update notebooks and documentation

1 files changed, 96 insertions, 96 deletions

diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb
index 56caa8a..baffef4 100644
--- a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb
+++ b/docs/source/auto_examples/plot_otda_mapping_colors_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": [
+        "%matplotlib inline"
+      ]
+    },
     {
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "\n# OT for image color adaptation with mapping estimation\n\n\nOT for domain adaptation with image color adaptation [6] with mapping\nestimation [8].\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n    discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n    1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n    discrete optimal transport\", Neural Information Processing Systems (NIPS),\n    2016.\n\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+      ]
+    },
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "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\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": [], 
+      "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\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)"
+      ]
+    },
     {
+      "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\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\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": [
         "Domain adaptation for pixel distribution transfer\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)\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\nImage_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\nImage_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n\not_mapping_linear = ot.da.MappingTransport(\n    mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\nX1tl = ot_mapping_linear.transform(Xs=X1)\nImage_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n\not_mapping_gaussian = ot.da.MappingTransport(\n    mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\nX1tn = ot_mapping_gaussian.transform(Xs=X1)  # use the estimated mapping\nImage_mapping_gaussian = minmax(mat2im(X1tn, I1.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)\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\nImage_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\nImage_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n\not_mapping_linear = ot.da.MappingTransport(\n    mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\nX1tl = ot_mapping_linear.transform(Xs=X1)\nImage_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n\not_mapping_gaussian = ot.da.MappingTransport(\n    mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\nX1tn = ot_mapping_gaussian.transform(Xs=X1)  # use the estimated mapping\nImage_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))"
+      ]
+    },
     {
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "Plot original images\n--------------------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+      ]
+    },
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "pl.figure(1, figsize=(6.4, 3))\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')\npl.tight_layout()"
-      ], 
-      "outputs": [], 
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
-    }, 
+      },
+      "outputs": [],
+      "source": [
+        "pl.figure(1, figsize=(6.4, 3))\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')\npl.tight_layout()"
+      ]
+    },
     {
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "Plot pixel values distribution\n------------------------------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+      ]
+    },
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "pl.figure(2, figsize=(6.4, 5))\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, 5))\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": [
         "Plot transformed images\n-----------------------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+      ]
+    },
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "pl.figure(2, figsize=(10, 5))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 3, 2)\npl.imshow(Image_emd)\npl.axis('off')\npl.title('EmdTransport')\n\npl.subplot(2, 3, 5)\npl.imshow(Image_sinkhorn)\npl.axis('off')\npl.title('SinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(Image_mapping_linear)\npl.axis('off')\npl.title('MappingTransport (linear)')\n\npl.subplot(2, 3, 6)\npl.imshow(Image_mapping_gaussian)\npl.axis('off')\npl.title('MappingTransport (gaussian)')\npl.tight_layout()\n\npl.show()"
-      ], 
-      "outputs": [], 
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
+      },
+      "outputs": [],
+      "source": [
+        "pl.figure(2, figsize=(10, 5))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 3, 2)\npl.imshow(Image_emd)\npl.axis('off')\npl.title('EmdTransport')\n\npl.subplot(2, 3, 5)\npl.imshow(Image_sinkhorn)\npl.axis('off')\npl.title('SinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(Image_mapping_linear)\npl.axis('off')\npl.title('MappingTransport (linear)')\n\npl.subplot(2, 3, 6)\npl.imshow(Image_mapping_gaussian)\npl.axis('off')\npl.title('MappingTransport (gaussian)')\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"
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+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
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