Add exercise 7
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function centroids = computeCentroids(X, idx, K)
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%COMPUTECENTROIDS returs the new centroids by computing the means of the
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%data points assigned to each centroid.
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% centroids = COMPUTECENTROIDS(X, idx, K) returns the new centroids by
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% computing the means of the data points assigned to each centroid. It is
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% given a dataset X where each row is a single data point, a vector
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% idx of centroid assignments (i.e. each entry in range [1..K]) for each
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% example, and K, the number of centroids. You should return a matrix
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% centroids, where each row of centroids is the mean of the data points
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% assigned to it.
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%
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% Useful variables
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[m n] = size(X);
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% You need to return the following variables correctly.
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centroids = zeros(K, n);
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% ====================== YOUR CODE HERE ======================
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% Instructions: Go over every centroid and compute mean of all points that
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% belong to it. Concretely, the row vector centroids(i, :)
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% should contain the mean of the data points assigned to
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% centroid i.
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%
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% Note: You can use a for-loop over the centroids to compute this.
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%
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% =============================================================
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end
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function [h, display_array] = displayData(X, example_width)
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%DISPLAYDATA Display 2D data in a nice grid
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% [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data
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% stored in X in a nice grid. It returns the figure handle h and the
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% displayed array if requested.
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% Set example_width automatically if not passed in
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if ~exist('example_width', 'var') || isempty(example_width)
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example_width = round(sqrt(size(X, 2)));
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end
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% Gray Image
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colormap(gray);
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% Compute rows, cols
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[m n] = size(X);
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example_height = (n / example_width);
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% Compute number of items to display
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display_rows = floor(sqrt(m));
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display_cols = ceil(m / display_rows);
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% Between images padding
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pad = 1;
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% Setup blank display
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display_array = - ones(pad + display_rows * (example_height + pad), ...
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pad + display_cols * (example_width + pad));
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% Copy each example into a patch on the display array
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curr_ex = 1;
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for j = 1:display_rows
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for i = 1:display_cols
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if curr_ex > m,
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break;
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end
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% Copy the patch
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% Get the max value of the patch
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max_val = max(abs(X(curr_ex, :)));
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display_array(pad + (j - 1) * (example_height + pad) + (1:example_height), ...
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pad + (i - 1) * (example_width + pad) + (1:example_width)) = ...
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reshape(X(curr_ex, :), example_height, example_width) / max_val;
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curr_ex = curr_ex + 1;
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end
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if curr_ex > m,
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break;
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end
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end
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% Display Image
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h = imagesc(display_array, [-1 1]);
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% Do not show axis
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axis image off
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drawnow;
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end
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function drawLine(p1, p2, varargin)
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%DRAWLINE Draws a line from point p1 to point p2
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% DRAWLINE(p1, p2) Draws a line from point p1 to point p2 and holds the
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% current figure
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plot([p1(1) p2(1)], [p1(2) p2(2)], varargin{:});
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end
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%% Machine Learning Online Class
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% Exercise 7 | Principle Component Analysis and K-Means Clustering
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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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% pca.m
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% projectData.m
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% recoverData.m
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% computeCentroids.m
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% findClosestCentroids.m
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% kMeansInitCentroids.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: Find Closest Centroids ====================
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% To help you implement K-Means, we have divided the learning algorithm
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% into two functions -- findClosestCentroids and computeCentroids. In this
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% part, you shoudl complete the code in the findClosestCentroids function.
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%
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fprintf('Finding closest centroids.\n\n');
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% Load an example dataset that we will be using
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load('ex7data2.mat');
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% Select an initial set of centroids
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K = 3; % 3 Centroids
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initial_centroids = [3 3; 6 2; 8 5];
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% Find the closest centroids for the examples using the
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% initial_centroids
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idx = findClosestCentroids(X, initial_centroids);
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fprintf('Closest centroids for the first 3 examples: \n')
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fprintf(' %d', idx(1:3));
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fprintf('\n(the closest centroids should be 1, 3, 2 respectively)\n');
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ===================== Part 2: Compute Means =========================
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% After implementing the closest centroids function, you should now
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% complete the computeCentroids function.
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%
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fprintf('\nComputing centroids means.\n\n');
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% Compute means based on the closest centroids found in the previous part.
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centroids = computeCentroids(X, idx, K);
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fprintf('Centroids computed after initial finding of closest centroids: \n')
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fprintf(' %f %f \n' , centroids');
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fprintf('\n(the centroids should be\n');
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fprintf(' [ 2.428301 3.157924 ]\n');
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fprintf(' [ 5.813503 2.633656 ]\n');
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fprintf(' [ 7.119387 3.616684 ]\n\n');
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =================== Part 3: K-Means Clustering ======================
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% After you have completed the two functions computeCentroids and
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% findClosestCentroids, you have all the necessary pieces to run the
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% kMeans algorithm. In this part, you will run the K-Means algorithm on
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% the example dataset we have provided.
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%
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fprintf('\nRunning K-Means clustering on example dataset.\n\n');
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% Load an example dataset
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load('ex7data2.mat');
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% Settings for running K-Means
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K = 3;
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max_iters = 10;
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% For consistency, here we set centroids to specific values
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% but in practice you want to generate them automatically, such as by
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% settings them to be random examples (as can be seen in
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% kMeansInitCentroids).
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initial_centroids = [3 3; 6 2; 8 5];
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% Run K-Means algorithm. The 'true' at the end tells our function to plot
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% the progress of K-Means
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[centroids, idx] = runkMeans(X, initial_centroids, max_iters, true);
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fprintf('\nK-Means Done.\n\n');
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ============= Part 4: K-Means Clustering on Pixels ===============
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% In this exercise, you will use K-Means to compress an image. To do this,
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% you will first run K-Means on the colors of the pixels in the image and
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% then you will map each pixel on to it's closest centroid.
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%
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% You should now complete the code in kMeansInitCentroids.m
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%
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fprintf('\nRunning K-Means clustering on pixels from an image.\n\n');
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% Load an image of a bird
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A = double(imread('bird_small.png'));
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% If imread does not work for you, you can try instead
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% load ('bird_small.mat');
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A = A / 255; % Divide by 255 so that all values are in the range 0 - 1
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% Size of the image
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img_size = size(A);
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% Reshape the image into an Nx3 matrix where N = number of pixels.
