69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
#!/usr/bin/python
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# -*- coding: utf8 -*-
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import numpy as np
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from statsmodels.tsa.tsatools import lagmat
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from pyFTS.common import fts
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from pyFTS.probabilistic import ProbabilityDistribution
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class KNearestNeighbors(fts.FTS):
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"""
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K-Nearest Neighbors
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"""
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def __init__(self, **kwargs):
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super(KNearestNeighbors, self).__init__(**kwargs)
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self.name = "kNN"
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self.shortname = "kNN"
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self.detail = "K-Nearest Neighbors"
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self.is_high_order = True
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self.has_point_forecasting = True
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self.has_interval_forecasting = True
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self.has_probability_forecasting = True
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self.benchmark_only = True
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self.min_order = 1
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self.alpha = kwargs.get("alpha", 0.05)
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self.lag = None
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self.k = kwargs.get("k", 30)
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self.uod = None
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def train(self, data, **kwargs):
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self.data = np.array(data)
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def knn(self, sample):
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if self.order == 1:
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dist = np.apply_along_axis(lambda x: (x - sample) ** 2, 0, self.data)
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ix = np.argsort(dist) + 1
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else:
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dist = []
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for k in np.arange(self.order, len(self.data)):
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dist.append(sum([ (self.data[k - kk] - sample[kk])**2 for kk in range(self.order)]))
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ix = np.argsort(np.array(dist)) + self.order + 1
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ix2 = np.clip(ix[:self.k], 0, len(self.data)-1)
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return self.data[ix2]
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def forecast_distribution(self, data, **kwargs):
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ret = []
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smooth = kwargs.get("smooth", "KDE")
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alpha = kwargs.get("alpha", None)
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uod = self.get_UoD()
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for k in np.arange(self.order, len(data)):
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sample = data[k-self.order : k]
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forecasts = self.knn(sample)
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dist = ProbabilityDistribution.ProbabilityDistribution(smooth, uod=uod, data=forecasts,
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name="", **kwargs)
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ret.append(dist)
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return ret
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