analysis pkg v 1.0.0.12

analysis.py v 1.2.0.004

analysis-master/analysis-amd64/analysis.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: analysis
-Version: 1.0.0.11
+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

analysis-master/analysis-amd64/analysis.egg-info/SOURCES.txt

@@ -1,13 +1,15 @@
 setup.py
 analysis/__init__.py
 analysis/analysis.py
-analysis/glicko2.py
 analysis/regression.py
 analysis/titanlearn.py
-analysis/trueskill.py
 analysis/visualization.py
 analysis.egg-info/PKG-INFO
 analysis.egg-info/SOURCES.txt
 analysis.egg-info/dependency_links.txt
 analysis.egg-info/requires.txt
 analysis.egg-info/top_level.txt
+analysis/metrics/__init__.py
+analysis/metrics/elo.py
+analysis/metrics/glicko2.py
+analysis/metrics/trueskill.py

analysis-master/analysis-amd64/analysis/analysis.py

@@ -7,10 +7,17 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.2.0.003"
+__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
@@ -275,22 +282,19 @@ __all__ = [
     'z_normalize',
     'histo_analysis',
     'regression',
-    'elo',
+    'Metrics',
-    'glicko2',
-    'trueskill',
     'RegressionMetrics',
     'ClassificationMetrics',
     'kmeans',
     'pca',
     'decisiontree',
-    'knn_classifier',
+    'KNN',
-    'knn_regressor',
     'NaiveBayes',
     'SVM',
     'random_forest_classifier',
     'random_forest_regressor',
     'CorrelationTests',
-    'RegressionTests',
+    'StatisticalTests',
     # all statistics functions left out due to integration in other functions
 ]
 
@@ -301,6 +305,7 @@ __all__ = [
 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
@@ -467,11 +472,11 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
 class Metrics:
 
-    def elo(starting_score, opposing_score, observed, N, K):
+    def elo(self, starting_score, opposing_score, observed, N, K):
 
         return Elo.calculate(starting_score, opposing_score, observed, N, K)
 
-    def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
+    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)
 
@@ -479,7 +484,7 @@ class Metrics:
 
         return (player.rating, player.rd, player.vol)
 
-    def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
+    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 = []
 
@@ -584,7 +589,7 @@ def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "
 
 class KNN:
 
-    def knn_classifier(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
+    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()
@@ -593,7 +598,7 @@ class KNN:
 
         return model, ClassificationMetrics(predictions, labels_test)
 
-    def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
+    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)
@@ -716,203 +721,203 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
 
 class CorrelationTests:
 
-    def anova_oneway(*args): #expects arrays of samples
+    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(x, y):
+    def pearson(self, x, y):
 
         results = scipy.stats.pearsonr(x, y)
         return {"r-value": results[0], "p-value": results[1]}
 
-    def spearman(a, b = None, axis = 0, nan_policy = 'propagate'):
+    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(x,y):
+    def point_biserial(self, x,y):
 
         results = scipy.stats.pointbiserialr(x, y)
         return {"r-value": results[0], "p-value": results[1]}
 
-    def kendall(x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
+    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(x, y, rank = True, weigher = None, additive = True):
+    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(x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
+    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(a, popmean, axis = 0, nan_policy = 'propagate'):
+    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(a, b, equal = True, nan_policy = 'propagate'):
+    def ttest_independent(self, a, b, equal = True, nan_policy = 'propagate'):
 
-        results = scipt.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy)
+        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(o1, o2, equal = True):
+    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(a, b, axis = 0, nan_policy='propagate'):
+    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(rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'):
+    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(f_obs, f_exp = None, ddof = None, axis = 0):
+    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(f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None):
+    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(x, y, alternative = 'two_sided', mode = 'auto'):
+    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(x, y, t = (0.4, 0.8)):
+    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(x, y, use_continuity = True, alternative = None):
+    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(rank_values):
+    def mw_tiecorrection(self, rank_values):
 
         results = scipy.stats.tiecorrect(rank_values)
         return {"correction-factor": results}
 
-    def rankdata(a, method = 'average'):
+    def rankdata(self, a, method = 'average'):
 
         results = scipy.stats.rankdata(a, method = method)
         return results
 
-    def wilcoxon_ranksum(a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test
+    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(x, y = None, method = 'wilcox', correction = False, alternative = 'two-sided'):
+    def wilcoxon_signedrank(self, x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'):
 
-        results = scipy.stats.wilcoxon(x, y = y, method = method, correction = correction, alternative = alternative)
+        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(*args, nan_policy = 'propagate'):
+    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(*args):
+    def friedman_chisquare(self, *args):
 
         results = scipy.stats.friedmanchisquare(*args)
         return {"chisquared-value": results[0], "p-value": results[1]}
 
-    def bm_wtest(x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'):
+    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(pvalues, method = 'fisher', weights = None):
+    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(x):
+    def jb_fitness(self, x):
 
         results = scipy.stats.jarque_bera(x)
         return {"jb-value": results[0], "p-value": results[1]}
 
-    def ab_equality(x, y):
+    def ab_equality(self, x, y):
 
         results = scipy.stats.ansari(x, y)
         return {"ab-value": results[0], "p-value": results[1]}
 
