use AI::NNFlex::Backprop;
use AI::NNFlex::Dataset;

my $network = AI::NNFlex::Backprop->new(
                learningrate=>.00000001,
                fahlmanconstant=>0,
                momentum=>0.4,
                bias=>1);


$network->add_layer(    nodes=>2,
            activationfunction=>"linear");


$network->add_layer(    nodes=>2,
            activationfunction=>"linear");

$network->add_layer(    nodes=>1,
            activationfunction=>"linear");


$network->init();

# Taken from Mesh ex_add.pl
my $dataset = AI::NNFlex::Dataset->new([
[ 1,   1   ], [ 2    ],
[ 500, 500 ], [ 1000 ],
[ 1,   2   ], [ 3    ],
[ 2,   2   ], [ 4    ],
[ 20,  20  ], [ 40   ],
[ 50,  50  ], [ 100  ],
[ 60,  40  ], [ 100  ],
[ 100, 100 ], [ 200  ],
[ 150, 150 ], [ 300  ],
[ 10,  10  ], [ 20   ],
[ 15,  15  ], [ 30   ],
[ 12,  8   ], [ 20   ],
]);

my $err = 10;
# Stop after 4096 epochs -- don't want to wait more than that
for ( my $i = 0; ($err > 0.001) && ($i < 40096); $i++ ) {
    $err = $dataset->learn($network);
    print "Epoch = $i error = $err\n";
}

foreach (@{$dataset->run($network)})
{
    foreach (@$_){print $_}
    print "\n";
}



# foreach my $a ( 1..10 ) {
#     foreach my $b ( 1..10 ) {
#     my($ans) = $a+$b;
#     my($nnans) = @{$network->run([$a,$b])};
#     print "[$a] [$b] ans=$ans but nnans=$nnans\n" unless $ans == $nnans;
#     }
# }