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Merge branch 'master' of https://github.com/titanscout2022/tr2022-strategy

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ltcptgeneral 2019-09-27 09:49:40 -05:00
commit 2c00f5b26e
1 changed files with 7 additions and 6 deletions
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data analysis/analysis/regression.py
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@ -1,7 +1,7 @@
# 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 should be imported as a python module using 'import cudaregress'
# this should be included in the local directory or environment variable # 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:
@ -12,7 +12,8 @@ __version__ = "1.0.0.002"
__changelog__ = """ __changelog__ = """
1.0.0.002: 1.0.0.002:
-Added more parameters to log, exponential, polynomial -Added more parameters to log, exponential, polynomial
- -Added SigmoidalRegKernelArthur, because Arthur apparently needs
to train the scaling and shifting of sigmoids
1.0.0.001: 1.0.0.001:
-initial release, with linear, log, exponential, polynomial, and sigmoid kernels -initial release, with linear, log, exponential, polynomial, and sigmoid kernels
@ -56,7 +57,7 @@ def factorial(n):
def num_poly_terms(num_vars, power): def num_poly_terms(num_vars, power):
if power == 0: if power == 0:
return 0 return 0
return int(factorial(num_vars+power-1) / factorial(power) / factorial(num_vars-1)) + nt(num_vars, power-1) 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): def take_all_pwrs(vec,pwr):
#todo: vectorize (kinda) #todo: vectorize (kinda)
@ -106,7 +107,7 @@ class SigmoidalRegKernelArthur():
self.weights=torch.rand(num_vars, requires_grad=True, device=device) self.weights=torch.rand(num_vars, requires_grad=True, device=device)
self.in_bias=torch.rand(1, 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.scal_mult=torch.rand(1, requires_grad=True, device=device)
self.out_bias==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] self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
def forward(self,mtx): def forward(self,mtx):
long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
@ -123,7 +124,7 @@ class LogRegKernel():
self.weights=torch.rand(num_vars, requires_grad=True, device=device) self.weights=torch.rand(num_vars, requires_grad=True, device=device)
self.in_bias=torch.rand(1, 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.scal_mult=torch.rand(1, requires_grad=True, device=device)
self.out_bias==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] self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
def forward(self,mtx): def forward(self,mtx):
long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
@ -140,7 +141,7 @@ class ExpRegKernel():
self.weights=torch.rand(num_vars, requires_grad=True, device=device) self.weights=torch.rand(num_vars, requires_grad=True, device=device)
self.in_bias=torch.rand(1, 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.scal_mult=torch.rand(1, requires_grad=True, device=device)
self.out_bias==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] self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
def forward(self,mtx): def forward(self,mtx):
long_in_bias=self.in_bias.repeat([1,mtx.size()[1]]) long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])