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Running mean and standard deviation
# From https://www.johndcook.com/blog/standard_deviation/
# and https://github.com/modestyachts/ARS
class RunningStat(object):
def __init__(self, shape=None):
self._n = 0
self._M = np.zeros(shape, dtype = np.float64)
self._S = np.zeros(shape, dtype = np.float64)
self._M2 = np.zeros(shape, dtype = np.float64)
def copy(self):
other = RunningStat()
other._n = self._n
other._M = np.copy(self._M)
other._S = np.copy(self._S)
return other
def push(self, x):
x = np.asarray(x)
# Unvectorized update of the running statistics.
assert x.shape == self._M.shape, ("x.shape = {}, self.shape = {}"
.format(x.shape, self._M.shape))
n1 = self._n
self._n += 1
if self._n == 1:
self._M[...] = x
else:
delta = x - self._M
deltaM2 = np.square(x) - self._M2
self._M[...] += delta / self._n
self._S[...] += delta * delta * n1 / self._n
def update(self, other):
n1 = self._n
n2 = other._n
n = n1 + n2
delta = self._M - other._M
delta2 = delta * delta
M = (n1 * self._M + n2 * other._M) / n
S = self._S + other._S + delta2 * n1 * n2 / n
self._n = n
self._M = M
self._S = S
def __repr__(self):
return '(n={}, mean_mean={}, mean_std={})'.format(
self.n, np.mean(self.mean), np.mean(self.std))
@property
def n(self):
return self._n
@property
def mean(self):
return self._M
@property
def var(self):
return self._S / (self._n - 1) if self._n > 1 else np.square(self._M)
@property
def std(self):
return np.sqrt(self.var)
@property
def shape(self):
return self._M.shape
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