python source code of gaussian

import numpy as np

from numbers import Number
from abcpy.probabilisticmodels import ProbabilisticModel, Continuous, InputConnector

import rpy2.robjects as robjects
import rpy2.robjects.numpy2ri
rpy2.robjects.numpy2ri.activate()

robjects.r('''
       source('gaussian_model.R')
''')

r_simple_gaussian = robjects.globalenv['simple_gaussian']

class Gaussian(ProbabilisticModel, Continuous):

    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]
        seed = rng.randint(np.iinfo(np.int32).max)

        # Do the actual forward simulation
        vector_of_k_samples = list(r_simple_gaussian(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
    y_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.continousmodels import Uniform
    prior = Uniform([[150, 5],[200, 25]])
    
    # define the model
    model = Gaussian([prior])

    # 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 backend
    from abcpy.backends import BackendDummy as Backend
    backend = Backend()
    
    # define sampling scheme
    from abcpy.inferences import PMCABC
    sampler = PMCABC([model], distance_calculator, backend)
    
    # sample from scheme
    T, n_sample, n_samples_per_param = 3, 250, 10
    eps_arr = np.array([.75])
    epsilon_percentile = 10
    journal = sampler.sample([y_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')

if __name__  == "__main__":
    journal = infer_parameters()
    analyse_journal(journal)