init ex3

Author: atupal

demo_snippet/infolit/file::memory:

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+function [h, display_array] = displayData(X, example_width)
+%DISPLAYDATA Display 2D data in a nice grid
+%   [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data
+%   stored in X in a nice grid. It returns the figure handle h and the 
+%   displayed array if requested.
+
+% Set example_width automatically if not passed in
+if ~exist('example_width', 'var') || isempty(example_width) 
+	example_width = round(sqrt(size(X, 2)));
+end
+
+% Gray Image
+colormap(gray);
+
+% Compute rows, cols
+[m n] = size(X);
+example_height = (n / example_width);
+
+% Compute number of items to display
+display_rows = floor(sqrt(m));
+display_cols = ceil(m / display_rows);
+
+% Between images padding
+pad = 1;
+
+% Setup blank display
+display_array = - ones(pad + display_rows * (example_height + pad), ...
+                       pad + display_cols * (example_width + pad));
+
+% Copy each example into a patch on the display array
+curr_ex = 1;
+for j = 1:display_rows
+	for i = 1:display_cols
+		if curr_ex > m, 
+			break; 
+		end
+		% Copy the patch
+		
+		% Get the max value of the patch
+		max_val = max(abs(X(curr_ex, :)));
+		display_array(pad + (j - 1) * (example_height + pad) + (1:example_height), ...
+		              pad + (i - 1) * (example_width + pad) + (1:example_width)) = ...
+						reshape(X(curr_ex, :), example_height, example_width) / max_val;
+		curr_ex = curr_ex + 1;
+	end
+	if curr_ex > m, 
+		break; 
+	end
+end
+
+% Display Image
+h = imagesc(display_array, [-1 1]);
+
+% Do not show axis
+axis image off
+
+drawnow;
+
+end

machine_learning_snippet/Stanford Course- Maching Learning/machine-learning-ex1/ex1/ex1_multi.asv

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+%% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all
+
+%  Instructions
+%  ------------
+%
+%  This file contains code that helps you get started on the
+%  linear exercise. You will need to complete the following functions
+%  in this exericse:
+%
+%     lrCostFunction.m (logistic regression cost function)
+%     oneVsAll.m
+%     predictOneVsAll.m
+%     predict.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
+
+%% Setup the parameters you will use for this part of the exercise
+input_layer_size  = 400;  % 20x20 Input Images of Digits
+num_labels = 10;          % 10 labels, from 1 to 10
+                          % (note that we have mapped "0" to label 10)
+
+%% =========== Part 1: Loading and Visualizing Data =============
+%  We start the exercise by first loading and visualizing the dataset.
+%  You will be working with a dataset that contains handwritten digits.
+%
+
+% Load Training Data
+fprintf('Loading and Visualizing Data ...\n')
+
+load('ex3data1.mat'); % training data stored in arrays X, y
+m = size(X, 1);
+
+% Randomly select 100 data points to display
+rand_indices = randperm(m);
+sel = X(rand_indices(1:100), :);
+
+displayData(sel);
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+%% ============ Part 2a: Vectorize Logistic Regression ============
+%  In this part of the exercise, you will reuse your logistic regression
+%  code from the last exercise. You task here is to make sure that your
+%  regularized logistic regression implementation is vectorized. After
+%  that, you will implement one-vs-all classification for the handwritten
+%  digit dataset.
+%
+
+% Test case for lrCostFunction
+fprintf('\nTesting lrCostFunction() with regularization');
+
+theta_t = [-2; -1; 1; 2];
+X_t = [ones(5,1) reshape(1:15,5,3)/10];
+y_t = ([1;0;1;0;1] >= 0.5);
+lambda_t = 3;
+[J grad] = lrCostFunction(theta_t, X_t, y_t, lambda_t);
+
+fprintf('\nCost: %f\n', J);
+fprintf('Expected cost: 2.534819\n');
+fprintf('Gradients:\n');
+fprintf(' %f \n', grad);
+fprintf('Expected gradients:\n');
+fprintf(' 0.146561\n -0.548558\n 0.724722\n 1.398003\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+%% ============ Part 2b: One-vs-All Training ============
+fprintf('\nTraining One-vs-All Logistic Regression...\n')
+
+lambda = 0.1;
+[all_theta] = oneVsAll(X, y, num_labels, lambda);
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+
+%% ================ Part 3: Predict for One-Vs-All ================
+
+pred = predictOneVsAll(all_theta, X);
+
+fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
+