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ltcptgeneral 2020-03-09 22:58:11 -05:00
parent 2605f7c29f
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.gitignore vendored
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@ -18,4 +18,5 @@ data analysis/arthur_pull.ipynb
data analysis/keys.txt
data analysis/check_for_new_matches.ipynb
data analysis/test.ipynb
data analysis/visualize_pit.ipynb
data analysis/visualize_pit.ipynb
data analysis/config/keys.config

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Metadata-Version: 2.1
Name: analysis
Version: 1.0.0.8
Summary: analysis package developed by Titan Scouting for The Red Alliance
Home-page: https://github.com/titanscout2022/tr2022-strategy
Author: The Titan Scouting Team
Author-email: titanscout2022@gmail.com
License: GNU General Public License v3.0
Description: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

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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/requires.txt
analysis.egg-info/top_level.txt

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numba
numpy
scipy
scikit-learn
six
matplotlib

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analysis

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# 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__ = "1.1.13.006"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.13.006:
- cleaned up imports
1.1.13.005:
- cleaned up package
1.1.13.004:
- small fixes to regression to improve performance
1.1.13.003:
- filtered nans from regression
1.1.13.002:
- removed torch requirement, and moved Regression back to regression.py
1.1.13.001:
- bug fix with linear regression not returning a proper value
- cleaned up regression
- fixed bug with polynomial regressions
1.1.13.000:
- fixed all regressions to now properly work
1.1.12.006:
- fixed bg with a division by zero in histo_analysis
1.1.12.005:
- fixed numba issues by removing numba from elo, glicko2 and trueskill
1.1.12.004:
- renamed gliko to glicko
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 <learthurgo@gmail.com>",
"Jacob Levine <jlevine@imsa.edu>",
)
__all__ = [
'load_csv',
'basic_stats',
'z_score',
'z_normalize',
'histo_analysis',
'regression',
'elo',
'glicko2',
'trueskill',
'RegressionMetrics',
'ClassificationMetrics',
'kmeans',
'pca',
'decisiontree',
'knn_classifier',
'knn_regressor',
'NaiveBayes',
'SVM',
'random_forest_classifier',
'random_forest_regressor',
'Glicko2',
# 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 scipy
from scipy import *
import sklearn
from sklearn import *
from analysis import trueskill as Trueskill
class error(ValueError):
pass
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):
if(len(hist_data[0]) > 2):
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]
else:
return None
def regression(inputs, outputs, args): # inputs, outputs expects N-D array
X = np.array(inputs)
y = np.array(outputs)
regressions = []
if 'lin' in args: # formula: ax + b
try:
def func(x, a, b):
return a * x + b
popt, pcov = scipy.optimize.curve_fit(func, X, y)
regressions.append((popt.flatten().tolist(), None))
except Exception as e:
pass
if 'log' in args: # formula: a log (b(x + c)) + d
try:
def func(x, a, b, c, d):
return a * np.log(b*(x + c)) + d
popt, pcov = scipy.optimize.curve_fit(func, X, y)
regressions.append((popt.flatten().tolist(), None))
except Exception as e:
pass
if 'exp' in args: # formula: a e ^ (b(x + c)) + d
try:
def func(x, a, b, c, d):
return a * np.exp(b*(x + c)) + d
popt, pcov = scipy.optimize.curve_fit(func, X, y)
regressions.append((popt.flatten().tolist(), None))
except Exception as e:
pass
if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
inputs = np.array([inputs])
outputs = np.array([outputs])
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 tanh (b(x + c)) + d
try:
def func(x, a, b, c, d):
return a * np.tanh(b*(x + c)) + d
popt, pcov = scipy.optimize.curve_fit(func, X, y)
regressions.append((popt.flatten().tolist(), None))
except Exception as e:
pass
return regressions
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))
def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
player = Glicko2(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)
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 Glicko2:
_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()

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@ -1,220 +0,0 @@
# 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__ = "1.0.0.004"
# changelog should be viewed using print(analysis.regression.__changelog__)
__changelog__ = """
1.0.0.004:
- bug fixes
- fixed changelog
1.0.0.003:
- bug fixes
1.0.0.002:
-Added more parameters to log, exponential, polynomial
-Added SigmoidalRegKernelArthur, because Arthur apparently needs
to train the scaling and shifting of sigmoids
1.0.0.001:
-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
-already vectorized (except for polynomial generation) and CUDA-optimized
"""
__author__ = (
"Jacob Levine <jlevine@imsa.edu>",
"Arthur Lu <learthurgo@gmail.com>"
)
__all__ = [
'factorial',
'take_all_pwrs',
'num_poly_terms',
'set_device',
'LinearRegKernel',
'SigmoidalRegKernel',
'LogRegKernel',
'PolyRegKernel',
'ExpRegKernel',
'SigmoidalRegKernelArthur',
'SGDTrain',
'CustomTrain'
]
import torch
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

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@ -1,122 +0,0 @@
# 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 <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
__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)

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@ -1,907 +0,0 @@
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)

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@ -1,34 +0,0 @@
# 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 <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'graphloss',
]
import matplotlib.pyplot as plt
def graphloss(losses):
x = range(0, len(losses))
plt.plot(x, losses)
plt.show()

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@ -1 +0,0 @@
python3 setup.py sdist bdist_wheel

View File

@ -1,790 +0,0 @@
# 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__ = "1.1.13.006"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.13.006:
- cleaned up imports
1.1.13.005:
- cleaned up package
1.1.13.004:
- small fixes to regression to improve performance
1.1.13.003:
- filtered nans from regression
1.1.13.002:
- removed torch requirement, and moved Regression back to regression.py
1.1.13.001:
- bug fix with linear regression not returning a proper value
- cleaned up regression
- fixed bug with polynomial regressions
1.1.13.000:
- fixed all regressions to now properly work
1.1.12.006:
- fixed bg with a division by zero in histo_analysis
1.1.12.005:
- fixed numba issues by removing numba from elo, glicko2 and trueskill
1.1.12.004:
- renamed gliko to glicko
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 <learthurgo@gmail.com>",
"Jacob Levine <jlevine@imsa.edu>",
)
__all__ = [
'load_csv',
'basic_stats',
'z_score',
'z_normalize',
'histo_analysis',
'regression',
'elo',
'glicko2',
'trueskill',
'RegressionMetrics',
'ClassificationMetrics',
'kmeans',
'pca',
'decisiontree',
'knn_classifier',
'knn_regressor',
'NaiveBayes',
'SVM',
'random_forest_classifier',
'random_forest_regressor',
'Glicko2',
# 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 scipy
from scipy import *
import sklearn
from sklearn import *
from analysis import trueskill as Trueskill
class error(ValueError):
pass
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):
if(len(hist_data[0]) > 2):
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]
else:
return None
def regression(inputs, outputs, args): # inputs, outputs expects N-D array
X = np.array(inputs)
y = np.array(outputs)
regressions = []
if 'lin' in args: # formula: ax + b
try:
def func(x, a, b):
return a * x + b
popt, pcov = scipy.optimize.curve_fit(func, X, y)
regressions.append((popt.flatten().tolist(), None))
except Exception as e:
pass
if 'log' in args: # formula: a log (b(x + c)) + d
try:
def func(x, a, b, c, d):
return a * np.log(b*(x + c)) + d
popt, pcov = scipy.optimize.curve_fit(func, X, y)
regressions.append((popt.flatten().tolist(), None))
except Exception as e:
pass
if 'exp' in args: # formula: a e ^ (b(x + c)) + d
try:
def func(x, a, b, c, d):
return a * np.exp(b*(x + c)) + d
popt, pcov = scipy.optimize.curve_fit(func, X, y)
regressions.append((popt.flatten().tolist(), None))
except Exception as e:
pass
if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
inputs = np.array([inputs])
outputs = np.array([outputs])
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 tanh (b(x + c)) + d
try:
def func(x, a, b, c, d):
return a * np.tanh(b*(x + c)) + d
popt, pcov = scipy.optimize.curve_fit(func, X, y)
regressions.append((popt.flatten().tolist(), None))
except Exception as e:
pass
return regressions
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))
def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
player = Glicko2(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)
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 Glicko2:
_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()

