexikon/src/shared/ui/code/Monaco.tsx new file mode 100644 index 0000000..e69de29 diff --git a/lexikon/src/shared/ui/icons.tsx b/lexikon/src/shared/ui/icons.tsx new file mode 100644 index 0000000..6de5430 --- /dev/null +++ b/lexikon/src/shared/ui/icons.tsx @@ -0,0 +1,183 @@ +import * as React from "react"; + +import { IconSvgProps } from "shared/types/icons"; + +export const Logo: React.FC = ({ + size = 36, + width, + height, + ...props +}) => ( + + + +); + +export const DiscordIcon: React.FC = ({ + size = 24, + width, + height, + ...props +}) => { + return ( + + + + ); +}; + +export const TwitterIcon: React.FC = ({ + size = 24, + width, + height, + ...props +}) => { + return ( + + + + ); +}; + +export const GithubIcon: React.FC = ({ + size = 24, + width, + height, + ...props +}) => { + return ( + + + + ); +}; + +export const MoonFilledIcon = ({ + size = 24, + width, + height, + ...props +}: IconSvgProps) => ( + + + +); + +export const SunFilledIcon = ({ + size = 24, + width, + height, + ...props +}: IconSvgProps) => ( + + + + + + +); + +export const HeartFilledIcon = ({ + size = 24, + width, + height, + ...props +}: IconSvgProps) => ( + + + +); + +export const SearchIcon = (props: IconSvgProps) => ( + + + + +); diff --git a/lexikon/src/shared/ui/markdown/MDXPreview.tsx b/lexikon/src/shared/ui/markdown/MDXPreview.tsx new file mode 100644 index 0000000..ecb279b --- /dev/null +++ b/lexikon/src/shared/ui/markdown/MDXPreview.tsx @@ -0,0 +1,38 @@ +"use client"; + +import React, { useMemo } from "react"; +import Markdown from "react-markdown"; +import rehypeKatex from "rehype-katex"; +import remarkGfm from "remark-gfm"; +import remarkMath from "remark-math"; + +import "katex/dist/katex.min.css"; +import { mdxComponents } from "shared/ui/markdown/mdxComponents"; +import { preprocessMath } from "shared/utils/markdown"; +import { cn } from "shared/utils/tailwind"; + +interface MDXPreviewProps extends React.HTMLAttributes { + content: string; + className?: string; +} + +export default function MDXPreview({ + content, + className, + ...props +}: MDXPreviewProps) { + // wrap math blocks properly for parser + const text = useMemo(() => preprocessMath(content), [content]); + + return ( +
+ + {text} + +
+ ); +} diff --git a/lexikon/src/shared/ui/markdown/mdxComponents.tsx b/lexikon/src/shared/ui/markdown/mdxComponents.tsx new file mode 100644 index 0000000..506dc4c --- /dev/null +++ b/lexikon/src/shared/ui/markdown/mdxComponents.tsx @@ -0,0 +1,158 @@ +// filepath: src/global/lib/markdownComponents.tsx +"use client"; + +import React from "react"; +import { Prism as SyntaxHighlighter } from "react-syntax-highlighter"; + +// Mapping MDX elements to styled components +export const mdxComponents = { + h1: ({ children, ...props }: React.HTMLAttributes) => ( +

