-
Notifications
You must be signed in to change notification settings - Fork 8
/
synthp.py
451 lines (379 loc) · 21.8 KB
/
synthp.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
'''
Parameters for synthetic examples using pykalman
(For linear models specify all, non-linear, just the ones that are relevant)
'''
import numpy as np
import os
from utils.misc import loadHDF5,saveHDF5
def nlinear_trans(z,fxn_params = {}, ns=None):
return 2*np.sin(z)+z
def linear_trans(z,fxn_params = {},ns=None):
return z+0.05
def linear_obs(z,fxn_params = {},ns=None):
return 0.5*z
params_synthetic = {}
params_synthetic['synthetic9'] = {}
params_synthetic['synthetic9']['trans_fxn'] = linear_trans
params_synthetic['synthetic9']['obs_fxn'] = linear_obs
params_synthetic['synthetic9']['dim_obs'] = 1
params_synthetic['synthetic9']['dim_stoc'] = 1
params_synthetic['synthetic9']['params'] = {}
params_synthetic['synthetic9']['trans_cov'] = 10.
params_synthetic['synthetic9']['trans_cov_full'] = 10.
params_synthetic['synthetic9']['trans_drift'] = 0.05
params_synthetic['synthetic9']['trans_mult'] = 1.
params_synthetic['synthetic9']['obs_cov'] = 20.
params_synthetic['synthetic9']['obs_cov_full'] = 20.
params_synthetic['synthetic9']['obs_drift'] = 0.
params_synthetic['synthetic9']['obs_mult'] = 0.5
params_synthetic['synthetic9']['init_mu'] = 0.
params_synthetic['synthetic9']['init_cov'] = 1.
params_synthetic['synthetic9']['init_cov_full'] = 1.
params_synthetic['synthetic9']['baseline'] = 'KF'
params_synthetic['synthetic9']['docstr'] = '$z_t\sim\mathcal{N}(z_{t-1}+0.05,10)$\n$x_t\sim\mathcal{N}(0.5z_t,20)$'
params_synthetic['synthetic10'] = {}
params_synthetic['synthetic10']['trans_fxn'] = nlinear_trans
params_synthetic['synthetic10']['obs_fxn'] = linear_obs
params_synthetic['synthetic10']['dim_obs'] = 1
params_synthetic['synthetic10']['dim_stoc'] = 1
params_synthetic['synthetic10']['params'] = {}
params_synthetic['synthetic10']['trans_cov'] = 5.
params_synthetic['synthetic10']['trans_cov_full'] = 5.
params_synthetic['synthetic10']['trans_drift'] = 0.
params_synthetic['synthetic10']['trans_mult'] = 1.
params_synthetic['synthetic10']['obs_cov'] = 5.
params_synthetic['synthetic10']['obs_cov_full']= 5.
params_synthetic['synthetic10']['obs_drift'] = 0.
params_synthetic['synthetic10']['obs_mult'] = 0.5
params_synthetic['synthetic10']['init_mu'] = 0.
params_synthetic['synthetic10']['init_cov'] = 0.01
params_synthetic['synthetic10']['init_cov_full']= 0.01
params_synthetic['synthetic10']['baseline'] = 'UKF'
params_synthetic['synthetic10']['docstr'] = '$z_t\sim\mathcal{N}(2\sin(z_{t-1})+z_{t-1},5)$\n$x_t\sim\mathcal{N}(0.5z_t,5)$'
def nlinear_trans_learn(z, fxn_params = {}, ns=None):
assert z.ndim == 3,'expecting 3d'
assert z.shape[2]== 2,'expecting 2 dim'
z_1 = z[:,:,[0]]
z_2 = z[:,:,[1]]
f_1 = 0.2*z_1+np.tanh(fxn_params['alpha']*z_2)
f_2 = 0.2*z_2+np.sin(fxn_params['beta']*z_1)
return np.concatenate([f_1,f_2],axis=2)
def obs_learn(z,fxn_params = {}, ns = None):
assert z.ndim == 3,'expecting 3d'
return 0.5*z
params_synthetic['synthetic11'] = {}
params_synthetic['synthetic11']['params'] = {}
params_synthetic['synthetic11']['params']['alpha'] = 0.5
params_synthetic['synthetic11']['params']['beta'] = -0.1
params_synthetic['synthetic11']['trans_fxn'] = nlinear_trans_learn
params_synthetic['synthetic11']['obs_fxn'] = obs_learn
params_synthetic['synthetic11']['dim_obs'] = 2
params_synthetic['synthetic11']['dim_stoc'] = 2
params_synthetic['synthetic11']['trans_cov'] = 1.
