From 6f898d6772459e068c16abe3ca83dbb1cd405512 Mon Sep 17 00:00:00 2001 From: art Date: Tue, 3 Mar 2020 20:30:54 -0600 Subject: [PATCH] packagefied analysis (finally) --- data analysis/__pycache__/data.cpython-37.pyc | Bin 2703 -> 0 bytes .../analysis.egg-info/PKG-INFO | 15 + .../analysis.egg-info/SOURCES.txt | 11 + .../analysis.egg-info/dependency_links.txt | 1 + .../analysis.egg-info/top_level.txt | 1 + .../.ipynb_checkpoints/analysis-checkpoint.py | 0 .../analysis/__init__.py | 0 .../__pycache__/__init__.cpython-37.pyc | Bin .../__pycache__/analysis.cpython-36.pyc | Bin .../__pycache__/analysis.cpython-37.pyc | Bin .../__pycache__/regression.cpython-37.pyc | Bin .../__pycache__/titanlearn.cpython-37.pyc | Bin .../__pycache__/trueskill.cpython-37.pyc | Bin .../analysis/analysis.py | 0 .../analysis/regression.py | 434 ++++---- .../analysis/titanlearn.py | 0 .../analysis/trueskill.py | 0 .../analysis/visualization.py | 0 .../build/lib/analysis/__init__.py | 0 .../build/lib/analysis/analysis.py | 952 ++++++++++++++++++ .../build/lib/analysis/regression.py | 217 ++++ .../build/lib/analysis/titanlearn.py | 122 +++ .../build/lib/analysis/trueskill.py | 907 +++++++++++++++++ .../build/lib/analysis/visualization.py | 34 + .../dist/analysis-1.0.0.0-py3-none-any.whl | Bin 0 -> 21422 bytes .../dist/analysis-1.0.0.0.tar.gz | Bin 0 -> 19298 bytes data analysis/{ => analysis-master}/setup.py | 0 data analysis/superscript.py | 4 - 28 files changed, 2477 insertions(+), 221 deletions(-) delete mode 100644 data analysis/__pycache__/data.cpython-37.pyc create mode 100644 data analysis/analysis-master/analysis.egg-info/PKG-INFO create mode 100644 data analysis/analysis-master/analysis.egg-info/SOURCES.txt create mode 100644 data analysis/analysis-master/analysis.egg-info/dependency_links.txt create mode 100644 data analysis/analysis-master/analysis.egg-info/top_level.txt rename data analysis/{ => analysis-master}/analysis/.ipynb_checkpoints/analysis-checkpoint.py (100%) rename data analysis/{ => analysis-master}/analysis/__init__.py (100%) rename data analysis/{ => analysis-master}/analysis/__pycache__/__init__.cpython-37.pyc (100%) rename data analysis/{ => analysis-master}/analysis/__pycache__/analysis.cpython-36.pyc (100%) rename data analysis/{ => analysis-master}/analysis/__pycache__/analysis.cpython-37.pyc (100%) rename data analysis/{ => analysis-master}/analysis/__pycache__/regression.cpython-37.pyc (100%) rename data analysis/{ => analysis-master}/analysis/__pycache__/titanlearn.cpython-37.pyc (100%) rename data analysis/{ => analysis-master}/analysis/__pycache__/trueskill.cpython-37.pyc (100%) rename data analysis/{ => analysis-master}/analysis/analysis.py (100%) rename data analysis/{ => analysis-master}/analysis/regression.py (97%) rename data analysis/{ => analysis-master}/analysis/titanlearn.py (100%) rename data analysis/{ => 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--git a/data analysis/analysis-master/analysis.egg-info/SOURCES.txt b/data analysis/analysis-master/analysis.egg-info/SOURCES.txt new file mode 100644 index 00000000..ea473c34 --- /dev/null +++ b/data analysis/analysis-master/analysis.egg-info/SOURCES.txt @@ -0,0 +1,11 @@ +setup.py +analysis/__init__.py +analysis/analysis.py +analysis/regression.py +analysis/titanlearn.py +analysis/trueskill.py +analysis/visualization.py +analysis.egg-info/PKG-INFO +analysis.egg-info/SOURCES.txt +analysis.egg-info/dependency_links.txt +analysis.egg-info/top_level.txt \ No newline at end of file diff --git a/data analysis/analysis-master/analysis.egg-info/dependency_links.txt b/data analysis/analysis-master/analysis.egg-info/dependency_links.txt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/data analysis/analysis-master/analysis.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/data analysis/analysis-master/analysis.egg-info/top_level.txt b/data analysis/analysis-master/analysis.egg-info/top_level.txt new file mode 100644 index 00000000..09ad3be3 --- /dev/null +++ b/data analysis/analysis-master/analysis.egg-info/top_level.txt @@ -0,0 +1 @@ +analysis diff --git a/data analysis/analysis/.ipynb_checkpoints/analysis-checkpoint.py b/data analysis/analysis-master/analysis/.ipynb_checkpoints/analysis-checkpoint.py similarity index 100% rename from data analysis/analysis/.ipynb_checkpoints/analysis-checkpoint.py rename to data analysis/analysis-master/analysis/.ipynb_checkpoints/analysis-checkpoint.py diff --git a/data analysis/analysis/__init__.py b/data analysis/analysis-master/analysis/__init__.py similarity index 100% rename from data analysis/analysis/__init__.py rename to data analysis/analysis-master/analysis/__init__.py diff --git a/data analysis/analysis/__pycache__/__init__.cpython-37.pyc b/data analysis/analysis-master/analysis/__pycache__/__init__.cpython-37.pyc similarity index 100% rename from data 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analysis/analysis-master/analysis/analysis.py diff --git a/data analysis/analysis/regression.py b/data analysis/analysis-master/analysis/regression.py similarity index 97% rename from data analysis/analysis/regression.py rename to data analysis/analysis-master/analysis/regression.py index 4ebc101a..6cbe7868 100644 --- a/data analysis/analysis/regression.py +++ b/data analysis/analysis-master/analysis/regression.py @@ -1,217 +1,217 @@ -# 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 regression' -# 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(regression.__changelog__) -__changelog__ = """ - 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 ", -) - -__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)) + 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 - -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=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 +# 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 regression' +# 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(regression.__changelog__) +__changelog__ = """ + 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 ", +) + +__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)) + 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 + +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=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 diff --git a/data analysis/analysis/titanlearn.py b/data analysis/analysis-master/analysis/titanlearn.py similarity index 100% rename from data analysis/analysis/titanlearn.py rename to data analysis/analysis-master/analysis/titanlearn.py diff --git a/data analysis/analysis/trueskill.py b/data analysis/analysis-master/analysis/trueskill.py similarity index 100% rename from data analysis/analysis/trueskill.py rename to data analysis/analysis-master/analysis/trueskill.py diff --git a/data analysis/analysis/visualization.py b/data analysis/analysis-master/analysis/visualization.py similarity index 100% rename from data analysis/analysis/visualization.py rename to data analysis/analysis-master/analysis/visualization.py diff --git a/data analysis/analysis-master/build/lib/analysis/__init__.py b/data analysis/analysis-master/build/lib/analysis/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/data analysis/analysis-master/build/lib/analysis/analysis.py b/data analysis/analysis-master/build/lib/analysis/analysis.py new file mode 100644 index 00000000..40c12eac --- /dev/null +++ b/data analysis/analysis-master/build/lib/analysis/analysis.py @@ -0,0 +1,952 @@ +# Titan Robotics Team 2022: Data Analysis Module +# Written by Arthur Lu & Jacob Levine +# Notes: +# this should be imported as a python module using 'import analysis' +# this should be included in the local directory or environment variable +# this module has been optimized for multhreaded computing +# current benchmark of optimization: 1.33 times faster +# setup: + +__version__ = "1.1.12.003" + +# changelog should be viewed using print(analysis.