代码搜索：Quantization
找到约 3,139 项符合「Quantization」的源代码
代码结果 3,139
www.eeworm.com/read/349842/10796835
m dslvq.m
function [features, targets, w] = DSLVQ(train_features, train_targets, Nmu, region, plot_on)
%Reduce the number of data points using distinction sensitive linear vector quantization
%Inputs:
% t
www.eeworm.com/read/462846/7194327
m prob8_1.m
%Vector Quantization of an image using generalized Lloyd algorithm
%This version yields quantized images with various codebook size from 2 to 256, so that it can draw a RD curve.
%you can slightly m
www.eeworm.com/read/451878/7454741
m vqlbg.m
function r = vqlbg(d,k)
% VQLBG Vector quantization using the Linde-Buzo-Gray algorithme
%
% Inputs: d contains training data vectors (one per column)
% k is number of centroids required
www.eeworm.com/read/442927/7641734
m vqkmeans.m
function [center, U, distortion, allCenter] = vqKmeans(data, clusterNum, plotOpt)
% vqKmeans: Vector quantization using K-means clustering (Forgy's batch-mode method)
% Usage: [center, U, distortion
www.eeworm.com/read/146291/12660960
m dslvq.m
function [features, targets, w] = DSLVQ(train_features, train_targets, Nmu, region, plot_on)
%Reduce the number of data points using distinction sensitive linear vector quantization
%Inputs:
% t
www.eeworm.com/read/316604/13520472
m dslvq.m
function [features, targets, w] = DSLVQ(train_features, train_targets, Nmu, region, plot_on)
%Reduce the number of data points using distinction sensitive linear vector quantization
%Inputs:
% t
www.eeworm.com/read/313956/13578216
m lloyds.m
function [partition, codebook, distor, rel_distor] = lloyds(training_set, ini_codebook, tol, plot_flag);
%LLOYDS Optimizes scalar quantization partition and codebook.
% [PARTITION, CODEBOOK] =
www.eeworm.com/read/313956/13578241
htm hcomfrmt.htm
Communications Toolbox Source Coding
Source Coding
The source coding category includes quantization, data
compression, data expander, and differential pulse
code demodulati
www.eeworm.com/read/359185/6352539
m dslvq.m
function [features, targets, w] = DSLVQ(train_features, train_targets, Nmu, region, plot_on)
%Reduce the number of data points using distinction sensitive linear vector quantization
%Inputs:
% t
www.eeworm.com/read/493206/6398549
m dslvq.m
function [features, targets, w] = DSLVQ(train_features, train_targets, Nmu, region, plot_on)
%Reduce the number of data points using distinction sensitive linear vector quantization
%Inputs:
% t