e067d6e72e53409cd58f3aa88312f0cdf0e41133,tslearn/clustering.py,KShape,_update_centroids,#KShape#Any#,422

Before Change

    def _update_centroids(self, X):
        for k in range(self.n_clusters):
            self.cluster_centers_[k] = numpy.empty((X.shape[1], X.shape[2]))  // TODO: Shape Extraction (Alg. 2 in paper)

    def _cross_dists(self, X):
        return numpy.empty((X.shape[0], self.n_clusters))  // TODO using Alg. 1

After Change

    def _update_centroids(self, X):
        for k in range(self.n_clusters):
            self.cluster_centers_[k] = self._shape_extraction(X, k)
        self.cluster_centers_ = TimeSeriesScalerMeanVariance(mu=0., std=1.).fit_transform(self.cluster_centers_)

    def _cross_dists(self, X):
        return 1. - cdist_normalized_cc(X, self.cluster_centers_, norms1=numpy.array([-1.]), norms2=numpy.array([-1.]),
                                        self_similarity=False)  // TODO: provide norms

Instances