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@ -1,220 +0,0 @@
# 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__ = "1.0.0.004"
# changelog should be viewed using print(analysis.regression.__changelog__)
__changelog__ = """
1.0.0.004:
- bug fixes
- fixed changelog
1.0.0.003:
- bug fixes
1.0.0.002:
-Added more parameters to log, exponential, polynomial
-Added SigmoidalRegKernelArthur, because Arthur apparently needs
to train the scaling and shifting of sigmoids
1.0.0.001:
-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
-already vectorized (except for polynomial generation) and CUDA-optimized
"""
__author__ = (
"Jacob Levine <jlevine@imsa.edu>",
"Arthur Lu <learthurgo@gmail.com>"
)
__all__ = [
'factorial',
'take_all_pwrs',
'num_poly_terms',
'set_device',
'LinearRegKernel',
'SigmoidalRegKernel',
'LogRegKernel',
'PolyRegKernel',
'ExpRegKernel',
'SigmoidalRegKernelArthur',
'SGDTrain',
'CustomTrain'
]
import torch
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

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@ -1,122 +0,0 @@
# 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 <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
__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)

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@ -1,907 +0,0 @@
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)

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@ -1,34 +0,0 @@
# 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 <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'graphloss',
]
import matplotlib.pyplot as plt
def graphloss(losses):
x = range(0, len(losses))
plt.plot(x, losses)
plt.show()

Binary file not shown.

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@ -1,27 +0,0 @@
import setuptools
setuptools.setup(
name="analysis", # Replace with your own username
version="1.0.0.008",
author="The Titan Scouting Team",
author_email="titanscout2022@gmail.com",
description="analysis package developed by Titan Scouting for The Red Alliance",
long_description="",
long_description_content_type="text/markdown",
url="https://github.com/titanscout2022/tr2022-strategy",
packages=setuptools.find_packages(),
install_requires=[
"numba",
"numpy",
"scipy",
"scikit-learn",
"six",
"matplotlib"
],
license = "GNU General Public License v3.0",
classifiers=[
"Programming Language :: Python :: 3",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
)

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@ -1 +0,0 @@
2020ilch

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@ -1,2 +0,0 @@
mongodb+srv://api-user-new:titanscout2022@2022-scouting-4vfuu.mongodb.net/test?authSource=admin&replicaSet=2022-scouting-shard-0&readPreference=primary&appname=MongoDB%20Compass&ssl=true
UDvKmPjPRfwwUdDX1JxbmkyecYBJhCtXeyVk9vmO2i7K0Zn4wqQPMfzuEINXJ7e5

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@ -1,14 +0,0 @@
balls-blocked,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
balls-collected,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
balls-lower-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
balls-lower-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
balls-started,basic_stats,historical_analyss,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
balls-upper-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
balls-upper-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
wheel-mechanism
low-balls
high-balls
wheel-success
strategic-focus
climb-mechanism
attitude

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@ -1,102 +0,0 @@
import requests
import pymongo
import pandas as pd
import time
def pull_new_tba_matches(apikey, competition, cutoff):
api_key= apikey
x=requests.get("https://www.thebluealliance.com/api/v3/event/"+competition+"/matches/simple", headers={"X-TBA-Auth_Key":api_key})
out = []
for i in x.json():
if (i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm"):
out.append({"match" : i['match_number'], "blue" : list(map(lambda x: int(x[3:]), i['alliances']['blue']['team_keys'])), "red" : list(map(lambda x: int(x[3:]), i['alliances']['red']['team_keys'])), "winner": i["winning_alliance"]})
return out
def get_team_match_data(apikey, competition, team_num):
client = pymongo.MongoClient(apikey)
db = client.data_scouting
mdata = db.matchdata
out = {}
for i in mdata.find({"competition" : competition, "team_scouted": team_num}):
out[i['match']] = i['data']
return pd.DataFrame(out)
def get_team_pit_data(apikey, competition, team_num):
client = pymongo.MongoClient(apikey)
db = client.data_scouting
mdata = db.pitdata
out = {}
return mdata.find_one({"competition" : competition, "team_scouted": team_num})["data"]
def get_team_metrics_data(apikey, competition, team_num):
client = pymongo.MongoClient(apikey)
db = client.data_processing
mdata = db.team_metrics
return mdata.find_one({"competition" : competition, "team": team_num})
def unkeyify_2l(layered_dict):
out = {}
for i in layered_dict.keys():
add = []
sortkey = []
for j in layered_dict[i].keys():
add.append([j,layered_dict[i][j]])
add.sort(key = lambda x: x[0])
out[i] = list(map(lambda x: x[1], add))
return out
def get_match_data_formatted(apikey, competition):
client = pymongo.MongoClient(apikey)
db = client.data_scouting
mdata = db.teamlist
x=mdata.find_one({"competition":competition})
out = {}
for i in x:
try:
out[int(i)] = unkeyify_2l(get_team_match_data(apikey, competition, int(i)).transpose().to_dict())
except:
pass
return out
def get_pit_data_formatted(apikey, competition):
client = pymongo.MongoClient(apikey)
db = client.data_scouting
mdata = db.teamlist
x=mdata.find_one({"competition":competition})
out = {}
for i in x:
try:
out[int(i)] = get_team_pit_data(apikey, competition, int(i))
except:
pass
return out
def push_team_tests_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_tests"):
client = pymongo.MongoClient(apikey)
db = client[dbname]
mdata = db[colname]
mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "data" : data}, True)
def push_team_metrics_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_metrics"):
client = pymongo.MongoClient(apikey)
db = client[dbname]
mdata = db[colname]
mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "metrics" : data}, True)
def push_team_pit_data(apikey, competition, variable, data, dbname = "data_processing", colname = "team_pit"):
client = pymongo.MongoClient(apikey)
db = client[dbname]
mdata = db[colname]
mdata.replace_one({"competition" : competition, "variable": variable}, {"competition" : competition, "variable" : variable, "data" : data}, True)
def get_analysis_flags(apikey, flag):
client = pymongo.MongoClient(apikey)
db = client.data_processing
mdata = db.flags
return mdata.find_one({flag:{"$exists":True}})
def set_analysis_flags(apikey, flag, data):
client = pymongo.MongoClient(apikey)
db = client.data_processing
mdata = db.flags
return mdata.replace_one({flag:{"$exists":True}}, data, True)

