fann_io.c

python 神经网络 数据挖掘 python实现的神经网络算法

		{
#else
		if(strncmp(read_version, "FANN_FLO_1.1\n", strlen("FANN_FLO_1.1\n")) == 0)
		{
#endif
			free(read_version);
			return fann_create_from_fd_1_1(conf, configuration_file);
		}

#ifndef FIXEDFANN
		/* Maintain compatibility with 2.0 version that doesnt have scale parameters. */
		if(strncmp(read_version, "FANN_FLO_2.0\n", strlen("FANN_FLO_2.0\n")) != 0 &&
		   strncmp(read_version, "FANN_FLO_2.1\n", strlen("FANN_FLO_2.1\n")) != 0)
#else
		if(strncmp(read_version, "FANN_FIX_2.0\n", strlen("FANN_FIX_2.0\n")) != 0 &&
		   strncmp(read_version, "FANN_FIX_2.1\n", strlen("FANN_FIX_2.1\n")) != 0)
#endif
		{
			free(read_version);
			fann_error(NULL, FANN_E_WRONG_CONFIG_VERSION, configuration_file);

			return NULL;
		}
	}

	free(read_version);

#ifdef FIXEDFANN
    fann_scanf("%u", "decimal_point", &decimal_point);
	multiplier = 1 << decimal_point;
#endif

    fann_scanf("%u", "num_layers", &num_layers);

	ann = fann_allocate_structure(num_layers);
	if(ann == NULL)
	{
		return NULL;
	}

    fann_scanf("%f", "learning_rate", &ann->learning_rate);
    fann_scanf("%f", "connection_rate", &ann->connection_rate);
    fann_scanf("%u", "network_type", (unsigned int *)&ann->network_type);
	fann_scanf("%f", "learning_momentum", &ann->learning_momentum);
	fann_scanf("%u", "training_algorithm", (unsigned int *)&ann->training_algorithm);
	fann_scanf("%u", "train_error_function", (unsigned int *)&ann->train_error_function);
	fann_scanf("%u", "train_stop_function", (unsigned int *)&ann->train_stop_function);
	fann_scanf("%f", "cascade_output_change_fraction", &ann->cascade_output_change_fraction);
	fann_scanf("%f", "quickprop_decay", &ann->quickprop_decay);
	fann_scanf("%f", "quickprop_mu", &ann->quickprop_mu);
	fann_scanf("%f", "rprop_increase_factor", &ann->rprop_increase_factor);
	fann_scanf("%f", "rprop_decrease_factor", &ann->rprop_decrease_factor);
	fann_scanf("%f", "rprop_delta_min", &ann->rprop_delta_min);
	fann_scanf("%f", "rprop_delta_max", &ann->rprop_delta_max);
	fann_scanf("%f", "rprop_delta_zero", &ann->rprop_delta_zero);
	fann_scanf("%u", "cascade_output_stagnation_epochs", &ann->cascade_output_stagnation_epochs);
	fann_scanf("%f", "cascade_candidate_change_fraction", &ann->cascade_candidate_change_fraction);
	fann_scanf("%u", "cascade_candidate_stagnation_epochs", &ann->cascade_candidate_stagnation_epochs);
	fann_scanf("%u", "cascade_max_out_epochs", &ann->cascade_max_out_epochs);
	fann_scanf("%u", "cascade_max_cand_epochs", &ann->cascade_max_cand_epochs);	
	fann_scanf("%u", "cascade_num_candidate_groups", &ann->cascade_num_candidate_groups);

	fann_scanf(FANNSCANF, "bit_fail_limit", &ann->bit_fail_limit);
	fann_scanf(FANNSCANF, "cascade_candidate_limit", &ann->cascade_candidate_limit);
	fann_scanf(FANNSCANF, "cascade_weight_multiplier", &ann->cascade_weight_multiplier);


	fann_scanf("%u", "cascade_activation_functions_count", &ann->cascade_activation_functions_count);

	/* reallocate mem */
	ann->cascade_activation_functions = 
		(enum fann_activationfunc_enum *)realloc(ann->cascade_activation_functions, 
		ann->cascade_activation_functions_count * sizeof(enum fann_activationfunc_enum));
	if(ann->cascade_activation_functions == NULL)
	{
		fann_error((struct fann_error*)ann, FANN_E_CANT_ALLOCATE_MEM);
		fann_destroy(ann);
		return NULL;
	}

	fscanf(conf, "cascade_activation_functions=");
	for(i = 0; i < ann->cascade_activation_functions_count; i++)
		fscanf(conf, "%u ", (unsigned int *)&ann->cascade_activation_functions[i]);
	
	fann_scanf("%u", "cascade_activation_steepnesses_count", &ann->cascade_activation_steepnesses_count);

