Exercise:PCA in 2D

习题的链接:Exercise:PCA in 2D

pca_2d.m

close all

%%================================================================
%% Step : Load data
% We have provided the code to load data from pcaData.txt into x.
% x is a * matrix, where the kth column x(:,k) corresponds to
% the kth data point.Here we provide the code to load natural image data into x.
% You do not need to change the code below. x = load('pcaData.txt','-ascii');
figure();
scatter(x(, :), x(, :));
title('Raw data'); %%================================================================
%% Step 1a: Implement PCA to obtain U
% Implement PCA to obtain the rotation matrix U, which is the eigenbasis
% sigma. % -------------------- YOUR CODE HERE --------------------
%u = zeros(size(x, )); %You need to compute this
sigma = (x*x') ./ size(x,2); %covariance matrix
[u,s,v] = svd(sigma); % --------------------------------------------------------
hold on
plot([ u(,)], [ u(,)]);
plot([ u(,)], [ u(,)]);
scatter(x(, :), x(, :));
hold off %%================================================================
%% Step 1b: Compute xRot, the projection on to the eigenbasis
% Now, compute xRot by projecting the data on to the basis defined
% by U. Visualize the points by performing a scatter plot. % -------------------- YOUR CODE HERE --------------------
%xRot = zeros(size(x)); % You need to compute this
xRot = u'*x; % -------------------------------------------------------- % Visualise the covariance matrix. You should see a line across the
% diagonal against a blue background.
figure();
scatter(xRot(, :), xRot(, :));
title('xRot'); %%================================================================
%% Step : Reduce the number of dimensions from to .
% Compute xRot again (this time projecting to dimension).
% Then, compute xHat by projecting the xRot back onto the original axes
% to see the effect of dimension reduction % -------------------- YOUR CODE HERE --------------------
k = ; % Use k = and project the data onto the first eigenbasis
%xHat = zeros(size(x)); % You need to compute this
%Recovering an Approximation of the Data
xRot(k+:size(x,), :) = ;
xHat = u*xRot; % --------------------------------------------------------
figure();
scatter(xHat(, :), xHat(, :));
title('xHat'); %%================================================================
%% Step : PCA Whitening
% Complute xPCAWhite and plot the results. epsilon = 1e-;
% -------------------- YOUR CODE HERE --------------------
%xPCAWhite = zeros(size(x)); % You need to compute this
xPCAWhite = diag( ./ sqrt(diag(s)+epsilon)) * u' * x; % --------------------------------------------------------
figure();
scatter(xPCAWhite(, :), xPCAWhite(, :));
title('xPCAWhite'); %%================================================================
%% Step : ZCA Whitening
% Complute xZCAWhite and plot the results. % -------------------- YOUR CODE HERE --------------------
%xZCAWhite = zeros(size(x)); % You need to compute this
xZCAWhite = u * xPCAWhite; % --------------------------------------------------------
figure();
scatter(xZCAWhite(, :), xZCAWhite(, :));
title('xZCAWhite'); %% Congratulations! When you have reached this point, you are done!
% You can now move onto the next PCA exercise. :)

【DeepLearning】Exercise:PCA in 2D的更多相关文章

  1. 【DeepLearning】Exercise:PCA and Whitening

    Exercise:PCA and Whitening 习题链接:Exercise:PCA and Whitening pca_gen.m %%============================= ...

  2. 【DeepLearning】Exercise:Convolution and Pooling

    Exercise:Convolution and Pooling 习题链接:Exercise:Convolution and Pooling cnnExercise.m %% CS294A/CS294 ...

  3. 【DeepLearning】Exercise:Softmax Regression

    Exercise:Softmax Regression 习题的链接:Exercise:Softmax Regression softmaxCost.m function [cost, grad] = ...

  4. 【DeepLearning】Exercise:Learning color features with Sparse Autoencoders

    Exercise:Learning color features with Sparse Autoencoders 习题链接:Exercise:Learning color features with ...

  5. 【DeepLearning】Exercise: Implement deep networks for digit classification

    Exercise: Implement deep networks for digit classification 习题链接:Exercise: Implement deep networks fo ...

  6. 【DeepLearning】Exercise:Self-Taught Learning

    Exercise:Self-Taught Learning 习题链接:Exercise:Self-Taught Learning feedForwardAutoencoder.m function [ ...

  7. 【DeepLearning】Exercise:Vectorization

    Exercise:Vectorization 习题的链接:Exercise:Vectorization 注意点: MNIST图片的像素点已经经过归一化. 如果再使用Exercise:Sparse Au ...

  8. 【DeepLearning】Exercise:Sparse Autoencoder

    Exercise:Sparse Autoencoder 习题的链接:Exercise:Sparse Autoencoder 注意点: 1.训练样本像素值需要归一化. 因为输出层的激活函数是logist ...

  9. 【UFLDL】Exercise: Convolutional Neural Network

    这个exercise需要完成cnn中的forward pass,cost,error和gradient的计算.需要弄清楚每一层的以上四个步骤的原理,并且要充分利用matlab的矩阵运算.大概把过程总结 ...

随机推荐

  1. SQL操作查漏补缺

    SQL教程地址:http://www.w3school.com.cn/sql/index.asp TOP 子句 TOP 子句用于规定要返回的记录的数目. 对于拥有数千条记录的大型表来说,TOP 子句是 ...

  2. 大数据开发实战:Stream SQL实时开发三

    4.聚合操作 4.1.group by 操作 group by操作是实际业务场景(如实时报表.实时大屏等)中使用最为频繁的操作.通常实时聚合的主要源头数据流不会包含丰富的上下文信息,而是经常需要实时关 ...

  3. jdbc操作数据库并自动获取字段类型

    //获取改功能编码的关联功能 public void getLinkdb(String gnbianma){ PreparedStatement pstmt = null; ResultSet rs ...

  4. 技能|三次简化一张图:一招理解LSTM/GRU门控机制

    作者 | 张皓 引言 RNN是深度学习中用于处理时序数据的关键技术, 目前已在自然语言处理, 语音识别, 视频识别等领域取得重要突破, 然而梯度消失现象制约着RNN的实际应用.LSTM和GRU是两种目 ...

  5. Web安全学习规划

    一名合格的Web安全工程师是要具备很多的知识点,不但要对网站架构熟悉,通讯协议,测试流程与测试工具使用,漏洞利用脚本编写,还有需要经验的积累等. 互联网进入下半场,竞争越发的激烈,能与人工智能比肩的热 ...

  6. Java:双向链表反转实现

    有个小需求要求实现一个双向链表的反转于是就有了下边代码: 链表元素结构定义: package com.util; public class LinkedNode<T>{ private T ...

  7. 3D打印机切片与控制软件

    3D模型必须经由两个软件的处理来完成打印程序:切片与传送.切片软件会将模型细分成可以打印的薄度,然后计算其打印路径.3d打印机客户端软件再把这系列动作传送到硬件,并提供控制其他功能的控制介面.了解您的 ...

  8. Git 以分支的方式同时管理多个项目

    你是否遇到过这样的问题: 你的客户在你们这边做了N个项目,而项目之间又存在着某些业务关联(数据库访问等) 之前你可能是这样处理的,为客户的每个项目创建单独的Git版本 PC项目 手机项目 微信项目 其 ...

  9. HDU 4059 The Boss on Mars(容斥原理 + 四次方求和)

    传送门 The Boss on Mars Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Oth ...

  10. JavaScript 上下文环境和作用域,以及 call、apply 和 bind【转载+翻译+整理】

    --看到这篇文章,翻译国外的,虽说写得有点矫情,但总体来看,还是相当不错的- 本文内容 我在哪儿?你又是谁 ? this? 用 apply 和 call 掌控上下文环境 bind 之美 本文将说明上下 ...