matlab normalization

Using meand stddev

 function [featureIn,meanFeatIn, stdDevFeatIn] = mynorm_train(featureIn)
meanFeatIn = mean(featureIn,1);
stdDevFeatIn = std(featureIn,1,1);
noSample = size(featureIn,1);
for i=1:noSample
    featureIn(i,:) = (featureIn(i,:) - meanFeatIn) ./ stdDevFeatIn ;
end
end

 function [testFeatureIn] = mynorm_test(testFeatureIn,meanFeatIn,stdDevFeatIn)
    noSample = size(testFeatureIn,1);
    noInputFeat = size(testFeatureIn,2);
    for i=1:noSample
            testFeatureIn(i,1:noInputFeat) = (testFeatureIn(i,1:noInputFeat) - meanFeatIn ) ./ stdDevFeatIn;          
    end  
end





Using range

 function [ N_feature,feature_range,feature_bases ] = normalize( features )
%NORMALIZE Summary of this function goes here
%   Detailed explanation goes here
% samples are in rows

for NoF = 1:size(features,2)
    F_min(NoF) = min(features(:,NoF));
    F_max(NoF) = max(features(:,NoF));
  
    feature_range(NoF) = (F_max(NoF)-F_min(NoF))/2;
    feature_bases(NoF) = (F_max(NoF)+F_min(NoF))/2;
  
    for NoS = 1:size(features,1)
        if (feature_range(NoF) ~=0)
            N_feature(NoS,NoF) = (features(NoS,NoF)-feature_bases(NoF))/feature_range(NoF);
        else
            N_feature(NoS,NoF)=features(NoS,NoF)-feature_bases(NoF);
        end
    end
end

end
   
function [ feature ] = normalize_t( t_features,range,bases )
%NORMALIZE_T Summary of this function goes here
%   Detailed explanation goes here
range = repmat(range,size(t_features,1),1);
bases = repmat(bases,size(t_features,1),1);
feature = (t_features - bases)./range;
end