def generate_mc(self, states, window, T):
# set transition
p = np.array([[0.2, 0.8], [0.6, 0.4]])
mc = MarkovChain(p, states)
# set of transions
transitions = mc.walk(int(T / window))
data = []
for i in range(len(transitions)):
for c in self.generate_zipf(float(transitions[i]), window):
data.append(c)
return data
def generate_mc(self, states, window, T, available=False):
# set transition
p = np.array([[0.4, 0.6], [0.75, 0.25]])
mc = MarkovChain(p, states)
# set of transions
transitions = mc.walk(int(T / window))
data = [];
for transition in range(len(transitions)):
for c in self.generate_zipf(float(transitions[transition]), window, transition=transition,
available=available):
data.append(c)
return data