本文转自：
http://blog.sina.com.cn/s/blog_890c6aa30101av9x.html

MATLAB命令行下验证Java版本命令

version -java

配置MATLAB调用Java库

Finish Java codes.
Create Java library file, i.e., .jar file.
Put created .jar file to one of directories Matlab uses for storing libraries, and add corresponding path to
Matlab configuration file, $MATLABINSTALLDIR\$MatlabVersion\toolbox\local\classpath.txt.

配置MATLAB调用Weka

下载weka
安装weka
在环境变量的系统变量中的Path中加入jre6(或者其他的)中bin文件夹的绝对路径，如：
C:\Program Files\Java\jre1.8.0_77\bin;
查找MATLAB配置文件classpath.txt
which classpath.txt %使用这个命令可以查找classpath.txt的位置
修改配置文件classpath.txt
edit classpath.txt
在classpath.txt配置文件中将weka安装目录下的weka.jar的绝对安装路径填入，如：
C:\Program Files\Weka-3-8\weka.jar
重启MATLAB
运行如下命令：
attributes = javaObject(‘weka.core.FastVector’);
%如果MATLAB没有报错，就说明配置成功了
Matlab在调用weka中的类时，经常遇见heap space溢出的情况，我们需要设置较大的堆栈，设置方法是：
Matlab->File->Preference->General->Java Heap Memory, 然后设置适当的值。

Matlab调用Weka示例
代码来自：
http://cn.mathworks.com/matlabcentral/fileexchange/37311-smoteboost
http://www.mathworks.com/matlabcentral/fileexchange/37315-rusboost

clc;
clear all;
close all;file = 'data.csv'; % Dataset% Reading training file
data = dlmread(file);
label = data(:,end);% Extracting positive data points
idx = (label==1);
pos_data = data(idx,:);
row_pos = size(pos_data,1);% Extracting negative data points
neg_data = data(~idx,:);
row_neg = size(neg_data,1);% Random permuation of positive and negative data points
p = randperm(row_pos);
n = randperm(row_neg);% 80-20 split for training and test
tstpf = p(1:round(row_pos/5));
tstnf = n(1:round(row_neg/5));
trpf = setdiff(p, tstpf);
trnf = setdiff(n, tstnf);train_data = [pos_data(trpf,:);neg_data(trnf,:)];
test_data = [pos_data(tstpf,:);neg_data(tstnf,:)];% Decision Tree
prediction = SMOTEBoost(train_data,test_data,'tree',false);
disp ('    Label   Probability');
disp ('-----------------------------');
disp (prediction);

