# Matlab Linear Regression Sample Code

Three type of datasets have been analyzed for this technique:

(1) Linearly separable data(LS)

(2) Inseparable data(NLS)

(3) Banana data (BD)

For a binary classification problem. Split the datasets into 70% training and 30% testing randomly in five folds. This is a old code done as a part of an assignment. Compatibility with newer versions may happen.

% This is for binary class problem, where we are fitting in k dimension.

%This is the code for classification using linear regression.

function linearRegression_LSData()
disp(‘..Starting linear regression Algorithm….’);

```%reading data

data = A.data;
[N,col]= size(data);

vtot=[0, 0, 0, 0,0, 0,  0 , 0];

%5 folds with 70-30 ratio
```

for i = 1:5

```    P=.3;
groups=data(:,3);
[train,test] = crossvalind('holdout',groups, P);
train1= data(train, 1: 3);
test1=data(test, 1:3);
[trainLengthRow, trainLengthCol]=size(train1);
[rowtest,coltest]= size(test1);

trainingSet = train1(1:trainLengthRow, 1 : trainLengthCol -1 );
trainingLabels = train1(1:trainLengthRow, trainLengthCol );

testSet = test1(1:rowtest, 1 : coltest -1 );
testLabels = test1(1:rowtest, coltest );

disp('training length');
disp(trainLengthCol);
disp(trainLengthRow);
disp(rowtest);
disp(coltest);

weights(1:trainLengthCol)=0;
[weights] = train1LinearReg(trainingSet,trainingLabels);
disp(weights);

[correctlyClassified,count0,count1,unClassified,v] =  testLinearReg(testSet,testLabels, weights);
vtot = vtot +v ;
```

end
disp(‘TP, TN, FP, FN, TP/(TP+FP), TP/P, 2PR / (P+R) , correctlyClassified/trainLengthRow’);
%taking average of all quantaties of TP, TN, FP, FN etc.
vtot = vtot ./ 5
end

%Method to do train the Linear Regression Model
%——-Paramaeters————
%trainingSet: The training set
%trainingLabels: The traingin labels corresponding to training Set
%————Return Type——————-
%weights: weights computed by linear regression
function [weights] = train1LinearReg(trainingSet,trainingLabels)

```[trainLengthRow, trainLengthCol] = size(trainingSet);
weights(1:trainLengthCol+1)=0;
Y=trainingLabels;
X(1:trainLengthRow, 1:trainLengthCol+1) = 0;
X(1:trainLengthRow, 1)= 1;
X(1:trainLengthRow, 2:trainLengthCol+1) = trainingSet;
disp(X);
disp(Y);
Z= X'* X;
weights = (inv(Z)) *(X' * Y);

%the following is for display of plots and surfaces
y1=weights' * X';

%Plotting the regression line
for p=1:trainLengthRow
x1(p)= trainingSet(p,1);
end;

for p=1:trainLengthRow
x2(p)= trainingSet(p,2);
end;

for p=1:trainLengthRow
yOrginal(p)= trainingLabels(p);
end;

k=5;

figure
scatter3(x1,x2,trainingLabels,10);
axis([-1*k,k,-1*k,k,-1*k,k])

figure
plot3(x1,x2,y1);
axis([-1*k,k,-1*k,k,-1*k,k])

xx=(-1*k:1:k);
yy=(-1*k:1:k);
[xx1,yy1]=meshgrid(xx,yy);

%drawing the surface
sum = -1 .* (weights(1)+weights(2).*xx1+weights(3).*yy1);
zz= 1 ./(1 + expm(sum));
figure
surf(xx1,yy1,zz);
title('Surface');
xlabel('x');
ylabel('y')
zlabel('z');
```

end

%This function tests the test set with testlabels and the computed outputs
%———–paramaters———–
%testSet: The Test Set for testing
%testLables: Corresponding test labels
%weights: weights computed from the training
%—————Return—————–
% correctlyClassified: correctly classified number of samples
% unClassified: 10 unclassified samples
%v: vector that stores TP ;TN;FP; FN ; P; R; F; Accuracy
%count0: Number of class -1 unclassified upto max val of 5
%count1: Number of class +1 unclassified upto max val of 5
function [correctlyClassified,count0,count1,unClassified,v] = testLinearReg(testSet,testLabels, weights)

```correctlyClassified = 0;
count0 = 0; count1=0;   TP=0;    TN=0;     FP=0;     FN =0; P=0; R=0; F=0;

[testLengthRow,testLengthCol]=size(testSet);
unClassified(1:10 ,1: testLengthCol) = 0;

% checking accuracy by  number of correctly classified

for k=(1: testLengthRow )
x=[1, testSet(k,1:testLengthCol)];
O1=    x' .* weights ;

%computing the value of vector with plane
sum =0;
for p=1:length(O1)
sum = sum +O1(p);
end

% setting the outputs
if(sum > 0)
O=1;
else
O=-1;
end

%    error as output approaching target
if (O == testLabels(k))
% correctly classified examples
correctlyClassified=correctlyClassified+1;

%compute  TP, TN
if(testLabels(k)==1)
TP = TP+1;
else
TN = TN +1;
end

else
% wrongly classified examples
if(testLabels(k)==1)
FN = FN+1;
else
FP = FP +1;
end
%storing 5 misclassified  classes from each class
if(count1<5 && testLabels(k)==1)
count1 = count1 + 1;
unClassified(count1,1: testLengthCol) = testSet(k,1: testLengthCol);
end
if(count0<5 && testLabels(k)==-1 )
count0 = count0 + 1;
unClassified(count0,1: testLengthCol) = testSet(k,1: testLengthCol);
end
end

end

k
P= TP/(TP+FP)
R=  TP/(TP+FN)
v=[TP,    TN,     FP,     FN,     P,     R,      2*P*R / (P+R) , correctlyClassified/testLengthRow]
disp('TP,    TN,     FP,     FN,     TP/(TP+FP),      TP/P,      2*P*R / (P+R) , correctlyClassified/trainLengthRow');

unClassified;
accuracy = correctlyClassified/testLengthRow  ;
accuracy
```

end