From 2daed73aaa86417bdc51a1077cc444b81a55125d Mon Sep 17 00:00:00 2001 From: Dev Singh Date: Fri, 6 Mar 2020 10:21:23 -0600 Subject: [PATCH] 10:21 unverified --- .../__pycache__/__init__.cpython-37.pyc | Bin 161 -> 0 bytes .../__pycache__/analysis.cpython-37.pyc | Bin 34218 -> 0 bytes analysis-master/build.sh | 0 .../build/lib/analysis/analysis.py | 226 +----------------- .../dist/analysis-1.0.0.6-py3-none-any.whl | Bin 21564 -> 20139 bytes analysis-master/dist/analysis-1.0.0.6.tar.gz | Bin 19456 -> 18395 bytes data analysis/__pycache__/data.cpython-37.pyc | Bin 4251 -> 4240 bytes .../analysis/__init__.py | 0 .../__pycache__/__init__.cpython-37.pyc | Bin 0 -> 150 bytes .../__pycache__/analysis.cpython-36.pyc | Bin .../__pycache__/analysis.cpython-37.pyc | Bin 0 -> 26518 bytes .../__pycache__/regression.cpython-37.pyc | Bin .../__pycache__/titanlearn.cpython-37.pyc | Bin .../__pycache__/trueskill.cpython-37.pyc | Bin 32174 -> 32168 bytes .../analysis/analysis.py | 226 +----------------- 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z6B-DLmVu(0S5%j>kEZONCmY)6t>o!h%YO3eZeHEPi_@n5Z$%ThmoY;W7M|x<%jwAmYL0S2YrUL<9`++wOJTW zV=1s;ZhXghZoDu)o^K#tn$I&9Fgl)z{_uSPM;1qs*Tur", - "Arthur Lu " - ) - - __all__ = [ - 'factorial', - 'take_all_pwrs', - 'num_poly_terms', - 'set_device', - 'LinearRegKernel', - 'SigmoidalRegKernel', - 'LogRegKernel', - 'PolyRegKernel', - 'ExpRegKernel', - 'SigmoidalRegKernelArthur', - 'SGDTrain', - 'CustomTrain' - ] - - global device - - device = "cuda:0" if torch.torch.cuda.is_available() else "cpu" - - #todo: document completely - - def set_device(self, 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 - - 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 (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 - - 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 - - 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 - - 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 - - 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 - class Glicko2: _tau = 0.5 @@ -1016,4 +792,4 @@ class Glicko2: def did_not_compete(self): - self._preRatingRD() \ No newline at end of file + self._preRatingRD() diff --git a/analysis-master/dist/analysis-1.0.0.6-py3-none-any.whl b/analysis-master/dist/analysis-1.0.0.6-py3-none-any.whl index bc4762c9623b23ebe761e28a1631aa4b25352971..46500f86c269d4a7f915abefc51f01369e29690f 100644 GIT binary patch delta 9361 zcmZ8{Wl$X2vi0EZ5G1%0+yVsm;5r0%cXu*_yAA{iE`z&Ea19V-aCdhL7V@2QU)8I3 zzg^Y4y1G}dAG=o7w(cr}tIUDJQddGiBm@8er~u&r>trl$?0`UP^{i04UrA&Dz-Q-V zWm?cZtAA<*ktqyQVMwTHpq#!1QlWCDAjER1?MIzAI&_dl7=(z|vU0`#gYIMfUtVz-1_GT7gJIb*pLe1Yx_8GUa(^GmxA 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'ExpRegKernel', - 'SigmoidalRegKernelArthur', - 'SGDTrain', - 'CustomTrain' - ] - - global device - - device = "cuda:0" if torch.torch.cuda.is_available() else "cpu" - - #todo: document completely - - def set_device(self, 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 - - 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 (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 - - 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 - - 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 - - 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 - - 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 - class Glicko2: _tau = 0.5 @@ -1016,4 +792,4 @@ class Glicko2: def did_not_compete(self): - self._preRatingRD() \ No newline at end of file + self._preRatingRD() diff --git a/analysis-master/analysis/regression.py b/data analysis/analysis/regression.py similarity index 100% rename from analysis-master/analysis/regression.py rename to data analysis/analysis/regression.py diff --git a/analysis-master/analysis/titanlearn.py b/data analysis/analysis/titanlearn.py similarity index 100% rename from analysis-master/analysis/titanlearn.py rename to data analysis/analysis/titanlearn.py diff --git a/analysis-master/analysis/trueskill.py b/data analysis/analysis/trueskill.py similarity index 100% rename from analysis-master/analysis/trueskill.py rename to data analysis/analysis/trueskill.py diff --git a/analysis-master/analysis/visualization.py b/data analysis/analysis/visualization.py similarity index 100% rename from analysis-master/analysis/visualization.py rename to data analysis/analysis/visualization.py