Pushing the docs to dev/ for branch: main, commit fbc56edab96c52ecefa9a67cb7d925b500e72207

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,15 +15,13 @@
 #          Kevin Hughes
 # License: BSD 3 clause
 
-from sklearn.decomposition import PCA
+# %%
+# Create the data
+# ---------------
 
 import numpy as np
-import matplotlib.pyplot as plt
-from scipy import stats
 
-
-# #############################################################################
-# Create the data
+from scipy import stats
 
 e = np.exp(1)
 np.random.seed(4)
@@ -51,8 +49,18 @@ def pdf(x):
 b /= norm
 
 
-# #############################################################################
+# %%
 # Plot the figures
+# ----------------
+
+from sklearn.decomposition import PCA
+
+import matplotlib.pyplot as plt
+
+# unused but required import for doing 3d projections with matplotlib < 3.2
+import mpl_toolkits.mplot3d  # noqa: F401
+
+
 def plot_figs(fig_num, elev, azim):
     fig = plt.figure(fig_num, figsize=(4, 3))
     plt.clf()

dev/_downloads/6304a55e6fa4d75c8e8d11b4ea9a8679/plot_pca_3d.py

@@ -26,7 +26,43 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Gael Varoquaux\n#          Jaques Grobler\n#          Kevin Hughes\n# License: BSD 3 clause\n\nfrom sklearn.decomposition import PCA\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import stats\n\n\n# #############################################################################\n# Create the data\n\ne = np.exp(1)\nnp.random.seed(4)\n\n\ndef pdf(x):\n    return 0.5 * (stats.norm(scale=0.25 / e).pdf(x) + stats.norm(scale=4 / e).pdf(x))\n\n\ny = np.random.normal(scale=0.5, size=(30000))\nx = np.random.normal(scale=0.5, size=(30000))\nz = np.random.normal(scale=0.1, size=len(x))\n\ndensity = pdf(x) * pdf(y)\npdf_z = pdf(5 * z)\n\ndensity *= pdf_z\n\na = x + y\nb = 2 * y\nc = a - b + z\n\nnorm = np.sqrt(a.var() + b.var())\na /= norm\nb /= norm\n\n\n# #############################################################################\n# Plot the figures\ndef plot_figs(fig_num, elev, azim):\n    fig = plt.figure(fig_num, figsize=(4, 3))\n    plt.clf()\n    ax = fig.add_subplot(111, projection=\"3d\", elev=elev, azim=azim)\n    ax.set_position([0, 0, 0.95, 1])\n\n    ax.scatter(a[::10], b[::10], c[::10], c=density[::10], marker=\"+\", alpha=0.4)\n    Y = np.c_[a, b, c]\n\n    # Using SciPy's SVD, this would be:\n    # _, pca_score, Vt = scipy.linalg.svd(Y, full_matrices=False)\n\n    pca = PCA(n_components=3)\n    pca.fit(Y)\n    V = pca.components_.T\n\n    x_pca_axis, y_pca_axis, z_pca_axis = 3 * V\n    x_pca_plane = np.r_[x_pca_axis[:2], -x_pca_axis[1::-1]]\n    y_pca_plane = np.r_[y_pca_axis[:2], -y_pca_axis[1::-1]]\n    z_pca_plane = np.r_[z_pca_axis[:2], -z_pca_axis[1::-1]]\n    x_pca_plane.shape = (2, 2)\n    y_pca_plane.shape = (2, 2)\n    z_pca_plane.shape = (2, 2)\n    ax.plot_surface(x_pca_plane, y_pca_plane, z_pca_plane)\n    ax.w_xaxis.set_ticklabels([])\n    ax.w_yaxis.set_ticklabels([])\n    ax.w_zaxis.set_ticklabels([])\n\n\nelev = -40\nazim = -80\nplot_figs(1, elev, azim)\n\nelev = 30\nazim = 20\nplot_figs(2, elev, azim)\n\nplt.show()"
+        "# Authors: Gael Varoquaux\n#          Jaques Grobler\n#          Kevin Hughes\n# License: BSD 3 clause"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create the data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nfrom scipy import stats\n\ne = np.exp(1)\nnp.random.seed(4)\n\n\ndef pdf(x):\n    return 0.5 * (stats.norm(scale=0.25 / e).pdf(x) + stats.norm(scale=4 / e).pdf(x))\n\n\ny = np.random.normal(scale=0.5, size=(30000))\nx = np.random.normal(scale=0.5, size=(30000))\nz = np.random.normal(scale=0.1, size=len(x))\n\ndensity = pdf(x) * pdf(y)\npdf_z = pdf(5 * z)\n\ndensity *= pdf_z\n\na = x + y\nb = 2 * y\nc = a - b + z\n\nnorm = np.sqrt(a.var() + b.var())\na /= norm\nb /= norm"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the figures\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.decomposition import PCA\n\nimport matplotlib.pyplot as plt\n\n# unused but required import for doing 3d projections with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\n\n\ndef plot_figs(fig_num, elev, azim):\n    fig = plt.figure(fig_num, figsize=(4, 3))\n    plt.clf()\n    ax = fig.add_subplot(111, projection=\"3d\", elev=elev, azim=azim)\n    ax.set_position([0, 0, 0.95, 1])\n\n    ax.scatter(a[::10], b[::10], c[::10], c=density[::10], marker=\"+\", alpha=0.4)\n    Y = np.c_[a, b, c]\n\n    # Using SciPy's SVD, this would be:\n    # _, pca_score, Vt = scipy.linalg.svd(Y, full_matrices=False)\n\n    pca = PCA(n_components=3)\n    pca.fit(Y)\n    V = pca.components_.T\n\n    x_pca_axis, y_pca_axis, z_pca_axis = 3 * V\n    x_pca_plane = np.r_[x_pca_axis[:2], -x_pca_axis[1::-1]]\n    y_pca_plane = np.r_[y_pca_axis[:2], -y_pca_axis[1::-1]]\n    z_pca_plane = np.r_[z_pca_axis[:2], -z_pca_axis[1::-1]]\n    x_pca_plane.shape = (2, 2)\n    y_pca_plane.shape = (2, 2)\n    z_pca_plane.shape = (2, 2)\n    ax.plot_surface(x_pca_plane, y_pca_plane, z_pca_plane)\n    ax.w_xaxis.set_ticklabels([])\n    ax.w_yaxis.set_ticklabels([])\n    ax.w_zaxis.set_ticklabels([])\n\n\nelev = -40\nazim = -80\nplot_figs(1, elev, azim)\n\nelev = 30\nazim = 20\nplot_figs(2, elev, azim)\n\nplt.show()"
       ]
     }
   ],