logitregkernel.m

一个matlab的工具包,里面包括一些分类器 例如 KNN KMEAN SVM NETLAB 等等有很多.

function [Y_compute, Y_prob] = LogitRegKernel(para, X_train, Y_train, X_test, Y_test, num_class)

global temp_model_file preprocess;

Y_compute = zeros(size(Y_test)); Y_prob = zeros(size(Y_test));
if (num_class > 2)
    error('LogitRegKernel: The class number is larger than 2!');
end;

class_set = GetClassSet(Y_train);
p = str2num(char(ParseParameter(para, {'-Kernel';'-KernelParam'; '-RegFactor'; '-PairFactor'; '-CostFactor'}, {'0';'0.05';'1';'1';'1'}, 1)));
kerneltype = p(1);
kernelpara =  p(2); 
regftr = p(3);
pairftr = p(4);
costfactor = p(5);

X_train_ext = [X_train ones(size(X_train, 1), 1)];
X_train_ext = X_train_ext(1:size(X_train_ext, 1), :);
Y_train  = Y_train(1:size(Y_train, 1), :);
X_test_ext = [X_test ones(size(X_test, 1), 1)];

ConPair1 = []; ConPair2 = []; LabelPair = [];
if (preprocess.ConstraintAvailable == 1) & (preprocess.ShotAvailable == 1),  
    one_array = ones(sum(preprocess.constraintUsed == 1), 1);
    ConPair1 = [preprocess.ConPair1(preprocess.constraintUsed == 1, :) one_array];
    ConPair2 = [preprocess.ConPair2(preprocess.constraintUsed == 1, :) one_array];
    LabelPair = preprocess.LabelPair(preprocess.constraintUsed == 1);
end;
X_ext = [X_train_ext; ConPair1; ConPair2];

Logit_Y_prob = zeros(size(X_test, 1), 1);
beta = []; 
if (~isempty(X_train)),
    % Convert the binary labels into +/-1
    Y_train = (Y_train == class_set(1)) - (Y_train ~= class_set(1));
    beta = ordinalKernel(Y_train, X_train_ext, LabelPair, ConPair1, ConPair2, kerneltype, kernelpara, regftr, pairftr, costfactor);
    fid = fopen(temp_model_file, 'w');
    if (fid > 0),
        fprintf('Writing to %s .... \n', temp_model_file);  
        fprintf(fid, 'File: %s\n', preprocess.input_file); 
        fprintf(fid, 'N: %d\n', size(Y_train, 1)); 
        fprintf(fid, '%d ', class_set); fprintf(fid, '\n');     
        format_str = ''; 
        for i = 1:size(X_ext,2)+1, format_str = strcat(format_str, '%f,'); end;
        format_str = strcat(format_str, '\n');
        fprintf(fid, format_str, [beta X_ext]');
        fclose(fid);    
    end;
else
    fid = fopen(temp_model_file, 'r');
    if (fid > 0),
        fgets(fid);
        line = fgetl(fid); num = sscanf(line, 'N: %d');
        line = fgetl(fid); class_set = sscanf(line, '%d');      
        input = fscanf(fid, '%f,');
        input = reshape(input, [], num)';
        beta = input(:, 1); X_ext = input(:, 2:size(input, 2));
        fclose(fid);    
        preprocess.ClassSet = class_set;
    end;    
end;

Logit_Y_prob = ordinalKernelPredict(beta, X_ext, X_test_ext, kerneltype, kernelpara);

% Y_prob = (exp(Logit_Y_prob) ./ (1 + exp(Logit_Y_prob))) .* (Logit_Y_prob >= 0) + (1 ./ (1 + exp(Logit_Y_prob))) .* (Logit_Y_prob < 0);
Y_prob = exp(Logit_Y_prob) ./ (1 + exp(Logit_Y_prob));
Y_compute = class_set(1) * (Logit_Y_prob >= 0) + class_set(2) * (Logit_Y_prob < 0);