__changelog__) +__changelog__ = """changelog: + 1.1.12.003: + - removed depreciated code + 1.1.12.002: + - removed team first time trueskill instantiation in favor of integration in superscript.py + 1.1.12.001: + - improved readibility of regression outputs by stripping tensor data + - used map with lambda to acheive the improved readibility + - lost numba jit support with regression, and generated_jit hangs at execution + - TODO: reimplement correct numba integration in regression + 1.1.12.000: + - temporarily fixed polynomial regressions by using sklearn's PolynomialFeatures + 1.1.11.010: + - alphabeticaly ordered import lists + 1.1.11.009: + - bug fixes + 1.1.11.008: + - bug fixes + 1.1.11.007: + - bug fixes + 1.1.11.006: + - tested min and max + - bug fixes + 1.1.11.005: + - added min and max in basic_stats + 1.1.11.004: + - bug fixes + 1.1.11.003: + - bug fixes + 1.1.11.002: + - consolidated metrics + - fixed __all__ + 1.1.11.001: + - added test/train split to RandomForestClassifier and RandomForestRegressor + 1.1.11.000: + - added RandomForestClassifier and RandomForestRegressor + - note: untested + 1.1.10.000: + - added numba.jit to remaining functions + 1.1.9.002: + - kernelized PCA and KNN + 1.1.9.001: + - fixed bugs with SVM and NaiveBayes + 1.1.9.000: + - added SVM class, subclasses, and functions + - note: untested + 1.1.8.000: + - added NaiveBayes classification engine + - note: untested + 1.1.7.000: + - added knn() + - added confusion matrix to decisiontree() + 1.1.6.002: + - changed layout of __changelog to be vscode friendly + 1.1.6.001: + - added additional hyperparameters to decisiontree() + 1.1.6.000: + - fixed __version__ + - fixed __all__ order + - added decisiontree() + 1.1.5.003: + - added pca + 1.1.5.002: + - reduced import list + - added kmeans clustering engine + 1.1.5.001: + - simplified regression by using .to(device) + 1.1.5.000: + - added polynomial regression to regression(); untested + 1.1.4.000: + - added trueskill() + 1.1.3.002: + - renamed regression class to Regression, regression_engine() to regression gliko2_engine class to Gliko2 + 1.1.3.001: + - changed glicko2() to return tuple instead of array + 1.1.3.000: + - added glicko2_engine class and glicko() + - verified glicko2() accuracy + 1.1.2.003: + - fixed elo() + 1.1.2.002: + - added elo() + - elo() has bugs to be fixed + 1.1.2.001: + - readded regrression import + 1.1.2.000: + - integrated regression.py as regression class + - removed regression import + - fixed metadata for regression class + - fixed metadata for analysis class + 1.1.1.001: + - regression_engine() bug fixes, now actaully regresses + 1.1.1.000: + - added regression_engine() + - added all regressions except polynomial + 1.1.0.007: + - updated _init_device() + 1.1.0.006: + - removed useless try statements + 1.1.0.005: + - removed impossible outcomes + 1.1.0.004: + - added performance metrics (r^2, mse, rms) + 1.1.0.003: + - resolved nopython mode for mean, median, stdev, variance + 1.1.0.002: + - snapped (removed) majority of uneeded imports + - forced object mode (bad) on all jit + - TODO: stop numba complaining about not being able to compile in nopython mode + 1.1.0.001: + - removed from sklearn import * to resolve uneeded wildcard imports + 1.1.0.000: + - removed c_entities,nc_entities,obstacles,objectives from __all__ + - applied numba.jit to all functions + - depreciated and removed stdev_z_split + - cleaned up histo_analysis to include numpy and numba.jit optimizations + - depreciated and removed all regression functions in favor of future pytorch optimizer + - depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data) + - optimized z_normalize using sklearn.preprocessing.normalize + - TODO: implement kernel/function based pytorch regression optimizer + 1.0.9.000: + - refactored + - numpyed everything + - removed stats in favor of numpy functions + 1.0.8.005: + - minor fixes + 1.0.8.004: + - removed a few unused dependencies + 1.0.8.003: + - added p_value function + 1.0.8.002: + - updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001 + 1.0.8.001: + - refactors + - bugfixes + 1.0.8.000: + - depreciated histo_analysis_old + - depreciated debug + - altered basic_analysis to take array data instead of filepath + - refactor + - optimization + 1.0.7.002: + - bug fixes + 1.0.7.001: + - bug fixes + 1.0.7.000: + - added tanh_regression (logistical regression) + - bug fixes + 1.0.6.005: + - added z_normalize function to normalize dataset + - bug fixes + 1.0.6.004: + - bug fixes + 1.0.6.003: + - bug fixes + 1.0.6.002: + - bug fixes + 1.0.6.001: + - corrected __all__ to contain all of the functions + 1.0.6.000: + - added calc_overfit, which calculates two measures of overfit, error and performance + - added calculating overfit to optimize_regression + 1.0.5.000: + - added optimize_regression function, which is a sample function to find the optimal regressions + - optimize_regression function filters out some overfit funtions (functions with r^2 = 1) + - planned addition: overfit detection in the optimize_regression function + 1.0.4.002: + - added __changelog__ + - updated debug function with log and exponential regressions + 1.0.4.001: + - added log regressions + - added exponential regressions + - added log_regression and exp_regression to __all__ + 1.0.3.008: + - added debug function to further consolidate functions + 1.0.3.007: + - added builtin benchmark function + - added builtin random (linear) data generation function + - added device initialization (_init_device) + 1.0.3.006: + - reorganized the imports list to be in alphabetical order + - added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives + 1.0.3.005: + - major bug fixes + - updated historical analysis + - depreciated old historical analysis + 1.0.3.004: + - added __version__, __author__, __all__ + - added polynomial regression + - added root mean squared function + - added r squared function + 1.0.3.003: + - bug fixes + - added c_entities + 1.0.3.002: + - bug fixes + - added nc_entities, obstacles, objectives + - consolidated statistics.py to analysis.py + 1.0.3.001: + - compiled 1d, column, and row basic stats into basic stats function + 1.0.3.000: + - added historical analysis function + 1.0.2.xxx: + - added z score test + 1.0.1.xxx: + - major bug fixes + 1.0.0.xxx: + - added loading csv + - added 1d, column, row basic stats +""" + +__author__ = ( + "Arthur Lu ", + "Jacob Levine ", +) + +__all__ = [ + '_init_device', + 'load_csv', + 'basic_stats', + 'z_score', + 'z_normalize', + 'histo_analysis', + 'regression', + 'elo', + 'gliko2', + 'trueskill', + 'RegressionMetrics', + 'ClassificationMetrics', + 'kmeans', + 'pca', + 'decisiontree', + 'knn_classifier', + 'knn_regressor', + 'NaiveBayes', + 'SVM', + 'random_forest_classifier', + 'random_forest_regressor', + 'Regression', + 'Gliko2', + # all statistics functions left out due to integration in other functions +] + +# now back to your regularly scheduled programming: + +# imports (now in alphabetical order! v 1.0.3.006): + +import csv +import numba +from numba import jit +import numpy as np +import math +import sklearn +from sklearn import * +import torch +try: + from analysis import trueskill as Trueskill +except: + import trueskill as Trueskill + +class error(ValueError): + pass + +def _init_device(): # initiates computation device for ANNs + device = 'cuda:0' if torch.cuda.is_available() else 'cpu' + return device + +def load_csv(filepath): + with open(filepath, newline='') as csvfile: + file_array = np.array(list(csv.reader(csvfile))) + csvfile.close() + return file_array + +# expects 1d array +@jit(forceobj=True) +def basic_stats(data): + + data_t = np.array(data).astype(float) + + _mean = mean(data_t) + _median = median(data_t) + _stdev = stdev(data_t) + _variance = variance(data_t) + _min = npmin(data_t) + _max = npmax(data_t) + + return _mean, _median, _stdev, _variance, _min, _max + +# returns z score with inputs of point, mean and standard deviation of spread +@jit(forceobj=True) +def z_score(point, mean, stdev): + score = (point - mean) / stdev + + return score + +# expects 2d array, normalizes across all axes +@jit(forceobj=True) +def z_normalize(array, *args): + + array = np.array(array) + for arg in args: + array = sklearn.preprocessing.normalize(array, axis = arg) + + return array + +@jit(forceobj=True) +# expects 2d array of [x,y] +def histo_analysis(hist_data): + + hist_data = np.array(hist_data) + derivative = np.array(len(hist_data) - 1, dtype = float) + t = np.diff(hist_data) + derivative = t[1] / t[0] + np.sort(derivative) + + return basic_stats(derivative)[0], basic_stats(derivative)[3] + +def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array + + regressions = [] + Regression().set_device(ndevice) + + if 'lin' in args: # formula: ax + b + + model = Regression().SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor([outputs]).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True) + params = model[0].parameters + params[:] = map(lambda x: x.item(), params) + regressions.append((params, model[1][::-1][0])) + + if 'log' in args: # formula: a log (b(x + c)) + d + + model = Regression().SGDTrain(Regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True) + params = model[0].parameters + params[:] = map(lambda x: x.item(), params) + regressions.append((params, model[1][::-1][0])) + + if 'exp' in args: # formula: a e ^ (b(x + c)) + d + + model = Regression().SGDTrain(Regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True) + params = model[0].parameters + params[:] = map(lambda x: x.item(), params) + regressions.append((params, model[1][::-1][0])) + + if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ... + + plys = [] + limit = len(outputs[0]) + + for i in range(2, limit): + + model = sklearn.preprocessing.PolynomialFeatures(degree = i) + model = sklearn.pipeline.make_pipeline(model, sklearn.linear_model.LinearRegression()) + model = model.fit(np.rot90(inputs), np.rot90(outputs)) + + params = model.steps[1][1].intercept_.tolist() + params = np.append(params, model.steps[1][1].coef_[0].tolist()[1::]) + params.flatten() + params = params.tolist() + + plys.append(params) + + regressions.append(plys) + + if 'sig' in args: # formula: a sig (b(x + c)) + d | sig() = 1/(1 + e ^ -x) + + model = Regression().SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True) + params = model[0].parameters + params[:] = map(lambda x: x.item(), params) + regressions.append((params, model[1][::-1][0])) + + return regressions + +@jit(nopython=True) +def elo(starting_score, opposing_score, observed, N, K): + + expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N)) + + return starting_score + K*(np.sum(observed) - np.sum(expected)) + +@jit(forceobj=True) +def gliko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations): + + player = Gliko2(rating = starting_score, rd = starting_rd, vol = starting_vol) + + player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations) + + return (player.rating, player.rd, player.vol) + +@jit(forceobj=True) +def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]] + + team_ratings = [] + + for team in teams_data: + team_temp = [] + for player in team: + player = Trueskill.Rating(player[0], player[1]) + team_temp.append(player) + team_ratings.append(team_temp) + + return Trueskill.rate(teams_data, observations) + +class RegressionMetrics(): + + def __new__(cls, predictions, targets): + + return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets) + + def r_squared(self, predictions, targets): # assumes equal size inputs + + return sklearn.metrics.r2_score(targets, predictions) + + def mse(self, predictions, targets): + + return sklearn.metrics.mean_squared_error(targets, predictions) + + def rms(self, predictions, targets): + + return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions)) + +class ClassificationMetrics(): + + def __new__(cls, predictions, targets): + + return cls.cm(cls, predictions, targets), cls.cr(cls, predictions, targets) + + def cm(self, predictions, targets): + + return sklearn.metrics.confusion_matrix(targets, predictions) + + def cr(self, predictions, targets): + + return sklearn.metrics.classification_report(targets, predictions) + +@jit(nopython=True) +def mean(data): + + return np.mean(data) + +@jit(nopython=True) +def median(data): + + return np.median(data) + +@jit(nopython=True) +def stdev(data): + + return np.std(data) + +@jit(nopython=True) +def variance(data): + + return np.var(data) + +@jit(nopython=True) +def npmin(data): + + return np.amin(data) + +@jit(nopython=True) +def npmax(data): + + return np.amax(data) + +@jit(forceobj=True) +def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"): + + kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm) + kernel.fit(data) + predictions = kernel.predict(data) + centers = kernel.cluster_centers_ + + return centers, predictions + +@jit(forceobj=True) +def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None): + + kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state) + + return