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converted space indentation to tab indentation

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ltcptgeneral
2020-05-01 16:15:07 -05:00
parent e3623dec5b
commit 98c48897e0
12 changed files with 2060 additions and 2060 deletions
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1204
analysis-master/analysis/analysis.py
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4
analysis-master/analysis/metrics/elo.py
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@@ -2,6 +2,6 @@ import numpy as np
def calculate(starting_score, opposing_score, observed, N, K):
expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
return starting_score + K*(np.sum(observed) - np.sum(expected))
return starting_score + K*(np.sum(observed) - np.sum(expected))

158
analysis-master/analysis/metrics/glicko2.py
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@@ -1,99 +1,99 @@
import math
class Glicko2:
_tau = 0.5
_tau = 0.5
def getRating(self):
return (self.__rating * 173.7178) + 1500
def getRating(self):
return (self.__rating * 173.7178) + 1500
def setRating(self, rating):
self.__rating = (rating - 1500) / 173.7178
def setRating(self, rating):
self.__rating = (rating - 1500) / 173.7178
rating = property(getRating, setRating)
rating = property(getRating, setRating)
def getRd(self):
return self.__rd * 173.7178
def getRd(self):
return self.__rd * 173.7178
def setRd(self, rd):
self.__rd = rd / 173.7178
def setRd(self, rd):
self.__rd = rd / 173.7178
rd = property(getRd, setRd)
def __init__(self, rating = 1500, rd = 350, vol = 0.06):
rd = property(getRd, setRd)
def __init__(self, rating = 1500, rd = 350, vol = 0.06):
self.setRating(rating)
self.setRd(rd)
self.vol = vol
def _preRatingRD(self):
self.setRating(rating)
self.setRd(rd)
self.vol = vol
def _preRatingRD(self):
self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
def update_player(self, rating_list, RD_list, outcome_list):
self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
def update_player(self, rating_list, RD_list, outcome_list):
rating_list = [(x - 1500) / 173.7178 for x in rating_list]
RD_list = [x / 173.7178 for x in RD_list]
rating_list = [(x - 1500) / 173.7178 for x in rating_list]
RD_list = [x / 173.7178 for x in RD_list]
v = self._v(rating_list, RD_list)
self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
self._preRatingRD()
self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
tempSum = 0
for i in range(len(rating_list)):
tempSum += self._g(RD_list[i]) * \
(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
self.__rating += math.pow(self.__rd, 2) * tempSum
def _newVol(self, rating_list, RD_list, outcome_list, v):
v = self._v(rating_list, RD_list)
self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
self._preRatingRD()
self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
tempSum = 0
for i in range(len(rating_list)):
tempSum += self._g(RD_list[i]) * \
(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
self.__rating += math.pow(self.__rd, 2) * tempSum
def _newVol(self, rating_list, RD_list, outcome_list, v):
i = 0
delta = self._delta(rating_list, RD_list, outcome_list, v)
a = math.log(math.pow(self.vol, 2))
tau = self._tau
x0 = a
x1 = 0
while x0 != x1:
# New iteration, so x(i) becomes x(i-1)
x0 = x1
d = math.pow(self.__rating, 2) + v + math.exp(x0)
h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
(math.pow(self.__rating, 2) + v) \
/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
x1 = x0 - (h1 / h2)
i = 0
delta = self._delta(rating_list, RD_list, outcome_list, v)
a = math.log(math.pow(self.vol, 2))
tau = self._tau
x0 = a
x1 = 0
while x0 != x1:
# New iteration, so x(i) becomes x(i-1)
x0 = x1
d = math.pow(self.__rating, 2) + v + math.exp(x0)
h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
(math.pow(self.__rating, 2) + v) \
/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
x1 = x0 - (h1 / h2)
return math.exp(x1 / 2)
def _delta(self, rating_list, RD_list, outcome_list, v):
return math.exp(x1 / 2)
def _delta(self, rating_list, RD_list, outcome_list, v):
tempSum = 0
for i in range(len(rating_list)):
tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
return v * tempSum
def _v(self, rating_list, RD_list):
tempSum = 0
for i in range(len(rating_list)):
tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
return v * tempSum
def _v(self, rating_list, RD_list):
tempSum = 0
for i in range(len(rating_list)):
tempE = self._E(rating_list[i], RD_list[i])
tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
return 1 / tempSum
def _E(self, p2rating, p2RD):
tempSum = 0
for i in range(len(rating_list)):
tempE = self._E(rating_list[i], RD_list[i])
tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
return 1 / tempSum
def _E(self, p2rating, p2RD):
return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
(self.__rating - p2rating)))
def _g(self, RD):
return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
(self.__rating - p2rating)))
def _g(self, RD):
return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
def did_not_compete(self):
return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
def did_not_compete(self):
self._preRatingRD()
self._preRatingRD()

