updated markov for general case (incomplete)
This commit is contained in:
3
.gitignore
vendored
3
.gitignore
vendored
@@ -1,2 +1,5 @@
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# ignore matplotlib
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# ignore matplotlib
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./bayes-learning/packages/matplotlib/*
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./bayes-learning/packages/matplotlib/*
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# ignore sublime workspace
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*markov.sublime-workspace
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125
markov/markov.py
125
markov/markov.py
@@ -11,7 +11,7 @@ seedNum = 27
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""" Simulation parameters """
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""" Simulation parameters """
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# Should we simulate more than once?
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# Should we simulate more than once?
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setSim = True
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setSim = True
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simNum = 1
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simNum = 5000
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""" Define our data """
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""" Define our data """
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# The Statespace
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# The Statespace
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@@ -23,23 +23,32 @@ transitionName = np.array([['LL', 'Lw', 'LW'],
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['WL', 'Ww', 'WW']])
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['WL', 'Ww', 'WW']])
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# Probabilities Matrix (transition matrix)
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# Probabilities Matrix (transition matrix)
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# Fill in
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transitionMatrix = np.array([[0.6, 0.1, 0.3],
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transitionMatrix = None
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[0.1, 0.7, 0.2],
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[0.2, 0.2, 0.6]])
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# Starting state
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# Starting state
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startingState = 'w'
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# startingState = 'L'
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initial_dist = None
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initial_dist = np.array([1, 0, 0])
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# initial_dist = None
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# Steps to run
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# Steps to run
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stepTime = 2
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stepTime = 5
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# End state you want to find probabilites of
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# End state you want to find probabilites of
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endState = 'W'
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endState = 'L'
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# Get P_steps
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""" Find the transition matrix for the given number of steps
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p_steps = False
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This finds the probabilities of going from step i to step j
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in N number of steps, where N is your step size
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E.g 10 steps would give P_10 which is prob of going from step i
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to step j in exactly 10 steps """
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transition_matrix_step = True
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# Get Stationary Dist
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""" Get Stationary distribution of the Markov Chain
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stat_dist = False
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This tells you what % of time you spend in each state if you
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simulated the Markov Chain for a high number of steps
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THIS NEEDS THE INTIIAL DISTRIBUTION SETTING """
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stat_dist = True
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# A class that implements the Markov chain to forecast the state/mood:
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# A class that implements the Markov chain to forecast the state/mood:
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@@ -72,56 +81,91 @@ class markov(object):
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return np.random.seed(num)
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return np.random.seed(num)
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@staticmethod
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@staticmethod
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def p_steps(transitionMatrix, initial_dist, steps):
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def transition_matrix_step(transitionMatrix, steps):
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for _ in itertools.repeat(None, steps):
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for _ in itertools.repeat(None, steps):
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initial_dist = transitionMatrix.T.dot(initial_dist)
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step_mat = np.matmul(transitionMatrix, transitionMatrix)
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return initial_dist
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return step_mat
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@staticmethod
