changed data analysis folder to data-analysis

added egg-info and build folders to git ignore
deleted egg-info and build folders

.gitignore

@@ -21,4 +21,6 @@ data analysis/test.ipynb
 data analysis/visualize_pit.ipynb
 data analysis/config/keys.config
 analysis-master/analysis/__pycache__/
-data analysis/__pycache__/
+data analysis/__pycache__/
+analysis-master/analysis.egg-info/
+analysis-master/build/

analysis-master/analysis.egg-info/PKG-INFO

@@ -1,14 +0,0 @@
-Metadata-Version: 2.1
-Name: analysis
-Version: 1.0.0.12
-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

analysis-master/analysis.egg-info/SOURCES.txt

@@ -1,15 +0,0 @@
-setup.py
-analysis/__init__.py
-analysis/analysis.py
-analysis/regression.py
-analysis/titanlearn.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
-analysis/metrics/__init__.py
-analysis/metrics/elo.py
-analysis/metrics/glicko2.py
-analysis/metrics/trueskill.py

analysis-master/analysis.egg-info/dependency_links.txt

@@ -1 +0,0 @@
-

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

@@ -1,6 +0,0 @@
-numba
-numpy
-scipy
-scikit-learn
-six
-matplotlib

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

@@ -1 +0,0 @@
-analysis

analysis-master/build/lib/analysis/analysis.py

@@ -1,923 +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 'from analysis 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__ = "1.2.0.004"
-
-# changelog should be viewed using print(analysis.__changelog__)
-__changelog__ = """changelog:
-    1.2.0.004:
-        - fixed __all__ to reflected the correct functions and classes
-        - fixed CorrelationTests and StatisticalTests class functions to require self invocation
-        - added missing math import
-        - fixed KNN class functions to require self invocation
-        - fixed Metrics class functions to require self invocation
-        - various spelling fixes in CorrelationTests and StatisticalTests
-    1.2.0.003:
-        - bug fixes with CorrelationTests and StatisticalTests
-        - moved glicko2 and trueskill to the metrics subpackage
-        - moved elo to a new metrics subpackage
-    1.2.0.002:
-        - fixed docs
-    1.2.0.001:
-        - fixed docs
-    1.2.0.000:
-        - cleaned up wild card imports with scipy and sklearn
-        - added CorrelationTests class
-        - added StatisticalTests class
-        - added several correlation tests to CorrelationTests
-        - added several statistical tests to StatisticalTests
-    1.1.13.009:
-        - moved elo, glicko2, trueskill functions under class Metrics
-    1.1.13.008:
-        - moved Glicko2 to a seperate package
-    1.1.13.007:
-        - fixed bug with trueskill
-    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',
-    'Metrics',
-    'RegressionMetrics',
-    'ClassificationMetrics',
-    'kmeans',
-    'pca',
-    'decisiontree',
-    'KNN',
-    'NaiveBayes',
-    'SVM',
-    'random_forest_classifier',
-    'random_forest_regressor',
-    'CorrelationTests',
-    'StatisticalTests',
-    # 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
-from analysis.metrics import elo as Elo
-from analysis.metrics import glicko2 as Glicko2
-import math
-import numba
-from numba import jit
-import numpy as np
-import scipy
-from scipy import optimize, stats
-import sklearn
-from sklearn import preprocessing, pipeline, linear_model, metrics, cluster, decomposition, tree, neighbors, naive_bayes, svm, model_selection, ensemble
-from analysis.metrics 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
-
-class Metrics:
-
-    def elo(self, starting_score, opposing_score, observed, N, K):
-
-        return Elo.calculate(starting_score, opposing_score, observed, N, K)
-
-    def glicko2(self, starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
-
-        player = Glicko2.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(self, 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 = team_temp + (player,)
-            team_ratings.append(team_temp)
-
-        return Trueskill.rate(team_ratings, ranks=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
-
-class KNN:
-
-    def knn_classifier(self, 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(self, 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 CorrelationTests:
-
-    def anova_oneway(self, *args): #expects arrays of samples
-
-        results = scipy.stats.f_oneway(*args)
-        return {"F-value": results[0], "p-value": results[1]}
-
-    def pearson(self, x, y):
-
-        results = scipy.stats.pearsonr(x, y)
-        return {"r-value": results[0], "p-value": results[1]}
-
-    def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'):
-
-        results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy)
-        return {"r-value": results[0], "p-value": results[1]}
-
-    def point_biserial(self, x,y):
-
-        results = scipy.stats.pointbiserialr(x, y)
-        return {"r-value": results[0], "p-value": results[1]}
-
-    def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
-
-        results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method)
-        return {"tau": results[0], "p-value": results[1]}
-
-    def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True):
-
-        results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive)
-        return {"tau": results[0], "p-value": results[1]}
-
-    def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
-
-        results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state)
-        return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value
-
-class StatisticalTests:
-
-    def ttest_onesample(self, a, popmean, axis = 0, nan_policy = 'propagate'):
-
-        results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy)
-        return {"t-value": results[0], "p-value": results[1]}
-
-    def ttest_independent(self, a, b, equal = True, nan_policy = 'propagate'):
-
-        results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy)
-        return {"t-value": results[0], "p-value": results[1]}
-
-    def ttest_statistic(self, o1, o2, equal = True):
-
-        results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal)
-        return {"t-value": results[0], "p-value": results[1]}
-
-    def ttest_related(self, a, b, axis = 0, nan_policy='propagate'):
-
-        results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy)
-        return {"t-value": results[0], "p-value": results[1]}
-
-    def ks_fitness(self, rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'):
-