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@ -1,59 +0,0 @@
import data as d
from analysis import analysis as an
import pymongo
import operator
def load_config(file):
config_vector = {}
file = an.load_csv(file)
for line in file[1:]:
config_vector[line[0]] = line[1:]
return (file[0][0], config_vector)
def get_metrics_processed_formatted(apikey, competition):
client = pymongo.MongoClient(apikey)
db = client.data_scouting
mdata = db.teamlist
x=mdata.find_one({"competition":competition})
out = {}
for i in x:
try:
out[int(i)] = d.get_team_metrics_data(apikey, competition, int(i))
except:
pass
return out
def main():
apikey = an.load_csv("keys.txt")[0][0]
tbakey = an.load_csv("keys.txt")[1][0]
competition, config = load_config("config.csv")
metrics = get_metrics_processed_formatted(apikey, competition)
elo = {}
gl2 = {}
for team in metrics:
elo[team] = metrics[team]["metrics"]["elo"]["score"]
gl2[team] = metrics[team]["metrics"]["gl2"]["score"]
elo = {k: v for k, v in sorted(elo.items(), key=lambda item: item[1])}
gl2 = {k: v for k, v in sorted(gl2.items(), key=lambda item: item[1])}
for team in elo:
print("teams sorted by elo:")
print("" + str(team) + " | " + str(elo[team]))
print("*"*25)
for team in gl2:
print("teams sorted by glicko2:")
print("" + str(team) + " | " + str(gl2[team]))
main()

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# Titan Robotics Team 2022: Superscript Script
# Written by Arthur Lu & Jacob Levine
# Notes:
# setup:
__version__ = "0.0.5.000"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
0.0.5.000:
improved user interface
0.0.4.002:
- removed unessasary code
0.0.4.001:
- fixed bug where X range for regression was determined before sanitization
- better sanitized data
0.0.4.000:
- fixed spelling issue in __changelog__
- addressed nan bug in regression
- fixed errors on line 335 with metrics calling incorrect key "glicko2"
- fixed errors in metrics computing
0.0.3.000:
- added analysis to pit data
0.0.2.001:
- minor stability patches
- implemented db syncing for timestamps
- fixed bugs
0.0.2.000:
- finalized testing and small fixes
0.0.1.004:
- finished metrics implement, trueskill is bugged
0.0.1.003:
- working
0.0.1.002:
- started implement of metrics
0.0.1.001:
- cleaned up imports
0.0.1.000:
- tested working, can push to database
0.0.0.009:
- tested working
- prints out stats for the time being, will push to database later
0.0.0.008:
- added data import
- removed tba import
- finished main method
0.0.0.007:
- added load_config
- optimized simpleloop for readibility
- added __all__ entries
- added simplestats engine
- pending testing
0.0.0.006:
- fixes
0.0.0.005:
- imported pickle
- created custom database object
0.0.0.004:
- fixed simpleloop to actually return a vector
0.0.0.003:
- added metricsloop which is unfinished
0.0.0.002:
- added simpleloop which is untested until data is provided
0.0.0.001:
- created script
- added analysis, numba, numpy imports
"""
__author__ = (
"Arthur Lu <learthurgo@gmail.com>",
"Jacob Levine <jlevine@imsa.edu>",
)
__all__ = [
"main",
"load_config",
"simpleloop",
"simplestats",
"metricsloop"
]
# imports:
from analysis import analysis as an
import data as d
import numpy as np
import matplotlib.pyplot as plt
from os import system, name
from pathlib import Path
import time
import warnings
def main():
warnings.filterwarnings("ignore")
while(True):
current_time = time.time()
print("[OK] time: " + str(current_time))
start = time.time()
config = load_config(Path("config/stats.config"))
competition = an.load_csv(Path("config/competition.config"))[0][0]
print("[OK] configs loaded")
apikey = an.load_csv(Path("config/keys.config"))[0][0]
tbakey = an.load_csv(Path("config/keys.config"))[1][0]
print("[OK] loaded keys")
previous_time = d.get_analysis_flags(apikey, "latest_update")
if(previous_time == None):
d.set_analysis_flags(apikey, "latest_update", 0)
previous_time = 0
else:
previous_time = previous_time["latest_update"]
print("[OK] analysis backtimed to: " + str(previous_time))
print("[OK] loading data")
start = time.time()
data = d.get_match_data_formatted(apikey, competition)
pit_data = d.pit = d.get_pit_data_formatted(apikey, competition)
print("[OK] loaded data in " + str(time.time() - start) + " seconds")
print("[OK] running tests")
start = time.time()
results = simpleloop(data, config)
print("[OK] finished tests in " + str(time.time() - start) + " seconds")
print("[OK] running metrics")
start = time.time()
metricsloop(tbakey, apikey, competition, previous_time)
print("[OK] finished metrics in " + str(time.time() - start) + " seconds")
print("[OK] running pit analysis")
start = time.time()
pit = pitloop(pit_data, config)
print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
print("[OK] pushing to database")
start = time.time()
push_to_database(apikey, competition, results, pit)
print("[OK] pushed to database in " + str(time.time() - start) + " seconds")
clear()
def clear():
# for windows
if name == 'nt':
_ = system('cls')
# for mac and linux(here, os.name is 'posix')
else:
_ = system('clear')
def load_config(file):
config_vector = {}
file = an.load_csv(file)
for line in file:
config_vector[line[0]] = line[1:]
return config_vector
def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
return_vector = {}
for team in data:
variable_vector = {}
for variable in data[team]:
test_vector = {}
variable_data = data[team][variable]
if(variable in tests):
for test in tests[variable]:
test_vector[test] = simplestats(variable_data, test)
else:
pass
variable_vector[variable] = test_vector
return_vector[team] = variable_vector
return return_vector
def simplestats(data, test):
data = np.array(data)
data = data[np.isfinite(data)]
ranges = list(range(len(data)))
if(test == "basic_stats"):
return an.basic_stats(data)
if(test == "historical_analysis"):
return an.histo_analysis([ranges, data])
if(test == "regression_linear"):
return an.regression(ranges, data, ['lin'])
if(test == "regression_logarithmic"):
return an.regression(ranges, data, ['log'])
if(test == "regression_exponential"):
return an.regression(ranges, data, ['exp'])
if(test == "regression_polynomial"):
return an.regression(ranges, data, ['ply'])
if(test == "regression_sigmoidal"):
return an.regression(ranges, data, ['sig'])
def push_to_database(apikey, competition, results, pit):
for team in results:
d.push_team_tests_data(apikey, competition, team, results[team])
for variable in pit:
d.push_team_pit_data(apikey, competition, variable, pit[variable])
def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
elo_N = 400
elo_K = 24
matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
red = {}
blu = {}
for match in matches:
red = load_metrics(apikey, competition, match, "red")
blu = load_metrics(apikey, competition, match, "blue")
elo_red_total = 0
elo_blu_total = 0
gl2_red_score_total = 0
gl2_blu_score_total = 0
gl2_red_rd_total = 0
gl2_blu_rd_total = 0
gl2_red_vol_total = 0
gl2_blu_vol_total = 0
for team in red:
elo_red_total += red[team]["elo"]["score"]
gl2_red_score_total += red[team]["gl2"]["score"]
gl2_red_rd_total += red[team]["gl2"]["rd"]
gl2_red_vol_total += red[team]["gl2"]["vol"]
for team in blu:
elo_blu_total += blu[team]["elo"]["score"]
gl2_blu_score_total += blu[team]["gl2"]["score"]
gl2_blu_rd_total += blu[team]["gl2"]["rd"]
gl2_blu_vol_total += blu[team]["gl2"]["vol"]
red_elo = {"score": elo_red_total / len(red)}
blu_elo = {"score": elo_blu_total / len(blu)}
red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
if(match["winner"] == "red"):
observations = {"red": 1, "blu": 0}
elif(match["winner"] == "blue"):
observations = {"red": 0, "blu": 1}
else:
observations = {"red": 0.5, "blu": 0.5}
red_elo_delta = an.elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
blu_elo_delta = an.elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
red_gl2_delta = {"score": new_red_gl2_score - red_gl2["score"], "rd": new_red_gl2_rd - red_gl2["rd"], "vol": new_red_gl2_vol - red_gl2["vol"]}
blu_gl2_delta = {"score": new_blu_gl2_score - blu_gl2["score"], "rd": new_blu_gl2_rd - blu_gl2["rd"], "vol": new_blu_gl2_vol - blu_gl2["vol"]}
for team in red:
red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
for team in blu:
blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
temp_vector = {}
temp_vector.update(red)
temp_vector.update(blu)
for team in temp_vector:
d.push_team_metrics_data(apikey, competition, team, temp_vector[team])
def load_metrics(apikey, competition, match, group_name):
group = {}
for team in match[group_name]:
db_data = d.get_team_metrics_data(apikey, competition, team)
if d.get_team_metrics_data(apikey, competition, team) == None:
elo = {"score": 1500}
gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
ts = {"mu": 25, "sigma": 25/3}
#d.push_team_metrics_data(apikey, competition, team, {"elo":elo, "gl2":gl2,"trueskill":ts})
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
else:
metrics = db_data["metrics"]
elo = metrics["elo"]
gl2 = metrics["gl2"]
ts = metrics["ts"]
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
return group
def pitloop(pit, tests):
return_vector = {}
for team in pit:
for variable in pit[team]:
if(variable in tests):
if(not variable in return_vector):
return_vector[variable] = []
return_vector[variable].append(pit[team][variable])
return return_vector
main()
"""
Metrics Defaults:
elo starting score = 1500
elo N = 400
elo K = 24
gl2 starting score = 1500
gl2 starting rd = 350
gl2 starting vol = 0.06
"""

