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1605042.py
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import numpy as np
import sys
import scipy.stats
import math
import random
from numpy.core.fromnumeric import argmax, shape, size
def main():
stateNames=['"El Nino"','"La Nina"']
dataFile = open(sys.argv[1], "r")
parametersFile= open(sys.argv[2], "r")
totalStates=int(parametersFile.readline().split()[0])
transitionMatrix=[]
for i in range(totalStates):
temp=[float(x) for x in parametersFile.readline().split()]
transitionMatrix.append(temp)
a=np.array(transitionMatrix).T
for i in range(a.shape[0]):
a[i][i]=a[i][i]-1
a[-1]=np.ones(a.shape[0])
b=np.zeros(a.shape[0])
b[-1]=1
initialProbability=np.linalg.solve(a,b)
means=[float(x) for x in parametersFile.readline().split()]
variance=[float(x) for x in parametersFile.readline().split()]
standardDeviations=[math.sqrt(x) for x in variance]
parametersFile.close()
observations=[float(x) for x in dataFile.readlines()]
dataFile.close()
s=np.zeros((len(observations),totalStates))
stateTrack=np.zeros((len(observations)-1,totalStates))
for i in range(s.shape[1]):
# print(scipy.stats.norm(means[i],standardDeviations[i]).pdf(observations[0]))
s[0][i]=np.log(initialProbability[i]*scipy.stats.norm(means[i],standardDeviations[i]).pdf(observations[0]))
for observation in range(s[1:].shape[0]):
for state in range(s.shape[1]):
temp=[]
for i in range(totalStates):
# if observation==0 and state==0:
# print(transitionMatrix[i][state])
# print(scipy.stats.norm(means[state],standardDeviations[state]).pdf(observations[observation+1]))
temp.append(s[observation][i]+np.log(transitionMatrix[i][state]*scipy.stats.norm(means[state],standardDeviations[state]).pdf(observations[observation+1])))
s[observation+1][state]=max(temp)
stateTrack[observation][state]=int(argmax(temp))
# estimatedStateSequence=s.argmax(axis=1)
outputfile=open("viterbi_output_wo_learning.txt",'w')
# for i in estimatedStateSequence:
# outputfile.write(stateNames[i]+"\n")
estimatedStateSequence=[]
estimatedStateSequence.append(s[-1].argmax())
for i in range(len(observations)-2,-1,-1):
temp=int(estimatedStateSequence[-1])
i=int(i)
estimatedStateSequence.append(stateTrack[i][temp])
for i in reversed(estimatedStateSequence):
outputfile.write(stateNames[int(i)]+"\n")
# np.savetxt("probs.txt",s)
outputfile.close()
threshold=0.0000000000000000
dif=1
random.seed(13)
for i in range(totalStates):
for j in range(totalStates):
transitionMatrix[i][j]=random.uniform(0,1)
transitionMatrix[i]=transitionMatrix[i]/np.sum(transitionMatrix[i])
means[i]=random.uniform(0,300)
variance[i]=random.uniform(0,20)
standardDeviations[i]=math.sqrt(variance[i])
# print(transitionMatrix)
# print(means)
# print(variance)
while dif>threshold:
a=np.array(transitionMatrix).T
for i in range(a.shape[0]):
a[i][i]=a[i][i]-1
a[-1]=np.ones(a.shape[0])
b=np.zeros(a.shape[0])
b[-1]=1
initialProbability=np.linalg.solve(a,b)
#-------Forward--------#
f=np.zeros((len(observations),totalStates))
for i in range(f.shape[1]):
f[0][i]=initialProbability[i]*scipy.stats.norm(means[i],standardDeviations[i]).pdf(observations[0])
f[0]=f[0]/np.sum(f[0])
for observation in range(f[1:].shape[0]):
for state in range(f.shape[1]):
temp=0
for i in range(totalStates):
temp+=(f[observation][i]*transitionMatrix[i][state]*scipy.stats.norm(means[state],standardDeviations[state]).pdf(observations[observation+1]))
f[observation+1][state]=temp
f[observation+1]=f[observation+1]/np.sum(f[observation+1])
# print(np.sum(f[-1]))
# np.savetxt("forward.txt",f)
#-------Backward--------#
b=np.zeros((len(observations),totalStates))
for i in range(b.shape[1]):