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% Each row will contain the Red, Green and Blue pixel values
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% This gives us our dataset matrix X that we will use K-Means on.
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X = reshape(A, img_size(1) * img_size(2), 3);
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% Run your K-Means algorithm on this data
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% You should try different values of K and max_iters here
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K = 16;
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max_iters = 10;
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% When using K-Means, it is important the initialize the centroids
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% randomly.
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% You should complete the code in kMeansInitCentroids.m before proceeding
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initial_centroids = kMeansInitCentroids(X, K);
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% Run K-Means
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[centroids, idx] = runkMeans(X, initial_centroids, max_iters);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================= Part 5: Image Compression ======================
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% In this part of the exercise, you will use the clusters of K-Means to
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% compress an image. To do this, we first find the closest clusters for
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% each example. After that, we
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fprintf('\nApplying K-Means to compress an image.\n\n');
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% Find closest cluster members
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idx = findClosestCentroids(X, centroids);
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% Essentially, now we have represented the image X as in terms of the
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% indices in idx.
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% We can now recover the image from the indices (idx) by mapping each pixel
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% (specified by it's index in idx) to the centroid value
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X_recovered = centroids(idx,:);
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% Reshape the recovered image into proper dimensions
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X_recovered = reshape(X_recovered, img_size(1), img_size(2), 3);
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% Display the original image
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subplot(1, 2, 1);
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imagesc(A);
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title('Original');
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% Display compressed image side by side
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subplot(1, 2, 2);
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imagesc(X_recovered)
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title(sprintf('Compressed, with %d colors.', K));
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% Machine Learning Online Class
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% Exercise 7 | Principle Component Analysis and K-Means Clustering
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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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% pca.m
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% projectData.m
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% recoverData.m
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% computeCentroids.m
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% findClosestCentroids.m
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% kMeansInitCentroids.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: Load Example Dataset ===================
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% We start this exercise by using a small dataset that is easily to
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% visualize
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%
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fprintf('Visualizing example dataset for PCA.\n\n');
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% The following command loads the dataset. You should now have the
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% variable X in your environment
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load ('ex7data1.mat');
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% Visualize the example dataset
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plot(X(:, 1), X(:, 2), 'bo');
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axis([0.5 6.5 2 8]); axis square;
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =============== Part 2: Principal Component Analysis ===============
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% You should now implement PCA, a dimension reduction technique. You
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% should complete the code in pca.m
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%
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fprintf('\nRunning PCA on example dataset.\n\n');
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% Before running PCA, it is important to first normalize X
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[X_norm, mu, sigma] = featureNormalize(X);
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% Run PCA
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[U, S] = pca(X_norm);
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% Compute mu, the mean of the each feature
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% Draw the eigenvectors centered at mean of data. These lines show the
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% directions of maximum variations in the dataset.
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hold on;
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drawLine(mu, mu + 1.5 * S(1,1) * U(:,1)', '-k', 'LineWidth', 2);
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drawLine(mu, mu + 1.5 * S(2,2) * U(:,2)', '-k', 'LineWidth', 2);
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hold off;
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fprintf('Top eigenvector: \n');
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fprintf(' U(:,1) = %f %f \n', U(1,1), U(2,1));
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fprintf('\n(you should expect to see -0.707107 -0.707107)\n');
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =================== Part 3: Dimension Reduction ===================
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% You should now implement the projection step to map the data onto the
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% first k eigenvectors. The code will then plot the data in this reduced
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% dimensional space. This will show you what the data looks like when
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% using only the corresponding eigenvectors to reconstruct it.
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%
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% You should complete the code in projectData.m
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%
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fprintf('\nDimension reduction on example dataset.\n\n');
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% Plot the normalized dataset (returned from pca)
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plot(X_norm(:, 1), X_norm(:, 2), 'bo');
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axis([-4 3 -4 3]); axis square
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% Project the data onto K = 1 dimension
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K = 1;
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Z = projectData(X_norm, U, K);
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fprintf('Projection of the first example: %f\n', Z(1));
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fprintf('\n(this value should be about 1.481274)\n\n');
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X_rec = recoverData(Z, U, K);
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fprintf('Approximation of the first example: %f %f\n', X_rec(1, 1), X_rec(1, 2));
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fprintf('\n(this value should be about -1.047419 -1.047419)\n\n');
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% Draw lines connecting the projected points to the original points
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hold on;
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plot(X_rec(:, 1), X_rec(:, 2), 'ro');
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for i = 1:size(X_norm, 1)
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drawLine(X_norm(i,:), X_rec(i,:), '--k', 'LineWidth', 1);
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end
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hold off
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =============== Part 4: Loading and Visualizing Face 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
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%
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fprintf('\nLoading face dataset.\n\n');
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% Load Face dataset
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load ('ex7faces.mat')
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% Display the first 100 faces in the dataset
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displayData(X(1:100, :));
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =========== Part 5: PCA on Face Data: Eigenfaces ===================
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% Run PCA and visualize the eigenvectors which are in this case eigenfaces
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% We display the first 36 eigenfaces.
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%
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fprintf(['\nRunning PCA on face dataset.\n' ...
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'(this mght take a minute or two ...)\n\n']);
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% Before running PCA, it is important to first normalize X by subtracting
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% the mean value from each feature
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[X_norm, mu, sigma] = featureNormalize(X);
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% Run PCA
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[U, S] = pca(X_norm);
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% Visualize the top 36 eigenvectors found
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displayData(U(:, 1:36)');
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ============= Part 6: Dimension Reduction for Faces =================
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% Project images to the eigen space using the top k eigenvectors
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% If you are applying a machine learning algorithm
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fprintf('\nDimension reduction for face dataset.\n\n');
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K = 100;
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Z = projectData(X_norm, U, K);
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fprintf('The projected data Z has a size of: ')
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fprintf('%d ', size(Z));
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fprintf('\n\nProgram paused. Press enter to continue.\n');
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pause;
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%% ==== Part 7: Visualization of Faces after PCA Dimension Reduction ====
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% Project images to the eigen space using the top K eigen vectors and
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% visualize only using those K dimensions
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% Compare to the original input, which is also displayed
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fprintf('\nVisualizing the projected (reduced dimension) faces.\n\n');
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K = 100;
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X_rec = recoverData(Z, U, K);
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% Display normalized data
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subplot(1, 2, 1);
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displayData(X_norm(1:100,:));
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title('Original faces');
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axis square;
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% Display reconstructed data from only k eigenfaces
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subplot(1, 2, 2);
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displayData(X_rec(1:100,:));
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title('Recovered faces');
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axis square;
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% === Part 8(a): Optional (ungraded) Exercise: PCA for Visualization ===
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% One useful application of PCA is to use it to visualize high-dimensional
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% data. In the last K-Means exercise you ran K-Means on 3-dimensional
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% pixel colors of an image. We first visualize this output in 3D, and then
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% apply PCA to obtain a visualization in 2D.
