update sd assumption
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9d6cc4c15e
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5
main.py
5
main.py
@ -10,7 +10,8 @@ from scipy.stats import norm
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def train(data):
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def train(data):
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D = pd.DataFrame(data)
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D = pd.DataFrame(data)
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D["qoi"] = D["obs"].apply(lambda o: np.sum(o, axis=0) / np.sqrt(len(o)))
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sd = np.array([1.0, 0.25, 0.5, 0.1])
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D["qoi"] = D["obs"].apply(lambda o: np.sum(o, axis=0) / sd / np.sqrt(len(o)))
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D["i"] = D["lam"].apply(lambda l: norm.pdf(l).prod())
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D["i"] = D["lam"].apply(lambda l: norm.pdf(l).prod())
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D["o"] = D["qoi"].apply(lambda q: norm.pdf(q).prod())
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D["o"] = D["qoi"].apply(lambda q: norm.pdf(q).prod())
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Q = np.array(D["qoi"].to_list()).reshape(-1, 4)
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Q = np.array(D["qoi"].to_list()).reshape(-1, 4)
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@ -19,6 +20,7 @@ def train(data):
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D["u"] = D["i"] * D["o"] / D["p"]
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D["u"] = D["i"] * D["o"] / D["p"]
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mud_point_idx = D["u"].argmax()
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mud_point_idx = D["u"].argmax()
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mud_point = D["lam"].iloc[mud_point_idx]
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mud_point = D["lam"].iloc[mud_point_idx]
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print(f"MUD Point ({mud_point_idx}: {mud_point}")
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return mud_point
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return mud_point
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@ -44,5 +46,4 @@ def test(decision=np.array([-0.09, -0.71, -0.43, -0.74]), seed=1992):
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if __name__ == "__main__":
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if __name__ == "__main__":
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data = pickle.load(open("data.pkl", "rb"))
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data = pickle.load(open("data.pkl", "rb"))
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mud_point = train(data)
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mud_point = train(data)
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print(f"MUD Point: {mud_point}")
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test(mud_point)
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test(mud_point)
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