View File

@ -1,59 +0,0 @@
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
import matplotlib.pyplot as plt
import data as d
import pymongo
# %%
def get_pit_variable_data(apikey, competition):
client = pymongo.MongoClient(apikey)
db = client.data_processing
mdata = db.team_pit
out = {}
return mdata.find()
# %%
def get_pit_variable_formatted(apikey, competition):
temp = get_pit_variable_data(apikey, competition)
out = {}
for i in temp:
out[i["variable"]] = i["data"]
return out
# %%
pit = get_pit_variable_formatted("mongodb+srv://api-user-new:titanscout2022@2022-scouting-4vfuu.mongodb.net/test?authSource=admin&replicaSet=2022-scouting-shard-0&readPreference=primary&appname=MongoDB%20Compass&ssl=true", "2020ilch")
# %%
import matplotlib.pyplot as plt
import numpy as np
# %%
fig, ax = plt.subplots(1, len(pit), sharey=True, figsize=(80,15))
i = 0
for variable in pit:
ax[i].hist(pit[variable])
ax[i].invert_xaxis()
ax[i].set_xlabel('')
ax[i].set_ylabel('Frequency')
ax[i].set_title(variable)
plt.yticks(np.arange(len(pit[variable])))
i+=1
plt.show()
# %%

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@ -1,944 +0,0 @@
# 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 not been optimized for multhreaded computing
# number of easter eggs: 2
# setup:
__version__ = "1.0.9.000"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
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 <arthurlu@ttic.edu>, "
"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'_init_device',
'c_entities',
'nc_entities',
'obstacles',
'objectives',
'load_csv',
'basic_stats',
'z_score',
'z_normalize',
'stdev_z_split',
'histo_analysis',
'poly_regression',
'log_regression',
'exp_regression',
'r_squared',
'rms',
'calc_overfit',
'strip_data',
'optimize_regression',
'select_best_regression',
'basic_analysis',
# 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):
from bisect import bisect_left, bisect_right
import collections
import csv
from decimal import Decimal
import functools
from fractions import Fraction
from itertools import groupby
import math
import matplotlib
import numbers
import numpy as np
import pandas
import random
import scipy
from scipy.optimize import curve_fit
from scipy import stats
from sklearn import *
# import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002
import time
import torch
class error(ValueError):
pass
def _init_device(setting, arg): # initiates computation device for ANNs
if setting == "cuda":
try:
return torch.device(setting + ":" + str(arg) if torch.cuda.is_available() else "cpu")
except:
raise error("could not assign cuda or cpu")
elif setting == "cpu":
try:
return torch.device("cpu")
except:
raise error("could not assign cpu")
else:
raise error("specified device does not exist")
def load_csv(filepath):
with open(filepath, newline='') as csvfile:
file_array = np.array(list(csv.reader(csvfile)))
csvfile.close()
return file_array
# data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row
def basic_stats(data, method, arg):
if method == 'debug':
return "basic_stats requires 3 args: data, mode, arg; where data is data to be analyzed, mode is an int from 0 - 2 depending on type of analysis (by column or by row) and is only applicable to 2d arrays (for 1d arrays use mode 1), and arg is row/column number for mode 1 or mode 2; function returns: [mean, median, mode, stdev, variance]"
if method == "1d" or method == 0:
data_t = np.array(data).astype(float)
_mean = mean(data_t)
_median = median(data_t)
try:
_mode = mode(data_t)
except:
_mode = None
try:
_stdev = stdev(data_t)
except:
_stdev = None
try:
_variance = variance(data_t)
except:
_variance = None
return _mean, _median, _mode, _stdev, _variance
"""
elif method == "column" or method == 1:
c_data = []
c_data_sorted = []
for i in data:
try:
c_data.append(float(i[arg]))
except:
pass
_mean = mean(c_data)
_median = median(c_data)
try:
_mode = mode(c_data)
except:
_mode = None
try:
_stdev = stdev(c_data)
except:
_stdev = None
try:
_variance = variance(c_data)
except:
_variance = None
return _mean, _median, _mode, _stdev, _variance
elif method == "row" or method == 2:
r_data = []
for i in range(len(data[arg])):
r_data.append(float(data[arg][i]))
_mean = mean(r_data)
_median = median(r_data)
try:
_mode = mode(r_data)
except:
_mode = None
try:
_stdev = stdev(r_data)
except:
_stdev = None
try:
_variance = variance(r_data)
except:
_variance = None
return _mean, _median, _mode, _stdev, _variance
else:
raise error("method error")
"""
# returns z score with inputs of point, mean and standard deviation of spread
def z_score(point, mean, stdev):
score = (point - mean) / stdev
return score
# mode is either 'x' or 'y' or 'both' depending on the variable(s) to be normalized
def z_normalize(x, y, mode):
x_norm = np.array().astype(float)
y_norm = np.array().astype(float)
mean = 0
stdev = 0
if mode == 'x':
_mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0)
for i in range(0, len(x), 1):
x_norm.append(z_score(x[i], _mean, _stdev))
return x_norm, y
if mode == 'y':
_mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0)
for i in range(0, len(y), 1):
y_norm.append(z_score(y[i], _mean, _stdev))
return x, y_norm
if mode == 'both':
_mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0)
for i in range(0, len(x), 1):
x_norm.append(z_score(x[i], _mean, _stdev))
_mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0)
for i in range(0, len(y), 1):
y_norm.append(z_score(y[i], _mean, _stdev))
return x_norm, y_norm
else:
return error('method error')
# returns n-th percentile of spread given mean, standard deviation, lower z-score, and upper z-score
def stdev_z_split(mean, stdev, delta, low_bound, high_bound):