params_synthetic['synthetic11']['trans_cov_full'] = None
params_synthetic['synthetic11']['trans_drift'] = 0.
params_synthetic['synthetic11']['trans_mult'] = 1.
params_synthetic['synthetic11']['obs_cov'] = 0.1
params_synthetic['synthetic11']['obs_cov_full'] = None
params_synthetic['synthetic11']['obs_drift'] = 0.
params_synthetic['synthetic11']['obs_mult'] = 0.5
params_synthetic['synthetic11']['init_mu'] = 0.
params_synthetic['synthetic11']['init_cov'] = 0.01
params_synthetic['synthetic11']['init_cov_full'] = None
params_synthetic['synthetic11']['baseline'] = 'None'
params_synthetic['synthetic11']['docstr'] = '$z_t\sim\mathcal{N}([0.2z_{t-1}^0+\\text{tanh}(\\alpha z_{t-1}^1); 0.2z_{t-1}^1+\\sin(\\beta z_{t-1}^0)] ,'+str(params_synthetic['synthetic11']['trans_cov'])+')$\n$x_t\sim\mathcal{N}(0.5z_t,'+str(params_synthetic['synthetic11']['obs_cov'])+')$'
"""
Synthetic [12,13,14] to check scalability of the inference algorithm
"""
SAVEDIR = os.path.dirname(os.path.realpath(__file__))+'/synthetic'
if not os.path.exists(SAVEDIR+'/linear-matrices.h5'):
os.system('mkdir -p '+SAVEDIR)
print 'Creating linear matrices'
linmat = {}
np.random.seed(0)
linmat['Wtrans_10'] = np.random.randn(10,10)*0.05
linmat['Wtrans_100'] = np.random.randn(100,100)*0.05
linmat['Wtrans_250'] = np.random.randn(250,250)*0.05
linmat['btrans_10'] = np.random.randn(10,)*0.05
linmat['btrans_100'] = np.random.randn(100,)*0.05
linmat['btrans_250'] = np.random.randn(250,)*0.05
linmat['Wobs_10'] = np.random.randn(10,20)*0.05
linmat['Wobs_100'] = np.random.randn(100,200)*0.05
linmat['Wobs_250'] = np.random.randn(250,500)*0.05
saveHDF5(SAVEDIR+'/linear-matrices.h5',linmat)
saved_matrices = linmat
else:
print 'Loading linear matrices'
saved_matrices = loadHDF5(SAVEDIR+'/linear-matrices.h5')
if not os.path.exists(SAVEDIR+'/linear-matrices-2.h5'):
os.system('mkdir -p '+SAVEDIR)
print 'Creating linear matrices'
linmat = {}
np.random.seed(0)
linmat['Wtrans_10'] = np.random.randn(10,10)*0.05
linmat['Wtrans_100'] = np.random.randn(100,100)*0.05
linmat['Wtrans_250'] = np.random.randn(250,250)*0.05
linmat['btrans_10'] = np.random.randn(10,)*0.05
linmat['btrans_100'] = np.random.randn(100,)*0.05
linmat['btrans_250'] = np.random.randn(250,)*0.05
linmat['Wobs_10'] = np.random.randn(10,10)*0.05
linmat['Wobs_100'] = np.random.randn(100,100)*0.05
linmat['Wobs_250'] = np.random.randn(250,250)*0.05
#Diagonal
def setupMatrices(dim, mult=0.05):
di = np.diag_indices(dim)
bt = np.random.randn(dim,)*mult
Wt = np.zeros((dim,dim))
Wt[di] = np.random.randn(dim,)*mult
We = np.zeros((dim,dim))
We[di] = np.random.randn(dim,)*mult
return Wt,bt,We
