Update doc + notebooks

docs/cache_nbrun

@@ -1 +1 @@
-{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_stochastic.ipynb": "e2c520150378ae4635f74509f687fa01", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_convolutional_barycenter.ipynb": "a72bb3716a1baaffd81ae267a673f9b6", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"}
+{"plot_otda_mapping_colors_images.ipynb": "cc8bf9a857f52e4a159fe71dfda19018", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_otda_color_images.ipynb": "f804d5806c7ac1a0901e4542b1eaa77b", "plot_stochastic.ipynb": "e18253354c8c1d72567a4259eb1094f7", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_convolutional_barycenter.ipynb": "a72bb3716a1baaffd81ae267a673f9b6", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"}

docs/source/auto_examples/index.rst

@@ -229,7 +229,7 @@ This is a gallery of all the POT example files.
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example presents a way of transferring colors between two image with Optimal Transport as ...">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example presents a way of transferring colors between two images with Optimal Transport as...">
 
 .. only:: html
 

docs/source/auto_examples/plot_otda_color_images.ipynb

@@ -1,144 +1,144 @@
 {
-  "nbformat_minor": 0, 
-  "nbformat": 4, 
   "cells": [
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "%matplotlib inline"
-      ], 
-      "outputs": [], 
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
-    }, 
-    {
+      },
+      "outputs": [],
       "source": [
-        "\n# OT for image color adaptation\n\n\nThis example presents a way of transferring colors between two image\nwith Optimal Transport as introduced in [6]\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport.\nSIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+        "%matplotlib inline"
+      ]
+    },
     {
-      "execution_count": null, 
-      "cell_type": "code", 
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "# Authors: Remi Flamary <remi.flamary@unice.fr>\n#          Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n    \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n    return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n    \"\"\"Converts back a matrix to an image\"\"\"\n    return X.reshape(shape)\n\n\ndef minmax(I):\n    return np.clip(I, 0, 1)"
-      ], 
-      "outputs": [], 
+        "\n# OT for image color adaptation\n\n\nThis example presents a way of transferring colors between two images\nwith Optimal Transport as introduced in [6]\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport.\nSIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
-    }, 
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Remi Flamary <remi.flamary@unice.fr>\n#          Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n    \"\"\"Converts an image to matrix (one pixel per line)\"\"\"\n    return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n    \"\"\"Converts back a matrix to an image\"\"\"\n    return X.reshape(shape)\n\n\ndef minmax(I):\n    return np.clip(I, 0, 1)"
+      ]
+    },
     {
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "Generate data\n-------------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+      ]
+    },
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]"
-      ], 
-      "outputs": [], 
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
-    }, 
+      },
+      "outputs": [],
+      "source": [
+        "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]"
+      ]
+    },
     {
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "Plot original image\n-------------------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+      ]
+    },
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "pl.figure(1, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')"
-      ], 
-      "outputs": [], 
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
-    }, 
+      },
+      "outputs": [],
+      "source": [
+        "pl.figure(1, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')"
+      ]
+    },
     {
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "Scatter plot of colors\n----------------------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+      ]
+    },
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "pl.figure(2, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()"
-      ], 
-      "outputs": [], 
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
-    }, 
+      },
+      "outputs": [],
+      "source": [
+        "pl.figure(2, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()"
+      ]
+    },
     {
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+      ]
+    },
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# prediction between images (using out of sample prediction as in [6])\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\ntransp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n\ntransp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\ntransp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(transp_Xs_emd, I1.shape))\nI2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n\nI1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\nI2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))"
-      ], 
-      "outputs": [], 
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
-    }, 
+      },
+      "outputs": [],
+      "source": [
+        "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# prediction between images (using out of sample prediction as in [6])\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\ntransp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\ntransp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(transp_Xs_emd, I1.shape))\nI2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n\nI1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\nI2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))"
+      ]
+    },
     {
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "Plot new images\n---------------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+      ]
+    },
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "pl.figure(3, figsize=(8, 4))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(2, 3, 2)\npl.imshow(I1t)\npl.axis('off')\npl.title('Image 1 Adapt')\n\npl.subplot(2, 3, 3)\npl.imshow(I1te)\npl.axis('off')\npl.title('Image 1 Adapt (reg)')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\n\npl.subplot(2, 3, 5)\npl.imshow(I2t)\npl.axis('off')\npl.title('Image 2 Adapt')\n\npl.subplot(2, 3, 6)\npl.imshow(I2te)\npl.axis('off')\npl.title('Image 2 Adapt (reg)')\npl.tight_layout()\n\npl.show()"
-      ], 
-      "outputs": [], 
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
+      },
+      "outputs": [],
+      "source": [
+        "pl.figure(3, figsize=(8, 4))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(2, 3, 2)\npl.imshow(I1t)\npl.axis('off')\npl.title('Image 1 Adapt')\n\npl.subplot(2, 3, 3)\npl.imshow(I1te)\npl.axis('off')\npl.title('Image 1 Adapt (reg)')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\n\npl.subplot(2, 3, 5)\npl.imshow(I2t)\npl.axis('off')\npl.title('Image 2 Adapt')\n\npl.subplot(2, 3, 6)\npl.imshow(I2te)\npl.axis('off')\npl.title('Image 2 Adapt (reg)')\npl.tight_layout()\n\npl.show()"
+      ]
     }
-  ], 
+  ],
   "metadata": {
     "kernelspec": {
-      "display_name": "Python 2", 
-      "name": "python2", 
-      "language": "python"
-    }, 
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
     "language_info": {
-      "mimetype": "text/x-python", 
-      "nbconvert_exporter": "python", 
-      "name": "python", 
-      "file_extension": ".py", 
-      "version": "2.7.12", 
-      "pygments_lexer": "ipython2", 
       "codemirror_mode": {
-        "version": 2, 
-        "name": "ipython"
-      }
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
     }
-  }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

