Pushing the docs to dev/ for branch: master, commit ae8223356b5e3618ce5a0e18070b03272a81c36b

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -0,0 +1,54 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n=============================================================================\n t-SNE: The effect of various perplexity values on the shape\n=============================================================================\n\nAn illustration of t-SNE on the two concentric circles and the S-curve\ndatasets for different perplexity values.\n\nWe observe a tendency towards clearer shapes as the preplexity value increases.\n\nThe size, the distance and the shape of clusters may vary upon initialization,\nperplexity values and does not always convey a meaning.\n\nAs shown below, t-SNE for higher perplexities finds meaningful topology of\ntwo concentric circles, however the size and the distance of the circles varies\nslightly from the original. Contrary to the two circles dataset, the shapes\nvisually diverge from S-curve topology on the S-curve dateset even for\nlarger perplexity values.\n\nFor further details, \"How to Use t-SNE Effectively\"\nhttp://distill.pub/2016/misread-tsne/ provides a good discussion of the\neffects of various parameters, as well as interactive plots to explore\nthose effects.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Author: Narine Kokhlikyan <[email protected]>\n# License: BSD\n\nprint(__doc__)\n\nimport matplotlib.pyplot as plt\n\nfrom matplotlib.ticker import NullFormatter\nfrom sklearn import manifold, datasets\nfrom time import time\n\nn_samples = 500\nn_components = 2\n(fig, subplots) = plt.subplots(2, 5, figsize=(15, 8))\nperplexities = [5, 50, 100, 150]\n\nX, y = datasets.make_circles(n_samples=n_samples, factor=.5, noise=.05)\n\nred = y == 0\ngreen = y == 1\n\nax = subplots[0][0]\nax.scatter(X[red, 0], X[red, 1], c=\"r\")\nax.scatter(X[green, 0], X[green, 1], c=\"g\")\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\nfor i, perplexity in enumerate(perplexities):\n    ax = subplots[0][i + 1]\n\n    t0 = time()\n    tsne = manifold.TSNE(n_components=n_components, init='random',\n                         random_state=0, perplexity=perplexity)\n    Y = tsne.fit_transform(X)\n    t1 = time()\n    print(\"circles, perplexity=%d in %.2g sec\" % (perplexity, t1 - t0))\n    ax.set_title(\"Perplexity=%d\" % perplexity)\n    ax.scatter(Y[red, 0], Y[red, 1], c=\"r\")\n    ax.scatter(Y[green, 0], Y[green, 1], c=\"g\")\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    ax.axis('tight')\n\n# Another example using s-curve\nX, color = datasets.samples_generator.make_s_curve(n_samples, random_state=0)\n\nax = subplots[1][0]\nax.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral)\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\n\nfor i, perplexity in enumerate(perplexities):\n    ax = subplots[1][i + 1]\n\n    t0 = time()\n    tsne = manifold.TSNE(n_components=n_components, init='random',\n                         random_state=0, perplexity=perplexity)\n    Y = tsne.fit_transform(X)\n    t1 = time()\n    print(\"S-curve, perplexity=%d in %.2g sec\" % (perplexity, t1 - t0))\n\n    ax.set_title(\"Perplexity=%d\" % perplexity)\n    ax.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    ax.axis('tight')\n\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.1"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

dev/_downloads/auto_examples_python.zip

@@ -0,0 +1,94 @@
+"""
+=============================================================================
+ t-SNE: The effect of various perplexity values on the shape
+=============================================================================
+
+An illustration of t-SNE on the two concentric circles and the S-curve
+datasets for different perplexity values.
+
+We observe a tendency towards clearer shapes as the preplexity value increases.
+
+The size, the distance and the shape of clusters may vary upon initialization,
+perplexity values and does not always convey a meaning.
+
+As shown below, t-SNE for higher perplexities finds meaningful topology of
+two concentric circles, however the size and the distance of the circles varies
+slightly from the original. Contrary to the two circles dataset, the shapes
+visually diverge from S-curve topology on the S-curve dateset even for
+larger perplexity values.
+
+For further details, "How to Use t-SNE Effectively"
+http://distill.pub/2016/misread-tsne/ provides a good discussion of the
+effects of various parameters, as well as interactive plots to explore
+those effects.
+"""
+
+# Author: Narine Kokhlikyan <[email protected]>
+# License: BSD
+
+print(__doc__)
+
+import matplotlib.pyplot as plt
+
+from matplotlib.ticker import NullFormatter
+from sklearn import manifold, datasets
+from time import time
+
+n_samples = 500
+n_components = 2
+(fig, subplots) = plt.subplots(2, 5, figsize=(15, 8))
+perplexities = [5, 50, 100, 150]
+
+X, y = datasets.make_circles(n_samples=n_samples, factor=.5, noise=.05)
+
+red = y == 0
+green = y == 1
+
+ax = subplots[0][0]
+ax.scatter(X[red, 0], X[red, 1], c="r")
+ax.scatter(X[green, 0], X[green, 1], c="g")
+ax.xaxis.set_major_formatter(NullFormatter())
+ax.yaxis.set_major_formatter(NullFormatter())
+plt.axis('tight')
+
+for i, perplexity in enumerate(perplexities):
+    ax = subplots[0][i + 1]
+
+    t0 = time()
+    tsne = manifold.TSNE(n_components=n_components, init='random',
+                         random_state=0, perplexity=perplexity)
+    Y = tsne.fit_transform(X)
+    t1 = time()
+    print("circles, perplexity=%d in %.2g sec" % (perplexity, t1 - t0))
+    ax.set_title("Perplexity=%d" % perplexity)
+    ax.scatter(Y[red, 0], Y[red, 1], c="r")
+    ax.scatter(Y[green, 0], Y[green, 1], c="g")
+    ax.xaxis.set_major_formatter(NullFormatter())
+    ax.yaxis.set_major_formatter(NullFormatter())
+    ax.axis('tight')
+
+# Another example using s-curve
+X, color = datasets.samples_generator.make_s_curve(n_samples, random_state=0)
+
+ax = subplots[1][0]
+ax.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral)
+ax.xaxis.set_major_formatter(NullFormatter())
+ax.yaxis.set_major_formatter(NullFormatter())
+
+for i, perplexity in enumerate(perplexities):
+    ax = subplots[1][i + 1]
+
+    t0 = time()
+    tsne = manifold.TSNE(n_components=n_components, init='random',
+                         random_state=0, perplexity=perplexity)
+    Y = tsne.fit_transform(X)
+    t1 = time()
+    print("S-curve, perplexity=%d in %.2g sec" % (perplexity, t1 - t0))
+
+    ax.set_title("Perplexity=%d" % perplexity)
+    ax.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
+    ax.xaxis.set_major_formatter(NullFormatter())
+    ax.yaxis.set_major_formatter(NullFormatter())
+    ax.axis('tight')
+
+plt.show()