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BEGIN{
    file_output_path = ARGV[2]
    ARGV[2]=""
}
 
function array_random(r_array,a_length)
{
    for(i=0;i<a_length;i++)
        r_array[i] = rand()
}
 
function array_print(array,astring)
{
    if(astring!="")
        print astring
 
    for(i in array)
        print array[i]
   
    print "\n"
}
 
function array_print_dual(array1,array2,astring)
{
    if(astring!="")
        print astring
 
    for(i in array1)
        print array1[i] "\t\t\t[" array2[i] "]"
   
    print "\n"
}
 
function array_length(array)
{
    for(i in array)
        j++
 
    return j
}
 
function array_dot(r_array,array1,array2)
{
    for(i in array1)
        r_array[i] = array1[i]*array2[i]
}
 
function array_mult(r_array,array1,array2)
{
    for(i in array1)
        r_array[i] = array1[i]*array2[i]
}
 
function array_add_update(array1,array2)
{
    for(i in array1)
        array1[i]+=array2[i]
}
 
 
function array_sigmoid(r_array,array)
{
    for(i in array)
        r_array[i]=(1/(1+exp(-array[i])))
}
 
function array_sigmoid_d(r_array,array)
{
    for(i in array)
        r_array[i]=exp(array[i])/((1+exp(array[i]))^2)
}
 
function array_mse(array1,array2)
{
    elems=0
    for(i in array1)
    {
        elems++;
        mse+=(array1[i]-array2[i])^2
    }
   
    return mse/elems
}
 
function array_2diff(r_array,array1,array2)
{
    for(i in array1)
        r_array[i]=2*(array1[i]-array2[i])
}
 
function array_sample(r_array,a_length)
{
    for(i=0;i<a_length;i++)
        r_array[i] = rand()*200
}
 
function array_normalize(r_array,array)
{
    array_max=array[0]
    array_min=array[0]
 
    for(i in array)
    {
        if(array[i]>array_max)
         array_max=array[i]
        if(array[i]<array_min)
         array_min=array[i]
    }
    for(i in array)
        r_array[i]= (array[i] - array_min)/(array_max-array_min)
}
 
function array_rev_normalize(r_array,n_array,o_array)
{
    array_max=o_array[0]
    array_min=o_array[0]
 
    for(i in o_array)
    {
        if(o_array[i]>array_max)
         array_max=o_array[i]
        if(o_array[i]<array_min)
         array_min=o_array[i]
    }
 
    for(i in n_array)
    {
        r_array[i]=n_array[i]*(array_max-array_min)+array_min
    }
}
 
function array_print_ann(array1,array2,header,file)
{
    print header >> file
 
    for(i in array1)
    {
        print array1[i] "," array2[i] > file
 
    }
}
 
END{
 
    input_size =10
 
    array_sample(sample_data,input_size)
    array_normalize(normalized_array,sample_data)
 
    array_random(weights1,input_size)
    array_random(weights2,input_size)
 
    for(iterations=0;iterations<15000;iterations++)
    {
    #######################
    # Forward Propogation #
    #######################
        array_dot(dot_product_array,normalized_array,weights1)
        array_sigmoid(layer1_output,dot_product_array)      # Layer1 output
 
        array_dot(dot_product_array,layer1_output,weights2)
        array_sigmoid(layer2_output,dot_product_array)      # Layer2 output
 
    #######################
    # Backpropogation     #
    #######################
        # Derivative Weights 2
        array_2diff(layer2_output_2diff,normalized_array,layer2_output)
        array_sigmoid_d(layer2_output_deriv,layer2_output)
        array_mult(layer2_mult,layer2_output_2diff,layer2_output_deriv)
        array_dot(d_weights2,layer1_output,layer2_mult)
        # Derivative Weights 1
        array_sigmoid_d(layer1_output_deriv,layer1_output)
        array_dot(d_temp,layer2_mult,weights2)
        array_mult(d_temp2,d_temp,layer1_output_deriv)
        array_dot(d_weights1,normalized_array,d_temp2)
 
        array_add_update(weights1,d_weights1)
        array_add_update(weights2,d_weights2)
 
        array_dot(dot_product_array,normalized_array,weights1)
        array_sigmoid(layer1_output,dot_product_array)      # Layer1 output
 
        array_dot(dot_product_array,layer1_output,weights2)
        array_sigmoid(layer2_output,dot_product_array)      # Layer2 output
    }
 
    print "\n\n---:::Results [" iterations " Iterations]:::---\n"
 
    array_rev_normalize(fully_trained_output,layer2_output,sample_data)
 
    array_print_dual(sample_data,normalized_array,"*Original Data Input*   [Normalized]")
    array_print_dual(fully_trained_output,layer2_output,"*Fully Trained Output*  [Normalized]")
 
    array_print_ann(sample_data,fully_trained_output,"Input Data,Trained Data",file_output_path)
 
    print "MSE= " array_mse(fully_trained_output,sample_data)
}