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analysis.py v 1.1.12.005
analysis pkg v 1.0.0.002
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@@ -1,6 +1,6 @@
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Metadata-Version: 2.1
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Name: analysis
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Version: 1.0.0.1
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Version: 1.0.0.2
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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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Author: The Titan Scouting Team
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# current benchmark of optimization: 1.33 times faster
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# setup:
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__version__ = "1.1.12.004"
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__version__ = "1.1.12.005"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.1.12.005:
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- fixed numba issues by removing numba from elo, glicko2 and trueskill
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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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@@ -384,14 +386,12 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
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return regressions
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@jit(nopython=True)
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def elo(starting_score, opposing_score, observed, N, K):
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expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
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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 glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
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player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
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@@ -400,7 +400,6 @@ def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_
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return (player.rating, player.rd, player.vol)
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@jit(forceobj=True)
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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)]]
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team_ratings = []
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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__ = "1.1.12.004"
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__version__ = "1.1.12.005"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.1.12.005:
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- fixed numba issues by removing numba from elo, glicko2 and trueskill
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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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@@ -384,14 +386,12 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
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return regressions
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@jit(nopython=True)
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def elo(starting_score, opposing_score, observed, N, K):
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expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
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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 glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
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player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
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@@ -400,7 +400,6 @@ def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_
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return (player.rating, player.rd, player.vol)
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@jit(forceobj=True)
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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)]]
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team_ratings = []
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analysis-master/dist/analysis-1.0.0.2.tar.gz
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@@ -2,7 +2,7 @@ import setuptools
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setuptools.setup(
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name="analysis", # Replace with your own username
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version="1.0.0.001",
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version="1.0.0.002",
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author="The Titan Scouting Team",
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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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