analysis.py v 1.1.12.004

This commit is contained in:
ltcptgeneral 2020-03-04 17:52:07 -06:00
parent 70b2ff1151
commit d57d1ebc6d

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@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "1.1.12.003" __version__ = "1.1.12.004"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.1.12.004:
- renamed gliko to glicko
1.1.12.003: 1.1.12.003:
- removed depreciated code - removed depreciated code
1.1.12.002: 1.1.12.002:
@ -235,7 +237,7 @@ __all__ = [
'histo_analysis', 'histo_analysis',
'regression', 'regression',
'elo', 'elo',
'gliko2', 'glicko2',
'trueskill', 'trueskill',
'RegressionMetrics', 'RegressionMetrics',
'ClassificationMetrics', 'ClassificationMetrics',
@ -249,7 +251,7 @@ __all__ = [
'random_forest_classifier', 'random_forest_classifier',
'random_forest_regressor', 'random_forest_regressor',
'Regression', 'Regression',
'Gliko2', 'Glicko2',
# all statistics functions left out due to integration in other functions # all statistics functions left out due to integration in other functions
] ]
@ -390,9 +392,9 @@ def elo(starting_score, opposing_score, observed, N, K):
return starting_score + K*(np.sum(observed) - np.sum(expected)) return starting_score + K*(np.sum(observed) - np.sum(expected))
@jit(forceobj=True) @jit(forceobj=True)
def gliko2(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 = Gliko2(rating = starting_score, rd = starting_rd, vol = starting_vol) player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations) player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
@ -852,7 +854,7 @@ class Regression:
optim.step() optim.step()
return kernel return kernel
class Gliko2: class Glicko2:
_tau = 0.5 _tau = 0.5