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diff --git a/Wass_Matcher.py b/Wass_Matcher.py
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+++ b/Wass_Matcher.py
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+import ot
+from sklearn.preprocessing import normalize
+from lapjv import lapjv
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import euclidean_distances
+from sklearn.externals.joblib import Parallel, delayed
+from sklearn.utils import check_array
+from sklearn.metrics.scorer import check_scoring
+from pathos.multiprocessing import ProcessingPool as Pool
+from sklearn.metrics import euclidean_distances
+import numpy as np
+
+class Wasserstein_Matcher(KNeighborsClassifier):
+    """
+    Implements a nearest neighbors classifier for input distributions using the Wasserstein distance as metric.
+    Source and target distributions are l_1 normalized before computing the Wasserstein distance.
+    Wasserstein is parametrized by the distances between the individual points of the distributions.
+    """
+    def __init__(self, W_embed, n_neighbors=1, n_jobs=1, verbose=False, sinkhorn= False, sinkhorn_reg=0.1):
+        """
+        Initialization of the class.
+        Arguments
+        ---------
+        W_embed: embeddings of the words, np.array
+        verbose: True/False
+        """
+        self.sinkhorn = sinkhorn
+        self.sinkhorn_reg = sinkhorn_reg
+        self.W_embed = W_embed
+        self.verbose = verbose
+        super(Wasserstein_Matcher, self).__init__(n_neighbors=n_neighbors, n_jobs=n_jobs, metric='precomputed', algorithm='brute')
+
+    def _wmd(self, i, row, X_train):
+        union_idx = np.union1d(X_train[i].indices, row.indices)
+        W_minimal = self.W_embed[union_idx]
+        W_dist = euclidean_distances(W_minimal)
+        bow_i = X_train[i, union_idx].A.ravel()
+        bow_j = row[:, union_idx].A.ravel()
+        if self.sinkhorn:
+            return  ot.sinkhorn2(bow_i, bow_j, W_dist, self.sinkhorn_reg, numItermax=50, method='sinkhorn_stabilized',)[0]
+        else:
+            return  ot.emd2(bow_i, bow_j, W_dist)
+
+    def _wmd_row(self, row):
+        X_train = self._fit_X
+        n_samples_train = X_train.shape[0]
+        return [self._wmd(i, row, X_train) for i in range(n_samples_train)]
+
+    def _pairwise_wmd(self, X_test, X_train=None):
+        n_samples_test = X_test.shape[0]
+
+        if X_train is None:
+            X_train = self._fit_X
+        pool = Pool(nodes=self.n_jobs) # Parallelization of the calculation of the distances
+        dist  = pool.map(self._wmd_row, X_test)
+        return np.array(dist)
+
+    def fit(self, X, y): # X_train_idf
+        X = check_array(X, accept_sparse='csr', copy=True) # check if array is sparse
+        X = normalize(X, norm='l1', copy=False)
+        return super(Wasserstein_Matcher, self).fit(X, y) # X_train_idf, np_ones(document collection size)
+
+    def predict(self, X):
+        X = check_array(X, accept_sparse='csr', copy=True)
+        X = normalize(X, norm='l1', copy=False)
+        dist = self._pairwise_wmd(X)
+        dist = dist * 1000 # for lapjv, small floating point numbers are evil
+        return super(Wasserstein_Matcher, self).predict(dist)
+
+    def kneighbors(self, X, n_neighbors=1): # X : X_train_idf
+        X = check_array(X, accept_sparse='csr', copy=True)
+        X = normalize(X, norm='l1', copy=False)
+        dist = self._pairwise_wmd(X)
+        dist = dist * 1000 # for lapjv, small floating point numbers are evil
+        return lapjv(dist) # and here is the matching part
+