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Copy pathFinal_code_for_Kaggle.m
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Final_code_for_Kaggle.m
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dataset=load('C:/Users/tjnai/Downloads/hw2data/hw2data/q3_2_data.mat');
x=dataset.trD;
y=dataset.trLb;
loss = zeros(40);
eta0 = 1;
eta1 = 100;
k = size(unique(y),1); %2
[d,n] = size(x);
w = zeros(d,k);
c=0.001;
m=containers.Map([1,2,3,4,5,6,7,8,9,10],[1,2,3,4,5,6,7,8,9,10]);
for epoch=1:1000
eta = eta0/(eta1+epoch);
randindex = randperm(n);%random indices
totalloss=0;
for i=1:n
%Finding Y hat first
index=randindex(i);
x_i=x(:,index);
y_i=m(y(index));
temp_w=w;
temp_w(:,y_i)=(-1*inf);
[temp_val,y_hat]=max(temp_w'*x_i);
%getting l
l = max((w(:,y_hat)'*x_i-w(:,y_i)'*x_i+1),0);
for j=1:k
if j==y_i
if l>0
der_y_i=(w(:,y_i))./n - c.*(x_i);
else
der_y_i = (w(:,y_i))./n;
end
w(:,j) = w(:,j) - (eta*der_y_i);
elseif j==y_hat
if l>0
der_y_hat=(w(:,y_hat))./n + c.*(x_i);
else
der_y_hat =(w(:,y_hat))./n;
end
w(:,j) = w(:,j) - (eta*der_y_hat);
else
w(:,j) = w(:,j) - (eta*((w(:,j))./n));
end
end
l = max((w(:,y_hat)'*x_i-w(:,y_i)'*x_i+1),0); %Calculating again loss
totalloss = totalloss + (sum(vecnorm(w).^2))/(2*n) + c*l;
end
loss(epoch) = totalloss;
end
[y_hat_train,y_hat_train_ind] = max(w'*x);
y_hat_train_ind=y_hat_train_ind';
y_actual_train =zeros(n,1);
for i=1:n
y_actual_train(i)=m(y(i));
end
accuracy=sum(y_actual_train==y_hat_train_ind)/n;
error=sum(y_actual_train ~= y_hat_train_ind)/n;
%dataset=load('C:/Users/tjnai/Downloads/hw2data/hw2data/q3_2_data.mat');
x_val=dataset.valD;
y_val=dataset.valLb;
loss = zeros(40);
[d_val,n_val]=size(x_val);
eta0 = 1;
eta1 = 100;
k = size(unique(y_val),1); %2
for epoch=1:1000
eta = eta0/(eta1+epoch);
randindex = randperm(n_val);%random indices
totalloss=0;
for i=1:n_val
%Finding Y hat first
index=randindex(i);
x_i=x_val(:,index);
y_i=m(y_val(index));
temp_w=w;
temp_w(:,y_i)=(-1*inf);
[temp_val,y_hat]=max(temp_w'*x_i);
%getting l
l = max((w(:,y_hat)'*x_i-w(:,y_i)'*x_i+1),0);
for j=1:k
if j==y_i
if l>0
der_y_i=(w(:,y_i))./n_val - c.*(x_i);
else
der_y_i = (w(:,y_i))./n_val;
end
w(:,j) = w(:,j) - (eta*der_y_i);
elseif j==y_hat
if l>0
der_y_hat=(w(:,y_hat))./n_val + c.*(x_i);
else
der_y_hat =(w(:,y_hat))./n_val;
end
w(:,j) = w(:,j) - (eta*der_y_hat);
else
w(:,j) = w(:,j) - (eta*(w(:,j))./n_val);
end
end
l = max((w(:,y_hat)'*x_i-w(:,y_i)'*x_i+1),0); %Calculating again loss
totalloss = totalloss + (sum(vecnorm(w).^2))/(2*n_val) + c*l;
end
loss(epoch) = totalloss;
end
plot(loss);
[y_hat_train,y_hat_train_ind] = max(w'*x);
y_hat_train_ind=y_hat_train_ind';
y_actual_train =zeros(n,1);
for i=1:n
y_actual_train(i)=m(y(i));
end
accuracy_new=sum(y_actual_train==y_hat_train_ind)/n;
error_new=sum(y_actual_train ~= y_hat_train_ind)/n;