diff options
Diffstat (limited to 'numpy/core/defmatrix.py')
| -rw-r--r-- | numpy/core/defmatrix.py | 20 |
1 files changed, 10 insertions, 10 deletions
diff --git a/numpy/core/defmatrix.py b/numpy/core/defmatrix.py index 85eab179f..de37a2686 100644 --- a/numpy/core/defmatrix.py +++ b/numpy/core/defmatrix.py @@ -390,11 +390,11 @@ class matrix(N.ndarray): ----- The standard deviation is the square root of the average of the squared deviations from the mean, i.e. var = - sqrt(mean(abs(x - x.mean())**2)). The computed standard - deviation is computed by dividing by the number of elements, - N-ddof. The option ddof defaults to zero, that is, a biased - estimate. Note that for complex numbers std takes the absolute - value before squaring, so that the result is always real + sqrt(mean(abs(x - x.mean())**2)). The computed standard + deviation is computed by dividing by the number of elements, + N-ddof. The option ddof defaults to zero, that is, a biased + estimate. Note that for complex numbers std takes the absolute + value before squaring, so that the result is always real and nonnegative. """ @@ -439,11 +439,11 @@ class matrix(N.ndarray): ----- The variance is the average of the squared deviations from the - mean, i.e. var = mean(abs(x - x.mean())**2). The mean is - computed by dividing by N-ddof, where N is the number of elements. - The argument ddof defaults to zero; for an unbiased estimate - supply ddof=1. Note that for complex numbers the absolute value - is taken before squaring, so that the result is always real + mean, i.e. var = mean(abs(x - x.mean())**2). The mean is + computed by dividing by N-ddof, where N is the number of elements. + The argument ddof defaults to zero; for an unbiased estimate + supply ddof=1. Note that for complex numbers the absolute value + is taken before squaring, so that the result is always real and nonnegative. """ return N.ndarray.var(self, axis, dtype, out)._align(axis) |