Training Data
Step 1	:	Prepare a training data matrix X of N number with features of d.
Step 2	:	Prepare training data target label $t$.
Step 3	:	Determine the number of neurons H in the hidden layer.
Step 4	:	Create a matrix of initial weight values $w$ of size $H\times d$.
Step 5	:	Fill $w$ with a random value.
Step 6	:	Calculate the output hidden layer initialization matrix,            ${\mathrm{H}}_{\mathrm{init}}=\mathrm{X}.{\mathrm{w}}^{\mathrm{T}}$	(8)
Step 7	:	Calculate the hidden layer output matrix using a sigmoid function.
Step 8	:	Count ${\mathrm{H}}^{†}$,          ${\mathrm{H}}^{†}={\left({\mathrm{H}}^{\mathrm{T}}.\mathrm{H}\right)}^{-1}.{\mathrm{H}}^{\mathrm{T}}$	(9)
Step 9	:	Calculate output weight,             $\mathsf{\beta }={\mathrm{H}}^{†}.\mathrm{t}$	(10)
Step 10	:	Calculate output value,             $\mathrm{O}=\mathrm{H}.\mathsf{\beta }$	(11)
Testing Data
Step 1	:	Prepare a testing data matrix X of N number with features of d.
Step 2	:	Calculate the output initialization matrix for the hidden layer using step 6.
Step 3	:	Calculate the output matrix for the hidden layer using step 7.
Step 4	:	Calculate the output value using step 10.