b45f35862afbca09ff1c70be1cc4d1d6ca6c9617,pyriemann/classification.py,MDM,_predict_distances,#MDM#Any#,46

Before Change

        dist = numpy.empty((Nt, Nc))

        for m in range(Nc):
            for k in range(Nt):
                dist[k, m] = distance(covtest[k, :, :], self.covmeans[m],
                                      metric=self.metric_dist)
        return dist

    def predict(self, covtest):
        dist = self._predict_distances(covtest)

After Change

        if self.n_jobs == 1:
            dist = [distance(covtest, self.covmeans[m], self.metric_dist)
                    for m in range(Nc)]
        else:
            dist = Parallel(n_jobs=self.n_jobs)(delayed(distance)(covtest, self.covmeans[m], self.metric_dist) for m in range(Nc))

        dist = numpy.concatenate(dist, axis=1)
        return dist

Instances