linmat['Wtrans_10_diag'], linmat['btrans_10_diag'], linmat['Wobs_10_diag'] = setupMatrices(10)
linmat['Wtrans_100_diag'],linmat['btrans_100_diag'],linmat['Wobs_100_diag'] = setupMatrices(100)
linmat['Wtrans_250_diag'],linmat['btrans_250_diag'],linmat['Wobs_250_diag'] = setupMatrices(250)
#Check that this works
saveHDF5(SAVEDIR+'/linear-matrices-2.h5',linmat)
saved_matrices_2= linmat
else:
print 'Loading linear matrices'
saved_matrices_2= loadHDF5(SAVEDIR+'/linear-matrices-2.h5')
params_synthetic['synthetic12'] = {}
params_synthetic['synthetic12']['dim_obs'] = 20
params_synthetic['synthetic12']['dim_stoc'] = 10
params_synthetic['synthetic12']['params'] = {}
params_synthetic['synthetic12']['trans_cov'] = 10.
params_synthetic['synthetic12']['trans_cov_full'] = np.zeros((10,10))
np.fill_diagonal(params_synthetic['synthetic12']['trans_cov_full'], 10.)
params_synthetic['synthetic12']['trans_drift'] = saved_matrices['btrans_10']
params_synthetic['synthetic12']['trans_mult'] = saved_matrices['Wtrans_10']
params_synthetic['synthetic12']['obs_cov'] = 20.
params_synthetic['synthetic12']['obs_cov_full']= np.zeros((20,20))
np.fill_diagonal(params_synthetic['synthetic12']['obs_cov_full'], 20.)
params_synthetic['synthetic12']['obs_drift'] = np.ones((20,))*0.
params_synthetic['synthetic12']['obs_mult'] = saved_matrices['Wobs_10']
def linear_trans_s12(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices['Wtrans_10'])+saved_matrices['btrans_10']
def linear_obs_s12(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices['Wobs_10'])
params_synthetic['synthetic12']['trans_fxn'] = linear_trans_s12
params_synthetic['synthetic12']['obs_fxn'] = linear_obs_s12
params_synthetic['synthetic12']['init_mu'] = np.ones((10,))*0.
params_synthetic['synthetic12']['init_cov'] = 0.1
params_synthetic['synthetic12']['init_cov_full'] = np.zeros((10,10))
np.fill_diagonal(params_synthetic['synthetic12']['init_cov_full'],0.1)
params_synthetic['synthetic12']['baseline'] = 'KF'
params_synthetic['synthetic12']['docstr'] = '$z_t\sim\mathcal{N}(W_tz_{t-1}+b_t,10)$\n$x_t\sim\mathcal{N}(W_oz_t,20)$'
params_synthetic['synthetic13'] = {}
params_synthetic['synthetic13']['dim_obs'] = 200
params_synthetic['synthetic13']['dim_stoc'] = 100
params_synthetic['synthetic13']['params'] = {}
params_synthetic['synthetic13']['trans_cov'] = 10.
params_synthetic['synthetic13']['trans_cov_full'] = np.zeros((100,100))
np.fill_diagonal(params_synthetic['synthetic13']['trans_cov_full'],10.)
params_synthetic['synthetic13']['trans_drift'] = saved_matrices['btrans_100']
params_synthetic['synthetic13']['trans_mult'] = saved_matrices['Wtrans_100']
params_synthetic['synthetic13']['obs_cov'] = 20.
