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comparison validationCurve.m @ 0:0f14514e907f

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initial commit
author Jordi Gutiérrez Hermoso <jordigh@octave.org>
date Sun, 20 Nov 2011 23:26:16 -0500
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children 4b6c21158ba6
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-1:000000000000 0:0f14514e907f
1 function [lambda_vec, error_train, error_val] = ...
2 validationCurve(X, y, Xval, yval)
3 %VALIDATIONCURVE Generate the train and validation errors needed to
4 %plot a validation curve that we can use to select lambda
5 % [lambda_vec, error_train, error_val] = ...
6 % VALIDATIONCURVE(X, y, Xval, yval) returns the train
7 % and validation errors (in error_train, error_val)
8 % for different values of lambda. You are given the training set (X,
9 % y) and validation set (Xval, yval).
10 %
11
12 % Selected values of lambda (you should not change this)
13 lambda_vec = [0 0.001 0.003 0.01 0.03 0.1 0.3 1 3 10]';
14
15 % You need to return these variables correctly.
16 error_train = zeros(length(lambda_vec), 1);
17 error_val = zeros(length(lambda_vec), 1);
18
19 % ====================== YOUR CODE HERE ======================
20 % Instructions: Fill in this function to return training errors in
21 % error_train and the validation errors in error_val. The
22 % vector lambda_vec contains the different lambda parameters
23 % to use for each calculation of the errors, i.e,
24 % error_train(i), and error_val(i) should give
25 % you the errors obtained after training with
26 % lambda = lambda_vec(i)
27 %
28 % Note: You can loop over lambda_vec with the following:
29 %
30 % for i = 1:length(lambda_vec)
31 % lambda = lambda_vec(i);
32 % % Compute train / val errors when training linear
33 % % regression with regularization parameter lambda
34 % % You should store the result in error_train(i)
35 % % and error_val(i)
36 % ....
37 %
38 % end
39 %
40 %
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51 % =========================================================================
52
53 end