-
Notifications
You must be signed in to change notification settings - Fork 35
/
test_distributions.py
451 lines (335 loc) · 13.6 KB
/
test_distributions.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
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
from __future__ import (division, print_function)
from yahmm.yahmm import *
from nose.tools import with_setup
import random
import numpy as np
random.seed(0)
np.random.seed(0)
def setup():
'''
No setup or teardown needs to be done in this case.
'''
pass
def teardown():
'''
No setup or teardown needs to be done in this case.
'''
pass
@with_setup( setup, teardown )
def test_normal():
'''
Test that the normal distribution implementation is correct.
'''
d = NormalDistribution( 5, 2 )
e = NormalDistribution( 5., 2. )
assert d.log_probability( 5 ) == -1.6120857137642188
assert d.log_probability( 5 ) == e.log_probability( 5 )
assert d.log_probability( 5 ) == d.log_probability( 5. )
assert d.log_probability( 0 ) == -4.737085713764219
assert d.log_probability( 0 ) == e.log_probability( 0. )
d.from_sample( [ 5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4 ] )
assert d.parameters == [ 4.916666666666667, 0.75920279826202286 ]
assert d.log_probability( 4 ) != e.log_probability( 4 )
assert d.log_probability( 4 ) == -1.3723678499651766
assert d.log_probability( 18 ) == -149.13140399454429
assert d.log_probability( 1e8 ) == -8674697942168743.0
d = NormalDistribution( 5, 1e-10 )
assert d.log_probability( 1e100 ) == -4.9999999999999994e+219
d.from_sample( [ 0, 2, 3, 2, 100 ], weights=[ 0, 5, 2, 3, 200 ] )
assert d.parameters == [ 95.342857142857142, 20.827558927640887 ]
assert d.log_probability( 50 ) == -6.325011936564346
d = NormalDistribution( 5, 2 )
d.from_sample( [ 0, 5, 3, 5, 7, 3, 4, 5, 2 ], inertia=0.5 )
assert round( d.parameters[0], 4 ) == 4.3889
assert round( d.parameters[1], 4 ) == 1.9655
d.summarize( [ 0, 2 ], weights=[0, 5] )
d.summarize( [ 3, 2 ], weights=[2, 3] )
d.summarize( [ 100 ], weights=[200] )
d.from_summaries()
assert round( d.parameters[0], 4 ) == 95.3429
assert round( d.parameters[1], 4 ) == 20.8276
d.freeze()
d.from_sample( [ 0, 1, 1, 2, 3, 2, 1, 2, 2 ] )
assert round( d.parameters[0], 4 ) == 95.3429
assert round( d.parameters[1], 4 ) == 20.8276
d.thaw()
d.from_sample( [ 5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4 ] )
assert d.parameters == [ 4.916666666666667, 0.75920279826202286 ]
@with_setup( setup, teardown )
def test_uniform():
'''
Test that the uniform distribution implementation is correct.
'''
d = UniformDistribution( 0, 10 )
assert d.log_probability( 2.34 ) == -2.3025850929940455
assert d.log_probability( 2 ) == d.log_probability( 8 )
assert d.log_probability( 10 ) == d.log_probability( 3.4 )
assert d.log_probability( 1.7 ) == d.log_probability( 9.7 )
assert d.log_probability( 10.0001 ) == float( "-inf" )
assert d.log_probability( -0.0001 ) == float( "-inf" )
for i in xrange( 10 ):
data = np.random.randn( 100 ) * 100
d.from_sample( data )
assert d.parameters == [ data.min(), data.max() ]
for i in xrange( 100 ):
sample = d.sample()
assert data.min() <= sample <= data.max()
d = UniformDistribution( 0, 10 )
d.from_sample( [ -5, 20 ], inertia=0.5 )
assert d.parameters[0] == -2.5
assert d.parameters[1] == 15
d.from_sample( [ -100, 100 ], inertia=1.0 )
assert d.parameters[0] == -2.5
assert d.parameters[1] == 15
d.summarize( [ 0, 50, 2, 24, 28 ] )
d.summarize( [ -20, 7, 8, 4 ] )
d.from_summaries( inertia=0.75 )
assert d.parameters[0] == -6.875
assert d.parameters[1] == 23.75
d.summarize( [ 0, 100 ] )
d.summarize( [ 100, 200 ] )
d.from_summaries()
assert d.parameters[0] == 0
assert d.parameters[1] == 200
d.freeze()
d.from_sample( [ 0, 1, 6, 7, 8, 3, 4, 5, 2 ] )
assert d.parameters == [ 0, 200 ]
d.thaw()
d.from_sample( [ 0, 1, 6, 7, 8, 3, 4, 5, 2 ] )
assert d.parameters == [ 0, 8 ]
@with_setup( setup, teardown )
def test_discrete():
'''
Test that the discrete distribution implementation is correct.
