对“视觉机器学习20讲配套仿真代码”的研究心得---Random Forest

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%功能：演示随机森林算法在计算机视觉中的应用

%环境：Win7，Matlab2012b

%Modi: NUDT-VAP

%时间：2015-4-10

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path = ['E:\works\book\7（机器学习20讲）\Code\5、Random Forest\'];

data1 = textread([path 'satimage.tra']);

data2 = textread([path 'satimage.txt']);

% path = 'C:\Users\Administrator\Documents\MATLAB\';

% data = textread(path + 'srbct.txt');

% In this data set, each row represents a sample,

% and each column represents a kind of variable(feature, attribute).

% !! So we should transpose "x" and "xts" below.

[m1, n1] = size(data1);

[m2, n2] = size(data2);

ntest = m2; % The number of test set;

ntrain = m1; % The number of training set;

% Above lines we randomly select 2/3 data as training data,

% and remaining 1/3 data as test data.

x = (data1(1 : ntrain, 1 : n1 - 1));

x = x';

cl = (data1(1 : ntrain, n1));

xts = (data2(1 : ntest, 1 : n2 - 1));

xts = xts';

clts = (data2(1 : ntest, n2));

% Above lines we acquire x, cl, xts and clts from randomData;

nclass = 6;

% The data set has 4 classes.

classwt = 0;

% Here we set all class the same weight 1.

% It can also be written as "classwt = [1 1 1 1];".

cat0 = 0;

% Here we set it having no categorical variables.

runParam = [6 1 50 10 1 0];

% Here we set mtry = 80, ndsize = 1, jbt = 60, look = 10, lookcls = 1, mdim2nd = 0;

impOpt = [0 0 0];

% Here we set imp = 0, Interact = 0, impn = 0;

proCom = [0 0 0 0 0];

% Here we set nprox = 0, nrnn = 0, noutlier = 0, nscale = 0, nprot = 0;

missingVal = 0;

% Here we set missingVal = 0, that means we use the "Default Value" for missingVal.

% That is, code = -999.0, missingfill = 0;

saveForest = [0 0 0];

% Here we set isaverf = 0, isavepar = 0, isavefill = 0;

runForest = [0 0];

% Here we set irunrf = 0, ireadpar = 0;

outParam = [1,0,0,0,0,0,0,0,0,0];

% Here we set isumout = 1 to show a classification summary.

msm = 1 : 36;

% Here we use all 2308 variables, we can also use msm = 0 to use all variables.

seed = 4351;

x = single(x); %get train x

cl = int32(cl); %get train label

xts = single(xts); %get test x

clts = int32(clts); %get test label

classwt = single(classwt);

cat0 = int32(cat0);

msm = int32(msm);

runParam = int32(runParam);

impOpt = int32(impOpt);

proCom = int32(proCom);

missingVal = single(missingVal);

saveForest = int32(saveForest);

runForest = int32(runForest);

outParam = int32(outParam);

seed = int32(seed);

[errtr, errts, prox, trees, predictts, varimp, scale] = ...

RF(nclass, x, cl, xts, clts, classwt, cat0, msm, runParam, impOpt, ...

proCom, missingVal, saveForest, runForest, outParam, seed, 'satimage');

&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&

function [errtr, errts, prox, trees, predictts, varimp, scale] = ...

runRF(trainData, testData, classwt, cat0, msm, runParam, ...

impOpt, proCom, missingVal, saveForest, runForest, outParam, seed)

if (trainData == testData)

data = textread(trainData);

[m, n] = size(data);

randomNum = randperm(m);

randomData = data(randomNum, :);

ntest = ceil(m / 3);

ntrain = m - ntest;

x = (randomData(1 : ntrain, 1 : n - 1));

x = x';

cl = (randomData(1 : ntrain, n));

xts = (data(ntrain + 1 : m, 1 : n - 1));

xts = xts';

clts = (data(ntrain + 1 : m, n));

else

data1 = textread(trainData);

data2 = textread(testData);

[m1, n1] = size(data1);

[m2, n2] = size(data2);

ntest = m2;

ntrain = m1;

x = (data1(1 : ntrain, 1 : n1 - 1));

x = x';

cl = (data1(1 : ntrain, n1));

xts = (data2(1 : ntest, 1 : n2 - 1));

xts = xts';

clts = (data2(1 : ntest, n2));

end

if (nargin < 13)

seed = 4351;

end

if (nargin < 12)

outParam = 0;

end

if (nargin < 11)

runForest = 0;

end

if (nargin < 10)

saveForest = 0;

end

if (nargin < 9)

missingVal = 0;

end

if (nargin < 8)

proCom = 0;

end

if (nargin < 7)

impOpt = 0;

end

if (nargin < 6)

runParam = 0;

end

if (nargin < 5)

msm = 0;

end

if (nargin < 4)

cat0 = 0;

end

if (nargin < 3)

classwt = 0;

end

if (nargin < 2)

xts = 0;

clts = 0;

end

uniquecl = unique(cl);

nclass = size(uniquecl, 1) * size(uniquecl, 2);

remain = trainData;

while true

[dataName, remain] = strtok(remain,'\');

if isempty(remain)

break;

end

end

dataName = strtok(dataName, '.');

x = single(x); %get train x

cl = int32(cl); %get train label

xts = single(xts); %get test x

clts = int32(clts); %get test label

classwt = single(classwt);

cat0 = int32(cat0);

msm = int32(msm);

runParam = int32(runParam);

impOpt = int32(impOpt);

proCom = int32(proCom);

missingVal = single(missingVal);

saveForest = int32(saveForest);

runForest = int32(runForest);

outParam = int32(outParam);

seed = int32(seed);

[errtr, errts, prox, trees, predictts, varimp, scale] = ...

RF(nclass, x, cl, xts, clts, classwt, cat0, msm, runParam, impOpt, proCom, ...

missingVal, saveForest, runForest, outParam, seed, dataName);

errtr = errtr';

errts = errts';

prox = prox';

predictts = predictts';

varimp = varimp';

scale = scale';

end

&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&

path = ['E:\works\book\7（机器学习20讲）\Code\5、Random Forest\'];

trainData = [path 'satimage.tra'];

testData = [path 'satimage.txt'];

classwt=0;

cat0 = 0;

runParam = [6 1 50 10 1 0];

impOpt = [0 0 0];

proCom = [0 4435 0 0 0];

missingVal=0;

saveForest=[1 0 0];

runForest=[0 0];

outParam = [1,0,0,0,0,0,0,0,0,0];

msm = 0;

seed = 4351;

[errtr, errts, prox, trees, predictts, varimp, scale] = ...

runRF(trainData, testData, classwt, cat0, msm, runParam, ...

impOpt, proCom, missingVal, saveForest, runForest, outParam, seed);