Rosenblatt感知器算法用python实现及结果

Rosenblatt感知器算法是一种二元分类算法，它可以根据给定的训练数据集，学习到一个超平面，将数据集分为两个部分。

以下是用Python实现Rosenblatt感知器算法的代码和结果：

import numpy as np

class Perceptron:
    def __init__(self, learning_rate=0.1, num_iterations=100):
        self.learning_rate = learning_rate
        self.num_iterations = num_iterations

    def fit(self, X, y):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features + 1)
        X = np.c_[X, np.ones(n_samples)]
        for _ in range(self.num_iterations):
            for i in range(n_samples):
                if y[i] * np.dot(X[i], self.weights) <= 0:
                    self.weights += self.learning_rate * y[i] * X[i]

    def predict(self, X):
        n_samples, _ = X.shape
        X = np.c_[X, np.ones(n_samples)]
        y_pred = np.sign(np.dot(X, self.weights))
        return y_pred

我们可以用以下代码测试这个算法：

from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X, y = make_blobs(n_samples=1000, centers=2, n_features=2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

perceptron = Perceptron()
perceptron.fit(X_train, y_train)
y_pred = perceptron.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))

输出结果为：

Accuracy: 1.0

可以看到，这个算法在这个数据集上取得了100%的准确率。

需要注意的是，Rosenblatt感知器算法只适用于线性可分的数据集。对于非线性可分的数据集，我们需要使用其他的分类算法。