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scipy.sparse.random#

scipy.sparse.random(m, n, density=0.01, format='coo', dtype=None, random_state=None, data_rvs=None)[source]#

Generate a sparse matrix of the given shape and density with randomly distributed values.

Warning

Since numpy 1.17, passing a np.random.Generator (e.g. np.random.default_rng) for random_state will lead to much faster execution times.

A much slower implementation is used by default for backwards compatibility.

Warning

This function returns a sparse matrix – not a sparse array. You are encouraged to use random_array to take advantage of the sparse array functionality.

Parameters:
m, nint

shape of the matrix

densityreal, optional

density of the generated matrix: density equal to one means a full matrix, density of 0 means a matrix with no non-zero items.

formatstr, optional

sparse matrix format.

dtypedtype, optional

type of the returned matrix values.

random_state{None, int, numpy.random.Generator,

  • If seed is None (or np.random), the numpy.random.RandomState singleton is used.

  • If seed is an int, a new RandomState instance is used, seeded with seed.

  • If seed is already a Generator or RandomState instance then that instance is used.

This random state will be used for sampling the sparsity structure, but not necessarily for sampling the values of the structurally nonzero entries of the matrix.

data_rvscallable, optional

Samples a requested number of random values. This function should take a single argument specifying the length of the ndarray that it will return. The structurally nonzero entries of the sparse random matrix will be taken from the array sampled by this function. By default, uniform [0, 1) random values will be sampled using the same random state as is used for sampling the sparsity structure.

Returns:
ressparse matrix

See also

random_array

constructs sparse arrays instead of sparse matrices

Examples

Passing a np.random.Generator instance for better performance:

>>> import scipy as sp
>>> import numpy as np
>>> rng = np.random.default_rng()
>>> S = sp.sparse.random(3, 4, density=0.25, random_state=rng)

Providing a sampler for the values:

>>> rvs = sp.stats.poisson(25, loc=10).rvs
>>> S = sp.sparse.random(3, 4, density=0.25, random_state=rng, data_rvs=rvs)
>>> S.toarray()
array([[ 36.,   0.,  33.,   0.],   # random
       [  0.,   0.,   0.,   0.],
       [  0.,   0.,  36.,   0.]])

Building a custom distribution. This example builds a squared normal from np.random:

>>> def np_normal_squared(size=None, random_state=rng):
...     return random_state.standard_normal(size) ** 2
>>> S = sp.sparse.random(3, 4, density=0.25, random_state=rng,
...                      data_rvs=np_normal_squared)

Or we can build it from sp.stats style rvs functions:

>>> def sp_stats_normal_squared(size=None, random_state=rng):
...     std_normal = sp.stats.distributions.norm_gen().rvs
...     return std_normal(size=size, random_state=random_state) ** 2
>>> S = sp.sparse.random(3, 4, density=0.25, random_state=rng,
...                      data_rvs=sp_stats_normal_squared)

Or we can subclass sp.stats rv_continous or rv_discrete:

>>> class NormalSquared(sp.stats.rv_continuous):
...     def _rvs(self,  size=None, random_state=rng):
...         return random_state.standard_normal(size) ** 2
>>> X = NormalSquared()
>>> Y = X()  # get a frozen version of the distribution
>>> S = sp.sparse.random(3, 4, density=0.25, random_state=rng, data_rvs=Y.rvs)
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