1f64e8d111a67ed8c97f9251f299805496b3644f,tslearn/svm.py,TimeSeriesSVC,predict_log_proba,#TimeSeriesSVC#Any#,265

Before Change

        return self.svm_estimator_.decision_function(sklearn_X)

    def predict_log_proba(self, X):
        if self.kernel in self.variable_length_kernels:
            X = check_array(X, allow_nd=True, force_all_finite=False)
        else:
            X = check_array(X, allow_nd=True)
        check_is_fitted(self, ["svm_estimator_", "_X_fit"])
        X = check_dims(X, self._X_fit)

        if self.kernel in self.variable_length_kernels:
            assert self.kernel == "gak"
            sklearn_X = cdist_gak(X, self._X_fit,
                                  sigma=numpy.sqrt(self.gamma / 2.))
        else:
            sklearn_X = _prepare_ts_datasets_sklearn(X)
        return self.svm_estimator_.predict_log_proba(sklearn_X)

    def predict_proba(self, X):
        if self.kernel in self.variable_length_kernels:

After Change

        check_is_fitted(self, ["svm_estimator_", "_X_fit"])
        return self.svm_estimator_.decision_function(sklearn_X)

    def predict_log_proba(self, X):
        sklearn_X, _ = self._preprocess_sklearn(X, set_X_fit=False)
        check_is_fitted(self, ["svm_estimator_", "_X_fit"])
        return self.svm_estimator_.predict_log_proba(sklearn_X)

    def predict_proba(self, X):

Instances