代码搜索：para
找到约 10,000 项符合「para」的源代码
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www.eeworm.com/read/368162/9708244
asm tss.asm
DATA SEGMENT PARA PUBLIC 'DATA'
X DW 40
Y DW 40
MUL_RAX DB 3
GEWEI DB 0 ;个位
SHIWEI DB 0
www.eeworm.com/read/367152/9779895
m wavesmoo.m
% wavesmoo - Wavelet Smoothing
%
% function [ny] = wavesmoo(x,y,xwant,para,wavelet,wpara)
%
% INPUTS
% ======
% x : independent scalars (row vector)
% y : dependent vector (col vectors)
% (in
www.eeworm.com/read/415313/11076730
m ldakernel_classify.m
% LDAKernel_classify: implementation for kernel linear discriminant analysis
%
% Parameters:
% para: parameters
% 1. RegFactor: regularization factor, default: 0.1
% 2. Kernel: kernel type,
www.eeworm.com/read/415313/11076737
m mcbagging.m
% MCBagging: implementation for Bagging meta-classifier
%
% Parameters:
% classifier: base classifier
% para: parameters
% 1. Iter: number of iteration, default: 10
% 2. SampleRatio: boots
www.eeworm.com/read/415313/11076747
m mcupsampling.m
% MCUpSampling: implementation for up sampling
%
% Parameters:
% classifier: base classifier
% para: parameters
% 1. PosRatio: ratio of positive examples after sampling, default: 10
% X_trai
www.eeworm.com/read/415313/11076813
m decisionstump.m
% DecisionStump: implementation for decision stump
%
% Parameters:
% para: parameters
% 1. CostFactor: weighting between postive data and negative data, default: 1
% 2. Threshold: decision t
www.eeworm.com/read/132622/14082736
asm open.asm
public begi
desg segment para 'data'
filename label byte
maxname db 16
namelen db ?
pathnam db 16 dup (' ')
endcde db 0
handle dw ?
www.eeworm.com/read/109732/15551252
asm open.asm
public begi
desg segment para 'data'
filename label byte
maxname db 16
namelen db ?
pathnam db 16 dup (' ')
endcde db 0
handle dw ?
www.eeworm.com/read/105723/15660796
cpp sbasic.cpp
/*----------------------Simple BASIC!-----------------*/
#include"bastype.cpp"
#include"type.cpp"
#include"edit.cpp"
#include"para.cpp"
#define titlecrc 2857
/*-------------DATA------------*
www.eeworm.com/read/191902/8417331
m cart.m
function D = CART(train_features, train_targets, params, region)
% Classify using classification and regression trees
% Inputs:
% features - Train features
% targets - Train targets
% para