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art 2020-02-18 16:16:57 -06:00
parent 9da4322aa9
commit 978a9a9a25
2 changed files with 23 additions and 11 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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@ -7,10 +7,15 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "1.1.12.000" __version__ = "1.1.12.001"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.1.12.001:
- improved readibility of regression outputs by stripping tensor data
- used map with lambda to acheive the improved readibility
- lost numba jit support with regression, and generated_jit hangs at execution
- TODO: reimplement correct numba integration in regression
1.1.12.000: 1.1.12.000:
- temporarily fixed polynomial regressions by using sklearn's PolynomialFeatures - temporarily fixed polynomial regressions by using sklearn's PolynomialFeatures
1.1.11.010: 1.1.11.010:
@ -318,28 +323,33 @@ def histo_analysis(hist_data):
return basic_stats(derivative)[0], basic_stats(derivative)[3] return basic_stats(derivative)[0], basic_stats(derivative)[3]
@jit(forceobj=True)
def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array
regressions = [] regressions = []
Regression().set_device(ndevice) Regression().set_device(ndevice)
if 'lin' in args: if 'lin' in args: # formula: ax + b
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])) params = model[0].parameters
params[:] = map(lambda x: x.item(), params)
regressions.append((params, model[1][::-1][0]))
if 'log' in args: if 'log' in args: # formula: a log (b(x + c)) + d
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])) params = model[0].parameters
params[:] = map(lambda x: x.item(), params)
regressions.append((params, model[1][::-1][0]))
if 'exp' in args: if 'exp' in args: # formula: a e ^ (b(x + c)) + d
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])) params = model[0].parameters
params[:] = map(lambda x: x.item(), params)
regressions.append((params, model[1][::-1][0]))
if 'ply' in args: if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
plys = [] plys = []
limit = len(outputs[0]) limit = len(outputs[0])
@ -374,10 +384,12 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
regressions.append(plys) regressions.append(plys)
""" """
if 'sig' in args: 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)
regressions.append((model[0].parameters, model[1][::-1][0])) params = model[0].parameters
params[:] = map(lambda x: x.item(), params)
regressions.append((params, model[1][::-1][0]))
return regressions return regressions