1462
analysis-master/analysis/metrics/trueskill.py
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352
analysis-master/analysis/regression.py
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@@ -9,38 +9,38 @@ __version__ = "1.0.0.004"
# changelog should be viewed using print(analysis.regression.__changelog__)
__changelog__ = """
1.0.0.004:
- bug fixes
- fixed changelog
1.0.0.003:
- bug fixes
1.0.0.002:
-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:
-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
-already vectorized (except for polynomial generation) and CUDA-optimized
1.0.0.004:
- bug fixes
- fixed changelog
1.0.0.003:
- bug fixes
1.0.0.002:
-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:
-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
-already vectorized (except for polynomial generation) and CUDA-optimized
"""
__author__ = (
"Jacob Levine <jlevine@imsa.edu>",
"Arthur Lu <learthurgo@gmail.com>"
"Jacob Levine <jlevine@imsa.edu>",
"Arthur Lu <learthurgo@gmail.com>"
)
__all__ = [
'factorial',
'take_all_pwrs',
'num_poly_terms',
'set_device',
'LinearRegKernel',
'SigmoidalRegKernel',
'LogRegKernel',
'PolyRegKernel',
'ExpRegKernel',
'SigmoidalRegKernelArthur',
'SGDTrain',
'CustomTrain'
'factorial',
'take_all_pwrs',
'num_poly_terms',
'set_device',
'LinearRegKernel',
'SigmoidalRegKernel',
'LogRegKernel',
'PolyRegKernel',
'ExpRegKernel',
'SigmoidalRegKernelArthur',
'SGDTrain',
'CustomTrain'
]
import torch
@@ -52,169 +52,169 @@ device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
#todo: document completely
def set_device(self, new_device):
device=new_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
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)
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 (self.scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
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 (self.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 (self.scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
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 (self.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 (self.scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
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 (self.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=self.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 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):
#TODO: Vectorize the last part
cols=[]
for i in torch.t(mtx):
cols.append(self.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
parameters= []
weights=None
bias=None
power=None
def __init__(self, num_vars, power):
self.power=power
num_terms=self.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 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):
#TODO: Vectorize the last part
cols=[]
for i in torch.t(mtx):
cols.append(self.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(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)
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
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(self, 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
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

