Re: error: d:\download\D:\Downloads\Octave_ex: Invalid argument

[Patrick Pintelon]

    I do it now. Thanks !!

On Thu, Feb 20, 2020, 6:10 PM Patrick Pintelon <address@hidden> wrote:

Thanks for replyi,g.

Concerns this script: below

k.reg.


On 21/02/2020 00:01, nrjank wrote:
> are you trying to post directly to the web archive at nabble.com?  You can
> instead try emailing directly to address@hidden
>
> just use plain text in your email and it should come through fine.
>
>
>
> --
> Sent from: https://octave.1599824.n4.nabble.com/Octave-General-f1599825.html
>
>
--
Greta & Patrick Pintelon
address@hidden
address@hidden

----------------------------------ex3.m------------------------------------------------

%% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all
% ZIE vraag onderaan van pp op https://iotmakerblog.wordpress.com/2016/06/21/03-octave-programmingmulti-class-classication-and-neural-networks/comment-page-1/?unapproved=593&moderation-hash=28c913ab5ea52e693c1aa13a35c3a47c#comment-593

%  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);

 

-- 
Greta & Patrick Pintelon
address@hidden
address@hidden