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@staticmethod
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def stationary_dist(transitionMatrix, initial_dist, steps):
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def stationary_dist(transitionMatrix, initial_dist, steps):
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for _ in itertools.repeat(None, steps):
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w, v = np.linalg.eig(transitionMatrix.T)
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initial_dist = transitionMatrix.T.dot(initial_dist)
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j_stationary = np.argmin(abs(w - 1.0))
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return initial_dist
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p_stationary = v[:, j_stationary].real
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p_stationary /= p_stationary.sum()
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return p_stationary
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@functools.lru_cache(maxsize=128)
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@functools.lru_cache(maxsize=128)
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def forecast(self):
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def forecast(self):
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print(f'Start state: {self.currentState}')
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print(f'Start state: {self.currentState}\n')
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# Shall store the sequence of states taken
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# Shall store the sequence of states taken
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self.stateList = [self.currentState]
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self.stateList = [self.currentState]
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i = 0
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i = 0
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# To calculate the probability of the stateList
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# To calculate the probability of the stateList
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self.prob = 1
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self.prob = 1
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while i != self.steps:
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while i != self.steps:
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if self.currentState == 'L':
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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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self.change = np.random.choice(self.transitionName[0],
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replace=True,
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replace=True,
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p=transitionMatrix[0])
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p=transitionMatrix[0])
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if self.change == 'LL':
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if self.change == self.transitionName[0][0]:
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self.prob = self.prob * 0.8
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self.prob = self.prob * self.transitionMatrix[0][0]
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self.stateList.append('L')
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self.stateList.append(self.states[0])
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pass
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pass
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elif self.change == 'Lw':
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elif self.change == self.transitionName[0][1]:
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self.prob = self.prob * self.transitionMatrix[0][1]
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self.currentState = self.states[1]
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self.stateList.append(self.states[1])
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else:
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self.prob == self.transitionMatrix[0][2]
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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 == "w":
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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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if self.change == "ww":
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self.prob = self.prob * 0.15
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self.prob = self.prob * 0.15
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self.currentState = 'w'
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self.stateList.append("w")
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self.stateList.append('w')
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pass
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elif self.change == "wL":
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self.prob = self.prob * 0.8
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self.currentState = "L"
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self.stateList.append("L")
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else:
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else:
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self.prob = self.prob * 0.05
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self.prob = self.prob * 0.05
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self.currentState = "W"
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self.currentState = "W"
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self.stateList.append("W")
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self.stateList.append("W")
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elif self.currentState == "w":
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# Fill in
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pass
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elif self.currentState == "W":
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elif self.currentState == "W":
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# Fill in