docs/source/auto_examples/plot_otda_color_images.py

@@ -4,7 +4,7 @@
 OT for image color adaptation
 =============================
 
-This example presents a way of transferring colors between two image
+This example presents a way of transferring colors between two images
 with Optimal Transport as introduced in [6]
 
 [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).
@@ -27,7 +27,7 @@
 
 
 def im2mat(I):
-    """Converts and image to matrix (one pixel per line)"""
+    """Converts an image to matrix (one pixel per line)"""
     return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
 
 
@@ -115,8 +115,8 @@ def minmax(I):
 transp_Xs_emd = ot_emd.transform(Xs=X1)
 transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)
 
-transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)
-transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)
+transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)
+transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)
 
 I1t = minmax(mat2im(transp_Xs_emd, I1.shape))
 I2t = minmax(mat2im(transp_Xt_emd, I2.shape))

docs/source/auto_examples/plot_otda_color_images.rst

@@ -7,7 +7,7 @@
 OT for image color adaptation
 =============================
 
-This example presents a way of transferring colors between two image
+This example presents a way of transferring colors between two images
 with Optimal Transport as introduced in [6]
 
 [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).
@@ -34,7 +34,7 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
 
 
     def im2mat(I):
-        """Converts and image to matrix (one pixel per line)"""
+        """Converts an image to matrix (one pixel per line)"""
         return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
 
 
@@ -168,8 +168,8 @@ Instantiate the different transport algorithms and fit them
     transp_Xs_emd = ot_emd.transform(Xs=X1)
     transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)
 
-    transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)
-    transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)
+    transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)
+    transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)
 
     I1t = minmax(mat2im(transp_Xs_emd, I1.shape))
     I2t = minmax(mat2im(transp_Xt_emd, I2.shape))
@@ -235,11 +235,13 @@ Plot new images
 
 
 
-**Total running time of the script:** ( 3 minutes  16.469 seconds)
+**Total running time of the script:** ( 3 minutes  55.541 seconds)
 
 
 
-.. container:: sphx-glr-footer
+.. only :: html
+
+ .. container:: sphx-glr-footer
 
 
   .. container:: sphx-glr-download
@@ -252,6 +254,9 @@ Plot new images
 
      :download:`Download Jupyter notebook: plot_otda_color_images.ipynb <plot_otda_color_images.ipynb>`
 
-.. rst-class:: sphx-glr-signature
 
-    `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_