kernel.fit_transform(data) + +@jit(forceobj=True) +def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth) + model = model.fit(data_train,labels_train) + predictions = model.predict(data_test) + metrics = ClassificationMetrics(predictions, labels_test) + + return model, metrics + +@jit(forceobj=True) +def knn_classifier(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.neighbors.KNeighborsClassifier() + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetrics(predictions, labels_test) + +def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None): + + data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) + model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs) + model.fit(data_train, outputs_train) + predictions = model.predict(data_test) + + return model, RegressionMetrics(predictions, outputs_test) + +class NaiveBayes: + + def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetrics(predictions, labels_test) + + def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetrics(predictions, labels_test) + + def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetrics(predictions, labels_test) + + def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetrics(predictions, labels_test) + +class SVM: + + class CustomKernel: + + def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state): + + return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) + + class StandardKernel: + + def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None): + + return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) + + class PrebuiltKernel: + + class Linear: + + def __new__(cls): + + return sklearn.svm.SVC(kernel = 'linear') + + class Polynomial: + + def __new__(cls, power, r_bias): + + return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias) + + class RBF: + + def __new__(cls, gamma): + + return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma) + + class Sigmoid: + + def __new__(cls, r_bias): + + return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias) + + def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs + + return kernel.fit(train_data, train_outputs) + + def eval_classification(self, kernel, test_data, test_outputs): + + predictions = kernel.predict(test_data) + + return ClassificationMetrics(predictions, test_outputs) + + def eval_regression(self, kernel, test_data, test_outputs): + + predictions = kernel.predict(test_data) + + return RegressionMetrics(predictions, test_outputs) + +def random_forest_classifier(data, labels, test_size, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight) + kernel.fit(data_train, labels_train) + predictions = kernel.predict(data_test) + + return kernel, ClassificationMetrics(predictions, labels_test) + +def random_forest_regressor(data, outputs, test_size, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False): + + data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) + kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start) + kernel.fit(data_train, outputs_train) + predictions = kernel.predict(data_test) + + return kernel, RegressionMetrics(predictions, outputs_test) + +class Regression: + + # Titan Robotics Team 2022: CUDA-based Regressions Module + # Written by Arthur Lu & Jacob Levine + # Notes: + # this module has been automatically inegrated into analysis.py, and should be callable as a class from the package + # this module is cuda-optimized and vectorized (except for one small part) + # setup: + + __version__ = "1.0.0.003" + + # changelog should be viewed using print(analysis.regression.__changelog__) + __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 ", + "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 Gliko2: + + _tau = 0.5 + + def getRating(self): + return (self.__rating * 173.7178) + 1500 + + def setRating(self, rating): + self.__rating = (rating - 1500) / 173.7178 + + rating = property(getRating, setRating) + + def getRd(self): + return self.__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): + + 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): + + 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): + + 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): + + 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): + + 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): + + self._preRatingRD() \ No newline at end of file diff --git a/data analysis/analysis-master/build/lib/analysis/regression.py b/data analysis/analysis-master/build/lib/analysis/regression.py new file mode 100644 index 00000000..6cbe7868 --- /dev/null +++ b/data analysis/analysis-master/build/lib/analysis/regression.py @@ -0,0 +1,217 @@ +# 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 regression' +# 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(regression.__changelog__) +__changelog__ = """ + 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 ", +) + +__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)) + 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 + +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=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 diff --git a/data analysis/analysis-master/build/lib/analysis/titanlearn.py b/data analysis/analysis-master/build/lib/analysis/titanlearn.py new file mode 100644 index 00000000..b69d36e3 --- /dev/null +++ b/data analysis/analysis-master/build/lib/analysis/titanlearn.py @@ -0,0 +1,122 @@ +# Titan Robotics Team 2022: ML Module +# Written by Arthur Lu & Jacob Levine +# Notes: +# this should be imported as a python module using 'import titanlearn' +# this should be included in the local directory or environment variable +# this module is optimized for multhreaded computing +# this module learns from its mistakes far faster than 2022's captains +# setup: + +__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 +""" + +__author__ = ( + "Arthur Lu ," + "Jacob Levine ," + ) + +__all__ = [ + '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') + +class net(torch.nn.Module): #template for standard neural net + 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 dataloader(dataset, batch_size, num_workers, shuffle = True): + + 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 = [] + + for epoch in range(epochs): # loop over the dataset multiple times + + for i, data in enumerate(trainloader, 0): + + inputs = data[0].to(device) + labels = data[1].to(device) + + optimizer.zero_grad() + + outputs = net(inputs) + loss = criterion(outputs, labels.to(torch.float)) + + loss.backward() + optimizer.step() + + # monitoring steps below + + 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") + +def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size): + + 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) \ No newline at end of file diff --git a/data analysis/analysis-master/build/lib/analysis/trueskill.py b/data analysis/analysis-master/build/lib/analysis/trueskill.py new file mode 100644 index 00000000..116357df --- /dev/null +++ b/data analysis/analysis-master/build/lib/analysis/trueskill.py @@ -0,0 +1,907 @@ +from __future__ import absolute_import + +from itertools import chain +import math + +from six import iteritems +from six.moves import map, range, zip +from six import iterkeys + +import copy +try: + from numbers import Number +except ImportError: + Number = (int, long, float, complex) + +inf = float('inf') + +class Gaussian(object): + #: Precision, the inverse of the variance. + pi = 0 + #: Precision adjusted mean, the precision multiplied by the mean. + tau = 0 + + def __init__(self, mu=None, sigma=None, pi=0, tau=0): + if mu is not None: + if sigma is None: + raise TypeError('sigma argument is needed') + elif sigma == 0: + raise ValueError('sigma**2 should be greater than 0') + pi = sigma ** -2 + tau = pi * mu + self.pi = pi + self.tau = tau + + @property + def mu(self): + return self.pi and self.tau / self.pi + + @property + def sigma(self): + return math.sqrt(1 / self.pi) if self.pi else inf + + def __mul__(self, other): + pi, tau = self.pi + other.pi, self.tau + other.tau + return Gaussian(pi=pi, tau=tau) + + def __truediv__(self, other): + pi, tau = self.pi - other.pi, self.tau - other.tau + return Gaussian(pi=pi, tau=tau) + + __div__ = __truediv__ # for Python 2 + + def __eq__(self, other): + return self.pi == other.pi and self.tau == other.tau + + def __lt__(self, other): + return self.mu < other.mu + + def __le__(self, other): + return self.mu <= other.mu + + def __gt__(self, other): + return self.mu > other.mu + + def __ge__(self, other): + return self.mu >= other.mu + + def __repr__(self): + return 'N(mu={:.3f}, sigma={:.3f})'.format(self.mu, self.sigma) + + def _repr_latex_(self): + latex = r'\mathcal{{ N }}( {:.3f}, {:.3f}^2 )'.format(self.mu, self.sigma) + return '$%s$' % latex + +class Matrix(list): + def __init__(self, src, height=None, width=None): + if callable(src): + f, src = src, {} + size = [height, width] + if not height: + def set_height(height): + size[0] = height + size[0] = set_height + if not width: + def set_width(width): + size[1] = width + size[1] = set_width + try: + for (r, c), val in f(*size): + src[r, c] = val + except TypeError: + raise TypeError('A callable src must return an interable ' + 'which generates a tuple containing ' + 'coordinate and value') + height, width = tuple(size) + if height is None or width is None: + raise TypeError('A callable src must call set_height and ' + 'set_width if the size is non-deterministic') + if isinstance(src, list): + is_number = lambda x: isinstance(x, Number) + unique_col_sizes = set(map(len, src)) + everything_are_number = filter(is_number, sum(src, [])) + if len(unique_col_sizes) != 1 or not everything_are_number: + raise ValueError('src must be a rectangular array of numbers') + two_dimensional_array = src + elif isinstance(src, dict): + if not height or not width: + w = h = 0 + for r, c in iterkeys(src): + if not height: + h = max(h, r + 1) + if not width: + w = max(w, c + 1) + if not height: + height = h + if not width: + width = w + two_dimensional_array = [] + for r in range(height): + row = [] + two_dimensional_array.append(row) + for c in range(width): + row.append(src.get((r, c), 0)) + else: + raise TypeError('src must be a list or dict or callable') + super(Matrix, self).__init__(two_dimensional_array) + + @property + def height(self): + return len(self) + + @property + def width(self): + return len(self[0]) + + def transpose(self): + height, width = self.height, self.width + src = {} + for c in range(width): + for r in range(height): + src[c, r] = self[r][c] + return type(self)(src, height=width, width=height) + + def minor(self, row_n, col_n): + height, width = self.height, self.width + if not (0 <= row_n < height): + raise ValueError('row_n should be between 0 and %d' % height) + elif not (0 <= col_n < width): + raise ValueError('col_n should be between 0 and %d' % width) + two_dimensional_array = [] + for r in range(height): + if r == row_n: + continue + row = [] + two_dimensional_array.append(row) + for c in range(width): + if c == col_n: + continue + row.append(self[r][c]) + return type(self)(two_dimensional_array) + + def determinant(self): + height, width = self.height, self.width + if height != width: + raise ValueError('Only square matrix can calculate a determinant') + tmp, rv = copy.deepcopy(self), 1. + for c in range(width - 1, 0, -1): + pivot, r = max((abs(tmp[r][c]), r) for r in range(c + 1)) + pivot = tmp[r][c] + if not pivot: + return 0. + tmp[r], tmp[c] = tmp[c], tmp[r] + if r != c: + rv = -rv + rv *= pivot + fact = -1. / pivot + for r in range(c): + f = fact * tmp[r][c] + for x in range(c): + tmp[r][x] += f * tmp[c][x] + return rv * tmp[0][0] + + def adjugate(self): + height, width = self.height, self.width + if height != width: + raise ValueError('Only square matrix can be adjugated') + if height == 2: + a, b = self[0][0], self[0][1] + c, d = self[1][0], self[1][1] + return type(self)([[d, -b], [-c, a]]) + src = {} + for r in range(height): + for c in range(width): + sign = -1 if (r + c) % 2 else 1 + src[r, c] = self.minor(r, c).determinant() * sign + return type(self)(src, height, width) + + def inverse(self): + if self.height == self.width == 1: + return type(self)([[1. / self[0][0]]]) + return (1. / self.determinant()) * self.adjugate() + + def __add__(self, other): + height, width = self.height, self.width + if (height, width) != (other.height, other.width): + raise ValueError('Must be same size') + src = {} + for r in range(height): + for c in range(width): + src[r, c] = self[r][c] + other[r][c] + return type(self)(src, height, width) + + def __mul__(self, other): + if self.width != other.height: + raise ValueError('Bad size') + height, width = self.height, other.width + src = {} + for r in range(height): + for c in range(width): + src[r, c] = sum(self[r][x] * other[x][c] + for x in range(self.width)) + return type(self)(src, height, width) + + def __rmul__(self, other): + if not isinstance(other, Number): + raise TypeError('The operand should be a number') + height, width = self.height, self.width + src = {} + for r in range(height): + for c in range(width): + src[r, c] = other * self[r][c] + return type(self)(src, height, width) + + def __repr__(self): + return '{}({})'.format(type(self).__name__, super(Matrix, self).