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analysis.py v 1.1.12.003
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# current benchmark of optimization: 1.33 times faster
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# setup:
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__version__ = "1.1.12.002"
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__version__ = "1.1.12.003"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.1.12.003:
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- removed depreciated code
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1.1.12.002:
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- removed team first time trueskill instantiation in favor of integration in superscript.py
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1.1.12.001:
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@ -371,21 +373,6 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
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regressions.append(plys)
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""" non functional and dep
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plys = []
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if power_limit == None:
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power_limit = len(outputs[0]) - 1
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for i in range(2, power_limit):
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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)
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plys.append((model[0].parameters, model[1][::-1][0]))
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regressions.append(plys)
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"""
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if 'sig' in args: # formula: a sig (b(x + c)) + d | sig() = 1/(1 + e ^ -x)
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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)
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