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analysis pkg 1.0.0.001
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Metadata-Version: 2.1
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Metadata-Version: 2.1
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Name: analysis
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Name: analysis
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Version: 1.0.0.0
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Version: 1.0.0.1
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Summary: analysis package developed by Titan Scouting for The Red Alliance
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Summary: analysis package developed by Titan Scouting for The Red Alliance
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Home-page: https://github.com/titanscout2022/tr2022-strategy
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Home-page: https://github.com/titanscout2022/tr2022-strategy
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Author: The Titan Scouting Team
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Author: The Titan Scouting Team
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@ -7,10 +7,12 @@
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# current benchmark of optimization: 1.33 times faster
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# current benchmark of optimization: 1.33 times faster
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# setup:
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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 should be viewed using print(analysis.__changelog__)
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__changelog__ = """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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1.1.12.003:
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- removed depreciated code
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- removed depreciated code
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1.1.12.002:
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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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'histo_analysis',
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'regression',
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'regression',
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'elo',
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'elo',
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'gliko2',
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'glicko2',
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'trueskill',
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'trueskill',
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'RegressionMetrics',
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'RegressionMetrics',
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'ClassificationMetrics',
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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_classifier',
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'random_forest_regressor',
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'random_forest_regressor',
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'Regression',
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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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# all statistics functions left out due to integration in other functions
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]
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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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return starting_score + K*(np.sum(observed) - np.sum(expected))
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@jit(forceobj=True)
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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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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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optim.step()
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return kernel
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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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_tau = 0.5
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analysis-master/dist/analysis-1.0.0.0.tar.gz
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analysis-master/dist/analysis-1.0.0.1.tar.gz
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analysis-master/dist/analysis-1.0.0.1.tar.gz
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@ -2,7 +2,7 @@ import setuptools
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setuptools.setup(
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setuptools.setup(
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name="analysis", # Replace with your own username
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name="analysis", # Replace with your own username
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version="1.0.0.000",
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version="1.0.0.001",
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author="The Titan Scouting Team",
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author="The Titan Scouting Team",
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author_email="titanscout2022@gmail.com",
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author_email="titanscout2022@gmail.com",
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description="analysis package developed by Titan Scouting for The Red Alliance",
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description="analysis package developed by Titan Scouting for The Red Alliance",
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@ -163,6 +163,14 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
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red = load_metrics(apikey, competition, matches, "red")
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red = load_metrics(apikey, competition, matches, "red")
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blu = load_metrics(apikey, competition, matches, "blue")
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blu = load_metrics(apikey, competition, matches, "blue")
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elo_red_total = 0
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elo_blu_total = 0
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gl2_red_total = 0
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gl2_blu_total + 0
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for team in red:
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return
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return
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def load_metrics(apikey, competition, matches, group_name):
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def load_metrics(apikey, competition, matches, group_name):
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@ -177,7 +185,7 @@ def load_metrics(apikey, competition, matches, group_name):
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if d.get_team_metrics_data(apikey, competition, team) == None:
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if d.get_team_metrics_data(apikey, competition, team) == None:
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elo = {"score": 1500, "N": 1500, "K": 1500}
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elo = {"score": 1500}
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gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
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gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
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ts = {"mu": 25, "sigma": 25/3}
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ts = {"mu": 25, "sigma": 25/3}
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@ -202,8 +210,8 @@ testing()
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Metrics Defaults:
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Metrics Defaults:
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elo starting score = 1500
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elo starting score = 1500
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elo N = 1500
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elo N = 400
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elo K = 32
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elo K = 24
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gl2 starting score = 1500
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gl2 starting score = 1500
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gl2 starting rd = 350
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gl2 starting rd = 350
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