bigram = (tag1,tag2)
transition_probability = tag_bigrams[bigram] / tag_unigrams[tag1]
sum_of_probabilities += transition_probability
basic_model.add_transition(state1, state2, transition_probability)
#==============================================================
# finalize the model
#==============================================================
# NOTE: YOU SHOULD NOT NEED TO MODIFY ANYTHING BELOW THIS LINE
basic_model.bake()
print("Number of nodes or states: ", basic_model.node_count())
print("Number of edges: ", basic_model.edge_count())
assert all(tag in set(s.name for s in basic_model.states) for tag in data.training_set.tagset), \
"Every state in your network should use the name of the associated tag, which must be one of the training set tags."
assert basic_model.edge_count() == 168, \
("Your network should have an edge from the start node to each state, one edge between every " +
"pair of tags (states), and an edge from each state to the end node.")
HTML('<div class="alert alert-block alert-success">Your HMM network topology looks good!</div>')
#==============================================================
# evaluate train / test data set metrics
#==============================================================
model.add_transition(model.start, rainy_state, 0.5)
# add sunny day transitions (we already know estimates of these probabilities
# from the problem statement)
model.add_transition(sunny_state, sunny_state, 0.8) # 80% sunny->sunny
model.add_transition(sunny_state, rainy_state, 0.2) # 20% sunny->rainy
# TODO: add rainy day transitions using the probabilities specified in the transition table
model.add_transition(rainy_state, sunny_state, 0.4) # 40% rainy->sunny
model.add_transition(rainy_state, rainy_state, 0.6) # 60% rainy->rainy
# finally, call the .bake() method to finalize the model
model.bake()
assert model.edge_count() == 6, "There should be two edges from model.start, two from Rainy, and two from Sunny"
assert model.node_count() == 4, "The states should include model.start, model.end, Rainy, and Sunny"
print("Great! You've finished the model.")
show_model(model, figsize=(5, 5), filename="example.png", overwrite=True, show_ends=False)
column_order = ["Example Model-start", "Sunny", "Rainy", "Example Model-end"] # Override the Pomegranate default order
column_names = [s.name for s in model.states]
order_index = [column_names.index(c) for c in column_order]
# re-order the rows/columns to match the specified column order
transitions = model.dense_transition_matrix()[:, order_index][order_index, :]
from pomegranate import HiddenMarkovModel, DiscreteDistribution, State
import numpy as np
model = HiddenMarkovModel(name='weather')
sunny_emissions = DiscreteDistribution({'yes': 0.1, 'no': 0.9})
sunny_state = State(sunny_emissions, name='sunny')
rainy_emissions = DiscreteDistribution({'yes': 0.8, 'no': 0.2})
rainy_state = State(rainy_emissions, name='rainy')
model.add_states(sunny_state, rainy_state)
model.add_transition(model.start, sunny_state, 0.5)
model.add_transition(model.start, rainy_state, 0.5)
model.add_transition(sunny_state, rainy_state, 0.2)
model.add_transition(sunny_state, sunny_state, 0.8)
model.add_transition(rainy_state, rainy_state, 0.6)
model.add_transition(rainy_state, sunny_state, 0.4)
model.bake()
states = np.array([s.name for s in model.states])
print('{} states: {}'.format(model.node_count(), states))
transitions = model.dense_transition_matrix()
print('{} transitions probabilities between states \n{}'.format(
model.edge_count(), transitions))