-    def bartlett_variance(*args):
+    def bartlett_variance(self, *args):
 
         results = scipy.stats.bartlett(*args)
         return {"t-value": results[0], "p-value": results[1]}
 
-    def levene_variance(*args, center = 'median', proportiontocut = 0.05):
+    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(x):
+    def sw_normality(self, x):
 
         results = scipy.stats.shapiro(x)
         return {"w-value": results[0], "p-value": results[1]}
 
-    def shapiro(x):
+    def shapiro(self, x):
 
         return "destroyed by facts and logic"
 
-    def ad_onesample(x, dist = 'norm'):
+    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(samples, midrank = True):
+    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(x, n = None, p = 0.5, alternative = 'two-sided'):
+    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(*args, center = 'median', proportiontocut = 0.05):
+    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(*args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'):
+    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(x, y, axis = 0):
+    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(a, axis = 0, nan_policy = 'propogate'):
+    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(a, axis = 0, nan_policy = 'propogate'):
+    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(a, axis = 0, nan_policy = 'propogate'):
+    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/analysis-amd64/build/lib/analysis/analysis.py

@@ -1,16 +1,37 @@
 # Titan Robotics Team 2022: Data Analysis Module
 # Written by Arthur Lu & Jacob Levine
 # Notes:
-#    this should be imported as a python module using 'import analysis'
+#    this should be 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__ = "1.1.13.009"
+__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:
@@ -261,20 +282,19 @@ __all__ = [
     'z_normalize',
     'histo_analysis',
     'regression',
-    'elo',
+    'Metrics',
-    'glicko2',
-    'trueskill',
     'RegressionMetrics',
     'ClassificationMetrics',
     'kmeans',
     'pca',
     'decisiontree',
-    'knn_classifier',
+    'KNN',
-    'knn_regressor',
     'NaiveBayes',
     'SVM',
     'random_forest_classifier',
     'random_forest_regressor',
+    'CorrelationTests',
+    'StatisticalTests',
     # all statistics functions left out due to integration in other functions
 ]
 
@@ -283,15 +303,17 @@ __all__ = [
 # imports (now in alphabetical order! v 1.0.3.006):
 
 import csv
-from analysis import glicko2 as Glicko2
+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 *
+from scipy import optimize, stats
 import sklearn
-from sklearn import *
+from sklearn import preprocessing, pipeline, linear_model, metrics, cluster, decomposition, tree, neighbors, naive_bayes, svm, model_selection, ensemble
-from analysis import trueskill as Trueskill
+from analysis.metrics import trueskill as Trueskill
 
 class error(ValueError):
     pass
@@ -450,13 +472,11 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
 class Metrics:
 
-    def elo(starting_score, opposing_score, observed, N, K):
+    def elo(self, starting_score, opposing_score, observed, N, K):
 
-        expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
+        return Elo.calculate(starting_score, opposing_score, observed, N, K)
 
-        return starting_score + K*(np.sum(observed) - np.sum(expected))
+    def glicko2(self, starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
-
-    def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
 
         player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
 
@@ -464,7 +484,7 @@ class Metrics:
 
         return (player.rating, player.rd, player.vol)
 
-    def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
+    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 = []
 
@@ -569,7 +589,7 @@ def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "
 
 class KNN:
 
-    def knn_classifier(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
+    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()
@@ -578,7 +598,7 @@ class KNN:
 
         return model, ClassificationMetrics(predictions, labels_test)
 
-    def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
+    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)
@@ -698,3 +718,206 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
     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/analysis-amd64/build/lib/analysis/metrics/elo.py

@@ -0,0 +1,7 @@
+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/analysis-amd64/build/lib/analysis/metrics/glicko2.py

@@ -0,0 +1,99 @@
+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/analysis-amd64/build/lib/analysis/metrics/trueskill.py

