forked from anubhabghosh/danse
-
Notifications
You must be signed in to change notification settings - Fork 0
/
parameters.py
288 lines (265 loc) · 9.34 KB
/
parameters.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
#####################################################
# Creator: Anubhab Ghosh
# Feb 2023
#####################################################
# This function is used to define the parameters of the model
import numpy as np
import math
import torch
from utils.utils import dB_to_lin, partial_corrupt
from ssm_models import LinearSSM
import torch
from torch.autograd.functional import jacobian
torch.manual_seed(10)
delta_t = 0.02 # Hardcoded for now
delta_t_test = 0.04 # Hardcoded for now
J_gen = 5
J_test = 5 # hardcoded for now
def A_fn(z):
return np.array([
[-10, 10, 0],
[28, -1, -z],
[0, z, -8.0/3]
])
def h_fn(z):
return z
"""
# The KalmanNet implementation
def f_lorenz(x):
B = torch.Tensor([[[0, 0, 0],[0, 0, -1],[0, 1, 0]], torch.zeros(3,3), torch.zeros(3,3)]).type(torch.FloatTensor)
C = torch.Tensor([[-10, 10, 0],
[ 28, -1, 0],
[ 0, 0, -8/3]]).type(torch.FloatTensor)
#A = torch.add(torch.einsum('nhw,wa->nh', B, x).T,C)
#A = torch.einsum('kn,nij->ij',x.reshape((1,-1)),B) #(torch.add(torch.reshape(torch.matmul(B, x),(3,3)).T,C))
A = (torch.add(torch.reshape(torch.matmul(B, x),(3,3)).T,C))
# Taylor Expansion for F
F = torch.eye(3)
J = J_test
for j in range(1,J+1):
F_add = (torch.matrix_power(A*delta_t, j)/math.factorial(j))
F = torch.add(F, F_add)
return torch.matmul(F, x)
"""
def f_lorenz_danse_test_ukf(x, dt):
x = torch.from_numpy(x).type(torch.FloatTensor)
B = torch.Tensor([[[0, 0, 0],[0, 0, -1],[0, 1, 0]], torch.zeros(3,3), torch.zeros(3,3)]).type(torch.FloatTensor)
C = torch.Tensor([[-10, 10, 0],
[ 28, -1, 0],
[ 0, 0, -8/3]]).type(torch.FloatTensor)
#A = torch.add(torch.einsum('nhw,wa->nh', B, x).T,C)
A = torch.einsum('kn,nij->ij',x.reshape((1,-1)),B)
#A = torch.reshape(torch.matmul(B, x),(3,3)).T # For KalmanNet
A += C
#delta = delta_t # Hardcoded for now
# Taylor Expansion for F
F = torch.eye(3)
J = J_test # Hardcoded for now
for j in range(1,J+1):
F_add = (torch.matrix_power(A*delta_t_test, j)/math.factorial(j))
F = torch.add(F, F_add)
return torch.matmul(F, x).numpy()
def f_lorenz_danse_ukf(x, dt):
x = torch.from_numpy(x).type(torch.FloatTensor)
B = torch.Tensor([[[0, 0, 0],[0, 0, -1],[0, 1, 0]], torch.zeros(3,3), torch.zeros(3,3)]).type(torch.FloatTensor)
C = torch.Tensor([[-10, 10, 0],
[ 28, -1, 0],
[ 0, 0, -8/3]]).type(torch.FloatTensor)
#A = torch.add(torch.einsum('nhw,wa->nh', B, x).T,C)
A = torch.einsum('kn,nij->ij',x.reshape((1,-1)),B)
#A = torch.reshape(torch.matmul(B, x),(3,3)).T # For KalmanNet
A += C
#delta = delta_t # Hardcoded for now
# Taylor Expansion for F
F = torch.eye(3)
J = J_test # Hardcoded for now
for j in range(1,J+1):
F_add = (torch.matrix_power(A*delta_t, j)/math.factorial(j))
F = torch.add(F, F_add)
return torch.matmul(F, x).numpy()
def f_lorenz_danse_test(x):
B = torch.Tensor([[[0, 0, 0],[0, 0, -1],[0, 1, 0]], torch.zeros(3,3), torch.zeros(3,3)]).type(torch.FloatTensor)
C = torch.Tensor([[-10, 10, 0],
[ 28, -1, 0],
[ 0, 0, -8/3]]).type(torch.FloatTensor)
A = torch.einsum('kn,nij->ij',x.reshape((1,-1)),B) + C
#delta_t = 0.02 # Hardcoded for now
# Taylor Expansion for F
F = torch.eye(3)
J = J_test # Hardcoded for now
for j in range(1,J+1):
F_add = (torch.matrix_power(A*delta_t_test, j)/math.factorial(j))
F = torch.add(F, F_add)
return torch.matmul(F, x)
def f_lorenz_danse(x):
B = torch.Tensor([[[0, 0, 0],[0, 0, -1],[0, 1, 0]], torch.zeros(3,3), torch.zeros(3,3)]).type(torch.FloatTensor)