	/* reallocate mem */
	ann->cascade_activation_steepnesses = 
		(fann_type *)realloc(ann->cascade_activation_steepnesses, 
		ann->cascade_activation_steepnesses_count * sizeof(fann_type));
	if(ann->cascade_activation_steepnesses == NULL)
	{
		fann_error((struct fann_error*)ann, FANN_E_CANT_ALLOCATE_MEM);
		fann_destroy(ann);
		return NULL;
	}

	fscanf(conf, "cascade_activation_steepnesses=");
	for(i = 0; i < ann->cascade_activation_steepnesses_count; i++)
		fscanf(conf, FANNSCANF" ", &ann->cascade_activation_steepnesses[i]);

#ifdef FIXEDFANN
	ann->decimal_point = decimal_point;
	ann->multiplier = multiplier;
#endif

#ifdef FIXEDFANN
	fann_update_stepwise(ann);
#endif

#ifdef DEBUG
	printf("creating network with %d layers\n", num_layers);
	printf("input\n");
#endif

	fscanf(conf, "layer_sizes=");
	/* determine how many neurons there should be in each layer */
	for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++)
	{
		if(fscanf(conf, "%u ", &layer_size) != 1)
		{
			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_CONFIG, "layer_sizes", configuration_file);
			fann_destroy(ann);
			return NULL;
		}
		/* we do not allocate room here, but we make sure that
		 * last_neuron - first_neuron is the number of neurons */
		layer_it->first_neuron = NULL;
		layer_it->last_neuron = layer_it->first_neuron + layer_size;
		ann->total_neurons += layer_size;
#ifdef DEBUG
		if(ann->network_type == FANN_NETTYPE_SHORTCUT && layer_it != ann->first_layer)
		{
			printf("  layer       : %d neurons, 0 bias\n", layer_size);
		}
		else
		{
			printf("  layer       : %d neurons, 1 bias\n", layer_size - 1);
		}
#endif
	}

	ann->num_input = ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1;
	ann->num_output = ((ann->last_layer - 1)->last_neuron - (ann->last_layer - 1)->first_neuron);
	if(ann->network_type == FANN_NETTYPE_LAYER)
	{
		/* one too many (bias) in the output layer */
		ann->num_output--;
	}

#ifndef FIXEDFANN
#define SCALE_LOAD( what, where )											\
	fscanf( conf, #what "_" #where "=" );									\
	for(i = 0; i < ann->num_##where##put; i++)								\
	{																		\
		if(fscanf( conf, "%f ", (float *)&ann->what##_##where[ i ] ) != 1)  \
		{																	\
			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_CONFIG, #what "_" #where, configuration_file); \
			fann_destroy(ann); 												\
			return NULL;													\
		}																	\
	}
	
	if(fscanf(conf, "scale_included=%u\n", &scale_included) == 1 && scale_included == 1)
	{
		fann_allocate_scale(ann);
		SCALE_LOAD( scale_mean,			in )
		SCALE_LOAD( scale_deviation,	in )
		SCALE_LOAD( scale_new_min,		in )
		SCALE_LOAD( scale_factor,		in )
	
		SCALE_LOAD( scale_mean,			out )
		SCALE_LOAD( scale_deviation,	out )
		SCALE_LOAD( scale_new_min,		out )
		SCALE_LOAD( scale_factor,		out )
	}
#undef SCALE_LOAD
#endif
	
	/* allocate room for the actual neurons */
	fann_allocate_neurons(ann);
	if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
	{
		fann_destroy(ann);
		return NULL;
	}

	last_neuron = (ann->last_layer - 1)->last_neuron;
	fscanf(conf, "neurons (num_inputs, activation_function, activation_steepness)=");
	for(neuron_it = ann->first_layer->first_neuron; neuron_it != last_neuron; neuron_it++)
	{
		if(fscanf
		   (conf, "(%u, %u, " FANNSCANF ") ", &num_connections, (unsigned int *)&neuron_it->activation_function,
			&neuron_it->activation_steepness) != 3)
		{
			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_NEURON, configuration_file);
			fann_destroy(ann);
			return NULL;
		}
		neuron_it->first_con = ann->total_connections;
		ann->total_connections += num_connections;
		neuron_it->last_con = ann->total_connections;
	}

	fann_allocate_connections(ann);
	if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
	{
		fann_destroy(ann);
		return NULL;
	}

	connected_neurons = ann->connections;
	weights = ann->weights;
	first_neuron = ann->first_layer->first_neuron;

	fscanf(conf, "connections (connected_to_neuron, weight)=");
	for(i = 0; i < ann->total_connections; i++)
	{
		if(fscanf(conf, "(%u, " FANNSCANF ") ", &input_neuron, &weights[i]) != 2)
		{
			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_CONNECTIONS, configuration_file);
			fann_destroy(ann);
			return NULL;
		}
		connected_neurons[i] = first_neuron + input_neuron;
	}