152
analysis-master/analysis/titanlearn.py
View File

@@ -11,112 +11,112 @@ __version__ = "2.0.1.001"
#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
2.0.1.001:
- removed matplotlib import
- removed graphloss()
2.0.1.000:
- added net, dataset, dataloader, and stdtrain template definitions
- added graphloss function
2.0.0.001:
- added clear functions
2.0.0.000:
- complete rewrite planned
- depreciated 1.0.0.xxx versions
- added simple training loop
1.0.0.xxx:
-added generation of ANNS, basic SGD training
2.0.1.001:
- removed matplotlib import
- removed graphloss()
2.0.1.000:
- added net, dataset, dataloader, and stdtrain template definitions
- added graphloss function
2.0.0.001:
- added clear functions
2.0.0.000:
- complete rewrite planned
- depreciated 1.0.0.xxx versions
- added simple training loop
1.0.0.xxx:
-added generation of ANNS, basic SGD training
"""
__author__ = (
"Arthur Lu <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
"Arthur Lu <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'clear',
'net',
'dataset',
'dataloader',
'train',
'stdtrainer',
]
'clear',
'net',
'dataset',
'dataloader',
'train',
'stdtrainer',
]
import torch
from os import system, name
import numpy as np
def clear():
if name == 'nt':
_ = system('cls')
else:
_ = system('clear')
if name == 'nt':
_ = system('cls')
else:
_ = system('clear')
class net(torch.nn.Module): #template for standard neural net
def __init__(self):
super(Net, self).__init__()
def forward(self, input):
pass
def __init__(self):
super(Net, self).__init__()
def forward(self, input):
pass
class dataset(torch.utils.data.Dataset): #template for standard dataset
def __init__(self):
super(torch.utils.data.Dataset).__init__()
def __getitem__(self, index):
pass
def __len__(self):
pass
def __init__(self):
super(torch.utils.data.Dataset).__init__()
def __getitem__(self, index):
pass
def __len__(self):
pass
def dataloader(dataset, batch_size, num_workers, shuffle = True):
return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels)
dataset_len = trainloader.dataset.__len__()
iter_count = 0
running_loss = 0
running_loss_list = []
dataset_len = trainloader.dataset.__len__()
iter_count = 0
running_loss = 0
running_loss_list = []
for epoch in range(epochs): # loop over the dataset multiple times
for epoch in range(epochs): # loop over the dataset multiple times
for i, data in enumerate(trainloader, 0):
for i, data in enumerate(trainloader, 0):
inputs = data[0].to(device)
labels = data[1].to(device)
inputs = data[0].to(device)
labels = data[1].to(device)
optimizer.zero_grad()
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels.to(torch.float))
loss.backward()
optimizer.step()
outputs = net(inputs)
loss = criterion(outputs, labels.to(torch.float))
loss.backward()
optimizer.step()
# monitoring steps below
# monitoring steps below
iter_count += 1
running_loss += loss.item()
running_loss_list.append(running_loss)
clear()
iter_count += 1
running_loss += loss.item()
running_loss_list.append(running_loss)
clear()
print("training on: " + device)
print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
print("current batch loss: " + str(loss.item))
print("running loss: " + str(running_loss / iter_count))
return net, running_loss_list
print("finished training")
print("training on: " + device)
print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
print("current batch loss: " + str(loss.item))
print("running loss: " + str(running_loss / iter_count))
return net, running_loss_list
print("finished training")
def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = net.to(device)
criterion = criterion.to(device)
optimizer = optimizer.to(device)
trainloader = dataloader
return train(device, net, epochs, trainloader, optimizer, criterion)
net = net.to(device)
criterion = criterion.to(device)
optimizer = optimizer.to(device)
trainloader = dataloader
return train(device, net, epochs, trainloader, optimizer, criterion)

24
analysis-master/analysis/visualization.py
View File

@@ -10,25 +10,25 @@ __version__ = "1.0.0.000"
#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.0.0.000:
- created visualization.py
- added graphloss()
- added imports
1.0.0.000:
- created visualization.py
- added graphloss()
- added imports
"""
__author__ = (
"Arthur Lu <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
"Arthur Lu <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'graphloss',
]
'graphloss',
]
import matplotlib.pyplot as plt
def graphloss(losses):
x = range(0, len(losses))
plt.plot(x, losses)
plt.show()
x = range(0, len(losses))
plt.plot(x, losses)
plt.show()

36
analysis-master/setup.py
View File

@@ -3,24 +3,24 @@ import setuptools
requirements = []
with open("requirements.txt", 'r') as file:
for line in file:
requirements.append(line)
for line in file:
requirements.append(line)
setuptools.setup(
name="analysis",
version="1.0.0.012",
author="The Titan Scouting Team",
author_email="titanscout2022@gmail.com",
description="analysis package developed by Titan Scouting for The Red Alliance",
long_description="",
long_description_content_type="text/markdown",
url="https://github.com/titanscout2022/tr2022-strategy",
packages=setuptools.find_packages(),
install_requires=requirements,
license = "GNU General Public License v3.0",
classifiers=[
"Programming Language :: Python :: 3",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
name="analysis",
version="1.0.0.012",
author="The Titan Scouting Team",
author_email="titanscout2022@gmail.com",
description="analysis package developed by Titan Scouting for The Red Alliance",
long_description="",
long_description_content_type="text/markdown",
url="https://github.com/titanscout2022/tr2022-strategy",
packages=setuptools.find_packages(),
install_requires=requirements,
license = "GNU General Public License v3.0",
classifiers=[
"Programming Language :: Python :: 3",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
)