代码搜索:preprocessing

找到约 856 项符合「preprocessing」的源代码

代码结果 856
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c separate-1.c

/* Copyright 2003 Free Software Foundation. */ /* { dg-do compile } */ /* { dg-options "-save-temps" } */ /* Make sure we report errors in the right line, even if separate preprocessing is used.
www.eeworm.com/read/162614/5530999

c undef2.c

/* C99 6.10.8 para 4: None of [the predefined macro names] shall be the subject of a #define or an #undef preprocessing directive. We pass -fno-show-column as otherwise dejagnu gets confused.
www.eeworm.com/read/452695/7436416

c program13_01.c

/* Program 13.1 Debugging using preprocessing directives */ #include #include #include /* Macro to generate pseudo-random number from 0 to NumValues */ #define ran
www.eeworm.com/read/436207/1850640

m opt_sphere.m

function A = opt_sphere(X, S, D, maxiter) % Preprocessing: First sphere the data sphereMult = cov(X)^-0.5; sphereX = X*sphereMult; s = size(sphereX); N = s(1); % number of examples d = s
www.eeworm.com/read/357319/3020495

bat preprocess.bat

@echo off SET OUTDIR=..\..\..\run cd src SET FILE=.\build if NOT "-%1" == "-" SET FILE=%1 echo Preprocessing template: %FILE%.pas ..\..\JvExVCL\src\dpp.exe .\%FILE%.pas -I..\..\..\common >
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bat preprocess.bat

@echo off SET OUTDIR=..\..\..\run cd src SET FILE=.\build if NOT "-%1" == "-" SET FILE=%1 echo Preprocessing template: %FILE%.pas dpp.exe .\%FILE%.pas -I..\..\..\common >NUL if "%FILE
www.eeworm.com/read/471682/6888004

c program13_01.c

/* Program 13.1 Debugging using preprocessing directives */ #include #include #include /* Macro to generate pseudo-random number from 0 to NumValues */ #define ran
www.eeworm.com/read/192096/8406805

htm ch16_07.htm

Recipe 16.6. Preprocessing Input (Perl Cookbook)
www.eeworm.com/read/143706/12850023

m test_validate.m

function run = test_validate(D, classifier) global preprocess; clear run; % The statistics of dataset [X, Y, num_data, num_feature] = Preprocessing(D); num_class = length(preprocess.ClassSet)
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m train_validate.m

function run = train_validate(D, classifier) global preprocess; clear run; % The statistics of dataset [X, Y, num_data, num_feature] = Preprocessing(D); num_class = length(preprocess.ClassSet