@@ -0,0 +1,907 @@
+from __future__ import absolute_import
+
+from itertools import chain
+import math
+
+from six import iteritems
+from six.moves import map, range, zip
+from six import iterkeys
+
+import copy
+try:
+    from numbers import Number
+except ImportError:
+    Number = (int, long, float, complex)
+
+inf = float('inf')
+
+class Gaussian(object):
+    #: Precision, the inverse of the variance.
+    pi = 0
+    #: Precision adjusted mean, the precision multiplied by the mean.
+    tau = 0
+
+    def __init__(self, mu=None, sigma=None, pi=0, tau=0):
+        if mu is not None:
+            if sigma is None:
+                raise TypeError('sigma argument is needed')
+            elif sigma == 0:
+                raise ValueError('sigma**2 should be greater than 0')
+            pi = sigma ** -2
+            tau = pi * mu
+        self.pi = pi
+        self.tau = tau
+
+    @property
+    def mu(self):
+        return self.pi and self.tau / self.pi
+
+    @property
+    def sigma(self):
+        return math.sqrt(1 / self.pi) if self.pi else inf
+
+    def __mul__(self, other):
+        pi, tau = self.pi + other.pi, self.tau + other.tau
+        return Gaussian(pi=pi, tau=tau)
+
+    def __truediv__(self, other):
+        pi, tau = self.pi - other.pi, self.tau - other.tau
+        return Gaussian(pi=pi, tau=tau)
+
+    __div__ = __truediv__  # for Python 2
+
+    def __eq__(self, other):
+        return self.pi == other.pi and self.tau == other.tau
+
+    def __lt__(self, other):
+        return self.mu < other.mu
+
+    def __le__(self, other):
+        return self.mu <= other.mu
+
+    def __gt__(self, other):
+        return self.mu > other.mu
+
+    def __ge__(self, other):
+        return self.mu >= other.mu
+
+    def __repr__(self):
+        return 'N(mu={:.3f}, sigma={:.3f})'.format(self.mu, self.sigma)
+
+    def _repr_latex_(self):
+        latex = r'\mathcal{{ N }}( {:.3f}, {:.3f}^2 )'.format(self.mu, self.sigma)
+        return '$%s$' % latex
+
+class Matrix(list):
+    def __init__(self, src, height=None, width=None):
+        if callable(src):
+            f, src = src, {}
+            size = [height, width]
+            if not height:
+                def set_height(height):
+                    size[0] = height
+                size[0] = set_height
+            if not width:
+                def set_width(width):
+                    size[1] = width
+                size[1] = set_width
+            try:
+                for (r, c), val in f(*size):
+                    src[r, c] = val
+            except TypeError:
+                raise TypeError('A callable src must return an interable '
+                                'which generates a tuple containing '
+                                'coordinate and value')
+            height, width = tuple(size)
+            if height is None or width is None:
+                raise TypeError('A callable src must call set_height and '
+                                'set_width if the size is non-deterministic')
+        if isinstance(src, list):
+            is_number = lambda x: isinstance(x, Number)
+            unique_col_sizes = set(map(len, src))
+            everything_are_number = filter(is_number, sum(src, []))
+            if len(unique_col_sizes) != 1 or not everything_are_number:
+                raise ValueError('src must be a rectangular array of numbers')
+            two_dimensional_array = src
+        elif isinstance(src, dict):
+            if not height or not width:
+                w = h = 0
+                for r, c in iterkeys(src):
+                    if not height:
+                        h = max(h, r + 1)
+                    if not width:
+                        w = max(w, c + 1)
+                if not height:
+                    height = h
+                if not width:
+                    width = w
+            two_dimensional_array = []
+            for r in range(height):
+                row = []
+                two_dimensional_array.append(row)
+                for c in range(width):
+                    row.append(src.get((r, c), 0))
+        else:
+            raise TypeError('src must be a list or dict or callable')
+        super(Matrix, self).__init__(two_dimensional_array)
+
+    @property
+    def height(self):
+        return len(self)
+
+    @property
+    def width(self):
+        return len(self[0])
+
+    def transpose(self):
+        height, width = self.height, self.width
+        src = {}
+        for c in range(width):
+            for r in range(height):
+                src[c, r] = self[r][c]
+        return type(self)(src, height=width, width=height)
+
+    def minor(self, row_n, col_n):
+        height, width = self.height, self.width
+        if not (0 <= row_n < height):
+            raise ValueError('row_n should be between 0 and %d' % height)
+        elif not (0 <= col_n < width):
+            raise ValueError('col_n should be between 0 and %d' % width)
+        two_dimensional_array = []
+        for r in range(height):
+            if r == row_n:
+                continue
+            row = []
+            two_dimensional_array.append(row)
+            for c in range(width):
+                if c == col_n:
+                    continue
+                row.append(self[r][c])
+        return type(self)(two_dimensional_array)
+
+    def determinant(self):
+        height, width = self.height, self.width
+        if height != width:
+            raise ValueError('Only square matrix can calculate a determinant')
