Use new unit tests interface.
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GPG Key ID:
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@ -101,10 +101,10 @@ It's useful to contribute `.mod` files that test some aspect of Dynare that is n
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### Unit tests
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So-called unit tests allow the test suite to check the correct functioning of the MATLAB/Octave functions contained in Dynare. To add a unit test you need to
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1. add the keyword ` % --*-- Unitary tests --*--` at the end of the `function` header to tell the testsuite that the file contains unit tests.
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1. Add the particular tests at the end of the file after a `return` by
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1. Starting a test with `%@test:INTEGER`
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So-called unit tests allow the test suite to check the correct functioning of the MATLAB/Octave functions contained in Dynare. To add a unit test you need to
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1. add the `return % --*-- Unit tests --*--` at the end of the `function` to tell the testsuite that the file contains unit tests.
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1. Add the particular tests at the end of the file after the `return` statement by
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1. Starting a test with `%@test:INTEGER`
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2. Adding a MATLAB/Octave test code that provides a pass/fail indicator `T` that takes on `true` if the test passed.
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3. Closing the test with `%@eof:INTEGER`
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where `INTEGER` denotes the number of the test.
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@ -112,9 +112,9 @@ So-called unit tests allow the test suite to check the correct functioning of th
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An example testing the correct functionality of mode-computations for a normal distribution is
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```
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function m = compute_prior_mode(hyperparameters,shape) % --*-- Unitary tests --*--
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function m = compute_prior_mode(hyperparameters,shape)
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return
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return % --*-- Unit tests --*--
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%@test:1
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% Normal density
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@ -1,4 +1,4 @@
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function [nodes, weights] = cubature_with_gaussian_weight(d,n,method) % --*-- Unitary tests --*--
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function [nodes, weights] = cubature_with_gaussian_weight(d, n, method)
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% Computes nodes and weights for a n-order cubature with gaussian weight.
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%
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@ -17,7 +17,7 @@ function [nodes, weights] = cubature_with_gaussian_weight(d,n,method) % --*-- U
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% ∫ f(x) × e dx
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% -∞
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% Copyright © 2012-2019 Dynare Team
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% Copyright © 2012-2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -108,7 +108,7 @@ m(:,2) = e(n,i)-e(n,j);
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m(:,3) = -m(:,2);
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m(:,4) = -m(:,1);
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return
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return % --*-- Unit tests --*--
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%@test:1
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d = 4;
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@ -267,4 +267,4 @@ if t(1)
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t(4) = dassert(m3,zeros(d,1),1e-12);
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T = all(t);
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end
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%@eof:6
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%@eof:6
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@ -1,4 +1,4 @@
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function [a, b] = beta_specification(mu, sigma2, lb, ub, name) % --*-- Unitary tests --*--
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function [a, b] = beta_specification(mu, sigma2, lb, ub, name)
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% Returns the hyperparameters of the beta distribution given the expectation and variance.
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%
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@ -12,7 +12,7 @@ function [a, b] = beta_specification(mu, sigma2, lb, ub, name) % --*-- Unitary
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% - a [double] First hyperparameter of the Beta density.
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% - b [double] Second hyperparameter of the Beta density.
