Template
28 lines
836 B
Python
28 lines
836 B
Python
def train_logreg(X, y, random_state=42):
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model = LogisticRegression(solver='liblinear', random_state=random_state)
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model.fit(X, y)
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return model
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def predict_logreg(inX, model):
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return int(model.predict([inX])[0])
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def load_data(filepath):
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data = np.genfromtxt(filepath, delimiter=' ', missing_values='?', filling_values=0)
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labels = data[:, -1].astype(int)
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data = data[:, :-1]
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labels = np.where((labels == 2) | (labels == 3), 0, labels)
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if data.shape[1] >= 3:
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data = np.delete(data, 2, axis=1)
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scaler = StandardScaler()
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data = scaler.fit_transform(data)
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return data, labels
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# 去掉不相关特征
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data = np.delete(data, [2] + list(range(23, 28)), axis=1)
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# 数据标准化
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scaler = StandardScaler()
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data = scaler.fit_transform(data)
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return data, labels |