generated from LufsX/Challenge-kNN
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+19
-11
@@ -58,6 +58,7 @@ def load_data(file_path):
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return features, labels
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return features, labels
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import numpy as np
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def classify0(inX, dataSet, labels, k=3):
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def classify0(inX, dataSet, labels, k=3):
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"""
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"""
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@@ -85,24 +86,31 @@ def classify0(inX, dataSet, labels, k=3):
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return -1
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return -1
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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
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return normalized
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if __name__ == "__main__":
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if __name__ == "__main__":
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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 # Prevent division by zero
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return normalized
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train_file_path = "datingTrainSet2.tsv"
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train_file_path = "datingTrainSet2.tsv"
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train_features, train_labels = load_data(train_file_path)
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train_features, train_labels = load_data(train_file_path)
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test_file_path = "datingTestSet2.tsv"
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test_file_path = "datingTestSet2.tsv"
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test_features, test_labels = load_data(test_file_path)
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test_features, test_labels = load_data(test_file_path)
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# 归一化特征
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# Normalize features
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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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correct_count = 0
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correct_count = 0
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for i in range(len(test_features)):
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for i in range(len(test_features)):
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test_feature = test_features[i].reshape(1, -1)
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test_feature = test_features[i].reshape(1, -1)
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