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jacob fix poly regression!

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art 2019-10-08 13:35:32 -05:00
parent c824087335
commit f47be637a0
2 changed files with 10 additions and 10 deletions
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data analysis/analysis/__pycache__/analysis.cpython-37.pyc
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data analysis/analysis/analysis.py
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@ -262,26 +262,26 @@ def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterat
if 'lin' in args: if 'lin' in args:
model = Regression.SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor([outputs]).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) model = Regression.SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor([outputs]).to(torch.float).cuda(), 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).cuda(), torch.tensor(outputs).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) model = Regression.SGDTrain(Regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor(outputs).to(torch.float).cuda(), 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).cuda(), torch.tensor(outputs).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) model = Regression.SGDTrain(Regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor(outputs).to(torch.float).cuda(), 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 'poly' in args: #if 'ply' in args:
#TODO because Jacob hasnt fixed regression.py #TODO because Jacob hasnt fixed regression.py
if 'sig' in args: if 'sig' in args:
model = Regression.SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor(outputs).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) model = Regression.SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor(outputs).to(torch.float).cuda(), 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]))
else: else:
@ -290,26 +290,26 @@ def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterat
if 'linear' in args: if 'linear' in args:
model = Regression.SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) model = Regression.SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), 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), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) model = Regression.SGDTrain(Regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), 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), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) model = Regression.SGDTrain(Regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), 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 'poly' in args: #if 'ply' in args:
#TODO because Jacob hasnt fixed regression.py #TODO because Jacob hasnt fixed regression.py
if 'sig' in args: if 'sig' in args:
model = Regression.SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) model = Regression.SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), 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