mars.tensor.var#

mars.tensor.var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=None, combine_size=None)[source]#

Compute the variance along the specified axis.

Returns the variance of the tensor elements, a measure of the spread of a distribution. The variance is computed for the flattened tensor by default, otherwise over the specified axis.

Parameters

  • a (array_like) – Tensor containing numbers whose variance is desired. If a is not a tensor, a conversion is attempted.

  • axis (None or int or tuple of ints, optional) –

    Axis or axes along which the variance is computed. The default is to compute the variance of the flattened array.

    If this is a tuple of ints, a variance is performed over multiple axes, instead of a single axis or all the axes as before.

  • dtype (data-type, optional) – Type to use in computing the variance. For arrays of integer type the default is float32; for tensors of float types it is the same as the tensor type.

  • out (Tensor, optional) – Alternate output array in which to place the result. It must have the same shape as the expected output, but the type is cast if necessary.

  • ddof (int, optional) – “Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is zero.

  • keepdims (bool, optional) –

    If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input tensor.

    If the default value is passed, then keepdims will not be passed through to the var method of sub-classes of Tensor, however any non-default value will be. If the sub-classes sum method does not implement keepdims any exceptions will be raised.

  • combine_size (int, optional) – The number of chunks to combine.

Returns

variance – If out=None, returns a new tensor containing the variance; otherwise, a reference to the output tensor is returned.

Return type

Tensor, see dtype parameter above

See also

std, mean, nanmean, nanstd, nanvar

Notes

The variance is the average of the squared deviations from the mean, i.e., var = mean(abs(x - x.mean())**2).

The mean is normally calculated as x.sum() / N, where N = len(x). If, however, ddof is specified, the divisor N - ddof is used instead. In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of a hypothetical infinite population. ddof=0 provides a maximum likelihood estimate of the variance for normally distributed variables.

Note that for complex numbers, the absolute value is taken before squaring, so that the result is always real and nonnegative.

For floating-point input, the variance is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-accuracy accumulator using the dtype keyword can alleviate this issue.

Examples

>>> import mars.tensor as mt

>>> a = mt.array([[1, 2], [3, 4]])
>>> mt.var(a).execute()
1.25
>>> mt.var(a, axis=0).execute()
array([ 1.,  1.])
>>> mt.var(a, axis=1).execute()
array([ 0.25,  0.25])

In single precision, var() can be inaccurate:

>>> a = mt.zeros((2, 512*512), dtype=mt.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> mt.var(a).execute()
0.20250003

Computing the variance in float64 is more accurate:

>>> mt.var(a, dtype=mt.float64).execute()
0.20249999932944759
>>> ((1-0.55)**2 + (0.1-0.55)**2)/2
0.2025