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scipy.stats.Logistic.

# sample

Logistic.sample(shape=(), *, method=None, rng=None)

  Random sample from the distribution.

  Parameters:
    shape tuple 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.

    rng `numpy.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_rng` to 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 `sample` produce new quasi-independent, low-discrepancy sequences.

  References

  [1]

  Sampling (statistics), Wikipedia, https://en.wikipedia.org/wiki/Sampling_(statistics)

  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)