__repr__()) + + def _repr_latex_(self): + rows = [' && '.join(['%.3f' % cell for cell in row]) for row in self] + latex = r'\begin{matrix} %s \end{matrix}' % r'\\'.join(rows) + return '$%s$' % latex + +def _gen_erfcinv(erfc, math=math): + def erfcinv(y): + """The inverse function of erfc.""" + if y >= 2: + return -100. + elif y <= 0: + return 100. + zero_point = y < 1 + if not zero_point: + y = 2 - y + t = math.sqrt(-2 * math.log(y / 2.)) + x = -0.70711 * \ + ((2.30753 + t * 0.27061) / (1. + t * (0.99229 + t * 0.04481)) - t) + for i in range(2): + err = erfc(x) - y + x += err / (1.12837916709551257 * math.exp(-(x ** 2)) - x * err) + return x if zero_point else -x + return erfcinv + +def _gen_ppf(erfc, math=math): + erfcinv = _gen_erfcinv(erfc, math) + def ppf(x, mu=0, sigma=1): + return mu - sigma * math.sqrt(2) * erfcinv(2 * x) + return ppf + +def erfc(x): + z = abs(x) + t = 1. / (1. + z / 2.) + r = t * math.exp(-z * z - 1.26551223 + t * (1.00002368 + t * ( + 0.37409196 + t * (0.09678418 + t * (-0.18628806 + t * ( + 0.27886807 + t * (-1.13520398 + t * (1.48851587 + t * ( + -0.82215223 + t * 0.17087277 + ))) + ))) + ))) + return 2. - r if x < 0 else r + +def cdf(x, mu=0, sigma=1): + return 0.5 * erfc(-(x - mu) / (sigma * math.sqrt(2))) + + +def pdf(x, mu=0, sigma=1): + return (1 / math.sqrt(2 * math.pi) * abs(sigma) * + math.exp(-(((x - mu) / abs(sigma)) ** 2 / 2))) + +ppf = _gen_ppf(erfc) + +def choose_backend(backend): + if backend is None: # fallback + return cdf, pdf, ppf + elif backend == 'mpmath': + try: + import mpmath + except ImportError: + raise ImportError('Install "mpmath" to use this backend') + return mpmath.ncdf, mpmath.npdf, _gen_ppf(mpmath.erfc, math=mpmath) + elif backend == 'scipy': + try: + from scipy.stats import norm + except ImportError: + raise ImportError('Install "scipy" to use this backend') + return norm.cdf, norm.pdf, norm.ppf + raise ValueError('%r backend is not defined' % backend) + +def available_backends(): + backends = [None] + for backend in ['mpmath', 'scipy']: + try: + __import__(backend) + except ImportError: + continue + backends.append(backend) + return backends + +class Node(object): + + pass + +class Variable(Node, Gaussian): + + def __init__(self): + self.messages = {} + super(Variable, self).__init__() + + def set(self, val): + delta = self.delta(val) + self.pi, self.tau = val.pi, val.tau + return delta + + def delta(self, other): + pi_delta = abs(self.pi - other.pi) + if pi_delta == inf: + return 0. + return max(abs(self.tau - other.tau), math.sqrt(pi_delta)) + + def update_message(self, factor, pi=0, tau=0, message=None): + message = message or Gaussian(pi=pi, tau=tau) + old_message, self[factor] = self[factor], message + return self.set(self / old_message * message) + + def update_value(self, factor, pi=0, tau=0, value=None): + value = value or Gaussian(pi=pi, tau=tau) + old_message = self[factor] + self[factor] = value * old_message / self + return self.set(value) + + def __getitem__(self, factor): + return self.messages[factor] + + def __setitem__(self, factor, message): + self.messages[factor] = message + + def __repr__(self): + args = (type(self).__name__, super(Variable, self).__repr__(), + len(self.messages), '' if len(self.messages) == 1 else 's') + return '<%s %s with %d connection%s>' % args + + +class Factor(Node): + + def __init__(self, variables): + self.vars = variables + for var in variables: + var[self] = Gaussian() + + def down(self): + return 0 + + def up(self): + return 0 + + @property + def var(self): + assert len(self.vars) == 1 + return self.vars[0] + + def __repr__(self): + args = (type(self).__name__, len(self.vars), + '' if len(self.vars) == 1 else 's') + return '<%s with %d connection%s>' % args + + +class PriorFactor(Factor): + + def __init__(self, var, val, dynamic=0): + super(PriorFactor, self).__init__([var]) + self.val = val + self.dynamic = dynamic + + def down(self): + sigma = math.sqrt(self.val.sigma ** 2 + self.dynamic ** 2) + value = Gaussian(self.val.mu, sigma) + return self.var.update_value(self, value=value) + + +class LikelihoodFactor(Factor): + + def __init__(self, mean_var, value_var, variance): + super(LikelihoodFactor, self).__init__([mean_var, value_var]) + self.mean = mean_var + self.value = value_var + self.variance = variance + + def calc_a(self, var): + return 1. / (1. + self.variance * var.pi) + + def down(self): + # update value. + msg = self.mean / self.mean[self] + a = self.calc_a(msg) + return self.value.update_message(self, a * msg.pi, a * msg.tau) + + def up(self): + # update mean. + msg = self.value / self.value[self] + a = self.calc_a(msg) + return self.mean.update_message(self, a * msg.pi, a * msg.tau) + + +class SumFactor(Factor): + + def __init__(self, sum_var, term_vars, coeffs): + super(SumFactor, self).__init__([sum_var] + term_vars) + self.sum = sum_var + self.terms = term_vars + self.coeffs = coeffs + + def down(self): + vals = self.terms + msgs = [var[self] for var in vals] + return self.update(self.sum, vals, msgs, self.coeffs) + + def up(self, index=0): + coeff = self.coeffs[index] + coeffs = [] + for x, c in enumerate(self.coeffs): + try: + if x == index: + coeffs.append(1. / coeff) + else: + coeffs.append(-c / coeff) + except ZeroDivisionError: + coeffs.append(0.) + vals = self.terms[:] + vals[index] = self.sum + msgs = [var[self] for var in vals] + return self.update(self.terms[index], vals, msgs, coeffs) + + def update(self, var, vals, msgs, coeffs): + pi_inv = 0 + mu = 0 + for val, msg, coeff in zip(vals, msgs, coeffs): + div = val / msg + mu += coeff * div.mu + if pi_inv == inf: + continue + try: + # numpy.float64 handles floating-point error by different way. + # For example, it can just warn RuntimeWarning on n/0 problem + # instead of throwing ZeroDivisionError. So div.pi, the + # denominator has to be a built-in float. + pi_inv += coeff ** 2 / float(div.pi) + except ZeroDivisionError: + pi_inv = inf + pi = 1. / pi_inv + tau = pi * mu + return var.update_message(self, pi, tau) + + +class TruncateFactor(Factor): + + def __init__(self, var, v_func, w_func, draw_margin): + super(TruncateFactor, self).__init__([var]) + self.v_func = v_func + self.w_func = w_func + self.draw_margin = draw_margin + + def up(self): + val = self.var + msg = self.var[self] + div = val / msg + sqrt_pi = math.sqrt(div.pi) + args = (div.tau / sqrt_pi, self.draw_margin * sqrt_pi) + v = self.v_func(*args) + w = self.w_func(*args) + denom = (1. - w) + pi, tau = div.pi / denom, (div.tau + sqrt_pi * v) / denom + return val.update_value(self, pi, tau) + +#: Default initial mean of ratings. +MU = 25. +#: Default initial standard deviation of ratings. +SIGMA = MU / 3 +#: Default distance that guarantees about 76% chance of winning. +BETA = SIGMA / 2 +#: Default dynamic factor. +TAU = SIGMA / 100 +#: Default draw probability of the game. +DRAW_PROBABILITY = .10 +#: A basis to check reliability of the result. +DELTA = 0.0001 + + +def calc_draw_probability(draw_margin, size, env=None): + if env is None: + env = global_env() + return 2 * env.cdf(draw_margin / (math.sqrt(size) * env.beta)) - 1 + + +def calc_draw_margin(draw_probability, size, env=None): + if env is None: + env = global_env() + return env.ppf((draw_probability + 1) / 2.) * math.sqrt(size) * env.beta + + +def _team_sizes(rating_groups): + team_sizes = [0] + for group in rating_groups: + team_sizes.append(len(group) + team_sizes[-1]) + del team_sizes[0] + return team_sizes + + +def _floating_point_error(env): + if env.backend == 'mpmath': + msg = 'Set "mpmath.mp.dps" to higher' + else: + msg = 'Cannot calculate correctly, set backend to "mpmath"' + return FloatingPointError(msg) + + +class Rating(Gaussian): + def __init__(self, mu=None, sigma=None): + if isinstance(mu, tuple): + mu, sigma = mu + elif isinstance(mu, Gaussian): + mu, sigma = mu.mu, mu.sigma + if mu is None: + mu = global_env().mu + if sigma is None: + sigma = global_env().sigma + super(Rating, self).