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self.change = np.random.choice(self.transitionName[2],
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pass
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replace=True,
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p=transitionMatrix[2])
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if self.change == "WW":
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self.prob = self.prob * 0.05
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self.stateList.append("W")
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pass
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elif self.change == "WL":
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self.prob = self.prob * 0.8
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self.currentState = "L"
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self.stateList.append("L")
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else:
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self.prob = self.prob * 0.15
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self.currentState = "w"
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self.stateList.append("w")
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i += 1
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i += 1
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print(f'Possible states: {self.stateList}')
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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'End state after {self.steps} steps: {self.currentState}')
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print(f'Probability of all the possible sequence of states:'
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print(f'Probability of this specific path:'
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f' {self.prob}\n')
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f' {self.prob:.4f} or {self.prob:.2%}\n')
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return self.stateList
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return self.stateList
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def checker(item, name):
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try:
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item is not None
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except Exception:
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raise Exception(f'{name} is not set - set it and try again.')
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def main(*args, **kwargs):
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def main(*args, **kwargs):
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global startingState
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global startingState
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try:
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try:
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@@ -163,18 +207,21 @@ def main(*args, **kwargs):
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stepTime).forecast())
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stepTime).forecast())
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for list in list_state:
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for list in list_state:
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if(list[-1] == f'{endState!s}'):
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if(list[-1] == f'{endState!s}'):
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print(True, list)
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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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count += 1
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count += 1
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else:
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else:
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print(False, list)
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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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if setSim is False:
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if setSim is False:
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simNum = 1
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simNum = 1
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print(f'\nThe probability of starting in {startingState} and finishing'
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print(f'\nThe probability of starting in {startingState} and finishing'
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f' in {endState} after {stepTime} steps is {(count / simNum):.2%}')
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f' in {endState} after {stepTime} steps is {(count / simNum):.2%}\n')
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if p_steps:
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if transition_matrix_step:
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print(f'P_{stepTime} is '
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print(f'P_{stepTime} is: \n'
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f'{markov.p_steps(transitionMatrix, initial_dist, stepTime)}')
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f'{markov.transition_matrix_step(transitionMatrix, stepTime)}\n')
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if stat_dist:
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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(f'Stat dist is {markov.stationary_dist(transitionMatrix,initial_dist, stepTime)}')
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@@ -1,942 +0,0 @@
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{
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"auto_complete":
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{
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"selected_items":
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[
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[
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"mea",
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"mean_absolute_error\tfunction"
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],
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[
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"roc",
