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Classe « gaussian_kde »

Méthode scipy.stats.gaussian_kde.set_bandwidth

Signature de la méthode set_bandwidth

def set_bandwidth(self, bw_method=None) 

Description

help(gaussian_kde.set_bandwidth)

Compute the estimator bandwidth with given method.

The new bandwidth calculated after a call to `set_bandwidth` is used
for subsequent evaluations of the estimated density.

Parameters
----------
bw_method : str, scalar or callable, optional
    The method used to calculate the estimator bandwidth.  This can be
    'scott', 'silverman', a scalar constant or a callable.  If a
    scalar, this will be used directly as `kde.factor`.  If a callable,
    it should take a `gaussian_kde` instance as only parameter and
    return a scalar.  If None (default), nothing happens; the current
    `kde.covariance_factor` method is kept.

Notes
-----
.. versionadded:: 0.11

Examples
--------
>>> import numpy as np
>>> import scipy.stats as stats
>>> x1 = np.array([-7, -5, 1, 4, 5.])
>>> kde = stats.gaussian_kde(x1)
>>> xs = np.linspace(-10, 10, num=50)
>>> y1 = kde(xs)
>>> kde.set_bandwidth(bw_method='silverman')
>>> y2 = kde(xs)
>>> kde.set_bandwidth(bw_method=kde.factor / 3.)
>>> y3 = kde(xs)

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> ax.plot(x1, np.full(x1.shape, 1 / (4. * x1.size)), 'bo',
...         label='Data points (rescaled)')
>>> ax.plot(xs, y1, label='Scott (default)')
>>> ax.plot(xs, y2, label='Silverman')
>>> ax.plot(xs, y3, label='Const (1/3 * Silverman)')
>>> ax.legend()
>>> plt.show()

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