How to use the abcpy.continuousmodels.Uniform function in abcpy

def test_sample(self):
        # setup backend
        dummy = BackendDummy()

        # define a uniform prior distribution
        mu = Uniform([[-5.0], [5.0]], name='mu')
        sigma = Uniform([[0.0], [10.0]], name='sigma')
        # define a Gaussian model
        self.model = Normal([mu,sigma])

        # define sufficient statistics for the model
        stat_calc = Identity(degree=2, cross=0)
        
        # define a distance function
        dist_calc = Euclidean(stat_calc)

        # create fake observed data
        y_obs = [np.array(9.8)]

        # use the rejection sampling scheme
        sampler = RejectionABC([self.model], [dist_calc], dummy, seed = 1)
        journal = sampler.sample([y_obs], 10, 1, 10)
        mu_sample = np.array(journal.get_parameters()['mu'])

"""

        if not isinstance(parameters, list):
            raise TypeError('Input for Uniform has to be of type list.')
        if len(parameters)<2:
            raise ValueError('Input for Uniform has to be of length 2.')
        if not isinstance(parameters[0], list):
            raise TypeError('Each boundary for Uniform has to be of type list.')
        if not isinstance(parameters[1], list):
            raise TypeError('Each boundary for Uniform has to be of type list.')
        if len(parameters[0]) != len(parameters[1]):
            raise ValueError('Length of upper and lower bound have to be equal.')

        self._dimension = len(parameters[0])
        input_parameters = InputConnector.from_list(parameters)
        super(Uniform, self).__init__(input_parameters, name)
        self.visited = False

import numpy as np

"""An example showing how to implement a bayesian network in ABCpy"""

# The data corresponding to model_1 defined below
y_obs_1 = [3.872486707973337, 4.6735380808674405, 3.9703538990858376, 4.11021272048805, 4.211048655421368, 4.154817956586653, 4.0046893064392695, 4.01891381384729, 4.123804757702919, 4.014941267301294, 3.888174595940634, 4.185275142948246, 4.55148774469135, 3.8954427675259016, 4.229264035335705, 3.839949451328312, 4.039402553532825, 4.128077814241238, 4.361488645531874, 4.086279074446419, 4.370801602256129, 3.7431697332475466, 4.459454162392378, 3.8873973643008255, 4.302566721487124, 4.05556051626865, 4.128817316703757, 3.8673704442215984, 4.2174459453805015, 4.202280254493361, 4.072851400451234, 3.795173229398952, 4.310702877332585, 4.376886328810306, 4.183704734748868, 4.332192463368128, 3.9071312388426587, 4.311681374107893, 3.55187913252144, 3.318878360783221, 4.187850500877817, 4.207923106081567, 4.190462065625179, 4.2341474252986036, 4.110228694304768, 4.1589891480847765, 4.0345604687633045, 4.090635481715123, 3.1384654393449294, 4.20375641386518, 4.150452690356067, 4.015304457401275, 3.9635442007388195, 4.075915739179875, 3.5702080541929284, 4.722333310410388, 3.9087618197155227, 4.3990088006390735, 3.968501165774181, 4.047603645360087, 4.109184340976979, 4.132424805281853, 4.444358334346812, 4.097211737683927, 4.288553086265748, 3.8668863066511303, 3.8837108501541007]

# The data corresponding to model_2 defined below
y_obs_2 = [2.7179657436207805, 2.124647285937229, 3.07193407853297, 2.335024761813643, 2.871893855192, 3.4332002458233837, 3.649996835818173, 3.50292335102711, 2.815638168018455, 2.3581613289315992, 2.2794821846395568, 2.8725835459926503, 3.5588573782815685, 2.26053126526137, 1.8998143530749971, 2.101110815311782, 2.3482974964831573, 2.2707679029919206, 2.4624550491079225, 2.867017757972507, 3.204249152084959, 2.4489542437714213, 1.875415915801106, 2.5604889644872433, 3.891985093269989, 2.7233633223405205, 2.2861070389383533, 2.9758813233490082, 3.1183403287267755, 2.911814060853062, 2.60896794303205, 3.5717098647480316, 3.3355752461779824, 1.99172284546858, 2.339937680892163, 2.9835630207301636, 2.1684912355975774, 3.014847335983034, 2.7844122961916202, 2.752119871525148, 2.1567428931391635, 2.5803629307680644, 2.7326646074552103, 2.559237193255186, 3.13478196958166, 2.388760269933492, 3.2822443541491815, 2.0114405441787437, 3.0380056368041073, 2.4889680313769724, 2.821660164621084, 3.343985964873723, 3.1866861970287808, 4.4535037154856045, 3.0026333138006027, 2.0675706089352612, 2.3835301730913185, 2.584208398359566, 3.288077633446465, 2.6955853384148183, 2.918315169739928, 3.2464814419322985, 2.1601516779909433, 3.231003347780546, 1.0893224045062178, 0.8032302688764734, 2.868438615047827]