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% Copyright © 2015-2017 Dynare Team
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% Copyright © 2015-2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -66,67 +66,69 @@ end
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a = (1-mu)*mu*mu/sigma2-mu;
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b = a*(1/mu-1);
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return % --*-- Unit tests --*--
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%@test:1
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%$ try
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%$ [a, b] = beta_specification(.5, .05);
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%$ t(1) = true;
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%$ catch
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%$ t(1) = false;
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%$ end
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%$
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%$ if t(1)
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%$ t(2) = abs(0.5-a/(a+b))<1e-12;
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%$ t(3) = abs(0.05-a*b/((a+b)^2*(a+b+1)))<1e-12;
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%$ end
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%$ T = all(t);
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try
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[a, b] = beta_specification(.5, .05);
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t(1) = true;
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catch
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t(1) = false;
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end
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if t(1)
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t(2) = abs(0.5-a/(a+b))<1e-12;
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t(3) = abs(0.05-a*b/((a+b)^2*(a+b+1)))<1e-12;
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end
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T = all(t);
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%@eof:1
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%@test:2
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%$ try
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%$ [a, b] = beta_specification(0.5, .05, 1, 2);
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%$ t(1) = false;
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%$ catch
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%$ t(1) = true;
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%$ end
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%$
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%$ T = all(t);
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try
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[a, b] = beta_specification(0.5, .05, 1, 2);
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t(1) = false;
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catch
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t(1) = true;
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end
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T = all(t);
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%@eof:2
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%@test:3
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%$ try
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%$ [a, b] = beta_specification(2.5, .05, 1, 2);
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%$ t(1) = false;
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%$ catch
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%$ t(1) = true;
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%$ end
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%$
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%$ T = all(t);
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try
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[a, b] = beta_specification(2.5, .05, 1, 2);
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t(1) = false;
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catch
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t(1) = true;
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end
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T = all(t);
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%@eof:3
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%@test:4
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%$ try
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%$ [a, b] = beta_specification(.4, .6*.4+1e-4);
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%$ t(1) = false;
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%$ catch
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%$ t(1) = true;
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%$ end
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%$
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%$ T = all(t);
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try
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[a, b] = beta_specification(.4, .6*.4+1e-4);
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t(1) = false;
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catch
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t(1) = true;
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end
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T = all(t);
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%@eof:4
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%@test:5
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%$ try
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%$ [a, b] = beta_specification(.4, .6*.4+1e-6);
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%$ t(1) = false;
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%$ catch
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%$ t(1) = true;
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%$ end
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%$ try
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%$ [a, b] = beta_specification(.4, .6*.4-1e-6);
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%$ t(2) = true;
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%$ catch
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%$ t(2) = false;
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%$ end
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%$
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%$ T = all(t);
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try
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[a, b] = beta_specification(.4, .6*.4+1e-6);
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t(1) = false;
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catch
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t(1) = true;
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end
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try
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[a, b] = beta_specification(.4, .6*.4-1e-6);
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t(2) = true;
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catch
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t(2) = false;
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end
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T = all(t);
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%@eof:5
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@ -1,4 +1,4 @@
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function m = compute_prior_mode(hyperparameters,shape) % --*-- Unitary tests --*--
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function m = compute_prior_mode(hyperparameters,shape)
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% This function computes the mode of the prior distribution given the (two, three or four) hyperparameters
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% of the prior distribution.
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%
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@ -23,7 +23,7 @@ function m = compute_prior_mode(hyperparameters,shape) % --*-- Unitary tests --*
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% [3] The uniform distribution has an infinity of modes. In this case the function returns the prior mean.
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% [4] For the beta distribution we can have 1, 2 or an infinity of modes.
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% Copyright © 2009-2017 Dynare Team
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% Copyright © 2009-2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -102,140 +102,142 @@ switch shape
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error('Unknown prior shape!')
