pyhrf.stats.random.
BetaGenerator
(mean=0.5, var=0.1)¶Bases: pyhrf.stats.random.RandomGenerator
Class encapsulating the beta random generator of numpy
generate
(size)¶pyhrf.stats.random.
GammaGenerator
(mean=1.0, var=1.0)¶Bases: pyhrf.stats.random.RandomGenerator
Class encapsulating the gamma random generator of numpy
generate
(size)¶pyhrf.stats.random.
GaussianGenerator
(mean=0.0, var=1.0)¶Bases: pyhrf.stats.random.RandomGenerator
Class encapsulating the gaussian random generator of numpy
generate
(size)¶pyhrf.stats.random.
IndependentMixtureLaw
(states, generators)¶Class handling the generation of values following an indenpendent mixture law. Requires the prior generator of label values.
generate
()¶Generate realisations of the mixture law.
pyhrf.stats.random.
LogNormalGenerator
(meanLogN=1.0, varLogN=1.0)¶Bases: pyhrf.stats.random.RandomGenerator
Class encapsulating the log normal generator of numpy
generate
(size)¶pyhrf.stats.random.
RandomGenerator
¶B Abstract class to ensure the definition of the function generate.
generate
(size)¶pyhrf.stats.random.
UniformGenerator
(minV=0.0, maxV=1.0)¶Bases: pyhrf.stats.random.RandomGenerator
Class encapsulating the random generator
generate
(size)¶pyhrf.stats.random.
ZeroGenerator
¶Bases: pyhrf.stats.random.RandomGenerator
Class encapsulating the null distribution !!!!!!!!!
generate
(size)¶pyhrf.stats.random.
gm_sample
(means, variances, props, n=1)¶pyhrf.stats.random.
rand
(d0, d1, ..., dn)¶Random values in a given shape.
Create an array of the given shape and populate it with
random samples from a uniform distribution
over [0, 1)
.
Parameters: | d1, .., dn (d0,) – The dimensions of the returned array, should all be positive. If no argument is given a single Python float is returned. |
---|---|
Returns: | out – Random values. |
Return type: | ndarray, shape (d0, d1, ..., dn) |
See also
random()
Notes
This is a convenience function. If you want an interface that takes a shape-tuple as the first argument, refer to np.random.random_sample .
Examples
>>> np.random.rand(3,2)
array([[ 0.14022471, 0.96360618], #random
[ 0.37601032, 0.25528411], #random
[ 0.49313049, 0.94909878]]) #random
pyhrf.stats.random.
randn
(d0, d1, ..., dn)¶Return a sample (or samples) from the “standard normal” distribution.
If positive, int_like or int-convertible arguments are provided,
randn generates an array of shape (d0, d1, ..., dn)
, filled
with random floats sampled from a univariate “normal” (Gaussian)
distribution of mean 0 and variance 1 (if any of the are
floats, they are first converted to integers by truncation). A single
float randomly sampled from the distribution is returned if no
argument is provided.
This is a convenience function. If you want an interface that takes a tuple as the first argument, use numpy.random.standard_normal instead.
Parameters: | d1, .., dn (d0,) – The dimensions of the returned array, should be all positive. If no argument is given a single Python float is returned. |
---|---|
Returns: | Z – A (d0, d1, ..., dn) -shaped array of floating-point samples from
the standard normal distribution, or a single such float if
no parameters were supplied. |
Return type: | ndarray or float |
See also
random.standard_normal()
Notes
For random samples from , use:
sigma * np.random.randn(...) + mu
Examples
>>> np.random.randn()
2.1923875335537315 #random
Two-by-four array of samples from N(3, 6.25):
>>> 2.5 * np.random.randn(2, 4) + 3
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random
pyhrf.stats.random.
rpnorm
(n, m, s)¶Random numbers from the positive normal distribution. rpnorm(n,m,s) is a vector of length n with random entries, generated from a positive normal distribution with mean m and standard deviation s.
Original matlab code from: (c) Vincent Mazet, 06/2005 Centre de Recherche en Automatique de Nancy, France vincent.mazet@cran.uhp-nancy.fr
Reference: V. Mazet, D. Brie, J. Idier, ‘Simulation of Positive Normal Variables using several Proposal Distributions’, IEEE Workshop Statistical Signal Processing 2005, july 17-20 2005, Bordeaux, France.
Adapted by Thomas VINCENT: thomas.vincent@cea.fr
pyhrf.stats.random.
truncRandn
(size, mu=0.0, sigma=1.0, a=0.0, b=inf)¶