mxnet.np.random.logistic

logistic(loc=0.0, scale=1.0, size=None, device=None, out=None)

Draw samples from a logistic distribution.

Samples are drawn from a logistic distribution with specified parameters, loc (location or mean, also median), and scale (>0).

Parameters

  • loc (float or array_like of floats, optional) – Parameter of the distribution. Default is 0.

  • scale (float or array_like of floats, optional) – Parameter of the distribution. Must be non-negative. Default is 1.

  • size (int or tuple of ints, optional) – 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 loc and scale are both scalars. Otherwise, np.broadcast(loc, scale).size samples are drawn.

  • device (Device, optional) – Device context of output, default is current device.

  • out (ndarray, optional) – Store output to an existing ndarray.

Returns

out – Drawn samples from the parameterized logistic distribution.

Return type

ndarray or scalar

Examples

Draw samples from the distribution: >>> loc, scale = 10, 1 >>> s = np.random.logistic(loc, scale, 10000) >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, bins=50) # plot against distribution >>> def logist(x, loc, scale): … return np.exp((loc-x)/scale)/(scale*(1+np.exp((loc-x)/scale))**2) >>> lgst_val = logist(bins, loc, scale) >>> plt.plot(bins, lgst_val * count.max() / lgst_val.max()) >>> plt.show()