From eefe262dca78863c01a6789597b3153324992c76 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Geron?= Date: Thu, 15 Mar 2018 18:51:08 +0100 Subject: [PATCH] Fix typo (pca->rbf_pca), fixes #192 --- 08_dimensionality_reduction.ipynb | 108 ++++++++---------------------- 1 file changed, 28 insertions(+), 80 deletions(-) diff --git a/08_dimensionality_reduction.ipynb b/08_dimensionality_reduction.ipynb index d1bf0d2..5f0de13 100644 --- a/08_dimensionality_reduction.ipynb +++ b/08_dimensionality_reduction.ipynb @@ -31,9 +31,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# To support both python 2 and python 3\n", @@ -77,9 +75,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "np.random.seed(4)\n", @@ -120,9 +116,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "X_centered = X - X.mean(axis=0)\n", @@ -134,9 +128,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "m, n = X.shape\n", @@ -157,9 +149,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "W2 = Vt.T[:, :2]\n", @@ -169,9 +159,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "X2D_using_svd = X2D" @@ -194,9 +182,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", @@ -251,9 +237,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "X3D_inv = pca.inverse_transform(X2D)" @@ -301,9 +285,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "X3D_inv_using_svd = X2D_using_svd.dot(Vt[:2, :])" @@ -436,9 +418,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from matplotlib.patches import FancyArrowPatch\n", @@ -466,9 +446,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "axes = [-1.8, 1.8, -1.3, 1.3, -1.0, 1.0]\n", @@ -563,9 +541,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import make_swiss_roll\n", @@ -785,9 +761,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from six.moves import urllib\n", @@ -798,9 +772,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -814,9 +786,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "pca = PCA()\n", @@ -837,9 +807,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "pca = PCA(n_components=0.95)\n", @@ -867,9 +835,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "pca = PCA(n_components = 154)\n", @@ -880,9 +846,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_digits(instances, images_per_row=5, **options):\n", @@ -921,9 +885,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "X_reduced_pca = X_reduced" @@ -956,9 +918,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "X_recovered_inc_pca = inc_pca.inverse_transform(X_reduced)" @@ -981,9 +941,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "X_reduced_inc_pca = X_reduced" @@ -1038,9 +996,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "filename = \"my_mnist.data\"\n", @@ -1060,9 +1016,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "del X_mm" @@ -1091,9 +1045,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "rnd_pca = PCA(n_components=154, svd_solver=\"randomized\", random_state=42)\n", @@ -1221,9 +1173,7 @@ { "cell_type": "code", "execution_count": 55, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "X, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)" @@ -1232,9 +1182,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import KernelPCA\n", @@ -1285,7 +1233,7 @@ "source": [ "plt.figure(figsize=(6, 5))\n", "\n", - "X_inverse = pca.inverse_transform(X_reduced_rbf)\n", + "X_inverse = rbf_pca.inverse_transform(X_reduced_rbf)\n", "\n", "ax = plt.subplot(111, projection='3d')\n", "ax.view_init(10, -70)\n", @@ -2339,7 +2287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.4" }, "nav_menu": { "height": "352px",