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Add continuous distributions to dpnp.random.Generator - #3158
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Port NumPy's continuous samplers to device kernels: exponential, gamma, beta, chisquare, standard_t, f, lognormal, standard_cauchy, wald, pareto, weibull, power, rayleigh, laplace, gumbel, logistic, triangular and vonmises. The rejection samplers return NaN for device parameters that would make them loop forever.
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Array API standard conformance tests for dpnp=0.22.0dev0=np2py314h8d9cdd5_28 ran successfully. |
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Coverage is 83.518% — feature/device-rng-continuous into feature/device-rng-generator. No base build found for |
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This PR adds the continuous distributions to
dpnp.random.Generator, as part of the series splitting #3147. It is stacked on #3157The samplers are device ports of NumPy's algorithms:
exponential,standard_exponential,pareto,weibull,power,rayleighgamma,standard_gamma,beta,chisquare,standard_t,flognormal,standard_cauchy,wald,laplace,gumbel,logistic,triangular,vonmisesParameters on the device aren't checked, since that would wait for the device. The rejection samplers instead return NaN for parameters that would make them loop forever, including valid ones that round badly in single precision