46cb0b5b2cf2d836a2cf96be95d4e25a4004675e,tslearn/clustering.py,KShape,_fit_one_init,#KShape#Any#Any#Any#,729

Before Change

        n_samples, sz, d = X.shape
        self.labels_ = rs.randint(self.n_clusters, size=n_samples)
        if initial_centroids is not None:
            self.cluster_centers_ = initial_centroids.copy()
        else:
            self.cluster_centers_ = rs.randn(self.n_clusters, sz, d)
        self._norms_centroids = numpy.linalg.norm(self.cluster_centers_, axis=(1, 2))
        old_inertia = numpy.inf

        for it in range(self.max_iter):
            old_cluster_centers = self.cluster_centers_.copy()
            self._update_centroids(X)
            self._assign(X)
            if self.verbose:
                print("%.3f" % self.inertia_, end=" --> ")

After Change

    def _fit_one_init(self, X, x_squared_norms, rs):
        n_ts, _, d = X.shape
        sz = min([ts_size(ts) for ts in X])
        if hasattr(self.init, "__array__"):
            self.cluster_centers_ = self.init.copy()
        else:
            self.cluster_centers_ = _k_init(X[:, :sz, :].reshape((n_ts, -1)),

Instances