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"roc_auc_score\tfunction"
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],
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[
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"mean",
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"mean_squared_error\tfunction"
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],
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[
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"r",
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"r2_score\tfunction"
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],
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[
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"prec",
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"precision_score\tfunction"
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],
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[
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"re",
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"recall_score\tfunction"
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],
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[
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"lin",
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"lin_reg\tstatement"
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],
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[
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"X",
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"X_test\tstatement"
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],
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[
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"dec",
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"dec_tree\tstatement"
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],
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[
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"yt",
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"y_train\tstatement"
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],
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[
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"y_",
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"y_train\tstatement"
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],
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[
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"ran",
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"ran_for\tstatement"
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],
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[
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"Random",
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"RandomForestRegressor\tclass"
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],
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[
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"Dec",
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"DecisionTreeRegressor\tclass"
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],
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[
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"instant",
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"instaniated_models\tstatement"
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],
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[
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"i",
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"i"
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],
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[
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"train",
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"train_test_split\tfunction"
|
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],
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[
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"tes",
|
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"train_test_split\tfunction"
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],
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[
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"model",
|
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"model_selection\tmodule"
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],
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[
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"min",
|
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"min_samples_leaf\tparam"
|
|
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],
|
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[
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"max",
|
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"max_depth\tparam"
|
|
||||||
],
|
|
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[
|
|
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"accur",
|
|
||||||
"accuracy_score\tfunction"
|
|
||||||
],
|
|
||||||
[
|
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||||||
"y",
|
|
||||||
"y_pred\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"reg",
|
|
||||||
"reg_coef\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"V",
|
|
||||||
"Var_Y"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"Var",
|
|
||||||
"Var_X"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"li",
|
|
||||||
"bmi_life_model"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"cla",
|
|
||||||
"classmethod\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"tester",
|
|
||||||
"tester1"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"clas",
|
|
||||||
"class\tNew Class"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"int",
|
|
||||||
"int\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"set",
|
|
||||||
"setattr\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"st",
|
|
||||||
"Standalone"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"M",
|
|
||||||
"MyClass\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"m",
|
|
||||||
"my_printer\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"from",
|
|
||||||
"fromBirthYear\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"class",
|
|
||||||
"classmethod\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"par",
|
|
||||||
"partialmethod\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"db",
|
|
||||||
"initialize_db"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"get",
|
|
||||||