b[-1][i]=1
b[-1]=b[-1]/np.sum(b[-1])
for observation in range(len(observations)-2,-1,-1):
for state in range(b.shape[1]):
temp=0
for i in range(totalStates):
temp+=(b[observation+1][i]*transitionMatrix[state][i]*scipy.stats.norm(means[i],standardDeviations[i]).pdf(observations[observation+1]))
b[observation][state]=temp
b[observation]=b[observation]/np.sum(b[observation])
# np.savetxt("backward.txt",b)
#-------pi_star--------#
pi_star=np.zeros((len(observations),totalStates))
for i in range(len(observations)):
for j in range(totalStates):
pi_star[i][j]=f[i][j]*b[i][j]
pi_star[i]=pi_star[i]/np.sum(pi_star[i])
# np.savetxt("pi_star.txt",pi_star)
#-------pi_star_star--------#
pi_star_star=np.zeros((len(observations)-1,totalStates*totalStates))
for i in range(len(observations)-1):
for j in range(totalStates):
for k in range(totalStates):
pi_star_star[i][j*totalStates+k]=f[i][j]*transitionMatrix[j][k]*scipy.stats.norm(means[k],standardDeviations[k]).pdf(observations[i+1])*b[i+1][k]
pi_star_star[i]=pi_star_star[i]/np.sum(pi_star_star[i])
# np.savetxt("pi_star_star.txt",pi_star_star)
dif=0
temp=np.sum(pi_star_star,axis=0)
for i in range(totalStates):
nsum=0
for j in range(totalStates):
nsum+=temp[i*totalStates+j]
for j in range(totalStates):
dif+=abs(transitionMatrix[i][j]-(temp[i*totalStates+j]/nsum))
transitionMatrix[i][j]=temp[i*totalStates+j]/nsum
temp=np.sum(pi_star,axis=0).reshape(totalStates,1)
pisum=np.zeros((totalStates,1))
for i in range(len(observations)):
for j in range(totalStates):
pisum[j][0]+=pi_star[i][j]*observations[i]
pisum=pisum/temp
for i in range(totalStates):
dif+=abs(means[i]-pisum[i][0])
means[i]=pisum[i][0]
pisum=np.zeros((totalStates,1))
for i in range(len(observations)):
for j in range(totalStates):
pisum[j][0]+=pi_star[i][j]*(observations[i]-means[j])**2
pisum=pisum/temp
for i in range(totalStates):
dif+=abs(variance[i]-pisum[i][0])
variance[i]=pisum[i][0]
standardDeviations=[math.sqrt(x) for x in variance]
outputfile=open("parameters_learned.txt",'w')
outputfile.write(str(totalStates)+"\n")
for i in range(totalStates):
for j in range(totalStates):
outputfile.write(str(transitionMatrix[i][j])+" ")
outputfile.write("\n")
for i in range(totalStates):
outputfile.write(str(means[i])+" ")
outputfile.write("\n")
for i in range(totalStates):
outputfile.write(str(variance[i])+" ")
outputfile.write("\n")
a=np.array(transitionMatrix).T
for i in range(a.shape[0]):
a[i][i]=a[i][i]-1
a[-1]=np.ones(a.shape[0])
b=np.zeros(a.shape[0])
b[-1]=1
initialProbability=np.linalg.solve(a,b)
for i in range(totalStates):
outputfile.write(str(initialProbability[i])+" ")
outputfile.close()
s=np.zeros((len(observations),totalStates))
stateTrack=np.zeros((len(observations)-1,totalStates))
for i in range(s.shape[1]):
s[0][i]=np.log(initialProbability[i]*scipy.stats.norm(means[i],standardDeviations[i]).pdf(observations[0]))
for observation in range(s[1:].shape[0]):
for state in range(s.shape[1]):
temp=[]
for i in range(totalStates):
temp.append(s[observation][i]+np.log(transitionMatrix[i][state]*scipy.stats.norm(means[state],standardDeviations[state]).pdf(observations[observation+1])))
s[observation+1][state]=max(temp)
stateTrack[observation][state]=int(argmax(temp))
outputfile=open("viterbi_output_after_learning.txt",'w')
estimatedStateSequence=[]
estimatedStateSequence.append(s[-1].argmax())
for i in range(len(observations)-2,-1,-1):
temp=int(estimatedStateSequence[-1])
i=int(i)
estimatedStateSequence.append(stateTrack[i][temp])
for i in reversed(estimatedStateSequence):
outputfile.write(stateNames[int(i)]+"\n")
outputfile.close()
if __name__ == "__main__":
main()