Machine Learning week 7 quiz: programming assignment-Support Vector Machines
2024-04-29 19:05:53
一、ex6.m
%% Machine Learning Online Class
% Exercise 6 | Support Vector Machines
%
% Instructions
% ------------
%
% This file contains code that helps you get started on the
% exercise. You will need to complete the following functions:
%
% gaussianKernel.m
% dataset3Params.m
% processEmail.m
% emailFeatures.m
%
% For this exercise, you will not need to change any code in this file,
% or any other files other than those mentioned above.
%%% Initialization
clear ; close all; clc%% =============== Part 1: Loading and Visualizing Data ================
% We start the exercise by first loading and visualizing the dataset.
% The following code will load the dataset into your environment and plot
% the data.
%fprintf('Loading and Visualizing Data ...\n')% Load from ex6data1:
% You will have X, y in your environment
load('ex6data1.mat');% Plot training data
plotData(X, y);fprintf('Program paused. Press enter to continue.\n');
pause;%% ==================== Part 2: Training Linear SVM ====================
% The following code will train a linear SVM on the dataset and plot the
% decision boundary learned.
%% Load from ex6data1:
% You will have X, y in your environment
load('ex6data1.mat');fprintf('\nTraining Linear SVM ...\n')% You should try to change the C value below and see how the decision
% boundary varies (e.g., try C = 1000)
C = 1;
model = svmTrain(X, y, C, @linearKernel, 1e-3, 20);
visualizeBoundaryLinear(X, y, model);fprintf('Program paused. Press enter to continue.\n');
pause;%% =============== Part 3: Implementing Gaussian Kernel ===============
% You will now implement the Gaussian kernel to use
% with the SVM. You should complete the code in gaussianKernel.m
%
fprintf('\nEvaluating the Gaussian Kernel ...\n')x1 = [1 2 1]; x2 = [0 4 -1]; sigma = 2;
sim = gaussianKernel(x1, x2, sigma);fprintf(['Gaussian Kernel between x1 = [1; 2; 1], x2 = [0; 4; -1], sigma = 0.5 :' ...'\n\t%f\n(this value should be about 0.324652)\n'], sim);fprintf('Program paused. Press enter to continue.\n');
pause;%% =============== Part 4: Visualizing Dataset 2 ================
% The following code will load the next dataset into your environment and
% plot the data.
%fprintf('Loading and Visualizing Data ...\n')% Load from ex6data2:
% You will have X, y in your environment
load('ex6data2.mat');% Plot training data
plotData(X, y);fprintf('Program paused. Press enter to continue.\n');
pause;%% ========== Part 5: Training SVM with RBF Kernel (Dataset 2) ==========
% After you have implemented the kernel, we can now use it to train the
% SVM classifier.
%
fprintf('\nTraining SVM with RBF Kernel (this may take 1 to 2 minutes) ...\n');% Load from ex6data2:
% You will have X, y in your environment
load('ex6data2.mat');% SVM Parameters
C = 1; sigma = 0.1;% We set the tolerance and max_passes lower here so that the code will run
% faster. However, in practice, you will want to run the training to
% convergence.
model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
visualizeBoundary(X, y, model);fprintf('Program paused. Press enter to continue.\n');
pause;%% =============== Part 6: Visualizing Dataset 3 ================
% The following code will load the next dataset into your environment and
% plot the data.
%fprintf('Loading and Visualizing Data ...\n')% Load from ex6data3:
% You will have X, y in your environment
load('ex6data3.mat');% Plot training data
plotData(X, y);fprintf('Program paused. Press enter to continue.\n');
pause;%% ========== Part 7: Training SVM with RBF Kernel (Dataset 3) ==========% This is a different dataset that you can use to experiment with. Try
% different values of C and sigma here.
% % Load from ex6data3:
% You will have X, y in your environment
load('ex6data3.mat');% Try different SVM Parameters here
[C, sigma] = dataset3Params(X, y, Xval, yval);% Train the SVM
model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
visualizeBoundary(X, y, model);fprintf('Program paused. Press enter to continue.\n');
pause;
二、gaussianKernel.m
function sim = gaussianKernel(x1, x2, sigma)
%RBFKERNEL returns a radial basis function kernel between x1 and x2
% sim = gaussianKernel(x1, x2) returns a gaussian kernel between x1 and x2
% and returns the value in sim% Ensure that x1 and x2 are column vectors
x1 = x1(:); x2 = x2(:);% You need to return the following variables correctly.
sim = 0; % 1*1% ====================== YOUR CODE HERE ======================
% Instructions: Fill in this function to return the similarity between x1
% and x2 computed using a Gaussian kernel with bandwidth
% sigma
%
%square_diff = sum((x1 - x2) .^ 2);
sim = exp(-square_diff / 2 /(sigma^2));% =============================================================end三、dataset3Params.m
function [C, sigma] = dataset3Params(X, y, Xval, yval)
%EX6PARAMS returns your choice of C and sigma for Part 3 of the exercise
%where you select the optimal (C, sigma) learning parameters to use for SVM
%with RBF kernel
% [C, sigma] = EX6PARAMS(X, y, Xval, yval) returns your choice of C and
% sigma. You should complete this function to return the optimal C and
% sigma based on a cross-validation set.