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close all; close all; clc
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% Re-load the image from the previous exercise and run K-Means on it
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% For this to work, you need to complete the K-Means assignment first
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A = double(imread('bird_small.png'));
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% If imread does not work for you, you can try instead
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% load ('bird_small.mat');
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A = A / 255;
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img_size = size(A);
|
||||
X = reshape(A, img_size(1) * img_size(2), 3);
|
||||
K = 16;
|
||||
max_iters = 10;
|
||||
initial_centroids = kMeansInitCentroids(X, K);
|
||||
[centroids, idx] = runkMeans(X, initial_centroids, max_iters);
|
||||
|
||||
% Sample 1000 random indexes (since working with all the data is
|
||||
% too expensive. If you have a fast computer, you may increase this.
|
||||
sel = floor(rand(1000, 1) * size(X, 1)) + 1;
|
||||
|
||||
% Setup Color Palette
|
||||
palette = hsv(K);
|
||||
colors = palette(idx(sel), :);
|
||||
|
||||
% Visualize the data and centroid memberships in 3D
|
||||
figure;
|
||||
scatter3(X(sel, 1), X(sel, 2), X(sel, 3), 10, colors);
|
||||
title('Pixel dataset plotted in 3D. Color shows centroid memberships');
|
||||
fprintf('Program paused. Press enter to continue.\n');
|
||||
pause;
|
||||
|
||||
%% === Part 8(b): Optional (ungraded) Exercise: PCA for Visualization ===
|
||||
% Use PCA to project this cloud to 2D for visualization
|
||||
|
||||
% Subtract the mean to use PCA
|
||||
[X_norm, mu, sigma] = featureNormalize(X);
|
||||
|
||||
% PCA and project the data to 2D
|
||||
[U, S] = pca(X_norm);
|
||||
Z = projectData(X_norm, U, 2);
|
||||
|
||||
% Plot in 2D
|
||||
figure;
|
||||
plotDataPoints(Z(sel, :), idx(sel), K);
|
||||
title('Pixel dataset plotted in 2D, using PCA for dimensionality reduction');
|
||||
fprintf('Program paused. Press enter to continue.\n');
|
||||
pause;
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -0,0 +1,17 @@
|
||||
function [X_norm, mu, sigma] = featureNormalize(X)
|
||||
%FEATURENORMALIZE Normalizes the features in X
|
||||
% FEATURENORMALIZE(X) returns a normalized version of X where
|
||||
% the mean value of each feature is 0 and the standard deviation
|
||||
% is 1. This is often a good preprocessing step to do when
|
||||
% working with learning algorithms.
|
||||
|
||||
mu = mean(X);
|
||||
X_norm = bsxfun(@minus, X, mu);
|
||||
|
||||
sigma = std(X_norm);
|
||||
X_norm = bsxfun(@rdivide, X_norm, sigma);
|
||||
|
||||
|
||||
% ============================================================
|
||||
|
||||
end
|
||||
@ -0,0 +1,33 @@
|
||||
function idx = findClosestCentroids(X, centroids)
|
||||
%FINDCLOSESTCENTROIDS computes the centroid memberships for every example
|
||||
% idx = FINDCLOSESTCENTROIDS (X, centroids) returns the closest centroids
|
||||
% in idx for a dataset X where each row is a single example. idx = m x 1
|
||||
% vector of centroid assignments (i.e. each entry in range [1..K])
|
||||
%
|
||||
|
||||
% Set K
|
||||
K = size(centroids, 1);
|
||||
|
||||
% You need to return the following variables correctly.
|
||||
idx = zeros(size(X,1), 1);
|
||||
|
||||
% ====================== YOUR CODE HERE ======================
|
||||
% Instructions: Go over every example, find its closest centroid, and store
|
||||
% the index inside idx at the appropriate location.
|
||||
% Concretely, idx(i) should contain the index of the centroid
|
||||
% closest to example i. Hence, it should be a value in the
|
||||
% range 1..K
|
||||
%
|
||||
% Note: You can use a for-loop over the examples to compute this.
|
||||
%
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
% =============================================================
|
||||
|
||||
end
|
||||
|
||||
@ -0,0 +1,26 @@
|
||||
function centroids = kMeansInitCentroids(X, K)
|
||||
%KMEANSINITCENTROIDS This function initializes K centroids that are to be
|
||||
%used in K-Means on the dataset X
|
||||
% centroids = KMEANSINITCENTROIDS(X, K) returns K initial centroids to be
|
||||
% used with the K-Means on the dataset X
|
||||
%
|
||||
|
||||
% You should return this values correctly
|
||||
centroids = zeros(K, size(X, 2));
|
||||
|
||||
% ====================== YOUR CODE HERE ======================
|
||||
% Instructions: You should set centroids to randomly chosen examples from
|
||||
% the dataset X
|
||||
%
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
% =============================================================
|
||||
|
||||
end
|
||||
|
||||
@ -0,0 +1,31 @@
|
||||
function [U, S] = pca(X)
|
||||
%PCA Run principal component analysis on the dataset X
|
||||
% [U, S, X] = pca(X) computes eigenvectors of the covariance matrix of X
|
||||
% Returns the eigenvectors U, the eigenvalues (on diagonal) in S
|
||||
%
|
||||
|
||||
% Useful values
|
||||
[m, n] = size(X);
|
||||
|
||||
% You need to return the following variables correctly.
|
||||
U = zeros(n);
|
||||
S = zeros(n);
|
||||
|
||||
% ====================== YOUR CODE HERE ======================
|
||||
% Instructions: You should first compute the covariance matrix. Then, you
|
||||
% should use the "svd" function to compute the eigenvectors
|
||||
% and eigenvalues of the covariance matrix.
|
||||
%
|
||||
% Note: When computing the covariance matrix, remember to divide by m (the
|
||||
% number of examples).
