-
Notifications
You must be signed in to change notification settings - Fork 33
/
discretemodels.py
398 lines (301 loc) · 12.7 KB
/
discretemodels.py
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
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
from abcpy.probabilisticmodels import ProbabilisticModel, Discrete, Hyperparameter, InputConnector
import numpy as np
from scipy.special import comb
from scipy.stats import poisson, bernoulli
class Bernoulli(Discrete, ProbabilisticModel):
def __init__(self, parameters, name='Bernoulli'):
"""This class implements a probabilistic model following a bernoulli distribution.
Parameters
----------
parameters: list
A list containing one entry, the probability of the distribution.
name: string
The name that should be given to the probabilistic model in the journal file.
"""
if not isinstance(parameters, list):
raise TypeError('Input for Bernoulli has to be of type list.')
if len(parameters)!=1:
raise ValueError('Input for Bernoulli has to be of length 1.')
self._dimension = len(parameters)
input_parameters = InputConnector.from_list(parameters)
super(Bernoulli, self).__init__(input_parameters, name)
self.visited = False
def _check_input(self, input_values):
"""
Checks parameter values sampled from the parents.
"""
if len(input_values) > 1:
return False
# test whether probability is in the interval [0,1]
if input_values[0]<0 or input_values[0]>1:
return False
return True
def _check_output(self, parameters):
"""
Checks parameter values given as fixed values. Returns False iff it is not an integer
"""
if not isinstance(parameters[0], (int, np.int32, np.int64)):
return False
return True
def forward_simulate(self, input_values, k, rng=np.random.RandomState()):
"""
Samples from the bernoulli distribution associtated with the probabilistic model.
Parameters
----------
input_values: list
List of input parameters, in the same order as specified in the InputConnector passed to the init function
k: integer
The number of samples to be drawn.
rng: random number generator
The random number generator to be used.
Returns
-------
list: [np.ndarray]
A list containing the sampled values as np-array.
"""
result = np.array(rng.binomial(1, input_values[0], k))
return [np.array([x]) for x in result]
def get_output_dimension(self):
return self._dimension
def pmf(self, input_values, x):
"""Evaluates the probability mass function at point x.
Parameters
----------
input_values: list
List of input parameters, in the same order as specified in the InputConnector passed to the init function
x: float
The point at which the pmf should be evaluated.
Returns
-------
float:
The pmf evaluated at point x.
"""
probability = input_values[0]
pmf = bernoulli(probability).pmf(x)
self.calculated_pmf = pmf
return pmf
class Binomial(Discrete, ProbabilisticModel):
def __init__(self, parameters, name='Binomial'):
"""
This class implements a probabilistic model following a binomial distribution.
Parameters
----------
parameters: list
Contains the probabilistic models and hyperparameters from which the model derives. Note that the first
entry of the list, n, an integer and has to be larger than or equal to 0, while the second entry, p, has to be in the
interval [0,1].
name: string
The name that should be given to the probabilistic model in the journal file.
"""
if not isinstance(parameters, list):
raise TypeError('Input for Binomial has to be of type list.')
if len(parameters)!=2:
raise ValueError('Input for Binomial has to be of length 2.')
self._dimension = 1
input_parameters = InputConnector.from_list(parameters)
super(Binomial, self).__init__(input_parameters, name)
self.visited = False
def _check_input(self, input_values):
"""Raises an Error iff:
- The number of trials is less than 0
- The number of trials is not an integer
- The success probability is not in [0,1]
"""
if len(input_values) != 2:
raise TypeError('Number of input parameters is exactly 2.')
# test whether number of trial is an integer
if not isinstance(input_values[0], (int, np.int32, np.int64)):
raise TypeError('Input parameter for number of trials has to be an integer.')
# test whether probability is in the interval [0,1]
if input_values[1] < 0 or input_values[1] > 1:
return False
# test whether number of trial less than 0
if input_values[0] < 0:
return False
return True
def _check_output(self, parameters):
if not isinstance(parameters[0], (int, np.int32, np.int64)):
return False
return True
def forward_simulate(self, input_values, k, rng=np.random.RandomState()):
"""
Samples from a binomial distribution using the current values for each probabilistic model from which the model derives.
Parameters
----------
input_values: list
List of input parameters, in the same order as specified in the InputConnector passed to the init function
k: integer
The number of samples that should be drawn.
rng: Random number generator
Defines the random number generator to be used. The default value uses a random seed to initialize the generator.
Returns
-------
list: [np.ndarray]
A list containing the sampled values as np-array.
"""
result = rng.binomial(input_values[0], input_values[1], k)
return [np.array([x]) for x in result]
def get_output_dimension(self):
return self._dimension
def pmf(self, input_values, x):
"""
Calculates the probability mass function at point x.
Parameters
----------
input_values: list
List of input parameters, in the same order as specified in the InputConnector passed to the init function
x: list
The point at which the pmf should be evaluated.