# The prior information changing the class size and social background, depending on school location
from abcpy.continuousmodels import Uniform, Normal
school_location = Uniform([[0.2,0.2],[0.3,0.3]])

# The average class size of a certain school
class_size = Normal([[school_location_score[0]], [0.1]])

# The average social background of a student in a certain school
background = Normal([[school_location_score[1]], [0.1]])

# The average grade obtained in the exam
grade = Normal([[4.5],[0.25]])

# The grade a student of a certain school receives
model_1 = grade-class_size-background

# A quantity that determines whether a student will receive a scholarship
scholarship = Normal([[2],[0.5]])

def infer_parameters_pmc():
    # define observation for true parameters mean=170, 65
    rng = np.random.RandomState()
    y_obs = [np.array(rng.multivariate_normal([170, 65], np.eye(2), 1).reshape(2,))]

    # define prior
    from abcpy.continuousmodels import Uniform
    mu0 = Uniform([[150], [200]], )
    mu1 = Uniform([[25], [100]], )

    # define the model
    height_weight_model = NestedBivariateGaussian([mu0, mu1])

    # define statistics
    from abcpy.statistics import Identity
    statistics_calculator = Identity(degree = 2, cross = False)

    from abcpy.approx_lhd import SynLikelihood
    approx_lhd = SynLikelihood(statistics_calculator)

    # define sampling scheme
    from abcpy.inferences import PMC
    sampler = PMC([height_weight_model], [approx_lhd], backend, seed=1)

    # sample from scheme

def infer_parameters():
    # define observation for true parameters mean=170, 65
    rng = np.random.RandomState()
    y_obs = [np.array(rng.multivariate_normal([170, 65], np.eye(2), 1).reshape(2,))]

    # define prior
    from abcpy.continuousmodels import Uniform
    mu0 = Uniform([[150], [200]], )
    mu1 = Uniform([[25], [100]], )

    # define the model
    height_weight_model = NestedBivariateGaussian([mu0, mu1])

    # define statistics
    from abcpy.statistics import Identity
    statistics_calculator = Identity(degree = 2, cross = False)

    from abcpy.approx_lhd import SynLiklihood
    approx_lhd = SynLiklihood(statistics_calculator)

    # define sampling scheme    
    from abcpy.inferences import PMC
    sampler = PMC([height_weight_model], [approx_lhd], backend, seed=1)

    # sample from scheme

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.continuousmodels import Uniform
    mu = Uniform([[150], [200]], name='mu')
    sigma = Uniform([[5], [25]], name='sigma')

    # define the model
    from abcpy.continuousmodels import Normal
    height = Normal([mu, sigma], name='height')

    # define statistics
    from abcpy.statistics import Identity
    statistics_calculator = Identity(degree = 2, cross = False)

    # define distance
    from abcpy.distances import Euclidean
    distance_calculator = Euclidean(statistics_calculator)

    # define sampling scheme
    from abcpy.inferences import PMCABC
    sampler = PMCABC([height], [distance_calculator], backend, seed=1)

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.continuousmodels 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 BackendSpark as Backend
    from abcpy.backends import BackendDummy as Backend
    backend = Backend()