"getattr\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"name",
|
|
||||||
"__name__"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"__get",
|
|
||||||
"__getattribute__\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"kwargs",
|
|
||||||
"kwargs"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"func",
|
|
||||||
"functools\tmodule"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"wra",
|
|
||||||
"wrapper_slow_down\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"slo",
|
|
||||||
"slowDownDecorator\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"slow",
|
|
||||||
"slowDown\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"init",
|
|
||||||
"init"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"call",
|
|
||||||
"callable"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"Slo",
|
|
||||||
"Slow_Down_Decorator\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"Slow",
|
|
||||||
"Slow_Down\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"partial",
|
|
||||||
"partial\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"coun",
|
|
||||||
"count_down\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"num",
|
|
||||||
"num_calls\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"sum",
|
|
||||||
"sum_squares"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"wrapper",
|
|
||||||
"wrapper_count_calls\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"n",
|
|
||||||
"num_calls"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"str",
|
|
||||||
"str\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"repea",
|
|
||||||
"repeat_partial\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"parti",
|
|
||||||
"repeat_partial_wrapper"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"repeat",
|
|
||||||
"repeatN\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"wrapper_rep",
|
|
||||||
"wrapper_repeatN\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"de",
|
|
||||||
"decorator_repeat\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"Call",
|
|
||||||
"callable\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"R",
|
|
||||||
"ReferenceError\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"gree",
|
|
||||||
"greeter_func\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"wrap",
|
|
||||||
"wrapper_debug\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"__",
|
|
||||||
"__name__"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"kw",
|
|
||||||
"kwargs_repr\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"args",
|
|
||||||
"args_repr"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"ge",
|
|
||||||
"get_type\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"no",
|
|
||||||
"not_during_the_night\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"ex",
|
|
||||||
"executable\tinstance"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"script",
|
|
||||||
"script_file\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"sc",
|
|
||||||
"script_file\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"Ke",
|
|
||||||
"KeyError\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"folder",
|
|
||||||
"folder"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"ar",
|
|
||||||
"arguments\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"local",
|
|
||||||
"locals_val\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"arguments",
|
|
||||||
"arguments"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"sctr",
|
|
||||||
"script2_main\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"f",
|
|
||||||
"f"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"read_",
|
|
||||||
"read_excel\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"read",
|
|
||||||
"read_csv\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"is",
|
|
||||||
"is_tasty"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"prin",
|
|
||||||
"print_sound"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"w",
|
|
||||||
"wheels"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"pr",
|
|
||||||
"print_sound"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"date",
|
|
||||||
"datetime\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"p",
|
|
||||||
"print_wheels\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"File",
|
|
||||||
"FileNotFoundError\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"borg",
|
|
||||||
"borg"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"log",
|
|
||||||
"log"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"conver",
|
|
||||||
"convert_to_uppercase\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"hell",
|
|
||||||
"hellow_world\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"__m",
|
|
||||||
"__name__\tinstance"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"fu",
|
|
||||||
"func\tparam"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"fun",
|
|
||||||
"functools\tmodule"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"funct",
|
|
||||||
"function_call\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"d",
|
|
||||||
"double_volume\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"s",
|
|
||||||
"surface_area\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"new",
|
|
||||||
"new_length\tparam"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"Cube",
|
|
||||||
"Cube"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"area",
|
|
||||||
"area_2\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"tri",
|
|
||||||
"tri_area\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"triangle",
|
|
||||||
"triangle_area\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"bas",
|
|
||||||
"base_area\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"sl",
|
|
||||||
"slant_height\tparam"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"heigh",
|
|
||||||
"height_tripled\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"h",
|
|
||||||
"height_doubled\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"__in",
|
|
||||||
"__init__\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"ba",
|
|
||||||
"base_area"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"garb",
|
|
||||||
"garbage_check\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"len",
|
|
||||||
"length\tparam"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"__init",
|
|
||||||
"__init_subclass__\tfunction"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"Square",
|
