49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
import numpy as np
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from pyFTS.common import FuzzySet,FLR,Transformations
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from pyFTS import fts
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class HighOrderFTS(fts.FTS):
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def __init__(self, name, **kwargs):
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super(HighOrderFTS, self).__init__(1, name)
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self.is_high_order = True
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self.min_order = 2
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self.name = "Hwang High Order FTS"
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self.shortname = "Hwang" + name
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self.detail = "Hwang"
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def forecast(self, data, **kwargs):
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ndata = self.doTransformations(data)
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cn = np.array([0.0 for k in range(len(self.sets))])
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ow = np.array([[0.0 for k in range(len(self.sets))] for z in range(self.order - 1)])
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rn = np.array([[0.0 for k in range(len(self.sets))] for z in range(self.order - 1)])
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ft = np.array([0.0 for k in range(len(self.sets))])
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ret = []
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for t in np.arange(self.order-1, len(ndata)):
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for s in range(len(self.sets)):
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cn[s] = self.sets[s].membership(ndata[t])
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for w in range(self.order - 1):
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ow[w, s] = self.sets[s].membership(ndata[t - w])
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rn[w, s] = ow[w, s] * cn[s]
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ft[s] = max(ft[s], rn[w, s])
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mft = max(ft)
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out = 0.0
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count = 0.0
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for s in range(len(self.sets)):
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if ft[s] == mft:
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out = out + self.sets[s].centroid
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count += 1.0
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ret.append(out / count)
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
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return ret
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def train(self, data, sets, order=1, parameters=None):
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self.sets = sets
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self.order = order |