Is there a way to remove double from the variable?

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Lester Lim 2013-1-24

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/59716-is-there-a-way-to-remove-double-from-the-variable

In the workspace, my variable is: lableimage < 359x476x3 double > Whenever I reshape it into a single column, the result is not 170884x1 but 512652x1. Also, because its double, the LDA is not working. Any suggestions?

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Lester Lim 2013-1-24

在 MATLAB Online 中打开

classdef LDA < handle %inherit from handle otherwise we must store value (object) each time when we change some propertie value

%function would need to return obj(this)

    properties
        Samples, %array of feature vectors
        Classes, %class labels for samples
        EigenVectors,
        EigenValues,
        BetweenScatter, %SB
        WithinScatter,  %SW
        NumberOfClasses
    end
    properties(Access = 'private')
        ClassSubsetIndexes %start and end indicies where one class begins and ends in variable "Classes"
        TotalMean,
        MeanPerClass
    end
    methods
        function this = LDA(samples, classes)
            this.Samples = samples;
            this.Classes = classes;
            this.ClassSubsetIndexes = LDA.GetClassSubsetIndexes(classes);
            this.NumberOfClasses = size(this.ClassSubsetIndexes, 1);
            [this.TotalMean this.MeanPerClass] = this.CalculateMean(samples);          
        end
        function Compute(this)
            SB = this.CalculateBetweenScatter();
            SW = this.CalculateWithinScatter();
            [eigVectors, eigValues] = eig(SB, SW);
            eigValues = diag(eigValues);
            %--------- sort eig values and vectors ---------%
            sortedValues = sort(eigValues,'descend');
            [c, ind] = sort(eigValues,'descend'); %store indicies
            sortedVectors = eigVectors(:,ind); % reorder columns 
            this.EigenVectors = sortedVectors;
            this.EigenValues = sortedValues;
            this.BetweenScatter = SB;
            this.WithinScatter = SW;
        end
        function projectedSamples = Transform(this, samples, 
numOfDiscriminants)
            vectors = this.EigenVectors(:, 1:numOfDiscriminants);
            class(samples)
            class(vectors)
            size(samples)
            size(vectors)
            %transformed sample is scalar (projection on a hyperplane)
            projectedSamples = samples * vectors; 
        end
        function measure = CalculateFLDMeasure(this, numOfDiscriminants)
            SB = this.BetweenScatter;
            SW = this.WithinScatter;
            vectors = this.EigenVectors(:, 1:numOfDiscriminants);
            measure = det(vectors' * SB * vectors) / det(vectors' * SW * vectors);
        end
    end
    methods(Access = 'private')
        function [totalMean meanPerClass] = CalculateMean(this, samples)
            for classIdx=1 : 1 : length(this.ClassSubsetIndexes)
                startIdx = this.ClassSubsetIndexes(classIdx, 1);
                endIdx = this.ClassSubsetIndexes(classIdx, 2);
                meanPerClass(classIdx, :) = mean( samples
(startIdx:endIdx, :), 1);
            end
            totalMean = mean(meanPerClass, 1); %global average value
        end
        function SW = CalculateWithinScatter(this)
            featureLength = size(this.Samples, 2);
            SW = zeros(featureLength, featureLength);
            for classIdx=1 : 1 : length(this.ClassSubsetIndexes)
                startIdx = this.ClassSubsetIndexes(classIdx, 1);
                endIdx = this.ClassSubsetIndexes(classIdx, 2);
                classSamples = this.Samples(startIdx:endIdx, :);
                classMean = this.MeanPerClass(classIdx, :);
                Sw_Class = LDA.CalculateScatterMatrix(classSamples, classMean);
                SW = SW + Sw_Class;
            end
        end
        function SB = CalculateBetweenScatter(this)
            featureLength = size(this.Samples, 2);
            SB = zeros(featureLength, featureLength);
             for classIdx=1 : 1 : length(this.ClassSubsetIndexes)
                 startIdx = this.ClassSubsetIndexes(classIdx, 1);
                 endIdx = this.ClassSubsetIndexes(classIdx, 2);
                 numberOfSamplesInClass = endIdx - startIdx + 1;
                 classMean = this.MeanPerClass(classIdx, :);
                 %because my vector is row-vector
                 Sb_class = (classMean - this.TotalMean)' * (classMean - this.TotalMean); 
                 Sb_class = numberOfSamplesInClass * Sb_class;
                 SB = SB + Sb_class;
             end
        end
    end
    methods(Static, Access = 'private')
        function subset = GetClassSubsetIndexes(classes)
            subset=[];
            oldClassLabel = NaN;
            for i=1 : 1 : length(classes)
                if oldClassLabel ~= classes(i)
                    oldClassLabel = classes(i);
                    subset = cat(1, subset, i);
                end
            end
            %now put end indicies
            for i=2 : 1 : size(subset,1)
                endIndex = subset(i, 1);
                subset(i-1, 2) = endIndex-1;
            end
            subset(size(subset,1), 2) = length(classes);           
        end
        function Sw_class = CalculateScatterMatrix(classSamples, classMean)
            featureLength = size(classSamples, 2);
            Sw_class = zeros(featureLength, featureLength);
            for sampleIdx=1 : 1 : size(classSamples, 1)
                covariance = (classSamples(sampleIdx, :) - classMean);
                covariance = covariance' * covariance; %because my vector is row-vector
                Sw_class = Sw_class + covariance;
            end          
        end
    end

end

Lester Lim 2013-1-24

在 MATLAB Online 中打开

Line error : projectedSamples = samples * vectors;

---------------------------------------------------

Error:

Error using * MTIMES is not fully supported for integer classes. At least one input must be scalar. To compute elementwise TIMES, use TIMES (.*) instead.

Error in LDA/Transform (line 60) projectedSamples = samples * vectors;

---------------------------------------------------------------------

Input: data = importdata('LDA data.mat')

features=data(:,1:end-1); %split data without labels

lable=data(:,end); %get the labels

trainSamples = features;%training samples

trainClasses = lable;%training labels

I2 = reshape(I,[],1);

testSamples = I2;%test samples

lableimage = reshape(handles.lableimage,[],1);

testClasses = lableimage;%test labels

mLDA = LDA(trainSamples, trainClasses);

mLDA.Compute();

transformedTrainSamples = mLDA.Transform(trainSamples, 1);

transformedTestSamples = mLDA.Transform(testSamples, 1);

calculatedClases = knnclassify(transformedTestSamples, transformedTrainSamples, trainClasses);

simmilarity = [];

for i = 1 : 1 : length(testClasses)

similarity(i) = ( testClasses{i} == calculatedClases{i} );

end

accuracy = sum(similarity) / length(testClasses);

fprintf('Testing: Accuracy is: %f %%\n', accuracy*100);

guidata(hObject, handles);

Walter Roberson 2013-1-24

The mtimes problem is being discussed in another earlier thread.

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Answer 1

Walter Roberson 2013-1-24

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/59716-is-there-a-way-to-remove-double-from-the-variable#answer_72194

在 MATLAB Online 中打开

To convert an array such as labelImage from being double to being uint8, use

newArray = uint8(labelImage);

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Lester Lim 2013-1-24

Im working on it...

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Is there a way to remove double from the variable?

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