You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
70 lines
2.1 KiB
Matlab
70 lines
2.1 KiB
Matlab
10 years ago
|
%% 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 2: 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.
|
||
|
|
%
|
||
|
|
|
||
|
|
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 ================
|
||
|
|
% After ...
|
||
|
|
pred = predictOneVsAll(all_theta, X);
|
||
|
|
|
||
|
|
fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
|
||
|
|
|