__init__(mu, sigma) + + def __int__(self): + return int(self.mu) + + def __long__(self): + return long(self.mu) + + def __float__(self): + return float(self.mu) + + def __iter__(self): + return iter((self.mu, self.sigma)) + + def __repr__(self): + c = type(self) + args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma) + return '%s(mu=%.3f, sigma=%.3f)' % args + + +class TrueSkill(object): + def __init__(self, mu=MU, sigma=SIGMA, beta=BETA, tau=TAU, + draw_probability=DRAW_PROBABILITY, backend=None): + self.mu = mu + self.sigma = sigma + self.beta = beta + self.tau = tau + self.draw_probability = draw_probability + self.backend = backend + if isinstance(backend, tuple): + self.cdf, self.pdf, self.ppf = backend + else: + self.cdf, self.pdf, self.ppf = choose_backend(backend) + + def create_rating(self, mu=None, sigma=None): + if mu is None: + mu = self.mu + if sigma is None: + sigma = self.sigma + return Rating(mu, sigma) + + def v_win(self, diff, draw_margin): + x = diff - draw_margin + denom = self.cdf(x) + return (self.pdf(x) / denom) if denom else -x + + def v_draw(self, diff, draw_margin): + abs_diff = abs(diff) + a, b = draw_margin - abs_diff, -draw_margin - abs_diff + denom = self.cdf(a) - self.cdf(b) + numer = self.pdf(b) - self.pdf(a) + return ((numer / denom) if denom else a) * (-1 if diff < 0 else +1) + + def w_win(self, diff, draw_margin): + x = diff - draw_margin + v = self.v_win(diff, draw_margin) + w = v * (v + x) + if 0 < w < 1: + return w + raise _floating_point_error(self) + + def w_draw(self, diff, draw_margin): + abs_diff = abs(diff) + a, b = draw_margin - abs_diff, -draw_margin - abs_diff + denom = self.cdf(a) - self.cdf(b) + if not denom: + raise _floating_point_error(self) + v = self.v_draw(abs_diff, draw_margin) + return (v ** 2) + (a * self.pdf(a) - b * self.pdf(b)) / denom + + def validate_rating_groups(self, rating_groups): + # check group sizes + if len(rating_groups) < 2: + raise ValueError('Need multiple rating groups') + elif not all(rating_groups): + raise ValueError('Each group must contain multiple ratings') + # check group types + group_types = set(map(type, rating_groups)) + if len(group_types) != 1: + raise TypeError('All groups should be same type') + elif group_types.pop() is Rating: + raise TypeError('Rating cannot be a rating group') + # normalize rating_groups + if isinstance(rating_groups[0], dict): + dict_rating_groups = rating_groups + rating_groups = [] + keys = [] + for dict_rating_group in dict_rating_groups: + rating_group, key_group = [], [] + for key, rating in iteritems(dict_rating_group): + rating_group.append(rating) + key_group.append(key) + rating_groups.append(tuple(rating_group)) + keys.append(tuple(key_group)) + else: + rating_groups = list(rating_groups) + keys = None + return rating_groups, keys + + def validate_weights(self, weights, rating_groups, keys=None): + if weights is None: + weights = [(1,) * len(g) for g in rating_groups] + elif isinstance(weights, dict): + weights_dict, weights = weights, [] + for x, group in enumerate(rating_groups): + w = [] + weights.append(w) + for y, rating in enumerate(group): + if keys is not None: + y = keys[x][y] + w.append(weights_dict.get((x, y), 1)) + return weights + + def factor_graph_builders(self, rating_groups, ranks, weights): + flatten_ratings = sum(map(tuple, rating_groups), ()) + flatten_weights = sum(map(tuple, weights), ()) + size = len(flatten_ratings) + group_size = len(rating_groups) + # create variables + rating_vars = [Variable() for x in range(size)] + perf_vars = [Variable() for x in range(size)] + team_perf_vars = [Variable() for x in range(group_size)] + team_diff_vars = [Variable() for x in range(group_size - 1)] + team_sizes = _team_sizes(rating_groups) + # layer builders + def build_rating_layer(): + for rating_var, rating in zip(rating_vars, flatten_ratings): + yield PriorFactor(rating_var, rating, self.tau) + def build_perf_layer(): + for rating_var, perf_var in zip(rating_vars, perf_vars): + yield LikelihoodFactor(rating_var, perf_var, self.beta ** 2) + def build_team_perf_layer(): + for team, team_perf_var in enumerate(team_perf_vars): + if team > 0: + start = team_sizes[team - 1] + else: + start = 0 + end = team_sizes[team] + child_perf_vars = perf_vars[start:end] + coeffs = flatten_weights[start:end] + yield SumFactor(team_perf_var, child_perf_vars, coeffs) + def build_team_diff_layer(): + for team, team_diff_var in enumerate(team_diff_vars): + yield SumFactor(team_diff_var, + team_perf_vars[team:team + 2], [+1, -1]) + def build_trunc_layer(): + for x, team_diff_var in enumerate(team_diff_vars): + if callable(self.draw_probability): + # dynamic draw probability + team_perf1, team_perf2 = team_perf_vars[x:x + 2] + args = (Rating(team_perf1), Rating(team_perf2), self) + draw_probability = self.draw_probability(*args) + else: + # static draw probability + draw_probability = self.draw_probability + size = sum(map(len, rating_groups[x:x + 2])) + draw_margin = calc_draw_margin(draw_probability, size, self) + if ranks[x] == ranks[x + 1]: # is a tie? + v_func, w_func = self.v_draw, self.w_draw + else: + v_func, w_func = self.v_win, self.w_win + yield TruncateFactor(team_diff_var, + v_func, w_func, draw_margin) + # build layers + return (build_rating_layer, build_perf_layer, build_team_perf_layer, + build_team_diff_layer, build_trunc_layer) + + def run_schedule(self, build_rating_layer, build_perf_layer, + build_team_perf_layer, build_team_diff_layer, + build_trunc_layer, min_delta=DELTA): + if min_delta <= 0: + raise ValueError('min_delta must be greater than 0') + layers = [] + def build(builders): + layers_built = [list(build()) for build in builders] + layers.extend(layers_built) + return layers_built + # gray arrows + layers_built = build([build_rating_layer, + build_perf_layer, + build_team_perf_layer]) + rating_layer, perf_layer, team_perf_layer = layers_built + for f in chain(*layers_built): + f.down() + # arrow #1, #2, #3 + team_diff_layer, trunc_layer = build([build_team_diff_layer, + build_trunc_layer]) + team_diff_len = len(team_diff_layer) + for x in range(10): + if team_diff_len == 1: + # only two teams + team_diff_layer[0].down() + delta = trunc_layer[0].up() + else: + # multiple teams + delta = 0 + for x in range(team_diff_len - 1): + team_diff_layer[x].down() + delta = max(delta, trunc_layer[x].up()) + team_diff_layer[x].up(1) # up to right variable + for x in range(team_diff_len - 1, 0, -1): + team_diff_layer[x].down() + delta = max(delta, trunc_layer[x].up()) + team_diff_layer[x].up(0) # up to left variable + # repeat until to small update + if delta <= min_delta: + break + # up both ends + team_diff_layer[0].up(0) + team_diff_layer[team_diff_len - 1].up(1) + # up the remainder of the black arrows + for f in team_perf_layer: + for x in range(len(f.vars) - 1): + f.up(x) + for f in perf_layer: + f.up() + return layers + + def rate(self, rating_groups, ranks=None, weights=None, min_delta=DELTA): + rating_groups, keys = self.validate_rating_groups(rating_groups) + weights = self.validate_weights(weights, rating_groups, keys) + group_size = len(rating_groups) + if ranks is None: + ranks = range(group_size) + elif len(ranks) != group_size: + raise ValueError('Wrong ranks') + # sort rating groups by rank + by_rank = lambda x: x[1][1] + sorting = sorted(enumerate(zip(rating_groups, ranks, weights)), + key=by_rank) + sorted_rating_groups, sorted_ranks, sorted_weights = [], [], [] + for x, (g, r, w) in sorting: + sorted_rating_groups.append(g) + sorted_ranks.append(r) + # make weights to be greater than 0 + sorted_weights.append(max(min_delta, w_) for w_ in w) + # build factor graph + args = (sorted_rating_groups, sorted_ranks, sorted_weights) + builders = self.factor_graph_builders(*args) + args = builders + (min_delta,) + layers = self.run_schedule(*args) + # make result + rating_layer, team_sizes = layers[0], _team_sizes(sorted_rating_groups) + transformed_groups = [] + for start, end in zip([0] + team_sizes[:-1], team_sizes): + group = [] + for f in rating_layer[start:end]: + group.append(Rating(float(f.var.mu), float(f.var.sigma))) + transformed_groups.append(tuple(group)) + by_hint = lambda x: x[0] + unsorting = sorted(zip((x for x, __ in sorting), transformed_groups), + key=by_hint) + if keys is None: + return [g for x, g in unsorting] + # restore the structure with input dictionary keys + return [dict(zip(keys[x], g)) for x, g in unsorting] + + def quality(self, rating_groups, weights=None): + rating_groups, keys = self.validate_rating_groups(rating_groups) + weights = self.validate_weights(weights, rating_groups, keys) + flatten_ratings = sum(map(tuple, rating_groups), ()) + flatten_weights = sum(map(tuple, weights), ()) + length = len(flatten_ratings) + # a vector of all of the skill means + mean_matrix = Matrix([[r.mu] for r in flatten_ratings]) + # a matrix whose diagonal values are the variances (sigma ** 2) of each + # of the players. + def variance_matrix(height, width): + variances = (r.sigma ** 2 for r in flatten_ratings) + for x, variance in enumerate(variances): + yield (x, x), variance + variance_matrix = Matrix(variance_matrix, length, length) + # the player-team assignment and comparison matrix + def rotated_a_matrix(set_height, set_width): + t = 0 + for r, (cur, _next) in enumerate(zip(rating_groups[:-1], + rating_groups[1:])): + for x in range(t, t + len(cur)): + yield (r, x), flatten_weights[x] + t += 1 + x += 1 + for x in range(x, x + len(_next)): + yield (r, x), -flatten_weights[x] + set_height(r + 1) + set_width(x + 1) + rotated_a_matrix = Matrix(rotated_a_matrix) + a_matrix = rotated_a_matrix.transpose() + # match quality further derivation + _ata = (self.beta ** 2) * rotated_a_matrix * a_matrix + _atsa = rotated_a_matrix * variance_matrix * a_matrix + start = mean_matrix.transpose() * a_matrix + middle = _ata + _atsa + end = rotated_a_matrix * mean_matrix + # make result + e_arg = (-0.5 * start * middle.inverse() * end).determinant() + s_arg = _ata.determinant() / middle.determinant() + return math.exp(e_arg) * math.sqrt(s_arg) + + def expose(self, rating): + k = self.mu / self.sigma + return rating.mu - k * rating.sigma + + def make_as_global(self): + return setup(env=self) + + def __repr__(self): + c = type(self) + if callable(self.draw_probability): + f = self.draw_probability + draw_probability = '.'.join([f.__module__, f.__name__]) + else: + draw_probability = '%.1f%%' % (self.draw_probability * 100) + if self.backend is None: + backend = '' + elif isinstance(self.backend, tuple): + backend = ', backend=...' + else: + backend = ', backend=%r' % self.backend + args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma, + self.beta, self.tau, draw_probability, backend) + return ('%s(mu=%.3f, sigma=%.3f, beta=%.3f, tau=%.3f, ' + 'draw_probability=%s%s)' % args) + + +def rate_1vs1(rating1, rating2, drawn=False, min_delta=DELTA, env=None): + if env is None: + env = global_env() + ranks = [0, 0 if drawn else 1] + teams = env.rate([(rating1,), (rating2,)], ranks, min_delta=min_delta) + return teams[0][0], teams[1][0] + + +def quality_1vs1(rating1, rating2, env=None): + if env is None: + env = global_env() + return env.quality([(rating1,), (rating2,)]) + + +def global_env(): + try: + global_env.__trueskill__ + except AttributeError: + # setup the default environment + setup() + return global_env.__trueskill__ + + +def setup(mu=MU, sigma=SIGMA, beta=BETA, tau=TAU, + draw_probability=DRAW_PROBABILITY, backend=None, env=None): + if env is None: + env = TrueSkill(mu, sigma, beta, tau, draw_probability, backend) + global_env.__trueskill__ = env + return env + + +def rate(rating_groups, ranks=None, weights=None, min_delta=DELTA): + return global_env().rate(rating_groups, ranks, weights, min_delta) + + +def quality(rating_groups, weights=None): + return global_env().quality(rating_groups, weights) + + +def expose(rating): + return global_env().expose(rating) \ No newline at end of file diff --git a/data analysis/analysis-master/build/lib/analysis/visualization.py b/data analysis/analysis-master/build/lib/analysis/visualization.py new file mode 100644 index 00000000..72358662 --- /dev/null +++ b/data analysis/analysis-master/build/lib/analysis/visualization.py @@ -0,0 +1,34 @@ +# Titan Robotics Team 2022: Visualization Module +# Written by Arthur Lu & Jacob Levine +# Notes: +# this should be imported as a python module using 'import visualization' +# this should be included in the local directory or environment variable +# fancy +# setup: + +__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 +""" + +__author__ = ( + "Arthur Lu ," + "Jacob Levine ," + ) + +__all__ = [ + 'graphloss', + ] + +import matplotlib.pyplot as plt + +def graphloss(losses): + + x = range(0, len(losses)) + plt.plot(x, losses) + plt.show() \ No newline at end of file diff --git a/data analysis/analysis-master/dist/analysis-1.0.0.0-py3-none-any.whl b/data analysis/analysis-master/dist/analysis-1.0.0.0-py3-none-any.whl new file mode 100644 index 0000000000000000000000000000000000000000..3f86af4324c2f00b6aa24a16cd106c017df900ca GIT binary patch literal 21422 zcmZ6xQ>-vduq?W4+qP}nwr$(CZQJ%*-?DAnwt4>jaPK}jo%EzLNxe(v@_S8fjPrsO{P&Qn$p zGL#?$0-(W1O6|n=`As9kOQq%Rm>8E*ft@Za)n8oPV#45q@|VSq2eXe|IJZ531iW$Kr!Cfb|sf~*xujYLVzH!K&V2aHPr zNs4ZiY(1j`bcW4tv02kmQ#wR+tf$!mcR1d4)KZ0XCVBdFqOPba)VSI&l!*GF(U_H& z1EP#`X+(D=qCVG{E~S}+gDbkwR%2y}DmK8tp)(-takqC24w8sv*%pnODq+ zh^%qQd#ssV?0Vpy>vQ6+--I<>vwS+?-SlycXY+sMR4UH14-BTruU&@)gz$lo=T%A` z1`k-&h~$bTR`{VS%-;hZSn_UpC^Lj1qHba-r0h;Dcyzo`X)Y<2FwsQ$FhKK_N+8Z> 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