代码搜索：pca
找到约 3,578 项符合「pca」的源代码
代码结果 3,578
www.eeworm.com/read/293288/6934009
pca
www.eeworm.com/read/319478/13450807
for pca.for
www.eeworm.com/read/191902/8417242
m pca.m
function [features, targets, UW, m] = PCA(features, targets, dimension, region)
%Reshape the data points using the principal component analysis
%Inputs:
% train_features - Input features
% train
www.eeworm.com/read/189063/8492655
m pca.m
function [scores,loads,ssq,res,q,tsq] = pca(data,plots,scl,lv)
%PCA Principal components analysis
% This function uses the svd to perform pca on a data matrix.
% It is assumed that samples are ro
www.eeworm.com/read/289970/8514478
m pca.m
clear all
clc
format long
load 11.001
xdata(1,:)=X11;
load 11.002
xdata(2,:)=X11;
load 11.003;
xdata(3,:)=X11;
load 11.004
xdata(4,:)=X11;
% aa=[1 2 3 5;2 4 5 1;3 5 6 2];
% b=[2 3 4 5];
% aa(1,:).
www.eeworm.com/read/289743/8529880
m pca.m
function [mappedX, mapping] = pca(X, no_dims)
%PCA Perform the PCA algorithm
%
% [mappedX, mapping] = pca(X, no_dims)
%
% The function runs PCA on a set of datapoints X. The variable
% no_dims sets
www.eeworm.com/read/432786/8573520
m pca.m
%一个修改后的PCA进行人脸识别的Matlab代码
% calc xmean,sigma and its eigen decomposition
function [frobenius_dis,train_time] = u2dpca;
clear all;
cv = zeros(92/1,92/1);
train_samples = zeros(112/1,92/1,200);
www.eeworm.com/read/388439/8609296
m pca.m
function [scores,loads,ssq,res,q,tsq] = pca(data,plots,scl,lv)
%PCA Principal components analysis
% This function uses the svd to perform pca on a data matrix.
% It is assumed that samples are ro
www.eeworm.com/read/288527/8626453
m pca.m
function [scores,loads,ssq,res,q,tsq] = pca(data,plots,scl,lv)
%PCA Principal components analysis
% This function uses the svd to perform pca on a data matrix.
% It is assumed that samples are ro