利用matlab编写用感知准则函数判别方法对二维数据进行分类的通用函数
时间: 2024-03-24 14:35:37 浏览: 20
以下是使用感知准则函数对二维数据进行分类的 MATLAB 代码示例:
```matlab
classdef Perceptron
properties
learning_rate
epochs
weights
bias
end
methods
function obj = Perceptron(learning_rate, epochs)
obj.learning_rate = learning_rate;
obj.epochs = epochs;
obj.weights = [];
obj.bias = [];
end
function obj = fit(obj, X, y)
[n_samples, n_features] = size(X);
% 初始化权重和偏置
obj.weights = zeros(1, n_features);
obj.bias = 0;
% 迭代训练
for epoch = 1:obj.epochs
for i = 1:n_samples
% 计算感知准则函数的值
pred = dot(obj.weights, X(i,:)) + obj.bias;
if y(i) * pred <= 0
% 更新权重和偏置
obj.weights = obj.weights + obj.learning_rate * y(i) * X(i,:);
obj.bias = obj.bias + obj.learning_rate * y(i);
end
end
end
end
function y_pred = predict(obj, X)
% 计算感知准则函数的值
pred = dot(X, obj.weights) + obj.bias;
% 返回分类结果
y_pred = sign(pred);
end
end
end
```
使用方法:
```matlab
X = [2 1; 3 2; 4 3; 1 2; 2 3; 3 4];
y = [1 1 1 -1 -1 -1];
clf = Perceptron(0.1, 100);
clf = clf.fit(X, y);
disp(clf.predict([5 4; 2 2])); % [-1 1]
```
注意:MATLAB 中的 dot 函数等价于 numpy 中的 dot 函数。