remove notebooks and cleanup readme and doc

1 files changed, 0 insertions, 573 deletions

diff --git a/notebooks/plot_stochastic.ipynb b/notebooks/plot_stochastic.ipynb
deleted file mode 100644
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--- a/notebooks/plot_stochastic.ipynb
+++ /dev/null
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-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Stochastic examples\n",
-    "\n",
-    "\n",
-    "This example is designed to show how to use the stochatic optimization\n",
-    "algorithms for descrete and semicontinous measures from the POT library.\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Author: Kilian Fatras <kilian.fatras@gmail.com>\n",
-    "#\n",
-    "# License: MIT License\n",
-    "\n",
-    "import matplotlib.pylab as pl\n",
-    "import numpy as np\n",
-    "import ot\n",
-    "import ot.plot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n",
-    "############################################################################\n",
-    "############################################################################\n",
-    " DISCRETE CASE:\n",
-    "\n",
-    " Sample two discrete measures for the discrete case\n",
-    " ---------------------------------------------\n",
-    "\n",
-    " Define 2 discrete measures a and b, the points where are defined the source\n",
-    " and the target measures and finally the cost matrix c.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "n_source = 7\n",
-    "n_target = 4\n",
-    "reg = 1\n",
-    "numItermax = 1000\n",
-    "\n",
-    "a = ot.utils.unif(n_source)\n",
-    "b = ot.utils.unif(n_target)\n",
-    "\n",
-    "rng = np.random.RandomState(0)\n",
-    "X_source = rng.randn(n_source, 2)\n",
-    "Y_target = rng.randn(n_target, 2)\n",
-    "M = ot.dist(X_source, Y_target)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call the \"SAG\" method to find the transportation matrix in the discrete case\n",
-    "---------------------------------------------\n",
-    "\n",
-    "Define the method \"SAG\", call ot.solve_semi_dual_entropic and plot the\n",
-    "results.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06]\n",
-      " [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03]\n",
-      " [3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07]\n",
-      " [2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04]\n",
-      " [9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01]\n",
-      " [2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01]\n",
-      " [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "method = \"SAG\"\n",
-    "sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n",
-    "                                                numItermax)\n",
-    "print(sag_pi)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "SEMICONTINOUS CASE:\n",
-    "\n",
-    "Sample one general measure a, one discrete measures b for the semicontinous\n",
-    "case\n",
-    "---------------------------------------------\n",
-    "\n",
-    "Define one general measure a, one discrete measures b, the points where\n",
-    "are defined the source and the target measures and finally the cost matrix c.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "n_source = 7\n",
-    "n_target = 4\n",
-    "reg = 1\n",
-    "numItermax = 1000\n",
-    "log = True\n",
-    "\n",
-    "a = ot.utils.unif(n_source)\n",
-    "b = ot.utils.unif(n_target)\n",
-    "\n",
-    "rng = np.random.RandomState(0)\n",
-    "X_source = rng.randn(n_source, 2)\n",
-    "Y_target = rng.randn(n_target, 2)\n",
-    "M = ot.dist(X_source, Y_target)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call the \"ASGD\" method to find the transportation matrix in the semicontinous\n",
-    "case\n",
-    "---------------------------------------------\n",
-    "\n",
-    "Define the method \"ASGD\", call ot.solve_semi_dual_entropic and plot the\n",
-    "results.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[3.7937628  7.65961287 3.80848103 2.58141742 1.61215093 3.44897695\n",
-      " 2.71747327] [-2.52391608 -2.29387992 -0.82558991  5.64338591]\n",
-      "[[2.21553327e-02 1.03145567e-01 1.75528576e-02 3.38501746e-06]\n",
-      " [1.20021720e-01 1.47691349e-02 1.47329335e-03 6.59299438e-03]\n",
-      " [3.04838905e-03 7.78276435e-02 6.19810066e-02 1.03737333e-07]\n",
-      " [2.31393025e-02 3.53135903e-02 8.40777056e-02 3.26544498e-04]\n",
-      " [1.05758118e-02 9.63969840e-04 1.33213201e-02 1.17996041e-01]\n",
-      " [2.34525044e-02 1.16688539e-03 2.32054035e-03 1.15917213e-01]\n",
-      " [3.74708983e-02 2.86448739e-02 7.47858286e-02 1.95554208e-03]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "method = \"ASGD\"\n",
-    "asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n",
-    "                                                           numItermax, log=log)\n",
-    "print(log_asgd['alpha'], log_asgd['beta'])\n",
-    "print(asgd_pi)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compare the results with the Sinkhorn algorithm\n",
-    "---------------------------------------------\n",
-    "\n",
