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from typing import Dict

from systemds.operator import OperationNode
from systemds.script_building.dag import DAGNode
from systemds.utils.consts import VALID_INPUT_TYPES

__all__ = ['l2svm', 'lm']


def l2svm(x: DAGNode, y: DAGNode, **kwargs: Dict[str, VALID_INPUT_TYPES]) -> OperationNode:
    """
    Perform L2SVM on matrix with labels given.

    :param x: Input dataset
    :param y: Input labels in shape of one column
    :param kwargs: Dictionary of extra arguments 
    :return: `OperationNode` containing the model fit.
    """
    x._check_matrix_op()
    params_dict = {'X': x, 'Y': y}
    params_dict.update(kwargs)
    return OperationNode(x.sds_context, 'l2svm', named_input_nodes=params_dict)


def lm(x: DAGNode, y: DAGNode, **kwargs: Dict[str, VALID_INPUT_TYPES]) -> OperationNode:
    """
    Performs LM on matrix with labels given.

    :param x: Input dataset
    :param y: Input labels in shape of one column
    :param kwargs: Dictionary of extra arguments 
    :return: `OperationNode` containing the model fit.
    """

    x._check_matrix_op()
    if x._np_array.size == 0:
        raise ValueError("Found array with 0 feature(s) (shape={s}) while a minimum of 1 is required."
                         .format(s=x._np_array.shape))
    if y._np_array.size == 0:
        raise ValueError("Found array with 0 feature(s) (shape={s}) while a minimum of 1 is required."
                         .format(s=y._np_array.shape))

    params_dict = {'X': x, 'y': y}
    params_dict.update(kwargs)
    return OperationNode(x.sds_context, 'lm', named_input_nodes=params_dict)


def kmeans(x: DAGNode, **kwargs: Dict[str, VALID_INPUT_TYPES]) -> OperationNode:
    """
    Perfoms KMeans on matrix input.

    :param x: Input dataset to perform K-Means on.
    :param k: The Number of centroids to use for the algorithm.
    :param runs: The Number of concurrent instances of K-Means to run (with different initial centroids).
    :param max_iter: The Maximum number of iterations to run the K-Means algorithm for.
    :param eps: Tolerance for the algorithm to declare convergence using WCSS change ratio.
    :param is_verbose: Boolean flag if the algorithm should be run in a verbose manner.
    :param avg_sample_size_per_centroid: The Average Number of records per centroid in the data samples.
    """

    x._check_matrix_op()
    if x._np_array.size == 0:
        raise ValueError("Found array with 0 feature(s) (shape={s}) while a minimum of 1 is required."
                         .format(s=x._np_array.shape))

    if 'k' in kwargs.keys() and kwargs.get('k') < 1:
        raise ValueError("Invalid number of clusters in K means, number must be integer above 0")


    params_dict = {'X': x}
    params_dict.update(kwargs)
    return OperationNode(x.sds_context, 'kmeans', named_input_nodes=params_dict)