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