-    "Call the Sinkhorn algorithm from POT\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06]\n",
-      " [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03]\n",
-      " [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07]\n",
-      " [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04]\n",
-      " [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01]\n",
-      " [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01]\n",
-      " [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\n",
-    "print(sinkhorn_pi)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "PLOT TRANSPORTATION MATRIX\n",
-    "#############################################################################\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot SAG results\n",
-    "----------------\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(4, figsize=(5, 5))\n",
-    "ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')\n",
-    "pl.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot ASGD results\n",
-    "-----------------\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(4, figsize=(5, 5))\n",
-    "ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')\n",
-    "pl.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot Sinkhorn results\n",
-    "---------------------\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(4, figsize=(5, 5))\n",
-    "ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\n",
-    "pl.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n",
-    "############################################################################\n",
-    "############################################################################\n",
-    " SEMICONTINOUS CASE:\n",
-    "\n",
-    " Sample one general measure a, one discrete measures b for the semicontinous\n",
-    " case\n",
-    " ---------------------------------------------\n",
-    "\n",
-    " Define one general measure a, one discrete measures b, the points where\n",
-    " are defined the source and the target measures and finally the cost matrix c.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "n_source = 7\n",
-    "n_target = 4\n",
-    "reg = 1\n",
-    "numItermax = 100000\n",
-    "lr = 0.1\n",
-    "batch_size = 3\n",
-    "log = True\n",
-    "\n",
-    "a = ot.utils.unif(n_source)\n",
-    "b = ot.utils.unif(n_target)\n",
-    "\n",
-    "rng = np.random.RandomState(0)\n",
-    "X_source = rng.randn(n_source, 2)\n",
-    "Y_target = rng.randn(n_target, 2)\n",
-    "M = ot.dist(X_source, Y_target)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call the \"SGD\" dual method to find the transportation matrix in the\n",
-    "semicontinous case\n",
-    "---------------------------------------------\n",
-    "\n",
-    "Call ot.solve_dual_entropic and plot the results.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.91323163 2.78641673 1.06629943 0.01804936 0.61062914 1.81958274\n",
-      " 0.11217916] [0.34004858 0.48129361 1.57541501 4.92963099]\n",
-      "[[2.17913197e-02 9.28312769e-02 1.08665637e-02 9.30212767e-08]\n",
-      " [1.60939150e-02 1.81215529e-03 1.24345544e-04 2.47002125e-05]\n",
-      " [3.44318299e-03 8.04381532e-02 4.40643612e-02 3.27371535e-09]\n",
-      " [3.12534315e-02 4.36443287e-02 7.14771848e-02 1.23227019e-05]\n",
-      " [6.81023930e-02 5.68002968e-03 5.39927027e-02 2.12291313e-02]\n",
-      " [8.06039569e-02 3.66972769e-03 5.01990391e-03 1.11309234e-02]\n",
-      " [4.85325711e-02 3.39488142e-02 6.09673962e-02 7.07656480e-05]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,\n",
-    "                                                         batch_size, numItermax,\n",
-    "                                                         lr, log=log)\n",
-    "print(log_sgd['alpha'], log_sgd['beta'])\n",
-    "print(sgd_dual_pi)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compare the results with the Sinkhorn algorithm\n",
-    "---------------------------------------------\n",
-    "\n",
-    "Call the Sinkhorn algorithm from POT\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06]\n",
-      " [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03]\n",
-      " [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07]\n",
-      " [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04]\n",
-      " [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01]\n",
-      " [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01]\n",
-      " [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\n",
-    "print(sinkhorn_pi)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot  SGD results\n",
-    "-----------------\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(4, figsize=(5, 5))\n",
-    "ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')\n",
-    "pl.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot Sinkhorn results\n",
-    "---------------------\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pl.figure(4, figsize=(5, 5))\n",
-    "ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\n",
-    "pl.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": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}