Untitled2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os,sys\n",
    "#module_path = os.path.abspath(os.path.join('/Users/vayer/Documents/OT/Python/GW_tests/'))\n",
    "#if module_path not in sys.path:\n",
    "#    sys.path.append(module_path)\n",
    "from graph import *\n",
    "import copy\n",
    "import NN,time\n",
    "\n",
    "from ot_distances import Gromov_Wasserstein_distance,Wasserstein_distance\n",
    "\n",
    "\n",
    "from ts import*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os,sys\n",
    "from graph import *\n",
    "import copy\n",
    "import NN,time\n",
    "from ot_distances import Gromov_Wasserstein_distance,Wasserstein_distance\n",
    "from ts import*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path='./UCR_TS_Archive_2015/Lighting2/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset=create_ts(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sodTnSqkYSplJqYOwUkrUZl233lL5WtmwvvoNcX/7Ogz76ntbCDVDp25rMVWR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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b63828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset[3][0].plot_ts(color='r')\n",
    "dataset[2][0].plot_ts()\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OO27Fgw8+gHK5DKA2EOrLX/436/377rsXc+fujLGxMbz66qu4/vqr8cwzT2P16lV44IH7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yQ0FMdm45cdFv67TSFf9fPBrSRePbUnT09wq96+DqeTf5HmkFvfM83prlsWrnBYOFRS+vDDD5Mk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\n14wOtoykgG2SVr190UeJaKXb/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rvtNk4//XTe8Y53jFun529zzJo1i1//+tf1clm0aBFx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\nrEgmk3PrNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110525470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TS().plot_sample_ts_dataset(dataset,N=10,lw=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dist=Wasserstein_distance().ts_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2 µs, sys: 0 ns, total: 2 µs\n",
      "Wall time: 5.01 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0021151535508380954"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "dist(dataset[0][0],dataset[36][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vo/GXfFLs2nopP+u41lc7xvmE/jGvZw5hzTK0YeOaOG69XB5LnBSrWRfWaDX2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\nySAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110d94978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(dataset[0][0].distance_matrix())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dist=Gromov_distance(epsilon=1,max_iter=12,method='time').ts_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs\n",
      "Wall time: 5.01 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.078017983317746079"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "dist(dataset[0][0],dataset[36][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dist=Gromov_Wasserstein_distance(epsilon=1,ratio=0.5,method='time').ts_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "start=time.time()\n",
    "dist(dataset[0][0],dataset[36][0])\n",
    "end=time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29447388648986816"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z//JV+c1GJyvH4ftIpbSHsAsMg9zXKQUAoFfxxfPNimJxfla9/Ln6Hz/8rI57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MnKX1EXF2Hrc4nzB2Xr3QdtTdk5a99+lC5ZcVbfC2TfzcK9R0CjOavcd9XX/RPjN7IgdLQ7e/+v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\nVEREpvsLF4R9SGoDA7MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11327a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset[0][0].plot_ts(color='r')\n",
    "dataset[36][0].plot_ts()\n",
    "pl.legend([dataset[0][1],dataset[36][1]])\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Some tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On est sur un problème à 5 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path='./UCR_TS_Archive_2015/Lighting2/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train=create_ts(path,t='TRAIN')\n",
    "test=create_ts(path,t='TEST')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train,y_train=zip(*train)\n",
    "x_test,y_test=zip(*test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf_wgw=NN.Ts_WGW_1NN_Classifier(epsilon=10,ratio=2.8,verbose=True,normalize_distance=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "0.413463830947876\n"
     ]
    }
   ],
   "source": [
    "start=time.time()\n",
    "clf_wgw.compute_similarity(x_train[0],x_train[1])\n",
    "end=time.time()\n",
    "print(end-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "637"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train[0].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8362.199999999999"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.6*181*77"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute():\n",
    "    start=time.time()\n",
    "    preds=clf_emd.fit(np.array(x_train),np.array(y_train)).predict(np.array(x_test))\n",
    "    end=time.time()\n",
    "    return end-start,preds "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV, cross_val_score, KFold\n",
    "import numpy as np\n",
    "import os,sys\n",
    "from graph import *\n",
    "import copy\n",
    "import NN,time\n",
    "from ot_distances import Gromov_Wasserstein_distance,Gromov_distance,Wasserstein_distance\n",
    "from ts import*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path='./UCR_TS_Archive_2015/Lighting2/'\n",
