感知机分类 python

以下是一个简单的感知机分类器的 Python 代码示例：

import numpy as np

class Perceptron:
    def __init__(self, learning_rate=0.01, n_iterations=1000):
        self.learning_rate = learning_rate
        self.n_iterations = n_iterations

    def train(self, X, y):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0

        for _ in range(self.n_iterations):
            for i in range(n_samples):
                if y[i] * self.predict(X[i]) <= 0:
                    self.weights += self.learning_rate * y[i] * X[i]
                    self.bias += self.learning_rate * y[i]

    def predict(self, x):
        linear_output = np.dot(x, self.weights) + self.bias
        return np.where(linear_output >= 0, 1, -1)

使用该代码可以训练一个二元分类器。下面是一个使用示例：

X_train = np.array([[1, 2], [2, 1], [3, 3], [4, 3]])
y_train = np.array([1, 1, -1, -1])

classifier = Perceptron()
classifier.train(X_train, y_train)

X_test = np.array([[1, 1], [4, 4], [5, 5]])
print(classifier.predict(X_test))  # 输出 [1, -1, -1]

在上述示例中，我们首先使用四个样本和它们的标签进行训练。然后，我们使用训练好的分类器来预测三个测试样本的类别。