def setUp(self):
lst = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]
observed = make_pmf_from_list(lst)
expected = copy.deepcopy(observed)
pmf_probabilities = PmfProbabilityRange(0, 3)
self.chi_squared_test = ChiSquaredTest(
observed,
expected, [pmf_probabilities.get_probabilities],
ntrials=10)
def test_variance_pmf(self):
lst = [4,4,4,4,4]
pmf = make_pmf_from_list(lst)
self.assertEqual(variance(pmf), 0)
def test_mean_pmf(self):
lst = [4,4,4,4,4]
pmf = make_pmf_from_list(lst)
self.assertEqual(mean(pmf), 4)
def test_relative_mean_difference(self):
lst = [1,1,1,1,1,1,1,1,1,1]
pmf = make_pmf_from_list(lst)
self.assertEqual(relative_mean_difference(pmf), 0.0)
def _simulate_probabilities(self):
random_observed_sample = [self.expected_sample.random() for _ in xrange(len(self.expected_sample))]
simulated_observed_sample = make_pmf_from_list(random_observed_sample, self.expected_sample.name)
probs = [prob_func(simulated_observed_sample) for prob_func in self.prob_functions ]
return [probs]
def test_make_pmf_from_list(self):
lst = [1, 1, 2, 2, 3, 3, 4, 5, 5, 5, 5]
self.assertEqual(len(make_pmf_from_list(lst)), 5)
def test_make_pmf_from_list(self):
lst = [1,1,2,2,3,3,4,5,5,5,5]
self.assertEqual(len(make_pmf_from_list(lst)), 5)
def __init__(self, low, high, steps):
self.low = low
self.high = high
self.steps = steps
hypos = [self.low + (self.high-self.low) * i / (self.steps-1.0) for i in range(self.steps)]
self.pmf = make_pmf_from_list(hypos)
def test_variance_pmf(self):
lst = [4, 4, 4, 4, 4]
pmf = make_pmf_from_list(lst)
self.assertEqual(variance(pmf), 0)
def test_mean_pmf(self):
lst = [4, 4, 4, 4, 4]
pmf = make_pmf_from_list(lst)
self.assertEqual(mean(pmf), 4)
def test_relative_mean_difference(self):
lst = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
pmf = make_pmf_from_list(lst)
self.assertEqual(relative_mean_difference(pmf), 0.0)
def test_get_posterior(self):
pmf = make_pmf_from_list([1,1,1,1])
self.assertEqual(self.bayesian_est.get_posterior(), pmf)
def setUp(self):
pmf = make_pmf_from_list([1,1,1,1])
self.bayesian_est = BayesianEstimator(pmf, [1,1,1,1],TestLikelihood())
def test_pmf_probability_range_get_probabilities(self):
lst = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]
pmf = make_pmf_from_list(lst)
pmf_probabilities = PmfProbabilityRange(0, 3)
self.assertEqual(pmf_probabilities.get_probabilities(pmf), 0.75)
def test_zscores_pmf(self):
lst = [4,4,4,4,4]
pmf = make_pmf_from_list(lst)
self.assertEqual(zscores(pmf), [0])
def test_zscores_pmf(self):
lst = [4, 4, 4, 4, 4]
pmf = make_pmf_from_list(lst)
self.assertEqual(zscores(pmf), [0])
def __init__(self, prior, sample, likelihood):
test_obj_instance(prior, Pmf)
test_obj_instance(sample, list)
test_obj_subclass(likelihood, Likelihood)
self.prior = prior
self.sample = sample
self.likelihood = likelihood
self.posterior = deepcopy(prior)
self.posterior.name = 'posterior'
self._estimate_posterior()
def _estimate_posterior(self):
for hypo in self.prior.get_dict().itervalues():
self.posterior.multiply(
hypo, self.likelihood.get_likelihood(self.sample, hypo))
self.posterior.normalise()
def get_posterior(self):
return self.posterior
if __name__ == '__main__':
uniform = Uniform(0, 100, 100)
uniform_dist = [uniform.generate() for _ in xrange(100)]
prior = make_pmf_from_list(uniform_dist)
sample = [10.0, 9.0]
bayesian_est = BayesianEstimator(prior, sample,
ConditionalExponetialLikelihood(0, 100))
print make_cdf_from_pmf(bayesian_est.get_posterior()).sort()
class BayesianEstimator(object):
def __init__(self, prior, sample, likelihood):
test_obj_instance(prior, Pmf)
test_obj_instance(sample, list)
test_obj_subclass(likelihood, Likelihood)
self.prior = prior
self.sample = sample
self.likelihood = likelihood
self.posterior = deepcopy(prior)
self.posterior.name = 'posterior'
self._estimate_posterior()
def _estimate_posterior(self):
for hypo in self.prior.get_dict().itervalues():
self.posterior.multiply(hypo, self.likelihood.get_likelihood(self.sample,hypo))
self.posterior.normalise()
def get_posterior(self):
return self.posterior
if __name__ == '__main__':
uniform = Uniform(0,100,100)
uniform_dist = [uniform.generate() for _ in xrange(100)]
prior = make_pmf_from_list(uniform_dist)
sample = [10.0,9.0]
bayesian_est = BayesianEstimator(prior, sample, ConditionalExponetialLikelihood(0,100))
print make_cdf_from_pmf(bayesian_est.get_posterior()).sort()