    "train=create_ts(path,t='TRAIN')\n",
    "test=create_ts(path,t='TEST')\n",
    "x_train,y_train=zip(*train)\n",
    "x_test,y_test=zip(*test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_jobs=1\n",
    "n_splits=2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tuned_parameters = [{'epsilon':list(np.linspace(0.01,50,1)),\n",
    "\t\t\t\t\t 'ratio':list(np.linspace(0.01,2.5,1))\n",
    "                     ,'method':['time']\n",
    "                     ,'normalize_distance':[True]\n",
    "                     ,'features_metric':['sqeuclidean']\n",
    "                     ,'timedistance':['sqeuclidean']}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wgw_1NN=NN.Ts_WGW_1NN_Classifier()\n",
    "clf = GridSearchCV(wgw_1NN, tuned_parameters, cv=n_splits,scoring='accuracy',verbose=1,n_jobs=n_jobs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 1 candidates, totalling 2 fits\n",
      "Les distances entre pour chaque point et eux mêmes sont calculées\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=2.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Les distances entre pour chaque point et eux mêmes sont calculées\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    4.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Les distances entre pour chaque point et eux mêmes sont calculées\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=2, error_score='raise',\n",
       "       estimator=Ts_WGW_1NN_Classifier(epsilon=1, features_metric='sqeuclidean', method='time',\n",
       "           normalize_distance=False, ratio=1, timedistance='sqeuclidean'),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid=[{'epsilon': [0.01], 'normalize_distance': [True], 'timedistance': ['sqeuclidean'], 'features_metric': ['sqeuclidean'], 'method': ['time'], 'ratio': [0.01]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(np.array(x_train[0:4]).reshape(-1,1),np.array(y_train[0:4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clf.best_estimator_.similarities_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yo method\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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     "execution_count": 8,
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   "source": [
    "clf.best_estimator_.similarities_dict"
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  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ils sont diff  <ts.TS object at 0x10fcfd940> <ts.TS object at 0x10fcfdfd0> 0 0\n",
      "sheisse_xy entre  0 0\n",
      "sheisse_yy entre  0 0\n",
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      "sheisse_xy entre  2 0\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd898> <ts.TS object at 0x10fcfdfd0> 3 0\n",
      "sheisse_xy entre  3 0\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd940> <ts.TS object at 0x10fe5ff28> 0 1\n",
      "sheisse_xy entre  0 1\n",
      "sheisse_yy entre  0 0\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd7f0> <ts.TS object at 0x10fe5ff28> 1 1\n",
      "sheisse_xy entre  1 1\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd978> <ts.TS object at 0x10fe5ff28> 2 1\n",
      "sheisse_xy entre  2 1\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd898> <ts.TS object at 0x10fe5ff28> 3 1\n",
      "sheisse_xy entre  3 1\n"
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       "array([-1, -1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(np.array(x_test)[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ils sont diff  <ts.TS object at 0x10fcfd940> <ts.TS object at 0x10fcfdfd0> 0 0\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd7f0> <ts.TS object at 0x10fcfdfd0> 1 0\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd978> <ts.TS object at 0x10fcfdfd0> 2 0\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd898> <ts.TS object at 0x10fcfdfd0> 3 0\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd940> <ts.TS object at 0x10fe5ff28> 0 1\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd7f0> <ts.TS object at 0x10fe5ff28> 1 1\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd978> <ts.TS object at 0x10fe5ff28> 2 1\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd898> <ts.TS object at 0x10fe5ff28> 3 1\n"
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "clf.predict(np.array(x_test)[0:2])"
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ils sont diff  <ts.TS object at 0x10fcfd940> <ts.TS object at 0x10fcfdfd0> 0 0\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd7f0> <ts.TS object at 0x10fcfdfd0> 1 0\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd978> <ts.TS object at 0x10fcfdfd0> 2 0\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd898> <ts.TS object at 0x10fcfdfd0> 3 0\n",
      "Ils sont diff  <ts.TS object at 0x10fcfd940> <ts.TS object at 0x10fe5ff28> 0 1\n",
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      "Ils sont diff  <ts.TS object at 0x10fcfd978> <ts.TS object at 0x10fe5ff28> 2 1\n",
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "clf.best_estimator_.predict(np.array(x_test)[0:2])"
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  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clf.best_estimator_.similarities_dict)"
