perceptronalgo.tex

\documentclass{article}
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\begin{document}
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% #Classical perceptron algorithm
\begin{algorithm}[ht]
	\caption{Classical online perceptron training} 	
	\begin{algorithmic}[1]
    \Require A dataset $\mathcal{D}=\{ (x^{(1)},y^{(1)}), (x^{(2)}, y^{(2)} ) ,\ldots, ( x^{(n)},y^{(n)})\} \in (\mathbb{R}^{m} \times\{0,1\})^{n}$
    \Ensure A vector of weights $w$ such that $y^{(j)}(w^\top x^{(i)}) > 0$ for all $j \in \{1, \dots, n\}$
		\State Initialize ${w}:={0} \in \mathbb{R}^{m+1}$
		\State $\mathrm{continue}=0$
		  \Do
			\For {every data point $\left(x^{(j)}, y^{(j)}\right) \in \mathcal{D}$}
				\State Compute the prediction $\hat{y}^{[i]}:=\sigma\left(\mathbf{x}^{[i]T} {w}_{i}+b\right)$
        		\State Compute the $\mathrm{error}:=\left(\hat{y}^{(i)}-{y}^{(i)}\right)$
        		\State $\mathrm{continue} = \mathrm{continue} + |\mathrm{error}|$
        		\State Update the weights ${w}_{i}^{'}:={w}_{i}+\Delta{w}$
        \EndFor
        
		\doWhile{$\mathrm{continue}!=0$}        
	\end{algorithmic}
\end{algorithm}

\end{document}