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Mixture distribusions
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# -*- coding: utf-8 -*- | |
""" | |
Created on Mon Aug 10 14:40:04 2015 | |
@author: ryuhei | |
""" | |
import numpy as np | |
from basic_distributions import ( | |
ProbabilityDistribution, Multinomial, Gaussian, Poisson | |
) | |
class MixtureDistributionBase(ProbabilityDistribution): | |
def __init__(self, K, weights): | |
assert K == len(weights) | |
self.K = K | |
self.weights = weights | |
self.multinomial = Multinomial(weights) | |
self.mixture_components = None | |
def __call__(self, num_examples=10000, complete_data=False): | |
z = [] | |
x = [] | |
for i in xrange(num_examples): | |
z_i = self.multinomial(1)[0] | |
x_i = self.mixture_components[z_i](1)[0] | |
z.append(z_i) | |
x.append(x_i) | |
if complete_data: | |
return (np.array(z), np.array(x)) | |
else: | |
return np.array(x) | |
class MixtureOfGaussians(MixtureDistributionBase): | |
def __init__(self, K=3, weights=[0.1, 0.7, 0.2], | |
means=[-15, 0, 30], stds=[1, 10, 2]): | |
assert K == len(means) == len(stds) | |
super(MixtureOfGaussians, self).__init__(K, weights) | |
self.means = means | |
self.stds = stds | |
self.mixture_components = [ | |
Gaussian(means[k], stds[k]) for k in range(K)] | |
class MixtureOfPoissons(MixtureDistributionBase): | |
def __init__(self, K=3, weights=[0.3, 0.3, 0.4], | |
means=[2, 20, 50]): | |
assert K == len(means) | |
super(MixtureOfPoissons, self).__init__(K, weights) | |
self.means = means | |
self.mixture_components = [Poisson(means[k]) for k in range(K)] | |
if __name__ == '__main__': | |
distributions = [MixtureOfGaussians(), # 0 | |
MixtureOfPoissons(), # 1 | |
] | |
dist_type = 0 | |
sampler = distributions[dist_type] | |
x = sampler(10000) | |
sampler.visualize(x) | |
print "Distribution: ", sampler.get_name() | |
print "Parameters: ", sampler.get_params() |
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This script requires basic_distribusions.py .