import numpy as np
from pyFTS.common import FuzzySet,FLR,Transformations
from pyFTS import fts


class HighOrderFTS(fts.FTS):
    def __init__(self, name, **kwargs):
        super(HighOrderFTS, self).__init__(1, name)
        self.is_high_order = True
        self.min_order = 2
        self.name = "Hwang High Order FTS"
        self.shortname = "Hwang" + name
        self.detail = "Hwang"

    def forecast(self, data, **kwargs):

        ndata = self.doTransformations(data)

        cn = np.array([0.0 for k in range(len(self.sets))])
        ow = np.array([[0.0 for k in range(len(self.sets))] for z in range(self.order - 1)])
        rn = np.array([[0.0 for k in range(len(self.sets))] for z in range(self.order - 1)])
        ft = np.array([0.0 for k in range(len(self.sets))])

        ret = []

        for t in np.arange(self.order-1, len(ndata)):

            for s in range(len(self.sets)):
                cn[s] = self.sets[s].membership(ndata[t])
                for w in range(self.order - 1):
                    ow[w, s] = self.sets[s].membership(ndata[t - w])
                    rn[w, s] = ow[w, s] * cn[s]
                    ft[s] = max(ft[s], rn[w, s])
            mft = max(ft)
            out = 0.0
            count = 0.0
            for s in range(len(self.sets)):
                if ft[s] == mft:
                    out = out + self.sets[s].centroid
                    count += 1.0
            ret.append(out / count)

        ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])

        return ret

    def train(self, data, sets, order=1, parameters=None):
        self.sets = sets
        self.order = order