alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-12-27 01:59:08 +00:00
Code Issues Packages Projects Releases Wiki Activity

analysis.py v 1.1.5.000

Browse Source
This commit is contained in:
ltcptgeneral 2019-10-09 23:58:08 -05:00
parent 7c121d48fc
commit ff2f0787ae
3 changed files with 27 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
data analysis/analysis/__pycache__/analysis.cpython-37.pyc
View File

Binary file not shown.

BIN
data analysis/analysis/__pycache__/trueskill.cpython-37.pyc
View File

Binary file not shown.

33
data analysis/analysis/analysis.py
View File

@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "1.1.4.000" __version__ = "1.1.5.000"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.1.5.000:
- added polynomial regression to regression(); untested
1.1.4.000: 1.1.4.000:
- added trueskill() - added trueskill()
1.1.3.002: 1.1.3.002:
@ -251,7 +253,12 @@ 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) @jit(forceobj=True)
def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01): # inputs, outputs expects N-D array def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01, power_limit = None): # inputs, outputs expects N-D array
if power_limit == None:
power_limit = len(outputs[0])
else:
power_limit += 1
regressions = [] regressions = []
@ -274,9 +281,16 @@ def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterat
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])) regressions.append((model[0].parameters, model[1][::-1][0]))
#if 'ply' in args: if 'ply' in args:
#TODO because Jacob hasnt fixed regression.py plys = []
for i in range(2, power_limit):
model = Regression.SGDTrain(Regression.PolyRegKernel(len(inputs),i), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor(outputs).to(torch.float).cuda(), 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: if 'sig' in args:
@ -302,9 +316,16 @@ def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterat
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])) regressions.append((model[0].parameters, model[1][::-1][0]))
#if 'ply' in args: if 'ply' in args:
#TODO because Jacob hasnt fixed regression.py plys = []
for i in range(2, power_limit):
model = Regression.SGDTrain(Regression.PolyRegKernel(len(inputs),i), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), 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: if 'sig' in args: