使用感知器学习找到两个可分离点集的线性分类器方程

Question

我想编写一个matlab函数，使用一个单层感知器找到2个可分离点集的线性分类器的方程。我有2个文件：

脚本文件 - run.m：

x_1 = [3, 3, 2, 4, 5];
y_1 = [3, 4, 5, 2, 2];
x_2 = [6, 7, 5, 9, 8];
y_2 = [3, 3, 4, 2, 5];

target_array = [0 0 0 0 0 1 1 1 1 1];

[ func ] = classify_perceptron([x_1 x_2; y_1 y_2], target_array);
x = -2:10;
y = arrayfun(func, x);

plot(x_1, y_1, 'o', x_2, y_2, 'X', x, y);
axis([-2, 10, -2, 10]);

classify_perceptron.m

function [ func ] = classify_perceptron( points, target )
    % points - matrix of x,y coordinates
    % target - array of expected results
    % func - function handler which appropriately classifies a point
    %        given by x, y arguments supplied to this function

    target_arr = target;
    weights = rand(1, 2);
    translation = rand();

    for i=1:size(points, 2)
        flag = true;
        while flag
            result = weights * points(:, i) + translation;
            y = result > 0;
            e = target_arr(1, i) - y;

            if e ~= 0
                weights = weights + (e * points(:, i))';
                translation = translation + e;
            else
                flag = false;
            end
        end
    end

    func = @(x)(-(translation + (weights(1, 1) * x)) / weights(1, 2));

    return
end

问题在于我不知道我在哪里犯了导致错误结果的错误。看起来线的斜率是正确的，但是平移应该更大一些。我真的很感谢指出我正确的方向。我得到的结果如下图所示：

Answer 1

好的，所以我取得了重大进展。万一有人遇到同样的问题我会向你提出解决方案。通过添加变量learning_rate = 0.1并将迭代迭代的循环打包到另一个循环中来解决问题，该循环迭代次数与变量epochs (e.g. 300)中指定的次数相同。