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genome.h
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genome.h
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/*
* Copyright (C) 2014-2018 Philippe Aubertin.
* All rights reserved.
* Redistribution and use in source and binary forms, with or without
* modification, are permitted 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 the author nor the names of other contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE AUTHOR 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.
*/
#ifndef _CRITTERS_GENOME_H_
#define _CRITTERS_GENOME_H_
#include <stdint.h>
/* Number of neurons with a sigmoid-like activation function in the hidden layer.
* Must be a multiple of four. Can be zero. */
#define GENOME_HIDDEN_SIGMOID 0
/* Number of neurons with a gaussian-like activation function in the hidden layer.
* Must be a multiple of four. Can be zero. */
#define GENOME_HIDDEN_GAUSSIAN 0
/* Number of neurons with a rectifier activation function (ReLU) in the hidden layer.
* Must be a multiple of four. Can be zero. */
#define GENOME_HIDDEN_RELU 8
/* All weights are between plus or minus this value. */
#define GENOME_WEIGHT_AMPLITUDE 20.0
#define GENOME_HIDDEN_COUNT (GENOME_HIDDEN_SIGMOID + GENOME_HIDDEN_GAUSSIAN + GENOME_HIDDEN_RELU)
#define GENOME_INPUT_COUNT 8
#define GENOME_OUTPUT_COUNT 2
#define GENOME_HIDDEN_GENES (GENOME_HIDDEN_COUNT / 4)
#define GENOME_SIGMOID_GENES (GENOME_HIDDEN_SIGMOID / 4)
#define GENOME_GAUSSIAN_GENES (GENOME_HIDDEN_GAUSSIAN / 4)
#define GENOME_RELU_GENES (GENOME_HIDDEN_RELU / 4)
#define GENOME_HIDDEN_WEIGHTS (GENOME_INPUT_COUNT + 1) /* + 1 for bias */
#define GENOME_OUTPUT_WEIGHTS (GENOME_HIDDEN_COUNT + 1)
typedef struct genome_t genome_t;
/* A vector of four 32-bit floating-point values */
typedef float genome_f4_t __attribute__ ((vector_size (16)));
typedef union {
genome_f4_t v;
float f[4];
} gene_chunk_t __attribute__ ((aligned (16)));
typedef union gene_hidden_t {
gene_chunk_t chunk[GENOME_HIDDEN_WEIGHTS];
float f[4 * GENOME_HIDDEN_WEIGHTS];
} gene_hidden_t;
typedef union {
gene_chunk_t chunk[GENOME_OUTPUT_WEIGHTS];
float f[4 * GENOME_OUTPUT_WEIGHTS];
} gene_output_t;
struct genome_t {
gene_hidden_t hidden[GENOME_HIDDEN_GENES];
gene_output_t output;
uint32_t colour;
int ref_count;
genome_t *next;
} __attribute__ ((aligned (16)));
genome_t *genome_new(void);
void genome_free(genome_t *genome);
genome_t *genome_clone(genome_t *genome);
void genome_make_random(genome_t *genome);
void genome_make_baby(genome_t *genome, const genome_t *mommy, const genome_t *daddy);
void genome_dump(const genome_t *genome);
#endif