+        tmp, rv = copy.deepcopy(self), 1.
+        for c in range(width - 1, 0, -1):
+            pivot, r = max((abs(tmp[r][c]), r) for r in range(c + 1))
+            pivot = tmp[r][c]
+            if not pivot:
+                return 0.
+            tmp[r], tmp[c] = tmp[c], tmp[r]
+            if r != c:
+                rv = -rv
+            rv *= pivot
+            fact = -1. / pivot
+            for r in range(c):
+                f = fact * tmp[r][c]
+                for x in range(c):
+                    tmp[r][x] += f * tmp[c][x]
+        return rv * tmp[0][0]
+
+    def adjugate(self):
+        height, width = self.height, self.width
+        if height != width:
+            raise ValueError('Only square matrix can be adjugated')
+        if height == 2:
+            a, b = self[0][0], self[0][1]
+            c, d = self[1][0], self[1][1]
+            return type(self)([[d, -b], [-c, a]])
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                sign = -1 if (r + c) % 2 else 1
+                src[r, c] = self.minor(r, c).determinant() * sign
+        return type(self)(src, height, width)
+
+    def inverse(self):
+        if self.height == self.width == 1:
+            return type(self)([[1. / self[0][0]]])
+        return (1. / self.determinant()) * self.adjugate()
+
+    def __add__(self, other):
+        height, width = self.height, self.width
+        if (height, width) != (other.height, other.width):
+            raise ValueError('Must be same size')
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                src[r, c] = self[r][c] + other[r][c]
+        return type(self)(src, height, width)
+
+    def __mul__(self, other):
+        if self.width != other.height:
+            raise ValueError('Bad size')
+        height, width = self.height, other.width
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                src[r, c] = sum(self[r][x] * other[x][c]
+                                for x in range(self.width))
+        return type(self)(src, height, width)
+
+    def __rmul__(self, other):
+        if not isinstance(other, Number):
+            raise TypeError('The operand should be a number')
+        height, width = self.height, self.width
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                src[r, c] = other * self[r][c]
+        return type(self)(src, height, width)
+
+    def __repr__(self):
+        return '{}({})'.format(type(self).__name__, super(Matrix, self).__repr__())
+
+    def _repr_latex_(self):
+        rows = [' && '.join(['%.3f' % cell for cell in row]) for row in self]
+        latex = r'\begin{matrix} %s \end{matrix}' % r'\\'.join(rows)
+        return '$%s$' % latex
+
+def _gen_erfcinv(erfc, math=math):
+    def erfcinv(y):
+        """The inverse function of erfc."""
+        if y >= 2:
+            return -100.
+        elif y <= 0:
+            return 100.
+        zero_point = y < 1
+        if not zero_point:
+            y = 2 - y
+        t = math.sqrt(-2 * math.log(y / 2.))
+        x = -0.70711 * \
+            ((2.30753 + t * 0.27061) / (1. + t * (0.99229 + t * 0.04481)) - t)
+        for i in range(2):
+            err = erfc(x) - y
+            x += err / (1.12837916709551257 * math.exp(-(x ** 2)) - x * err)
+        return x if zero_point else -x
+    return erfcinv
+
+def _gen_ppf(erfc, math=math):
+    erfcinv = _gen_erfcinv(erfc, math)
+    def ppf(x, mu=0, sigma=1):
+        return mu - sigma * math.sqrt(2) * erfcinv(2 * x)
+    return ppf
+
+def erfc(x):
+    z = abs(x)
+    t = 1. / (1. + z / 2.)
+    r = t * math.exp(-z * z - 1.26551223 + t * (1.00002368 + t * (
+        0.37409196 + t * (0.09678418 + t * (-0.18628806 + t * (
+            0.27886807 + t * (-1.13520398 + t * (1.48851587 + t * (
+                -0.82215223 + t * 0.17087277
+            )))
+        )))
+    )))
+    return 2. - r if x < 0 else r
+
+def cdf(x, mu=0, sigma=1):
+    return 0.5 * erfc(-(x - mu) / (sigma * math.sqrt(2)))
+
+
+def pdf(x, mu=0, sigma=1):
+    return (1 / math.sqrt(2 * math.pi) * abs(sigma) *
+            math.exp(-(((x - mu) / abs(sigma)) ** 2 / 2)))
+
+ppf = _gen_ppf(erfc)
+
+def choose_backend(backend):
+    if backend is None:  # fallback
+        return cdf, pdf, ppf
+    elif backend == 'mpmath':
+        try:
+            import mpmath
+        except ImportError:
+            raise ImportError('Install "mpmath" to use this backend')
+        return mpmath.ncdf, mpmath.npdf, _gen_ppf(mpmath.erfc, math=mpmath)
+    elif backend == 'scipy':
+        try:
+            from scipy.stats import norm
+        except ImportError:
+            raise ImportError('Install "scipy" to use this backend')
+        return norm.cdf, norm.pdf, norm.ppf
+    raise ValueError('%r backend is not defined' % backend)
+
+def available_backends():
+    backends = [None]
+    for backend in ['mpmath', 'scipy']:
+        try:
+            __import__(backend)
+        except ImportError:
+            continue
+        backends.append(backend)
+    return backends
+
+class Node(object):
+
+    pass
+
+class Variable(Node, Gaussian):
+
+    def __init__(self):
+        self.messages = {}
+        super(Variable, self).__init__()
+
+    def set(self, val):
+        delta = self.delta(val)
+        self.pi, self.tau = val.pi, val.tau
+        return delta
+
+    def delta(self, other):
+        pi_delta = abs(self.pi - other.pi)
+        if pi_delta == inf:
+            return 0.
