def main():
#--------------------------------------------------------------------------
# 2-state Markov chain
#--------------------------------------------------------------------------
P = np.array([[0.8, 0.2], [0.1, 0.9]]) # Transition matrix
mc = MarkovChain(P, ['1', '2'])
mc.draw("../img/markov-chain-two-states.png")
#--------------------------------------------------------------------------
# 3-state Markov chain
#--------------------------------------------------------------------------
P = np.array([
[0.8, 0.1, 0.1],
[0.1, 0.7, 0.2],
[0.1, 0.7, 0.2],
])
mc = MarkovChain(P, ['A', 'B', 'C'])
mc.draw("../img/markov-chain-three-states.png")
#--------------------------------------------------------------------------
# 4-state Markov chain
#--------------------------------------------------------------------------
P = np.array([[0.8, 0.1, 0.1, 0.0], [0.1, 0.7, 0.0, 0.2],
[0.1, 0.0, 0.7, 0.2], [0.1, 0.0, 0.7, 0.2]])
mc = MarkovChain(P, ['1', '2', '3', '4'])
mc.draw("../img/markov-chain-four-states.png")
def __init__(self): path_ini = os.getcwd()+'/resources/models/mc_model/mc_initial.npy' path_trans = os.getcwd()+'/resources/models/mc_model/mc_transition.npy' trans = np.load(path_trans) pi = np.load(path_ini) self.model = MarkovChain(transition=trans, initial=pi, states=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18])
def plot_graph(self, mk_prop, Labels):
P = np.array([mk_prop]) # Transition matrix
mc = MarkovChain(P, Labels)
mc.draw()
("sjor", "data/usjavarornefni.txt", 3),
("sveit", "data/usveit.txt", 3),
("borg", "data/uthettbyli.txt", 3),
("vatn", "data/uvatnaornefni.txt", 4),
("land", "data/ulandornefni.txt", 4),
("jokull", "data/ujoklaornefni.txt", 3),
("kvk", "data/ukvknofn.txt", 3),
("kk", "data/ukknofn.txt", 3),
("milli", "data/umillinofn.txt", 3),
("gata", "data/ugotunofn.txt", 4),
("hus", "data/uhusanofn.txt", 3),
]
CHAINS = {}
for name, fname, order in name_files:
mc = MarkovChain(order=order, analyzer="char")
mc.fit(fname)
CHAINS[name] = mc
mc = MarkovChain(order=order, analyzer="char")
mc.fit(fname, reversed=True)
CHAINS[name + "_r"] = mc
NAMES = {}
for chain, fname, _ in name_files:
NAMES[chain] = set()
with open(fname) as f:
for name in f.readlines():
NAMES[chain].add(name.strip())
def is_bool(value):
if _ == (0, 0):
pass
else:
start += step_size
stop += step_size
# Slice sessions @sessions(None, None)
next_step = input.sessions(start, stop)
if _ >= (1, 1):
past_start = (start-step_size)
past_stop = (stop-step_size)
past_step = input.sessions(past_start, past_stop)
# Compute transition matrix next
mc_next = MarkovChain(next_step, states)
mc_next = mc_next.csr_sparse_matrix()
# Compute transition matrix past
mc_past = MarkovChain(past_step, states)
mc_past = mc_past.csr_sparse_matrix()
print('matrix done', datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'))
print('start clustering')
# DBSCAN
dbscan_next = Clustering(mc_next)
dbscan_past = Clustering(mc_past)
print("End clustering", datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'), '\n')
"""
