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analysis.py v 1.1.1.001

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ltcptgeneral 2019-09-30 13:37:19 -05:00
parent fd991401c4
commit c135ddb856
3 changed files with 16 additions and 12 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
# setup:
__version__ = "1.1.1.000"
__version__ = "1.1.1.001"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.1.001:
- regression_engine() bug fixes, now actaully regresses
1.1.1.000:
- added regression_engine()
- added all regressions except polynomial
@ -157,7 +159,7 @@ import numba
from numba import jit
import numpy as np
import math
import regression
from analysis import regression
from sklearn import metrics
from sklearn import preprocessing
import torch
@ -224,7 +226,7 @@ def histo_analysis(hist_data):
return basic_stats(derivative)[0], basic_stats(derivative)[3]
@jit(forceobj=True)
def regression_engine(device, inputs, outputs, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.1, *args):
def regression_engine(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01):
regressions = []
@ -234,18 +236,18 @@ def regression_engine(device, inputs, outputs, loss = torch.nn.MSELoss(), _itera
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]])
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)
regressions.append([model[0].parameter, model[1][::-1][0]])
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)
regressions.append([model[0].parameter, model[1][::-1][0]])
regressions.append([model[0].parameters, model[1][::-1][0]])
#if 'poly' in args:
@ -254,7 +256,7 @@ def regression_engine(device, inputs, outputs, loss = torch.nn.MSELoss(), _itera
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]])
regressions.append([model[0].parameters, model[1][::-1][0]])
else:
@ -263,17 +265,17 @@ def regression_engine(device, inputs, outputs, loss = torch.nn.MSELoss(), _itera
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]])
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)
regressions.append([model[0].parameter, model[1][::-1][0]])
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)
regressions.append([model[0].parameter, model[1][::-1][0]])
regressions.append([model[0].parameters, model[1][::-1][0]])
#if 'poly' in args:
@ -282,7 +284,9 @@ def regression_engine(device, inputs, outputs, loss = torch.nn.MSELoss(), _itera
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]])
regressions.append([model[0].parameters, model[1][::-1][0]])
return regressions
@jit(forceobj=True)
def r_squared(predictions, targets): # assumes equal size inputs