noncentral_chisquare(self, df, nonc, size=None, chunks='auto', **kwargs)
This docstring was copied from numpy.random.mtrand.RandomState.noncentral_chisquare.
Some inconsistencies with the Dask version may exist.
The noncentral $\chi^2$ distribution is a generalization of the $\chi^2$ distribution.
New code should use the noncentral_chisquare
method of a default_rng()
instance instead; please see the :None:ref:`random-quick-start`.
The probability density function for the noncentral Chi-square distribution is
$$P(x;df,nonc) = \sum^{\infty}_{i=0}\frac{e^{-nonc/2}(nonc/2)^{i}}{i!} P_{Y_{df+2i}}(x),$$where $Y_{q}$ is the Chi-square with q degrees of freedom.
Degrees of freedom, must be > 0.
Earlier NumPy versions required dfnum > 1.
Non-centrality, must be non-negative.
Output shape. If the given shape is, e.g., (m, n, k)
, then m * n * k
samples are drawn. If size is None
(default), a single value is returned if df
and nonc
are both scalars. Otherwise, np.broadcast(df, nonc).size
samples are drawn.
Drawn samples from the parameterized noncentral chi-square distribution.
Draw samples from a noncentral chi-square distribution.
Generator.noncentral_chisquare
which should be used for new code.
Draw values from the distribution and plot the histogram
This example is valid syntax, but we were not able to check execution>>> import matplotlib.pyplot as plt # doctest: +SKIP
... values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000), # doctest: +SKIP
... bins=200, density=True)
... plt.show() # doctest: +SKIP
Draw values from a noncentral chisquare with very small noncentrality, and compare to a chisquare.
This example is valid syntax, but we were not able to check execution>>> plt.figure() # doctest: +SKIP
... values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000), # doctest: +SKIP
... bins=np.arange(0., 25, .1), density=True)
... values2 = plt.hist(np.random.chisquare(3, 100000), # doctest: +SKIP
... bins=np.arange(0., 25, .1), density=True)
... plt.plot(values[1][0:-1], values[0]-values2[0], 'ob') # doctest: +SKIP
... plt.show() # doctest: +SKIP
Demonstrate how large values of non-centrality lead to a more symmetric distribution.
This example is valid syntax, but we were not able to check execution>>> plt.figure() # doctest: +SKIPSee :
... values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000), # doctest: +SKIP
... bins=200, density=True)
... plt.show() # doctest: +SKIP
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