z_split = np.array().astype(float)
i = low_bound
while True:
z_split.append(float((1 / (stdev * math.sqrt(2 * math.pi))) *
math.e ** (-0.5 * (((i - mean) / stdev) ** 2))))
i = i + delta
if i > high_bound:
break
return z_split
def histo_analysis(hist_data, delta, low_bound, high_bound):
if hist_data == 'debug':
return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and higher bounds in number of standard deviations')
derivative = []
for i in range(0, len(hist_data), 1):
try:
derivative.append(float(hist_data[i - 1]) - float(hist_data[i]))
except:
pass
derivative_sorted = sorted(derivative, key=int)
mean_derivative = basic_stats(derivative_sorted, "1d", 0)[0]
stdev_derivative = basic_stats(derivative_sorted, "1d", 0)[3]
predictions = []
pred_change = 0
i = low_bound
while True:
if i > high_bound:
break
try:
pred_change = mean_derivative + i * stdev_derivative
except:
pred_change = mean_derivative
predictions.append(float(hist_data[-1:][0]) + pred_change)
i = i + delta
return predictions
def poly_regression(x, y, power):
if x == "null": # if x is 'null', then x will be filled with integer points between 1 and the size of y
x = []
for i in range(len(y)):
print(i)
x.append(i + 1)
reg_eq = scipy.polyfit(x, y, deg=power)
eq_str = ""
for i in range(0, len(reg_eq), 1):
if i < len(reg_eq) - 1:
eq_str = eq_str + str(reg_eq[i]) + \
"*(z**" + str(len(reg_eq) - i - 1) + ")+"
else:
eq_str = eq_str + str(reg_eq[i]) + \
"*(z**" + str(len(reg_eq) - i - 1) + ")"
vals = []
for i in range(0, len(x), 1):
z = x[i]
try:
exec("vals.append(" + eq_str + ")")
except:
pass
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
return [eq_str, _rms, r2_d2]
def log_regression(x, y, base):
x_fit = []
for i in range(len(x)):
try:
# change of base for logs
x_fit.append(np.log(x[i]) / np.log(base))
except:
pass
# y = reg_eq[0] * log(x, base) + reg_eq[1]
reg_eq = np.polyfit(x_fit, y, 1)
q_str = str(reg_eq[0]) + "* (np.log(z) / np.log(" + \
str(base) + "))+" + str(reg_eq[1])
vals = []
for i in range(len(x)):
z = x[i]
try:
exec("vals.append(" + eq_str + ")")
except:
pass
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
return eq_str, _rms, r2_d2
def exp_regression(x, y, base):
y_fit = []
for i in range(len(y)):
try:
# change of base for logs
y_fit.append(np.log(y[i]) / np.log(base))
except:
pass
# y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1])
reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y_fit))
eq_str = "(" + str(base) + "**(" + \
str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))"
vals = []
for i in range(len(x)):
z = x[i]
try:
exec("vals.append(" + eq_str + ")")
except:
pass
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
return eq_str, _rms, r2_d2
def tanh_regression(x, y):
def tanh(x, a, b, c, d):
return a * np.tanh(b * (x - c)) + d
reg_eq = np.float64(curve_fit(tanh, np.array(x), np.array(y))[0]).tolist()
eq_str = str(reg_eq[0]) + " * np.tanh(" + str(reg_eq[1]) + \
"*(z - " + str(reg_eq[2]) + ")) + " + str(reg_eq[3])
vals = []
for i in range(len(x)):
z = x[i]
try:
exec("vals.append(" + eq_str + ")")
except:
pass
_rms = rms(vals, y)
r2_d2 = r_squared(vals, y)
return eq_str, _rms, r2_d2
def r_squared(predictions, targets): # assumes equal size inputs
return metrics.r2_score(np.array(targets), np.array(predictions))
def rms(predictions, targets): # assumes equal size inputs
_sum = 0
for i in range(0, len(targets), 1):
_sum = (targets[i] - predictions[i]) ** 2
return float(math.sqrt(_sum / len(targets)))
def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
# performance overfit = performance(train) - performance(test) where performance is r^2
# error overfit = error(train) - error(test) where error is rms; biased towards smaller values
vals = []
for i in range(0, len(x_test), 1):
z = x_test[i]
exec("vals.append(" + equation + ")")
r2_test = r_squared(vals, y_test)
rms_test = rms(vals, y_test)
return r2_train - r2_test
def strip_data(data, mode):
if mode == "adam": # x is the row number, y are the data
pass
if mode == "eve": # x are the data, y is the column number
pass
else:
raise error("mode error")
# _range in poly regression is the range of powers tried, and in log/exp it is the inverse of the stepsize taken from -1000 to 1000
def optimize_regression(x, y, _range, resolution):
# usage not: for demonstration purpose only, performance is shit
if type(resolution) != int:
raise error("resolution must be int")
x_train = x
y_train = []
for i in range(len(y)):
y_train.append(float(y[i]))
x_test = []
y_test = []
for i in range(0, math.floor(len(x) * 0.5), 1):
index = random.randint(0, len(x) - 1)
x_test.append(x[index])
y_test.append(float(y[index]))
x_train.pop(index)
y_train.pop(index)
#print(x_train, x_test)
#print(y_train, y_test)
eqs = []
rmss = []
r2s = []
for i in range(0, _range + 1, 1):
try:
x, y, z = poly_regression(x_train, y_train, i)
eqs.append(x)
rmss.append(y)
r2s.append(z)
except:
pass
for i in range(1, 100 * resolution + 1):
try:
x, y, z = exp_regression(x_train, y_train, float(i / resolution))
eqs.append(x)
rmss.append(y)
r2s.append(z)
except:
pass
for i in range(1, 100 * resolution + 1):
try:
x, y, z = log_regression(x_train, y_train, float(i / resolution))
eqs.append(x)
rmss.append(y)
r2s.append(z)
except:
pass
try:
x, y, z = tanh_regression(x_train, y_train)
eqs.append(x)
rmss.append(y)
r2s.append(z)
except:
pass
# marks all equations where r2 = 1 as they 95% of the time overfit the data
for i in range(0, len(eqs), 1):
if r2s[i] == 1:
eqs[i] = ""
rmss[i] = ""
r2s[i] = ""
while True: # removes all equations marked for removal
try:
