Pushing the docs to dev/ for branch: master, commit 8342548ae18b36d814440b46ed154c96bd8e15ce

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,62 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.linear_model import OrthogonalMatchingPursuit\nfrom sklearn.linear_model import OrthogonalMatchingPursuitCV\nfrom sklearn.datasets import make_sparse_coded_signal\n\nn_components, n_features = 512, 100\nn_nonzero_coefs = 17\n\n# generate the data\n###################\n\n# y = Xw\n# |x|_0 = n_nonzero_coefs\n\ny, X, w = make_sparse_coded_signal(n_samples=1,\n                                   n_components=n_components,\n                                   n_features=n_features,\n                                   n_nonzero_coefs=n_nonzero_coefs,\n                                   random_state=0)\n\nidx, = w.nonzero()\n\n# distort the clean signal"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "y_noisy = y + 0.05 * np.random.randn(len(y))\n\n# plot the sparse signal"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "plt.figure(figsize=(7, 7))\nplt.subplot(4, 1, 1)\nplt.xlim(0, 512)\nplt.title(\"Sparse signal\")\nplt.stem(idx, w[idx])\n\n# plot the noise-free reconstruction"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)\nomp.fit(X, y)\ncoef = omp.coef_\nidx_r, = coef.nonzero()\nplt.subplot(4, 1, 2)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noise-free measurements\")\nplt.stem(idx_r, coef[idx_r])\n\n# plot the noisy reconstruction"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "omp.fit(X, y_noisy)\ncoef = omp.coef_\nidx_r, = coef.nonzero()\nplt.subplot(4, 1, 3)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements\")\nplt.stem(idx_r, coef[idx_r])\n\n# plot the noisy reconstruction with number of non-zeros set by CV"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "omp_cv = OrthogonalMatchingPursuitCV(cv=5)\nomp_cv.fit(X, y_noisy)\ncoef = omp_cv.coef_\nidx_r, = coef.nonzero()\nplt.subplot(4, 1, 4)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements with CV\")\nplt.stem(idx_r, coef[idx_r])\n\nplt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)\nplt.suptitle('Sparse signal recovery with Orthogonal Matching Pursuit',\n             fontsize=16)\nplt.show()"
+        "print(__doc__)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.linear_model import OrthogonalMatchingPursuit\nfrom sklearn.linear_model import OrthogonalMatchingPursuitCV\nfrom sklearn.datasets import make_sparse_coded_signal\n\nn_components, n_features = 512, 100\nn_nonzero_coefs = 17\n\n# generate the data\n\n# y = Xw\n# |x|_0 = n_nonzero_coefs\n\ny, X, w = make_sparse_coded_signal(n_samples=1,\n                                   n_components=n_components,\n                                   n_features=n_features,\n                                   n_nonzero_coefs=n_nonzero_coefs,\n                                   random_state=0)\n\nidx, = w.nonzero()\n\n# distort the clean signal\ny_noisy = y + 0.05 * np.random.randn(len(y))\n\n# plot the sparse signal\nplt.figure(figsize=(7, 7))\nplt.subplot(4, 1, 1)\nplt.xlim(0, 512)\nplt.title(\"Sparse signal\")\nplt.stem(idx, w[idx])\n\n# plot the noise-free reconstruction\nomp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)\nomp.fit(X, y)\ncoef = omp.coef_\nidx_r, = coef.nonzero()\nplt.subplot(4, 1, 2)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noise-free measurements\")\nplt.stem(idx_r, coef[idx_r])\n\n# plot the noisy reconstruction\nomp.fit(X, y_noisy)\ncoef = omp.coef_\nidx_r, = coef.nonzero()\nplt.subplot(4, 1, 3)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements\")\nplt.stem(idx_r, coef[idx_r])\n\n# plot the noisy reconstruction with number of non-zeros set by CV\nomp_cv = OrthogonalMatchingPursuitCV(cv=5)\nomp_cv.fit(X, y_noisy)\ncoef = omp_cv.coef_\nidx_r, = coef.nonzero()\nplt.subplot(4, 1, 4)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements with CV\")\nplt.stem(idx_r, coef[idx_r])\n\nplt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)\nplt.suptitle('Sparse signal recovery with Orthogonal Matching Pursuit',\n             fontsize=16)\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -18,7 +18,6 @@
 n_nonzero_coefs = 17
 
 # generate the data
-###################
 
 # y = Xw
 # |x|_0 = n_nonzero_coefs
@@ -32,20 +31,16 @@
 idx, = w.nonzero()
 
 # distort the clean signal
-##########################
 y_noisy = y + 0.05 * np.random.randn(len(y))
 
 # plot the sparse signal
-########################
 plt.figure(figsize=(7, 7))
 plt.subplot(4, 1, 1)
 plt.xlim(0, 512)
 plt.title("Sparse signal")
 plt.stem(idx, w[idx])
 
 # plot the noise-free reconstruction
-####################################
-
 omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
 omp.fit(X, y)
 coef = omp.coef_
@@ -56,7 +51,6 @@
 plt.stem(idx_r, coef[idx_r])
 
 # plot the noisy reconstruction
-###############################
 omp.fit(X, y_noisy)
 coef = omp.coef_
 idx_r, = coef.nonzero()
@@ -66,7 +60,6 @@
 plt.stem(idx_r, coef[idx_r])
 
 # plot the noisy reconstruction with number of non-zeros set by CV
-##################################################################
 omp_cv = OrthogonalMatchingPursuitCV(cv=5)
 omp_cv.fit(X, y_noisy)
 coef = omp_cv.coef_