构建伯努利朴素贝叶斯模型通过x和y对模型进行训练。然后让模型同时预测X第3至最后一行数据对应的y值并输出。

由于没有给出具体的数据集，以下是伪代码实现：

定义伯努利朴素贝叶斯模型类

class BernoulliNB: def init(self): self.class_prior = None self.feature_prob = None

def fit(self, X, y):
    # 计算类先验概率
    self.class_prior = np.zeros(2)
    self.class_prior[0] = np.sum(y == 0) / len(y)
    self.class_prior[1] = np.sum(y == 1) / len(y)

    # 计算条件概率
    self.feature_prob = np.zeros((2, X.shape[1]))
    for i in range(X.shape[1]):
        self.feature_prob[0, i] = np.sum((X[:, i] == 0) & (y == 0)) / np.sum(y == 0)
        self.feature_prob[1, i] = np.sum((X[:, i] == 0) & (y == 1)) / np.sum(y == 1)

def predict(self, X):
    y_pred = np.zeros(X.shape[0])
    for i in range(X.shape[0]):
        prob_0 = self.class_prior[0]
        prob_1 = self.class_prior[1]
        for j in range(X.shape[1]):
            if X[i, j] == 0:
                prob_0 *= self.feature_prob[0, j]
                prob_1 *= self.feature_prob[1, j]
            else:
                prob_0 *= (1 - self.feature_prob[0, j])
                prob_1 *= (1 - self.feature_prob[1, j])
        y_pred[i] = 0 if prob_0 > prob_1 else 1
    return y_pred

导入数据

X = np.loadtxt('data.txt')[:, :-1] y = np.loadtxt('data.txt')[:, -1]

训练模型

model = BernoulliNB() model.fit(X, y)

预测并输出结果

X_test = np.loadtxt('test_data.txt')[:, :-1] y_test_pred = model.predict(X_test) print(y_test_pred