C = torch.Tensor([[-10, 10, 0],
[ 28, -1, 0],
[ 0, 0, -8/3]]).type(torch.FloatTensor)
A = torch.einsum('kn,nij->ij',x.reshape((1,-1)),B) + C
#delta_t = 0.02 # Hardcoded for now
# Taylor Expansion for F
F = torch.eye(3)
J = J_test # Hardcoded for now
for j in range(1,J+1):
F_add = (torch.matrix_power(A*delta_t, j)/math.factorial(j))
F = torch.add(F, F_add)
return torch.matmul(F, x)
def f_sinssm_fn(z, alpha=0.9, beta=1.1, phi=0.1*math.pi, delta=0.01):
return alpha * torch.sin(beta * z + phi) + delta
def h_sinssm_fn(z, a=1, b=1, c=0):
return a * (b * z + c)
def get_H_DANSE(type_, n_states, n_obs):
if type_ == "LinearSSM":
return LinearSSM(n_states=n_states, n_obs=n_obs).construct_H()
elif type_ == "LorenzSSM":
return np.eye(n_obs, n_states)
elif type_ == "SinusoidalSSM":
return jacobian(h_sinssm_fn, torch.randn(n_states,)).numpy()
def get_parameters(N=1000, T=100, n_states=5, n_obs=5, q2=1.0, r2=1.0,
inverse_r2_dB=40, nu_dB=0, device='cpu'):
#H_DANSE = np.eye(n_obs, n_states) # Lorenz attractor model
#H_DANSE = LinearSSM(n_states=n_states, n_obs=n_obs).construct_H() # Linear SSM
H_DANSE = None
r2 = 1.0 / dB_to_lin(inverse_r2_dB)
q2 = dB_to_lin(nu_dB - inverse_r2_dB)
ssm_parameters_dict = {
# Parameters of the linear model
"LinearSSM":{
"n_states":n_states,
"n_obs":n_obs,
"F":None,
"G":np.zeros((n_states,1)),
"H":None,
"mu_e":np.zeros((n_states,)),
"mu_w":np.zeros((n_obs,)),
"inverse_r2_dB":inverse_r2_dB,
"nu_dB":nu_dB,
"q2":q2,
"r2":r2,
"N":N,
"T":T,
"Q":None,
"R":None
},
# Parameters of the Lorenz Attractor model
"LorenzSSM":{
"n_states":n_states,
"n_obs":n_obs,
"J":J_gen,
"delta":delta_t,
"A_fn":A_fn,
"h_fn":h_fn,
"delta_d":0.02,
"decimate":False,
"mu_e":np.zeros((n_states,)),
"mu_w":np.zeros((n_obs,)),
"inverse_r2_dB":inverse_r2_dB,
"nu_dB":nu_dB,
"use_Taylor":True
},
# Parameters of the Sinusoidal SSM
"SinusoidalSSM":{
"n_states":n_states,
"alpha":0.9,
"beta":1.1,
"phi":0.1*math.pi,
"delta":0.01,
"a":1.0,
"b":1.0,
"c":0.0,
"decimate":False,
"mu_e":np.zeros((n_states,)),
"mu_w":np.zeros((n_obs,)),
"inverse_r2_dB":inverse_r2_dB,
"nu_dB":nu_dB,
"use_Taylor":False
},
}
estimators_dict={
# Parameters of the DANSE estimator
"danse":{
"n_states":n_states,
"n_obs":n_obs,
"mu_w":np.zeros((n_obs,)),
"C_w":np.eye(n_obs,n_obs)*r2,
"H":H_DANSE,
"mu_x0":np.zeros((n_states,)),
"C_x0":np.eye(n_states,n_states),
"batch_size":64,
"rnn_type":"gru",
"device":device,
"rnn_params_dict":{
"gru":{
"model_type":"gru",
"input_size":n_obs,
"output_size":n_states,
"n_hidden":40,
"n_layers":2,
"lr":1e-2,
"num_epochs":2000,
"min_delta":5e-2,
"n_hidden_dense":32,
"device":device
},
"rnn":{
"model_type":"gru",
"input_size":n_obs,
"output_size":n_states,
"n_hidden":40,
"n_layers":2,
"lr":1e-3,
"num_epochs":300,
"min_delta":1e-3,
"n_hidden_dense":32,
"device":device
},
"lstm":{
"model_type":"lstm",
"input_size":n_obs,
"output_size":n_states,
"n_hidden":50,
"n_layers":2,
"lr":1e-3,
"num_epochs":300,
"min_delta":1e-3,
"n_hidden_dense":32,
"device":device
}
}
},
# Parameters of the Model-based filters - KF, EKF, UKF
"KF":{
"n_states":n_states,
"n_obs":n_obs
},
"EKF":{
"n_states":n_states,
"n_obs":n_obs
},
"UKF":{
"n_states":n_states,
"n_obs":n_obs,
"n_sigma":n_states*2,
"kappa":0.0,
"alpha":1e-3
},
"KNetUoffline":{
"n_states":n_states,
"n_obs":n_obs,
"n_layers":1,
"N_E":10_0,
"N_CV":100,
"N_T":10_0,
"unsupervised":True,
"data_file_specification":'Ratio_{}---R_{}---T_{}',
"model_file_specification":'Ratio_{}---R_{}---T_{}---unsupervised_{}',
"nu_dB":0.0,
"lr":1e-3,
"weight_decay":1e-6,
"num_epochs":100,
"batch_size":100,
"device":device
}
}
return ssm_parameters_dict, estimators_dict