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# This file is distributed under the same license as the mars package.
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#: ../../source/reference/tensor/generated/mars.tensor.var.rst:2
msgid "mars.tensor.var"
msgstr ""

#: mars.tensor.var:1 of
msgid "Compute the variance along the specified axis."
msgstr ""

#: mars.tensor.var:3 of
msgid ""
"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."
msgstr ""

#: mars.tensor.var of
msgid "Parameters"
msgstr ""

#: mars.tensor.var:7 of
msgid ""
"Tensor containing numbers whose variance is desired.  If `a` is not a "
"tensor, a conversion is attempted."
msgstr ""

#: mars.tensor.var:10 of
msgid ""
"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."
msgstr ""

#: mars.tensor.var:10 of
msgid ""
"Axis or axes along which the variance is computed.  The default is to "
"compute the variance of the flattened array."
msgstr ""

#: mars.tensor.var:13 of
msgid ""
"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."
msgstr ""

#: mars.tensor.var:16 of
msgid ""
"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."
msgstr ""

#: mars.tensor.var:20 of
msgid ""
"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."
msgstr ""

#: mars.tensor.var:24 of
msgid ""
"\"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."
msgstr ""

#: mars.tensor.var:28 of
msgid ""
"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."
msgstr ""

#: mars.tensor.var:28 of
msgid ""
"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."
msgstr ""

#: mars.tensor.var:32 of
msgid ""
"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."
msgstr ""

#: mars.tensor.var:38 of
msgid "The number of chunks to combine."
msgstr ""

#: mars.tensor.var of
msgid "Returns"
msgstr ""

#: mars.tensor.var:41 of
msgid ""
"**variance** -- If ``out=None``, returns a new tensor containing the "
"variance; otherwise, a reference to the output tensor is returned."
msgstr ""

#: mars.tensor.var of
msgid "Return type"
msgstr ""

#: mars.tensor.var:45 of
msgid ":func:`std`, :func:`mean`, :func:`nanmean`, :func:`nanstd`, :func:`nanvar`"
msgstr ""

#: mars.tensor.var:48 of
msgid "Notes"
msgstr ""

#: mars.tensor.var:49 of
msgid ""
"The variance is the average of the squared deviations from the mean, "
"i.e.,  ``var = mean(abs(x - x.mean())**2)``."
msgstr ""

#: mars.tensor.var:52 of
msgid ""
"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."
msgstr ""

#: mars.tensor.var:59 of
msgid ""
"Note that for complex numbers, the absolute value is taken before "
"squaring, so that the result is always real and nonnegative."
msgstr ""

#: mars.tensor.var:62 of
msgid ""
"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."
msgstr ""

#: mars.tensor.var:69 of
msgid "Examples"
msgstr ""

#: mars.tensor.var:80 of
msgid "In single precision, var() can be inaccurate:"
msgstr ""

#: mars.tensor.var:88 of
msgid "Computing the variance in float64 is more accurate:"
msgstr ""

