schuetoliver
/
handson-ml
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'edition2'

main
Aurélien Geron 2021-10-10 14:19:39 +13:00
parent afe7f381e7 3f9ff484a6
commit 5822444ba7
2 changed files with 33 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
03_classification.ipynb
View File

@ -197,16 +197,24 @@
"def plot_digits(instances, images_per_row=10, **options):\n", "def plot_digits(instances, images_per_row=10, **options):\n",
" size = 28\n", " size = 28\n",
" images_per_row = min(len(instances), images_per_row)\n", " images_per_row = min(len(instances), images_per_row)\n",
" images = [instance.reshape(size,size) for instance in instances]\n", " # This is equivalent to n_rows = ceil(len(instances) / images_per_row):\n",
" n_rows = (len(instances) - 1) // images_per_row + 1\n", " n_rows = (len(instances) - 1) // images_per_row + 1\n",
" row_images = []\n", "\n",
" # Append empty images to fill the end of the grid, if needed:\n",
" n_empty = n_rows * images_per_row - len(instances)\n", " n_empty = n_rows * images_per_row - len(instances)\n",
" images.append(np.zeros((size, size * n_empty)))\n", " padded_instances = np.concatenate([instances, np.zeros((n_empty, size * size))], axis=0)\n",
" for row in range(n_rows):\n", "\n",
" rimages = images[row * images_per_row : (row + 1) * images_per_row]\n", " # Reshape the array so it's organized as a grid containing 28×28 images:\n",
" row_images.append(np.concatenate(rimages, axis=1))\n", " image_grid = padded_instances.reshape((n_rows, images_per_row, size, size))\n",
" image = np.concatenate(row_images, axis=0)\n", "\n",
" plt.imshow(image, cmap = mpl.cm.binary, **options)\n", " # Combine axes 0 and 2 (vertical image grid axis, and vertical image axis),\n",
" # and axes 1 and 3 (horizontal axes). We first need to move the axes that we\n",
" # want to combine next to each other, using transpose(), and only then we\n",
" # can reshape:\n",
" big_image = image_grid.transpose(0, 2, 1, 3).reshape(n_rows * size,\n",
" images_per_row * size)\n",
" # Now that we have a big image, we just need to show it:\n",
" plt.imshow(big_image, cmap = mpl.cm.binary, **options)\n",
" plt.axis(\"off\")" " plt.axis(\"off\")"
] ]
}, },

25
08_dimensionality_reduction.ipynb
View File

@ -947,19 +947,28 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# EXTRA\n",
"def plot_digits(instances, images_per_row=5, **options):\n", "def plot_digits(instances, images_per_row=5, **options):\n",
" size = 28\n", " size = 28\n",
" images_per_row = min(len(instances), images_per_row)\n", " images_per_row = min(len(instances), images_per_row)\n",
" images = [instance.reshape(size,size) for instance in instances]\n", " # This is equivalent to n_rows = ceil(len(instances) / images_per_row):\n",
" n_rows = (len(instances) - 1) // images_per_row + 1\n", " n_rows = (len(instances) - 1) // images_per_row + 1\n",
" row_images = []\n", "\n",
" # Append empty images to fill the end of the grid, if needed:\n",
" n_empty = n_rows * images_per_row - len(instances)\n", " n_empty = n_rows * images_per_row - len(instances)\n",
" images.append(np.zeros((size, size * n_empty)))\n", " padded_instances = np.concatenate([instances, np.zeros((n_empty, size * size))], axis=0)\n",
" for row in range(n_rows):\n", "\n",
" rimages = images[row * images_per_row : (row + 1) * images_per_row]\n", " # Reshape the array so it's organized as a grid containing 28×28 images:\n",
" row_images.append(np.concatenate(rimages, axis=1))\n", " image_grid = padded_instances.reshape((n_rows, images_per_row, size, size))\n",
" image = np.concatenate(row_images, axis=0)\n", "\n",
" plt.imshow(image, cmap = mpl.cm.binary, **options)\n", " # Combine axes 0 and 2 (vertical image grid axis, and vertical image axis),\n",
" # and axes 1 and 3 (horizontal axes). We first need to move the axes that we\n",
" # want to combine next to each other, using transpose(), and only then we\n",
" # can reshape:\n",
" big_image = image_grid.transpose(0, 2, 1, 3).reshape(n_rows * size,\n",
" images_per_row * size)\n",
" # Now that we have a big image, we just need to show it:\n",
" plt.imshow(big_image, cmap = mpl.cm.binary, **options)\n",
" plt.axis(\"off\")" " plt.axis(\"off\")"
] ]
}, },