RandomState.standard_exponential()

RandomState.standard_exponential(size=None)

Draw samples from the standard exponential distribution.

standard_exponential is identical to the exponential distribution with a scale parameter of 1.

Parameters:

Parameters:	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. Default is None, in which case a single value is returned.
Returns:	out : float or ndarray Drawn samples.

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. Default is None, in which case a single value is returned.

Returns:

out : float or ndarray

Drawn samples.

Examples

Output a 3x8000 array:

>>> n = np.random.standard_exponential((3, 8000))

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.random.RandomState.standard_exponential.html

doc_NumPy

2017-01-10 18:19:48

Comments

Popular Articles

RandomState.standard_normal() RandomState.standard_normal(size=None) Draw samples from a standard Normal distribution (mean=0, stdev=1). Parameters: size : int or tuple of in

RandomState.standard_t() RandomState.standard_t(df, size=None) Draw samples from a standard Student?s t distribution with df degrees of freedom. A special case of the hyp

RandomState.standard_gamma() RandomState.standard_gamma(shape, size=None) Draw samples from a standard Gamma distribution. Samples are drawn from a Gamma distribution with sp

RandomState.standard_cauchy() RandomState.standard_cauchy(size=None) Draw samples from a standard Cauchy distribution with mode = 0. Also known as the Lorentz distribution. Pa

numpy.random.standard_exponential() numpy.random.standard_exponential(size=None) Draw samples from the standard exponential distribution. standard_exponential is identical to the ex