def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m, axis=0)
y = y - _Nmean(y, axis=0)
if bias:
fact = N * 1.0
else:
fact = N - 1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
def mean(x, axis=0):
return _Nmean(x, axis)
def mean(x, axis=0):
return _Nmean(x, axis)
