# Source code for pyFTS.partitioners.FCM

    """
    S. T. Li, Y. C. Cheng, and S. Y. Lin, “A FCM-based deterministic forecasting model for fuzzy time series,”
    Comput. Math. Appl., vol. 56, no. 12, pp. 3052–3063, Dec. 2008. DOI: 10.1016/j.camwa.2008.07.033.
    """
    import numpy as np
    import pandas as pd
    import math
    import random as rnd
    import functools, operator
    from pyFTS.common import FuzzySet, Membership
    from pyFTS.partitioners import partitioner

    def fuzzy_distance(x, y):
        if isinstance(x, (list, tuple, np.ndarray)):
            tmp = functools.reduce(operator.add, [(x[k] - y[k]) ** 2 for k in range(0, len(x))])
        else:
            tmp = (x - y) ** 2
        return math.sqrt(tmp)

    def membership(val, vals):
        soma = 0
        for k in vals:
            if k == 0:
                k = 1
            soma = soma + (val / k) ** 2

        return soma

    def fuzzy_cmeans(k, data, size, m, deltadist=0.001):
        data_length = len(data)

        # Centroid initialization
        centroids = [data[rnd.randint(0, data_length - 1)] for kk in range(0, k)]

        # Membership table
        membership_table = np.zeros((k, data_length)) #[[0 for kk in range(0, k)] for xx in range(0, data_length)]

        mean_change = 1000

        m_exp = 1 / (m - 1)

        iterations = 0

        while iterations < 1000 and mean_change > deltadist:

            mean_change = 0
            inst_count = 0
            for instance in data:

                dist_groups = np.zeros(k) #[0 for xx in range(0, k)]

                for group_count, group in enumerate(centroids):
                    dist_groups[group_count] = fuzzy_distance(group, instance)

                dist_groups_total = functools.reduce(operator.add, [xk for xk in dist_groups])

                for grp in range(0, k):
                    if dist_groups[grp] == 0:
                        membership_table[inst_count][grp] = 1
                    else:
                        membership_table[inst_count][grp] = 1 / membership(dist_groups[grp], dist_groups)
                        # membership_table[inst_count][grp] = 1/(dist_groups[grp] / dist_grupos_total)
                        # membership_table[inst_count][grp] = (1/(dist_groups[grp]**2))**m_exp / (1/(dist_grupos_total**2))**m_exp

                inst_count = inst_count + 1

            for group_count, group in enumerate(centroids):
                if size > 1:
                    oldgrp = [xx for xx in group]
                    for atr in range(0, size):
                        soma = functools.reduce(operator.add,
                                                [membership_table[xk][group_count] * data[xk][atr] for xk in range(0, data_length)])
                        norm = functools.reduce(operator.add, [membership_table[xk][group_count] for xk in range(0, data_length)])
                        centroids[group_count][atr] = soma / norm
                else:
                    oldgrp = group
                    soma = functools.reduce(operator.add,
                                            [membership_table[xk][group_count] * data[xk] for xk in range(0, data_length)])
                    norm = functools.reduce(operator.add, [membership_table[xk][group_count] for xk in range(0, data_length)])
                    centroids[group_count] = soma / norm

                mean_change = mean_change + fuzzy_distance(oldgrp, group)

            mean_change = mean_change / k
            iterations = iterations + 1

        return centroids

    class FCMPartitioner(partitioner.Partitioner):
        """

        """

        def __init__(self, **kwargs):
            super(FCMPartitioner, self).__init__(name="FCM", **kwargs)

        def build(self, data):
            sets = {}

            kwargs = {'type': self.type, 'variable': self.variable}

            centroids = fuzzy_cmeans(self.partitions, data, 1, 2)
            centroids.append(self.max)
            centroids.append(self.min)
            centroids = list(set(centroids))
            centroids.sort()
            for c in np.arange(1, len(centroids) - 1):
                _name = self.get_name(c)
                if self.membership_function == Membership.trimf:
                    sets[_name] = FuzzySet.FuzzySet(_name, Membership.trimf,
                                                    [round(centroids[c - 1], 3), round(centroids[c], 3),
                                                     round(centroids[c + 1], 3)],
                                                    round(centroids[c], 3), **kwargs)
                elif self.membership_function == Membership.trapmf:
                    q1 = (round(centroids[c], 3) - round(centroids[c - 1], 3)) / 2
                    q2 = (round(centroids[c + 1], 3) - round(centroids[c], 3)) / 2
                    sets[_name] = FuzzySet.FuzzySet(_name, Membership.trimf,
                                                    [round(centroids[c - 1], 3), round(centroids[c], 3) - q1,
                                                     round(centroids[c], 3) + q2, round(centroids[c + 1], 3)],
                                                    round(centroids[c], 3), **kwargs)

            return sets