Random (methods.seeding.random
)¶
Random is the simplest MF initialization method.
The entries of factors are drawn from a uniform distribution over [0, max(target matrix)). Generated matrix factors are sparse matrices with the default density parameter of 0.01.
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class
nimfa.methods.seeding.random.
Random
¶ Bases:
object
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gen_dense
(dim1, dim2)¶ Return randomly initialized
numpy.matrix
matrix of specified dimensions.Parameters: - dim1 (int) – Dimension along first axis.
- dim2 (int) – Dimension along second axis.
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gen_sparse
(dim1, dim2)¶ Return randomly initialized sparse matrix of specified dimensions.
Parameters: - dim1 (int) – Dimension along first axis.
- dim2 (int) – Dimension along second axis.
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initialize
(V, rank, options)¶ Return initialized basis and mixture matrix (and additional factors if specified in :param:`Sn`, n = 1, 2, …, k). Initialized matrices are of the same type as passed target matrix.
Parameters: - V (One of the
scipy.sparse
sparse matrices types ornumpy.matrix
) – Target matrix, the matrix for MF method to estimate. - rank (int) – Factorization rank.
- options (dict) –
Specify the algorithm and model specific options (e.g. initialization of extra matrix factor, seeding parameters).
Option
Sn
, n = 1, 2, 3, …, k specifies additional k matrix factors which need to be initialized. The value of each option Sn is a tuple denoting matrix shape. Matrix factors are returned in the same order as their descriptions in input.Option
density
represents density of generated matrices. Density of 1 means a full matrix, density of 0 means a matrix with no nonzero items. Default value is 0.7. Density parameter is applied only if passed targetV
is an instance of onescipy.sparse
sparse types.
- V (One of the
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