HMMParamEstimator.py

# !/usr/bin/env python3
# HMM Set 2
# Estimate the Parameters of an HMM
# University of California, Santa Cruz - BME 205
# Biomolecular Engineering and Bioinformatics
# Name: (zmmason)
# Group Members: NONE

import sys
import numpy as np  # numpy version 1.19.3


class ParamHMM:
    """
    Estimate the Parameters of an HMM
    Input:  A sequence of emitted symbols x = x1 . . . xn in an alphabet ∑ and a path π = π1 . . . πn generated by a
            k-state HMM with unknown transition and emission probabilities.
    Output: A matrix of transition probabilities Transition and a matrix of emission probabilities Emission that
            maximize Pr(x,π) over all possible matrices of transition and emission probabilities.
    """

    def __init__(self, emission, alphabet, pi, states):
        """Constructor: saves attributes from the input file."""
        self.emission = [alphabet.index(i) for i in emission]  # list of index codes for each em in emission seq
        self.states = states
        self.pi = [states.index(i) for i in pi]
        self.alphabet = alphabet
        # initializing the matrix for emission and transition
        self.transitionMatrix = np.array([[float(0) for j in range(len(states))] for i in range(len(states))])
        self.emissionMatrix = np.array([[float(0) for j in range(len(alphabet))] for i in range(len(states))])

    def paramEst(self):
        """Compute the transition and emission matrices that maximize Pr(x,π)."""
        self.emissionMatrix[self.pi[0]][self.emission[0]] = 1  # initialize 1st prob
        prev = self.pi[0]  # initializing variable to hold previous index item
        for i in range(1, len(self.emission)):
            self.emissionMatrix[self.pi[i]][self.emission[i]] += 1  # counts frequency of occurrences
            self.transitionMatrix[prev][self.pi[i]] += 1  # counts frequency of occurrences
            prev = self.pi[i]  # reset new previous index
        for s in range(len(self.states)):
            if sum(self.emissionMatrix[s]) == 0:  # accounts for 0 frequency
                self.emissionMatrix[s] += 1
            self.emissionMatrix[s] = self.emissionMatrix[s]/sum(self.emissionMatrix[s])  # get Pr(emissions)
            if sum(self.transitionMatrix[s]) == 0:  # accounts for 0 frequency
                self.transitionMatrix[s] += 1
            self.transitionMatrix[s] = self.transitionMatrix[s]/sum(self.transitionMatrix[s])  # get Pr(transitions)
        roundTrans = np.round(self.transitionMatrix, 3)  # rounding array to 3rd dec place for formatting
        roundEmis = np.round(self.emissionMatrix, 3)
        roundTrans = roundTrans.tolist()  # turns matrix into list ro print
        roundEmis = roundEmis.tolist()
        return roundEmis, roundTrans

    def dataPrint(self, roundEmis, roundTrans):
        """Using the data from transition and emission matrices , print to specified formatting."""
        maxMatrix = ['\t'.join([str(x) for x in self.states])]
        for i in range(len(roundEmis)):  # printing emission matrix info
            roundTrans[i].insert(0, self.states[i])  # adding the state to the specific probabilities
            maxMatrix.append('\t'.join([str(x) for x in roundTrans[i]]))
        maxMatrix.append('--------')
        maxMatrix.append('\t' + '\t'.join([str(x) for x in self.alphabet]))
        for i in range(len(roundTrans)):  # printing transition matrix info
            roundEmis[i].insert(0, self.states[i])  # adding the state to the specific probabilities
            maxMatrix.append('\t'.join([str(x) for x in roundEmis[i]]))
        return maxMatrix


def main():
    """Estimate the Parameters of an HMM."""
    contents = []  # list to hold the contents of the dataset
    for line in sys.stdin:  # takes STDIN only
        contents.append(line.strip())
    param = ParamHMM(contents[0], contents[2].split(), contents[4], contents[6].split())
    roundEmis, roundTrans = param.paramEst()
    matrix = param.dataPrint(roundEmis, roundTrans)
    for data in matrix:
        print(data)


if __name__ == '__main__':
    main()