class HMMWrapper:
def __init__(self):
self.model = HiddenMarkovModel()
self.start = self.model.start
self.end = self.model.end
self.states_before_bake = []
self.states = None
def add_state(self, state, start_prob=0):
self.states_before_bake.append((state, start_prob))
self.model.add_state(state)
def add_transition(self, start_state, end_state, prob):
# print('adding from', start_state.name, 'to', end_state.name, prob)
self.model.add_transition(start_state, end_state, prob)
def bake(self):
starter_states_no_prob = []
free_start_prob = 1.0
for state in self.states_before_bake:
if 'none' not in state[0].name:
if not state[1]:
starter_states_no_prob.append(state)
else:
free_start_prob -= state[1]
print('asignado ' + str(state[1]) + ' a ' + state[0].name)
self.add_transition(self.start, state[0], state[1])
len_no_prob = len(starter_states_no_prob)
starter_prob = free_start_prob / len_no_prob
print(len_no_prob, starter_prob)
for state in starter_states_no_prob:
self.add_transition(self.start, state, starter_prob)
self.model.bake()
self.states = self.model.states
def make_states_from_alignment(self, first_state, last_state, seq_matrix,
name):
columns = column_clasify(seq_matrix)
zones = create_zones(columns)
grouped_states = group_states(zones, name)
add_states(self, grouped_states)
trans = calculate_transitions(first_state, last_state, grouped_states)
apply_transitions(self, trans)
def predict(self, *args, **kwargs):
return self.model.predict(*args, **kwargs)
def cluster(self):
if self.preprocessed_data is None:
print("No preprocessed_data attribute found")
return -1
if self.alg == "Kmeans":
from sklearn.cluster import KMeans
km = KMeans(n_clusters=self.K, precompute_distances=True)
km.fit(np.concatenate(
self.preprocessed_data)) #flattens all dates together
self.states = [km.predict(d) for d in self.preprocessed_data]
elif self.alg == "HMM":
from pomegranate import HiddenMarkovModel, MultivariateGaussianDistribution
distribution = MultivariateGaussianDistribution
hmm=HiddenMarkovModel().from_samples(distribution,n_components=self.K\
,X=self.preprocessed_data.copy())
self.states = [
np.array(hmm.predict(d.copy())) for d in self.preprocessed_data
]
else:
print("Unrecognised or undefined clustering algorithm.")
return -1
self.experiment_progress = 2
hmm.add_transition(s33, s33, 0.92)
hmm.add_transition(s33, s333, 0.01)
hmm.add_transition(s333, s1, 0.01)
hmm.add_transition(s333, s11, 0.01)
hmm.add_transition(s333, s111, 0.01)
hmm.add_transition(s333, s2, 0.01)
hmm.add_transition(s333, s22, 0.01)
hmm.add_transition(s333, s222, 0.01)
hmm.add_transition(s333, s3, 0.01)
hmm.add_transition(s333, s33, 0.01)
hmm.add_transition(s333, s333, 0.92)
hmm.bake()
hmm.fit(X) # , weights=w) hmm does not support weights in pomegranate
preds = hmm.predict(X)
probs = hmm.predict_proba(X)
data_thr['preds'] = pd.Series(preds).astype("category")
color_key = ["red", "blue", "yellow", "grey", "black", "purple", "pink",
"brown", "green", "orange"] # Spectral9
color_key = color_key[:len(set(preds))+2]
covs = np.array([np.array(hmm.states[m].distribution.parameters[1])
for m in range(9)])
means = np.array([np.array(hmm.states[m].distribution.parameters[0])
for m in range(9)])
# transform cov for non-standardizeed data:
covs = np.array([np.dot(np.diag(np.sqrt(scaler.var_)),
reunify_sequences(hm_model, mdd_states_sequences, background, [1, 1, 1, 1])
hm_model.bake()
a = 'a'
c = 'c'
g = 'g'
t = 't'
seq = numpy.array([
c, t, g, t, c, t, c, c, c, g, g, c, g, g, c, c, a, g, c, g, g, c, g, g, a,
a, c, c, t, g, t, g, c, g, a, g, t, g, g, a, t, g, c, g, g, a, a, g, c, c,
g, g, c, g, c, a, g, c, a, g, t, c, c, c, t, c, g, g, c, a, g, c, c, a, a,
g, g, t, a, a, g, c, a, g, a, g, g, c, t, g, c, g, c, c, c, c, t, t, c, g,
g, a, g, g, g, t, g, c, t, t, g, g, g, a, a, g, g, c, g, c, g, g, g, t, c,
g, a, g, c, c, a, g, t, g, g, c, t, g, c, t, g, c, g, c, g, t, c, g
])
hmm_predictions = hm_model.predict(seq)
#print("sequence: {}".format(' '.join(seq)))
#print("hmm pred: {}".format(' '.join(map( str, hmm_predictions))))
empar = []
for i, s in enumerate(seq):
empar.append((seq[i], hm_model.states[hmm_predictions[i]].name))
print(len(hm_model.states), hm_model.states[19])
print(empar)
second_model = insert_delete_main_hmm(og_matrix)
print(second_model.states)
#for idx, val in enumerate(final_percent):
# final_percent[idx] = {
# 'a': count_a[idx] / examples,
# 'c': count_c[idx] / examples,
