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analysis.py v 1.1.12.003

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art 2020-02-18 20:32:35 -06:00
parent 52d79ea25e
commit fe31db07f9
1 changed files with 3 additions and 16 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__ = "1.1.12.002" __version__ = "1.1.12.003"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.1.12.003:
- removed depreciated code
1.1.12.002: 1.1.12.002:
- removed team first time trueskill instantiation in favor of integration in superscript.py - removed team first time trueskill instantiation in favor of integration in superscript.py
1.1.12.001: 1.1.12.001:
@ -371,21 +373,6 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
regressions.append(plys) regressions.append(plys)
""" non functional and dep
plys = []
if power_limit == None:
power_limit = len(outputs[0]) - 1
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)
plys.append((model[0].parameters, model[1][::-1][0]))
regressions.append(plys)
"""
if 'sig' in args: # formula: a sig (b(x + c)) + d | sig() = 1/(1 + e ^ -x) if 'sig' in args: # formula: a sig (b(x + c)) + d | sig() = 1/(1 + e ^ -x)
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)