RPCL(Rival Penalized Competitive Learning)在matlab下的实现

时间：2021-01-15 21:59:51

RPCL是在线kmeans的改进版，相当于半自动的选取出k个簇心：一开始设定N个簇心，N>k，然后聚类。每次迭代中把第二近的簇心甩开一段距离。

所谓在线kmeans是就是，每次仅仅用一个样本来更新簇心位置，而不是用全部数据（“批量”）。

代码：

% RPCL, rival penalized competitive learning，改进版的Kmeans

% 根据http://www.cs.hmc.edu/~asampson/ap/ap.git移植而来，并根据RPCL原文进行了修改

clear;

close all;

iter_num = 10; % 最多迭代次数

%load 'stimuli.txt';

%data = stimuli; clear stimuli;

%load 'dita.txt';

%data=dita; clear dita;

load 'data.txt';

sample_num = size(data, 1);

sample_dimension = size(data, 2);

learning_rate = 0.1;

K=20; %簇心数量

cluster_wins = zeros(K,1);

clusters = zeros(K, sample_dimension);

minVal = min(data); % 各维度计算最小值

maxVal = max(data); % 各维度计算最大值

% 初始化簇心。根据DLZhu的建议，要在数据区域外面生成簇心。

for i=1:K

   %clusters(i,:) = [rand(), rand()]; % [0,1]x[0,1]平面上的散点

   %clusters(i, :) = minVal + (maxVal - minVal) * rand();

   clusters(i,:) = mypkg.genOutRand(minVal, maxVal);

   cluster_wins(i) = 0;

end

% clusters = [

%     [0.58900736, 0.8727472];

%     [0.42491971, 0.31954921];

%     [0.90917136, 0.70537815];

%     [0.1836478,  0.2356091];

%     [0.53538985, 0.35172357];

%     [0.01189786,  0.98610582]

% ];

figure;

scatter(data(:,1), data(:,2));

hold on;

scatter(clusters(:,1), clusters(:,2), 'red', 'filled');

title('原始数据:蓝圈；初始簇心：红点');

c = zeros(sample_num, 1); % 每个样本数据所属簇心编号

cluster_colors = [

    [0 0 1]; % blue

    [1 0 0]; % red

    [0 1 0]; % green

    [1 0 1]; % magenta

    [0 1 1]; % cyan

    [1 1 0]; % yellow

    ];

for iter=1:iter_num

    progress = iter / iter_num;

    indexes = randperm(sample_num); % 样本顺序的一个随机排序

    for h=1:sample_num

        i=indexes(h);

        % 遍历clusters，找到和data(j,:)最近的那个(winner)，以及第二近的(rival)

        gg = zeros(K,1);

        for j=1:K

            tt = norm(clusters(j,:) - data(i,:));

            gg(j) = tt * cluster_wins(j);

        end

        % 找出winner簇心，它的gg(`)值最小

        winner_idx = 1;

        min_val = gg(1);

        for j=1:K

            if (gg(j)<min_val)

                min_val = gg(j);

                winner_idx = j;

            end

        end

        % winner = clusters(winner_idx,:);

        % 找出winner簇心，它的gg(`)值第二小

        rival_idx = 1;

        min_val = gg(1)+gg(2);  %取前两元素的和，是为了避免gg(1)就是winner

        for j=1:K

            if (j==winner_idx)

                continue;

            end

            if (gg(j)<min_val)

                min_val = gg(j);

                rival_idx = j;

            end

        end

        % rival = clusters(rival_idx,:);

        cluster_wins(winner_idx) = cluster_wins(winner_idx) + 1;

        c(i) = winner_idx;

        % 对于winner，learn

        winner_learning_rate = learning_rate;

        clusters(winner_idx,:) = clusters(winner_idx,:) + winner_learning_rate*(data(i,:)-clusters(winner_idx,:));

        % 对于rival, de-learn

        % learn(rival, stimulus, -0.8 * self.learning_rate)

        rival_learning_rate = -0.8 * learning_rate;

        clusters(rival_idx,:) = clusters(rival_idx,:) + rival_learning_rate*(data(i,:)-clusters(rival_idx,:));

    end

    %if(rem(iter,10)~=0)

    %    continue;

    %end

    huck = figure;

    %for j=1:sample_num

    %    hold on;

    %    % plot(data(j,1), data(j,2), 'b--x', 'Color', cluster_colors(c(j),:), 'LineWidth', 2);

    %    plot([data(j,1), clusters(c(j),1)], [data(j,2), clusters(c(j),2)], 'Color', 'blue', 'Marker', 'o', 'MarkerFaceColor', cluster_colors(c(j),:), 'MarkerSize', 3);

    %end

    scatter(data(:,1), data(:,2));

    hold on;

    scatter(clusters(:,1), clusters(:,2), 'filled'); % 实心圆点，表示簇心初始位置

    title(['第', num2str(iter), '次迭代']);

    waitfor(huck);

end

disp('==========================');

final_c = unique(c);

final_clusters = clusters(final_c,:);

disp(final_clusters);

disp('Done.');

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