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wgw_1NN=NN.Ts_WGW_1NN_Classifier(**clf.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Les distances entre pour chaque point et eux mêmes sont calculées\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1,  1])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wgw_1NN.fit(np.array(x_train[0:4]).reshape(-1,1),np.array(y_train[0:4])).predict(np.array(x_test)[0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yo method\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0]==x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ts.TS at 0x1103f1a90>"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'epsilon': 0.01,\n",
       " 'features_metric': 'sqeuclidean',\n",
       " 'method': 'time',\n",
       " 'normalize_distance': True,\n",
       " 'ratio': 0.01,\n",
       " 'timedistance': 'sqeuclidean'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nTree=100\n",
    "depth=3\n",
    "c=20\n",
    "d=30\n",
    "dataset=build_binary_uniform_dataset(nTree1=nTree,nTree2=nTree,maxdepth=depth,c=c,d=d)\n",
    "X,y=zip(*dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "graph1=X[0]\n",
    "graph2=X[101]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/ot/bregman.py:344: RuntimeWarning: overflow encountered in exp\n",
      "  K = np.exp(-M / reg)\n",
      "//anaconda/lib/python3.5/site-packages/ot/bregman.py:347: RuntimeWarning: overflow encountered in multiply\n",
      "  Kp = (1 / a).reshape(-1, 1) * K\n",
      "/Users/vayer/Documents/OT/Python/GW_tests/WGW_2.py:69: RuntimeWarning: overflow encountered in multiply\n",
      "  log_struct['GW_dist'].append(np.sum(T*Cost))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n"
     ]
    }
   ],
   "source": [
    "cpts=[]\n",
    "temps=[]\n",
    "i=0\n",
    "N=10\n",
    "espilons=list(np.logspace(0.01,150,5))\n",
    "ratios=list(np.logspace(0.001,0.5,5))\n",
    "for i in range(N):\n",
    "    for eps in espilons:\n",
    "        for ra in ratios:\n",
    "    #for eps,ra in zip(espilons,ratios):\n",
    "        \n",
    "            graph1=X[random.randint(0,len(X)-1)]\n",
    "            graph2=X[random.randint(0,len(X)-1)]\n",
    "            start=time.time()\n",
    "            gw=Gromov_Wasserstein_distance(epsilon=eps,ratio=ra,method='weighted_shortest_path')\n",
    "            a=gw.graph_d(graph1,graph2)\n",
    "            end=time.time()\n",
    "            cpts.append(gw.log['cpt'])\n",
    "            temps.append(end-start)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gw=Gromov_Wasserstein_distance(epsilon=0.01,ratio=2,method='weighted_shortest_path')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/ot/bregman.py:344: RuntimeWarning: overflow encountered in exp\n",
      "  K = np.exp(-M / reg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gw.graph_d(graph1,graph2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "         nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gw.transp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GCSKiFO4FqfRnIrIYJIiIUrAiQRSPQYKIKAV7JIjiMUgQEaVgRYIoHoMEEVGKaI8EgwRR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\nizsYUq+9Zg8kku6PEfJZ2L/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mBYn9+4E777TNoePjwK231k/T31891bSIHomQ60i4G2F9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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ee6ea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cpts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GZweO79bC/U3VsBXALly4EOPj41BVFX5/NqIeHh5GJBJBd3d3wXOHhoYMjw0PD2P+/Pn6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\ngiAIgiAIoqr4/wF7/5+7iuKV4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115240b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(temps)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(cpts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os,sys\n",
    "from graph import *\n",
    "from ts import *\n",
    "import copy\n",
    "import NN,time\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, KFold\n",
    "from WGW_3 import WGW\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path='./UCR_TS_Archive_2015/Lighting2/'\n",
    "train=create_ts(path,t='TRAIN')\n",
    "test=create_ts(path,t='TEST')\n",
    "x_train,y_train=zip(*train)\n",
    "x_test,y_test=zip(*test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "size=50\n",
    "train=create_random_ts(N=size,a=0,b=300)\n",
    "test=create_random_ts(N=size,a=0,b=300)\n",
    "x_train,y_train=zip(*train)\n",
    "x_test,y_test=zip(*test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from ot_distances import Gromov_distance as GW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts1=x_train[0]\n",
    "ts2=x_train[1]\n",
    "C1=ts1.distance_matrix(method='time')\n",
    "C2=ts2.distance_matrix(method='time')\n",
    "\n",
    "t1masses = np.ones(len(ts1.values))/len(ts1.values)\n",
    "t2masses = np.ones(len(ts2.values))/len(ts2.values)\n",
    "w=WGW(np.zeros((C1.shape[0],C2.shape[0])),C1,C2,t1masses,t2masses,15,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def main(N=50):\n",
    "    import ot\n",
    "    import numpy as np\n",
    "    size=N\n",
    "    x1=list(np.random.uniform(low=0, high=300, size=(N,)))\n",
    "    times1=range(len(x1))\n",
    "    x2=list(np.random.uniform(low=0, high=300, size=(N,)))\n",
    "    times2=range(len(x2))\n",
    "    t1masses = np.ones(len(x1))/len(x1)\n",
    "    t2masses = np.ones(len(x2))/len(x2)\n",
    "    C=ot.dist(np.array(times1).reshape(-1,1),np.array(times1).reshape(-1,1))\n",