|
||||
%
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
% =========================================================================
|
||||
|
||||
end
|
||||
@ -0,0 +1,14 @@
|
||||
function plotDataPoints(X, idx, K)
|
||||
%PLOTDATAPOINTS plots data points in X, coloring them so that those with the same
|
||||
%index assignments in idx have the same color
|
||||
% PLOTDATAPOINTS(X, idx, K) plots data points in X, coloring them so that those
|
||||
% with the same index assignments in idx have the same color
|
||||
|
||||
% Create palette
|
||||
palette = hsv(K + 1);
|
||||
colors = palette(idx, :);
|
||||
|
||||
% Plot the data
|
||||
scatter(X(:,1), X(:,2), 15, colors);
|
||||
|
||||
end
|
||||
@ -0,0 +1,27 @@
|
||||
function plotProgresskMeans(X, centroids, previous, idx, K, i)
|
||||
%PLOTPROGRESSKMEANS is a helper function that displays the progress of
|
||||
%k-Means as it is running. It is intended for use only with 2D data.
|
||||
% PLOTPROGRESSKMEANS(X, centroids, previous, idx, K, i) plots the data
|
||||
% points with colors assigned to each centroid. With the previous
|
||||
% centroids, it also plots a line between the previous locations and
|
||||
% current locations of the centroids.
|
||||
%
|
||||
|
||||
% Plot the examples
|
||||
plotDataPoints(X, idx, K);
|
||||
|
||||
% Plot the centroids as black x's
|
||||
plot(centroids(:,1), centroids(:,2), 'x', ...
|
||||
'MarkerEdgeColor','k', ...
|
||||
'MarkerSize', 10, 'LineWidth', 3);
|
||||
|
||||
% Plot the history of the centroids with lines
|
||||
for j=1:size(centroids,1)
|
||||
drawLine(centroids(j, :), previous(j, :));
|
||||
end
|
||||
|
||||
% Title
|
||||
title(sprintf('Iteration number %d', i))
|
||||
|
||||
end
|
||||
|
||||
@ -0,0 +1,26 @@
|
||||
function Z = projectData(X, U, K)
|
||||
%PROJECTDATA Computes the reduced data representation when projecting only
|
||||
%on to the top k eigenvectors
|
||||
% Z = projectData(X, U, K) computes the projection of
|
||||
% the normalized inputs X into the reduced dimensional space spanned by
|
||||
% the first K columns of U. It returns the projected examples in Z.
|
||||
%
|
||||
|
||||
% You need to return the following variables correctly.
|
||||
Z = zeros(size(X, 1), K);
|
||||
|
||||
% ====================== YOUR CODE HERE ======================
|
||||
% Instructions: Compute the projection of the data using only the top K
|
||||
% eigenvectors in U (first K columns).
|
||||
% For the i-th example X(i,:), the projection on to the k-th
|
||||
% eigenvector is given as follows:
|
||||
% x = X(i, :)';
|
||||
% projection_k = x' * U(:, k);
|
||||
%
|
||||
|
||||
|
||||
|
||||
|
||||
% =============================================================
|
||||
|
||||
end
|
||||
@ -0,0 +1,28 @@
|
||||
function X_rec = recoverData(Z, U, K)
|
||||
%RECOVERDATA Recovers an approximation of the original data when using the
|
||||
%projected data
|
||||
% X_rec = RECOVERDATA(Z, U, K) recovers an approximation the
|
||||
% original data that has been reduced to K dimensions. It returns the
|
||||
% approximate reconstruction in X_rec.
|
||||
%
|
||||
|
||||
% You need to return the following variables correctly.
|
||||
X_rec = zeros(size(Z, 1), size(U, 1));
|
||||
|
||||
% ====================== YOUR CODE HERE ======================
|
||||
% Instructions: Compute the approximation of the data by projecting back
|
||||
% onto the original space using the top K eigenvectors in U.
|
||||
%
|
||||
% For the i-th example Z(i,:), the (approximate)
|
||||
% recovered data for dimension j is given as follows:
|
||||
% v = Z(i, :)';
|
||||
% recovered_j = v' * U(j, 1:K)';
|
||||
%
|
||||
% Notice that U(j, 1:K) is a row vector.
|
||||
%
|
||||
|
||||
|
||||
|
||||
% =============================================================
|
||||
|
||||
end
|
||||
@ -0,0 +1,64 @@
|
||||
function [centroids, idx] = runkMeans(X, initial_centroids, ...
|
||||
max_iters, plot_progress)
|
||||
%RUNKMEANS runs the K-Means algorithm on data matrix X, where each row of X
|
||||
%is a single example
|
||||
% [centroids, idx] = RUNKMEANS(X, initial_centroids, max_iters, ...