params_synthetic['synthetic13']['obs_cov_full']= np.zeros((200,200))
np.fill_diagonal(params_synthetic['synthetic13']['obs_cov_full'],20.)
params_synthetic['synthetic13']['obs_drift'] = np.ones((200,))*0.
params_synthetic['synthetic13']['obs_mult'] = saved_matrices['Wobs_100']
def linear_trans_s13(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices['Wtrans_100'])+saved_matrices['btrans_100']
def linear_obs_s13(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices['Wobs_100'])
params_synthetic['synthetic13']['trans_fxn'] = linear_trans_s13
params_synthetic['synthetic13']['obs_fxn'] = linear_obs_s13
params_synthetic['synthetic13']['init_mu'] = np.ones((100,))*0.
params_synthetic['synthetic13']['init_cov'] = 0.1
params_synthetic['synthetic13']['init_cov_full'] = np.zeros((100,100))
np.fill_diagonal(params_synthetic['synthetic13']['init_cov_full'],0.1)
params_synthetic['synthetic13']['baseline'] = 'KF'
params_synthetic['synthetic13']['docstr'] = '$z_t\sim\mathcal{N}(W_tz_{t-1}+b_t,10)$\n$x_t\sim\mathcal{N}(W_oz_t,20)$'
params_synthetic['synthetic14'] = {}
params_synthetic['synthetic14']['dim_obs'] = 500
params_synthetic['synthetic14']['dim_stoc'] = 250
params_synthetic['synthetic14']['params'] = {}
params_synthetic['synthetic14']['trans_cov'] = 10.
params_synthetic['synthetic14']['trans_cov_full'] = np.zeros((250,250))
np.fill_diagonal(params_synthetic['synthetic14']['trans_cov_full'],10.)
params_synthetic['synthetic14']['trans_drift'] = saved_matrices['btrans_250']
params_synthetic['synthetic14']['trans_mult'] = saved_matrices['Wtrans_250']
params_synthetic['synthetic14']['obs_cov'] = 20.
params_synthetic['synthetic14']['obs_cov_full'] = np.zeros((500,500))
np.fill_diagonal(params_synthetic['synthetic14']['obs_cov_full'],20.)
params_synthetic['synthetic14']['obs_drift'] = np.ones((500,))*0.
params_synthetic['synthetic14']['obs_mult'] = saved_matrices['Wobs_250']
def linear_trans_s14(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices['Wtrans_250'])+saved_matrices['btrans_250']
def linear_obs_s14(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices['Wobs_250'])
params_synthetic['synthetic14']['trans_fxn'] = linear_trans_s14
params_synthetic['synthetic14']['obs_fxn'] = linear_obs_s14
params_synthetic['synthetic14']['init_mu'] = np.ones((250,))*0.
params_synthetic['synthetic14']['init_cov'] = 0.1
params_synthetic['synthetic14']['init_cov_full'] = np.zeros((250,250))
np.fill_diagonal(params_synthetic['synthetic14']['init_cov_full'],0.1)
params_synthetic['synthetic14']['baseline'] = 'KF'
params_synthetic['synthetic14']['docstr'] = '$z_t\sim\mathcal{N}(W_tz_{t-1}+b_t,10)$\n$x_t\sim\mathcal{N}(W_oz_t,20)$'
"""
Dimobs and Dimz are the same
"""
params_synthetic['synthetic15'] = {}
params_synthetic['synthetic15']['dim_obs'] = 10
params_synthetic['synthetic15']['dim_stoc'] = 10
params_synthetic['synthetic15']['params'] = {}
params_synthetic['synthetic15']['trans_cov'] = 10.
params_synthetic['synthetic15']['trans_cov_full'] = np.zeros((10,10))
np.fill_diagonal(params_synthetic['synthetic15']['trans_cov_full'], 10.)