    "    for i  in range(1000000):\n",
    "        c=ot.sinkhorn(t1masses,t2masses,C,10,verbose=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-74c75d050d9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m#tens = w.constC-np.dot(w.hC1, w.T).dot(np.transpose(w.hC2))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;31m#tens = w.constC\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mtens\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhC1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhC2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#tens = w.constC-np.dot(w.hC1, w.T).dot(w.hC2.T)\n",
    "#Cost = w.G+w.alpha*tens\n",
    "for i  in range(1000000):\n",
    "    #gw=GW(epsilon=15,method='time')\n",
    "    #d=gw.ts_d(x_train[0],x_train[1])\n",
    "    c=ot.sinkhorn(t1masses,t2masses,C1,10,verbose=False)\n",
    "    #transpwgw,log= wgw.wgw(np.zeros((C1.shape[0],C2.shape[0])),C1,C2,t1masses,t2masses,'square_loss',15,alpha=1,max_iter=500,verbose=False,log=True)\n",
    "    #transpwgw,log=w.wgw(log=True)\n",
    "    #tens = w.constC-np.dot(w.hC1, w.T).dot(np.transpose(w.hC2))\n",
    "    #tens = w.constC\n",
    "    #tens =np.dot(w.hC1, w.T).dot(np.transpose(w.hC2))\n",
    "\n",
    "\n",
    "    #Cost = w.G+w.alpha*tens\n",
    "\n",
    "    #T = ot.sinkhorn(w.p, w.q, Cost, w.epsilon, numItermax=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-f9bf17da38cd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m  \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mtens\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0;32mdel\u001b[0m \u001b[0mtens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "N=600\n",
    "A=np.ones((N,N))\n",
    "B=np.ones((N,N))\n",
    "for i  in range(10000):\n",
    "    tens =np.dot(A, B.T)\n",
    "    del tens\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ts.TS at 0x10534db00>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
      "Construction des matrices de structures\n",
      "Les distances entre pour chaque point et eux mêmes sont calculées\n",
      "Construction des matrices de structures\n",
      "Les distances entre pour chaque point et eux mêmes sont calculées\n",
      "Construction des matrices de structures\n",
      "Les distances entre pour chaque point et eux mêmes sont calculées\n",
      "Construction des matrices de structures\n",
      "Les distances entre pour chaque point et eux mêmes sont calculées\n",
      "Construction des matrices de structures\n",
      "Les distances entre pour chaque point et eux mêmes sont calculées\n",
      "Construction des matrices de structures\n",
      "Les distances entre pour chaque point et eux mêmes sont calculées\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   13.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=Ts_GW_1NN_Classifier(epsilon=1, method='time', normalize_distance=False,\n",
       "           timedistance='sqeuclidean'),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid=[{'method': ['time'], 'epsilon': [15.0], 'normalize_distance': [True], 'timedistance': ['sqeuclidean']}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir_path='./'\n",
    "result_file='result_grid_lightning_WGW_test.csv'\n",
    "text_file = open(os.path.join(dir_path, result_file), 'w')\n",
    "\n",
    "n_splits=5\n",
    "n_jobs=1\n",
    "start_time = time.time()\n",
    "\n",
    "print('CV Nb_splits : ', n_splits, file=text_file)\n",
    "print('Data train size : ',len(x_train),file=text_file)\n",
    "print('Data test size : ',len(x_train),file=text_file)\n",
    "print('N_jobs : ',n_jobs,file=text_file)\n",
    "\n",
    "\n",
    "#tuned_parameters = [{'epsilon':list(np.linspace(0.01,50,5)),\n",
    "#\t\t\t\t\t 'ratio':list(np.linspace(0.01,2.5,5))\n",
    "#                    ,'method':['time']\n",
    "#                     ,'normalize_distance':[False,True]\n",
    "#                     ,'features_metric':['sqeuclidean']\n",
    "#                     ,'timedistance':['sqeuclidean']}]\n",
    "\n",
    "tuned_parameters = [{'epsilon':list(np.linspace(15,50,1)),'method':['time']\n",
    "                     ,'normalize_distance':[True]\n",
    "                     ,'timedistance':['sqeuclidean']}]\n",
    "\n",
    "print('Tuned tuned_parameters : ',tuned_parameters,file=text_file) \n",
    "\n",
    "wgw_1NN=NN.Ts_GW_1NN_Classifier()\n",
    "clf = GridSearchCV(wgw_1NN, tuned_parameters, cv=n_splits,scoring='accuracy',verbose=1)\n",
    "clf.fit(np.array(x_train).reshape(-1,1),np.array(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-9fb0bc084e47>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0mwgw_1NN\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTs_EMD_1NN_Classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwgw_1NN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuned_parameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m    943\u001b[0m             \u001b[0mtrain\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtest\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m         \"\"\"\n\u001b[0;32m--> 945\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    946\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, groups, parameter_iterable)\u001b[0m\n\u001b[1;32m    562\u001b[0m                                   \u001b[0mreturn_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_parameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m                                   