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Fix typo (pca->rbf_pca), fixes #192

main
Aurélien Geron 2018-03-15 18:51:08 +01:00
parent d9fbf7dd4c
commit eefe262dca
1 changed files with 28 additions and 80 deletions
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108
08_dimensionality_reduction.ipynb
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@ -31,9 +31,7 @@
{ {
"cell_type": "code", "cell_type": "code",
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"source": [ "source": [
"# To support both python 2 and python 3\n", "# To support both python 2 and python 3\n",
@ -77,9 +75,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"np.random.seed(4)\n", "np.random.seed(4)\n",
@ -120,9 +116,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"X_centered = X - X.mean(axis=0)\n", "X_centered = X - X.mean(axis=0)\n",
@ -134,9 +128,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 4,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"m, n = X.shape\n", "m, n = X.shape\n",
@ -157,9 +149,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 6,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"W2 = Vt.T[:, :2]\n", "W2 = Vt.T[:, :2]\n",
@ -169,9 +159,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 7,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"X2D_using_svd = X2D" "X2D_using_svd = X2D"
@ -194,9 +182,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 8,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.decomposition import PCA\n", "from sklearn.decomposition import PCA\n",
@ -251,9 +237,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 12,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"X3D_inv = pca.inverse_transform(X2D)" "X3D_inv = pca.inverse_transform(X2D)"
@ -301,9 +285,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 15,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"X3D_inv_using_svd = X2D_using_svd.dot(Vt[:2, :])" "X3D_inv_using_svd = X2D_using_svd.dot(Vt[:2, :])"
@ -436,9 +418,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 22,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from matplotlib.patches import FancyArrowPatch\n", "from matplotlib.patches import FancyArrowPatch\n",
@ -466,9 +446,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 23,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"axes = [-1.8, 1.8, -1.3, 1.3, -1.0, 1.0]\n", "axes = [-1.8, 1.8, -1.3, 1.3, -1.0, 1.0]\n",
@ -563,9 +541,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 26,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.datasets import make_swiss_roll\n", "from sklearn.datasets import make_swiss_roll\n",
@ -785,9 +761,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 31,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from six.moves import urllib\n", "from six.moves import urllib\n",
@ -798,9 +772,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 32,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.model_selection import train_test_split\n", "from sklearn.model_selection import train_test_split\n",
@ -814,9 +786,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 33, "execution_count": 33,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"pca = PCA()\n", "pca = PCA()\n",
@ -837,9 +807,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 35,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"pca = PCA(n_components=0.95)\n", "pca = PCA(n_components=0.95)\n",
@ -867,9 +835,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 38, "execution_count": 38,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"pca = PCA(n_components = 154)\n", "pca = PCA(n_components = 154)\n",
@ -880,9 +846,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 39, "execution_count": 39,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"def plot_digits(instances, images_per_row=5, **options):\n", "def plot_digits(instances, images_per_row=5, **options):\n",
@ -921,9 +885,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 41, "execution_count": 41,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"X_reduced_pca = X_reduced" "X_reduced_pca = X_reduced"
@ -956,9 +918,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 43, "execution_count": 43,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"X_recovered_inc_pca = inc_pca.inverse_transform(X_reduced)" "X_recovered_inc_pca = inc_pca.inverse_transform(X_reduced)"
@ -981,9 +941,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 45, "execution_count": 45,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"X_reduced_inc_pca = X_reduced" "X_reduced_inc_pca = X_reduced"
@ -1038,9 +996,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 48, "execution_count": 48,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"filename = \"my_mnist.data\"\n", "filename = \"my_mnist.data\"\n",
@ -1060,9 +1016,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 49, "execution_count": 49,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"del X_mm" "del X_mm"
@ -1091,9 +1045,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 51, "execution_count": 51,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"rnd_pca = PCA(n_components=154, svd_solver=\"randomized\", random_state=42)\n", "rnd_pca = PCA(n_components=154, svd_solver=\"randomized\", random_state=42)\n",
@ -1221,9 +1173,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 55, "execution_count": 55,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"X, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)" "X, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)"
@ -1232,9 +1182,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 56, "execution_count": 56,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.decomposition import KernelPCA\n", "from sklearn.decomposition import KernelPCA\n",
@ -1285,7 +1233,7 @@
"source": [ "source": [
"plt.figure(figsize=(6, 5))\n", "plt.figure(figsize=(6, 5))\n",
"\n", "\n",
"X_inverse = pca.inverse_transform(X_reduced_rbf)\n", "X_inverse = rbf_pca.inverse_transform(X_reduced_rbf)\n",
"\n", "\n",
"ax = plt.subplot(111, projection='3d')\n", "ax = plt.subplot(111, projection='3d')\n",
"ax.view_init(10, -70)\n", "ax.view_init(10, -70)\n",
@ -2339,7 +2287,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.5.2" "version": "3.6.4"
}, },
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"height": "352px", "height": "352px",