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end
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return % --*-- Unit tests --*--
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%@test:1
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%$ % Beta density
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%$ try
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%$ m1 = compute_prior_mode([2 1],1);
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%$ m2 = compute_prior_mode([2 5 1 4],1); % Wolfram Alpha: BetaDistribution[2,5]
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%$ t(1) = true;
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%$ catch
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%$ t(1) = false;
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%$ end
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%$
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%$ % Check the results
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%$ if t(1)
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%$ t(2) = dassert(m1,1,1e-6);
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%$ t(3) = dassert(m2,1/5*3+1,1e-6);
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%$ end
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%$ T = all(t);
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% Beta density
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try
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m1 = compute_prior_mode([2 1],1);
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m2 = compute_prior_mode([2 5 1 4],1); % Wolfram Alpha: BetaDistribution[2,5]
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t(1) = true;
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catch
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t(1) = false;
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end
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% Check the results
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if t(1)
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t(2) = dassert(m1,1,1e-6);
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t(3) = dassert(m2,1/5*3+1,1e-6);
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end
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T = all(t);
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%@eof:1
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%@test:2
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%$ % Gamma density
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%$ try
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%$ m1 = compute_prior_mode([1 2],2);
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%$ m2 = compute_prior_mode([9 0.5 1],2); % Wolfram Alpha: GammaDistribution[9,0.5]
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%$ t(1) = true;
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%$ catch
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%$ t(1) = false;
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%$ end
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%$
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%$ % Check the results
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%$ if t(1)
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%$ t(2) = dassert(m1,0,1e-6);
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%$ t(3) = dassert(m2,4+1,1e-6);
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%$ end
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%$ T = all(t);
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% Gamma density
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try
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m1 = compute_prior_mode([1 2],2);
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m2 = compute_prior_mode([9 0.5 1],2); % Wolfram Alpha: GammaDistribution[9,0.5]
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t(1) = true;
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catch
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t(1) = false;
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end
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% Check the results
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if t(1)
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t(2) = dassert(m1,0,1e-6);
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t(3) = dassert(m2,4+1,1e-6);
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end
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T = all(t);
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%@eof:2
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%@test:3
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%$ % Normal density
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%$ try
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%$ m1 = compute_prior_mode([1 1],3);
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%$ m2 = compute_prior_mode([2 5],3);
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%$ t(1) = true;
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%$ catch
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%$ t(1) = false;
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%$ end
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%$
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%$ % Check the results
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%$ if t(1)
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%$ t(2) = dassert(m1,1,1e-6);
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%$ t(3) = dassert(m2,2,1e-6);
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%$ end
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%$ T = all(t);
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% Normal density
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try
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m1 = compute_prior_mode([1 1],3);
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m2 = compute_prior_mode([2 5],3);
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t(1) = true;
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catch
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t(1) = false;
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end
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% Check the results
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if t(1)
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t(2) = dassert(m1,1,1e-6);
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t(3) = dassert(m2,2,1e-6);
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end
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T = all(t);
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%@eof:3
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%@test:4
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%$ % Inverse Gamma I density
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%$ try
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%$ m1 = compute_prior_mode([8 2],4);
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%$ m2 = compute_prior_mode([8 2 1],4);
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%$ t(1) = true;
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%$ catch
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%$ t(1) = false;
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%$ end
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%$
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%$ % Check the results
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%$ if t(1)
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%$ t(2) = dassert(m1,1.632993161855452,1e-6);
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%$ t(3) = dassert(m2,1.632993161855452+1,1e-6);
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%$ end
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%$ T = all(t);
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% Inverse Gamma I density
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try
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m1 = compute_prior_mode([8 2],4);
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m2 = compute_prior_mode([8 2 1],4);
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t(1) = true;
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catch
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t(1) = false;
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end
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% Check the results
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if t(1)
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t(2) = dassert(m1,1.632993161855452,1e-6);
