analysis pkg v 1.0.0.7

This commit is contained in:
ltcptgeneral 2020-03-06 20:32:41 -06:00
parent 484f266659
commit f5b2ae0811
19 changed files with 42 additions and 44 deletions

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Metadata-Version: 2.1 Metadata-Version: 2.1
Name: analysis Name: analysis
Version: 1.0.0.6 Version: 1.0.0.7
Summary: analysis package developed by Titan Scouting for The Red Alliance Summary: analysis package developed by Titan Scouting for The Red Alliance
Home-page: https://github.com/titanscout2022/tr2022-strategy Home-page: https://github.com/titanscout2022/tr2022-strategy
Author: The Titan Scouting Team Author: The Titan Scouting Team

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# Titan Robotics Team 2022: CUDA-based Regressions Module # Titan Robotics Team 2022: CUDA-based Regressions Module
# Written by Arthur Lu & Jacob Levine # Written by Arthur Lu & Jacob Levine
# Notes: # Notes:
# this should be imported as a python module using 'import regression' # this module has been automatically inegrated into analysis.py, and should be callable as a class from the package
# this should be included in the local directory or environment variable # this module is cuda-optimized and vectorized (except for one small part)
# this module is cuda-optimized and vectorized (except for one small part)
# setup: # setup:
__version__ = "1.0.0.002" __version__ = "1.0.0.003"
# changelog should be viewed using print(regression.__changelog__) # changelog should be viewed using print(analysis.regression.__changelog__)
__changelog__ = """ __changelog__ = """
1.0.0.002: 1.0.0.003:
-Added more parameters to log, exponential, polynomial - bug fixes
-Added SigmoidalRegKernelArthur, because Arthur apparently needs 1.0.0.002:
to train the scaling and shifting of sigmoids -Added more parameters to log, exponential, polynomial
-Added SigmoidalRegKernelArthur, because Arthur apparently needs
1.0.0.001: to train the scaling and shifting of sigmoids
-initial release, with linear, log, exponential, polynomial, and sigmoid kernels 1.0.0.001:
-already vectorized (except for polynomial generation) and CUDA-optimized -initial release, with linear, log, exponential, polynomial, and sigmoid kernels
-already vectorized (except for polynomial generation) and CUDA-optimized
""" """
__author__ = ( __author__ = (
"Jacob Levine <jlevine@imsa.edu>", "Jacob Levine <jlevine@imsa.edu>",
"Arthur Lu <learthurgo@gmail.com>"
) )
__all__ = [ __all__ = [
@ -39,35 +40,13 @@ __all__ = [
'CustomTrain' 'CustomTrain'
] ]
global device
# imports (just one for now):
import torch
device = "cuda:0" if torch.torch.cuda.is_available() else "cpu" device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
#todo: document completely #todo: document completely
def factorial(n): def set_device(self, new_device):
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)) + num_poly_terms(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 device=new_device
class LinearRegKernel(): class LinearRegKernel():
@ -154,20 +133,39 @@ class PolyRegKernel():
power=None power=None
def __init__(self, num_vars, power): def __init__(self, num_vars, power):
self.power=power self.power=power
num_terms=num_poly_terms(num_vars, power) num_terms=self.num_poly_terms(num_vars, power)
self.weights=torch.rand(num_terms, requires_grad=True, device=device) self.weights=torch.rand(num_terms, requires_grad=True, device=device)
self.bias=torch.rand(1, requires_grad=True, device=device) self.bias=torch.rand(1, requires_grad=True, device=device)
self.parameters=[self.weights,self.bias] self.parameters=[self.weights,self.bias]
def num_poly_terms(self,num_vars, power):
if power == 0:
return 0
return int(self.factorial(num_vars+power-1) / self.factorial(power) / self.factorial(num_vars-1)) + self.num_poly_terms(num_vars, power-1)
def factorial(self,n):
if n==0:
return 1
else:
return n*self.factorial(n-1)
def take_all_pwrs(self, vec, pwr):
#todo: vectorize (kinda)
combins=torch.combinations(vec, r=pwr, with_replacement=True)
out=torch.ones(combins.size()[0]).to(device).to(torch.float)
for i in torch.t(combins).to(device).to(torch.float):
out *= i
if pwr == 1:
return out
else:
return torch.cat((out,self.take_all_pwrs(vec, pwr-1)))
def forward(self,mtx): def forward(self,mtx):
#TODO: Vectorize the last part #TODO: Vectorize the last part
cols=[] cols=[]
for i in torch.t(mtx): for i in torch.t(mtx):
cols.append(take_all_pwrs(i,self.power)) cols.append(self.take_all_pwrs(i,self.power))
new_mtx=torch.t(torch.stack(cols)) new_mtx=torch.t(torch.stack(cols))
long_bias=self.bias.repeat([1,mtx.size()[1]]) long_bias=self.bias.repeat([1,mtx.size()[1]])
return torch.matmul(self.weights,new_mtx)+long_bias 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): def SGDTrain(self, kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):
optim=torch.optim.SGD(kernel.parameters, lr=learning_rate) optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
data_cuda=data.to(device) data_cuda=data.to(device)
ground_cuda=ground.to(device) ground_cuda=ground.to(device)
@ -192,7 +190,7 @@ def SGDTrain(kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, lea
optim.step() optim.step()
return kernel return kernel
def CustomTrain(kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False): def CustomTrain(self, kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):
data_cuda=data.to(device) data_cuda=data.to(device)
ground_cuda=ground.to(device) ground_cuda=ground.to(device)
if (return_losses): if (return_losses):

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@ -2,7 +2,7 @@ import setuptools
setuptools.setup( setuptools.setup(
name="analysis", # Replace with your own username name="analysis", # Replace with your own username
version="1.0.0.006", version="1.0.0.007",
author="The Titan Scouting Team", author="The Titan Scouting Team",
author_email="titanscout2022@gmail.com", author_email="titanscout2022@gmail.com",
description="analysis package developed by Titan Scouting for The Red Alliance", description="analysis package developed by Titan Scouting for The Red Alliance",