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热门代码： main.c GPIO I2C SPI UART timer interrupt ADC PWM LED
www.eeworm.com/read/349842/10796711

m interactive_learning.m

function D = Interactive_Learning(train_features, train_targets, params, region); % Classify using nearest neighbors and interactive learning % Inputs: % features- Train features % targets - Tr
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www.eeworm.com/read/444528/6965694

cpp brute.cpp

//---------------------------------------------------------------------- // File: brute.cpp // Programmer: Sunil Arya and David Mount // Description: Brute-force nearest neighbors // Last modified
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www.eeworm.com/read/459044/7283861

m ch5example14prg1.m

% ch5example14prg1.m clear; I=imread('colorbar.tif');% 或用 autumn.tif，sydney.JPG等图像文件 figure(1);imshow(I); I=double(I); [m,n,p]=size(I); I=-(0.75-0.125)./(255).*I+0.75; % 换算为0.125到0.75电平 R=I(:,
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www.eeworm.com/read/442927/7641730

m editdm.m

% Demo of editing technique for data reduction % Roger Jang, March 1997 use_pause = 1; % Collect 2500 data points [xx, yy, zz] = peaks(50); x = xx(:); y = yy(:); z = zz(:); axis_limit = [min
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www.eeworm.com/read/399996/7816686

m interactive_learning.m

function test_targets = Interactive_Learning(train_patterns, train_targets, test_patterns, params) % Classify using nearest neighbors and interactive learning % Inputs: % train_patterns - Train
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www.eeworm.com/read/299459/7850447

m knnrule.m

function model=knnrule(data,K) % KNNRULE Creates K-nearest neighbours classifier. % % Synopsis: % model=knnrule(data) % model=knnrule(data,K) % % Description: % It creates model of the K-nearest ne
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www.eeworm.com/read/397111/8067346

m knndd.m

%KNNDD K-Nearest neighbour data description method. % % W = KNNDD(A,FRACREJ,K,METHOD) % % Calculates the K-Nearest neighbour data description on dataset A. % Three methods are defined to compu
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www.eeworm.com/read/397099/8068792

m interactive_learning.m

function test_targets = Interactive_Learning(train_patterns, train_targets, test_patterns, params) % Classify using nearest neighbors and interactive learning % Inputs: % train_patterns - Train
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www.eeworm.com/read/245941/12770813

m interactive_learning.m

function test_targets = Interactive_Learning(train_patterns, train_targets, test_patterns, params) % Classify using nearest neighbors and interactive learning % Inputs: % train_patterns - Train
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www.eeworm.com/read/244800/12842945

c find_nn.c

#include "mex.h" #include void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] ) { /* Declare variables. */ int *
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