2020-12-11 21:33:20 +00:00
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from dataclasses import dataclass, field
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from typing import List
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import numpy as np
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from maths import gaussian
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@dataclass
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class Likelihood:
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forward: float # forward likelihood
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backward: float # backward likelihood
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@dataclass
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class TimeStep:
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states: List[Likelihood] = field(default_factory=list)
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@dataclass
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class Transition:
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pass
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class MarkovModel:
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def __init__(self, states: list, observations: list = list(), state_transitions: list = list()):
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self.observations = observations
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2020-12-23 20:12:08 +00:00
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self.state_transitions = state_transitions # use state number not state index, is padded by entry and exit probs
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2020-12-11 21:33:20 +00:00
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self.states = states # number of states
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# self.timesteps = list()
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self.forward = np.zeros((len(states), len(observations)))
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self.backward = np.zeros((len(states), len(observations)))
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2020-12-23 20:12:08 +00:00
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def get_other_state_index(self, state_in):
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"""For when state changes, get other index for retrieving state transitions (FOR 0 INDEXING)"""
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if state_in == 0:
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return 1
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elif state_in == 1:
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return 0
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else:
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print(f"invalid state index provided, ({state_in})")
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def get_other_state_number(self, state_in):
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"""For when state changes, get other number for retrieving state transitions (FOR 1 INDEXING)"""
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return self.get_other_state_index(state_in - 1) + 1
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2020-12-11 21:33:20 +00:00
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def populate_forward(self):
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for t, observation in enumerate(self.observations): # iterate through observations (time)
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2020-12-23 20:12:08 +00:00
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for state_index, state in enumerate(self.states):
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state_number = state_index + 1 # for easier reading (arrays 0-indexed, numbers start at 1)
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2020-12-11 21:33:20 +00:00
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if t == 0: # calcualte initial
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2020-12-23 20:12:08 +00:00
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self.forward[state_index, t] = self.state_transitions[0, state_number] * gaussian(observation, state.mean, state.std_dev)
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2020-12-11 21:33:20 +00:00
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else:
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2020-12-23 20:12:08 +00:00
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# each state for each time has two paths leading to
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other_index = self.get_other_state_index(state_index)
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other_number = other_index + 1 # for 1 indexing
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# previous value prob of changing from previous state to current
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this_to_this = self.forward[state_index, t - 1] * self.state_transitions[state_number, state_number]
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other_to_this = self.forward[other_index, t - 1] * self.state_transitions[other_number, state_number]
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self.forward[state_index, t] = (this_to_this + other_to_this) * gaussian(observation, state.mean, state.std_dev)
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@property
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def p_observations_forward(self):
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sum = 0
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for state_index, final_likelihood in enumerate(self.forward[:, -1]):
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sum += final_likelihood * self.state_transitions[state_index + 1, -1] # get exit prob from state transitions
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return sum
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#TODO finish
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def populate_backward(self):
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# initialise from exit probabilities
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self.backward[:, -1] = self.state_transitions[1:len(self.states) + 1, -1]
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for t, observation in list(enumerate(self.observations[1:]))[::-1]: # iterate backwards through observations (time)
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print(t, observation)
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for state_index, state in enumerate(self.states):
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state_number = state_index + 1 # for easier reading (arrays 0-indexed, numbers start at 1)
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other_index = self.get_other_state_index(state_index)
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other_number = other_index + 1 # for 1 indexing
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# previous value prob of changing from previous state to current
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this_to_this = self.backward[state_index, t + 1] * self.state_transitions[state_number, state_number]
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other_to_this = self.backward[other_index, t + 1] * self.state_transitions[other_number, state_number]
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self.backward[state_index, t] = (this_to_this + other_to_this) * gaussian(observation, state.mean, state.std_dev)
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#TODO finish
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@property
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def p_observations_backward(self):
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sum = 0
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for state_index, initial_likelihood in enumerate(self.backward[:, 0]):
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sum += self.state_transitions[0, state_index + 1] * gaussian(self.observations[0], self.states[state_index].mean, self.states[state_index].std_dev) * initial_likelihood
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return sum
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