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‎week7/Coursera _ Online Courses From Top Universities.pdf

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‎week7/_246c2a4e4c249f94c895f607ea1e6407_Lecture12.pdf

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‎week7/machine-learning-ex6.zip

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‎week7/machine-learning-ex6/ex6.pdf

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‎week7/machine-learning-ex6/ex6/dataset3Params.m

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function [C, sigma] = dataset3Params(X, y, Xval, yval)
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%DATASET3PARAMS returns your choice of C and sigma for Part 3 of the exercise
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%where you select the optimal (C, sigma) learning parameters to use for SVM
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%with RBF kernel
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% [C, sigma] = DATASET3PARAMS(X, y, Xval, yval) returns your choice of C and
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% sigma. You should complete this function to return the optimal C and
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% sigma based on a cross-validation set.
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%
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% You need to return the following variables correctly.
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C = 1;
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sigma = 0.3;
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% ====================== YOUR CODE HERE ======================
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% Instructions: Fill in this function to return the optimal C and sigma
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% learning parameters found using the cross validation set.
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% You can use svmPredict to predict the labels on the cross
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% validation set. For example,
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% predictions = svmPredict(model, Xval);
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% will return the predictions on the cross validation set.
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%
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% Note: You can compute the prediction error using
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% mean(double(predictions ~= yval))
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%
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% =========================================================================
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end

‎week7/machine-learning-ex6/ex6/emailFeatures.m

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function x = emailFeatures(word_indices)
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%EMAILFEATURES takes in a word_indices vector and produces a feature vector
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%from the word indices
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% x = EMAILFEATURES(word_indices) takes in a word_indices vector and
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% produces a feature vector from the word indices.
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% Total number of words in the dictionary
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n = 1899;
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% You need to return the following variables correctly.
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x = zeros(n, 1);
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% ====================== YOUR CODE HERE ======================
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% Instructions: Fill in this function to return a feature vector for the
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% given email (word_indices). To help make it easier to
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% process the emails, we have have already pre-processed each
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% email and converted each word in the email into an index in
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% a fixed dictionary (of 1899 words). The variable
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% word_indices contains the list of indices of the words
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% which occur in one email.
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%
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% Concretely, if an email has the text:
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%
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% The quick brown fox jumped over the lazy dog.
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%
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% Then, the word_indices vector for this text might look
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% like:
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%
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% 60 100 33 44 10 53 60 58 5
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%
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% where, we have mapped each word onto a number, for example:
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%
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% the -- 60
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% quick -- 100
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% ...
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%
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% (note: the above numbers are just an example and are not the
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% actual mappings).
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%
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% Your task is take one such word_indices vector and construct
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% a binary feature vector that indicates whether a particular
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% word occurs in the email. That is, x(i) = 1 when word i
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% is present in the email. Concretely, if the word 'the' (say,
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% index 60) appears in the email, then x(60) = 1. The feature
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% vector should look like:
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%
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% x = [ 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 ... 0 0 0 1 0 ..];
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%
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%
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% =========================================================================
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end

‎week7/machine-learning-ex6/ex6/emailSample1.txt

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> Anyone knows how much it costs to host a web portal ?
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>
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Well, it depends on how many visitors you're expecting.
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This can be anywhere from less than 10 bucks a month to a couple of $100.
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You should checkout http://www.rackspace.com/ or perhaps Amazon EC2
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if youre running something big..
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To unsubscribe yourself from this mailing list, send an email to:
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groupname-unsubscribe@egroups.com
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‎week7/machine-learning-ex6/ex6/emailSample2.txt

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Folks,
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my first time posting - have a bit of Unix experience, but am new to Linux.
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Just got a new PC at home - Dell box with Windows XP. Added a second hard disk
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for Linux. Partitioned the disk and have installed Suse 7.2 from CD, which went
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fine except it didn't pick up my monitor.
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I have a Dell branded E151FPp 15" LCD flat panel monitor and a nVidia GeForce4
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Ti4200 video card, both of which are probably too new to feature in Suse's default
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set. I downloaded a driver from the nVidia website and installed it using RPM.
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Then I ran Sax2 (as was recommended in some postings I found on the net), but
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it still doesn't feature my video card in the available list. What next?
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Another problem. I have a Dell branded keyboard and if I hit Caps-Lock twice,
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the whole machine crashes (in Linux, not Windows) - even the on/off switch is
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inactive, leaving me to reach for the power cable instead.
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If anyone can help me in any way with these probs., I'd be really grateful -
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I've searched the 'net but have run out of ideas.
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Or should I be going for a different version of Linux such as RedHat? Opinions
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welcome.
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Thanks a lot,
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Peter
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--
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Irish Linux Users' Group: ilug@linux.ie
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http://www.linux.ie/mailman/listinfo/ilug for (un)subscription information.
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List maintainer: listmaster@linux.ie
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‎week7/machine-learning-ex6/ex6/ex6.m