'''
d = DiscreteDistribution( { 'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25 } )
assert d.log_probability( 'C' ) == -1.3862943611198906
assert d.log_probability( 'A' ) == d.log_probability( 'C' )
assert d.log_probability( 'G' ) == d.log_probability( 'T' )
assert d.log_probability( 'a' ) == float( '-inf' )
seq = "ACGTACGTTGCATGCACGCGCTCTCGCGC"
d.from_sample( list( seq ) )
assert d.log_probability( 'C' ) == -0.9694005571881033
assert d.log_probability( 'A' ) == -1.9810014688665833
assert d.log_probability( 'T' ) == -1.575536360758419
seq = "ACGTGTG"
d.from_sample( list( seq ), weights=[0.,1.,2.,3.,4.,5.,6.] )
assert d.log_probability( 'A' ) == float( '-inf' )
assert d.log_probability( 'C' ) == -3.044522437723423
assert d.log_probability( 'G' ) == -0.5596157879354228
d.summarize( list("ACG"), weights=[0., 1., 2.] )
d.summarize( list("TGT"), weights=[3., 4., 5.] )
d.summarize( list("G"), weights=[6.] )
d.from_summaries()
assert d.log_probability( 'A' ) == float( '-inf' )
assert round( d.log_probability( 'C' ), 4 ) == -3.0445
assert round( d.log_probability( 'G' ), 4 ) == -0.5596
d = DiscreteDistribution( { 'A': 0.0, 'B': 1.0 } )
d.summarize( list( "ABABABAB" ) )
d.summarize( list( "ABAB" ) )
d.summarize( list( "BABABABABABABABABA" ) )
d.from_summaries( inertia=0.75 )
assert d.parameters[0] == { 'A': 0.125, 'B': 0.875 }
d = DiscreteDistribution( { 'A': 0.0, 'B': 1.0 } )
d.summarize( list( "ABABABAB" ) )
d.summarize( list( "ABAB" ) )
d.summarize( list( "BABABABABABABABABA" ) )
d.from_summaries( inertia=0.5 )
assert d.parameters[0] == { 'A': 0.25, 'B': 0.75 }
d.freeze()
d.from_sample( list('ABAABBAAAAAAAAAAAAAAAAAA') )
assert d.parameters[0] == { 'A': 0.25, 'B': 0.75 }
@with_setup( setup, teardown )
def test_lognormal():
'''
Test that the lognormal distribution implementation is correct.
'''
d = LogNormalDistribution( 5, 2 )
assert round( d.log_probability( 5 ), 4 ) == -4.6585
d.from_sample( [ 5.1, 5.03, 4.98, 5.05, 4.91, 5.2, 5.1, 5., 4.8, 5.21 ])
assert round( d.parameters[0], 4 ) == 1.6167
assert round( d.parameters[1], 4 ) == 0.0237
d.summarize( [5.1, 5.03, 4.98, 5.05] )
d.summarize( [4.91, 5.2, 5.1] )
d.summarize( [5., 4.8, 5.21] )
d.from_summaries()
assert round( d.parameters[0], 4 ) == 1.6167
assert round( d.parameters[1], 4 ) == 0.0237
@with_setup( setup, teardown )
def test_gamma():
'''
Test that the gamma distribution implementation is correct.
'''
d = GammaDistribution( 5, 2 )
assert round( d.log_probability( 4 ), 4 ) == -2.1671
d.from_sample( [ 2.3, 4.3, 2.7, 2.3, 3.1, 3.2, 3.4, 3.1, 2.9, 2.8 ] )
assert round( d.parameters[0], 4 ) == 31.8806
assert round( d.parameters[1], 4 ) == 10.5916
d = GammaDistribution( 2, 7 )
assert round( d.log_probability( 4 ), 4 ) != -2.1671
d.summarize( [2.3, 4.3, 2.7] )
d.summarize( [2.3, 3.1, 3.2] )
d.summarize( [3.4, 3.1, 2.9, 2.8] )
d.from_summaries()
assert round( d.parameters[0], 4 ) == 31.8806
assert round( d.parameters[1], 4 ) == 10.5916
@with_setup( setup, teardown )
def test_exponential():
'''
Test that the beta distribution implementation is correct.