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t(3) = dassert(m2,1.632993161855452+1,1e-6);
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end
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T = all(t);
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%@eof:4
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%@test:5
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%$ % Uniform density
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%$ try
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%$ m1 = compute_prior_mode([0.5 1/sqrt(12)],5);
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%$ m2 = compute_prior_mode([2 5 1 2],5);
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%$ t(1) = true;
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%$ catch
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%$ t(1) = false;
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%$ end
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%$
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%$ % Check the results
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%$ if t(1)
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%$ t(2) = dassert(m1,0.5,1e-6);
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%$ t(3) = dassert(m2,2,1e-6);
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%$ end
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%$ T = all(t);
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% Uniform density
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try
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m1 = compute_prior_mode([0.5 1/sqrt(12)],5);
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m2 = compute_prior_mode([2 5 1 2],5);
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t(1) = true;
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catch
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t(1) = false;
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end
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% Check the results
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if t(1)
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t(2) = dassert(m1,0.5,1e-6);
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t(3) = dassert(m2,2,1e-6);
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end
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T = all(t);
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%@eof:5
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%@test:6
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%$ % Inverse Gamma II density, parameterized with s and nu where s=2*beta and nu=2*alpha
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%$ try
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%$ m1 = compute_prior_mode([8 2],6); % Wolfram Alpha, parameterized with alpha beta: InversegammaDistribution[1,4]
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%$ m2 = compute_prior_mode([8 4 1],6); % Wolfram Alpha, parameterized with alpha beta: InversegammaDistribution[2,4]
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%$ t(1) = true;
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%$ catch
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%$ t(1) = false;
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%$ end
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%$
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%$ % Check the results
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%$ if t(1)
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%$ t(2) = dassert(m1,2,1e-6);
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%$ t(3) = dassert(m2,1+4/3,1e-6);
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%$ end
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%$ T = all(t);
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% Inverse Gamma II density, parameterized with s and nu where s=2*beta and nu=2*alpha
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try
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m1 = compute_prior_mode([8 2],6); % Wolfram Alpha, parameterized with alpha beta: InversegammaDistribution[1,4]
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m2 = compute_prior_mode([8 4 1],6); % Wolfram Alpha, parameterized with alpha beta: InversegammaDistribution[2,4]
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t(1) = true;
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catch
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t(1) = false;
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end
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% Check the results
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if t(1)
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t(2) = dassert(m1,2,1e-6);
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t(3) = dassert(m2,1+4/3,1e-6);
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end
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T = all(t);
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%@eof:6
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%@test:7
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%$ % Weibull density
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%$ try
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%$ m1 = compute_prior_mode([1 1],8);
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%$ m2 = compute_prior_mode([2 1 1],8); % Wolfram Alpha: WeibullDistribution[2,1]
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%$ t(1) = true;
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%$ catch
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%$ t(1) = false;
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%$ end
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%$
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%$ % Check the results
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%$ if t(1)
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%$ t(2) = dassert(m1,0,1e-6);
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%$ t(3) = dassert(m2,1+1/sqrt(2),1e-6);
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%$ end
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%$ T = all(t);
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% Weibull density
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try
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m1 = compute_prior_mode([1 1],8);
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m2 = compute_prior_mode([2 1 1],8); % Wolfram Alpha: WeibullDistribution[2,1]
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t(1) = true;
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catch
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t(1) = false;
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end
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% Check the results
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if t(1)
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t(2) = dassert(m1,0,1e-6);
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t(3) = dassert(m2,1+1/sqrt(2),1e-6);
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end
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T = all(t);
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%@eof:7
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%@test:8
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%$ % Unknown density
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%$ try
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%$ m1 = compute_prior_mode([1 1],7);
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%$ t(1) = false;
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%$ catch
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%$ t(1) = true;
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%$ end
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%$
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%$ T = all(t);
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%@eof:8
|
||||
% Unknown density
|
||||
try
|
||||
m1 = compute_prior_mode([1 1],7);
|
||||
t(1) = false;
|
||||
catch
|
||||
t(1) = true;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:8
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
function [a, b] = gamma_specification(mu, sigma2, lb, name) % --*-- Unitary tests --*--
|
||||
function [a, b] = gamma_specification(mu, sigma2, lb, name)
|
||||
|
||||
% Returns the hyperparameters of the gamma distribution given the expectation and variance.
|
||||
%
|
||||
|
|
@ -12,7 +12,7 @@ function [a, b] = gamma_specification(mu, sigma2, lb, name) % --*-- Unitary te
|
|||
% - a [double] First hyperparameter of the Gamma density (shape).
|
||||
% - b [double] Second hyperparameter of the Gamma density (scale).
