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Extended_data.py
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import torch
import math
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
#######################
### Size of DataSet ###
#######################
# Number of Training Examples
N_E = 1000
# Number of Cross Validation Examples
N_CV = 100
N_T = 200
# Sequence Length for Linear Case
T = 100
T_test = 100
#################
## Design #10 ###
#################
F10 = torch.tensor([[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])
H10 = torch.tensor([[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
############
## 2 x 2 ###
############
m = 2
n = 2
F = F10[0:m, 0:m]
H = torch.eye(2)
m1_0 = torch.tensor([[0.0], [0.0]])
# m1x_0_design = torch.tensor([[10.0], [-10.0]])
m2_0 = 0 * 0 * torch.eye(m)
#############
### 5 x 5 ###
#############
# m = 5
# n = 5
# F = F10[0:m, 0:m]
# H = H10[0:n, 10-m:10]
# m1_0 = torch.zeros(m, 1)
# # m1x_0_design = torch.tensor([[1.0], [-1.0], [2.0], [-2.0], [0.0]])
# m2_0 = 0 * 0 * torch.eye(m)
##############
## 10 x 10 ###
##############
# m = 10
# n = 10
# F = F10[0:m, 0:m]
# H = H10
# m1_0 = torch.zeros(m, 1)
# # m1x_0_design = torch.tensor([[10.0], [-10.0]])
# m2_0 = 0 * 0 * torch.eye(m)
# Inaccurate model knowledge based on matrix rotation
alpha_degree = 10
rotate_alpha = torch.tensor([alpha_degree/180*torch.pi])
cos_alpha = torch.cos(rotate_alpha)
sin_alpha = torch.sin(rotate_alpha)
rotate_matrix = torch.tensor([[cos_alpha, -sin_alpha],
[sin_alpha, cos_alpha]])
# print(rotate_matrix)
F_rotated = torch.mm(F,rotate_matrix) #inaccurate process model
H_rotated = torch.mm(H,rotate_matrix) #inaccurate observation model
def DataGen_True(SysModel_data, fileName, T):
SysModel_data.GenerateBatch(1, T, randomInit=False)
test_input = SysModel_data.Input
test_target = SysModel_data.Target
# torch.save({"True Traj":[test_target],
# "Obs":[test_input]},fileName)
torch.save([test_input, test_target], fileName)
def DataGen(SysModel_data, fileName, T, T_test,randomInit=False):
##################################
### Generate Training Sequence ###
##################################
SysModel_data.GenerateBatch(N_E, T, randomInit=randomInit)
training_input = SysModel_data.Input
training_target = SysModel_data.Target
####################################
### Generate Validation Sequence ###
####################################
SysModel_data.GenerateBatch(N_CV, T, randomInit=randomInit)
cv_input = SysModel_data.Input
cv_target = SysModel_data.Target
##############################
### Generate Test Sequence ###
##############################
SysModel_data.GenerateBatch(N_T, T_test, randomInit=randomInit)
test_input = SysModel_data.Input
test_target = SysModel_data.Target
#################
### Save Data ###
#################
torch.save([training_input, training_target, cv_input, cv_target, test_input, test_target], fileName)
def DataLoader(fileName):
[training_input, training_target, cv_input, cv_target, test_input, test_target] = torch.load(fileName, map_location=torch.device("cpu"))
return [training_input, training_target, cv_input, cv_target, test_input, test_target]
def DataLoader_GPU(fileName):
[training_input, training_target, cv_input, cv_target, test_input, test_target] = torch.utils.data.DataLoader(torch.load(fileName),pin_memory = False)
training_input = training_input.squeeze().to(torch.device("cuda:0"))
training_target = training_target.squeeze().to(torch.device("cuda:0"))
cv_input = cv_input.squeeze().to(torch.device("cuda:0"))
cv_target =cv_target.squeeze().to(torch.device("cuda:0"))
test_input = test_input.squeeze().to(torch.device("cuda:0"))
test_target = test_target.squeeze().to(torch.device("cuda:0"))
return [training_input, training_target, cv_input, cv_target, test_input, test_target]
def DecimateData(all_tensors, t_gen,t_mod, offset=0):
# ratio: defines the relation between the sampling time of the true process and of the model (has to be an integer)
ratio = round(t_mod/t_gen)
i = 0
all_tensors_out = all_tensors
for tensor in all_tensors:
tensor = tensor[:,(0+offset)::ratio]
if(i==0):
all_tensors_out = torch.cat([tensor], dim=0).view(1,all_tensors.size()[1],-1)
else:
all_tensors_out = torch.cat([all_tensors_out,tensor], dim=0)
i += 1
return all_tensors_out
def Decimate_and_perturbate_Data(true_process, delta_t, delta_t_mod, N_examples, h, lambda_r, offset=0):
# Decimate high resolution process
decimated_process = DecimateData(true_process, delta_t, delta_t_mod, offset)
noise_free_obs = getObs(decimated_process,h)
# Replicate for computation purposes
decimated_process = torch.cat(int(N_examples)*[decimated_process])
noise_free_obs = torch.cat(int(N_examples)*[noise_free_obs])
# Observations; additive Gaussian Noise
observations = noise_free_obs + torch.randn_like(decimated_process) * lambda_r
return [decimated_process, observations]
def getObs(sequences, h):
i = 0
sequences_out = torch.zeros_like(sequences)
for sequence in sequences:
for t in range(sequence.size()[1]):
sequences_out[i,:,t] = h(sequence[:,t])
i = i+1
return sequences_out
def Short_Traj_Split(data_target, data_input, T):
data_target = list(torch.split(data_target,T,2))
data_input = list(torch.split(data_input,T,2))
data_target.pop()
data_input.pop()
data_target = torch.squeeze(torch.cat(list(data_target), dim=0))
data_input = torch.squeeze(torch.cat(list(data_input), dim=0))
return [data_target, data_input]