BayesianOnlineChange-pointDetection-python-codes-/BOCD_Algorithms.py at master · Ralami1859/BayesianOnlineChange-pointDetection-python-codes- · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
from __future__ import division
import numpy as np
from BOCD_modules import *
import matplotlib.pyplot as plt


"""
--------------------------------------------------------------------------------------------------------------------------------------

Bayesian Online Change-point Detection original version

 Inputs:
  -- environment: numpy array of piece-wise Bernoulli distributions

 Outputs:
  -- ChangePointEstimation: numpy array of change-point estimations

--------------------------------------------------------------------------------------------------------------------------------------
"""

def BOCD(environment):
    #--------------- Initialization ---------------------
    Horizon = environment.size
    gamma = 1/Horizon # Switching Rate
    alphas = np.array([1])
    betas = np.array([1])
    ForecasterDistribution = np.array([1])
    ChangePointEstimation = np.array([])
    #-----------------------------------------------------
    #Interation with the environment ...
    print('Launching BOCD ...')
    for t in range(Horizon):
        EstimatedBestExpert = np.argmax(ForecasterDistribution) #Change-point estimation
        ChangePointEstimation = np.append(ChangePointEstimation,EstimatedBestExpert+1)
        reward = int ((np.random.uniform() < environment[t]) == True) #Get the observation from the environment
        ForecasterDistribution = updateForecasterDistribution(ForecasterDistribution, alphas, betas, reward, gamma)
        (alphas, betas) = updateLaplacePrediction(alphas, betas, reward) #Update the laplace predictor
    return ChangePointEstimation


"""
--------------------------------------------------------------------------------------------------------------------------------------

Bayesian Online Change-point Detection with original prior and restart

 Inputs:
  -- environment: numpy array of piece-wise Bernoulli distributions

 Outputs:
  -- ChangePointEstimation: numpy array of change-point estimations

--------------------------------------------------------------------------------------------------------------------------------------
"""


def BOCD_restart(environment):
    #--------------- Initialization ---------------------
    Horizon = environment.size
    gamma = 1/Horizon # Switching Rate
    alphas = np.array([1])
    betas = np.array([1])
    ForecasterDistribution = np.array([1])
    ChangePointEstimation = np.array([])
    Restart = 1  # Position of last restart
    #-----------------------------------------------------
    #Interation with the environment ...
    print('Launching BOCD with restart ... ')
    for t in range(Horizon):
        EstimatedBestExpert = np.argmax(ForecasterDistribution)
        # Restart precedure
        if not(EstimatedBestExpert == 0):
            # Reinitialization
            alphas = np.array([1])
            betas = np.array([1])
            ForecasterDistribution = np.array([1])
            Restart = t+1
        ChangePointEstimation = np.append(ChangePointEstimation,Restart+1)#Change-point estimation
        reward = int ((np.random.uniform() < environment[t]) == True) #Get the observation from the environment
        ForecasterDistribution = updateForecasterDistribution(ForecasterDistribution, alphas, betas, reward, gamma)
        (alphas, betas) = updateLaplacePrediction(alphas, betas, reward) #Update the laplace predictor
    return ChangePointEstimation


"""
--------------------------------------------------------------------------------------------------------------------------------------

Bayesian Online Change-point Detection modified without restart and simple prior

 Inputs:
  -- environment: numpy array of piece-wise Bernoulli distributions

 Outputs:
  -- ChangePointEstimation: numpy array of change-point estimations

--------------------------------------------------------------------------------------------------------------------------------------
"""

def BOCDm(environment):
    #--------------- Initialization ---------------------
    Horizon = environment.size
    gamma = 1/Horizon # Switching Rate
    alphas = np.array([1])
    betas = np.array([1])
    ForecasterDistribution = np.array([1])
    PseudoDist = np.array([1])
    ChangePointEstimation = np.array([])
    like1 = 1
    #-----------------------------------------------------
    #Interation with the environment ...
    print('Launching BOCD modified ... ')
    for t in range(Horizon):
    	EstimatedBestExpert = np.argmax(ForecasterDistribution)
    	ChangePointEstimation = np.append(ChangePointEstimation,EstimatedBestExpert+1) #Change-point estimation
    	reward = int ((np.random.uniform() < environment[t]) == True) #Get the observation from the environment
    	(ForecasterDistribution, PseudoDist, like1) = updateForecasterDistribution_m(ForecasterDistribution,PseudoDist, alphas, betas, reward, gamma, like1)
    	(alphas, betas) = updateLaplacePrediction(alphas, betas, reward) #Update the laplace predictor
    return ChangePointEstimation


"""
--------------------------------------------------------------------------------------------------------------------------------------

Bayesian Online Change-point Detection modified without restart and simple prior

 Inputs:
  -- environment: numpy array of piece-wise Bernoulli distributions

 Outputs:
  -- ChangePointEstimation: numpy array of change-point estimations

--------------------------------------------------------------------------------------------------------------------------------------
"""


def BOCDm_restart(environment):
    #--------------- Initialization ---------------------
    Horizon = environment.size
    gamma = 1/Horizon # Switching Rate
    alphas = np.array([1])
    betas = np.array([1])
    ForecasterDistribution = np.array([1])
    PseudoDist = np.array([1])
    ChangePointEstimation = np.array([])
    like1 = 1
    Restart = 1  # Position of last restart
    #------------------------------------------------------
    #Interation with the environment ...
    print('Launching BOCD modified with restart ...')
    for t in range(Horizon):
        EstimatedBestExpert = np.argmax(ForecasterDistribution)
        # Restart precedure
        if not(EstimatedBestExpert == 0):
            # Reinitialization
            alphas = np.array([1])
            betas = np.array([1])
            ForecasterDistribution = np.array([1])
            Restart = t+1
            like1 = 1
        ChangePointEstimation = np.append(ChangePointEstimation,Restart+1) #Change-point estimation
        reward = int ((np.random.uniform() < environment[t]) == True) #Get the observation from the environment
        (ForecasterDistribution, PseudoDist, like1) = updateForecasterDistribution_m(ForecasterDistribution,PseudoDist, alphas, betas, reward, gamma, like1)
        (alphas, betas) = updateLaplacePrediction(alphas, betas, reward) #Update the laplace predictor
    return ChangePointEstimation