sample — SciPy v1.15.3 Manual (original) (raw)

scipy.stats.Mixture.

Mixture.sample(shape=(), *, rng=None, method=None)[source]#

Random sample from the distribution.

Parameters:

shapetuple of ints, default: ()

The shape of the sample to draw. If the parameters of the distribution underlying the random variable are arrays of shape param_shape, the output array will be of shape shape + param_shape.

method{None, ‘formula’, ‘inverse_transform’}

The strategy used to produce the sample. By default (None), the infrastructure chooses between the following options, listed in order of precedence.

• 'formula': an implementation specific to the distribution
• 'inverse_transform': generate a uniformly distributed sample and return the inverse CDF at these arguments.

Not all method options are available for all distributions. If the selected method is not available, a _NotImplementedError`_will be raised.

rngnumpy.random.Generator or scipy.stats.QMCEngine, optional

Pseudo- or quasi-random number generator state. When rng is None, a new numpy.random.Generator is created using entropy from the operating system. Types other than numpy.random.Generator and_scipy.stats.QMCEngine_ are passed to numpy.random.default_rngto instantiate a Generator.

If rng is an instance of scipy.stats.QMCEngine configured to use scrambling and shape is not empty, then each slice along the zeroth axis of the result is a “quasi-independent”, low-discrepancy sequence; that is, they are distinct sequences that can be treated as statistically independent for most practical purposes. Separate calls to sampleproduce new quasi-independent, low-discrepancy sequences.

References

Examples

Instantiate a distribution with the desired parameters:

import numpy as np from scipy import stats X = stats.Uniform(a=0., b=1.)

Generate a pseudorandom sample:

x = X.sample((1000, 1)) octiles = (np.arange(8) + 1) / 8 np.count_nonzero(x <= octiles, axis=0) array([ 148, 263, 387, 516, 636, 751, 865, 1000]) # may vary

X = stats.Uniform(a=np.zeros((3, 1)), b=np.ones(2)) X.a.shape, (3, 2) x = X.sample(shape=(5, 4)) x.shape (5, 4, 3, 2)