added support vector machine example

Author: Quinn Liu

unsupervisedLearning/supportVectorMachines/dataset3Params.m

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+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;
+sigma = 0.3;
+
+% ====================== 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))
+%
+
+
+
+
+
+
+
+% =========================================================================
+
+end

unsupervisedLearning/supportVectorMachines/emailFeatures.m

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+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);
+
+% ====================== 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 ..];
+%
+%
+
+
+
+
+
+
+
+
+% =========================================================================
+    
+
+end

unsupervisedLearning/supportVectorMachines/emailSample1.txt

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+> Anyone knows how much it costs to host a web portal ?
+>
+Well, it depends on how many visitors you're expecting.
+This can be anywhere from less than 10 bucks a month to a couple of $100. 
+You should checkout http://www.rackspace.com/ or perhaps Amazon EC2 
+if youre running something big..
+
+To unsubscribe yourself from this mailing list, send an email to:
+[email protected]
+

unsupervisedLearning/supportVectorMachines/emailSample2.txt

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+Folks,
+ 
+my first time posting - have a bit of Unix experience, but am new to Linux.
+
+ 
+Just got a new PC at home - Dell box with Windows XP. Added a second hard disk
+for Linux. Partitioned the disk and have installed Suse 7.2 from CD, which went
+fine except it didn't pick up my monitor.
+ 
+I have a Dell branded E151FPp 15" LCD flat panel monitor and a nVidia GeForce4
+Ti4200 video card, both of which are probably too new to feature in Suse's default
+set. I downloaded a driver from the nVidia website and installed it using RPM.
+Then I ran Sax2 (as was recommended in some postings I found on the net), but
+it still doesn't feature my video card in the available list. What next?
+ 
+Another problem. I have a Dell branded keyboard and if I hit Caps-Lock twice,
+the whole machine crashes (in Linux, not Windows) - even the on/off switch is
+inactive, leaving me to reach for the power cable instead.
+ 
+If anyone can help me in any way with these probs., I'd be really grateful -
+I've searched the 'net but have run out of ideas.
+ 
+Or should I be going for a different version of Linux such as RedHat? Opinions
+welcome.
+ 
+Thanks a lot,
+Peter
+
+-- 
+Irish Linux Users' Group: [email protected]
+http://www.linux.ie/mailman/listinfo/ilug for (un)subscription information.
+List maintainer: [email protected]
+
+

unsupervisedLearning/supportVectorMachines/ex6.m

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+%% 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;
+