eqs.remove('')
rmss.remove('')
r2s.remove('')
except:
break
overfit = []
for i in range(0, len(eqs), 1):
overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test))
return eqs, rmss, r2s, overfit
def select_best_regression(eqs, rmss, r2s, overfit, selector):
b_eq = ""
b_rms = 0
b_r2 = 0
b_overfit = 0
ind = 0
if selector == "min_overfit":
ind = np.argmin(overfit)
b_eq = eqs[ind]
b_rms = rmss[ind]
b_r2 = r2s[ind]
b_overfit = overfit[ind]
if selector == "max_r2s":
ind = np.argmax(r2s)
b_eq = eqs[ind]
b_rms = rmss[ind]
b_r2 = r2s[ind]
b_overfit = overfit[ind]
return b_eq, b_rms, b_r2, b_overfit
def p_value(x, y): # takes 2 1d arrays
return stats.ttest_ind(x, y)[1]
# assumes that rows are the independent variable and columns are the dependant. also assumes that time flows from lowest column to highest column.
def basic_analysis(data):
row = len(data)
column = []
for i in range(0, row, 1):
column.append(len(data[i]))
column_max = max(column)
row_b_stats = []
row_histo = []
for i in range(0, row, 1):
row_b_stats.append(basic_stats(data, "row", i))
row_histo.append(histo_analysis(data[i], 0.67449, -0.67449, 0.67449))
column_b_stats = []
for i in range(0, column_max, 1):
column_b_stats.append(basic_stats(data, "column", i))
return[row_b_stats, column_b_stats, row_histo]
def benchmark(x, y):
start_g = time.time()
generate_data("data/data.csv", x, y, -10, 10)
end_g = time.time()
start_a = time.time()
basic_analysis("data/data.csv")
end_a = time.time()
return [(end_g - start_g), (end_a - start_a)]
def generate_data(filename, x, y, low, high):
file = open(filename, "w")
for i in range(0, y, 1):
temp = ""
for j in range(0, x - 1, 1):
temp = str(random.uniform(low, high)) + "," + temp
temp = temp + str(random.uniform(low, high))
file.write(temp + "\n")
def mean(data):
return np.mean(data)
def median(data):
return np.median(data)
def mode(data):
return np.argmax(np.bincount(data))
def stdev(data):
return np.std(data)
def variance(data):
return np.var(data)
"""
class StatisticsError(ValueError):
pass
def _sum(data, start=0):
count = 0
n, d = _exact_ratio(start)
partials = {d: n}
partials_get = partials.get
T = _coerce(int, type(start))
for typ, values in groupby(data, type):
T = _coerce(T, typ) # or raise TypeError
for n, d in map(_exact_ratio, values):
count += 1
partials[d] = partials_get(d, 0) + n
if None in partials:
total = partials[None]
assert not _isfinite(total)
else:
total = sum(Fraction(n, d) for d, n in sorted(partials.items()))
return (T, total, count)
def _isfinite(x):
try:
return x.is_finite() # Likely a Decimal.
except AttributeError:
return math.isfinite(x) # Coerces to float first.
def _coerce(T, S):
assert T is not bool, "initial type T is bool"
if T is S:
return T
if S is int or S is bool:
return T
if T is int:
return S
if issubclass(S, T):
return S
if issubclass(T, S):
return T
if issubclass(T, int):
return S
if issubclass(S, int):
return T
if issubclass(T, Fraction) and issubclass(S, float):
return S
if issubclass(T, float) and issubclass(S, Fraction):
return T
msg = "don't know how to coerce %s and %s"
raise TypeError(msg % (T.__name__, S.__name__))
def _exact_ratio(x):
try:
if type(x) is float or type(x) is Decimal:
return x.as_integer_ratio()
try:
return (x.numerator, x.denominator)
except AttributeError:
try:
return x.as_integer_ratio()
except AttributeError:
pass
except (OverflowError, ValueError):
assert not _isfinite(x)
return (x, None)
msg = "can't convert type '{}' to numerator/denominator"
raise TypeError(msg.format(type(x).__name__))
def _convert(value, T):
if type(value) is T:
return value
if issubclass(T, int) and value.denominator != 1:
T = float
try:
return T(value)
except TypeError:
if issubclass(T, Decimal):
return T(value.numerator) / T(value.denominator)
else:
raise
def _counts(data):
table = collections.Counter(iter(data)).most_common()
if not table:
return table
maxfreq = table[0][1]
for i in range(1, len(table)):
if table[i][1] != maxfreq:
table = table[:i]
break
return table
def _find_lteq(a, x):
i = bisect_left(a, x)
if i != len(a) and a[i] == x:
return i
raise ValueError
def _find_rteq(a, l, x):
i = bisect_right(a, x, lo=l)
if i != (len(a) + 1) and a[i - 1] == x:
return i - 1
raise ValueError
def _fail_neg(values, errmsg='negative value'):
for x in values:
if x < 0:
raise StatisticsError(errmsg)
yield x
def mean(data):
if iter(data) is data:
data = list(data)
n = len(data)
if n < 1:
raise StatisticsError('mean requires at least one data point')
T, total, count = _sum(data)
assert count == n
return _convert(total / n, T)
def median(data):
data = sorted(data)
n = len(data)
if n == 0:
raise StatisticsError("no median for empty data")
if n % 2 == 1:
return data[n // 2]
else:
i = n // 2
return (data[i - 1] + data[i]) / 2
def mode(data):
table = _counts(data)
if len(table) == 1:
return table[0][0]
elif table:
raise StatisticsError(
'no unique mode; found %d equally common values' % len(table)
)
else:
raise StatisticsError('no mode for empty data')
def _ss(data, c=None):
if c is None:
c = mean(data)
T, total, count = _sum((x - c)**2 for x in data)
U, total2, count2 = _sum((x - c) for x in data)
assert T == U and count == count2
total -= total2**2 / len(data)
assert not total < 0, 'negative sum of square deviations: %f' % total
return (T, total)
def variance(data, xbar=None):
if iter(data) is data:
data = list(data)
n = len(data)
if n < 2:
raise StatisticsError('variance requires at least two data points')
T, ss = _ss(data, xbar)
return _convert(ss / (n - 1), T)
def stdev(data, xbar=None):
var = variance(data, xbar)
try:
return var.sqrt()
except AttributeError:
return math.sqrt(var)
"""