params_synthetic['synthetic15']['trans_drift'] = saved_matrices_2['btrans_10']
params_synthetic['synthetic15']['trans_mult'] = saved_matrices_2['Wtrans_10']
params_synthetic['synthetic15']['obs_cov'] = 20.
params_synthetic['synthetic15']['obs_cov_full']= np.zeros((10,10))
np.fill_diagonal(params_synthetic['synthetic15']['obs_cov_full'], 20.)
params_synthetic['synthetic15']['obs_drift'] = np.ones((10,))*0.
params_synthetic['synthetic15']['obs_mult'] = saved_matrices_2['Wobs_10']
def linear_trans_s15(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wtrans_10'])+saved_matrices_2['btrans_10']
def linear_obs_s15(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wobs_10'])
params_synthetic['synthetic15']['trans_fxn'] = linear_trans_s15
params_synthetic['synthetic15']['obs_fxn'] = linear_obs_s15
params_synthetic['synthetic15']['init_mu'] = np.ones((10,))*0.
params_synthetic['synthetic15']['init_cov'] = 0.1
params_synthetic['synthetic15']['init_cov_full'] = np.zeros((10,10))
np.fill_diagonal(params_synthetic['synthetic15']['init_cov_full'],0.1)
params_synthetic['synthetic15']['baseline'] = 'KF'
params_synthetic['synthetic15']['docstr'] = '$z_t\sim\mathcal{N}(W_tz_{t-1}+b_t,10)$\n$x_t\sim\mathcal{N}(W_oz_t,20)$'
params_synthetic['synthetic16'] = {}
params_synthetic['synthetic16']['dim_obs'] = 100
params_synthetic['synthetic16']['dim_stoc'] = 100
params_synthetic['synthetic16']['params'] = {}
params_synthetic['synthetic16']['trans_cov'] = 10.
params_synthetic['synthetic16']['trans_cov_full'] = np.zeros((100,100))
np.fill_diagonal(params_synthetic['synthetic16']['trans_cov_full'],10.)
params_synthetic['synthetic16']['trans_drift'] = saved_matrices_2['btrans_100']
params_synthetic['synthetic16']['trans_mult'] = saved_matrices_2['Wtrans_100']
params_synthetic['synthetic16']['obs_cov'] = 20.
params_synthetic['synthetic16']['obs_cov_full']= np.zeros((100,100))
np.fill_diagonal(params_synthetic['synthetic16']['obs_cov_full'],20.)
params_synthetic['synthetic16']['obs_drift'] = np.ones((100,))*0.
params_synthetic['synthetic16']['obs_mult'] = saved_matrices_2['Wobs_100']
def linear_trans_s16(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wtrans_100'])+saved_matrices_2['btrans_100']
def linear_obs_s16(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wobs_100'])
params_synthetic['synthetic16']['trans_fxn'] = linear_trans_s16
params_synthetic['synthetic16']['obs_fxn'] = linear_obs_s16
params_synthetic['synthetic16']['init_mu'] = np.ones((100,))*0.
params_synthetic['synthetic16']['init_cov'] = 0.1
params_synthetic['synthetic16']['init_cov_full'] = np.zeros((100,100))
np.fill_diagonal(params_synthetic['synthetic16']['init_cov_full'],0.1)
params_synthetic['synthetic16']['baseline'] = 'KF'
params_synthetic['synthetic16']['docstr'] = '$z_t\sim\mathcal{N}(W_tz_{t-1}+b_t,10)$\n$x_t\sim\mathcal{N}(W_oz_t,20)$'
params_synthetic['synthetic17'] = {}
params_synthetic['synthetic17']['dim_obs'] = 250
params_synthetic['synthetic17']['dim_stoc'] = 250
params_synthetic['synthetic17']['params'] = {}
params_synthetic['synthetic17']['trans_cov'] = 10.
params_synthetic['synthetic17']['trans_cov_full'] = np.zeros((250,250))
np.fill_diagonal(params_synthetic['synthetic17']['trans_cov_full'],10.)
