import logging import numpy as np from numbers import Number from abcpy.probabilisticmodels import ProbabilisticModel, Continuous, InputConnector logging.basicConfig(level=logging.INFO) class Gaussian(ProbabilisticModel, Continuous): """ This class is an re-implementation of the `abcpy.continousmodels.Normal` for documentation purposes. """ def __init__(self, parameters, name='Gaussian'): # We expect input of type parameters = [mu, sigma] if not isinstance(parameters, list): raise TypeError('Input of Normal model is of type list') if len(parameters) != 2: raise RuntimeError('Input list must be of length 2, containing [mu, sigma].') input_connector = InputConnector.from_list(parameters) super().__init__(input_connector, name) def _check_input(self, input_values): # Check whether input has correct type or format if len(input_values) != 2: raise ValueError('Number of parameters of Normal model must be 2.') # Check whether input is from correct domain mu = input_values[0] sigma = input_values[1] if sigma < 0: return False return True def _check_output(self, values): if not isinstance(values, Number): raise ValueError('Output of the normal distribution is always a number.') # At this point values is a number (int, float); full domain for Normal is allowed return True def get_output_dimension(self): return 1 def forward_simulate(self, input_values, k, rng=np.random.RandomState()): # Extract the input parameters mu = input_values[0] sigma = input_values[1] # Do the actual forward simulation vector_of_k_samples = np.array(rng.normal(mu, sigma, k)) # Format the output to obey API result = [np.array([x]) for x in vector_of_k_samples] return result def pdf(self, input_values, x): mu = input_values[0] sigma = input_values[1] pdf = np.norm(mu,sigma).pdf(x) return pdf def infer_parameters(): # define observation for true parameters mean=170, std=15 height_obs = [160.82499176, 167.24266737, 185.71695756, 153.7045709, 163.40568812, 140.70658699, 169.59102084, 172.81041696, 187.38782738, 179.66358934, 176.63417241, 189.16082803, 181.98288443, 170.18565017, 183.78493886, 166.58387299, 161.9521899, 155.69213073, 156.17867343, 144.51580379, 170.29847515, 197.96767899, 153.36646527, 162.22710198, 158.70012047, 178.53470703, 170.77697743, 164.31392633, 165.88595994, 177.38083686, 146.67058471763457, 179.41946565658628, 238.02751620619537, 206.22458790620766, 220.89530574344568, 221.04082532837026, 142.25301427453394, 261.37656571434275, 171.63761180867033, 210.28121820385866, 237.29130237612236, 175.75558340169619, 224.54340549862235, 197.42448680731226, 165.88273684581381, 166.55094082844519, 229.54308602661584, 222.99844054358519, 185.30223966014586, 152.69149367593846, 206.94372818527413, 256.35498655339154, 165.43140916577741, 250.19273595481803, 148.87781549665536, 223.05547559193792, 230.03418198709608, 146.13611923127021, 138.24716809523139, 179.26755740864527, 141.21704876815426, 170.89587081800852, 222.96391329259626, 188.27229523693822, 202.67075179617672, 211.75963110985992, 217.45423324370509] # define prior from abcpy.continuousmodels import Uniform mu = Uniform([[150], [200]], ) sigma = Uniform([[5], [25]], ) # define the model from abcpy.continuousmodels import Normal as Gaussian height = Gaussian([mu, sigma], name='height') # define statistics from abcpy.statistics import Identity statistics_calculator = Identity(degree = 2, cross = False) # define distance from abcpy.distances import LogReg distance_calculator = LogReg(statistics_calculator) # define kernel from abcpy.perturbationkernel import DefaultKernel kernel = DefaultKernel([mu, sigma]) # define backend # Note, the dummy backend does not parallelize the code! from abcpy.backends import BackendDummy as Backend backend = Backend() # define sampling scheme from abcpy.inferences import PMCABC sampler = PMCABC([height], [distance_calculator], backend, kernel, seed=1) # sample from scheme T, n_sample, n_samples_per_param = 3, 250, 10 eps_arr = np.array([.75]) epsilon_percentile = 10 journal = sampler.sample([height_obs], T, eps_arr, n_sample, n_samples_per_param, epsilon_percentile) return journal def analyse_journal(journal): # output parameters and weights print(journal.parameters) print(journal.weights) # do post analysis print(journal.posterior_mean()) print(journal.posterior_cov()) print(journal.posterior_histogram()) # print configuration print(journal.configuration) # save and load journal journal.save("experiments.jnl") from abcpy.output import Journal new_journal = Journal.fromFile('experiments.jnl') # this code is for testing purposes only and not relevant to run the example import unittest class ExampleExtendModelGaussianPython(unittest.TestCase): def test_example(self): journal = infer_parameters() test_result = journal.posterior_mean()[0] expected_result = 177.02 self.assertLess(abs(test_result - expected_result), 2.) if __name__ == "__main__": journal = infer_parameters() analyse_journal(journal)