How to use the pycm.pycm_error.pycmVectorError function in pycm

:param actual_vector: Actual Vector
    :type actual_vector: python list or numpy array of any stringable objects
    :param predict_vector: Predicted Vector
    :type predict_vector: python list or numpy array of any stringable objects
    :param threshold : activation threshold function
    :type threshold : FunctionType (function or lambda)
    :param sample_weight : sample weights list
    :type sample_weight : list
    :return: matrix parameters as list
    """
    if isinstance(threshold, types.FunctionType):
        predict_vector = list(map(threshold, predict_vector))
        cm.predict_vector = predict_vector
    if not isinstance(actual_vector, (list, numpy.ndarray)) or not \
            isinstance(predict_vector, (list, numpy.ndarray)):
        raise pycmVectorError(VECTOR_TYPE_ERROR)
    if len(actual_vector) != len(predict_vector):
        raise pycmVectorError(VECTOR_SIZE_ERROR)
    if len(actual_vector) == 0 or len(predict_vector) == 0:
        raise pycmVectorError(VECTOR_EMPTY_ERROR)
    matrix_param = matrix_params_calc(
        actual_vector, predict_vector, sample_weight)
    if isinstance(sample_weight, (list, numpy.ndarray)):
        cm.weights = sample_weight

    return matrix_param

:param threshold : activation threshold function
    :type threshold : FunctionType (function or lambda)
    :param sample_weight : sample weights list
    :type sample_weight : list
    :return: matrix parameters as list
    """
    if isinstance(threshold, types.FunctionType):
        predict_vector = list(map(threshold, predict_vector))
        cm.predict_vector = predict_vector
    if not isinstance(actual_vector, (list, numpy.ndarray)) or not \
            isinstance(predict_vector, (list, numpy.ndarray)):
        raise pycmVectorError(VECTOR_TYPE_ERROR)
    if len(actual_vector) != len(predict_vector):
        raise pycmVectorError(VECTOR_SIZE_ERROR)
    if len(actual_vector) == 0 or len(predict_vector) == 0:
        raise pycmVectorError(VECTOR_EMPTY_ERROR)
    matrix_param = matrix_params_calc(
        actual_vector, predict_vector, sample_weight)
    if isinstance(sample_weight, (list, numpy.ndarray)):
        cm.weights = sample_weight

    return matrix_param

:param predict_vector: Predicted Vector
    :type predict_vector: python list or numpy array of any stringable objects
    :param threshold : activation threshold function
    :type threshold : FunctionType (function or lambda)
    :param sample_weight : sample weights list
    :type sample_weight : list
    :return: matrix parameters as list
    """
    if isinstance(threshold, types.FunctionType):
        predict_vector = list(map(threshold, predict_vector))
        cm.predict_vector = predict_vector
    if not isinstance(actual_vector, (list, numpy.ndarray)) or not \
            isinstance(predict_vector, (list, numpy.ndarray)):
        raise pycmVectorError(VECTOR_TYPE_ERROR)
    if len(actual_vector) != len(predict_vector):
        raise pycmVectorError(VECTOR_SIZE_ERROR)
    if len(actual_vector) == 0 or len(predict_vector) == 0:
        raise pycmVectorError(VECTOR_EMPTY_ERROR)
    matrix_param = matrix_params_calc(
        actual_vector, predict_vector, sample_weight)
    if isinstance(sample_weight, (list, numpy.ndarray)):
        cm.weights = sample_weight

    return matrix_param