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@ -1,2 +0,0 @@
python setup.py build_ext --inplace
pause

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@ -1 +0,0 @@
python setup.py build_ext --inplace

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@ -1,5 +0,0 @@
from distutils.core import setup
from Cython.Build import cythonize
setup(name='analysis',
ext_modules=cythonize("analysis.py"))

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@ -1 +0,0 @@
[{"outputType":{"type":"APK"},"apkInfo":{"type":"MAIN","splits":[],"versionCode":1,"versionName":"1.0","enabled":true,"outputFile":"app-debug.apk","fullName":"debug","baseName":"debug"},"path":"app-debug.apk","properties":{}}]

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@ -1,13 +0,0 @@
*.iml
.gradle
/local.properties
/.idea/caches
/.idea/libraries
/.idea/modules.xml
/.idea/workspace.xml
/.idea/navEditor.xml
/.idea/assetWizardSettings.xml
.DS_Store
/build
/captures
.externalNativeBuild

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@ -1,29 +0,0 @@
<component name="ProjectCodeStyleConfiguration">
<code_scheme name="Project" version="173">
<Objective-C-extensions>
<file>
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Import" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Macro" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Typedef" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Enum" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Constant" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Global" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Struct" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="FunctionPredecl" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Function" />
</file>
<class>
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Property" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Synthesize" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="InitMethod" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="StaticMethod" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="InstanceMethod" />
<option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="DeallocMethod" />
</class>
<extensions>
<pair source="cpp" header="h" fileNamingConvention="NONE" />
<pair source="c" header="h" fileNamingConvention="NONE" />
</extensions>
</Objective-C-extensions>
</code_scheme>
</component>

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@ -1,18 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="GradleSettings">
<option name="linkedExternalProjectsSettings">
<GradleProjectSettings>
<option name="distributionType" value="DEFAULT_WRAPPED" />
<option name="externalProjectPath" value="$PROJECT_DIR$" />
<option name="modules">
<set>
<option value="$PROJECT_DIR$" />
<option value="$PROJECT_DIR$/app" />
</set>
</option>
<option name="resolveModulePerSourceSet" value="false" />
</GradleProjectSettings>
</option>
</component>
</project>

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@ -1,9 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" languageLevel="JDK_11" project-jdk-name="11" project-jdk-type="JavaSDK">
<output url="file://$PROJECT_DIR$/build/classes" />
</component>
<component name="ProjectType">
<option name="id" value="Android" />
</component>
</project>

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@ -1,12 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="RunConfigurationProducerService">
<option name="ignoredProducers">
<set>
<option value="org.jetbrains.plugins.gradle.execution.test.runner.AllInPackageGradleConfigurationProducer" />
<option value="org.jetbrains.plugins.gradle.execution.test.runner.TestClassGradleConfigurationProducer" />
<option value="org.jetbrains.plugins.gradle.execution.test.runner.TestMethodGradleConfigurationProducer" />
</set>
</option>
</component>
</project>

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@ -1,6 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$/../../.." vcs="Git" />
</component>
</project>

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@ -1 +0,0 @@
/build

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@ -1,28 +0,0 @@
apply plugin: 'com.android.application'
android {
compileSdkVersion 28
defaultConfig {
applicationId "com.example.titanscouting"
minSdkVersion 16
targetSdkVersion 28
versionCode 1
versionName "1.0"
testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
}
buildTypes {
release {
minifyEnabled false
proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
}
}
}
dependencies {
implementation fileTree(dir: 'libs', include: ['*.jar'])
implementation 'com.android.support:appcompat-v7:28.0.0'
implementation 'com.android.support.constraint:constraint-layout:1.1.3'
testImplementation 'junit:junit:4.12'
androidTestImplementation 'com.android.support.test:runner:1.0.2'
androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
}

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@ -1,21 +0,0 @@
# Add project specific ProGuard rules here.
# You can control the set of applied configuration files using the
# proguardFiles setting in build.gradle.
#
# For more details, see
# http://developer.android.com/guide/developing/tools/proguard.html
# If your project uses WebView with JS, uncomment the following
# and specify the fully qualified class name to the JavaScript interface
# class:
#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
# public *;
#}
# Uncomment this to preserve the line number information for
# debugging stack traces.
#-keepattributes SourceFile,LineNumberTable
# If you keep the line number information, uncomment this to
# hide the original source file name.
#-renamesourcefileattribute SourceFile

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@ -1 +0,0 @@
[{"outputType":{"type":"APK"},"apkInfo":{"type":"MAIN","splits":[],"versionCode":1,"versionName":"1.0","enabled":true,"outputFile":"app-release.apk","fullName":"release","baseName":"release"},"path":"app-release.apk","properties":{}}]

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@ -1,26 +0,0 @@
package com.example.titanscouting;
import android.content.Context;
import android.support.test.InstrumentationRegistry;
import android.support.test.runner.AndroidJUnit4;
import org.junit.Test;
import org.junit.runner.RunWith;
import static org.junit.Assert.*;
/**
* Instrumented test, which will execute on an Android device.
*
* @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
*/
@RunWith(AndroidJUnit4.class)
public class ExampleInstrumentedTest {
@Test
public void useAppContext() {
// Context of the app under test.
Context appContext = InstrumentationRegistry.getTargetContext();
assertEquals("com.example.titanscouting", appContext.getPackageName());
}
}

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@ -1,28 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
package="com.example.titanscouting">
<uses-permission android:name="android.permission.INTERNET" />
<application
android:allowBackup="true"
android:icon="@mipmap/ic_launcher"
android:label="@string/app_name"
android:roundIcon="@drawable/binoculars_big"
android:supportsRtl="true"
android:theme="@style/AppTheme"
android:usesCleartextTraffic="true">
<activity android:name=".tits"></activity>
<activity android:name=".launcher">
<intent-filter>
<action android:name="android.intent.action.MAIN" />
<category android:name="android.intent.category.LAUNCHER" />
</intent-filter>
</activity>
<activity android:name=".MainActivity">
</activity>
</application>
</manifest>

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@ -1,32 +0,0 @@
package com.example.titanscouting;
import android.support.v7.app.AppCompatActivity;
import android.os.Bundle;
import android.webkit.WebView;
import android.webkit.WebSettings;
import android.webkit.WebViewClient;
public class MainActivity extends AppCompatActivity {
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
WebView myWebView = (WebView) findViewById(R.id.webview);
myWebView.getSettings().setJavaScriptEnabled(true);
myWebView.setWebViewClient(new WebViewClient());
myWebView.loadUrl("http://titanrobotics.ddns.net:60080/public/");
myWebView.getSettings().setJavaScriptEnabled(true);
myWebView.getSettings().setJavaScriptCanOpenWindowsAutomatically(true);
myWebView.getSettings().setDomStorageEnabled(true);
myWebView.getSettings().setDomStorageEnabled(true);
}
}

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@ -1,49 +0,0 @@
package com.example.titanscouting;
import android.support.v7.app.AppCompatActivity;
import android.os.Bundle;
import android.app.Activity;
import android.content.Intent;
import android.view.Menu;
import android.view.View;
import android.view.View.OnClickListener;
import android.widget.Button;
import android.widget.EditText;
public class launcher extends AppCompatActivity {
Button button;
EditText passField;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_launcher);
// Locate the button in activity_main.xml
button = (Button) findViewById(R.id.launch_button);
final EditText passField = (EditText)findViewById(R.id.editText);
// Capture button clicks
button.setOnClickListener(new OnClickListener() {
public void onClick(View arg0) {
// Start NewActivity.class
if(passField.getText().toString().equals("gimmetits")){
Intent myIntent = new Intent(launcher.this,
tits.class);
startActivity(myIntent);
}
else {
Intent myIntent = new Intent(launcher.this,
MainActivity.class);
startActivity(myIntent);
}
}
});
}
}

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@ -1,30 +0,0 @@
package com.example.titanscouting;
import android.content.Intent;
import android.support.v7.app.AppCompatActivity;
import android.os.Bundle;
import android.view.View;
import android.widget.Button;
import android.widget.EditText;
public class tits extends AppCompatActivity {
Button button;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_tits);
button = (Button) findViewById(R.id.button);
// Capture button clicks
button.setOnClickListener(new View.OnClickListener() {
public void onClick(View arg0) {
Intent myIntent = new Intent(tits.this,
MainActivity.class);
startActivity(myIntent);
}
});
}
}