%% You need to return the following variables correctly.
C = 1; % 1*1
sigma = 0.3; % 1*1% ====================== YOUR CODE HERE ======================
% Instructions: Fill in this function to return the optimal C and sigma
% learning parameters found using the cross validation set.
% You can use svmPredict to predict the labels on the cross
% validation set. For example,
% predictions = svmPredict(model, Xval);
% will return the predictions on the cross validation set.
%
% Note: You can compute the prediction error using
% mean(double(predictions ~= yval))
%set_values = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30];
results = [];
long = numel(set_values);
for i = 1:long
for j = 1:longC_temp = set_values(i);
sigma_temp = set_values(j);model = svmTrain(X, y, C_temp, @(x1, x2) gaussianKernel(x1, x2, sigma_temp));
predictions = svmPredict(model, Xval);pre_error = mean(double(predictions ~= yval));results = [results; C_temp, sigma_temp, pre_error];
end
end[smallest_error, idx] = min(results(:, 3));
C = results(idx, 1);
sigma = results(idx, 2);
% =========================================================================end四、processEmail.m
function word_indices = processEmail(email_contents)
%PROCESSEMAIL preprocesses a the body of an email and
%returns a list of word_indices
% word_indices = PROCESSEMAIL(email_contents) preprocesses
% the body of an email and returns a list of indices of the
% words contained in the email.
%% Load Vocabulary
vocabList = getVocabList();% Init return value
word_indices = [];% ========================== Preprocess Email ===========================% Find the Headers ( \n\n and remove )
% Uncomment the following lines if you are working with raw emails with the
% full headers% hdrstart = strfind(email_contents, ([char(10) char(10)]));
% email_contents = email_contents(hdrstart(1):end);% Lower case
email_contents = lower(email_contents);% Strip all HTML
% Looks for any expression that starts with < and ends with > and replace
% and does not have any < or > in the tag it with a space
email_contents = regexprep(email_contents, '<[^<>]+>', ' ');% Handle Numbers
% Look for one or more characters between 0-9
email_contents = regexprep(email_contents, '[0-9]+', 'number');% Handle URLS
% Look for strings starting with http:// or https://
email_contents = regexprep(email_contents, ...'(http|https)://[^\s]*', 'httpaddr');% Handle Email Addresses
% Look for strings with @ in the middle
email_contents = regexprep(email_contents, '[^\s]+@[^\s]+', 'emailaddr');% Handle $ sign
email_contents = regexprep(email_contents, '[$]+', 'dollar');% ========================== Tokenize Email ===========================% Output the email to screen as well
fprintf('\n==== Processed Email ====\n\n');% Process file
l = 0;while ~isempty(email_contents)% Tokenize and also get rid of any punctuation[str, email_contents] = ...strtok(email_contents, ...[' @$/#.-:&*+=[]?!(){},''">_<;%' char(10) char(13)]);% Remove any non alphanumeric charactersstr = regexprep(str, '[^a-zA-Z0-9]', '');% Stem the word % (the porterStemmer sometimes has issues, so we use a try catch block)try str = porterStemmer(strtrim(str)); catch str = ''; continue;end;% Skip the word if it is too shortif length(str) < 1continue;end% Look up the word in the dictionary and add to word_indices if% found% ====================== YOUR CODE HERE ======================% Instructions: Fill in this function to add the index of str to% word_indices if it is in the vocabulary. At this point% of the code, you have a stemmed word from the email in% the variable str. You should look up str in the% vocabulary list (vocabList). If a match exists, you% should add the index of the word to the word_indices% vector. Concretely, if str = 'action', then you should% look up the vocabulary list to find where in vocabList% 'action' appears. For example, if vocabList{18} =% 'action', then, you should add 18 to the word_indices % vector (e.g., word_indices = [word_indices ; 18]; ).% % Note: vocabList{idx} returns a the word with index idx in the% vocabulary list.% % Note: You can use strcmp(str1, str2) to compare two strings (str1 and% str2). It will return 1 only if the two strings are equivalent.%%%%%%%%%%%%%%%%%%%%%% NOT CORRECT %%%%%%%%%%%%%%%%%%%%%
%str2 = str(:);
%long_dic = numel(vocabList2);
%long_email = numel(str2);%for i = 1:long_email
%for j = 1:long_dic
%if 1 == strcmp(str2(i), vocabList2(j))
%word_indices = [word_indices ; j];
%break;
%end % if-end
%end
%end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%CORRECT
word_indices = [word_indices, find(ismember(vocabList, str))];% =============================================================% Print to screen, ensuring that the output lines are not too longif (l + length(str) + 1) > 78fprintf('\n');l = 0;endfprintf('%s ', str);l = l + length(str) + 1;end% Print footer
fprintf('\n\n=========================\n');end五、emailFeatures.m
function x = emailFeatures(word_indices)
%EMAILFEATURES takes in a word_indices vector and produces a feature vector
%from the word indices
% x = EMAILFEATURES(word_indices) takes in a word_indices vector and
% produces a feature vector from the word indices. % Total number of words in the dictionary
n = 1899;% You need to return the following variables correctly.