|
||||||
"Square\tclass"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"se",
|
|
||||||
"self"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"list",
|
|
||||||
"list_of_ips\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"su",
|
|
||||||
"sub_list\tstatement"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"def",
|
|
||||||
"def\tFunction"
|
|
||||||
]
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"buffers":
|
|
||||||
[
|
|
||||||
],
|
|
||||||
"build_system": "",
|
|
||||||
"build_system_choices":
|
|
||||||
[
|
|
||||||
],
|
|
||||||
"build_varint": "",
|
|
||||||
"command_palette":
|
|
||||||
{
|
|
||||||
"height": 0.0,
|
|
||||||
"last_filter": "",
|
|
||||||
"selected_items":
|
|
||||||
[
|
|
||||||
[
|
|
||||||
"replv",
|
|
||||||
"SublimeREPL: Python - virtualenv"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"ac",
|
|
||||||
"Virtualenv: Activate"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"activ",
|
|
||||||
"Virtualenv: Activate"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"repl v",
|
|
||||||
"SublimeREPL: Python - virtualenv"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"virtualen",
|
|
||||||
"Virtualenv: Activate"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"repl p",
|
|
||||||
"SublimeREPL: Python - virtualenv"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"repl py",
|
|
||||||
"SublimeREPL: Python - virtualenv"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"repel p",
|
|
||||||
"SublimeREPL: Python - virtualenv"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"error",
|
|
||||||
"SublimeLinter: Show All Errors"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"show",
|
|
||||||
"SublimeLinter: Show All Errors"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"rep",
|
|
||||||
"SublimeREPL: Python - virtualenv"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"python",
|
|
||||||
"SublimeREPL: Python - virtualenv"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"virtual",
|
|
||||||
"Virtualenv: Activate"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"python vi",
|
|
||||||
"SublimeREPL: Python - virtualenv"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"repl python",
|
|
||||||
"SublimeREPL: Python - virtualenv"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"virtua",
|
|
||||||
"Virtualenv: Activate"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"sublimerepl",
|
|
||||||
"Virtualenv: SublimeREPL - Python"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"install",
|
|
||||||
"Package Control: Install Package"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"virt",
|
|
||||||
"Virtualenv: Activate"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"virtu",
|
|
||||||
"Virtualenv: Activate"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"packa",
|
|
||||||
"Package Control: Install Package"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"vir",
|
|
||||||
"Virtualenv: New (venv)"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"install pa",
|
|
||||||
"Package Control: Install Package"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"insta",
|
|
||||||
"Package Control: Install Package"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"ins",
|
|
||||||
"Package Control: Install Package"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"brow",
|
|
||||||
"Preferences: Browse Packages"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"browse",
|
|
||||||
"Preferences: Browse Packages"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"remove",
|
|
||||||
"Package Control: Remove Package"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"Snippet: ",
|
|
||||||
"Snippet: For Loop"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"remo",
|
|
||||||
"Package Control: Remove Channel"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"show all",
|
|
||||||
"SublimeLinter: Show All Errors"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"prefs",
|
|
||||||
"Preferences: SublimeLinter Settings"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"package in",
|
|
||||||
"Package Control: Install Package"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"install pack",
|
|
||||||
"Package Control: Install Package"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"ayu: Activate theme",
|
|
||||||
"ayu: Activate theme"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"Browse Pack",
|
|
||||||
"Preferences: Browse Packages"
|
|
||||||
],
|
|
||||||
[
|
|
||||||
"Package Control: insta",
|
|
||||||
"Package Control: Install Package"
|
|
||||||
]
|
|
||||||
],
|
|
||||||
"width": 0.0
|
|
||||||
},
|
|
||||||
"console":
|
|
||||||
{
|
|
||||||
"height": 250.0,
|
|
||||||
"history":
|
|
||||||
[
|
|
||||||
"sublime.cache_path()",
|
|
||||||
"import urllib.request,os,hashlib; h = '6f4c264a24d933ce70df5dedcf1dcaee' + 'ebe013ee18cced0ef93d5f746d80ef60'; pf = 'Package Control.sublime-package'; ipp = sublime.installed_packages_path(); urllib.request.install_opener( urllib.request.build_opener( urllib.request.ProxyHandler()) ); by = urllib.request.urlopen( 'http://packagecontrol.io/' + pf.replace(' ', '%20')).read(); dh = hashlib.sha256(by).hexdigest(); print('Error validating download (got %s instead of %s), please try manual install' % (dh, h)) if dh != h else open(os.path.join( ipp, pf), 'wb' ).write(by)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"distraction_free":
|
|
||||||
{
|
|
||||||
"menu_visible": true,
|
|
||||||
"show_minimap": false,
|
|
||||||
"show_open_files": false,
|
|
||||||
"show_tabs": false,
|
|
||||||
"side_bar_visible": false,
|
|
||||||
"status_bar_visible": false
|
|
||||||
},
|
|
||||||
"expanded_folders":
|
|
||||||
[
|
|
||||||
],
|
|
||||||
"file_history":
|
|
||||||
[
|
|
||||||
"/home/dtomlinson/courses/Udacity Machine Learning - Introduction Nanodegree Program/python/Supervised Learning/Model Evaluation Metrics/Regression Metrics/regression.py",
|
|
||||||
"/home/dtomlinson/courses/Udacity Machine Learning - Introduction Nanodegree Program/python/Supervised Learning/Model Evaluation Metrics/Regression Metrics/tests2.py",
|
|
||||||
"/home/dtomlinson/courses/Udacity Machine Learning - Introduction Nanodegree Program/python/Supervised Learning/Model Evaluation Metrics/3. Testing your models/tests.py",
|
|
||||||
"/home/dtomlinson/courses/Udacity Machine Learning - Introduction Nanodegree Program/python/Supervised Learning/Model Evaluation Metrics/3. Testing your models/quiz.py",
