Bindings for numeric::random namespace

Classes
		builtins.object RandomGenerator WeightedSampler uniform_RG mt19937_RG standard_RG   class RandomGenerator(builtins.object)     Random number generator system     Methods defined here: __call__(...) from builtins.PyCapsule __call__(rosetta.numeric.random.RandomGenerator) -> float __init__(self, /, *args, **kwargs) Initialize self.  See help(type(self)) for accurate signature. __new__(*args, **kwargs) from builtins.type Create and return a new object.  See help(type) for accurate signature. assign(...) from builtins.PyCapsule assign(self : rosetta.numeric.random.RandomGenerator,  : rosetta.numeric.random.RandomGenerator) -> rosetta.numeric.random.RandomGenerator gaussian(...) from builtins.PyCapsule gaussian(rosetta.numeric.random.RandomGenerator) -> float   Get Gaussian distribution random number get_seed(...) from builtins.PyCapsule get_seed(rosetta.numeric.random.RandomGenerator) -> int   Return the seed used by this RNG. max(...) from builtins.PyCapsule max(rosetta.numeric.random.RandomGenerator) -> float min(...) from builtins.PyCapsule min(rosetta.numeric.random.RandomGenerator) -> float random_range(...) from builtins.PyCapsule random_range(self : rosetta.numeric.random.RandomGenerator, low : int, high : int) -> int   Returns a random int in the range specified by the arguments set_seed(...) from builtins.PyCapsule set_seed(*args, **kwargs) Overloaded function.   1. set_seed(self : rosetta.numeric.random.RandomGenerator, generator_type : str, seed : int) -> NoneType   Set the seed and the generator type synchronously.  Currently the two supported generator types are "standard"  and "mt19937" with the latter being the recommended form.   2. set_seed(self : rosetta.numeric.random.RandomGenerator, seed : int) -> NoneType   Return the seed used by this RNG. uniform(...) from builtins.PyCapsule uniform(rosetta.numeric.random.RandomGenerator) -> float   Return from range [0, 1] (?) uniform random number    The implementation of random_range leads me to believe this is  actually [0, 1), like most other random number generators.  -IWD   class WeightedSampler(builtins.object)       Methods defined here: __init__(...) from builtins.PyCapsule __init__(*args, **kwargs) Overloaded function.   1. __init__(rosetta.numeric.random.WeightedSampler) -> NoneType   2. __init__(self : rosetta.numeric.random.WeightedSampler, num_weights : int) -> NoneType   3. __init__(self : rosetta.numeric.random.WeightedSampler, weights : rosetta.utility.vector1_double) -> NoneType   4. __init__(self : rosetta.numeric.random.WeightedSampler,  : rosetta.numeric.random.WeightedSampler) -> NoneType __new__(*args, **kwargs) from builtins.type Create and return a new object.  See help(type) for accurate signature. add_weight(...) from builtins.PyCapsule add_weight(self : rosetta.numeric.random.WeightedSampler, weight : float) -> NoneType assign(...) from builtins.PyCapsule assign(self : rosetta.numeric.random.WeightedSampler,  : rosetta.numeric.random.WeightedSampler) -> rosetta.numeric.random.WeightedSampler   Copy operator clear(...) from builtins.PyCapsule clear(rosetta.numeric.random.WeightedSampler) -> NoneType random_sample(...) from builtins.PyCapsule random_sample(*args, **kwargs) Overloaded function.   1. random_sample(self : rosetta.numeric.random.WeightedSampler, randnum : float) -> int   2. random_sample(self : rosetta.numeric.random.WeightedSampler,  : rosetta.numeric.random.RandomGenerator) -> int resize(...) from builtins.PyCapsule resize(*args, **kwargs) Overloaded function.   1. resize(self : rosetta.numeric.random.WeightedSampler, num_weights : int) -> NoneType   2. resize(self : rosetta.numeric.random.WeightedSampler, num_weights : int, default_weight : float) -> NoneType set_weight(...) from builtins.PyCapsule set_weight(self : rosetta.numeric.random.WeightedSampler, weight_num : int, weight : float) -> NoneType size(...) from builtins.PyCapsule size(rosetta.numeric.random.WeightedSampler) -> int update_cumulative_distribution(...) from builtins.PyCapsule update_cumulative_distribution(rosetta.numeric.random.WeightedSampler) -> NoneType weights(...) from builtins.PyCapsule weights(*args, **kwargs) Overloaded function.   1. weights(rosetta.numeric.random.WeightedSampler) -> rosetta.utility.vector1_double   2. weights(self : rosetta.numeric.random.WeightedSampler, weights : rosetta.utility.vector1_double) -> NoneType   class mt19937_RG(uniform_RG)       Method resolution order: mt19937_RG uniform_RG builtins.object Methods defined here: __init__(...) from builtins.PyCapsule __init__(handle) -> NoneType __new__(*args, **kwargs) from builtins.type Create and return a new object.  