From daab3e8c5bba0892f61ff256d57d877e6f8abecb Mon Sep 17 00:00:00 2001 From: jlevine18 Date: Thu, 26 Sep 2019 13:34:42 -0500 Subject: [PATCH] wait arthur moved this --- data analysis/cudaregress.py | 217 ----------------------------------- 1 file changed, 217 deletions(-) delete mode 100644 data analysis/cudaregress.py diff --git a/data analysis/cudaregress.py b/data analysis/cudaregress.py deleted file mode 100644 index 2c268a34..00000000 --- a/data analysis/cudaregress.py +++ /dev/null @@ -1,217 +0,0 @@ -# Titan Robotics Team 2022: CUDA-based Regressions Module -# Written by Arthur Lu & Jacob Levine -# Notes: -# this should be imported as a python module using 'import cudaregress' -# this should be included in the local directory or environment variable -# this module is cuda-optimized and vectorized (except for one small part) -# setup: - -__version__ = "1.0.0.002" - -# changelog should be viewed using print(cudaregress.__changelog__) -__changelog__ = """ -1.0.0.002: - -Added more parameters to log, exponential, polynomial - - - -1.0.0.001: - -initial release, with linear, log, exponential, polynomial, and sigmoid kernels - -already vectorized (except for polynomial generation) and CUDA-optimized - -""" - -__author__ = ( - "Jacob Levine ", -) - -__all__ = [ - 'factorial', - 'take_all_pwrs', - 'num_poly_terms', - 'set_device', - 'LinearRegKernel', - 'SigmoidalRegKernel', - 'LogRegKernel', - 'PolyRegKernel', - 'ExpRegKernel', - 'SigmoidalRegKernelArthur', - 'SGDTrain', - 'CustomTrain' -] - - -# imports (just one for now): - -import torch - -device = "cuda:0" if torch.torch.cuda.is_available() else "cpu" - -#todo: document completely - -def factorial(n): - if n==0: - return 1 - else: - return n*factorial(n-1) -def num_poly_terms(num_vars, power): - if power == 0: - return 0 - return int(factorial(num_vars+power-1) / factorial(power) / factorial(num_vars-1)) + nt(num_vars, power-1) - -def take_all_pwrs(vec,pwr): - #todo: vectorize (kinda) - combins=torch.combinations(vec, r=pwr, with_replacement=True) - out=torch.ones(combins.size()[0]) - for i in torch.t(combins): - out *= i - return torch.cat(out,take_all_pwrs(vec, pwr-1)) - -def set_device(new_device): - global device - device=new_device - -class LinearRegKernel(): - parameters= [] - weights=None - bias=None - def __init__(self, num_vars): - self.weights=torch.rand(num_vars, requires_grad=True, device=device) - self.bias=torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.bias] - def forward(self,mtx): - long_bias=self.bias.repeat([1,mtx.size()[1]]) - return torch.matmul(self.weights,mtx)+long_bias - -class SigmoidalRegKernel(): - parameters= [] - weights=None - bias=None - sigmoid=torch.nn.Sigmoid() - def __init__(self, num_vars): - self.weights=torch.rand(num_vars, requires_grad=True, device=device) - self.bias=torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.bias] - def forward(self,mtx): - long_bias=self.bias.repeat([1,mtx.size()[1]]) - return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias) - -class SigmoidalRegKernelArthur(): - parameters= [] - weights=None - in_bias=None - scal_mult=None - out_bias=None - sigmoid=torch.nn.Sigmoid() - def __init__(self, num_vars): - self.weights=torch.rand(num_vars, requires_grad=True, device=device) - self.in_bias=torch.rand(1, requires_grad=True, device=device) - self.scal_mult=torch.rand(1, requires_grad=True, device=device) - self.out_bias==torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias] - def forward(self,mtx): - long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) - long_out_bias=self.out_bias.repeat([1,mtx.size()[1]]) - return (scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias - -class LogRegKernel(): - parameters= [] - weights=None - in_bias=None - scal_mult=None - out_bias=None - def __init__(self, num_vars): - self.weights=torch.rand(num_vars, requires_grad=True, device=device) - self.in_bias=torch.rand(1, requires_grad=True, device=device) - self.scal_mult=torch.rand(1, requires_grad=True, device=device) - self.out_bias==torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias] - def forward(self,mtx): - long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) - long_out_bias=self.out_bias.repeat([1,mtx.size()[1]]) - return (scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias - -class ExpRegKernel(): - parameters= [] - weights=None - in_bias=None - scal_mult=None - out_bias=None - def __init__(self, num_vars): - self.weights=torch.rand(num_vars, requires_grad=True, device=device) - self.in_bias=torch.rand(1, requires_grad=True, device=device) - self.scal_mult=torch.rand(1, requires_grad=True, device=device) - self.out_bias==torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias] - def forward(self,mtx): - long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) - long_out_bias=self.out_bias.repeat([1,mtx.size()[1]]) - return (scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias - -class PolyRegKernel(): - parameters= [] - weights=None - bias=None - power=None - def __init__(self, num_vars, power): - self.power=power - num_terms=num_poly_terms(num_vars, power) - self.weights=torch.rand(num_terms, requires_grad=True, device=device) - self.bias=torch.rand(1, requires_grad=True, device=device) - self.parameters=[self.weights,self.bias] - def forward(self,mtx): - #TODO: Vectorize the last part - cols=[] - for i in torch.t(mtx): - cols.append(take_all_pwrs(i,self.power)) - new_mtx=torch.t(torch.stack(cols)) - long_bias=self.bias.repeat([1,mtx.size()[1]]) - return torch.matmul(self.weights,new_mtx)+long_bias - -def SGDTrain(kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False): - optim=torch.optim.SGD(kernel.parameters, lr=learning_rate) - data_cuda=data.to(device) - ground_cuda=ground.to(device) - if (return_losses): - losses=[] - for i in range(iterations): - with torch.set_grad_enabled(True): - optim.zero_grad() - pred=kernel.forward(data_cuda) - ls=loss(pred,ground_cuda) - losses.append(ls.item()) - ls.backward() - optim.step() - return [kernel,losses] - else: - for i in range(iterations): - with torch.set_grad_enabled(True): - optim.zero_grad() - pred=kernel.forward(data_cuda) - ls=loss(pred,ground_cuda) - ls.backward() - optim.step() - return kernel - -def CustomTrain(kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False): - data_cuda=data.to(device) - ground_cuda=ground.to(device) - if (return_losses): - losses=[] - for i in range(iterations): - with torch.set_grad_enabled(True): - optim.zero_grad() - pred=kernel.forward(data) - ls=loss(pred,ground) - losses.append(ls.item()) - ls.backward() - optim.step() - return [kernel,losses] - else: - for i in range(iterations): - with torch.set_grad_enabled(True): - optim.zero_grad() - pred=kernel.forward(data_cuda) - ls=loss(pred,ground_cuda) - ls.backward() - optim.step() - return kernel