|
||||
% plot_progress) runs the K-Means algorithm on data matrix X, where each
|
||||
% row of X is a single example. It uses initial_centroids used as the
|
||||
% initial centroids. max_iters specifies the total number of interactions
|
||||
% of K-Means to execute. plot_progress is a true/false flag that
|
||||
% indicates if the function should also plot its progress as the
|
||||
% learning happens. This is set to false by default. runkMeans returns
|
||||
% centroids, a Kxn matrix of the computed centroids and idx, a m x 1
|
||||
% vector of centroid assignments (i.e. each entry in range [1..K])
|
||||
%
|
||||
|
||||
% Set default value for plot progress
|
||||
if ~exist('plot_progress', 'var') || isempty(plot_progress)
|
||||
plot_progress = false;
|
||||
end
|
||||
|
||||
% Plot the data if we are plotting progress
|
||||
if plot_progress
|
||||
figure;
|
||||
hold on;
|
||||
end
|
||||
|
||||
% Initialize values
|
||||
[m n] = size(X);
|
||||
K = size(initial_centroids, 1);
|
||||
centroids = initial_centroids;
|
||||
previous_centroids = centroids;
|
||||
idx = zeros(m, 1);
|
||||
|
||||
% Run K-Means
|
||||
for i=1:max_iters
|
||||
|
||||
% Output progress
|
||||
fprintf('K-Means iteration %d/%d...\n', i, max_iters);
|
||||
if exist('OCTAVE_VERSION')
|
||||
fflush(stdout);
|
||||
end
|
||||
|
||||
% For each example in X, assign it to the closest centroid
|
||||
idx = findClosestCentroids(X, centroids);
|
||||
|
||||
% Optionally, plot progress here
|
||||
if plot_progress
|
||||
plotProgresskMeans(X, centroids, previous_centroids, idx, K, i);
|
||||
previous_centroids = centroids;
|
||||
fprintf('Press enter to continue.\n');
|
||||
pause;
|
||||
end
|
||||
|
||||
% Given the memberships, compute new centroids
|
||||
centroids = computeCentroids(X, idx, K);
|
||||
end
|
||||
|
||||
% Hold off if we are plotting progress
|
||||
if plot_progress
|
||||
hold off;
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
@ -0,0 +1,576 @@
|
||||
function submit(partId, webSubmit)
|
||||
%SUBMIT Submit your code and output to the ml-class servers
|
||||
% SUBMIT() will connect to the ml-class server and submit your solution
|
||||
|
||||
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
|
||||
homework_id());
|
||||
if ~exist('partId', 'var') || isempty(partId)
|
||||
partId = promptPart();
|
||||
end
|
||||
|
||||
if ~exist('webSubmit', 'var') || isempty(webSubmit)
|
||||
webSubmit = 0; % submit directly by default
|
||||
end
|
||||
|
||||
% Check valid partId
|
||||
partNames = validParts();
|
||||
if ~isValidPartId(partId)
|
||||
fprintf('!! Invalid homework part selected.\n');
|
||||
fprintf('!! Expected an integer from 1 to %d.\n', numel(partNames) + 1);
|
||||
fprintf('!! Submission Cancelled\n');
|
||||
return
|
||||
end
|
||||
|
||||
if ~exist('ml_login_data.mat','file')
|
||||
[login password] = loginPrompt();
|
||||
save('ml_login_data.mat','login','password');
|
||||
else
|
||||
load('ml_login_data.mat');
|
||||
[login password] = quickLogin(login, password);
|
||||
save('ml_login_data.mat','login','password');
|
||||
end
|
||||
|
||||
if isempty(login)
|
||||
fprintf('!! Submission Cancelled\n');
|
||||
return
|
||||
end
|
||||
|
||||
fprintf('\n== Connecting to ml-class ... ');
|
||||
if exist('OCTAVE_VERSION')
|
||||
fflush(stdout);
|
||||
end
|
||||
|
||||
% Setup submit list
|
||||
if partId == numel(partNames) + 1
|
||||
submitParts = 1:numel(partNames);
|
||||
else
|
||||
submitParts = [partId];
|
||||
end
|
||||
|
||||
for s = 1:numel(submitParts)
|
||||
thisPartId = submitParts(s);
|
||||
if (~webSubmit) % submit directly to server
|
||||
[login, ch, signature, auxstring] = getChallenge(login, thisPartId);
|
||||
if isempty(login) || isempty(ch) || isempty(signature)
|
||||
% Some error occured, error string in first return element.
|
||||
fprintf('\n!! Error: %s\n\n', login);
|
||||
return
|
||||
end
|
||||
|
||||
% Attempt Submission with Challenge
|
||||
ch_resp = challengeResponse(login, password, ch);
|
||||
|
||||
[result, str] = submitSolution(login, ch_resp, thisPartId, ...
|
||||
output(thisPartId, auxstring), source(thisPartId), signature);
|
||||
|
||||
partName = partNames{thisPartId};
|
||||
|
||||
fprintf('\n== [ml-class] Submitted Assignment %s - Part %d - %s\n', ...
|
||||
homework_id(), thisPartId, partName);
|
||||
fprintf('== %s\n', strtrim(str));
|
||||
|
||||
if exist('OCTAVE_VERSION')
|
||||
fflush(stdout);
|
||||
end
|
||||
else
|
||||
[result] = submitSolutionWeb(login, thisPartId, output(thisPartId), ...
|
||||
source(thisPartId));
|
||||
result = base64encode(result);
|
||||
|
||||
fprintf('\nSave as submission file [submit_ex%s_part%d.txt (enter to accept default)]:', ...
|
||||
homework_id(), thisPartId);
|
||||
saveAsFile = input('', 's');
|
||||
if (isempty(saveAsFile))
|
||||
saveAsFile = sprintf('submit_ex%s_part%d.txt', homework_id(), thisPartId);
|
||||
end
|
||||
|
||||
fid = fopen(saveAsFile, 'w');
|
||||
if (fid)
|
||||
fwrite(fid, result);
|
||||
fclose(fid);
|
||||
fprintf('\nSaved your solutions to %s.\n\n', saveAsFile);
|
||||
fprintf(['You can now submit your solutions through the web \n' ...
|
||||
'form in the programming exercises. Select the corresponding \n' ...
|
||||
'programming exercise to access the form.\n']);
|
||||
|
||||
else
|
||||
fprintf('Unable to save to %s\n\n', saveAsFile);
|
||||
fprintf(['You can create a submission file by saving the \n' ...
|
||||
'following text in a file: (press enter to continue)\n\n']);
|
||||
pause;
|
||||
fprintf(result);
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
% ================== CONFIGURABLES FOR EACH HOMEWORK ==================
|
||||
|
||||
function id = homework_id()
|
||||
id = '7';
|
||||
end
|
||||
|
||||
function [partNames] = validParts()
|
||||
partNames = {
|
||||
'Find Closest Centroids (k-Means)', ...
|
||||
'Compute Centroid Means (k-Means)' ...
|
||||
'PCA', ...
|
||||
'Project Data (PCA)', ...
|
||||
'Recover Data (PCA)' ...
|
||||
};
|
||||
end
|
||||
|
||||
function srcs = sources()
|
||||
% Separated by part
|
||||
srcs = { { 'findClosestCentroids.m' }, ...
|
||||
{ 'computeCentroids.m' }, ...
|
||||
{ 'pca.m' }, ...
|
||||
{ 'projectData.m' }, ...
|
||||
{ 'recoverData.m' } ...