error_score=self.error_score)\n\u001b[0;32m--> 564\u001b[0;31m           \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameter_iterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    565\u001b[0m           for train, test in cv_iter)\n\u001b[1;32m    566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    756\u001b[0m             \u001b[0;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m             \u001b[0;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 758\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    759\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    760\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    606\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 608\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    609\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 571\u001b[0;31m         \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    572\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0mfit_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m         \u001b[0mtest_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    261\u001b[0m         \u001b[0mscore_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mfit_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_score\u001b[0;34m(estimator, X_test, y_test, scorer)\u001b[0m\n\u001b[1;32m    286\u001b[0m         \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 288\u001b[0;31m         \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    289\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'item'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/sklearn/metrics/scorer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, estimator, X, y_true, sample_weight)\u001b[0m\n\u001b[1;32m     89\u001b[0m         super(_PredictScorer, self).__call__(estimator, X, y_true,\n\u001b[1;32m     90\u001b[0m                                              sample_weight=sample_weight)\n\u001b[0;32m---> 91\u001b[0;31m         \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m             return self._sign * self._score_func(y_true, y_pred,\n",
      "\u001b[0;32m/Users/vayer/Documents/OT/Python/GW_tests/NN.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     55\u001b[0m                         \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m                     \u001b[0msimilarities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_similarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mXi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX_train\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_X\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m             \u001b[0;32melse\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                 \u001b[0mpool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/vayer/Documents/OT/Python/GW_tests/NN.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     55\u001b[0m                         \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m                     \u001b[0msimilarities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_similarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mXi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX_train\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_X\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m             \u001b[0;32melse\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                 \u001b[0mpool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/vayer/Documents/OT/Python/GW_tests/NN.py\u001b[0m in \u001b[0;36mcompute_similarity\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m     28\u001b[0m             \u001b[0msimilarity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilarities_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m             \u001b[0msimilarity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilarity_measure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilarities_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msimilarity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msimilarity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/vayer/Documents/OT/Python/GW_tests/ot_distances.py\u001b[0m in \u001b[0;36mts_d\u001b[0;34m(self, ts1, ts2)\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0mM\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m         \u001b[0mtransp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0memd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt1masses\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt2masses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/ot/lp/__init__.py\u001b[0m in \u001b[0;36memd\u001b[0;34m(a, b, M, numItermax, log)\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m     \u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0memd_c\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumItermax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m     \u001b[0mresult_code_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os,sys\n",
    "from graph import *\n",
    "from ts import *\n",
    "import copy\n",
    "import NN,time\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, KFold\n",
    "\n",
    "path='./UCR_TS_Archive_2015/Lighting2/'\n",
    "train=create_ts(path,t='TRAIN')\n",
    "test=create_ts(path,t='TEST')\n",
    "x_train,y_train=zip(*train)\n",
    "x_test,y_test=zip(*test)\n",
