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