'''
d = ExponentialDistribution( 3 )
assert round( d.log_probability( 8 ), 4 ) == -22.9014
d.from_sample( [ 2.7, 2.9, 3.8, 1.9, 2.7, 1.6, 1.3, 1.0, 1.9 ] )
assert round( d.parameters[0], 4 ) == 0.4545
d = ExponentialDistribution( 4 )
assert round( d.log_probability( 8 ), 4 ) != -22.9014
d.summarize( [2.7, 2.9, 3.8] )
d.summarize( [1.9, 2.7, 1.6] )
d.summarize( [1.3, 1.0, 1.9] )
d.from_summaries()
assert round( d.parameters[0], 4 ) == 0.4545
@with_setup( setup, teardown )
def test_inverse_gamma():
'''
Test that the inverse gamma distribution implementation is correct.
'''
d = InverseGammaDistribution( 4, 5 )
assert round( d.log_probability( 1.06 ), 4 ) == -0.2458
d.from_sample( [ 0.1, 0.2, 0.015, 0.1, 0.09, 0.08, 0.07, 0.075, 0.044 ] )
assert round( d.parameters[0], 4 ) == 1.9565
assert round( d.parameters[1], 4 ) == 0.1063
d = InverseGammaDistribution( 5, 4 )
assert round( d.log_probability( 1.06 ), 4 ) != -0.2458
d.summarize( [0.1, 0.2, 0.015] )
d.summarize( [0.1, 0.09, 0.08] )
d.summarize( [0.07, 0.075] )
d.summarize( [0.044] )
d.from_summaries()
assert round( d.parameters[0], 4 ) == 1.9565
assert round( d.parameters[1], 4 ) == 0.1063
@with_setup( setup, teardown )
def test_gaussian_kernel():
'''
Test that the Gaussian Kernel Density implementation is correct.
'''
d = GaussianKernelDensity( [ 0, 4, 3, 5, 7, 4, 2 ] )
assert round( d.log_probability( 3.3 ), 4 ) == -1.7042
d.from_sample( [ 1, 6, 8, 3, 2, 4, 7, 2] )
assert round( d.log_probability( 1.2 ), 4 ) == -2.0237
d.from_sample( [ 1, 0, 108 ], weights=[2., 3., 278.] )
assert round( d.log_probability( 110 ), 4 ) == -2.9368
assert round( d.log_probability( 0 ), 4 ) == -5.1262
d.summarize( [1, 6, 8, 3] )
d.summarize( [2, 4, 7] )
d.summarize( [2] )
d.from_summaries()
assert round( d.log_probability( 1.2 ), 4 ) == -2.0237
d.summarize( [ 1, 0, 108 ], weights=[2., 3., 278.] )
d.from_summaries()
assert round( d.log_probability( 110 ), 4 ) == -2.9368
assert round( d.log_probability( 0 ), 4 ) == -5.1262
d.freeze()
d.from_sample( [ 1, 3, 5, 4, 6, 7, 3, 4, 2 ] )
assert round( d.log_probability( 110 ), 4 ) == -2.9368
assert round( d.log_probability( 0 ), 4 ) == -5.1262
@with_setup( setup, teardown )
def test_triangular_kernel():
'''
Test that the Triangular Kernel Density implementation is correct.
'''
d = TriangleKernelDensity( [ 1, 6, 3, 4, 5, 2 ] )
assert round( d.log_probability( 6.5 ), 4 ) == -2.4849
d = TriangleKernelDensity( [1, 8, 100] )
assert round( d.log_probability( 6.5 ), 4 ) != -2.4849
d.summarize( [1, 6] )
d.summarize( [3, 4, 5] )
d.summarize( [2] )
d.from_summaries()
assert round( d.log_probability( 6.5 ), 4 ) == -2.4849
d.freeze()
d.from_sample( [ 1, 4, 6, 7, 3, 5, 7, 8, 3, 3, 4 ] )
assert round( d.log_probability( 6.5 ), 4 ) == -2.4849
@with_setup( setup, teardown )
def test_uniform_kernel():
'''
Test that the Uniform Kernel Density implementation is correct.
'''
d = UniformKernelDensity( [ 1, 3, 5, 6, 2, 2, 3, 2, 2 ] )
assert round( d.log_probability( 2.2 ), 4 ) == -0.4055
assert round( d.log_probability( 6.2 ), 4 ) == -2.1972
assert d.log_probability( 10 ) == float( '-inf' )
d = UniformKernelDensity( [ 1, 100, 200 ] )
assert round( d.log_probability( 2.2 ), 4 ) != -0.4055
assert round( d.log_probability( 6.2 ), 4 ) != -2.1972
d.summarize( [1, 3, 5, 6, 2] )
d.summarize( [2, 3, 2, 2] )
d.from_summaries()
assert round( d.log_probability( 2.2 ), 4 ) == -0.4055
assert round( d.log_probability( 6.2 ), 4 ) == -2.1972
@with_setup( setup, teardown )
def test_mixture():
'''
Test that the Mixture Distribution implementation is correct.
