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dtomlinson
2019-12-05 03:56:49 +00:00
parent b45c030c59
commit 6c84083c69
7 changed files with 124 additions and 43 deletions
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118
markov/markov.py
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@@ -18,14 +18,14 @@ simNum = 15000
states = np.array(['L', 'w', 'W'])
# Possible sequences of events
transitionName = np.array([['LL', 'Lw', 'LW'],
['wL', 'ww', 'wW'],
['WL', 'Ww', 'WW']])
transitionName = np.array(
[['LL', 'Lw', 'LW'], ['wL', 'ww', 'wW'], ['WL', 'Ww', 'WW']]
)
# Probabilities Matrix (transition matrix)
transitionMatrix = np.array([[0.6, 0.1, 0.3],
[0.1, 0.7, 0.2],
[0.2, 0.2, 0.6]])
transitionMatrix = np.array(
[[0.6, 0.1, 0.3], [0.1, 0.7, 0.2], [0.2, 0.2, 0.6]]
)
# Starting state - Given as a list of probabilities of starting
# in each state. To always start at the same state set one of them
@@ -69,9 +69,14 @@ class markov(object):
currentState: a string indicating the starting state
steps: an integer determining how many steps (or times) to simulate"""
def __init__(self, states: np.array, transitionName: np.array,
transitionMatrix: np.array, currentState: str,
steps: int):
def __init__(
self,
states: np.array,
transitionName: np.array,
transitionMatrix: np.array,
currentState: str,
steps: int,
):
super(markov, self).__init__()
self.states = states
self.list = list
@@ -110,10 +115,11 @@ class markov(object):
# To calculate the probability of the stateList
self.prob = 1
while i != self.steps:
import pdb; pdb.set_trace() # breakpoint 24e62119 //
if self.currentState == self.states[0]:
self.change = np.random.choice(self.transitionName[0],
replace=True,
p=transitionMatrix[0])
self.change = np.random.choice(
self.transitionName[0], replace=True, p=transitionMatrix[0]
)
if self.change == self.transitionName[0][0]:
self.prob = self.prob * self.transitionMatrix[0][0]
self.stateList.append(self.states[0])
@@ -127,9 +133,9 @@ class markov(object):
self.currentState = self.states[2]
self.stateList.append(self.states[2])
elif self.currentState == self.states[1]:
self.change = np.random.choice(self.transitionName[1],
replace=True,
p=transitionMatrix[1])
self.change = np.random.choice(
self.transitionName[1], replace=True, p=transitionMatrix[1]
)
if self.change == self.transitionName[1][0]:
self.prob = self.prob * self.transitionMatrix[1][0]
self.currentState = self.states[0]
@@ -143,9 +149,9 @@ class markov(object):
self.currentState = self.states[2]
self.stateList.append(self.states[2])
elif self.currentState == self.states[2]:
self.change = np.random.choice(self.transitionName[2],
replace=True,
p=transitionMatrix[2])
self.change = np.random.choice(
self.transitionName[2], replace=True, p=transitionMatrix[2]
)
if self.change == self.transitionName[2][0]:
self.prob = self.prob * self.transitionMatrix[2][0]
self.currentState = self.states[0]
@@ -161,8 +167,10 @@ class markov(object):
i += 1
print(f'Path Markov Chain took in this iteration: {self.stateList}')
print(f'End state after {self.steps} steps: {self.currentState}')
print(f'Probability of taking these exact steps in this order is:'
f' {self.prob:.2f} or {self.prob:.2%}\n')
print(
f'Probability of taking these exact steps in this order is:'
f' {self.prob:.2f} or {self.prob:.2%}\n'
)
return self.stateList
@@ -196,43 +204,69 @@ def main(*args, **kwargs):
if initial_dist is not None:
startingState = np.random.choice(states, p=initial_dist)
for _ in itertools.repeat(None, simNum):
markovChain = markov(states, transitionName,
transitionMatrix, startingState,
stepTime)
markovChain = markov(
states,
transitionName,
transitionMatrix,
startingState,
stepTime,
)
list_state.append(markovChain.forecast())
startingState = np.random.choice(states, p=initial_dist)
else:
for _ in itertools.repeat(None, simNum):
markovChain = markov(states, transitionName,
transitionMatrix, startingState,
stepTime)
markovChain = markov(
states,
transitionName,
transitionMatrix,
startingState,
stepTime,
)
list_state.append(markovChain.forecast())
else:
for _ in range(1, 2):
list_state.append(markov(states, transitionName,
transitionMatrix, startingState,
stepTime).forecast())
list_state.append(
markov(
states,
transitionName,
transitionMatrix,
startingState,
stepTime,
).forecast()
)
for list in list_state:
if(list[-1] == f'{endState!s}'):
print(f'SUCCESS - path ended in the requested state {endState!s}'
f':', list)
if list[-1] == f'{endState!s}':
print(
f'SUCCESS - path ended in the requested state {endState!s}'
f':',
list,
)
count += 1
else:
print(f'FAILURE - path did not end in the requested state'
f' {endState!s}:', list)
print(
f'FAILURE - path did not end in the requested state'
f' {endState!s}:',
list,
)
if setSim is False:
simNum = 1
print(f'\nTherefore the estimated probability of starting in'
f' {startingState} and finishing in {endState} after '
f'{stepTime} steps is '
f'{(count / simNum):.2%}.\n'
f'This is calculated by number of success/total steps\n')
print(
f'\nTherefore the estimated probability of starting in'
f' {startingState} and finishing in {endState} after '
f'{stepTime} steps is '
f'{(count / simNum):.2%}.\n'
f'This is calculated by number of success/total steps\n'
)
if transition_matrix_step:
print(f'P_{stepTime} is: \n'
f'{markov.transition_matrix_step(transitionMatrix, stepTime)}\n')
print(
f'P_{stepTime} is: \n'
f'{markov.transition_matrix_step(transitionMatrix, stepTime)}\n'
)
if stat_dist:
checker(initial_dist, 'initial distribution')
print(f'Stat dist is {markov.stationary_dist(transitionMatrix,initial_dist, stepTime)}')
print(
f'Stat dist is {markov.stationary_dist(transitionMatrix,initial_dist, stepTime)}'
)
if __name__ == '__main__':

1
markov/requirements.txt Normal file
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@@ -0,0 +1 @@
numpy==1.17.4