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numpy.random.chisquare(df, size=None)
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Draw samples from a chi-square distribution.
When
df
independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chi-square (see Notes). This distribution is often used in hypothesis testing.Parameters: df : int
Number of degrees of freedom.
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. Default is None, in which case a single value is returned.Returns: output : ndarray
Samples drawn from the distribution, packed in a
size
-shaped array.Raises: ValueError
When
df
<= 0 or when an inappropriatesize
(e.g.size=-1
) is given.Notes
The variable obtained by summing the squares of
df
independent, standard normally distributed random variables:is chi-square distributed, denoted
The probability density function of the chi-squared distribution is
where is the gamma function,
References
[R213] NIST ?Engineering Statistics Handbook? http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm Examples
>>> np.random.chisquare(2,4) array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272])
numpy.random.chisquare()
2017-01-10 18:17:59
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