Pushing the docs to dev/ for branch: main, commit d37988916405d1b2a59b1c54d409a2205e45f3e4

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,43 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Alexandre Gramfort, Gael Varoquaux\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.decomposition import PCA, FastICA\n\n# #############################################################################\n# Generate sample data\nrng = np.random.RandomState(42)\nS = rng.standard_t(1.5, size=(20000, 2))\nS[:, 0] *= 2.0\n\n# Mix data\nA = np.array([[1, 1], [0, 2]])  # Mixing matrix\n\nX = np.dot(S, A.T)  # Generate observations\n\npca = PCA()\nS_pca_ = pca.fit(X).transform(X)\n\nica = FastICA(random_state=rng)\nS_ica_ = ica.fit(X).transform(X)  # Estimate the sources\n\nS_ica_ /= S_ica_.std(axis=0)\n\n\n# #############################################################################\n# Plot results\n\n\ndef plot_samples(S, axis_list=None):\n    plt.scatter(\n        S[:, 0], S[:, 1], s=2, marker=\"o\", zorder=10, color=\"steelblue\", alpha=0.5\n    )\n    if axis_list is not None:\n        colors = [\"orange\", \"red\"]\n        for color, axis in zip(colors, axis_list):\n            axis /= axis.std()\n            x_axis, y_axis = axis\n            # Trick to get legend to work\n            plt.plot(0.1 * x_axis, 0.1 * y_axis, linewidth=2, color=color)\n            plt.quiver(\n                (0, 0),\n                (0, 0),\n                x_axis,\n                y_axis,\n                zorder=11,\n                width=0.01,\n                scale=6,\n                color=color,\n            )\n\n    plt.hlines(0, -3, 3)\n    plt.vlines(0, -3, 3)\n    plt.xlim(-3, 3)\n    plt.ylim(-3, 3)\n    plt.xlabel(\"x\")\n    plt.ylabel(\"y\")\n\n\nplt.figure()\nplt.subplot(2, 2, 1)\nplot_samples(S / S.std())\nplt.title(\"True Independent Sources\")\n\naxis_list = [pca.components_.T, ica.mixing_]\nplt.subplot(2, 2, 2)\nplot_samples(X / np.std(X), axis_list=axis_list)\nlegend = plt.legend([\"PCA\", \"ICA\"], loc=\"upper right\")\nlegend.set_zorder(100)\n\nplt.title(\"Observations\")\n\nplt.subplot(2, 2, 3)\nplot_samples(S_pca_ / np.std(S_pca_, axis=0))\nplt.title(\"PCA recovered signals\")\n\nplt.subplot(2, 2, 4)\nplot_samples(S_ica_ / np.std(S_ica_))\nplt.title(\"ICA recovered signals\")\n\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.36)\nplt.show()"
+        "# Authors: Alexandre Gramfort, Gael Varoquaux\n# License: BSD 3 clause"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Generate sample data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nfrom sklearn.decomposition import PCA, FastICA\n\nrng = np.random.RandomState(42)\nS = rng.standard_t(1.5, size=(20000, 2))\nS[:, 0] *= 2.0\n\n# Mix data\nA = np.array([[1, 1], [0, 2]])  # Mixing matrix\n\nX = np.dot(S, A.T)  # Generate observations\n\npca = PCA()\nS_pca_ = pca.fit(X).transform(X)\n\nica = FastICA(random_state=rng, whiten=\"arbitrary-variance\")\nS_ica_ = ica.fit(X).transform(X)  # Estimate the sources\n\nS_ica_ /= S_ica_.std(axis=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\n\ndef plot_samples(S, axis_list=None):\n    plt.scatter(\n        S[:, 0], S[:, 1], s=2, marker=\"o\", zorder=10, color=\"steelblue\", alpha=0.5\n    )\n    if axis_list is not None:\n        colors = [\"orange\", \"red\"]\n        for color, axis in zip(colors, axis_list):\n            axis /= axis.std()\n            x_axis, y_axis = axis\n            # Trick to get legend to work\n            plt.plot(0.1 * x_axis, 0.1 * y_axis, linewidth=2, color=color)\n            plt.quiver(\n                (0, 0),\n                (0, 0),\n                x_axis,\n                y_axis,\n                zorder=11,\n                width=0.01,\n                scale=6,\n                color=color,\n            )\n\n    plt.hlines(0, -3, 3)\n    plt.vlines(0, -3, 3)\n    plt.xlim(-3, 3)\n    plt.ylim(-3, 3)\n    plt.xlabel(\"x\")\n    plt.ylabel(\"y\")\n\n\nplt.figure()\nplt.subplot(2, 2, 1)\nplot_samples(S / S.std())\nplt.title(\"True Independent Sources\")\n\naxis_list = [pca.components_.T, ica.mixing_]\nplt.subplot(2, 2, 2)\nplot_samples(X / np.std(X), axis_list=axis_list)\nlegend = plt.legend([\"PCA\", \"ICA\"], loc=\"upper right\")\nlegend.set_zorder(100)\n\nplt.title(\"Observations\")\n\nplt.subplot(2, 2, 3)\nplot_samples(S_pca_ / np.std(S_pca_, axis=0))\nplt.title(\"PCA recovered signals\")\n\nplt.subplot(2, 2, 4)\nplot_samples(S_ica_ / np.std(S_ica_))\nplt.title(\"ICA recovered signals\")\n\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.36)\nplt.show()"
       ]
     }
   ],

dev/_downloads/4ee88a807e060ca374ab95e0d8d819ed/plot_ica_vs_pca.ipynb

@@ -32,13 +32,13 @@
 # Authors: Alexandre Gramfort, Gael Varoquaux
 # License: BSD 3 clause
 
+# %%
+# Generate sample data
+# --------------------
 import numpy as np
-import matplotlib.pyplot as plt
 
 from sklearn.decomposition import PCA, FastICA
 
-# #############################################################################
-# Generate sample data
 rng = np.random.RandomState(42)
 S = rng.standard_t(1.5, size=(20000, 2))
 S[:, 0] *= 2.0
@@ -51,14 +51,16 @@
 pca = PCA()
 S_pca_ = pca.fit(X).transform(X)
 
-ica = FastICA(random_state=rng)
+ica = FastICA(random_state=rng, whiten="arbitrary-variance")
 S_ica_ = ica.fit(X).transform(X)  # Estimate the sources
 
 S_ica_ /= S_ica_.std(axis=0)
 
 
-# #############################################################################
+# %%
 # Plot results
+# ------------
+import matplotlib.pyplot as plt
 
 
 def plot_samples(S, axis_list=None):