+        return max(abs(self.tau - other.tau), math.sqrt(pi_delta))
+
+    def update_message(self, factor, pi=0, tau=0, message=None):
+        message = message or Gaussian(pi=pi, tau=tau)
+        old_message, self[factor] = self[factor], message
+        return self.set(self / old_message * message)
+
+    def update_value(self, factor, pi=0, tau=0, value=None):
+        value = value or Gaussian(pi=pi, tau=tau)
+        old_message = self[factor]
+        self[factor] = value * old_message / self
+        return self.set(value)
+
+    def __getitem__(self, factor):
+        return self.messages[factor]
+
+    def __setitem__(self, factor, message):
+        self.messages[factor] = message
+
+    def __repr__(self):
+        args = (type(self).__name__, super(Variable, self).__repr__(),
+                len(self.messages), '' if len(self.messages) == 1 else 's')
+        return '<%s %s with %d connection%s>' % args
+
+
+class Factor(Node):
+
+    def __init__(self, variables):
+        self.vars = variables
+        for var in variables:
+            var[self] = Gaussian()
+
+    def down(self):
+        return 0
+
+    def up(self):
+        return 0
+
+    @property
+    def var(self):
+        assert len(self.vars) == 1
+        return self.vars[0]
+
+    def __repr__(self):
+        args = (type(self).__name__, len(self.vars),
+                '' if len(self.vars) == 1 else 's')
+        return '<%s with %d connection%s>' % args
+
+
+class PriorFactor(Factor):
+
+    def __init__(self, var, val, dynamic=0):
+        super(PriorFactor, self).__init__([var])
+        self.val = val
+        self.dynamic = dynamic
+
+    def down(self):
+        sigma = math.sqrt(self.val.sigma ** 2 + self.dynamic ** 2)
+        value = Gaussian(self.val.mu, sigma)
+        return self.var.update_value(self, value=value)
+
+
+class LikelihoodFactor(Factor):
+
+    def __init__(self, mean_var, value_var, variance):
+        super(LikelihoodFactor, self).__init__([mean_var, value_var])
+        self.mean = mean_var
+        self.value = value_var
+        self.variance = variance
+
+    def calc_a(self, var):
+        return 1. / (1. + self.variance * var.pi)
+
+    def down(self):
+        # update value.
+        msg = self.mean / self.mean[self]
+        a = self.calc_a(msg)
+        return self.value.update_message(self, a * msg.pi, a * msg.tau)
+
+    def up(self):
+        # update mean.
+        msg = self.value / self.value[self]
+        a = self.calc_a(msg)
+        return self.mean.update_message(self, a * msg.pi, a * msg.tau)
+
+
+class SumFactor(Factor):
+
+    def __init__(self, sum_var, term_vars, coeffs):
+        super(SumFactor, self).__init__([sum_var] + term_vars)
+        self.sum = sum_var
+        self.terms = term_vars
+        self.coeffs = coeffs
+
+    def down(self):
+        vals = self.terms
+        msgs = [var[self] for var in vals]
+        return self.update(self.sum, vals, msgs, self.coeffs)
+
+    def up(self, index=0):
+        coeff = self.coeffs[index]
+        coeffs = []
+        for x, c in enumerate(self.coeffs):
+            try:
+                if x == index:
+                    coeffs.append(1. / coeff)
+                else:
+                    coeffs.append(-c / coeff)
+            except ZeroDivisionError:
+                coeffs.append(0.)
+        vals = self.terms[:]
+        vals[index] = self.sum
+        msgs = [var[self] for var in vals]
+        return self.update(self.terms[index], vals, msgs, coeffs)
+
+    def update(self, var, vals, msgs, coeffs):
+        pi_inv = 0
+        mu = 0
+        for val, msg, coeff in zip(vals, msgs, coeffs):
+            div = val / msg
+            mu += coeff * div.mu
+            if pi_inv == inf:
+                continue
+            try:
+                # numpy.float64 handles floating-point error by different way.
+                # For example, it can just warn RuntimeWarning on n/0 problem
+                # instead of throwing ZeroDivisionError.  So div.pi, the
+                # denominator has to be a built-in float.
+                pi_inv += coeff ** 2 / float(div.pi)
+            except ZeroDivisionError:
+                pi_inv = inf
+        pi = 1. / pi_inv
+        tau = pi * mu
+        return var.update_message(self, pi, tau)
+
+
+class TruncateFactor(Factor):
+
+    def __init__(self, var, v_func, w_func, draw_margin):
+        super(TruncateFactor, self).__init__([var])
+        self.v_func = v_func
+        self.w_func = w_func
+        self.draw_margin = draw_margin
+
+    def up(self):
+        val = self.var
+        msg = self.var[self]
+        div = val / msg
+        sqrt_pi = math.sqrt(div.pi)
+        args = (div.tau / sqrt_pi, self.draw_margin * sqrt_pi)
+        v = self.v_func(*args)
+        w = self.w_func(*args)
+        denom = (1. - w)
+        pi, tau = div.pi / denom, (div.tau + sqrt_pi * v) / denom
+        return val.update_value(self, pi, tau)
+
+#: Default initial mean of ratings.
+MU = 25.
+#: Default initial standard deviation of ratings.
+SIGMA = MU / 3
+#: Default distance that guarantees about 76% chance of winning.
+BETA = SIGMA / 2
+#: Default dynamic factor.
+TAU = SIGMA / 100
+#: Default draw probability of the game.
+DRAW_PROBABILITY = .10
+#: A basis to check reliability of the result.
+DELTA = 0.0001
+
+
+def calc_draw_probability(draw_margin, size, env=None):
+    if env is None:
+        env = global_env()
+    return 2 * env.cdf(draw_margin / (math.sqrt(size) * env.beta)) - 1