|
||||
|
||||
% Copyright © 2015-2017 Dynare Team
|
||||
% Copyright © 2015-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -53,54 +53,56 @@ mu = mu-lb;
|
|||
b = sigma2/mu;
|
||||
a = mu/b;
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ try
|
||||
%$ [a, b] = gamma_specification(.5, 1);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(0.5-a*b)<1e-12;
|
||||
%$ t(3) = abs(1.0-a*b*b)<1e-12;
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
try
|
||||
[a, b] = gamma_specification(.5, 1);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(0.5-a*b)<1e-12;
|
||||
t(3) = abs(1.0-a*b*b)<1e-12;
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
||||
%@test:2
|
||||
%$ try
|
||||
%$ [a, b] = gamma_specification(2, 1);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(2.0-a*b)<1e-12;
|
||||
%$ t(3) = abs(1.0-a*b*b)<1e-12;
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
try
|
||||
[a, b] = gamma_specification(2, 1);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(2.0-a*b)<1e-12;
|
||||
t(3) = abs(1.0-a*b*b)<1e-12;
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:2
|
||||
|
||||
%@test:3
|
||||
%$ try
|
||||
%$ [a, b] = gamma_specification(2, 1, 3);
|
||||
%$ t(1) = false;
|
||||
%$ catch
|
||||
%$ t(1) = true;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
try
|
||||
[a, b] = gamma_specification(2, 1, 3);
|
||||
t(1) = false;
|
||||
catch
|
||||
t(1) = true;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:3
|
||||
|
||||
%@test:4
|
||||
%$ try
|
||||
%$ [a, b] = gamma_specification(2, Inf, 3);
|
||||
%$ t(1) = false;
|
||||
%$ catch
|
||||
%$ t(1) = true;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
try
|
||||
[a, b] = gamma_specification(2, Inf, 3);
|
||||
t(1) = false;
|
||||
catch
|
||||
t(1) = true;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:4
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
function [s,nu] = inverse_gamma_specification(mu, sigma2, lb, type, use_fzero_flag, name) % --*-- Unitary tests --*--
|
||||
function [s,nu] = inverse_gamma_specification(mu, sigma2, lb, type, use_fzero_flag, name)
|
||||
|
||||
% Computes the inverse Gamma hyperparameters from the prior mean and variance.
|
||||
%
|
||||
|
|
@ -21,7 +21,7 @@ function [s,nu] = inverse_gamma_specification(mu, sigma2, lb, type, use_fzero_fl
|
|||
% more often in finding an interval for nu containing a signe change because it expands the interval on both sides and eventually
|
||||
% violates the condition nu>2.
|
||||
|
||||
% Copyright © 2003-2017 Dynare Team
|
||||
% Copyright © 2003-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -147,62 +147,64 @@ else
|
|||
error('inverse_gamma_specification: unkown type')
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ try
|
||||
%$ [s, nu] = inverse_gamma_specification(.5, .05, 0, 1);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(0.5-sqrt(.5*s)*gamma(.5*(nu-1))/gamma(.5*nu))<1e-12;
|
||||
%$ t(3) = abs(0.05-s/(nu-2)+.5^2)<1e-12;
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
try
|
||||
[s, nu] = inverse_gamma_specification(.5, .05, 0, 1);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(0.5-sqrt(.5*s)*gamma(.5*(nu-1))/gamma(.5*nu))<1e-12;
|
||||
t(3) = abs(0.05-s/(nu-2)+.5^2)<1e-12;
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
||||
%@test:2
|
||||
%$ try
|
||||
%$ [s, nu] = inverse_gamma_specification(.5, .05, 0, 2);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(0.5-s/(nu-2))<1e-12;
|
||||
%$ t(3) = abs(0.05-2*.5^2/(nu-4))<1e-12;
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
try
|
||||
[s, nu] = inverse_gamma_specification(.5, .05, 0, 2);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(0.5-s/(nu-2))<1e-12;
|
||||
t(3) = abs(0.05-2*.5^2/(nu-4))<1e-12;
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:2
|
||||
|