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@ -1,34 +0,0 @@
<vector xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:aapt="http://schemas.android.com/aapt"
android:width="108dp"
android:height="108dp"
android:viewportWidth="108"
android:viewportHeight="108">
<path
android:fillType="evenOdd"
android:pathData="M32,64C32,64 38.39,52.99 44.13,50.95C51.37,48.37 70.14,49.57 70.14,49.57L108.26,87.69L108,109.01L75.97,107.97L32,64Z"
android:strokeWidth="1"
android:strokeColor="#00000000">
<aapt:attr name="android:fillColor">
<gradient
android:endX="78.5885"
android:endY="90.9159"
android:startX="48.7653"
android:startY="61.0927"
android:type="linear">
<item
android:color="#44000000"
android:offset="0.0" />
<item
android:color="#00000000"
android:offset="1.0" />
</gradient>
</aapt:attr>
</path>
<path
android:fillColor="#FFFFFF"
android:fillType="nonZero"
android:pathData="M66.94,46.02L66.94,46.02C72.44,50.07 76,56.61 76,64L32,64C32,56.61 35.56,50.11 40.98,46.06L36.18,41.19C35.45,40.45 35.45,39.3 36.18,38.56C36.91,37.81 38.05,37.81 38.78,38.56L44.25,44.05C47.18,42.57 50.48,41.71 54,41.71C57.48,41.71 60.78,42.57 63.68,44.05L69.11,38.56C69.84,37.81 70.98,37.81 71.71,38.56C72.44,39.3 72.44,40.45 71.71,41.19L66.94,46.02ZM62.94,56.92C64.08,56.92 65,56.01 65,54.88C65,53.76 64.08,52.85 62.94,52.85C61.8,52.85 60.88,53.76 60.88,54.88C60.88,56.01 61.8,56.92 62.94,56.92ZM45.06,56.92C46.2,56.92 47.13,56.01 47.13,54.88C47.13,53.76 46.2,52.85 45.06,52.85C43.92,52.85 43,53.76 43,54.88C43,56.01 43.92,56.92 45.06,56.92Z"
android:strokeWidth="1"
android:strokeColor="#00000000" />
</vector>

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@ -1,170 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<vector xmlns:android="http://schemas.android.com/apk/res/android"
android:width="108dp"
android:height="108dp"
android:viewportWidth="108"
android:viewportHeight="108">
<path
android:fillColor="#008577"
android:pathData="M0,0h108v108h-108z" />
<path
android:fillColor="#00000000"
android:pathData="M9,0L9,108"
android:strokeWidth="0.8"
android:strokeColor="#33FFFFFF" />
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android:fillColor="#00000000"
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android:fillColor="#00000000"
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android:fillColor="#00000000"
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android:strokeColor="#33FFFFFF" />
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android:strokeColor="#33FFFFFF" />
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View File

@ -1,42 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto"
xmlns:tools="http://schemas.android.com/tools"
android:layout_width="match_parent"
android:layout_height="match_parent"
tools:context=".launcher">
<Button
android:id="@+id/launch_button"
android:layout_width="253dp"
android:layout_height="56dp"
android:layout_marginStart="8dp"
android:layout_marginLeft="8dp"
android:layout_marginTop="8dp"
android:layout_marginEnd="8dp"
android:layout_marginRight="8dp"
android:layout_marginBottom="8dp"
android:text="Launch Titan Scouting"
app:layout_constraintBottom_toBottomOf="parent"
app:layout_constraintEnd_toEndOf="parent"
app:layout_constraintStart_toStartOf="parent"
app:layout_constraintTop_toTopOf="parent" />
<EditText
android:id="@+id/editText"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:layout_marginStart="8dp"
android:layout_marginLeft="8dp"
android:layout_marginTop="8dp"
android:layout_marginEnd="8dp"
android:layout_marginRight="8dp"
android:layout_marginBottom="8dp"
android:ems="10"
android:inputType="textPassword"
app:layout_constraintBottom_toBottomOf="parent"
app:layout_constraintEnd_toEndOf="parent"
app:layout_constraintStart_toStartOf="parent"
app:layout_constraintTop_toBottomOf="@+id/launch_button"
app:layout_constraintVertical_bias="0.0" />
</android.support.constraint.ConstraintLayout>

View File

@ -1,19 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto"
xmlns:tools="http://schemas.android.com/tools"
android:layout_width="match_parent"
android:layout_height="match_parent"
tools:context=".MainActivity">
<WebView
android:id="@+id/webview"
android:layout_width="0dp"
android:layout_height="0dp"
app:layout_constraintBottom_toBottomOf="parent"
app:layout_constraintEnd_toEndOf="parent"
app:layout_constraintStart_toStartOf="parent"
app:layout_constraintTop_toTopOf="parent"
app:layout_constraintVertical_bias="0.48000002" />
</android.support.constraint.ConstraintLayout>

View File

@ -1,36 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto"
xmlns:tools="http://schemas.android.com/tools"
android:layout_width="match_parent"
android:layout_height="match_parent"
tools:context=".tits">
<ImageView
android:id="@+id/imageView"
android:layout_width="372dp"
android:layout_height="487dp"
android:layout_marginTop="4dp"
android:layout_marginBottom="215dp"
app:layout_constraintBottom_toBottomOf="parent"
app:layout_constraintEnd_toEndOf="parent"
app:layout_constraintStart_toStartOf="parent"
app:layout_constraintTop_toTopOf="parent"
app:srcCompat="@drawable/uuh" />
<Button
android:id="@+id/button"
android:layout_width="198dp"
android:layout_height="86dp"
android:layout_marginStart="8dp"
android:layout_marginLeft="8dp"
android:layout_marginTop="8dp"
android:layout_marginEnd="8dp"
android:layout_marginRight="8dp"
android:layout_marginBottom="8dp"
android:text="Fuck Get Me Out"
app:layout_constraintBottom_toBottomOf="parent"
app:layout_constraintEnd_toEndOf="parent"
app:layout_constraintStart_toStartOf="parent"
app:layout_constraintTop_toBottomOf="@+id/imageView" />
</android.support.constraint.ConstraintLayout>

View File

@ -1,5 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
<background android:drawable="@drawable/ic_launcher_background" />
<foreground android:drawable="@drawable/ic_launcher_foreground" />
</adaptive-icon>

View File

@ -1,5 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
<background android:drawable="@drawable/ic_launcher_background" />
<foreground android:drawable="@drawable/ic_launcher_foreground" />
</adaptive-icon>

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@ -1,6 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<resources>
<color name="colorPrimary">#008577</color>
<color name="colorPrimaryDark">#00574B</color>
<color name="colorAccent">#D81B60</color>
</resources>

View File

@ -1,3 +0,0 @@
<resources>
<string name="app_name">TitanScout</string>
</resources>

View File

@ -1,11 +0,0 @@
<resources>
<!-- Base application theme. -->
<style name="AppTheme" parent="Theme.AppCompat.Light.NoActionBar">
<!-- Customize your theme here. -->
<item name="colorPrimary">@color/colorPrimary</item>
<item name="colorPrimaryDark">@color/colorPrimaryDark</item>
<item name="colorAccent">@color/colorAccent</item>
</style>
</resources>

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