+
+
+def calc_draw_margin(draw_probability, size, env=None):
+    if env is None:
+        env = global_env()
+    return env.ppf((draw_probability + 1) / 2.) * math.sqrt(size) * env.beta
+
+
+def _team_sizes(rating_groups):
+    team_sizes = [0]
+    for group in rating_groups:
+        team_sizes.append(len(group) + team_sizes[-1])
+    del team_sizes[0]
+    return team_sizes
+
+
+def _floating_point_error(env):
+    if env.backend == 'mpmath':
+        msg = 'Set "mpmath.mp.dps" to higher'
+    else:
+        msg = 'Cannot calculate correctly, set backend to "mpmath"'
+    return FloatingPointError(msg)
+
+
+class Rating(Gaussian):
+    def __init__(self, mu=None, sigma=None):
+        if isinstance(mu, tuple):
+            mu, sigma = mu
+        elif isinstance(mu, Gaussian):
+            mu, sigma = mu.mu, mu.sigma
+        if mu is None:
+            mu = global_env().mu
+        if sigma is None:
+            sigma = global_env().sigma
+        super(Rating, self).__init__(mu, sigma)
+
+    def __int__(self):
+        return int(self.mu)
+
+    def __long__(self):
+        return long(self.mu)
+
+    def __float__(self):
+        return float(self.mu)
+
+    def __iter__(self):
+        return iter((self.mu, self.sigma))
+
+    def __repr__(self):
+        c = type(self)
+        args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma)
+        return '%s(mu=%.3f, sigma=%.3f)' % args
+
+
+class TrueSkill(object):
+    def __init__(self, mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
+                 draw_probability=DRAW_PROBABILITY, backend=None):
+        self.mu = mu
+        self.sigma = sigma
+        self.beta = beta
+        self.tau = tau
+        self.draw_probability = draw_probability
+        self.backend = backend
+        if isinstance(backend, tuple):
+            self.cdf, self.pdf, self.ppf = backend
+        else:
+            self.cdf, self.pdf, self.ppf = choose_backend(backend)
+
+    def create_rating(self, mu=None, sigma=None):
+        if mu is None:
+            mu = self.mu
+        if sigma is None:
+            sigma = self.sigma
+        return Rating(mu, sigma)
+
+    def v_win(self, diff, draw_margin):
+        x = diff - draw_margin
+        denom = self.cdf(x)
+        return (self.pdf(x) / denom) if denom else -x
+
+    def v_draw(self, diff, draw_margin):
+        abs_diff = abs(diff)
+        a, b = draw_margin - abs_diff, -draw_margin - abs_diff
+        denom = self.cdf(a) - self.cdf(b)
+        numer = self.pdf(b) - self.pdf(a)
+        return ((numer / denom) if denom else a) * (-1 if diff < 0 else +1)
+
+    def w_win(self, diff, draw_margin):
+        x = diff - draw_margin
+        v = self.v_win(diff, draw_margin)
+        w = v * (v + x)
+        if 0 < w < 1:
+            return w
+        raise _floating_point_error(self)
+
+    def w_draw(self, diff, draw_margin):
+        abs_diff = abs(diff)
+        a, b = draw_margin - abs_diff, -draw_margin - abs_diff
+        denom = self.cdf(a) - self.cdf(b)
+        if not denom:
+            raise _floating_point_error(self)
+        v = self.v_draw(abs_diff, draw_margin)
+        return (v ** 2) + (a * self.pdf(a) - b * self.pdf(b)) / denom
+
+    def validate_rating_groups(self, rating_groups):
+        # check group sizes
+        if len(rating_groups) < 2:
+            raise ValueError('Need multiple rating groups')
+        elif not all(rating_groups):
+            raise ValueError('Each group must contain multiple ratings')
+        # check group types
+        group_types = set(map(type, rating_groups))
+        if len(group_types) != 1:
+            raise TypeError('All groups should be same type')
+        elif group_types.pop() is Rating:
+            raise TypeError('Rating cannot be a rating group')
+        # normalize rating_groups
+        if isinstance(rating_groups[0], dict):
+            dict_rating_groups = rating_groups
+            rating_groups = []
+            keys = []
+            for dict_rating_group in dict_rating_groups:
+                rating_group, key_group = [], []
+                for key, rating in iteritems(dict_rating_group):
+                    rating_group.append(rating)
+                    key_group.append(key)
+                rating_groups.append(tuple(rating_group))
+                keys.append(tuple(key_group))
+        else:
+            rating_groups = list(rating_groups)
+            keys = None
+        return rating_groups, keys
+
+    def validate_weights(self, weights, rating_groups, keys=None):
+        if weights is None:
+            weights = [(1,) * len(g) for g in rating_groups]
+        elif isinstance(weights, dict):
+            weights_dict, weights = weights, []
+            for x, group in enumerate(rating_groups):
+                w = []
+                weights.append(w)
+                for y, rating in enumerate(group):
+                    if keys is not None:
+                        y = keys[x][y]
+                    w.append(weights_dict.get((x, y), 1))
+        return weights
+
+    def factor_graph_builders(self, rating_groups, ranks, weights):
+        flatten_ratings = sum(map(tuple, rating_groups), ())
+        flatten_weights = sum(map(tuple, weights), ())
+        size = len(flatten_ratings)
+        group_size = len(rating_groups)
+        # create variables
+        rating_vars = [Variable() for x in range(size)]
+        perf_vars = [Variable() for x in range(size)]
+        team_perf_vars = [Variable() for x in range(group_size)]
