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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__ = "1.1.2.001"
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__version__ = "1.1.2.003"
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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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1.1.2.003:
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- fixed elo()
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1.1.2.002:
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- added elo()
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- elo() has bugs to be fixed
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1.1.2.001:
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1.1.2.001:
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- readded regrression import
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- readded regrression import
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1.1.2.000:
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1.1.2.000:
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@ -295,6 +300,13 @@ def regression_engine(device, inputs, outputs, args, loss = torch.nn.MSELoss(),
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return regressions
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return regressions
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@jit(nopython=True)
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def elo(starting_score, opposing_scores, observed, N, K):
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expected = 1/(1+10**((np.array(opposing_scores) - 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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@jit(forceobj=True)
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def r_squared(predictions, targets): # assumes equal size inputs
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def r_squared(predictions, targets): # assumes equal size inputs
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