See help(type) for accurate signature. assign(...) from builtins.PyCapsule assign(self : rosetta.numeric.random.mt19937_RG,  : rosetta.numeric.random.mt19937_RG) -> rosetta.numeric.random.mt19937_RG getRandom(...) from builtins.PyCapsule getRandom(rosetta.numeric.random.mt19937_RG) -> float getSeed(...) from builtins.PyCapsule getSeed(rosetta.numeric.random.mt19937_RG) -> int setSeed(...) from builtins.PyCapsule setSeed(*args, **kwargs) Overloaded function.   1. setSeed(self : rosetta.numeric.random.mt19937_RG, iseed : int) -> NoneType   Set seed and state   2. setSeed(self : rosetta.numeric.random.mt19937_RG,  : str) -> NoneType   Set seed and state   class standard_RG(uniform_RG)     Generator based on rand() < clib > function.     Method resolution order: standard_RG uniform_RG builtins.object Methods defined here: __init__(...) from builtins.PyCapsule __init__(handle) -> NoneType __new__(*args, **kwargs) from builtins.type Create and return a new object.  See help(type) for accurate signature. assign(...) from builtins.PyCapsule assign(self : rosetta.numeric.random.standard_RG,  : rosetta.numeric.random.standard_RG) -> rosetta.numeric.random.standard_RG getRandom(...) from builtins.PyCapsule getRandom(rosetta.numeric.random.standard_RG) -> float getSeed(...) from builtins.PyCapsule getSeed(rosetta.numeric.random.standard_RG) -> int setSeed(...) from builtins.PyCapsule setSeed(*args, **kwargs) Overloaded function.   1. setSeed(self : rosetta.numeric.random.standard_RG, seed : int) -> NoneType   2. setSeed(self : rosetta.numeric.random.standard_RG,  : str) -> NoneType   class uniform_RG(builtins.object)     Uniform random number generator     Methods defined here: __init__(self, /, *args, **kwargs) Initialize self.  See help(type(self)) for accurate signature. __new__(*args, **kwargs) from builtins.type Create and return a new object.  See help(type) for accurate signature. assign(...) from builtins.PyCapsule assign(self : rosetta.numeric.random.uniform_RG,  : rosetta.numeric.random.uniform_RG) -> rosetta.numeric.random.uniform_RG getRandom(...) from builtins.PyCapsule getRandom(rosetta.numeric.random.uniform_RG) -> float getSeed(...) from builtins.PyCapsule getSeed(rosetta.numeric.random.uniform_RG) -> int setSeed(...) from builtins.PyCapsule setSeed(*args, **kwargs) Overloaded function.   1. setSeed(self : rosetta.numeric.random.uniform_RG, seed : int) -> NoneType   2. setSeed(self : rosetta.numeric.random.uniform_RG, seed : str) -> NoneType

Functions
		do_recursion(...) method of builtins.PyCapsule instance do_recursion(r : rosetta.W128_T, a : rosetta.W128_T, b : rosetta.W128_T, c : rosetta.W128_T, lung : rosetta.W128_T) -> NoneType   This function represents the recursion formula.      output      a 128-bit part of the internal state array      a 128-bit part of the internal state array      a 128-bit part of the internal state array      a 128-bit part of the internal state array gaussian(...) method of builtins.PyCapsule instance gaussian() -> float gaussian_random_xform(...) method of builtins.PyCapsule instance gaussian_random_xform(angsd : float, movsd : float) -> rosetta.numeric.xyzTransform_double_t ini_func1(...) method of builtins.PyCapsule instance ini_func1(x : int) -> int ini_func2(...) method of builtins.PyCapsule instance ini_func2(x : int) -> int lshift128(...) method of builtins.PyCapsule instance lshift128(out : rosetta.W128_T, in : rosetta.W128_T, shift : int) -> NoneType random_normal(...) method of builtins.PyCapsule instance random_normal() -> rosetta.numeric.xyzVector_double_t random_range(...) method of builtins.PyCapsule instance random_range(low : int, high : int) -> int   Return a number uniformly drawn from the inclusive range between low  and high.  Threadsafe since each thread uses its own random generator. random_rotation(...) method of builtins.PyCapsule instance random_rotation() -> rosetta.numeric.xyzMatrix_double_t random_vector(...) method of builtins.PyCapsule instance random_vector() -> rosetta.numeric.xyzVector_double_t random_vector_spherical(...) method of builtins.PyCapsule instance random_vector_spherical() -> rosetta.numeric.xyzVector_double_t random_vector_unit_cube(...) method of builtins.PyCapsule instance random_vector_unit_cube() -> rosetta.numeric.xyzVector_double_t random_xform(...) method of builtins.PyCapsule instance random_xform() -> rosetta.numeric.xyzTransform_double_t reservoir_sample_accept_prob(...) method of builtins.PyCapsule instance reservoir_sample_accept_prob(n_wanted : int, n_seen : int) -> float   Returns the probability that the Nth value in a sequence  should be accepted using the reservoir sampling criterion.      If we've seen N values and we want to keep K of them,  the probability of the Nth value being accepted is min(K/N,1.0). rg(...) method of builtins.PyCapsule instance rg() -> numeric::random::RandomGenerator   Return the one-per-thread "singleton" random generator. sformat_idxof(...) method of builtins.PyCapsule instance sformat_idxof(i : int) -> int uniform(...) method of builtins.PyCapsule instance uniform() -> float