+        team_diff_vars = [Variable() for x in range(group_size - 1)]
+        team_sizes = _team_sizes(rating_groups)
+        # layer builders
+        def build_rating_layer():
+            for rating_var, rating in zip(rating_vars, flatten_ratings):
+                yield PriorFactor(rating_var, rating, self.tau)
+        def build_perf_layer():
+            for rating_var, perf_var in zip(rating_vars, perf_vars):
+                yield LikelihoodFactor(rating_var, perf_var, self.beta ** 2)
+        def build_team_perf_layer():
+            for team, team_perf_var in enumerate(team_perf_vars):
+                if team > 0:
+                    start = team_sizes[team - 1]
+                else:
+                    start = 0
+                end = team_sizes[team]
+                child_perf_vars = perf_vars[start:end]
+                coeffs = flatten_weights[start:end]
+                yield SumFactor(team_perf_var, child_perf_vars, coeffs)
+        def build_team_diff_layer():
+            for team, team_diff_var in enumerate(team_diff_vars):
+                yield SumFactor(team_diff_var,
+                                team_perf_vars[team:team + 2], [+1, -1])
+        def build_trunc_layer():
+            for x, team_diff_var in enumerate(team_diff_vars):
+                if callable(self.draw_probability):
+                    # dynamic draw probability
+                    team_perf1, team_perf2 = team_perf_vars[x:x + 2]
+                    args = (Rating(team_perf1), Rating(team_perf2), self)
+                    draw_probability = self.draw_probability(*args)
+                else:
+                    # static draw probability
+                    draw_probability = self.draw_probability
+                size = sum(map(len, rating_groups[x:x + 2]))
+                draw_margin = calc_draw_margin(draw_probability, size, self)
+                if ranks[x] == ranks[x + 1]:  # is a tie?
+                    v_func, w_func = self.v_draw, self.w_draw
+                else:
+                    v_func, w_func = self.v_win, self.w_win
+                yield TruncateFactor(team_diff_var,
+                                     v_func, w_func, draw_margin)
+        # build layers
+        return (build_rating_layer, build_perf_layer, build_team_perf_layer,
+                build_team_diff_layer, build_trunc_layer)
+
+    def run_schedule(self, build_rating_layer, build_perf_layer,
+                     build_team_perf_layer, build_team_diff_layer,
+                     build_trunc_layer, min_delta=DELTA):
+        if min_delta <= 0:
+            raise ValueError('min_delta must be greater than 0')
+        layers = []
+        def build(builders):
+            layers_built = [list(build()) for build in builders]
+            layers.extend(layers_built)
+            return layers_built
+        # gray arrows
+        layers_built = build([build_rating_layer,
+                              build_perf_layer,
+                              build_team_perf_layer])
+        rating_layer, perf_layer, team_perf_layer = layers_built
+        for f in chain(*layers_built):
+            f.down()
+        # arrow #1, #2, #3
+        team_diff_layer, trunc_layer = build([build_team_diff_layer,
+                                              build_trunc_layer])
+        team_diff_len = len(team_diff_layer)
+        for x in range(10):
+            if team_diff_len == 1:
+                # only two teams
+                team_diff_layer[0].down()
+                delta = trunc_layer[0].up()
+            else:
+                # multiple teams
+                delta = 0
+                for x in range(team_diff_len - 1):
+                    team_diff_layer[x].down()
+                    delta = max(delta, trunc_layer[x].up())
+                    team_diff_layer[x].up(1)  # up to right variable
+                for x in range(team_diff_len - 1, 0, -1):
+                    team_diff_layer[x].down()
+                    delta = max(delta, trunc_layer[x].up())
+                    team_diff_layer[x].up(0)  # up to left variable
+            # repeat until to small update
+            if delta <= min_delta:
+                break
+        # up both ends
+        team_diff_layer[0].up(0)
+        team_diff_layer[team_diff_len - 1].up(1)
+        # up the remainder of the black arrows
+        for f in team_perf_layer:
+            for x in range(len(f.vars) - 1):
+                f.up(x)
+        for f in perf_layer:
+            f.up()
+        return layers
+
+    def rate(self, rating_groups, ranks=None, weights=None, min_delta=DELTA):
+        rating_groups, keys = self.validate_rating_groups(rating_groups)
+        weights = self.validate_weights(weights, rating_groups, keys)
+        group_size = len(rating_groups)
+        if ranks is None:
+            ranks = range(group_size)
+        elif len(ranks) != group_size:
+            raise ValueError('Wrong ranks')
+        # sort rating groups by rank
+        by_rank = lambda x: x[1][1]
+        sorting = sorted(enumerate(zip(rating_groups, ranks, weights)),
+                         key=by_rank)
+        sorted_rating_groups, sorted_ranks, sorted_weights = [], [], []
+        for x, (g, r, w) in sorting:
+            sorted_rating_groups.append(g)
+            sorted_ranks.append(r)
+            # make weights to be greater than 0
+            sorted_weights.append(max(min_delta, w_) for w_ in w)
+        # build factor graph
