alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-11-10 06:54:44 +00:00
Code Issues Packages Projects Releases Wiki Activity

analysis?py v 1.1.11.008

Browse Source
This commit is contained in:
ltcptgeneral 2020-01-06 23:48:28 -06:00
parent a8bf4e46e9
commit 190fbf6cac
1 changed files with 9 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
data analysis/analysis/analysis.py
View File

@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.11.007"
__version__ = "1.1.11.008"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.11.008:
- bug fixes
1.1.11.007:
- bug fixes
1.1.11.006:
@ -319,21 +321,21 @@ def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterat
power_limit += 1
regressions = []
Regression.set_device(device)
Regression().set_device(device)
if 'lin' in args:
model = Regression.SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor([outputs]).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
model = Regression().SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor([outputs]).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
regressions.append((model[0].parameters, model[1][::-1][0]))
if 'log' in args:
model = Regression.SGDTrain(Regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
model = Regression().SGDTrain(Regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
regressions.append((model[0].parameters, model[1][::-1][0]))
if 'exp' in args:
model = Regression.SGDTrain(Regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
model = Regression().SGDTrain(Regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
regressions.append((model[0].parameters, model[1][::-1][0]))
if 'ply' in args:
@ -342,14 +344,14 @@ def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterat
for i in range(2, power_limit):
model = Regression.SGDTrain(Regression.PolyRegKernel(len(inputs),i), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations_ply * 10 ** i, learning_rate=lr_ply * 10 ** -i, return_losses=True)
model = Regression().SGDTrain(Regression.PolyRegKernel(len(inputs),i), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations_ply * 10 ** i, learning_rate=lr_ply * 10 ** -i, return_losses=True)
plys.append((model[0].parameters, model[1][::-1][0]))
regressions.append(plys)
if 'sig' in args:
model = Regression.SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
model = Regression().SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
regressions.append((model[0].parameters, model[1][::-1][0]))
return regressions