function prediction = SMOTEBoost (TRAIN,TEST,WeakLearn,ClassDist)
% This function implements the SMOTEBoost Algorithm. For more details on the
% theoretical description of the algorithm please refer to the following
% paper:
% N.V. Chawla, A.Lazarevic, L.O. Hall, K. Bowyer, "SMOTEBoost: Improving
% Prediction of Minority Class in Boosting, Journal of Knowledge Discovery
% in Databases: PKDD, 2003.
% Input: TRAIN = Training data as matrix
%        TEST = Test data as matrix
%        WeakLearn = String to choose algortihm. Choices are
%                    'svm','tree','knn' and 'logistic'.
%        ClassDist = true or false. true indicates that the class
%                    distribution is maintained while doing weighted
%                    resampling and before SMOTE is called at each
%                    iteration. false indicates that the class distribution
%                    is not maintained while resampling.
% Output: prediction = size(TEST,1)x 2 matrix. Col 1 is class labels for
%                      all instances. Col 2 is probability of the instances
%                      being classified as positive class.javaaddpath('weka.jar');%% Training SMOTEBoost
% Total number of instances in the training set
m = size(TRAIN,1);
POS_DATA = TRAIN(TRAIN(:,end)==1,:);
NEG_DATA = TRAIN(TRAIN(:,end)==0,:);
pos_size = size(POS_DATA,1);
neg_size = size(NEG_DATA,1);% Reorganize TRAIN by putting all the positive and negative exampels
% together, respectively.
TRAIN = [POS_DATA;NEG_DATA];% Converting training set into Weka compatible format
CSVtoARFF (TRAIN, 'train', 'train');
train_reader = javaObject('java.io.FileReader', 'train.arff');
train = javaObject('weka.core.Instances', train_reader);
train.setClassIndex(train.numAttributes() - 1);% Total number of iterations of the boosting method
T = 10;% W stores the weights of the instances in each row for every iteration of
% boosting. Weights for all the instances are initialized by 1/m for the
% first iteration.
W = zeros(1,m);
for i = 1:mW(1,i) = 1/m;
end% L stores pseudo loss values, H stores hypothesis, B stores (1/beta)
% values that is used as the weight of the % hypothesis while forming the
% final hypothesis. % All of the following are of length <=T and stores
% values for every iteration of the boosting process.
L = [];
H = {};
B = [];% Loop counter
t = 1;% Keeps counts of the number of times the same boosting iteration have been
% repeated
count = 0;% Boosting T iterations
while t <= T% LOG MESSAGEdisp (['Boosting iteration #' int2str(t)]);if ClassDist == true% Resampling POS_DATA with weights of positive examplePOS_WT = zeros(1,pos_size);sum_POS_WT = sum(W(t,1:pos_size));for i = 1:pos_sizePOS_WT(i) = W(t,i)/sum_POS_WT ;endRESAM_POS = POS_DATA(randsample(1:pos_size,pos_size,true,POS_WT),:);% Resampling NEG_DATA with weights of positive exampleNEG_WT = zeros(1,neg_size);sum_NEG_WT = sum(W(t,pos_size+1:m));for i = 1:neg_sizeNEG_WT(i) = W(t,pos_size+i)/sum_NEG_WT ;endRESAM_NEG = NEG_DATA(randsample(1:neg_size,neg_size,true,NEG_WT),:);% Resampled TRAIN is stored in RESAMPLEDRESAMPLED = [RESAM_POS;RESAM_NEG];% Calulating the percentage of boosting the positive class. 'pert'% is used as a parameter of SMOTEpert = ((neg_size-pos_size)/pos_size)*100;else % Indices of resampled trainRND_IDX = randsample(1:m,m,true,W(t,:));% Resampled TRAIN is stored in RESAMPLEDRESAMPLED = TRAIN(RND_IDX,:);% Calulating the percentage of boosting the positive class. 'pert'% is used as a parameter of SMOTEpos_size = sum(RESAMPLED(:,end)==1);neg_size = sum(RESAMPLED(:,end)==0);pert = ((neg_size-pos_size)/pos_size)*100;end% Converting resample training set into Weka compatible formatCSVtoARFF (RESAMPLED,'resampled','resampled');reader = javaObject('java.io.FileReader','resampled.arff');resampled = javaObject('weka.core.Instances',reader);resampled.setClassIndex(resampled.numAttributes()-1);% New SMOTE boosted data gets stored in Ssmote = javaObject('weka.filters.supervised.instance.SMOTE');pert = ((neg_size-pos_size)/pos_size)*100;smote.setPercentage(pert);smote.setInputFormat(resampled);S = weka.filters.Filter.useFilter(resampled, smote);% Training a weak learner. 'pred' is the weak hypothesis. However, the % hypothesis function is encoded in 'model'.switch WeakLearncase 'svm'model = javaObject('weka.classifiers.functions.SMO');case 'tree'model = javaObject('weka.classifiers.trees.J48');case 'knn'model = javaObject('weka.classifiers.lazy.IBk');model.setKNN(5);case 'logistic'model = javaObject('weka.classifiers.functions.Logistic');endmodel.buildClassifier(S);pred = zeros(m,1);for i = 0 : m - 1pred(i+1) = model.classifyInstance(train.instance(i));end% Computing the pseudo loss of hypothesis 'model'loss = 0;for i = 1:mif TRAIN(i,end)==pred(i)continue;elseloss = loss + W(t,i);endend% If count exceeds a pre-defined threshold (5 in the current% implementation), the loop is broken and rolled back to the state% where loss > 0.5 was not encountered.if count > 5L = L(1:t-1);H = H(1:t-1);B = B(1:t-1);disp ('          Too many iterations have loss > 0.5');disp ('          Aborting boosting...');break;end% If the loss is greater than 1/2, it means that an inverted% hypothesis would perform better. In such cases, do not take that% hypothesis into consideration and repeat the same iteration. 'count'% keeps counts of the number of times the same boosting iteration have% been repeatedif loss > 0.5count = count + 1;continue;elsecount = 1;end        L(t) = loss; % Pseudo-loss at each iterationH{t} = model; % Hypothesis function   beta = loss/(1-loss); % Setting weight update parameter 'beta'.B(t) = log(1/beta); % Weight of the hypothesis% At the final iteration there is no need to update the weights any% furtherif t==Tbreak;end% Updating weight    for i = 1:mif TRAIN(i,end)==pred(i)W(t+1,i) = W(t,i)*beta;elseW(t+1,i) = W(t,i);endend% Normalizing the weight for the next iterationsum_W = sum(W(t+1,:));for i = 1:mW(t+1,i) = W(t+1,i)/sum_W;end% Incrementing loop countert = t + 1;
end% The final hypothesis is calculated and tested on the test set
% simulteneously.%% Testing SMOTEBoost
n = size(TEST,1); % Total number of instances in the test setCSVtoARFF(TEST,'test','test');
test = 'test.arff';
test_reader = javaObject('java.io.FileReader', test);
test = javaObject('weka.core.Instances', test_reader);
test.setClassIndex(test.numAttributes() - 1);% Normalizing B
sum_B = sum(B);
for i = 1:size(B,2)B(i) = B(i)/sum_B;
endprediction = zeros(n,2);for i = 1:n% Calculating the total weight of the class labels from all the models% produced during boostingwt_zero = 0;wt_one = 0;for j = 1:size(H,2)p = H{j}.classifyInstance(test.instance(i-1));      if p==1wt_one = wt_one + B(j);else wt_zero = wt_zero + B(j);           endendif (wt_one > wt_zero)prediction(i,:) = [1 wt_one];elseprediction(i,:) = [0 wt_one];end
end

