45cc6da8a8242d041b71ee95e2caaff4569bcddb,tslearn/svm.py,TimeSeriesSVC,predict_log_proba,#TimeSeriesSVC#Any#,241

Before Change

        X = check_array(X, allow_nd=True)

        if len(X.shape) == 2:
            warnings.warn("2-Dimensional data passed. Assuming these are "
                          "{} 1-dimensional timeseries".format(X.shape[0]))
            X = X.reshape((X.shape) + (1,))

        sklearn_X = _prepare_ts_datasets_sklearn(X)
        return super(TimeSeriesSVC, self).predict_log_proba(sklearn_X)

    def predict_proba(self, X):

After Change

    def predict_log_proba(self, X):
        X = check_array(X, allow_nd=True)
        check_is_fitted(self, "svm_estimator_")

        sklearn_X = _prepare_ts_datasets_sklearn(X)
        return self.svm_estimator_.predict_log_proba(sklearn_X)

Instances