数据聚类源码程序 - matlab算法设计

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标题：数据聚类源码程序
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所属分类: 算法设计资源类型：文件大小: 2.14 KB 上传时间: 2016-01-25 19:11:56 下载次数: 14 资源积分：1分提供者: xiaopeng2 数据聚类源码程序

内容：
数据聚类源码程序，程序员在编程的过程中可以参考学习使用，希望对IT程序员有用，此源码程序简单易懂、方便阅读，有很好的学习价值！

function adaboostDemo

close all

clear all

clc

%% Settings

weakLearnerNum = 16; % how many weak classifiers will learn during AdaBoost training

displayTrainingProcess = 1; % whether display training process

%% generate samples

r = sqrt(rand(100,1)); % radius

t = 2*pi*rand(100,1); % angle

positive = [r.*cos(t), r.*sin(t)];

r = sqrt(3*rand(100,1)+1); % radius

t = 2*pi*rand(100,1); % angle

negative = [r.*cos(t), r.*sin(t)];

figure(1)

hold on

plot(positive(:,1),positive(:,2),'.r')

plot(negative(:,1),negative(:,2),'.b')

ezpolar(@(x)1);ezpolar(@(x)2);

axis equal

hold off

%% AdaBoost training process

data = [positive; negative];

labels = [ones(size(positive,1),1); -ones(size(negative,1),1)];

weights = [ones(size(positive,1),1) ./ (2*size(positive,1)); ones(size(negative,1),1) ./ (2*size(negative,1))];

weakLearners = zeros(weakLearnerNum,4); % [dimension, threshold, polarity, errorRate]

for t = 1 : weakLearnerNum

% re-normalize the weights

weights = weights ./ sum(weights);

% select best classifier

[dimension, threshold, polarity, errorRate] = selectBestClassifier(data, labels, weights);

% update sample weights

weights = updateSampleWeights(data, labels, weights, dimension, threshold, polarity, errorRate, displayTrainingProcess);

% load weak classifier

weakLearners(t,:) = [dimension, threshold, polarity, errorRate];

end

%% AdaBoosting predict process

figure(3)

hold on

for i = 1 : size(data,1)

% classify by weak classifiers

x = data(i,:);

if adaboostingPredict(x, weakLearners) == 1

plot(x(1), x(2), '.r');

else

plot(x(1), x(2), '.b');

end

ezpolar(@(x)1);ezpolar(@(x)2);

axis equal

hold off

%% Classify sample

function label = adaboostingPredict(x, weakLearners)

dimensions = weakLearners(:,1);

thresholds = weakLearners(:,2);

polarities = weakLearners(:,3);

errorRates = weakLearners(:,4);

beta = errorRates ./ (1-errorRates);

alpha = log(1./beta);

features = x(dimensions);

hypothesis = (polarities' .* features)' < (polarities .* thresholds);

label = (alpha' * hypothesis) > (sum(alpha) * 0.5);

%% Update samples weights

function weights = updateSampleWeights(data, labels, weights, dimension, threshold, polarity, errorRate, displayTrainingProcess)

% classify data by current threshold

positive = find(polarity.*data(:,dimension) < polarity*threshold);

negative = find(polarity.*data(:,dimension) >= polarity*threshold);

% find the correct samples

positive(find(labels(positive) ~= 1)) = [];

negative(find(labels(negative) ~= -1)) = [];

corrects = [positive; negative];

weights(corrects) = weights(corrects) .* (errorRate / (1-errorRate));

% plot current weak classifier and weighted samples

if displayTrainingProcess == 1

figure(2)

clf(figure(2),'reset')

hold on

for i = 1 : size(data,1)

color = 'y'; % default : incorrect classfied samples

if ~isempty(find(positive == i)) % correct classified positive samples

color = 'r';

end

if ~isempty(find(negative == i)) % correct classified negative samples

color = 'b';

end

plot(data(i,1), data(i,2), 'o', 'MarkerEdgeColor', 'k', 'MarkerFaceColor', color, 'MarkerSize', weights(i)*size(data,1)*10);

end

if dimension == 1

line([threshold, threshold], [-2, 2], 'LineWidth', 4, 'Color', [.8 .8 .8])

else

line([-2, 2], [threshold, threshold], 'LineWidth', 4, 'Color', [.8 .8 .8])

end

ezpolar(@(x)1);ezpolar(@(x)2);

axis equal

hold off

pause

end

%% Select best classifier

function [bestDim, bestThreshold, bestPolarity, bestErrorRate] = selectBestClassifier(data, labels, weights)

bestDim = 0;

bestThreshold = 0;

bestPolarity = 0;

bestErrorRate = sum(weights);

for dim = 1 : size(data,2)

[threshold, polarity, errorRate] = buildFeatureClassifier(data(:,dim), labels, weights);

if errorRate <= bestErrorRate

bestDim = dim;

bestThreshold = threshold;

bestPolarity = polarity;

bestErrorRate = errorRate;

end

%% Build feature classifier

function [bestThreshold, bestPolarity, bestErrorRate] = buildFeatureClassifier(data, labels, weights)

bestThreshold = 0;

bestPolarity = 0;

bestErrorRate = sum(weights);

% sort data

[data, index] = sort(data);

labels = labels(index);

weights = weights(index);

% generate possible splitters

splitters = data(1:end-1) + 0.5 * (data(2:end) - data(1:end-1));

for i = 1 : size(splitters,1)

threshold = splitters(i);

for polarity = -1 : 2 : 1 % polarity is (+) / (-)

positive = find(polarity.*data < polarity*threshold);

negative = find(polarity.*data >= polarity*threshold);

positive(find(labels(positive) == 1)) = []; % dimiss correct samples

negative(find(labels(negative) == -1)) = [];

incorrects = [positive; negative];

errorRate = sum(weights(incorrects));

if errorRate <= bestErrorRate

bestThreshold = threshold;

bestPolarity = polarity;

bestErrorRate = errorRate;

end

文件列表（点击上边下载按钮，如果是垃圾文件请在下面评价差评或者投诉）:

数据聚类源码程序/
数据聚类源码程序/adaboostDemo/
数据聚类源码程序/adaboostDemo/adaboostDemo.m

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