function r = CSVtoARFF (data, relation, type)
% csv to arff file converter% load the csv data
[rows cols] = size(data);% open the arff file for writing
farff = fopen(strcat(type,'.arff'), 'w');% print the relation part of the header
fprintf(farff, '@relation %s', relation);% Reading from the ARFF header
fid = fopen('ARFFheader.txt','r');
tline = fgets(fid);
while ischar(tline)tline = fgets(fid);fprintf(farff,'%s',tline);
end
fclose(fid);% Converting the data
for i = 1 : rows% print the attribute values for the data pointfor j = 1 : cols - 1if data(i,j) ~= -1 % check if it is a missing valuefprintf(farff, '%d,', data(i,j));elsefprintf(farff, '?,');endend% print the label for the data pointfprintf(farff, '%d\n', data(i,end));
end% close the file
fclose(farff);r = 0;

function model = ClassifierTrain(data,type)
% Training the classifier that would do the sample selectionjavaaddpath('weka.jar');CSVtoARFF(data,'train','train');
train_file = 'train.arff';
reader = javaObject('java.io.FileReader', train_file);
train = javaObject('weka.core.Instances', reader);
train.setClassIndex(train.numAttributes() - 1);
% options = javaObject('java.lang.String');switch typecase 'svm'model = javaObject('weka.classifiers.functions.SMO');kernel = javaObject('weka.classifiers.functions.supportVector.RBFKernel');model.setKernel(kernel);case 'tree'model = javaObject('weka.classifiers.trees.J48');% options = weka.core.Utils.splitOptions('-C 0.2');% model.setOptions(options);case 'knn'model = javaObject('weka.classifiers.lazy.IBk');model.setKNN(5);case 'logistic'model = javaObject('weka.classifiers.functions.Logistic');
endmodel.buildClassifier(train);

function prediction = ClassifierPredict(data,model)
% Predicting the labels of the test instances
% Input: data = test data
%        model = the trained model
%        type = type of classifier
% Output: prediction = prediction labelsjavaaddpath('weka.jar');CSVtoARFF(data,'test','test');
test_file = 'test.arff';
reader = javaObject('java.io.FileReader', test_file);
test = javaObject('weka.core.Instances', reader);
test.setClassIndex(test.numAttributes() - 1);prediction = [];
for i = 0 : size(data,1) - 1p = model.classifyInstance(test.instance(i));prediction = [prediction; p];
end

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MATLAB中调用Weka设置方法（转）及示例

MATLAB命令行下验证Java版本命令

配置MATLAB调用Java库

配置MATLAB调用Weka

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