• 홈
• FannWrapperFixed
• neural_net

C# 클래스 FannWrapperFixed.neural_net

파일 보기 프로젝트 열기: joelself/FannCSharp 1 사용 예제들

보호된 프로퍼티들

프로퍼티	타입	설명
swigCMemOwn	bool

공개 메소드들

메소드	설명
Dispose ( ) : void
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
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 ) : int
get_bias_array ( SWIGTYPE_p_unsigned_int bias ) : void
get_bit_fail ( ) : uint
get_bit_fail_limit ( ) : int
get_connection_array ( Connection connections ) : void
get_connection_rate ( ) : float
get_decimal_point ( ) : uint
get_errno ( ) : uint
get_errstr ( ) : string
get_layer_array ( SWIGTYPE_p_unsigned_int layers ) : void
get_learning_momentum ( ) : float
get_learning_rate ( ) : float
get_multiplier ( ) : uint
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 ( int min_weight, int max_weight ) : void
reset_MSE ( ) : void
reset_errno ( ) : void
reset_errstr ( ) : void
run ( int input ) : SWIGTYPE_p_int
save ( string configuration_file ) : bool
save_to_fixed ( string configuration_file ) : int
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 ( int steepness, int layer, int neuron ) : void
set_activation_steepness_hidden ( int steepness ) : void
set_activation_steepness_layer ( int steepness, int layer ) : void
set_activation_steepness_output ( int steepness ) : void
set_bit_fail_limit ( int bit_fail_limit ) : void
set_error_log ( SWIGTYPE_p_FILE log_file ) : void
set_learning_momentum ( float learning_momentum ) : void
set_learning_rate ( float learning_rate ) : void
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_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, int weight ) : void
set_weight_array ( Connection connections, uint num_connections ) : void
test ( int input, int desired_output ) : SWIGTYPE_p_int
test_data ( training_data data ) : float
to_fann ( ) : SWIGTYPE_p_fann

비공개 메소드들

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

메소드 상세

프로퍼티 상세