|
||||
};
|
||||
end
|
||||
|
||||
function out = output(partId, auxstring)
|
||||
% Random Test Cases
|
||||
X = reshape(sin(1:165), 15, 11);
|
||||
Z = reshape(cos(1:121), 11, 11);
|
||||
C = Z(1:5, :);
|
||||
idx = (1 + mod(1:15, 3))';
|
||||
if partId == 1
|
||||
idx = findClosestCentroids(X, C);
|
||||
out = sprintf('%0.5f ', idx(:));
|
||||
elseif partId == 2
|
||||
centroids = computeCentroids(X, idx, 3);
|
||||
out = sprintf('%0.5f ', centroids(:));
|
||||
elseif partId == 3
|
||||
[U, S] = pca(X);
|
||||
out = sprintf('%0.5f ', abs([U(:); S(:)]));
|
||||
elseif partId == 4
|
||||
X_proj = projectData(X, Z, 5);
|
||||
out = sprintf('%0.5f ', X_proj(:));
|
||||
elseif partId == 5
|
||||
X_rec = recoverData(X(:,1:5), Z, 5);
|
||||
out = sprintf('%0.5f ', X_rec(:));
|
||||
end
|
||||
end
|
||||
|
||||
% ====================== SERVER CONFIGURATION ===========================
|
||||
|
||||
% ***************** REMOVE -staging WHEN YOU DEPLOY *********************
|
||||
function url = site_url()
|
||||
url = 'http://class.coursera.org/ml-007';
|
||||
end
|
||||
|
||||
function url = challenge_url()
|
||||
url = [site_url() '/assignment/challenge'];
|
||||
end
|
||||
|
||||
function url = submit_url()
|
||||
url = [site_url() '/assignment/submit'];
|
||||
end
|
||||
|
||||
% ========================= CHALLENGE HELPERS =========================
|
||||
|
||||
function src = source(partId)
|
||||
src = '';
|
||||
src_files = sources();
|
||||
if partId <= numel(src_files)
|
||||
flist = src_files{partId};
|
||||
for i = 1:numel(flist)
|
||||
fid = fopen(flist{i});
|
||||
if (fid == -1)
|
||||
error('Error opening %s (is it missing?)', flist{i});
|
||||
end
|
||||
line = fgets(fid);
|
||||
while ischar(line)
|
||||
src = [src line];
|
||||
line = fgets(fid);
|
||||
end
|
||||
fclose(fid);
|
||||
src = [src '||||||||'];
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function ret = isValidPartId(partId)
|
||||
partNames = validParts();
|
||||
ret = (~isempty(partId)) && (partId >= 1) && (partId <= numel(partNames) + 1);
|
||||
end
|
||||
|
||||
function partId = promptPart()
|
||||
fprintf('== Select which part(s) to submit:\n');
|
||||
partNames = validParts();
|
||||
srcFiles = sources();
|
||||
for i = 1:numel(partNames)
|
||||
fprintf('== %d) %s [', i, partNames{i});
|
||||
fprintf(' %s ', srcFiles{i}{:});
|
||||
fprintf(']\n');
|
||||
end
|
||||
fprintf('== %d) All of the above \n==\nEnter your choice [1-%d]: ', ...
|
||||
numel(partNames) + 1, numel(partNames) + 1);
|
||||
selPart = input('', 's');
|
||||
partId = str2num(selPart);
|
||||
if ~isValidPartId(partId)
|
||||
partId = -1;
|
||||
end
|
||||
end
|
||||
|
||||
function [email,ch,signature,auxstring] = getChallenge(email, part)
|
||||
str = urlread(challenge_url(), 'post', {'email_address', email, 'assignment_part_sid', [homework_id() '-' num2str(part)], 'response_encoding', 'delim'});
|
||||
|
||||
str = strtrim(str);
|
||||
r = struct;
|
||||
while(numel(str) > 0)
|
||||
[f, str] = strtok (str, '|');
|
||||
[v, str] = strtok (str, '|');
|
||||
r = setfield(r, f, v);
|
||||
end
|
||||
|
||||
email = getfield(r, 'email_address');
|
||||
ch = getfield(r, 'challenge_key');
|
||||
signature = getfield(r, 'state');
|
||||
auxstring = getfield(r, 'challenge_aux_data');
|
||||
end
|
||||
|
||||
function [result, str] = submitSolutionWeb(email, part, output, source)
|
||||
|
||||
result = ['{"assignment_part_sid":"' base64encode([homework_id() '-' num2str(part)], '') '",' ...
|
||||
'"email_address":"' base64encode(email, '') '",' ...
|
||||
'"submission":"' base64encode(output, '') '",' ...
|
||||
'"submission_aux":"' base64encode(source, '') '"' ...
|
||||
'}'];
|
||||
str = 'Web-submission';
|
||||
end
|
||||
|
||||
function [result, str] = submitSolution(email, ch_resp, part, output, ...
|
||||
source, signature)
|
||||
|
||||
params = {'assignment_part_sid', [homework_id() '-' num2str(part)], ...
|
||||
'email_address', email, ...
|
||||
'submission', base64encode(output, ''), ...
|
||||
'submission_aux', base64encode(source, ''), ...
|
||||
'challenge_response', ch_resp, ...
|
||||
'state', signature};
|
||||
|
||||
str = urlread(submit_url(), 'post', params);
|
||||
|
||||
% Parse str to read for success / failure
|
||||
result = 0;
|
||||
|
||||
end
|
||||
|
||||
% =========================== LOGIN HELPERS ===========================
|
||||
|
||||
function [login password] = loginPrompt()
|
||||
% Prompt for password
|
||||
[login password] = basicPrompt();
|
||||
|
||||
if isempty(login) || isempty(password)
|
||||
login = []; password = [];
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function [login password] = basicPrompt()
|
||||
login = input('Login (Email address): ', 's');
|
||||
password = input('Password: ', 's');
|
||||
end
|
||||
|
||||
function [login password] = quickLogin(login,password)
|
||||
disp(['You are currently logged in as ' login '.']);
|
||||
cont_token = input('Is this you? (y/n - type n to reenter password)','s');
|
||||
if(isempty(cont_token) || cont_token(1)=='Y'||cont_token(1)=='y')
|
||||
return;
|
||||
else
|
||||
[login password] = loginPrompt();
|
||||
end
|
||||
end
|
||||
|
||||
function [str] = challengeResponse(email, passwd, challenge)
|
||||
str = sha1([challenge passwd]);
|
||||
end
|
||||
|
||||
% =============================== SHA-1 ================================
|
||||
|
||||
function hash = sha1(str)
|
||||
|
||||
% Initialize variables
|
||||
h0 = uint32(1732584193);
|
||||
h1 = uint32(4023233417);
|
||||
h2 = uint32(2562383102);
|
||||
h3 = uint32(271733878);
|
||||
h4 = uint32(3285377520);
|
||||
|
||||
% Convert to word array
|
||||
strlen = numel(str);
|
||||
|
||||
% Break string into chars and append the bit 1 to the message
|
||||
mC = [double(str) 128];
|
||||
mC = [mC zeros(1, 4-mod(numel(mC), 4), 'uint8')];
|
||||
|
||||
numB = strlen * 8;
|
||||
if exist('idivide')
|
||||
numC = idivide(uint32(numB + 65), 512, 'ceil');
|
||||
else
|
||||
numC = ceil(double(numB + 65)/512);
|
||||
end
|
||||
numW = numC * 16;
|
||||
mW = zeros(numW, 1, 'uint32');
|
||||
|
||||
idx = 1;
|
||||
for i = 1:4:strlen + 1
|
||||
mW(idx) = bitor(bitor(bitor( ...
