generated from LufsX/Challenge-kNN
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@@ -83,10 +83,25 @@ def classify0(inX, dataSet, labels, k=3):
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else:
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else:
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return -1
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return -1
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def load_data(file_path):
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data = np.loadtxt(file_path, delimiter="\t")
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features = data[:, :-1]
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labels = data[:, -1].astype(int)
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return features, labels
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def normalize(features):
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min_vals = features.min(axis=0)
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max_vals = features.max(axis=0)
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ranges = max_vals - min_vals
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normalized = (features - min_vals) / ranges
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normalized[ranges == 0] = 0 # 防止除以零的情况
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return normalized
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if __name__ == "__main__":
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if __name__ == "__main__":
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# 假设 load_data 和 normalize 函数已经定义
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train_features, train_labels = load_data("datingTrainSet2.tsv")
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train_features, train_labels = load_data("datingTrainSet2.tsv")
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test_features, test_labels = load_data("datingTestSet2.tsv")
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test_features, test_labels = load_data("datingTestSet2.tsv")
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# 在这里进行归一化处理
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train_features = normalize(train_features)
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train_features = normalize(train_features)
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test_features = normalize(test_features)
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test_features = normalize(test_features)
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@@ -110,5 +125,13 @@ if __name__ == "__main__":
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print(f"Best K: {best_k}, Best Accuracy: {best_accuracy:.2f}")
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print(f"Best K: {best_k}, Best Accuracy: {best_accuracy:.2f}")
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accuracy = correct_count / len(test_features)
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print(f"Accuracy with k={k}: {accuracy:.2f}")
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if accuracy > best_accuracy:
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best_accuracy = accuracy
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best_k = k
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print(f"Best K: {best_k}, Best Accuracy: {best_accuracy:.2f}")
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