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* Add auto formatting with black and isort * Reformat code * Ignore typing errors * Add note about line length * Add minimum version for isort * Add commit-checks * Update docker image * Fixed lost import (during last merge) * Fix opencv dependency
39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
from typing import Tuple
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import numpy as np
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class RunningMeanStd(object):
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def __init__(self, epsilon: float = 1e-4, shape: Tuple[int, ...] = ()):
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"""
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Calulates the running mean and std of a data stream
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https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
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:param epsilon: (float) helps with arithmetic issues
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:param shape: (tuple) the shape of the data stream's output
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"""
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self.mean = np.zeros(shape, np.float64)
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self.var = np.ones(shape, np.float64)
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self.count = epsilon
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def update(self, arr: np.ndarray) -> None:
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batch_mean = np.mean(arr, axis=0)
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batch_var = np.var(arr, axis=0)
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batch_count = arr.shape[0]
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self.update_from_moments(batch_mean, batch_var, batch_count)
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def update_from_moments(self, batch_mean: np.ndarray, batch_var: np.ndarray, batch_count: int) -> None:
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delta = batch_mean - self.mean
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tot_count = self.count + batch_count
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new_mean = self.mean + delta * batch_count / tot_count
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m_a = self.var * self.count
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m_b = batch_var * batch_count
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m_2 = m_a + m_b + np.square(delta) * self.count * batch_count / (self.count + batch_count)
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new_var = m_2 / (self.count + batch_count)
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new_count = batch_count + self.count
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self.mean = new_mean
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self.var = new_var
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self.count = new_count
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