• 홈
• FannWrapperDouble
• neural_net

C# 클래스 FannWrapperDouble.neural_net

파일 보기 프로젝트 열기: joelself/FannCSharp 1 사용 예제들

보호된 프로퍼티들

프로퍼티	타입	설명
swigCMemOwn	bool

공개 메소드들

메소드	설명
Dispose ( ) : void
cascadetrain_on_data ( training_data data, uint max_neurons, uint neurons_between_reports, float desired_error ) : void
cascadetrain_on_file ( string filename, uint max_neurons, uint neurons_between_reports, float desired_error ) : void
clear_scaling_params ( ) : bool
copy_from_struct_fann ( SWIGTYPE_p_fann other ) : void
create_from_file ( string configuration_file ) : bool
create_shortcut ( uint num_layers ) : bool
create_shortcut_array ( uint num_layers, SWIGTYPE_p_unsigned_int layers ) : bool
create_sparse ( float connection_rate, uint num_layers ) : bool
create_sparse_array ( float connection_rate, uint num_layers, SWIGTYPE_p_unsigned_int layers ) : bool
create_standard ( uint num_layers ) : bool
create_standard_array ( uint num_layers, SWIGTYPE_p_unsigned_int layers ) : bool
descale_input_ ( double descale_vector ) : void
descale_output_ ( double descale_vector ) : void
descale_train ( training_data data ) : void
destroy ( ) : void
disable_seed_rand ( ) : void
enable_seed_rand ( ) : void
get_MSE ( ) : float
get_activation_function ( int layer, int neuron ) : ActivationFunction
get_activation_steepness ( int layer, int neuron ) : double
get_bias_array ( SWIGTYPE_p_unsigned_int bias ) : void
get_bit_fail ( ) : uint
get_bit_fail_limit ( ) : double
get_cascade_activation_functions ( ) : SWIGTYPE_p_FANN__activation_function_enum
get_cascade_activation_functions_count ( ) : uint
get_cascade_activation_steepnesses ( ) : SWIGTYPE_p_double
get_cascade_activation_steepnesses_count ( ) : uint
get_cascade_candidate_change_fraction ( ) : float
get_cascade_candidate_limit ( ) : double
get_cascade_candidate_stagnation_epochs ( ) : uint
get_cascade_max_cand_epochs ( ) : uint
get_cascade_max_out_epochs ( ) : uint
get_cascade_num_candidate_groups ( ) : uint
get_cascade_num_candidates ( ) : uint
get_cascade_output_change_fraction ( ) : float
get_cascade_output_stagnation_epochs ( ) : uint
get_cascade_weight_multiplier ( ) : double
get_connection_array ( Connection connections ) : void
get_connection_rate ( ) : float
get_errno ( ) : uint
get_errstr ( ) : string
get_layer_array ( SWIGTYPE_p_unsigned_int layers ) : void
get_learning_momentum ( ) : float
get_learning_rate ( ) : float
get_network_type ( ) : NetworkType
get_num_input ( ) : uint
get_num_layers ( ) : uint
get_num_output ( ) : uint
get_quickprop_decay ( ) : float
get_quickprop_mu ( ) : float
get_rprop_decrease_factor ( ) : float
get_rprop_delta_max ( ) : float
get_rprop_delta_min ( ) : float
get_rprop_delta_zero ( ) : float
get_rprop_increase_factor ( ) : float
get_sarprop_step_error_shift ( ) : float
get_sarprop_step_error_threshold_factor ( ) : float
get_sarprop_temperature ( ) : float
get_sarprop_weight_decay_shift ( ) : float
get_total_connections ( ) : uint
get_total_neurons ( ) : uint
get_train_error_function ( ) : ErrorFunction
get_train_stop_function ( ) : StopFunction
get_training_algorithm ( ) : TrainingAlgorithm
init_weights ( training_data data ) : void
neural_net ( ) : FANNCSharp
neural_net ( NetworkType net_type, uint num_layers ) : FANNCSharp
neural_net ( NetworkType net_type, uint num_layers, SWIGTYPE_p_unsigned_int layers ) : FANNCSharp
neural_net ( SWIGTYPE_p_fann other ) : FANNCSharp
neural_net ( float connection_rate, uint num_layers ) : FANNCSharp
neural_net ( float connection_rate, uint num_layers, SWIGTYPE_p_unsigned_int layers ) : FANNCSharp
neural_net ( neural_net other ) : FANNCSharp
neural_net ( string configuration_file ) : FANNCSharp
print_connections ( ) : void
print_error ( ) : void
print_parameters ( ) : void
randomize_weights ( double min_weight, double max_weight ) : void
reset_MSE ( ) : void
reset_errno ( ) : void
reset_errstr ( ) : void
run ( double input ) : SWIGTYPE_p_double
save ( string configuration_file ) : bool
save_to_fixed ( string configuration_file ) : int
