mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-12-27 01:59:08 +00:00
analysis.py v 1.1.3.002
This commit is contained in:
art
parent
469dcd0ce2
commit
eade12bb71
@ -7,10 +7,12 @@
|
||||
# current benchmark of optimization: 1.33 times faster
|
||||
# setup:
|
||||
|
||||
__version__ = "1.1.3.001"
|
||||
__version__ = "1.1.3.002"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
1.1.3.002:
|
||||
- renamed regression class to Regression, regression_engine() to regression gliko2_engine class to Gliko2
|
||||
1.1.3.001:
|
||||
- changed glicko2() to return tuple instead of array
|
||||
1.1.3.000:
|
||||
@ -161,11 +163,14 @@ __all__ = [
|
||||
'z_score',
|
||||
'z_normalize',
|
||||
'histo_analysis',
|
||||
'regression_engine',
|
||||
'regression',
|
||||
'elo',
|
||||
'gliko2',
|
||||
'r_squared',
|
||||
'mse',
|
||||
'rms',
|
||||
'regression'
|
||||
'Regression',
|
||||
'Gliko2'
|
||||
# all statistics functions left out due to integration in other functions
|
||||
]
|
||||
|
||||
@ -243,27 +248,27 @@ def histo_analysis(hist_data):
|
||||
return basic_stats(derivative)[0], basic_stats(derivative)[3]
|
||||
|
||||
@jit(forceobj=True)
|
||||
def regression_engine(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01):
|
||||
def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01):
|
||||
|
||||
regressions = []
|
||||
|
||||
if 'cuda' in device:
|
||||
|
||||
regression.set_device(device)
|
||||
Regression.set_device(device)
|
||||
|
||||
if 'linear' 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]])
|
||||
|
||||
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]])
|
||||
|
||||
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]])
|
||||
|
||||
#if 'poly' in args:
|
||||
@ -272,26 +277,26 @@ def regression_engine(device, inputs, outputs, args, loss = torch.nn.MSELoss(),
|
||||
|
||||
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]])
|
||||
|
||||
else:
|
||||
|
||||
regression.set_device(device)
|
||||
Regression.set_device(device)
|
||||
|
||||
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]])
|
||||
|
||||
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]])
|
||||
|
||||
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]])
|
||||
|
||||
#if 'poly' in args:
|
||||
@ -300,7 +305,7 @@ def regression_engine(device, inputs, outputs, args, loss = torch.nn.MSELoss(),
|
||||
|
||||
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]])
|
||||
|
||||
return regressions
|
||||
@ -315,7 +320,7 @@ def elo(starting_score, opposing_scores, observed, N, K):
|
||||
@jit(forceobj=True)
|
||||
def gliko2(starting_score, starting_rd, starting_vol, opposing_scores, opposing_rd, observations):
|
||||
|
||||
player = gliko2_engine(rating = starting_score, rd = starting_rd, vol = starting_vol)
|
||||
player = Gliko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
|
||||
|
||||
player.update_player([x for x in opposing_scores], [x for x in opposing_rd], observations)
|
||||
|
||||
@ -356,7 +361,7 @@ def variance(data):
|
||||
|
||||
return np.var(data)
|
||||
|
||||
class regression:
|
||||
class Regression:
|
||||
|
||||
# Titan Robotics Team 2022: CUDA-based Regressions Module
|
||||
# Written by Arthur Lu & Jacob Levine
|
||||
@ -576,7 +581,7 @@ class regression:
|
||||
optim.step()
|
||||
return kernel
|
||||
|
||||
class gliko2_engine:
|
||||
class Gliko2:
|
||||
|
||||
_tau = 0.5
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user