实现了感知机模型，K最近邻模型，朴素贝叶斯模型

tests/model_test.rs

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+use ml_learn::{KNNModel, Model, NaiveBayesClassifier, Perceptron, TestDataSet, TrainDataset};
+use ndarray::Array2;
+
+#[test]
+fn perceptron_fit_and_predict() {
+    // 线性可分数据：x1 + x2 > 2 为正类
+    let input_data = Array2::from_shape_vec((6, 2), vec![
+        2.0, 2.0, 3.0, 1.0, 2.0, 3.0,   // "pos"
+        0.0, 0.0, 1.0, 0.0, 0.0, 1.0,   // "neg"
+    ]).unwrap();
+    let output_data = vec![
+        "pos".to_string(), "pos".to_string(), "pos".to_string(),
+        "neg".to_string(), "neg".to_string(), "neg".to_string(),
+    ];
+    let train = TrainDataset { input_data, output_data };
+
+    let mut model = Perceptron::new(1.0, 1000);
+    model.fit(&train);
+    assert_eq!(model.score(&train), 1.0);
+
+    let test = TestDataSet {
+        input_data: Array2::from_shape_vec((2, 2), vec![4.0, 4.0, -1.0, 0.0]).unwrap(),
+    };
+    let result = model.predict(&test);
+    assert_eq!(result.output_data, vec!["pos".to_string(), "neg".to_string()]);
+}
+
+#[test]
+fn knn_fit_and_predict() {
+    let input_data = Array2::from_shape_vec((6, 2), vec![
+        1.0, 1.0, 1.2, 0.8, 0.8, 1.2,   // "A"
+        5.0, 5.0, 5.2, 4.8, 4.8, 5.2,   // "B"
+    ]).unwrap();
+    let output_data = vec![
+        "A".to_string(), "A".to_string(), "A".to_string(),
+        "B".to_string(), "B".to_string(), "B".to_string(),
+    ];
+    let train = TrainDataset { input_data, output_data };
+
+    let mut model = KNNModel::new("base", 3);
+    model.fit(&train);
+    assert_eq!(model.score(&train), 1.0);
+
+    let test = TestDataSet {
+        input_data: Array2::from_shape_vec((2, 2), vec![1.1, 0.9, 5.1, 5.1]).unwrap(),
+    };
+    let result = model.predict(&test);
+    assert_eq!(result.output_data, vec!["A".to_string(), "B".to_string()]);
+}
+
+#[test]
+fn gaussian_naive_bayes_fit_and_predict() {
+    // 两个类别，单一连续特征，分布明显分开
+    let input_data = Array2::from_shape_vec((6, 1), vec![
+        1.0, 1.2, 0.8,   // "A"
+        5.0, 5.2, 4.8,   // "B"
+    ]).unwrap();
+    let output_data = vec![
+        "A".to_string(), "A".to_string(), "A".to_string(),
+        "B".to_string(), "B".to_string(), "B".to_string(),
+    ];
+    let train = TrainDataset { input_data, output_data };
+
+    let mut clf = NaiveBayesClassifier::new();
+    clf.create_classifier(vec!["GNB"]);
+    clf.fit(&train);
+    assert_eq!(clf.score(&train), 1.0);
+
+    let test = TestDataSet {
+        input_data: Array2::from_shape_vec((2, 1), vec![1.1, 5.1]).unwrap(),
+    };
+    let result = clf.predict(&test);
+    assert_eq!(result.output_data, vec!["A".to_string(), "B".to_string()]);
+}
+
+#[test]
+fn poisson_naive_bayes_fit_and_predict() {
+    // 计数特征：A 类 λ≈1，B 类 λ≈5
+    let input_data = Array2::from_shape_vec((6, 1), vec![
+        0.0, 1.0, 2.0,   // "A"
+        4.0, 5.0, 6.0,   // "B"
+    ]).unwrap();
+    let output_data = vec![
+        "A".to_string(), "A".to_string(), "A".to_string(),
+        "B".to_string(), "B".to_string(), "B".to_string(),
+    ];
+    let train = TrainDataset { input_data, output_data };
+
+    let mut clf = NaiveBayesClassifier::new();
+    clf.create_classifier(vec!["PoissonNB"]);
+    clf.fit(&train);
+    assert_eq!(clf.score(&train), 1.0);
+
+    let test = TestDataSet {
+        input_data: Array2::from_shape_vec((2, 1), vec![1.0, 5.0]).unwrap(),
+    };
+    let result = clf.predict(&test);
+    assert_eq!(result.output_data, vec!["A".to_string(), "B".to_string()]);
+}
+
+#[test]
+fn bernoulli_naive_bayes_fit_and_predict() {
+    // 0/1 特征：A 类大概率为 1，B 类大概率为 0
+    let input_data = Array2::from_shape_vec((6, 1), vec![
+        1.0, 1.0, 1.0,   // "A"
+        0.0, 0.0, 0.0,   // "B"
+    ]).unwrap();
+    let output_data = vec![
+        "A".to_string(), "A".to_string(), "A".to_string(),
+        "B".to_string(), "B".to_string(), "B".to_string(),
+    ];
+    let train = TrainDataset { input_data, output_data };
+
+    let mut clf = NaiveBayesClassifier::new();
+    clf.create_classifier(vec!["BernoulliNB"]);
+    clf.fit(&train);
+
+    let test = TestDataSet {
+        input_data: Array2::from_shape_vec((2, 1), vec![1.0, 0.0]).unwrap(),
+    };
+    let result = clf.predict(&test);
+    assert_eq!(result.output_data, vec!["A".to_string(), "B".to_string()]);
+}
+
+#[test]
+fn naive_bayes_mixed_layers() {
+    // 两个特征列分别用不同层：连续特征 GNB + 计数特征 PoissonNB
+    let input_data = Array2::from_shape_vec((6, 2), vec![
+        1.0, 0.0,
+        1.2, 1.0,
+        0.8, 2.0,   // "A"
+        5.0, 4.0,
+        5.2, 5.0,
+        4.8, 6.0,   // "B"
+    ]).unwrap();
+    let output_data = vec![
+        "A".to_string(), "A".to_string(), "A".to_string(),
+        "B".to_string(), "B".to_string(), "B".to_string(),
+    ];
+    let train = TrainDataset { input_data, output_data };
+
+    let mut clf = NaiveBayesClassifier::new();
+    clf.create_classifier(vec!["GNB", "PoissonNB"]);
+    clf.fit(&train);
+    assert_eq!(clf.score(&train), 1.0);
+}