def main():
model = HMM()
K = 3
Lambda = 0.1
student_list = pd.Series(range(5))
random_cluster = model.createRandomCluster(K, student_list, with_seed=True)
print('init_cluster', random_cluster)
obs_sequence = pd.Series([0, 1, 2, 3, 4])
# state_sequence = model.convertObsToStateSequence(obs_sequence, update_cluster)
error_dict = model.getErrorRateMatrix(random_cluster, K, 0.1)
switch_dict = model.getSwitchRateMatrix(K, Lambda)
print(error_dict)
print(switch_dict)
forward_llk = model.forward(obs_sequence, K, error_dict, switch_dict)
test_llk = model.logLikelihood(obs_sequence, K, error_dict, switch_dict)
print('forward llk', forward_llk)
print('llk', test_llk)
def main():
model = HMM()
K = 3
Lambda = 0.1
student_list = pd.Series(range(5))
random_cluster = model.createRandomCluster(K, student_list, with_seed=True)
print('init_cluster', random_cluster)
obs_sequence = pd.Series([0, 1, 2, 3, 4])
# state_sequence = model.convertObsToStateSequence(obs_sequence, update_cluster)
error_dict = model.getErrorRateMatrix(random_cluster, K, 0.1)
switch_dict = model.getSwitchRateMatrix(K, Lambda)
print(error_dict)
print(switch_dict)
forward_llk = model.forward(obs_sequence, K, error_dict, switch_dict)
test_llk = model.logLikelihood(obs_sequence, K, error_dict, switch_dict)
print('forward llk', forward_llk)
print('llk', test_llk)
from HMM.Model import HMM
import pandas as pd
if __name__ == '__main__':
# generate sim data
model = HMM()
Lambda = 0.2
epsilon = 0.01
K = 3
num_unique_students = 20
true_cluster_structure = model.createRandomCluster(
K, pd.Series(range(num_unique_students)), with_seed=True)
obs_seq_len = 100
sim_data = model.generateData(K,
obs_seq_len,
true_cluster_structure,
epsilon,
Lambda,
seed=1)
test_data = pd.DataFrame({'obs_seq': sim_data['sim_data']})
output_true_cluster_structure = pd.DataFrame(
{'clust_struct': true_cluster_structure})
test_data.to_csv('python_sim_data_seed_1.csv', sep=',', index=False)
output_true_cluster_structure.to_csv('python_cluster_structure_seed_1.csv',
sep=',',
index=False)
# initialization
init_cluster_structure = model.createRandomCluster(
from HMM.Model import HMM
import pandas as pd
if __name__ == '__main__':
# generate sim data
model = HMM()
Lambda = 0.2
epsilon = 0.01
K = 3
num_unique_students = 20
true_cluster_structure = model.createRandomCluster(K, pd.Series(range(num_unique_students)), with_seed=True)
obs_seq_len = 100
sim_data = model.generateData(K, obs_seq_len, true_cluster_structure, epsilon, Lambda, seed=1)
test_data = pd.DataFrame({'obs_seq':sim_data['sim_data']})
output_true_cluster_structure = pd.DataFrame({'clust_struct':true_cluster_structure})
test_data.to_csv('python_sim_data_seed_1.csv', sep=',', index=False)
output_true_cluster_structure.to_csv('python_cluster_structure_seed_1.csv', sep=',', index=False)
# initialization
init_cluster_structure = model.createRandomCluster(K, pd.Series(range(num_unique_students)))
init_params = dict()
init_params['Lambda'] = {'value': 0.1}
init_params['epsilon'] = {'value': 0.1}
init_params['cluster'] = {'value': true_cluster_structure}
K = 2
num_iter = 10000
thin = 100
chain = model.singleChainMLE([sim_data['sim_data']], K, init_params)
