C# 클래스 FannWrapperFloat.fannfloat

파일 보기 프로젝트 열기: joelself/FannCSharp

공개 메소드들

메소드 설명
fclose ( SWIGTYPE_p_FILE stream ) : int
fopen ( string filename, string mode ) : SWIGTYPE_p_FILE
test_data_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb ) : float
test_data_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb, floatVectorVector predicted_outputs ) : float
train_epoch_batch_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb ) : float
train_epoch_batch_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb, floatVectorVector predicted_outputs ) : float
train_epoch_incremental_mod ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data ) : float
train_epoch_incremental_mod ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, floatVectorVector predicted_outputs ) : float
train_epoch_irpropm_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb ) : float
train_epoch_irpropm_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb, floatVectorVector predicted_outputs ) : float
train_epoch_quickprop_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb ) : float
train_epoch_quickprop_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb, floatVectorVector predicted_outputs ) : float
train_epoch_sarprop_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb ) : float
train_epoch_sarprop_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb, floatVectorVector predicted_outputs ) : float

메소드 상세

fclose() 공개 정적인 메소드

public static fclose ( SWIGTYPE_p_FILE stream ) : int
stream FANNCSharp.SWIGTYPE_p_FILE
리턴 int

fopen() 공개 정적인 메소드

public static fopen ( string filename, string mode ) : SWIGTYPE_p_FILE
filename string
mode string
리턴 FANNCSharp.SWIGTYPE_p_FILE

test_data_parallel() 공개 정적인 메소드

public static test_data_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
threadnumb uint
리턴 float

test_data_parallel() 공개 정적인 메소드

public static test_data_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb, floatVectorVector predicted_outputs ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
threadnumb uint
predicted_outputs floatVectorVector
리턴 float

train_epoch_batch_parallel() 공개 정적인 메소드

public static train_epoch_batch_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
threadnumb uint
리턴 float

train_epoch_batch_parallel() 공개 정적인 메소드

public static train_epoch_batch_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb, floatVectorVector predicted_outputs ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
threadnumb uint
predicted_outputs floatVectorVector
리턴 float

train_epoch_incremental_mod() 공개 정적인 메소드

public static train_epoch_incremental_mod ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
리턴 float

train_epoch_incremental_mod() 공개 정적인 메소드

public static train_epoch_incremental_mod ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, floatVectorVector predicted_outputs ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
predicted_outputs floatVectorVector
리턴 float

train_epoch_irpropm_parallel() 공개 정적인 메소드

public static train_epoch_irpropm_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
threadnumb uint
리턴 float

train_epoch_irpropm_parallel() 공개 정적인 메소드

public static train_epoch_irpropm_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb, floatVectorVector predicted_outputs ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
threadnumb uint
predicted_outputs floatVectorVector
리턴 float

train_epoch_quickprop_parallel() 공개 정적인 메소드

public static train_epoch_quickprop_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
threadnumb uint
리턴 float

train_epoch_quickprop_parallel() 공개 정적인 메소드

public static train_epoch_quickprop_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb, floatVectorVector predicted_outputs ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
threadnumb uint
predicted_outputs floatVectorVector
리턴 float

train_epoch_sarprop_parallel() 공개 정적인 메소드

public static train_epoch_sarprop_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
threadnumb uint
리턴 float

train_epoch_sarprop_parallel() 공개 정적인 메소드

public static train_epoch_sarprop_parallel ( SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb, floatVectorVector predicted_outputs ) : float
ann FANNCSharp.SWIGTYPE_p_fann
data FANNCSharp.SWIGTYPE_p_fann_train_data
threadnumb uint
predicted_outputs floatVectorVector
리턴 float