2526879b1f941c887eeb24a267b5ea010e20d5d7,PyNomaly/loop.py,LocalOutlierProbability,_ssd,#LocalOutlierProbability#Any#,137

Before Change

    def _ssd(self, data_store):
        self.cluster_labels_u = np.unique(data_store[:, 0])
        ssd_dict = {}
        for cluster_id in self.cluster_labels_u:
            indices = np.where(data_store[:, 0] == cluster_id)
            cluster_distances = np.take(data_store[:, 1], indices)
            cluster_distances_nonan = cluster_distances[np.logical_not(np.isnan(cluster_distances))]
            ssd = np.sum(np.power(cluster_distances_nonan, 2))
            if ssd == 0.0:
                warnings.warn("Sum of square distances equals zero. Execution halted.", RuntimeWarning)
                sys.exit()
            ssd_dict[cluster_id] = ssd
        data_store = np.hstack((data_store, np.array([[ssd_dict[x] for x in data_store[:, 0].tolist()]]).T))
        return data_store

    def _standard_distances(self, data_store):
        return np.hstack(

After Change

            indices = np.where(data_store[:, 0] == cluster_id)
            cluster_distances = np.take(data_store[:, 1], indices).tolist()
            ssd = np.sum(np.power(cluster_distances[0], 2), axis=1)
            for i, j in zip(indices[0], ssd):
                ssd_array[i] = j
        data_store = np.hstack((data_store, ssd_array))
        return data_store

    def _standard_distances(self, data_store):

Instances