def __init__(self, id, parameters): self.id = id self.rng = RVGs.RNG(seed=id) self.params = parameters self.gillespie = Markov.Gillespie( transition_rate_matrix=parameters.rateMatrix) self.stateMonitor = PatientStateMonitor(parameters=parameters)
def __init__(self, id, parameters): """ initiates a patient :param id: ID of the patient :param parameters: an instance of the parameters class """ self.id = id self.rng = RVGs.RNG(seed=id) self.params = parameters self.stateMonitor = PatientStateMonitor(parameters=parameters)
def __init__(self, id, trans_rate_matrix): """ initiates a patient :param id: ID of the patient :param trans_rate_matrix: transition rate matrix """ self.id = id self.rng = RVGs.RNG(seed=id) # random number generator for this patient # gillespie algorithm self.gillespie = Markov.Gillespie(transition_rate_matrix=trans_rate_matrix) self.stateMonitor = PatientStateMonitor() # patient state monitor
def __populate_parameter_sets(self, diagnostic): # create a parameter set generator param_generator = ParameterGenerator(diagnostic=diagnostic) # create as many sets of parameters as the number of cohorts for i in range(len(self.ids)): # create a new random number generator for each parameter set rng = RVGs.RNG(seed=i) # get and store a new set of parameter self.param_sets.append(param_generator.get_new_parameters(rng=rng))
def __init__(self, id, parameters): """ initiates a patient :param id: ID of the patient :param parameters: an instance of the parameters class """ self.id = id self.rng = RVGs.RNG( seed=id) # random number generator for this patient self.params = parameters # gillespie algorithm self.gillespie = Markov.Gillespie( transition_rate_matrix=parameters.rateMatrix) self.stateMonitor = PatientStateMonitor( parameters=parameters) # patient state monitor
def get_obj_value(self, x, seed_index=0): """ returns one realization from x^2+noise """ # create a random number generator rng = RVGs.RNG(seed=seed_index) accum_penalty = 0 # accumulated penalty # test the feasibility if x[1] < 1: accum_penalty += self._penalty * pow(x[1] - 1, 2) x[1] = 1 return (x[0] + 1) * (x[0] + 1) + x[1] * x[1] + self._err.sample( rng) + accum_penalty
from tests.ProbDistributions import RVGtests as Tests import SimPy.RandomVariantGenerators as rndSupport # use numpy random number generator rng = rndSupport.RNG(1) print('') # tests Tests.test_rng(rng) Tests.test_bernoulli(rng, p=.2) Tests.test_beta(rng, a=2, b=5, loc=1,scale=2) Tests.test_exponential(rng, scale=10, loc=1) Tests.test_beta_binomial(rng, n=100, a=2, b=3, loc=1, scale=2) Tests.test_binomial(rng, n=1000, p=.2, loc=1) Tests.test_dirichlet(rng, a=[1, 2, 3]) Tests.test_empirical(rng, prob=[0.2, 0.2, 0.6]) Tests.test_gamma(rng, a=2, scale=4, loc=1) Tests.test_gamma_poisson(rng, a=2, gamma_scale=4, loc=1, scale=1) Tests.test_geometric(rng, p=.2, loc=1) Tests.test_johnsonsb(rng, a=10, b=3, loc=10, scale=100) Tests.test_johnsonsu(rng, a=10, b=3, loc=1, scale=2) Tests.test_lognormal(rng, s=0.2, loc=2, scale=1.1) Tests.test_multinomial(rng, n=1000, pvals=.2) Tests.test_negative_binomial(rng, n=10, p=.2, loc=1) Tests.test_normal(rng, loc=5, scale=1.2) Tests.test_poisson(rng, mu=2) Tests.test_triangular(rng, c=0.2, loc=6, scale=7) Tests.test_uniform(rng, loc=2, scale=7) Tests.test_uniform_discrete(rng, l=0, r=5)
def __init__(self, seed): self.rng = RVGs.RNG(seed=seed) self.beta = RVGs.Beta(a=1, b=2) self.sum = 0 self.seed = seed
def __init__(self, id, parameters): self.id = id self.rng = RVGs.RNG(seed=id) self.params = parameters self.stateMonitor = PatientStateMonitor(parameters=parameters)
def get_obj_value(self, x, seed_index=0): """ returns one realization from x^2+noise """ # create a random number generator rng = RVGs.RNG(seed=seed_index) return (x[0] + 1) * (x[0] + 1) + x[1] * x[1] + self._err.sample(rng)