#ifdef DEBUG
	printf("output\n");
#endif
	return ann;
}


/* INTERNAL FUNCTION
   Create a network from a configuration file descriptor. (backward compatible read of version 1.1 files)
 */
struct fann *fann_create_from_fd_1_1(FILE * conf, const char *configuration_file)
{
	unsigned int num_layers, layer_size, input_neuron, i, network_type, num_connections;
	unsigned int activation_function_hidden, activation_function_output;
#ifdef FIXEDFANN
	unsigned int decimal_point, multiplier;
#endif
	fann_type activation_steepness_hidden, activation_steepness_output;
	float learning_rate, connection_rate;
	struct fann_neuron *first_neuron, *neuron_it, *last_neuron, **connected_neurons;
	fann_type *weights;
	struct fann_layer *layer_it;
	struct fann *ann;

#ifdef FIXEDFANN
	if(fscanf(conf, "%u\n", &decimal_point) != 1)
	{
		fann_error(NULL, FANN_E_CANT_READ_CONFIG, "decimal_point", configuration_file);
		return NULL;
	}
	multiplier = 1 << decimal_point;
#endif

	if(fscanf(conf, "%u %f %f %u %u %u " FANNSCANF " " FANNSCANF "\n", &num_layers, &learning_rate,
		&connection_rate, &network_type, &activation_function_hidden,
		&activation_function_output, &activation_steepness_hidden,
		&activation_steepness_output) != 8)
	{
		fann_error(NULL, FANN_E_CANT_READ_CONFIG, "parameters", configuration_file);
		return NULL;
	}

	ann = fann_allocate_structure(num_layers);
	if(ann == NULL)
	{
		return NULL;
	}
	ann->connection_rate = connection_rate;
	ann->network_type = (enum fann_nettype_enum)network_type;
	ann->learning_rate = learning_rate;

#ifdef FIXEDFANN
	ann->decimal_point = decimal_point;
	ann->multiplier = multiplier;
#endif

#ifdef FIXEDFANN
	fann_update_stepwise(ann);
#endif

#ifdef DEBUG
	printf("creating network with learning rate %f\n", learning_rate);
	printf("input\n");
#endif

	/* determine how many neurons there should be in each layer */
	for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++)
	{
		if(fscanf(conf, "%u ", &layer_size) != 1)
		{
			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_NEURON, configuration_file);
			fann_destroy(ann);
			return NULL;
		}
		/* we do not allocate room here, but we make sure that
		 * last_neuron - first_neuron is the number of neurons */
		layer_it->first_neuron = NULL;
		layer_it->last_neuron = layer_it->first_neuron + layer_size;
		ann->total_neurons += layer_size;
#ifdef DEBUG
		if(ann->network_type == FANN_NETTYPE_SHORTCUT && layer_it != ann->first_layer)
		{
			printf("  layer       : %d neurons, 0 bias\n", layer_size);
		}
		else
		{
			printf("  layer       : %d neurons, 1 bias\n", layer_size - 1);
		}
#endif
	}

	ann->num_input = ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1;
	ann->num_output = ((ann->last_layer - 1)->last_neuron - (ann->last_layer - 1)->first_neuron);
	if(ann->network_type == FANN_NETTYPE_LAYER)
	{
		/* one too many (bias) in the output layer */
		ann->num_output--;
	}

	/* allocate room for the actual neurons */
	fann_allocate_neurons(ann);
	if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
	{
		fann_destroy(ann);
		return NULL;
	}

	last_neuron = (ann->last_layer - 1)->last_neuron;
	for(neuron_it = ann->first_layer->first_neuron; neuron_it != last_neuron; neuron_it++)
	{
		if(fscanf(conf, "%u ", &num_connections) != 1)
		{
			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_NEURON, configuration_file);
			fann_destroy(ann);
			return NULL;
		}
		neuron_it->first_con = ann->total_connections;
		ann->total_connections += num_connections;
		neuron_it->last_con = ann->total_connections;
	}

	fann_allocate_connections(ann);
	if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
	{
		fann_destroy(ann);
		return NULL;
	}

	connected_neurons = ann->connections;
	weights = ann->weights;
	first_neuron = ann->first_layer->first_neuron;

	for(i = 0; i < ann->total_connections; i++)
	{
		if(fscanf(conf, "(%u " FANNSCANF ") ", &input_neuron, &weights[i]) != 2)
		{
			fann_error((struct fann_error *) ann, FANN_E_CANT_READ_CONNECTIONS, configuration_file);
			fann_destroy(ann);
			return NULL;
		}
		connected_neurons[i] = first_neuron + input_neuron;
	}

	fann_set_activation_steepness_hidden(ann, activation_steepness_hidden);
	fann_set_activation_steepness_output(ann, activation_steepness_output);
	fann_set_activation_function_hidden(ann, (enum fann_activationfunc_enum)activation_function_hidden);
	fann_set_activation_function_output(ann, (enum fann_activationfunc_enum)activation_function_output);

#ifdef DEBUG
	printf("output\n");
#endif
	return ann;
}