+ {children} +

+ ), + h2: ({ children, ...props }: React.HTMLAttributes) => ( +

+ {children} +

+ ), + h3: ({ children, ...props }: React.HTMLAttributes) => ( +

+ {children} +

+ ), + p: ({ children, ...props }: React.HTMLAttributes) => ( +

+ {children} +

+ ), + a: ({ + children, + href, + ...props + }: React.AnchorHTMLAttributes) => ( + + {children || href} + + ), + ul: ({ children, ...props }: React.HTMLAttributes) => ( +
    + {children} +
+ ), + ol: ({ children, ...props }: React.HTMLAttributes) => ( +
    + {children} +
+ ), + li: ({ children, ...props }: React.LiHTMLAttributes) => ( +
  • + {children} +
    • + ), + blockquote: ({ + children, + ...props + }: React.HTMLAttributes) => ( +
    + {children} +
    + ), + hr: (props: React.HTMLAttributes) => ( +
    + ), + img: ({ alt, src, ...props }: React.ImgHTMLAttributes) => ( + {alt + ), + table: ({ children, ...props }: React.HTMLAttributes) => ( +
    + + {children} +
    +
    + ), + thead: ({ + children, + ...props + }: React.HTMLAttributes) => ( + + {children} + + ), + tbody: ({ + children, + ...props + }: React.HTMLAttributes) => ( + + {children} + + ), + th: ({ + children, + ...props + }: React.ThHTMLAttributes) => ( + + {children || "\u00A0"} + + ), + td: ({ + children, + ...props + }: React.TdHTMLAttributes) => ( + + {children || "\u00A0"} + + ), + code: ({ inline, className, children, ...props }: any) => { + const match = /language-(\w+)/.exec(className || ""); + + if (!inline && match) { + return ( + + {String(children).replace(/\n$/, "")} + + ); + } + + return ( + + {children} + + ); + }, + pre: ({ children, ...props }: React.HTMLAttributes) => ( + 
    +      {children}
+
    + ), +}; diff --git a/lexikon/src/shared/ui/primitives/ConfirmModal.tsx b/lexikon/src/shared/ui/primitives/ConfirmModal.tsx new file mode 100644 index 0000000..5459964 --- /dev/null +++ b/lexikon/src/shared/ui/primitives/ConfirmModal.tsx @@ -0,0 +1,89 @@ +"use client"; + +import React from "react"; +import { + Modal, + ModalContent, + ModalHeader, + ModalBody, + ModalFooter, + Button, + ModalProps, +} from "@heroui/react"; + +// Extend ModalProps to include standard modal control props like isOpen, onOpenChange +interface ConfirmModalProps extends Omit { + title: string; + children: React.ReactNode; // Message content + confirmText?: string; + cancelText?: string; + onConfirm: () => void | Promise; + isConfirmLoading?: boolean; + confirmButtonColor?: "primary" | "secondary" | "success" | "warning" | "danger" | "default"; +} + +/** + * A reusable modal component for confirming actions. + * Uses useDisclosure hook for state management. + * Example: + * const { isOpen, onOpen, onOpenChange } = useDisclosure(); + * // ... + * + * + * Are you sure you want to delete this item? + * + */ +export default function ConfirmModal({ + isOpen, + onOpenChange, + title, + children, + confirmText = "Confirm", + cancelText = "Cancel", + onConfirm, + isConfirmLoading = false, + confirmButtonColor = "primary", + ...modalProps // Pass remaining ModalProps like size, backdrop, etc. +}: ConfirmModalProps) { + const handleConfirmClick = async () => { + await onConfirm(); + // Optionally close modal after confirm action completes + // Or let the caller manage closing based on success/failure + // For simplicity, we close here. Caller can reopen on error if needed. + if (onOpenChange) { + onOpenChange(false); + } + }; + + return ( + + + {( + onClose // Use function child to get onClose + ) => ( + <> + {title} + {children} + + + + + + )} + + + ); +} diff --git a/lexikon/src/shared/ui/primitives/IconButton.tsx b/lexikon/src/shared/ui/primitives/IconButton.tsx new file mode 100644 index 0000000..6fb8691 --- /dev/null +++ b/lexikon/src/shared/ui/primitives/IconButton.tsx @@ -0,0 +1,39 @@ +import { Button } from "@heroui/button"; +import { Spinner } from "@heroui/react"; +import { LucideIcon } from "lucide-react"; + +interface IconButtonProps { + onClick: () => void; + disabled?: boolean; + icon: LucideIcon; + color?: "primary" | "secondary" | "success" | "warning" | "danger"; + busy?: boolean; +} + +const IconButton = ({ + onClick, + disabled, + icon, + color, + busy = false, +}: IconButtonProps) => { + const RenderedIcon = icon; + return ( + + ); +}; + +export default IconButton; diff --git a/lexikon/src/shared/ui/themeSwitch.tsx b/lexikon/src/shared/ui/themeSwitch.tsx new file mode 100644 index 0000000..931a813 --- /dev/null +++ b/lexikon/src/shared/ui/themeSwitch.tsx @@ -0,0 +1,86 @@ +"use client"; + +import { FC } from "react"; +import { SwitchProps, useSwitch } from "@heroui/switch"; +import { useTheme } from "next-themes"; +import { useIsSSR } from "@react-aria/ssr"; +import clsx from "clsx"; +import { Moon, Sun } from "lucide-react"; +import { Button } from "@heroui/button"; + +export interface ThemeSwitchProps { + className?: string; + classNames?: SwitchProps["classNames"]; +} + +export const ThemeSwitch: FC = ({ + className, + classNames, +}) => { + const { theme, setTheme } = useTheme(); + const isSSR = useIsSSR(); + + const onChange = () => { + theme === "light" ? setTheme("dark") : setTheme("light"); + }; + + const { + Component, + slots, + isSelected, + getBaseProps, + getInputProps, + getWrapperProps, + } = useSwitch({ + isSelected: theme === "light" || isSSR, + onChange, + }); + + // Explicitly type Component as any to avoid type errors + const SwitchComponent = Component as any; + + const wrapperProps = getWrapperProps(); + const { "aria-hidden": _removed, ...cleanedWrapperProps } = wrapperProps; + + return ( + <> + + +
    + +
    + + ); +}; diff --git a/lexikon/src/shared/utils/markdown.ts b/lexikon/src/shared/utils/markdown.ts new file mode 100644 index 0000000..dc74511 --- /dev/null +++ b/lexikon/src/shared/utils/markdown.ts @@ -0,0 +1,12 @@ +// filepath: src/global/lib/markdownUtils.ts + +/** + * Ensure block math sections are wrapped with newlines for proper parsing + */ +export function preprocessMath(content: string): string { + return content.replace(/\$\$(.*?)\$\$/gs, (match, p1) => + match.startsWith("$$\n") || match.endsWith("\n$$") + ? match + : `$$\n${p1}\n$$`, + ); +} diff --git a/lexikon/src/shared/utils/sequenceValidator.ts b/lexikon/src/shared/utils/sequenceValidator.ts new file mode 100644 index 0000000..3f5a42e --- /dev/null +++ b/lexikon/src/shared/utils/sequenceValidator.ts @@ -0,0 +1,112 @@ +// Worker sequence validation +export interface WorkerMeta { + name: string; + input: string; + output: string; +} + +export interface SequenceValidationResult { + isValid: boolean; + sequence: WorkerMeta[]; + error?: string; +} + +/** + * Validates and builds a sequence of workers such that each worker's output matches the next worker's input. + * Returns the ordered sequence if valid, or an error message if not. + */ +export function validateWorkerSequence(workers: WorkerMeta[]): SequenceValidationResult { + if (!workers.length) { + return { isValid: false, sequence: [], error: 'No workers provided.' }; + } + + // Map input/output for quick lookup + const inputMap = new Map(); + const outputMap = new Map(); + for (const worker of workers) { + inputMap.set(worker.input, worker); + outputMap.set(worker.output, worker); + } + + // Find the starting worker (whose input is not any other worker's output) + const startWorker = workers.find( + w => !outputMap.has(w.input) + ); + if (!startWorker) { + return { isValid: false, sequence: [], error: 'No valid starting worker found.' }; + } + + // Build the sequence + const sequence: WorkerMeta[] = [startWorker]; + let current = startWorker; + const used = new Set([current.name]); + + while (sequence.length < workers.length) { + const next = workers.find( + w => w.input === current.output && !used.has(w.name) + ); + if (!next) { + break; + } + sequence.push(next); + used.add(next.name); + current = next; + } + + if (sequence.length !