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%% Machine Learning Online Class
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% Exercise 6 | Support Vector Machines
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%
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% Instructions
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% ------------
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%
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% This file contains code that helps you get started on the
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% exercise. You will need to complete the following functions:
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%
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% gaussianKernel.m
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% dataset3Params.m
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% processEmail.m
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% emailFeatures.m
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%
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% For this exercise, you will not need to change any code in this file,
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% or any other files other than those mentioned above.
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%
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%% Initialization
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clear ; close all; clc
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%% =============== Part 1: Loading and Visualizing Data ================
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% We start the exercise by first loading and visualizing the dataset.
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% The following code will load the dataset into your environment and plot
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% the data.
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%
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fprintf('Loading and Visualizing Data ...\n')
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% Load from ex6data1:
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% You will have X, y in your environment
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load('ex6data1.mat');
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% Plot training data
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plotData(X, y);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ==================== Part 2: Training Linear SVM ====================
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% The following code will train a linear SVM on the dataset and plot the
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% decision boundary learned.
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%
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% Load from ex6data1:
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% You will have X, y in your environment
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load('ex6data1.mat');
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fprintf('\nTraining Linear SVM ...\n')
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% You should try to change the C value below and see how the decision
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% boundary varies (e.g., try C = 1000)
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C = 1;
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model = svmTrain(X, y, C, @linearKernel, 1e-3, 20);
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visualizeBoundaryLinear(X, y, model);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =============== Part 3: Implementing Gaussian Kernel ===============
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% You will now implement the Gaussian kernel to use
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% with the SVM. You should complete the code in gaussianKernel.m
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%
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fprintf('\nEvaluating the Gaussian Kernel ...\n')
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x1 = [1 2 1]; x2 = [0 4 -1]; sigma = 2;
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sim = gaussianKernel(x1, x2, sigma);
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fprintf(['Gaussian Kernel between x1 = [1; 2; 1], x2 = [0; 4; -1], sigma = %f :' ...
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'\n\t%f\n(for sigma = 2, this value should be about 0.324652)\n'], sigma, sim);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =============== Part 4: Visualizing Dataset 2 ================
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% The following code will load the next dataset into your environment and
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% plot the data.
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%
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fprintf('Loading and Visualizing Data ...\n')
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% Load from ex6data2:
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% You will have X, y in your environment
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load('ex6data2.mat');
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% Plot training data
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plotData(X, y);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ========== Part 5: Training SVM with RBF Kernel (Dataset 2) ==========
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% After you have implemented the kernel, we can now use it to train the
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% SVM classifier.
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%
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fprintf('\nTraining SVM with RBF Kernel (this may take 1 to 2 minutes) ...\n');
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% Load from ex6data2:
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% You will have X, y in your environment
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load('ex6data2.mat');
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% SVM Parameters
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C = 1; sigma = 0.1;
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% We set the tolerance and max_passes lower here so that the code will run
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% faster. However, in practice, you will want to run the training to
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% convergence.
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model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
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visualizeBoundary(X, y, model);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =============== Part 6: Visualizing Dataset 3 ================
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% The following code will load the next dataset into your environment and
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% plot the data.
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%
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fprintf('Loading and Visualizing Data ...\n')
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% Load from ex6data3:
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% You will have X, y in your environment
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load('ex6data3.mat');
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% Plot training data
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plotData(X, y);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ========== Part 7: Training SVM with RBF Kernel (Dataset 3) ==========
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% This is a different dataset that you can use to experiment with. Try
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% different values of C and sigma here.
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%
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% Load from ex6data3:
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% You will have X, y in your environment
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load('ex6data3.mat');
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% Try different SVM Parameters here
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[C, sigma] = dataset3Params(X, y, Xval, yval);
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% Train the SVM
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model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
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visualizeBoundary(X, y, model);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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