scale_input ( SWIGTYPE_p_double input_vector ) : void
scale_output ( SWIGTYPE_p_double output_vector ) : void
scale_train ( training_data data ) : void
set_activation_function ( ActivationFunction activation_function, int layer, int neuron ) : void
set_activation_function_hidden ( ActivationFunction activation_function ) : void
set_activation_function_layer ( ActivationFunction activation_function, int layer ) : void
set_activation_function_output ( ActivationFunction activation_function ) : void
set_activation_steepness ( double steepness, int layer, int neuron ) : void
set_activation_steepness_hidden ( double steepness ) : void
set_activation_steepness_layer ( double steepness, int layer ) : void
set_activation_steepness_output ( double steepness ) : void
set_bit_fail_limit ( double bit_fail_limit ) : void
set_callback ( global callback, global user_data ) : void
set_cascade_activation_functions ( SWIGTYPE_p_FANN__activation_function_enum cascade_activation_functions, uint cascade_activation_functions_count ) : void
set_cascade_activation_steepnesses ( double cascade_activation_steepnesses, uint cascade_activation_steepnesses_count ) : void
set_cascade_candidate_change_fraction ( float cascade_candidate_change_fraction ) : void
set_cascade_candidate_limit ( double cascade_candidate_limit ) : void
set_cascade_candidate_stagnation_epochs ( uint cascade_candidate_stagnation_epochs ) : void
set_cascade_max_cand_epochs ( uint cascade_max_cand_epochs ) : void
set_cascade_max_out_epochs ( uint cascade_max_out_epochs ) : void
set_cascade_num_candidate_groups ( uint cascade_num_candidate_groups ) : void
set_cascade_output_change_fraction ( float cascade_output_change_fraction ) : void
set_cascade_output_stagnation_epochs ( uint cascade_output_stagnation_epochs ) : void
set_cascade_weight_multiplier ( double cascade_weight_multiplier ) : void
set_error_log ( SWIGTYPE_p_FILE log_file ) : void
set_input_scaling_params ( training_data data, float new_input_min, float new_input_max ) : bool
set_learning_momentum ( float learning_momentum ) : void
set_learning_rate ( float learning_rate ) : void
set_output_scaling_params ( training_data data, float new_output_min, float new_output_max ) : bool
set_quickprop_decay ( float quickprop_decay ) : void
set_quickprop_mu ( float quickprop_mu ) : void
set_rprop_decrease_factor ( float rprop_decrease_factor ) : void
set_rprop_delta_max ( float rprop_delta_max ) : void
set_rprop_delta_min ( float rprop_delta_min ) : void
set_rprop_delta_zero ( float rprop_delta_zero ) : void
set_rprop_increase_factor ( float rprop_increase_factor ) : void
set_sarprop_step_error_shift ( float sarprop_step_error_shift ) : void
set_sarprop_step_error_threshold_factor ( float sarprop_step_error_threshold_factor ) : void
set_sarprop_temperature ( float sarprop_temperature ) : void
set_sarprop_weight_decay_shift ( float sarprop_weight_decay_shift ) : void
set_scaling_params ( training_data data, float new_input_min, float new_input_max, float new_output_min, float new_output_max ) : bool
set_train_error_function ( ErrorFunction train_error_function ) : void
set_train_stop_function ( StopFunction train_stop_function ) : void
set_training_algorithm ( TrainingAlgorithm training_algorithm ) : void
set_weight ( uint from_neuron, uint to_neuron, double weight ) : void
set_weight_array ( Connection connections, uint num_connections ) : void
test ( double input, double desired_output ) : SWIGTYPE_p_double
test_data ( training_data data ) : float
to_fann ( ) : SWIGTYPE_p_fann
train ( double input, double desired_output ) : void
train_epoch ( training_data data ) : float
train_on_data ( training_data data, uint max_epochs, uint epochs_between_reports, float desired_error ) : void
train_on_file ( string filename, uint max_epochs, uint epochs_between_reports, float desired_error ) : void

비공개 메소드들

메소드	설명
getCPtr ( neural_net obj ) : global::System.Runtime.InteropServices.HandleRef
neural_net ( global cPtr, bool cMemoryOwn ) : FANNCSharp

메소드 상세

프로퍼티 상세