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analysis.py v 1.1.1.000

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ltcptgeneral 2019-09-30 10:11:53 -05:00
parent 91d727b6ad
commit 941dd4838a
1 changed files with 65 additions and 2 deletions
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data analysis/analysis/analysis.py
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@ -7,10 +7,13 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.0.007"
__version__ = "1.1.1.000"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.1.000:
- added regression_engine()
- added all regressions except polynomial
1.1.0.007:
- updated _init_device()
1.1.0.006:
@ -154,6 +157,7 @@ import numba
from numba import jit
import numpy as np
import math
import regression
from sklearn import metrics
from sklearn import preprocessing
import torch
@ -219,7 +223,66 @@ def histo_analysis(hist_data):
return basic_stats(derivative)[0], basic_stats(derivative)[3]
#regressions
@jit(forceobj=True)
def regression_engine(device, inputs, outputs, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.1, *args):
regressions = []
if 'cuda' in 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)
regressions.append([model[0].parameter, 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)
regressions.append([model[0].parameter, 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)
regressions.append([model[0].parameter, model[1][::-1][0]])
#if 'poly' in args:
#TODO because Jacob hasnt fixed regression.py
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)
regressions.append([model[0].parameter, model[1][::-1][0]])
else:
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)
regressions.append([model[0].parameter, 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)
regressions.append([model[0].parameter, 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)
regressions.append([model[0].parameter, model[1][::-1][0]])
#if 'poly' in args:
#TODO because Jacob hasnt fixed regression.py
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)
regressions.append([model[0].parameter, model[1][::-1][0]])
@jit(forceobj=True)
def r_squared(predictions, targets): # assumes equal size inputs