== workers.length) { + return { isValid: false, sequence, error: 'Workers cannot be sequenced linearly.' }; + } + + // Validate the chain + for (let i = 0; i < sequence.length - 1; i++) { + if (sequence[i].output !== sequence[i + 1].input) { + return { isValid: false, sequence, error: 'Invalid sequence: output/input mismatch.' }; + } + } + + return { isValid: true, sequence }; +} +/** + * Sequence validator utility + * Validates that a sequence (array) meets certain criteria. + * + * Usage examples: + * isStrictlyIncreasing([1,2,3]) // true + * isStrictlyDecreasing([3,2,1]) // true + * isNonDecreasing([1,2,2,3]) // true + * isNonIncreasing([3,3,2,1]) // true + */ + +export function isStrictlyIncreasing(arr: T[], comparator: (a: T, b: T) => number = defaultComparator): boolean { + for (let i = 1; i < arr.length; i++) { + if (comparator(arr[i - 1], arr[i]) >= 0) return false; + } + return true; +} + +export function isStrictlyDecreasing(arr: T[], comparator: (a: T, b: T) => number = defaultComparator): boolean { + for (let i = 1; i < arr.length; i++) { + if (comparator(arr[i - 1], arr[i]) <= 0) return false; + } + return true; +} + +export function isNonDecreasing(arr: T[], comparator: (a: T, b: T) => number = defaultComparator): boolean { + for (let i = 1; i < arr.length; i++) { + if (comparator(arr[i - 1], arr[i]) > 0) return false; + } + return true; +} + +export function isNonIncreasing(arr: T[], comparator: (a: T, b: T) => number = defaultComparator): boolean { + for (let i = 1; i < arr.length; i++) { + if (comparator(arr[i - 1], arr[i]) < 0) return false; + } + return true; +} + +function defaultComparator(a: T, b: T): number { + if (typeof a === "number" && typeof b === "number") return a - b; + if (typeof a === "string" && typeof b === "string") return a.localeCompare(b); + throw new Error("Default comparator only supports number or string arrays. Provide a custom comparator."); +} diff --git a/lexikon/src/shared/utils/shader.ts b/lexikon/src/shared/utils/shader.ts new file mode 100644 index 0000000..2a5837b --- /dev/null +++ b/lexikon/src/shared/utils/shader.ts @@ -0,0 +1,17 @@ +import fs from "fs"; +import path from "path"; + +/** + * Loads shader file contents at build time + */ +export function loadShaderFile(relativeFilePath: string): string { + try { + const filePath = path.resolve(process.cwd(), relativeFilePath); + + return fs.readFileSync(filePath, "utf8"); + } catch (e) { + console.error(`Error loading shader file ${relativeFilePath}:`, e); + + return ""; // Return empty string as fallback + } +} diff --git a/lexikon/src/shared/utils/tailwind.ts b/lexikon/src/shared/utils/tailwind.ts new file mode 100644 index 0000000..8e96c80 --- /dev/null +++ b/lexikon/src/shared/utils/tailwind.ts @@ -0,0 +1,9 @@ +import { type ClassValue, clsx } from "clsx"; +import { twMerge } from "tailwind-merge"; + +/** + * Combines and merges class names using clsx and tailwind-merge + */ +export function cn(...inputs: ClassValue[]): string { + return twMerge(clsx(inputs)); +} diff --git a/lexikon/tsconfig.json b/lexikon/tsconfig.json new file mode 100644 index 0000000..b884451 --- /dev/null +++ b/lexikon/tsconfig.json @@ -0,0 +1,30 @@ +{ + "compilerOptions": { + "target": "es2018", + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "bundler", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "preserve", + "incremental": true, + "plugins": [ + { + "name": "next" + } + ], + "paths": { + "shared/*": ["./src/shared/*"], + "features/*": ["./src/features/*"], + "app/*": ["./src/app/*"], + "@/*": ["./*"] + } + }, + "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"], + "exclude": ["node_modules", "_archive", ".next"] +}