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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)
}
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