x = zeros(n, 1); % n*1% ====================== YOUR CODE HERE ======================
% Instructions: Fill in this function to return a feature vector for the
% given email (word_indices). To help make it easier to
% process the emails, we have have already pre-processed each
% email and converted each word in the email into an index in
% a fixed dictionary (of 1899 words). The variable
% word_indices contains the list of indices of the words
% which occur in one email.
%
% Concretely, if an email has the text:
%
% The quick brown fox jumped over the lazy dog.
%
% Then, the word_indices vector for this text might look
% like:
%
% 60 100 33 44 10 53 60 58 5
%
% where, we have mapped each word onto a number, for example:
%
% the -- 60
% quick -- 100
% ...
%
% (note: the above numbers are just an example and are not the
% actual mappings).
%
% Your task is take one such word_indices vector and construct
% a binary feature vector that indicates whether a particular
% word occurs in the email. That is, x(i) = 1 when word i
% is present in the email. Concretely, if the word 'the' (say,
% index 60) appears in the email, then x(60) = 1. The feature
% vector should look like:
%
% x = [ 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 ... 0 0 0 1 0 ..];
%
%x([word_indices]) = 1;% =========================================================================end六、submit results
Machine Learning week 7 quiz: programming assignment-Support Vector Machines相关推荐
- Machine Learning week 5 quiz: programming assignment-Multi-Neural Network Learning
一.ex4.m %% Machine Learning Online Class - Exercise 4 Neural Network Learning% Instructions % ------ ...
- Machine Learning week 4 quiz: programming assignment-Multi-class Classification and Neural Networks
一.ex3.m %% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all% Instructions % --------- ...
- Machine Learning week 9 quiz: programming assignment-Anomaly Detection and Recommender Systems
一.ex8.m %% Machine Learning Online Class % Exercise 8 | Anomaly Detection and Collaborative Filterin ...
- Machine Learning week 8 quiz: programming assignment-K-Means Clustering and PCA
一.ex7.m %% Machine Learning Online Class % Exercise 7 | Principle Component Analysis and K-Means Clu ...
- Machine Learning week 6 quiz: programming assignment-Regularized Linear Regression and Bias/Variance
一.ex5.m %% Machine Learning Online Class % Exercise 5 | Regularized Linear Regression and Bias-Varia ...
- Machine Learning week 3 quiz: programming assignment-Logistic Regression
一.ex2.m: the main .m file to call other function files % matlab%% Machine Learning Online Class - Ex ...
- Machine Learning Techniques 笔记：2-1 Linear Support Vector Machine
Linear Calssification:通过资料加权,看是正还是负,来对资料进行分类. PLA:把资料做未分类的线不止一条,那条线是最佳的分类?Margin最大? 我们学过的理论保障:VC bou ...
- Machine Learning week 7 quiz: Support Vector Machines
Support Vector Machines 5 试题 1. Suppose you have trained an SVM classifier with a Gaussian kernel, a ...
- Programming Exercise 6:Support Vector Machines
大家好,我是Mac Jiang,今天和大家分享一下coursera网站上Stanford University的Machine Learning公开课(吴恩达老师)课程第六次作业:Programmin ...
最新文章
- python 中cookie_Python 处理Cookie的菜鸟教程（一）Cookie库
- 【深度学习】深度学习语义分割理论与实战指南.pdf
- Ghost 2.16.3 发布，基于 Markdown 的在线写作平台
- 散列--数据结构与算法JavaScript描述(8)
- MySQL 5.6 for Windows 解压缩版配置安装
- 【干货】借助用户画像解决电商业务问题.pdf（附下载链接）
- php根据经纬度查询附近工人，并算出距离（tp3.2）
- AS3 键盘的事件与实现
- 调优jvm需要修改什么文件_JVM性能调优：基本概念介绍
- win7 sp1简体中文升级补丁包（64位）
- 泰坦尼克号生还率预测分析
- QImage 32bit转8bit
- JBPM学习入门（一） 配置JBPM运行环境
- Java Web——基于Jsp+Servlet的大学生社团管理系统
- WITH GRANT OPTION
- Grip编辑器增强版（UG二次开发工具，最好用的Grip编辑器，没有之一）
- 测试工作3年还在基础岗？可能只是因为你的工作能力差
- 什么是BCD码？BCD码编码规则
- 2020年全国各省、各个地级市、各县的10米分辨率的土地利用数据的制作方法与获取
- 关于单元测试框架GoogleTest——参考《百度文库》、大量博客