BaseISC.py 文件源码

def _fit(self,X,y=None):
        if isinstance(X, pyisc.DataObject) and y is None:
            assert y is None # Contained in the data object
            self.class_column = X.class_column
            if self.class_column >= 0:
                self.classes_ = X.classes_

            self._anomaly_detector._SetParams(
                0,
                -1 if X.class_column is None else X.class_column,
                self.anomaly_threshold,
                1 if self.is_clustering else 0
            )
            self._anomaly_detector._TrainData(X)
            return self
        if isinstance(X, ndarray):
            class_column = -1
            data_object = None
            assert X.ndim <= 2
            if X.ndim == 2:
                max_class_column = X.shape[1]
            else:
                max_class_column = 1
            if isinstance(y, list) or isinstance(y, ndarray):
                assert len(X) == len(y)
                class_column = max_class_column
                data_object = pyisc.DataObject(numpy.c_[X, y], class_column=class_column)
            elif y is None or int(y) == y and y > -1 and y <= max_class_column:
                self.class_column = y
                data_object = pyisc.DataObject(X,class_column=y)

            if data_object is not None:
                return self._fit(data_object)

        raise ValueError("Unknown type of data to fit X, y:", type(X), type(y))