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seed(seed=None)

The generator is a lagged Fibonacci method with 55-element internal state.

Parameters

seed : int, sequence, 'default', optional

If 'default', the random seed is reset to a default value.

If seed is a sequence containing 55 floating-point numbers in range [0,1], these are used to set the internal state of the generator.

If the value is an integer, the internal state is obtained from numpy.random.RandomState (MT19937) with the integer used as the initial seed.

If seed is omitted (None), numpy.random.rand is used to initialize the generator.

Seed the internal random number generator used in this ID package.

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy._lib._util.check_random_state scipy.sparse._construct.random scipy.optimize._basinhopping.basinhopping scipy.linalg.interpolative.seed scipy.optimize._basinhopping.RandomDisplacement scipy.optimize._differentialevolution.DifferentialEvolutionSolver scipy.linalg._sketches.cwt_matrix scipy.optimize._basinhopping.Metropolis scipy.optimize._optimize.check_grad scipy.optimize._differentialevolution.differential_evolution scipy.optimize._qap.quadratic_assignment scipy.sparse._construct.rand scipy.linalg._sketches.clarkson_woodruff_transform scipy.optimize._dual_annealing.StrategyChain scipy.optimize._dual_annealing.dual_annealing

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /scipy/linalg/interpolative.py#422
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