'''
d = MixtureDistribution( [ NormalDistribution( 5, 1 ),
NormalDistribution( 4, 4 ) ] )
assert round( d.log_probability( 6 ), 4 ) == -1.8018
assert round( d.log_probability( 5 ), 4 ) == -1.3951
assert round( d.log_probability( 4.5 ), 4 ) == -1.4894
d = MixtureDistribution( [ NormalDistribution( 5, 1 ),
NormalDistribution( 4, 4 ) ],
weights=[1., 7.] )
assert round( d.log_probability( 6 ), 4 ) == -2.2325
assert round( d.log_probability( 5 ), 4 ) == -2.0066
assert round( d.log_probability( 4.5 ), 4 ) == -2.0356
d = MixtureDistribution( [ NormalDistribution( 5, 1 ),
NormalDistribution( 10, 2 ),
ExponentialDistribution( 1 ),
GammaDistribution( 5, 2 ) ] )
@with_setup( setup, teardown )
def test_multivariate():
'''
Test that the Multivariate Distribution implementation is correct.
'''
d = MultivariateDistribution( [ NormalDistribution( 5, 2 ), ExponentialDistribution( 2 ) ] )
assert round( d.log_probability( (4,1) ), 4 ) == -3.0439
assert round( d.log_probability( ( 100, 0.001 ) ), 4 ) == -1129.0459
d = MultivariateDistribution( [ NormalDistribution( 5, 2 ), ExponentialDistribution( 2 ) ],
weights=[18., 1.] )
assert round( d.log_probability( (4,1) ), 4 ) == -32.5744
assert round( d.log_probability( (100, 0.001) ), 4 ) == -20334.5764
d.from_sample( [ (5, 1), (5.2, 1.7), (4.7, 1.9), (4.9, 2.4), (4.5, 1.2) ] )
assert round( d.parameters[0][0].parameters[0], 4 ) == 4.86
assert round( d.parameters[0][0].parameters[1], 4 ) == 0.2417
assert round( d.parameters[0][1].parameters[0], 4 ) == 0.6098
d = MultivariateDistribution( [ NormalDistribution( 5, 2 ),
UniformDistribution( 0, 10 ) ] )
d.from_sample( [ ( 0, 0 ), ( 5, 0 ), ( 3, 0 ), ( 5, -5 ), ( 7, 0 ),
( 3, 0 ), ( 4, 0 ), ( 5, 0 ), ( 2, 20) ], inertia=0.5 )
assert round( d.parameters[0][0].parameters[0], 4 ) == 4.3889
assert round( d.parameters[0][0].parameters[1], 4 ) == 1.9655
assert d.parameters[0][1].parameters[0] == -2.5
assert d.parameters[0][1].parameters[1] == 15
d.from_sample( [ ( 0, 0 ), ( 5, 0 ), ( 3, 0 ), ( 5, -5 ), ( 7, 0 ),
( 3, 0 ), ( 4, 0 ), ( 5, 0 ), ( 2, 20 ) ], inertia=0.75 )
assert round( d.parameters[0][0].parameters[0], 4 ) != 4.3889
assert round( d.parameters[0][0].parameters[1], 4 ) != 1.9655
assert d.parameters[0][1].parameters[0] != -2.5
assert d.parameters[0][1].parameters[1] != 15
d = MultivariateDistribution([ NormalDistribution( 5, 2 ),
UniformDistribution( 0, 10 ) ])
d.summarize([ ( 0, 0 ), ( 5, 0 ), ( 3, 0 ) ])
d.summarize([ ( 5, -5 ), ( 7, 0 ) ])
d.summarize([ ( 3, 0 ), ( 4, 0 ), ( 5, 0 ), ( 2, 20 ) ])
d.from_summaries( inertia=0.5 )
assert round( d.parameters[0][0].parameters[0], 4 ) == 4.3889
assert round( d.parameters[0][0].parameters[1], 4 ) == 1.9655
assert d.parameters[0][1].parameters[0] == -2.5
assert d.parameters[0][1].parameters[1] == 15
d.freeze()
d.from_sample( [ ( 1, 7 ), ( 7, 2 ), ( 2, 4), ( 2, 4 ), ( 1, 4 ) ] )
assert round( d.parameters[0][0].parameters[0], 4 ) == 4.3889
assert round( d.parameters[0][0].parameters[1], 4 ) == 1.9655
assert d.parameters[0][1].parameters[0] == -2.5
assert d.parameters[0][1].parameters[1] == 15