|
|
||||||
"/home/dtomlinson/courses/Udacity Machine Learning - Introduction Nanodegree Program/python/Supervised Learning/Model Evaluation Metrics/3. Testing your models/classification_metrics.py",
|
|
||||||
"/home/dtomlinson/courses/Udacity Machine Learning - Introduction Nanodegree Program/python/Supervised Learning/Model Evaluation Metrics/3. Testing your models/data.csv",
|
|
||||||
"/home/dtomlinson/git-repos/sublime/packages",
|
|
||||||
"/home/dtomlinson/courses/Udacity Machine Learning - Introduction Nanodegree Program/python/Supervised Learning/Ensemble Methods/spam_ensembles.py",
|
|
||||||
"/home/dtomlinson/git-repos/sublime/licesne",
|
|
||||||
"/home/dtomlinson/git-repos/sublime/.config/sublime-text-3/Packages/User/SublimeLinter.sublime-settings",
|
|
||||||
"/home/dtomlinson/git-repos/sublime/Projects/base.sublime-project",
|
|
||||||
"/home/dtomlinson/sublime/base.sublime-project",
|
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|
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}
|
|
||||||
@@ -29,18 +29,26 @@ transitionMatrix = np.array([[0.6, 0.1, 0.3],
|
|||||||
|
|
||||||
# Starting state
|
# Starting state
|
||||||
startingState = 'w'
|
startingState = 'w'
|
||||||
initial_dist = np.array([0.3, 0.3, 0.4])
|
initial_dist = np.array([0, 1, 0])
|
||||||
|
# initial_dist = None
|
||||||
|
|
||||||
# Steps to run
|
# Steps to run
|
||||||
stepTime = 2
|
stepTime = 100
|
||||||
# End state you want to find probabilites of
|
# End state you want to find probabilites of
|
||||||
endState = 'W'
|
endState = 'W'
|
||||||
|
|
||||||
# Get P_steps
|
""" Find the transition matrix for the given number of steps
|
||||||
p_steps = False
|
This finds the probabilities of going from step i to step j
|
||||||
|
in N number of steps, where N is your step size
|
||||||
|
E.g 10 steps would give P_10 which is prob of going from step i
|
||||||
|
to step j in exactly 10 steps """
|
||||||
|
transition_matrix_step = True
|
||||||
|
|
||||||
# Get Stationary Dist
|
""" Get Stationary distribution of the Markov Chain
|
||||||
stat_dist = False
|
This tells you what % of time you spend in each state if you
|
||||||
|
simulated the Markov Chain for a high number of steps
|
||||||
|
THIS NEEDS THE INTIIAL DISTRIBUTION SETTING """
|
||||||
|
stat_dist = True
|
||||||
|
|
||||||
|
|
||||||
# A class that implements the Markov chain to forecast the state/mood:
|
# A class that implements the Markov chain to forecast the state/mood:
|
||||||
@@ -73,20 +81,22 @@ class markov(object):
|
|||||||
return np.random.seed(num)
|
return np.random.seed(num)
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def p_steps(transitionMatrix, initial_dist, steps):
|
def transition_matrix_step(transitionMatrix, steps):
|
||||||
for _ in itertools.repeat(None, steps):
|
for _ in itertools.repeat(None, steps):
|
||||||
initial_dist = transitionMatrix.T.dot(initial_dist)
|
step_mat = np.matmul(transitionMatrix, transitionMatrix)
|
||||||
return initial_dist
|
return step_mat
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def stationary_dist(transitionMatrix, initial_dist, steps):
|
def stationary_dist(transitionMatrix, initial_dist, steps):
|
||||||
for _ in itertools.repeat(None, steps):
|
w, v = np.linalg.eig(transitionMatrix.T)
|
||||||
initial_dist = transitionMatrix.T.dot(initial_dist)
|
j_stationary = np.argmin(abs(w - 1.0))
|
||||||
return initial_dist
|
p_stationary = v[:, j_stationary].real
|
||||||
|
p_stationary /= p_stationary.sum()
|
||||||
|
return p_stationary
|
||||||
|
|
||||||
@functools.lru_cache(maxsize=128)
|
@functools.lru_cache(maxsize=128)
|
||||||
def forecast(self):
|
def forecast(self):
|
||||||
print(f'Start state: {self.currentState}')
|
print(f'Start state: {self.currentState}\n')
|
||||||
# Shall store the sequence of states taken
|
# Shall store the sequence of states taken
|
||||||
self.stateList = [self.currentState]
|
self.stateList = [self.currentState]
|
||||||
i = 0
|
i = 0
|
||||||
@@ -142,13 +152,20 @@ class markov(object):
|
|||||||
self.currentState = "w"
|
self.currentState = "w"
|
||||||
self.stateList.append("w")
|
self.stateList.append("w")
|
||||||
i += 1
|
i += 1
|
||||||
print(f'Possible states: {self.stateList}')
|
print(f'Path Markov Chain took in this iteration: {self.stateList}')
|
||||||
print(f'End state after {self.steps} steps: {self.currentState}')
|
print(f'End state after {self.steps} steps: {self.currentState}')
|
||||||
print(f'Probability of all the possible sequence of states:'
|
print(f'Probability of this specific path:'
|
||||||
f' {self.prob}\n')
|
f' {self.prob:.4f} or {self.prob:.2%}\n')
|
||||||
return self.stateList
|
return self.stateList
|
||||||
|
|
||||||
|
|
||||||
|
def checker(item, name):
|
||||||
|
try:
|
||||||
|
item is not None
|
||||||
|
except Exception:
|
||||||
|
raise Exception(f'{name} is not set - set it and try again.')
|
||||||
|
|
||||||
|
|
||||||
def main(*args, **kwargs):
|
def main(*args, **kwargs):
|
||||||
global startingState
|
global startingState
|
||||||
try:
|
try:
|
||||||
@@ -190,18 +207,21 @@ def main(*args, **kwargs):
|
|||||||
stepTime).forecast())
|
stepTime).forecast())
|
||||||
for list in list_state:
|
for list in list_state:
|
||||||
if(list[-1] == f'{endState!s}'):
|
if(list[-1] == f'{endState!s}'):
|
||||||
print(True, list)
|
print(f'SUCCESS - path ended in the requested state {endState!s}'
|
||||||
|
f':', list)
|
||||||
count += 1
|
count += 1
|
||||||
else:
|
else:
|
||||||
print(False, list)
|
print(f'FAILURE - path did not end in the requested state'
|
||||||
|
f' {endState!s}:', list)
|
||||||
if setSim is False:
|
if setSim is False:
|
||||||
simNum = 1
|
simNum = 1
|
||||||
print(f'\nThe probability of starting in {startingState} and finishing'
|
print(f'\nThe probability of starting in {startingState} and finishing'
|
||||||
f' in {endState} after {stepTime} steps is {(count / simNum):.2%}')
|
f' in {endState} after {stepTime} steps is {(count / simNum):.2%}\n')
|
||||||
if p_steps:
|
if transition_matrix_step:
|
||||||
print(f'P_{stepTime} is '
|
print(f'P_{stepTime} is: \n'
|
||||||
f'{markov.p_steps(transitionMatrix, initial_dist, stepTime)}')
|
f'{markov.transition_matrix_step(transitionMatrix, stepTime)}\n')
|
||||||
if stat_dist:
|
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)}')
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
Reference in New Issue
Block a user