| from typing import Tuple | |
| import numpy as np | |
| class RunningMeanStd(object): | |
| def __init__(self, epsilon: float = 1e-4, shape: Tuple[int, ...] = ()): | |
| """ | |
| Calulates the running mean and std of a data stream | |
| https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm | |
| :param epsilon: helps with arithmetic issues | |
| :param shape: the shape of the data stream's output | |
| """ | |
| self.mean = np.zeros(shape, np.float64) | |
| self.var = np.ones(shape, np.float64) | |
| self.count = epsilon | |
| def update(self, arr: np.ndarray) -> None: | |
| batch_mean = np.mean(arr, axis=0) | |
| batch_var = np.var(arr, axis=0) | |
| batch_count = arr.shape[0] | |
| self.update_from_moments(batch_mean, batch_var, batch_count) | |
| def update_from_moments(self, batch_mean: np.ndarray, batch_var: np.ndarray, batch_count: int) -> None: | |
| delta = batch_mean - self.mean | |
| tot_count = self.count + batch_count | |
| new_mean = self.mean + delta * batch_count / tot_count | |
| m_a = self.var * self.count | |
| m_b = batch_var * batch_count | |
| m_2 = m_a + m_b + np.square(delta) * self.count * batch_count / (self.count + batch_count) | |
| new_var = m_2 / (self.count + batch_count) | |
| new_count = batch_count + self.count | |
| self.mean = new_mean | |
| self.var = new_var | |
| self.count = new_count | |