31 lines
808 B
Python
31 lines
808 B
Python
from pyFTS.common import Transformations
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import numpy as np
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"""
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Kernel Density Estimation
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"""
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class KernelSmoothing(object):
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"""Kernel Density Estimation"""
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def __init__(self,h, method="epanechnikov"):
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self.h = h
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self.method = method
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self.transf = Transformations.Scale(min=0,max=1)
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def kernel(self, u):
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if self.method == "epanechnikov":
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return (3/4)*(1 - u**2)
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elif self.method == "gaussian":
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return (1/np.sqrt(2*np.pi))*np.exp(-0.5*u**2)
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elif self.method == "uniform":
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return 0.5
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def probability(self, x, data):
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l = len(data)
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ndata = self.transf.apply(data)
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nx = self.transf.apply(x)
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p = sum([self.kernel((nx - k)/self.h) for k in ndata]) / l*self.h
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return p |