代码搜索:Nearest

找到约 1,596 项符合「Nearest」的源代码

代码结果 1,596
www.eeworm.com/read/257078/11951319

m e0458.m

x = 0:1; y1 = sin(x); y2=cos(x);xi = 0:.25:1; yi1 = interp1(x,y1,xi),yi2= interp1(x,y2,xi) %线性插值方法 yi1 = interp1(x,y1,xi,'nearest'),yi2= interp1(x,y2,xi,'nearest') %最邻近插值
www.eeworm.com/read/232704/14185011

m interp1_example.m

%interp1_example.m %用不同插值方法对一维数据进行插值,并比较其不同 x = 0:1.2:10; y = sin(x); xi = 0:0.1:10; yi_nearest = interp1(x,y,xi,'nearset'); %最邻近插值 yi_linear = interp1(x,y,xi); %默认插值方法是线性插值 yi_sp
www.eeworm.com/read/215382/15062850

m interp1_example.m

%interp1_example.m %用不同插值方法对一维数据进行插值,并比较其不同 x = 0:1.2:10; y = sin(x); xi = 0:0.1:10; yi_nearest = interp1(x,y,xi,'nearset'); %最邻近插值 yi_linear = interp1(x,y,xi); %默认插值方法是线性插值 yi_sp
www.eeworm.com/read/269453/11097538

m interp1_example.m

%interp1_example.m %用不同插值方法对一维数据进行插值,并比较其不同 x = 0:1.2:10; y = sin(x); xi = 0:0.1:10; yi_nearest = interp1(x,y,xi,'nearset'); %最邻近插值 yi_linear = interp1(x,y,xi); %默认插值方法是线性插值 yi_sp
www.eeworm.com/read/200130/15440737

m interp1_example.m

%interp1_example.m %用不同插值方法对一维数据进行插值,并比较其不同 x = 0:1.2:10; y = sin(x); xi = 0:0.1:10; yi_nearest = interp1(x,y,xi,'nearset'); %最邻近插值 yi_linear = interp1(x,y,xi); %默认插值方法是线性插值 yi_sp
www.eeworm.com/read/467873/7003118

m knn.m

function [label_test] = knn(k, data_train, label_train, data_test) % knn - k nearest neighbours classifier error(nargchk(4,4,nargin)); dist = l2_distance(data_train, data_test); [sorted_dist, neares
www.eeworm.com/read/452512/7438846

c test.c

/* * Test program for glpng * by Ben Wyatt ben@wyatt100.freeserve.co.uk * Featuring a shameless plug for my stunt course program * Available from the same site as glpng * http://www.wyatt100
www.eeworm.com/read/136820/5852058

c test.c

/* * Test program for glpng * by Ben Wyatt ben@wyatt100.freeserve.co.uk * Featuring a shameless plug for my stunt course program * Available from the same site as glpng * http://www.wyatt100.free
www.eeworm.com/read/130196/5963045

m clustknb_new_w.m

function [C,R]=clustknb_new_w(XData, k, Weights, disp, mus) %[C,R,real_k,it]=clustknb_new(XData, k, Weights, mus=random_data_subset) % % 'online version' of the k-means-nearest-neighbourhood algorith