stable-baselines3/stable_baselines3/common/running_mean_std.py
Antonin RAFFIN 55912576ed
Cleanup docstring types (#169)
* Cleanup docstring types

* Update style

* Test with js hack

* Revert "Test with js hack"

This reverts commit d091f438e8851ab8d01b66628e06a104f5e5ec69.

* Fix types

* Fix typo

* Update CONTRIBUTING example
2020-10-02 20:05:55 +03:00

39 lines
1.4 KiB
Python

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