def construct(self):
firstMarginalsAsMatrix = np.matrix([self.feasibleMarginals[0], 1.0 - self.feasibleMarginals[0]])
secondMarginalsAsMatrix = np.matrix([self.feasibleMarginals[1], 1.0 - self.feasibleMarginals[1]])
#construct the path through the product distribution with those marginals
productDistributionWithFeasibleMarginals = pd.ProbabilityDistribution(self.k,self.l)
productDistributionWithFeasibleMarginals.setParametersProductDistAssocWithMarginals(firstMarginalsAsMatrix,
secondMarginalsAsMatrix)
return pdp.probabilityDistributionPath(productDistributionWithFeasibleMarginals)
def construct(self):
firstMarginalsAsMatrix = np.matrix(
[self.feasibleMarginals[0], 1.0 - self.feasibleMarginals[0]])
secondMarginalsAsMatrix = np.matrix(
[self.feasibleMarginals[1], 1.0 - self.feasibleMarginals[1]])
#construct the path through the product distribution with those marginals
productDistributionWithFeasibleMarginals = pd.ProbabilityDistribution(
self.k, self.l)
productDistributionWithFeasibleMarginals.setParametersProductDistAssocWithMarginals(
firstMarginalsAsMatrix, secondMarginalsAsMatrix)
return pdp.probabilityDistributionPath(
productDistributionWithFeasibleMarginals)
def get_p_eta(self,eta):
'''
Makes a distribution p_eta with uniform marginals and test statistic eta.
'''
l = self.l
k = self.k
prob_dist = pd.ProbabilityDistribution(k,l)
uniform_dist = pd.ProbabilityDistribution(k,l)
prob_dist_path = pdp.probabilityDistributionPath(uniform_dist)
prob_dist_parameters = prob_dist_path.distribution_at_specified_divergence_from_base_pos_t(eta)
prob_dist.distribution = prob_dist_parameters
return prob_dist
def get_p_eta(self, eta):
'''
Makes a distribution p_eta with uniform marginals and test statistic eta.
'''
l = self.l
k = self.k
prob_dist = pd.ProbabilityDistribution(k, l)
uniform_dist = pd.ProbabilityDistribution(k, l)
prob_dist_path = pdp.probabilityDistributionPath(uniform_dist)
prob_dist_parameters = prob_dist_path.distribution_at_specified_divergence_from_base_pos_t(
eta)
prob_dist.distribution = prob_dist_parameters
return prob_dist
def testRaiseNotImplementedError(self):
k, l = 3, 3
prob_dist = pd.ProbabilityDistribution(k, l)
probDistPath = pdp.probabilityDistributionPath(prob_dist)
probDistPath.tOfMarkedDistribution()
def testRaiseNotImplementedError(self):
k,l = 3,3
prob_dist = pd.ProbabilityDistribution(k,l)
probDistPath = pdp.probabilityDistributionPath(prob_dist)
probDistPath.tOfMarkedDistribution()