    "\n",
    "dir_path='./'\n",
    "result_file='result_grid_lightning_WGW_test.csv'\n",
    "text_file = open(os.path.join(dir_path, result_file), 'w')\n",
    "\n",
    "n_splits=5\n",
    "n_jobs=1\n",
    "start_time = time.time()\n",
    "\n",
    "print('CV Nb_splits : ', n_splits, file=text_file)\n",
    "print('Data train size : ',len(x_train),file=text_file)\n",
    "print('Data test size : ',len(x_train),file=text_file)\n",
    "print('N_jobs : ',n_jobs,file=text_file)\n",
    "\n",
    "\n",
    "#tuned_parameters = [{'epsilon':list(np.linspace(0.01,50,5)),\n",
    "#\t\t\t\t\t 'ratio':list(np.linspace(0.01,2.5,5))\n",
    "#                    ,'method':['time']\n",
    "#                     ,'normalize_distance':[False,True]\n",
    "#                     ,'features_metric':['sqeuclidean']\n",
    "#                     ,'timedistance':['sqeuclidean']}]\n",
    "\n",
    "tuned_parameters = [{'features_metric':['sqeuclidean']}]\n",
    "\n",
    "print('Tuned tuned_parameters : ',tuned_parameters,file=text_file) \n",
    "\n",
    "wgw_1NN=NN.Ts_EMD_1NN_Classifier()\n",
    "clf = GridSearchCV(wgw_1NN, tuned_parameters, cv=n_splits,scoring='accuracy',verbose=1,n_jobs=n_jobs)\n",
    "clf.fit(np.array(x_train).reshape(-1,1),np.array(y_train))\n",
    "\n",
    "\n",
    "print('--------------------------', file=text_file)\n",
    "print('--------------------------', file=text_file)\n",
    "print('', file=text_file)\n",
    "print(\"Best parameters set found on development set:\", file=text_file)\n",
    "print('', file=text_file)\n",
    "print(clf.best_params_, file=text_file)\n",
    "print('', file=text_file)\n",
    "print('--------------------------', file=text_file)\n",
    "print('--------------------------', file=text_file)\n",
    "print(\"Grid scores on development set:\", file=text_file)\n",
    "print('', file=text_file)\n",
    "means = clf.cv_results_['mean_test_score']\n",
    "stds = clf.cv_results_['std_test_score']\n",
    "for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "    print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "        % (mean, std * 2, params), file=text_file)\n",
    "print('', file=text_file)\n",
    "\n",
    "\n",
    "end_time = time.time()\n",
    "print('--------------------------', file=text_file)\n",
    "print('--------------------------', file=text_file)\n",
    "\n",
    "#nested_score = cross_val_score(clf, X=np.array(x_test), y=np.array(y_test)\n",
    "#\t,cv=n_splits,n_jobs=n_jobs,scoring='accuracy')\n",
    "#nested_scores = nested_score.mean()\n",
    "preds=clf.predict(np.array(x_test))\n",
    "nested_scores=np.sum(preds==np.array(y_test))/len(y_test)\n",
    "\n",
    "print('Score on test set with best_params_ : ',nested_scores,file=text_file)\n",
    "\n",
    "\n",
    "\n",
    "print('All Time :', end_time-start_time, file=text_file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "emd_1NN=NN.Ts_EMD_1NN_Classifier()\n",
    "wgw_1NN=NN.Ts_GW_1NN_Classifier(epsilon=15,method='time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path='./UCR_TS_Archive_2015/Lighting2/'\n",
    "train=create_ts(path,t='TRAIN')\n",
    "test=create_ts(path,t='TEST')\n",
    "x_train,y_train=zip(*train)\n",
    "x_test,y_test=zip(*test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-2cb473030e66>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10000000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwgw_1NN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_similarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/vayer/Documents/OT/Python/GW_tests/NN.py\u001b[0m in \u001b[0;36mcompute_similarity\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilarities_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilarity_measure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m             \u001b[0msimilarity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNormalized_1NN_Classifier\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_similarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Attention\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msimilarity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/vayer/Documents/OT/Python/GW_tests/NN.py\u001b[0m in \u001b[0;36mcompute_similarity\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcompute_similarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilarities_dict\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# ou suppose que les similarités sont symétriques\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m             \u001b[0msimilarity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilarities_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilarities_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/vayer/Documents/OT/Python/GW_tests/ts.py\u001b[0m in \u001b[0;36m__hash__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__hash__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mhash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mplot_ts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for i in range(10000000):\n",
    "    a=wgw_1NN.compute_similarity(x_train[0],x_train[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0060392277187339685"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emd_1NN.compute_similarity(x_train[0],x_train[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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