print "Sigma" sigma = mgi.sigma print sigma print "mu" mu = mgi.mu print mu #prior = ones((1, states_count)) * 0.5 #data = randn(dimensions_count, steps_count) #transmat = np.ones((states_count, states_count)) / states_count #mixmat = ones((1, mixture_components_count)) / mixture_components_count #mu = np.ones((mixture_components_count, 1, dimensions_count, states_count)) #sigma = np.ones((states_count, mixture_components_count, dimensions_count, dimensions_count)) model = HMM(threshold=1e-4, adjustSigma=1, adjustMu=1, adjustPrior=1, adjustTransmition=1, adjustMix=4, covarianceType='full', maxIter=10) model.fit(data, prior, transmat, mixmat, mu, sigma) print model.sigma print model.mu print model.transmissions print model.mix print model.prior print model.post_prob_mc print model.post_prob_mcand_mog
return width, height, angle
"""
HMM model with K=4 possible states and observations conditional to the chain are normally distributed.
"""
K = 4
seed = 1995
max_iter = 100
log_interval = 10
verbose = 1
clf = HMM(K=K, max_iter=max_iter, verbose=verbose, log_interval=log_interval)
clf = clf.fit(df_train.values)
print("Log-likelihood at convergence on train %.3f" % clf.lc)
print("Log-likelihood on test %.3f" %
clf._get_lc(df_test.values, clf.predict(df_test.values)))
fig, ax = plt.subplots(nrows=1, ncols=1)
plot_clusters(df_train.values, clf.mu, clf.labels, ax=ax)
widths = np.zeros(K)
heights = np.zeros(K)
angles = np.zeros(K)
for j in range(K):
widths[j], heights[j], angles[j] = extract_params_ellipse(clf.Sigma[j])
simulated data
"""
import numpy as np
from hmm import HMM
def get_input_data(N, T, V):
return np.random.randint(0, V, size=(N, T))
def get_state(H):
return np.array(range(0, H))
# simulation
V = [0, 1, 2, 3, 4]
X = get_input_data(20, 10, len(V))
Y = get_state(3)
# training
model = HMM()
model.fit(X, Y, V)
t = model.Transition
e = model.Emission
p = model.Pi
# decoding
model = HMM(t, e, p)
res = model.decode(np.random.randint(0, len(V), size=(2, 10)))
print(res)
feature_maker = Feature_Maker()
if model_type == "HMM":
# fit hidden markov model model
# -------------------------------------------------------------------------
if load_entities is True:
data_train = [[(t[0], t[2]) for t in sent] for sent in data_train]
data_test = [[(t[0], t[2]) for t in sent] for sent in data_test]
else:
data_train = [[(t[0], t[1]) for t in sent] for sent in data_train]
data_test = [[(t[0], t[1]) for t in sent] for sent in data_test]
hmm = HMM()
start_time = time.time()
hmm.fit(data_train)
print(f"Duration of training: {time.time() - start_time}")
# evaluation hmm
# -------------------------------------------------------------------------
# plot confusion matrix, calculate precision, recall, f1-score
hmm.evaluate(data_test)
# show misclassifications
features_test, labels_test = separate_labels_from_features(data_test)
predictions = hmm.predict(features_test)
show_misclassifications(data_test, predictions)
elif model_type == "NB":
# fit naive bayes model
# -------------------------------------------------------------------------
nb = Naive_Bayes()
#!/usr/bin/env python # encoding: utf-8 from __future__ import division, print_function import numpy as np from hmm import _stoch_map, HMM n_hid = 4 n_obs = 3 steps = 10 hmm_ref = HMM(n_obs, n_hid) hmm_test = HMM(n_obs, n_hid) obs = hmm_ref.get_sequence(steps) hmm_test.fit(obs) norm = lambda x: np.max(np.abs(x)) print(norm(hmm_ref._mkh - hmm_test._mkh)) print(norm(hmm_ref._mko - hmm_test._mko)) print(norm(hmm_ref._init - hmm_test._init))