|
||||
bitshift(uint32(mC(i)), 24), ...
|
||||
bitshift(uint32(mC(i+1)), 16)), ...
|
||||
bitshift(uint32(mC(i+2)), 8)), ...
|
||||
uint32(mC(i+3)));
|
||||
idx = idx + 1;
|
||||
end
|
||||
|
||||
% Append length of message
|
||||
mW(numW - 1) = uint32(bitshift(uint64(numB), -32));
|
||||
mW(numW) = uint32(bitshift(bitshift(uint64(numB), 32), -32));
|
||||
|
||||
% Process the message in successive 512-bit chs
|
||||
for cId = 1 : double(numC)
|
||||
cSt = (cId - 1) * 16 + 1;
|
||||
cEnd = cId * 16;
|
||||
ch = mW(cSt : cEnd);
|
||||
|
||||
% Extend the sixteen 32-bit words into eighty 32-bit words
|
||||
for j = 17 : 80
|
||||
ch(j) = ch(j - 3);
|
||||
ch(j) = bitxor(ch(j), ch(j - 8));
|
||||
ch(j) = bitxor(ch(j), ch(j - 14));
|
||||
ch(j) = bitxor(ch(j), ch(j - 16));
|
||||
ch(j) = bitrotate(ch(j), 1);
|
||||
end
|
||||
|
||||
% Initialize hash value for this ch
|
||||
a = h0;
|
||||
b = h1;
|
||||
c = h2;
|
||||
d = h3;
|
||||
e = h4;
|
||||
|
||||
% Main loop
|
||||
for i = 1 : 80
|
||||
if(i >= 1 && i <= 20)
|
||||
f = bitor(bitand(b, c), bitand(bitcmp(b), d));
|
||||
k = uint32(1518500249);
|
||||
elseif(i >= 21 && i <= 40)
|
||||
f = bitxor(bitxor(b, c), d);
|
||||
k = uint32(1859775393);
|
||||
elseif(i >= 41 && i <= 60)
|
||||
f = bitor(bitor(bitand(b, c), bitand(b, d)), bitand(c, d));
|
||||
k = uint32(2400959708);
|
||||
elseif(i >= 61 && i <= 80)
|
||||
f = bitxor(bitxor(b, c), d);
|
||||
k = uint32(3395469782);
|
||||
end
|
||||
|
||||
t = bitrotate(a, 5);
|
||||
t = bitadd(t, f);
|
||||
t = bitadd(t, e);
|
||||
t = bitadd(t, k);
|
||||
t = bitadd(t, ch(i));
|
||||
e = d;
|
||||
d = c;
|
||||
c = bitrotate(b, 30);
|
||||
b = a;
|
||||
a = t;
|
||||
|
||||
end
|
||||
h0 = bitadd(h0, a);
|
||||
h1 = bitadd(h1, b);
|
||||
h2 = bitadd(h2, c);
|
||||
h3 = bitadd(h3, d);
|
||||
h4 = bitadd(h4, e);
|
||||
|
||||
end
|
||||
|
||||
hash = reshape(dec2hex(double([h0 h1 h2 h3 h4]), 8)', [1 40]);
|
||||
|
||||
hash = lower(hash);
|
||||
|
||||
end
|
||||
|
||||
function ret = bitadd(iA, iB)
|
||||
ret = double(iA) + double(iB);
|
||||
ret = bitset(ret, 33, 0);
|
||||
ret = uint32(ret);
|
||||
end
|
||||
|
||||
function ret = bitrotate(iA, places)
|
||||
t = bitshift(iA, places - 32);
|
||||
ret = bitshift(iA, places);
|
||||
ret = bitor(ret, t);
|
||||
end
|
||||
|
||||
% =========================== Base64 Encoder ============================
|
||||
% Thanks to Peter John Acklam
|
||||
%
|
||||
|
||||
function y = base64encode(x, eol)
|
||||
%BASE64ENCODE Perform base64 encoding on a string.
|
||||
%
|
||||
% BASE64ENCODE(STR, EOL) encode the given string STR. EOL is the line ending
|
||||
% sequence to use; it is optional and defaults to '\n' (ASCII decimal 10).
|
||||
% The returned encoded string is broken into lines of no more than 76
|
||||
% characters each, and each line will end with EOL unless it is empty. Let
|
||||
% EOL be empty if you do not want the encoded string broken into lines.
|
||||
%
|
||||
% STR and EOL don't have to be strings (i.e., char arrays). The only
|
||||
% requirement is that they are vectors containing values in the range 0-255.
|
||||
%
|
||||
% This function may be used to encode strings into the Base64 encoding
|
||||
% specified in RFC 2045 - MIME (Multipurpose Internet Mail Extensions). The
|
||||
% Base64 encoding is designed to represent arbitrary sequences of octets in a
|
||||
% form that need not be humanly readable. A 65-character subset
|
||||
% ([A-Za-z0-9+/=]) of US-ASCII is used, enabling 6 bits to be represented per
|
||||
% printable character.
|
||||
%
|
||||
% Examples
|
||||
% --------
|
||||
%
|
||||
% If you want to encode a large file, you should encode it in chunks that are
|
||||
% a multiple of 57 bytes. This ensures that the base64 lines line up and
|
||||
% that you do not end up with padding in the middle. 57 bytes of data fills
|
||||
% one complete base64 line (76 == 57*4/3):
|
||||
%
|
||||
% If ifid and ofid are two file identifiers opened for reading and writing,
|
||||
% respectively, then you can base64 encode the data with
|
||||
%
|
||||
% while ~feof(ifid)
|
||||
% fwrite(ofid, base64encode(fread(ifid, 60*57)));
|
||||
% end
|
||||
%
|
||||
% or, if you have enough memory,
|
||||
%
|
||||
% fwrite(ofid, base64encode(fread(ifid)));
|
||||
%
|
||||
% See also BASE64DECODE.
