def __init__(self, exp_desc): """ :param exp_desc: an experiment descriptor object """ assert isinstance(exp_desc, ed.ExperimentDescriptor) self.exp_desc = exp_desc self.exp_dir = os.path.join(misc.get_root(), 'experiments', exp_desc.get_dir()) self.sim = misc.get_simulator(exp_desc.sim)
import numpy as np import matplotlib import matplotlib.pyplot as plt import misc import inference.mcmc as mcmc import inference.diagnostics.two_sample as two_sample import simulators.gaussian as sim import experiment_descriptor as ed import util.io import util.math import util.plot root = misc.get_root() rng = np.random.RandomState(42) prior = sim.Prior() model = sim.Model() true_ps, obs_xs = sim.get_ground_truth() # for mcmc thin = 10 n_mcmc_samples = 5000 burnin = 100 def get_true_samples(seed): """ Generates MCMC samples from the true posterior.
import os import numpy as np import ml.models.neural_nets as nn import ml.trainers as trainers import ml.loss_functions as lf import simulators.lotka_volterra as sim import misc import util.io import util.plot dir = os.path.join(misc.get_root(), 'results', 'lotka_volterra', 'other', 'failed_sims_model') def gen_data(n_data=100000, rng=np.random): """ Generates training data to fit the model. :param n_data: number of datapoints :param rng: random number generator """ res_file = os.path.join(dir, 'data') if os.path.exists(res_file + '.pkl'): ps, ys = util.io.load(res_file) else: