Remove unneeded comments, sync notebook with book code
Aurélien Geron
parent
8e97aab84b
commit
01d5df8e72
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@ -316,13 +316,6 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**This is the first code example in chapter 5:**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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@ -472,13 +465,6 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Here is second code example in the chapter:**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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@ -538,13 +524,6 @@
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"## Polynomial Kernel"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Next code example:**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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@ -553,10 +532,8 @@
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"source": [
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"from sklearn.svm import SVC\n",
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"\n",
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"poly_kernel_svm_clf = make_pipeline(\n",
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" StandardScaler(),\n",
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" SVC(kernel=\"poly\", degree=3, coef0=1, C=5)\n",
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")\n",
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"poly_kernel_svm_clf = make_pipeline(StandardScaler(),\n",
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" SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
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"poly_kernel_svm_clf.fit(X, y)"
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]
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},
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@ -677,23 +654,14 @@
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"## Gaussian RBF Kernel"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Next code example:**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"rbf_kernel_svm_clf = make_pipeline(\n",
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" StandardScaler(),\n",
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" SVC(kernel=\"rbf\", gamma=5, C=0.001)\n",
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")\n",
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"rbf_kernel_svm_clf = make_pipeline(StandardScaler(),\n",
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" SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
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"rbf_kernel_svm_clf.fit(X, y)"
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]
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},
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@ -751,29 +719,14 @@
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book – this code generates a simple linear dataset\n",
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"np.random.seed(42)\n",
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"m = 50\n",
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"X = 2 * np.random.rand(m, 1)\n",
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"y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Next code example:**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.svm import LinearSVR\n",
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"\n",
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"# not in the book – these 3 lines generate a simple linear dataset\n",
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"np.random.seed(42)\n",
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"X = 2 * np.random.rand(50, 1)\n",
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"y = 4 + 3 * X[:, 0] + np.random.randn(50)\n",
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"\n",
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"svm_reg = make_pipeline(StandardScaler(),\n",
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" LinearSVR(epsilon=0.5, random_state=42))\n",
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"svm_reg.fit(X, y)"
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@ -781,7 +734,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -839,32 +792,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book – this code generates a simple quadratic dataset\n",
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"np.random.seed(42)\n",
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"m = 50\n",
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"X = 2 * np.random.rand(m, 1) - 1\n",
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"y = (0.2 + 0.1 * X + 0.5 * X ** 2 + np.random.randn(m, 1) / 10).ravel()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Next code example:**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.svm import SVR\n",
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"\n",
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"# not in the book – these 3 lines generate a simple quadratic dataset\n",
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"np.random.seed(42)\n",
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"X = 2 * np.random.rand(50, 1) - 1\n",
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"y = 0.2 + 0.1 * X[:, 0] + 0.5 * X[:, 0] ** 2 + np.random.randn(50) / 10\n",
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"\n",
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"svm_poly_reg = make_pipeline(StandardScaler(),\n",
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" SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1))\n",
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"svm_poly_reg.fit(X, y)"
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@ -872,7 +810,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -913,7 +851,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -962,7 +900,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1010,7 +948,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1020,7 +958,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1081,7 +1019,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1094,7 +1032,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1108,7 +1046,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1117,7 +1055,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1128,7 +1066,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1159,7 +1097,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 33,
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"metadata": {
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"scrolled": true
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},
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@ -1237,7 +1175,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1264,7 +1202,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1292,7 +1230,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1351,7 +1289,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 37,
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"metadata": {},
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"outputs": [],
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@ -1362,7 +1300,7 @@
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1392,7 +1330,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"execution_count": 41,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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},
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{
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"cell_type": "code",
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"metadata": {},
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},
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"metadata": {},
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},
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"cell_type": "code",
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"metadata": {},
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},
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"source": [
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