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()