function roughNN(w, x)
#
# v = roughNN( w , x )
#
# returns the falue of the function v = f( x ) as currently estimated by
# a small NN with 1 input, 1 output, 3 hidden layers of 5 nodes each, and
# tanh activation function.
#
# Input:
#
# - w is the [ 76 x 1 ] real vector containing the weights of the NN,
#   i.e., w is made as follows:
#   [ 1 .. 5 ] are the [ 5 x 1 ] weigths of the first layer
#   [ 6 .. 10 ] are the [ 5 x 1 ] biases of the first layer
#   [ 11 .. 35 ] are the [ 5 x 5 ] weigths of the second layer
#   [ 36 .. 40 ] are the [ 5 x 1 ] biases of the second layer
#   [ 41 .. 65 ] are the [ 5 x 5 ] weigths of the third layer
#   [ 66 .. 70 ] are the [ 5 x 1 ] biases of the third layer
#   [ 71 .. 75 ] are the [ 5 x 1 ] weigths of the fourth (output) layer
#   [ 76 ] is the [ 1 x 1 ] bias of the fourth (output) layer
#
# - x is the real scalar containing the input of f()
#
# Output:
#
# - v (real, scalar): v = f( x ) as estimated by the NN with weights w
#
#{
# =======================================
# Author: Antonio Frangioni
# Date: 28-08-22
# Version 1.00
# Copyright Antonio Frangioni
# =======================================
#}

    g = tanh( (ones(5, 1) * x ) .* w( 1 : 5 ) + w( 6:10 ) )
    g = tanh( reshape( w( 11:35 ) , (5, 5) ) * g + w( 36:40 ) )
    g = tanh( reshape( w( 41:65 ) , (5, 5) ) * g + w( 66:70 ) )
    v = g' * w( 71:75 ) + w( 76 )

    return v
end