params_synthetic['synthetic17']['trans_drift'] = saved_matrices_2['btrans_250']
params_synthetic['synthetic17']['trans_mult'] = saved_matrices_2['Wtrans_250']
params_synthetic['synthetic17']['obs_cov'] = 20.
params_synthetic['synthetic17']['obs_cov_full'] = np.zeros((250,250))
np.fill_diagonal(params_synthetic['synthetic17']['obs_cov_full'],20.)
params_synthetic['synthetic17']['obs_drift'] = np.ones((250,))*0.
params_synthetic['synthetic17']['obs_mult'] = saved_matrices_2['Wobs_250']
def linear_trans_s17(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wtrans_250'])+saved_matrices_2['btrans_250']
def linear_obs_s17(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wobs_250'])
params_synthetic['synthetic17']['trans_fxn'] = linear_trans_s17
params_synthetic['synthetic17']['obs_fxn'] = linear_obs_s17
params_synthetic['synthetic17']['init_mu'] = np.ones((250,))*0.
params_synthetic['synthetic17']['init_cov'] = 0.1
params_synthetic['synthetic17']['init_cov_full'] = np.zeros((250,250))
np.fill_diagonal(params_synthetic['synthetic17']['init_cov_full'],0.1)
params_synthetic['synthetic17']['baseline'] = 'KF'
params_synthetic['synthetic17']['docstr'] = '$z_t\sim\mathcal{N}(W_tz_{t-1}+b_t,10)$\n$x_t\sim\mathcal{N}(W_oz_t,20)$'
"""
dimz and dimobs are the same with diagonal
"""
params_synthetic['synthetic18'] = {}
params_synthetic['synthetic18']['dim_obs'] = 10
params_synthetic['synthetic18']['dim_stoc'] = 10
params_synthetic['synthetic18']['params'] = {}
params_synthetic['synthetic18']['trans_cov'] = 10.
params_synthetic['synthetic18']['trans_cov_full'] = np.zeros((10,10))
np.fill_diagonal(params_synthetic['synthetic18']['trans_cov_full'], 10.)
params_synthetic['synthetic18']['trans_drift'] = saved_matrices_2['btrans_10_diag']
params_synthetic['synthetic18']['trans_mult'] = saved_matrices_2['Wtrans_10_diag']
params_synthetic['synthetic18']['obs_cov'] = 20.
params_synthetic['synthetic18']['obs_cov_full']= np.zeros((10,10))
np.fill_diagonal(params_synthetic['synthetic18']['obs_cov_full'], 20.)
params_synthetic['synthetic18']['obs_drift'] = np.ones((10,))*0.
params_synthetic['synthetic18']['obs_mult'] = saved_matrices_2['Wobs_10_diag']
def linear_trans_s18(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wtrans_10_diag'])+saved_matrices_2['btrans_10_diag']
def linear_obs_s18(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wobs_10_diag'])
params_synthetic['synthetic18']['trans_fxn'] = linear_trans_s18
params_synthetic['synthetic18']['obs_fxn'] = linear_obs_s18
params_synthetic['synthetic18']['init_mu'] = np.ones((10,))*0.
params_synthetic['synthetic18']['init_cov'] = 0.1
params_synthetic['synthetic18']['init_cov_full'] = np.zeros((10,10))
np.fill_diagonal(params_synthetic['synthetic18']['init_cov_full'],0.1)
params_synthetic['synthetic18']['baseline'] = 'KF'
params_synthetic['synthetic18']['docstr'] = '$z_t\sim\mathcal{N}(W_tz_{t-1}+b_t,10)$\n$x_t\sim\mathcal{N}(W_oz_t,20)$'
params_synthetic['synthetic19'] = {}
params_synthetic['synthetic19']['dim_obs'] = 100
params_synthetic['synthetic19']['dim_stoc'] = 100
params_synthetic['synthetic19']['params'] = {}
params_synthetic['synthetic19']['trans_cov'] = 10.
