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analysis.py v 1.1.11.009
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# current benchmark of optimization: 1.33 times faster
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# current benchmark of optimization: 1.33 times faster
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# setup:
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# setup:
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__version__ = "1.1.11.008"
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__version__ = "1.1.11.009"
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# changelog should be viewed using print(analysis.__changelog__)
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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__changelog__ = """changelog:
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1.1.11.009:
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- bug fixes
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1.1.11.008:
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1.1.11.008:
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- bug fixes
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- bug fixes
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1.1.11.007:
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1.1.11.007:
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@ -342,6 +344,10 @@ def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterat
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plys = []
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plys = []
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if power_limit == None:
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power_limit = len(outputs[0])
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for i in range(2, power_limit):
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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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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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