Returns
-------
Float
The evaluated pmf at point x.
"""
# If the provided point is not an integer, it is converted to one
x = int(x)
n = input_values[0]
p = input_values[1]
if(x>n):
pmf = 0
else:
pmf = comb(n,x)*pow(p,x)*pow((1-p),(n-x))
self.calculated_pmf = pmf
return pmf
class Poisson(Discrete, ProbabilisticModel):
def __init__(self, parameters, name='Poisson'):
"""This class implements a probabilistic model following a poisson distribution.
Parameters
----------
parameters: list
A list containing one entry, the mean of the distribution.
name: string
The name that should be given to the probabilistic model in the journal file.
"""
if not isinstance(parameters, list):
raise TypeError('Input for Poisson has to be of type list.')
if len(parameters)!=1:
raise ValueError('Input for Poisson has to be of length 1.')
self._dimension = 1
input_parameters = InputConnector.from_list(parameters)
super(Poisson, self).__init__(input_parameters, name)
self.visited = False
def _check_input(self, input_values):
"""Raises an error iff more than one parameter are given or the parameter given is smaller than 0."""
if len(input_values) > 1:
return False
# test whether the parameter is smaller than 0
if input_values[0]<0:
return False
return True
def _check_output(self, parameters):
if not isinstance(parameters[0], (int, np.int32, np.int64)):
return False
return True
def forward_simulate(self, input_values, k, rng=np.random.RandomState()):
"""
Samples k values from the defined possion distribution.
Parameters
----------
input_values: list
List of input parameters, in the same order as specified in the InputConnector passed to the init function
k: integer
The number of samples.
rng: random number generator
The random number generator to be used.
Returns
-------
list: [np.ndarray]
A list containing the sampled values as np-array.
"""
result = rng.poisson(int(input_values[0]), k)
return [np.array([x]) for x in result]
def get_output_dimension(self):
return self._dimension
def pmf(self, input_values, x):
"""Calculates the probability mass function of the distribution at point x.
Parameters
----------
input_values: list
List of input parameters, in the same order as specified in the InputConnector passed to the init function
x: integer
The point at which the pmf should be evaluated.
Returns
-------
Float
The evaluated pmf at point x.
"""
pmf = poisson(int(input_values[0])).pmf(x)
self.calculated_pmf = pmf
return pmf
class DiscreteUniform(Discrete, ProbabilisticModel):
def __init__(self, parameters, name='DiscreteUniform'):
"""This class implements a probabilistic model following a Discrete Uniform distribution.
Parameters
----------
parameters: list
A list containing two entries, the upper and lower bound of the range.
name: string
The name that should be given to the probabilistic model in the journal file.
"""
if not isinstance(parameters, list):
raise TypeError('Input for Discrete Uniform has to be of type list.')
if len(parameters) != 2:
raise ValueError('Input for Discrete Uniform has to be of length 2.')
self._dimension = 1
input_parameters = InputConnector.from_list(parameters)
super(DiscreteUniform, self).__init__(input_parameters, name)
self.visited = False
def _check_input(self, input_values):
# Check whether input has correct type or format
if len(input_values) != 2:
raise ValueError('Number of parameters of FloorField model must be 2.')
# Check whether input is from correct domain
lowerbound = input_values[0] # Lower bound
upperbound = input_values[1] # Upper bound
if not isinstance(lowerbound, (int, np.int64, np.int32, np.int16)) or not isinstance(upperbound, (int, np.int64, np.int32, np.int16)) or lowerbound >= upperbound:
return False
return True
def _check_output(self, parameters):
"""
Checks parameter values given as fixed values. Returns False iff it is not an integer
"""
if not isinstance(parameters[0], (int, np.int32, np.int64)):
return False
return True
def forward_simulate(self, input_values, k, rng=np.random.RandomState()):
"""
Samples from the Discrete Uniform distribution associated with the probabilistic model.
Parameters
----------
input_values: list
List of input parameters, in the same order as specified in the InputConnector passed to the init function
k: integer
The number of samples to be drawn.
rng: random number generator
The random number generator to be used.
Returns
-------
list: [np.ndarray]
A list containing the sampled values as np-array.
"""
result = np.array(rng.randint(input_values[0], input_values[1], size=k, dtype=np.int64))
return [np.array([x]) for x in result]
def get_output_dimension(self):
return self._dimension
def pmf(self, input_values, x):
"""Evaluates the probability mass function at point x.
Parameters
----------
input_values: list
List of input parameters, in the same order as specified in the InputConnector passed to the init function
x: float
The point at which the pmf should be evaluated.
Returns
-------
float:
The pmf evaluated at point x.
"""
upperbound, lowerbound = input_values[0], input_values[1]
pmf = 1. / (upperbound - lowerbound + 1)
self.calculated_pmf = pmf
return pmf