params_synthetic['synthetic19']['trans_cov_full'] = np.zeros((100,100))
np.fill_diagonal(params_synthetic['synthetic19']['trans_cov_full'],10.)
params_synthetic['synthetic19']['trans_drift'] = saved_matrices_2['btrans_100_diag']
params_synthetic['synthetic19']['trans_mult'] = saved_matrices_2['Wtrans_100_diag']
params_synthetic['synthetic19']['obs_cov'] = 20.
params_synthetic['synthetic19']['obs_cov_full']= np.zeros((100,100))
np.fill_diagonal(params_synthetic['synthetic19']['obs_cov_full'],20.)
params_synthetic['synthetic19']['obs_drift'] = np.ones((100,))*0.
params_synthetic['synthetic19']['obs_mult'] = saved_matrices_2['Wobs_100_diag']
def linear_trans_s19(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wtrans_100_diag'])+saved_matrices_2['btrans_100_diag']
def linear_obs_s19(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wobs_100_diag'])
params_synthetic['synthetic19']['trans_fxn'] = linear_trans_s19
params_synthetic['synthetic19']['obs_fxn'] = linear_obs_s19
params_synthetic['synthetic19']['init_mu'] = np.ones((100,))*0.
params_synthetic['synthetic19']['init_cov'] = 0.1
params_synthetic['synthetic19']['init_cov_full'] = np.zeros((100,100))
np.fill_diagonal(params_synthetic['synthetic19']['init_cov_full'],0.1)
params_synthetic['synthetic19']['baseline'] = 'KF'
params_synthetic['synthetic19']['docstr'] = '$z_t\sim\mathcal{N}(W_tz_{t-1}+b_t,10)$\n$x_t\sim\mathcal{N}(W_oz_t,20)$'
params_synthetic['synthetic20'] = {}
params_synthetic['synthetic20']['dim_obs'] = 250
params_synthetic['synthetic20']['dim_stoc'] = 250
params_synthetic['synthetic20']['params'] = {}
params_synthetic['synthetic20']['trans_cov'] = 10.
params_synthetic['synthetic20']['trans_cov_full'] = np.zeros((250,250))
np.fill_diagonal(params_synthetic['synthetic20']['trans_cov_full'],10.)
params_synthetic['synthetic20']['trans_drift'] = saved_matrices_2['btrans_250_diag']
params_synthetic['synthetic20']['trans_mult'] = saved_matrices_2['Wtrans_250_diag']
params_synthetic['synthetic20']['obs_cov'] = 20.
params_synthetic['synthetic20']['obs_cov_full'] = np.zeros((250,250))
np.fill_diagonal(params_synthetic['synthetic20']['obs_cov_full'],20.)
params_synthetic['synthetic20']['obs_drift'] = np.ones((250,))*0.
params_synthetic['synthetic20']['obs_mult'] = saved_matrices_2['Wobs_250_diag']
def linear_trans_s20(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wtrans_250_diag'])+saved_matrices_2['btrans_250_diag']
def linear_obs_s20(z,fxn_params = {},ns=None):
return np.dot(z,saved_matrices_2['Wobs_250_diag'])
params_synthetic['synthetic20']['trans_fxn'] = linear_trans_s20
params_synthetic['synthetic20']['obs_fxn'] = linear_obs_s20
params_synthetic['synthetic20']['init_mu'] = np.ones((250,))*0.
params_synthetic['synthetic20']['init_cov'] = 0.1
params_synthetic['synthetic20']['init_cov_full'] = np.zeros((250,250))
np.fill_diagonal(params_synthetic['synthetic20']['init_cov_full'],0.1)
params_synthetic['synthetic20']['baseline'] = 'KF'
params_synthetic['synthetic20']['docstr'] = '$z_t\sim\mathcal{N}(W_tz_{t-1}+b_t,10)$\n$x_t\sim\mathcal{N}(W_oz_t,20)$'