def infer_parameters():
    # The data corresponding to model_1 defined below
    grades_obs = [3.872486707973337, 4.6735380808674405, 3.9703538990858376, 4.11021272048805, 4.211048655421368, 4.154817956586653, 4.0046893064392695, 4.01891381384729, 4.123804757702919, 4.014941267301294, 3.888174595940634, 4.185275142948246, 4.55148774469135, 3.8954427675259016, 4.229264035335705, 3.839949451328312, 4.039402553532825, 4.128077814241238, 4.361488645531874, 4.086279074446419, 4.370801602256129, 3.7431697332475466, 4.459454162392378, 3.8873973643008255, 4.302566721487124, 4.05556051626865, 4.128817316703757, 3.8673704442215984, 4.2174459453805015, 4.202280254493361, 4.072851400451234, 3.795173229398952, 4.310702877332585, 4.376886328810306, 4.183704734748868, 4.332192463368128, 3.9071312388426587, 4.311681374107893, 3.55187913252144, 3.318878360783221, 4.187850500877817, 4.207923106081567, 4.190462065625179, 4.2341474252986036, 4.110228694304768, 4.1589891480847765, 4.0345604687633045, 4.090635481715123, 3.1384654393449294, 4.20375641386518, 4.150452690356067, 4.015304457401275, 3.9635442007388195, 4.075915739179875, 3.5702080541929284, 4.722333310410388, 3.9087618197155227, 4.3990088006390735, 3.968501165774181, 4.047603645360087, 4.109184340976979, 4.132424805281853, 4.444358334346812, 4.097211737683927, 4.288553086265748, 3.8668863066511303, 3.8837108501541007]

    # The prior information changing the class size and social background, depending on school location
    from abcpy.continuousmodels import Uniform, Normal
    school_location = Uniform([[0.2], [0.3]], )

    # The average class size of a certain school
    class_size = Normal([[school_location], [0.1]], )

    # The social background of a student
    background = Normal([[school_location], [0.1]], )

    # The grade a student would receive without any bias
    grade_without_additional_effects = Normal([[4.5], [0.25]], )

    # The grade a student of a certain school receives
    final_grade = grade_without_additional_effects-class_size-background

    # The data corresponding to model_2 defined below
    scholarship_obs = [2.7179657436207805, 2.124647285937229, 3.07193407853297, 2.335024761813643, 2.871893855192, 3.4332002458233837, 3.649996835818173, 3.50292335102711, 2.815638168018455, 2.3581613289315992, 2.2794821846395568, 2.8725835459926503, 3.5588573782815685, 2.26053126526137, 1.8998143530749971, 2.101110815311782, 2.3482974964831573, 2.2707679029919206, 2.4624550491079225, 2.867017757972507, 3.204249152084959, 2.4489542437714213, 1.875415915801106, 2.5604889644872433, 3.891985093269989, 2.7233633223405205, 2.2861070389383533, 2.9758813233490082, 3.1183403287267755, 2.911814060853062, 2.60896794303205, 3.5717098647480316, 3.3355752461779824, 1.99172284546858, 2.339937680892163, 2.9835630207301636, 2.1684912355975774, 3.014847335983034, 2.7844122961916202, 2.752119871525148, 2.1567428931391635, 2.5803629307680644, 2.7326646074552103, 2.559237193255186, 3.13478196958166, 2.388760269933492, 3.2822443541491815, 2.0114405441787437, 3.0380056368041073, 2.4889680313769724, 2.821660164621084, 3.343985964873723, 3.1866861970287808, 4.4535037154856045, 3.0026333138006027, 2.0675706089352612, 2.3835301730913185, 2.584208398359566, 3.288077633446465, 2.6955853384148183, 2.918315169739928, 3.2464814419322985, 2.1601516779909433, 3.231003347780546, 1.0893224045062178, 0.8032302688764734, 2.868438615047827]

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
    height = Normal([mu, sigma], )
    
    # 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])

def infer_parameters_smcabc():
    # define observation for true parameters mean=170, 65
    rng = np.random.RandomState()
    y_obs = [np.array(rng.multivariate_normal([170, 65], np.eye(2), 1).reshape(2,))]

    # define prior
    from abcpy.continuousmodels import Uniform
    mu0 = Uniform([[150], [200]], )
    mu1 = Uniform([[25], [100]], )

    # define the model
    height_weight_model = NestedBivariateGaussian([mu0, mu1])

    # define statistics
    from abcpy.statistics import Identity
    statistics_calculator = Identity(degree = 2, cross = False)

    # define distance
    from abcpy.distances import Euclidean
    distance_calculator = Euclidean(statistics_calculator)

    # define sampling scheme
    from abcpy.inferences import SMCABC
    sampler = SMCABC([height_weight_model], [distance_calculator], backend, seed=1)
    steps, n_samples, n_samples_per_param, epsilon = 2, 10, 1, 2000