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            double error;

            c_feed(n, &input[j*n->size.input], &output[j*n->size.output], 1);

            if (mse >= 0.0 || i == iterations - 1) {
                error = mean_square_error(n, &output[j*n->size.output]);
                if (error > max_error)
                    max_error = error;
            }
        }

        if (mse >= 0 && max_error <= mse)    /* Below their target! */
            break;
    }

    free(input);
    free(output);

    return max_error;
}

SV* c_infer(int handle, SV *array_ref)
{
    NEURAL_NETWORK *n = c_get_network(handle);
    int    i;
    AV     *perl_array, *result = newAV();

    /* feed the data */
    perl_array = get_array(array_ref);

    for (i = 0; i < n->size.input; i++)
        n->tmp[i] = get_float_element(perl_array, i);

    c_feed(n, n->tmp, NULL, 0); 

    /* read the results */
    for (i = 0; i < n->size.output; i++) {
        av_push(result, newSVnv(n->neuron.output[i]));
    }
    return newRV_noinc((SV*) result);
}

void c_feed(NEURAL_NETWORK *n, double *input, double *output, int learn)
{
    int i;

    for (i=0; i < n->size.input; i++) {
        n->neuron.input[i]  = input[i];
    }

    if (learn)
        for (i=0; i < n->size.output; i++)
            n->neuron.target[i] = output[i];

    c_feed_forward(n);

    if (learn) c_back_propagate(n); 
}

/*
 *  The original author of this code is M. Tim Jones <mtj@cogitollc.com> and
 *  written for the book "AI Application Programming", by Charles River Media.
 *
 *  It's been so heavily modified that it bears little resemblance to the
 *  original, but credit should be given where credit is due.  Therefore ...
 *
 *  Copyright (c) 2003 Charles River Media.  All rights reserved.
 * 
 *  Redistribution and use in source and binary forms, with or without
 *  modification, is hereby granted without fee provided that the following
 *  conditions are met:
 * 
 *    1.  Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer.  2.
 *    Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
 *    documentation and/or other materials provided with the distribution.  3.
 *    Neither the name of Charles River Media nor the names of its
 *    contributors may be used to endorse or promote products derived from
 *    this software without specific prior written permission.
 * 
 * THIS SOFTWARE IS PROVIDED BY CHARLES RIVER MEDIA AND CONTRIBUTORS 'AS IS'
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTIBILITY AND FITNESS FOR A PARTICULAR PURPOSE
 * ARE DISCLAIMED.  IN NO EVENT SHALL CHARLES RIVER MEDIA OR CONTRIBUTORS BE
 * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
 * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
 * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
 * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 * POSSIBILITY OF SUCH DAMAGE.
 */

MODULE = AI::NeuralNet::Simple	PACKAGE = AI::NeuralNet::Simple

PROTOTYPES: DISABLE

int
is_array_ref (ref)
	SV *	ref

AV *
get_array (aref)
	SV *	aref

float
get_float_element (array, index)
	AV *	array
	int	index

SV *
get_element (array, index)
	AV *	array
	int	index

AV *
get_array_from_aoa (aref, index)
	SV *	aref
	int	index

float
c_get_learn_rate (handle)
	int	handle