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# -*- coding: utf-8 -*- |
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""" |
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Created on Wed Dec 6 19:51:57 2017 |
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@author: sakurai |
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""" |
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import numpy as np |
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from basic_distributions import ( |
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ProbabilityDistribution, Multinomial, Gaussian, Poisson |
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) |
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class HiddenMarkovModelBase(ProbabilityDistribution): |
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def __init__(self, K, p_initial_state, transition_matrix): |
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assert K == len(transition_matrix) |
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self.K = K |
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self.transition_matrix = transition_matrix |
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self.p_initial_state = p_initial_state |
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self.multinomial_init = Multinomial(p_initial_state) |
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multinomials = [] |
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for k in range(K): |
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multinomials.append(Multinomial(transition_matrix[k])) |
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self.multinomials = multinomials |
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self.components = None |
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def __call__(self, num_examples=10000, complete_data=False): |
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z_1 = self.multinomial_init(1)[0] |
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x_1 = self.components[z_1](1)[0] |
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z = [z_1] |
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x = [x_1] |
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for i in range(1, num_examples): |
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z_prev = z[-1] |
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z_i = self.multinomials[z_prev](1)[0] |
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x_i = self.components[z_i](1)[0] |
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z.append(z_i) |
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x.append(x_i) |
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if complete_data: |
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return (np.array(z), np.array(x)) |
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else: |
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return np.array(x) |
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class GaussianHMM(HiddenMarkovModelBase): |
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def __init__(self, K=3, p_initial_state=[0.4, 0.3, 0.3], |
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transition_matrix=[[0.99, 0.009, 0.001], |
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[0.05, 0.9, 0.05], |
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[0.001, 0.009, 0.99]], |
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means=[-15, 0, 30], stds=[1, 10, 2]): |
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assert K == len(means) == len(stds) |
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super(GaussianHMM, self).__init__( |
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K, p_initial_state, transition_matrix) |
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self.means = means |
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self.stds = stds |
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self.components = [Gaussian(means[k], stds[k]) for k in range(K)] |
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class PoissonHMM(HiddenMarkovModelBase): |
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def __init__(self, K=3, p_initial_state=[0.4, 0.3, 0.3], |
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transition_matrix=[[0.99, 0.009, 0.001], |
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[0.05, 0.9, 0.05], |
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[0.001, 0.009, 0.99]], |
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means=[2, 20, 50]): |
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assert K == len(means) |
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super(PoissonHMM, self).__init__( |
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K, p_initial_state, transition_matrix) |
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self.means = means |
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self.components = [Poisson(means[k]) for k in range(K)] |
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if __name__ == '__main__': |
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distributions = [GaussianHMM(), # 0 |
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PoissonHMM(), # 1 |
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] |
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dist_type = 1 |
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sampler = distributions[dist_type] |
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x = sampler(10000) |
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sampler.visualize(x) |
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print("Distribution: ", sampler.get_name()) |
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print("Parameters: ", sampler.get_params()) |
