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analysis?py v 1.1.11.008

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ltcptgeneral 2020-01-06 23:48:28 -06:00
parent a8bf4e46e9
commit 190fbf6cac
1 changed files with 9 additions and 7 deletions
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data analysis/analysis/analysis.py
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@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "1.1.11.007" __version__ = "1.1.11.008"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.1.11.008:
- bug fixes
1.1.11.007: 1.1.11.007:
- bug fixes - bug fixes
1.1.11.006: 1.1.11.006:
@ -319,21 +321,21 @@ def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterat
power_limit += 1 power_limit += 1
regressions = [] regressions = []
Regression.set_device(device) Regression().set_device(device)
if 'lin' in args: 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])) regressions.append((model[0].parameters, model[1][::-1][0]))
if 'log' in args: 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])) regressions.append((model[0].parameters, model[1][::-1][0]))
if 'exp' in args: 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])) regressions.append((model[0].parameters, model[1][::-1][0]))
if 'ply' in args: 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): 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])) plys.append((model[0].parameters, model[1][::-1][0]))
regressions.append(plys) regressions.append(plys)
if 'sig' in args: 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])) regressions.append((model[0].parameters, model[1][::-1][0]))
return regressions return regressions