def torch_cov()

in utils/fid.py [0:0]

• 27 lines of code
• 7 McCabe index (conditional complexity)

def torch_cov(m, rowvar=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)
    m -= torch.mean(m, dim=1, keepdim=True)
    mt = m.t()  # if complex: mt = m.t().conj()
    return fact * m.matmul(mt).squeeze()