raw_random – Low-level random numbers

Raw random provides the random-number drawing functionality, that underlies the friendlier RandomStreams interface.

Reference

class raw_random.RandomStreamsBase(object)

This is the interface for the theano.tensor.shared_randomstreams.RandomStreams subclass

binomial(self, size=(), n=1, p=0.5, ndim=None):

Sample n times with probability of success p for each trial and return the number of successes.

If size is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information.

This wraps the numpy implementation, so it has the same behavior.

uniform(self, size=(), low=0.0, high=1.0, ndim=None):

Sample a tensor of the given size whose elements come from a uniform distribution between low and high.

If size is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information.

This wraps the numpy implementation, so it has the same bounds: [low, high[.

normal(self, size=(), avg=0.0, std=1.0, ndim=None):

Sample from a normal distribution centered on avg with the specified standard deviation (std)

If size is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information.

This wrap numpy implementation, so it have the same behavior.

random_integers(self, size=(), low=0, high=1, ndim=None):

Sample a random integer between low and high, both inclusive.

If size is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information.

This is a generalization of numpy.random.random_integers() to the case where low and high are tensors. Otherwise it behaves the same.

choice(self, size=(), a=2, replace=True, p=None, ndim=None, dtype='int64'):

Choose values from a with or without replacement. a can be a 1-D array or a positive scalar. If a is a scalar, the samples are drawn from the range [0, a[.

If size is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information.

This wraps the numpy implementation so it has the same behavior.

poisson(self, size=(), lam=None, ndim=None, dtype='int64'):

Draw samples from a Poisson distribution.

The Poisson distribution is the limit of the Binomial distribution for large N.

If size is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information.

This wraps the numpy implementation so it has the same behavior.

permutation(self, size=(), n=1, ndim=None):

Returns permutations of the integers between 0 and n-1, as many times as required by size. For instance, if size=(p,q), p*q permutations will be generated, and the output shape will be (p,q,n), because each permutation is of size n.

Theano tries to infer the number of dimensions from the length of size, but you may always specify it with ndim.

Note

The output will have ndim+1 dimensions.

This is a generalization of numpy.random.permutation() to tensors. Otherwise it behaves the same.

multinomial(self, size=(), n=1, pvals=[0.5, 0.5], ndim=None):

Sample n times from a multinomial distribution defined by probabilities pvals, as many times as required by size. For instance, if size=(p,q), p*q samples will be drawn, and the output shape will be (p,q,len(pvals)).

Theano tries to infer the number of dimensions from the length of size, but you may always specify it with ndim.

Note

The output will have ndim+1 dimensions.

This is a generalization of numpy.random.multinomial() to the case where n and pvals are tensors. Otherwise it behaves the same.

shuffle_row_elements(self, input):

Return a variable with every row (rightmost index) shuffled.

This uses a permutation random variable internally, available via the .permutation attribute of the return value.

class raw_random.RandomStateType(gof.Type)

A Type for variables that will take numpy.random.RandomState values.

raw_random.random_state_type(name=None)

Return a new Variable whose .type is random_state_type.

class raw_random.RandomFunction(gof.Op)

Op that draws random numbers from a numpy.RandomState object. This Op is parametrized to draw numbers from many possible distributions.

raw_random.uniform(random_state, size=None, low=0.0, high=1.0, ndim=None, dtype=None)

Sample from a uniform distribution between low and high.

If the size argument is ambiguous on the number of dimensions, the first argument may be a plain integer to supplement the missing information.

Returns:RandomVariable, NewRandomState
raw_random.binomial(random_state, size=None, n=1, p=0.5, ndim=None, dtype='int64')

Sample n times with probability of success p for each trial and return the number of successes.

If size is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information.

Returns:RandomVariable, NewRandomState
raw_random.normal(random_state, size=None, avg=0.0, std=1.0, ndim=None, dtype=None)

Sample from a normal distribution centered on avg with the specified standard deviation (std).

If size is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information.

Returns:RandomVariable, NewRandomState
raw_random.random_integers(random_state, size=None, low=0, high=1, ndim=None, dtype='int64')

Sample random integers in [low, high] to fill up size.

If size is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information.

Returns:RandomVariable, NewRandomState
raw_random.permutation(random_state, size=None, n=1, ndim=None, dtype='int64')

Returns permutations of the integers in [0, n[, as many times as required by size. For instance, if size=(p,q), p*q permutations will be generated, and the output shape will be (p,q,n), because each permutation is of size n.

If size is ambiguous on the number of dimensions, ndim may be a plain integer, which should correspond to len(size).

Note

The output will have ndim+1 dimensions.

Returns:RandomVariable, NewRandomState
raw_random.multinomial(random_state, size=None, p_vals=[0.5, 0.5], ndim=None, dtype='int64')

Sample from a multinomial distribution defined by probabilities pvals, as many times as required by size. For instance, if size=(p,q), p*q samples will be drawn, and the output shape will be (p,q,len(pvals)).

If size is ambiguous on the number of dimensions, ndim may be a plain integer, which should correspond to len(size).

Note

The output will have ndim+1 dimensions.

Returns:RandomVariable, NewRandomState