|
||||
|
||||
% Author: Peter John Acklam
|
||||
% Time-stamp: 2004-02-03 21:36:56 +0100
|
||||
% E-mail: pjacklam@online.no
|
||||
% URL: http://home.online.no/~pjacklam
|
||||
|
||||
if isnumeric(x)
|
||||
x = num2str(x);
|
||||
end
|
||||
|
||||
% make sure we have the EOL value
|
||||
if nargin < 2
|
||||
eol = sprintf('\n');
|
||||
else
|
||||
if sum(size(eol) > 1) > 1
|
||||
error('EOL must be a vector.');
|
||||
end
|
||||
if any(eol(:) > 255)
|
||||
error('EOL can not contain values larger than 255.');
|
||||
end
|
||||
end
|
||||
|
||||
if sum(size(x) > 1) > 1
|
||||
error('STR must be a vector.');
|
||||
end
|
||||
|
||||
x = uint8(x);
|
||||
eol = uint8(eol);
|
||||
|
||||
ndbytes = length(x); % number of decoded bytes
|
||||
nchunks = ceil(ndbytes / 3); % number of chunks/groups
|
||||
nebytes = 4 * nchunks; % number of encoded bytes
|
||||
|
||||
% add padding if necessary, to make the length of x a multiple of 3
|
||||
if rem(ndbytes, 3)
|
||||
x(end+1 : 3*nchunks) = 0;
|
||||
end
|
||||
|
||||
x = reshape(x, [3, nchunks]); % reshape the data
|
||||
y = repmat(uint8(0), 4, nchunks); % for the encoded data
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% Split up every 3 bytes into 4 pieces
|
||||
%
|
||||
% aaaaaabb bbbbcccc ccdddddd
|
||||
%
|
||||
% to form
|
||||
%
|
||||
% 00aaaaaa 00bbbbbb 00cccccc 00dddddd
|
||||
%
|
||||
y(1,:) = bitshift(x(1,:), -2); % 6 highest bits of x(1,:)
|
||||
|
||||
y(2,:) = bitshift(bitand(x(1,:), 3), 4); % 2 lowest bits of x(1,:)
|
||||
y(2,:) = bitor(y(2,:), bitshift(x(2,:), -4)); % 4 highest bits of x(2,:)
|
||||
|
||||
y(3,:) = bitshift(bitand(x(2,:), 15), 2); % 4 lowest bits of x(2,:)
|
||||
y(3,:) = bitor(y(3,:), bitshift(x(3,:), -6)); % 2 highest bits of x(3,:)
|
||||
|
||||
y(4,:) = bitand(x(3,:), 63); % 6 lowest bits of x(3,:)
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% Now perform the following mapping
|
||||
%
|
||||
% 0 - 25 -> A-Z
|
||||
% 26 - 51 -> a-z
|
||||
% 52 - 61 -> 0-9
|
||||
% 62 -> +
|
||||
% 63 -> /
|
||||
%
|
||||
% We could use a mapping vector like
|
||||
%
|
||||
% ['A':'Z', 'a':'z', '0':'9', '+/']
|
||||
%
|
||||
% but that would require an index vector of class double.
|
||||
%
|
||||
z = repmat(uint8(0), size(y));
|
||||
i = y <= 25; z(i) = 'A' + double(y(i));
|
||||
i = 26 <= y & y <= 51; z(i) = 'a' - 26 + double(y(i));
|
||||
i = 52 <= y & y <= 61; z(i) = '0' - 52 + double(y(i));
|
||||
i = y == 62; z(i) = '+';
|
||||
i = y == 63; z(i) = '/';
|
||||
y = z;
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% Add padding if necessary.
|
||||
%
|
||||
npbytes = 3 * nchunks - ndbytes; % number of padding bytes
|
||||
if npbytes
|
||||
y(end-npbytes+1 : end) = '='; % '=' is used for padding
|
||||
end
|
||||
|
||||
if isempty(eol)
|
||||
|
||||
% reshape to a row vector
|
||||
y = reshape(y, [1, nebytes]);
|
||||
|
||||
else
|
||||
|
||||
nlines = ceil(nebytes / 76); % number of lines
|
||||
neolbytes = length(eol); % number of bytes in eol string
|
||||
|
||||
% pad data so it becomes a multiple of 76 elements
|
||||
y = [y(:) ; zeros(76 * nlines - numel(y), 1)];
|
||||
y(nebytes + 1 : 76 * nlines) = 0;
|
||||
y = reshape(y, 76, nlines);
|
||||
|
||||
% insert eol strings
|
||||
eol = eol(:);
|
||||
y(end + 1 : end + neolbytes, :) = eol(:, ones(1, nlines));
|
||||
|
||||
% remove padding, but keep the last eol string
|
||||
m = nebytes + neolbytes * (nlines - 1);
|
||||
n = (76+neolbytes)*nlines - neolbytes;
|
||||
y(m+1 : n) = '';
|
||||
|
||||
% extract and reshape to row vector
|
||||
y = reshape(y, 1, m+neolbytes);
|
||||
|
||||
end
|
||||
|
||||
% output is a character array
|
||||
y = char(y);
|
||||
|
||||
end
|
||||
@ -0,0 +1,20 @@
|
||||
% submitWeb Creates files from your code and output for web submission.
|
||||
%
|
||||
% If the submit function does not work for you, use the web-submission mechanism.
|
||||
% Call this function to produce a file for the part you wish to submit. Then,
|
||||
% submit the file to the class servers using the "Web Submission" button on the
|
||||
% Programming Exercises page on the course website.
|
||||
%
|
||||
% You should call this function without arguments (submitWeb), to receive
|
||||
% an interactive prompt for submission; optionally you can call it with the partID
|
||||
% if you so wish. Make sure your working directory is set to the directory
|
||||
% containing the submitWeb.m file and your assignment files.
|
||||
|
||||
function submitWeb(partId)
|
||||
if ~exist('partId', 'var') || isempty(partId)
|
||||
partId = [];
|
||||
end
|
||||
|
||||
submit(partId, 1);
|
||||
end
|
||||
|
||||
Reference in New Issue