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73 lines
1.8 KiB
73 lines
1.8 KiB
import numpy as np
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class RunningStat():
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# tracks realtime mean and standard deviation without storing any data
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def __init__(self, priors=None, max_trackable=-1):
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self.max_trackable = max_trackable
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if priors is not None:
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# initialize from history
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self.M = priors[0]
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self.S = priors[1]
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self.n = priors[2]
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self.M_last = self.M
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self.S_last = self.S
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else:
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self.reset()
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def reset(self):
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self.M = 0.
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self.S = 0.
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self.M_last = 0.
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self.S_last = 0.
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self.n = 0
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def push_data(self, new_data):
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# short term memory hack
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if self.max_trackable < 0 or self.n < self.max_trackable:
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self.n += 1
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if self.n == 0:
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self.M_last = new_data
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self.M = self.M_last
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self.S_last = 0.
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else:
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self.M = self.M_last + (new_data - self.M_last) / self.n
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self.S = self.S_last + (new_data - self.M_last) * (new_data - self.M)
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self.M_last = self.M
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self.S_last = self.S
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def mean(self):
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return self.M
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def variance(self):
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if self.n >= 2:
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return self.S / (self.n - 1.)
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else:
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return 0
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def std(self):
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return np.sqrt(self.variance())
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def params_to_save(self):
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return [self.M, self.S, self.n]
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class RunningStatFilter():
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def __init__(self, raw_priors=None, filtered_priors=None, max_trackable=-1):
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self.raw_stat = RunningStat(raw_priors, -1)
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self.filtered_stat = RunningStat(filtered_priors, max_trackable)
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def reset(self):
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self.raw_stat.reset()
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self.filtered_stat.reset()
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def push_and_update(self, new_data):
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_std_last = self.raw_stat.std()
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self.raw_stat.push_data(new_data)
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_delta_std = self.raw_stat.std() - _std_last
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if _delta_std <= 0:
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self.filtered_stat.push_data(new_data)
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else:
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pass
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# self.filtered_stat.push_data(self.filtered_stat.mean())
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# class SequentialBayesian():
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