+        args = (sorted_rating_groups, sorted_ranks, sorted_weights)
+        builders = self.factor_graph_builders(*args)
+        args = builders + (min_delta,)
+        layers = self.run_schedule(*args)
+        # make result
+        rating_layer, team_sizes = layers[0], _team_sizes(sorted_rating_groups)
+        transformed_groups = []
+        for start, end in zip([0] + team_sizes[:-1], team_sizes):
+            group = []
+            for f in rating_layer[start:end]:
+                group.append(Rating(float(f.var.mu), float(f.var.sigma)))
+            transformed_groups.append(tuple(group))
+        by_hint = lambda x: x[0]
+        unsorting = sorted(zip((x for x, __ in sorting), transformed_groups),
+                           key=by_hint)
+        if keys is None:
+            return [g for x, g in unsorting]
+        # restore the structure with input dictionary keys
+        return [dict(zip(keys[x], g)) for x, g in unsorting]
+
+    def quality(self, rating_groups, weights=None):
+        rating_groups, keys = self.validate_rating_groups(rating_groups)
+        weights = self.validate_weights(weights, rating_groups, keys)
+        flatten_ratings = sum(map(tuple, rating_groups), ())
+        flatten_weights = sum(map(tuple, weights), ())
+        length = len(flatten_ratings)
+        # a vector of all of the skill means
+        mean_matrix = Matrix([[r.mu] for r in flatten_ratings])
+        # a matrix whose diagonal values are the variances (sigma ** 2) of each
+        # of the players.
+        def variance_matrix(height, width):
+            variances = (r.sigma ** 2 for r in flatten_ratings)
+            for x, variance in enumerate(variances):
+                yield (x, x), variance
+        variance_matrix = Matrix(variance_matrix, length, length)
+        # the player-team assignment and comparison matrix
+        def rotated_a_matrix(set_height, set_width):
+            t = 0
+            for r, (cur, _next) in enumerate(zip(rating_groups[:-1],
+                                                rating_groups[1:])):
+                for x in range(t, t + len(cur)):
+                    yield (r, x), flatten_weights[x]
+                    t += 1
+                x += 1
+                for x in range(x, x + len(_next)):
+                    yield (r, x), -flatten_weights[x]
+            set_height(r + 1)
+            set_width(x + 1)
+        rotated_a_matrix = Matrix(rotated_a_matrix)
+        a_matrix = rotated_a_matrix.transpose()
+        # match quality further derivation
+        _ata = (self.beta ** 2) * rotated_a_matrix * a_matrix
+        _atsa = rotated_a_matrix * variance_matrix * a_matrix
+        start = mean_matrix.transpose() * a_matrix
+        middle = _ata + _atsa
+        end = rotated_a_matrix * mean_matrix
+        # make result
+        e_arg = (-0.5 * start * middle.inverse() * end).determinant()
+        s_arg = _ata.determinant() / middle.determinant()
+        return math.exp(e_arg) * math.sqrt(s_arg)
+
+    def expose(self, rating):
+        k = self.mu / self.sigma
+        return rating.mu - k * rating.sigma
+
+    def make_as_global(self):
+        return setup(env=self)
+
+    def __repr__(self):
+        c = type(self)
+        if callable(self.draw_probability):
+            f = self.draw_probability
+            draw_probability = '.'.join([f.__module__, f.__name__])
+        else:
+            draw_probability = '%.1f%%' % (self.draw_probability * 100)
+        if self.backend is None:
+            backend = ''
+        elif isinstance(self.backend, tuple):
+            backend = ', backend=...'
+        else:
+            backend = ', backend=%r' % self.backend
+        args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma,
+                self.beta, self.tau, draw_probability, backend)
+        return ('%s(mu=%.3f, sigma=%.3f, beta=%.3f, tau=%.3f, '
+                'draw_probability=%s%s)' % args)
+
+
+def rate_1vs1(rating1, rating2, drawn=False, min_delta=DELTA, env=None):
+    if env is None:
+        env = global_env()
+    ranks = [0, 0 if drawn else 1]
+    teams = env.rate([(rating1,), (rating2,)], ranks, min_delta=min_delta)
+    return teams[0][0], teams[1][0]
+
+
+def quality_1vs1(rating1, rating2, env=None):
+    if env is None:
+        env = global_env()
+    return env.quality([(rating1,), (rating2,)])
+
+
+def global_env():
+    try:
+        global_env.__trueskill__
+    except AttributeError:
+        # setup the default environment
+        setup()
+    return global_env.__trueskill__
+
+
+def setup(mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
+          draw_probability=DRAW_PROBABILITY, backend=None, env=None):
+    if env is None:
+        env = TrueSkill(mu, sigma, beta, tau, draw_probability, backend)
+    global_env.__trueskill__ = env
+    return env
+
+
+def rate(rating_groups, ranks=None, weights=None, min_delta=DELTA):
+    return global_env().rate(rating_groups, ranks, weights, min_delta)
+
+
+def quality(rating_groups, weights=None):
+    return global_env().quality(rating_groups, weights)
+
+
+def expose(rating):
+    return global_env().expose(rating)

analysis-master/analysis-amd64/setup.py

@@ -8,7 +8,7 @@ with open("requirements.txt", 'r') as file:
 
 setuptools.setup(
     name="analysis",
-    version="1.0.0.011",
+    version="1.0.0.012",
     author="The Titan Scouting Team",
     author_email="titanscout2022@gmail.com",
     description="analysis package developed by Titan Scouting for The Red Alliance",