This is the same code with inplace flag.
import torch
def cov(m, rowvar=True, inplace=False):
'''Estimate a covariance matrix given data.
Covariance indicates the level to which two variables vary together.
If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`,
then the covariance matrix element `C_{ij}` is the covariance of
`x_i` and `x_j`. The element `C_{ii}` is the variance of `x_i`.
Args:
m: A 1-D or 2-D array containing multiple variables and observations.
Each row of `m` represents a variable, and each column a single
observation of all those variables.
rowvar: If `rowvar` is True, then each row represents a
variable, with observations in the columns. Otherwise, the
relationship is transposed: each column represents a variable,
while the rows contain observations.
Returns:
The covariance matrix of the variables.
'''
if m.dim() > 2:
raise ValueError('m has more than 2 dimensions')
if m.dim() < 2:
m = m.view(1, -1)
if not rowvar and m.size(0) != 1:
m = m.t()
# m = m.type(torch.double) # uncomment this line if desired
fact = 1.0 / (m.size(1) - 1)
if inplace:
m -= torch.mean(m, dim=1, keepdim=True)
else:
m = m - torch.mean(m, dim=1, keepdim=True)
mt = m.t() # if complex: mt = m.t().conj()
return fact * m.matmul(mt).squeeze()