代码搜索：TestData
找到约 755 项符合「TestData」的源代码
代码结果 755
www.eeworm.com/read/409299/2234858
svn-base splitdata2.m.svn-base
function [trainData, testData] = splitData2(d, trainFraction);
%A function to split a dataset into training and test
% Copyright (C) 2006 Charanpal Dhanjal
% This library is free software; y
www.eeworm.com/read/409299/2234962
svn-base dualgeneralfeaturesapprox.m.svn-base
function [testInfo, projectionInfo] = dualGeneralFeaturesApprox(trainData, testData, subspaceInfo, params)
%Compute kernel matrix approximation for dual general features using
%\tilde{K} = K - K_{j
www.eeworm.com/read/409299/2235047
svn-base dualpcaproject.m.svn-base
function [testInfo, projectionInfo] = dualPCAProject(trainData, testData, subspaceInfo, params)
%Extract new features on a test set
if (nargin ~= 4)
fprintf('%s\n', help(sprintf('%s', mfilena
www.eeworm.com/read/284357/8938248
m knn1.m
function [eachClass, ensembleClass, nearestSampleIndex, knnmat] = ...
knn(sampledata, testdata, k)
% KNN K-nearest neighbor rule for classification
% Usage:
% [EACH_CLASS, ENSEMBLE_CLASS, NEAREST
www.eeworm.com/read/183144/9177483
m test.m
function [rle,rab] = Test( num,testData,eigenVector,meanData,new_data,threshold)
%TEST Summary of this function goes here
% Detailed explanation goes here
% rle is the index of the picture that the
www.eeworm.com/read/360710/10080941
m knn.m
function [eachClass, ensembleClass, nearestSampleIndex, knnmat] = ...
knn(sampledata, testdata, k)
% KNN K-nearest neighbor rule for classification
% Usage:
% [EACH_CLASS, ENSEMBLE_CLASS, NEAREST
www.eeworm.com/read/154064/11990762
txt readmefirst.txt
Unzip this file into a writeable directory.
You should end up with a "Geocode" project, as well as Bitmaps and TestData directories.
This sample geocodes single-match zip codes, entered by the us
www.eeworm.com/read/357319/3024033
extra entries.extra
/.cvsignore////*///
/BaseTest.mdb////*///
/PhoneList.csv////*///
/TestData.csv////*///
/bin.txt////*///
D/Data///////
/grass.bmp////*///
/human.bmp////*///
D/ApplicationHookPlugin///////
D/Da
www.eeworm.com/read/362199/10013176
asv knn.asv
function [eachClass, nearestSampleIndex, knnmat] = ...
knn(sampledata, testdata, k)
% KNN K-nearest neighbor rule for classification
% Usage:
% [EACH_CLASS, NEAREST_SAMPLE_INDEX] = KNN(SAMPLE, INPUT,
www.eeworm.com/read/362199/10013178
m knn.m
function [eachClass, nearestSampleIndex, knnmat] = ...
knn(sampledata, testdata, k)
% KNN K-nearest neighbor rule for classification
% Usage:
% [EACH_CLASS, NEAREST_SAMPLE_INDEX] = KNN(SAMPLE, INPUT,