Package csb :: Package statistics :: Module rand |
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# Module rand

source code

Random number generators

Functions
gen_inv_gaussian(a, b, p, burnin=10) Sampler based on Gibbs sampling. | source code
inv_gaussian(mu=1.0, _lambda=1.0, shape=None) Generate random samples from inverse gaussian. | source code
probability_transform(shape, inv_cum, cum_min=0.0, cum_max=1.0) Generic sampler based on the probability transform. | source code
3 x 3 numpy array |
random_rotation(A, n_iter=10, initial_values=None) Generation of three-dimensional random rotations in fitting and matching problems, Habeck 2009. | source code
sample_dirichlet(alpha, n_samples=1) Sample points from a dirichlet distribution with parameter alpha. | source code
sample_from_histogram(p, n_samples=1) returns the indice of bin according to the histogram p | source code
numpy array |
sample_sphere3d(radius=1.0, n_samples=1) Sample points from 3D sphere. | source code
truncated_gamma(shape=None, alpha=1.0, beta=1.0, x_min=None, x_max=None) Generate random variates from a lower-and upper-bounded gamma distribution. | source code
truncated_normal(shape=None, mu=0.0, sigma=1.0, x_min=None, x_max=None) Generates random variates from a lower-and upper-bounded normal distribution | source code

Function Details

### gen_inv_gaussian(a, b, p, burnin=10) | source code
Sampler based on Gibbs sampling. Assumes scalar p.

### probability_transform(shape, inv_cum, cum_min=0.0, cum_max=1.0) | source code
Generic sampler based on the probability transform. Parameters: `shape` - shape of the random sample `inv_cum` - inversion of the cumulative density function from which one seeks to sample `cum_min` - lower value of the cumulative distribution `cum_max` - upper value of the cumulative distribution Returns: random variates of the PDF implied by the inverse cumulative distribution

### random_rotation(A, n_iter=10, initial_values=None) | source code
Generation of three-dimensional random rotations in fitting and matching problems, Habeck 2009. Generate random rotation R from:
       exp(trace(dot(transpose(A), R)))
Parameters: `A` (3 x 3 numpy array) - generating parameter `n_iter` (integer) - number of gibbs sampling steps `initial_values` (tuple) - initial euler angles alpha, beta and gamma Returns: 3 x 3 numpy array

### sample_dirichlet(alpha, n_samples=1) | source code
Sample points from a dirichlet distribution with parameter alpha. Parameters: `alpha` (array) - alpha parameter of a dirichlet distribution

### sample_from_histogram(p, n_samples=1) | source code
returns the indice of bin according to the histogram p Parameters: `p` (numpy.array) - histogram `n_samples` (integer) - number of samples to generate

### sample_sphere3d(radius=1.0, n_samples=1) | source code
Sample points from 3D sphere. Parameters: `radius` (float) - radius of the sphere `n_samples` (int) - number of samples to return Returns: numpy array n_samples times random cartesian coordinates inside the sphere

### truncated_gamma(shape=None, alpha=1.0, beta=1.0, x_min=None, x_max=None) | source code
Generate random variates from a lower-and upper-bounded gamma distribution. Parameters: `shape` - shape of the random sample `alpha` - shape parameter (alpha > 0.) `beta` - scale parameter (beta >= 0.) `x_min` - lower bound of variate `x_max` - upper bound of variate Returns: random variates of lower-bounded gamma distribution

### truncated_normal(shape=None, mu=0.0, sigma=1.0, x_min=None, x_max=None) | source code
Generates random variates from a lower-and upper-bounded normal distribution Parameters: `shape` - shape of the random sample `mu` - location parameter `sigma` - width of the distribution (sigma >= 0.) `x_min` - lower bound of variate `x_max` - upper bound of variate Returns: random variates of lower-bounded normal distribution