def main():
from hmm import load_hmm_model_from_files
fasta_file_path = r'data/chrom17.fasta'
emission_input_file = r'data/initial_emission.tsv'
transition_input_file = r'data/initial_transition.tsv'
hmm_model = load_hmm_model_from_files(emission_input_file, transition_input_file)
observation = load_fasta_file(fasta_file_path)
v, ptr = viterbi_algorithm(hmm_model, observation)
np.savez_compressed('output/viterbi_algorithm_output.npz', v=v, ptr=ptr)
path = restore_viterbi_path(v,ptr)
np.savez_compressed('output/path.npz', path=path)
q_3_1_c(path, observation)
size=len(observation),
replace=True))
f = forward_algorithm(hmm_model, random_observation)
print(f[1, -1])
def q_2_b_iii(hmm_model, observation):
"""
calculating the log probability of observing chrom17.fasta given the model.
:return:
"""
repetitive_observation = 'A'*len(observation)
f = forward_algorithm(hmm_model, repetitive_observation)
print(f[1, -1])
if __name__ == '__main__':
from hmm import load_hmm_model_from_files
fasta_file_path = r'data/chrom17.fasta'
observation = load_fasta_file(fasta_file_path)
emission_input_file = r'data/initial_emission.tsv'
transition_input_file = r'data/initial_transition.tsv'
hmm_model = load_hmm_model_from_files(emission_input_file, transition_input_file)
q_2_b_i(hmm_model, observation)
#q_2_b_ii(hmm_model, observation)
#q_2_b_iii(hmm_model, observation)