-        results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode)
-        return {"ks-value": results[0], "p-value": results[1]}
-
-    def chisquare(self, f_obs, f_exp = None, ddof = None, axis = 0):
-
-        results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis)
-        return {"chisquared-value": results[0], "p-value": results[1]}
-
-    def powerdivergence(self, f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None):
-
-        results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_)
-        return {"powerdivergence-value": results[0], "p-value": results[1]}
-
-    def ks_twosample(self, x, y, alternative = 'two_sided', mode = 'auto'):
-        
-        results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode)
-        return {"ks-value": results[0], "p-value": results[1]}
-
-    def es_twosample(self, x, y, t = (0.4, 0.8)):
-
-        results = scipy.stats.epps_singleton_2samp(x, y, t = t)
-        return {"es-value": results[0], "p-value": results[1]}
-
-    def mw_rank(self, x, y, use_continuity = True, alternative = None):
-
-        results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative)
-        return {"u-value": results[0], "p-value": results[1]}
-
-    def mw_tiecorrection(self, rank_values):
-
-        results = scipy.stats.tiecorrect(rank_values)
-        return {"correction-factor": results}
-
-    def rankdata(self, a, method = 'average'):
-
-        results = scipy.stats.rankdata(a, method = method)
-        return results
-
-    def wilcoxon_ranksum(self, a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test
-
-        results = scipy.stats.ranksums(a, b)
-        return {"u-value": results[0], "p-value": results[1]}
-
-    def wilcoxon_signedrank(self, x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'):
-
-        results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative)
-        return {"t-value": results[0], "p-value": results[1]}
-
-    def kw_htest(self, *args, nan_policy = 'propagate'):
-
-        results = scipy.stats.kruskal(*args, nan_policy = nan_policy)
-        return {"h-value": results[0], "p-value": results[1]}
-
-    def friedman_chisquare(self, *args):
-
-        results = scipy.stats.friedmanchisquare(*args)
-        return {"chisquared-value": results[0], "p-value": results[1]}
-
-    def bm_wtest(self, x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'):
-
-        results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy)
-        return {"w-value": results[0], "p-value": results[1]}
-
-    def combine_pvalues(self, pvalues, method = 'fisher', weights = None):
-
-        results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights)
-        return {"combined-statistic": results[0], "p-value": results[1]}
-
-    def jb_fitness(self, x):
-
-        results = scipy.stats.jarque_bera(x)
-        return {"jb-value": results[0], "p-value": results[1]}
-
-    def ab_equality(self, x, y):
-
-        results = scipy.stats.ansari(x, y)
-        return {"ab-value": results[0], "p-value": results[1]}
-
-    def bartlett_variance(self, *args):
-
-        results = scipy.stats.bartlett(*args)
-        return {"t-value": results[0], "p-value": results[1]}
-
-    def levene_variance(self, *args, center = 'median', proportiontocut = 0.05):
-
-        results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut)
-        return {"w-value": results[0], "p-value": results[1]}
-
-    def sw_normality(self, x):
-
-        results = scipy.stats.shapiro(x)
-        return {"w-value": results[0], "p-value": results[1]}
-
-    def shapiro(self, x):
-
-        return "destroyed by facts and logic"
-
-    def ad_onesample(self, x, dist = 'norm'):
-
-        results = scipy.stats.anderson(x, dist = dist)
-        return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
-    
-    def ad_ksample(self, samples, midrank = True):
-
-        results = scipy.stats.anderson_ksamp(samples, midrank = midrank)
-        return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
-
-    def binomial(self, x, n = None, p = 0.5, alternative = 'two-sided'):
-
-        results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative)
-        return {"p-value": results}
-
-    def fk_variance(self, *args, center = 'median', proportiontocut = 0.05):
-
-        results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut)
-        return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value
-
-    def mood_mediantest(self, *args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'):
-
-        results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy)
-        return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]}
-
-    def mood_equalscale(self, x, y, axis = 0):
-
-        results = scipy.stats.mood(x, y, axis = axis)
-        return {"z-score": results[0], "p-value": results[1]}
-
-    def skewtest(self, a, axis = 0, nan_policy = 'propogate'):
-
-        results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy)
-        return {"z-score": results[0], "p-value": results[1]}
-
-    def kurtosistest(self, a, axis = 0, nan_policy = 'propogate'):
-
-        results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy)
-        return {"z-score": results[0], "p-value": results[1]}
-
-    def normaltest(self, a, axis = 0, nan_policy = 'propogate'):
-
-        results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy)
-        return {"z-score": results[0], "p-value": results[1]}

analysis-master/build/lib/analysis/glicko2.py

@@ -1,99 +0,0 @@
-import math
-
-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()

analysis-master/build/lib/analysis/metrics/elo.py

@@ -1,7 +0,0 @@
-import numpy as np
-
-def calculate(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))

analysis-master/build/lib/analysis/metrics/glicko2.py

@@ -1,99 +0,0 @@
-import math
-
-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()

analysis-master/build/lib/analysis/metrics/trueskill.py

@@ -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)

analysis-master/build/lib/analysis/regression.py

@@ -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

analysis-master/build/lib/analysis/titanlearn.py

@@ -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)

analysis-master/build/lib/analysis/trueskill.py

@@ -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)

analysis-master/build/lib/analysis/visualization.py

@@ -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()

data-analysis/config/keys.config

@@ -0,0 +1,2 @@
+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