||||
%@test:3
|
||||
%$ try
|
||||
%$ [s, nu] = inverse_gamma_specification(.5, Inf, 0, 1);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(0.5-sqrt(.5*s)*gamma(.5*(nu-1))/gamma(.5*nu))<1e-12;
|
||||
%$ t(3) = isequal(nu, 2); %abs(0.05-2*.5^2/(nu-4))<1e-12;
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
try
|
||||
[s, nu] = inverse_gamma_specification(.5, Inf, 0, 1);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(0.5-sqrt(.5*s)*gamma(.5*(nu-1))/gamma(.5*nu))<1e-12;
|
||||
t(3) = isequal(nu, 2); %abs(0.05-2*.5^2/(nu-4))<1e-12;
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:3
|
||||
|
||||
%@test:4
|
||||
%$ try
|
||||
%$ [s, nu] = inverse_gamma_specification(.5, Inf, 0, 2);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(0.5-s/(nu-2))<1e-12;
|
||||
%$ t(3) = isequal(nu, 4);
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
try
|
||||
[s, nu] = inverse_gamma_specification(.5, Inf, 0, 2);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(0.5-s/(nu-2))<1e-12;
|
||||
t(3) = isequal(nu, 4);
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:4
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
function [ldens,Dldens,D2ldens] = lpdfgweibull(x,a,b,c) % --*-- Unitary tests --*--
|
||||
function [ldens,Dldens,D2ldens] = lpdfgweibull(x,a,b,c)
|
||||
|
||||
% Evaluates the logged Weibull PDF at x.
|
||||
%
|
||||
|
|
@ -16,7 +16,7 @@ function [ldens,Dldens,D2ldens] = lpdfgweibull(x,a,b,c) % --*-- Unitary tests -
|
|||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
|
||||
% Copyright © 2015-2020 Dynare Team
|
||||
% Copyright © 2015-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -96,344 +96,346 @@ if nargout>1
|
|||
end
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ try
|
||||
%$ lpdfgweibull(1.0);
|
||||
%$ t(1) = false;
|
||||
%$ catch
|
||||
%$ t(1) = true;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
try
|
||||
lpdfgweibull(1.0);
|
||||
t(1) = false;
|
||||
catch
|
||||
t(1) = true;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
||||
%@test:2
|
||||
%$ try
|
||||
%$ lpdfgweibull(1.0, .5);
|
||||
%$ t(1) = false;
|
||||
%$ catch
|
||||
%$ t(1) = true;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
try
|
||||
lpdfgweibull(1.0, .5);
|
||||
t(1) = false;
|
||||
catch
|
||||
t(1) = true;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:2
|
||||
|
||||
%@test:3
|
||||
%$ try
|
||||
%$ lpdfgweibull(ones(2,2), .5*ones(2,1), .1*ones(3,1));
|
||||
%$ t(1) = false;
|
||||
%$ catch
|
||||
%$ t(1) = true;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
try
|
||||
lpdfgweibull(ones(2,2), .5*ones(2,1), .1*ones(3,1));
|
||||
t(1) = false;
|
||||
catch
|
||||
t(1) = true;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:3
|
||||
|
||||
%@test:4
|
||||
%$ try
|
||||
%$ a = lpdfgweibull(-1, .5, .1);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = isinf(a);
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
try
|
||||
a = lpdfgweibull(-1, .5, .1);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = isinf(a);
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:4
|
||||
|
||||
%@test:5
|
||||
%$ try
|
||||
%$ a = lpdfgweibull(0, 1, 1);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(a-1.0)<1e-10;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
try
|
||||
a = lpdfgweibull(0, 1, 1);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(a-1.0)<1e-10;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:5
|
||||
|
||||
%@test:6
|
||||
%$ try
|
||||
%$ a = lpdfgweibull([0, -1], [1 1], [1 1]);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(a(1)-1.0)<1e-10;
|
||||
%$ t(3) = isinf(a(2));
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
try
|
||||
a = lpdfgweibull([0, -1], [1 1], [1 1]);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(a(1)-1.0)<1e-10;
|
||||
t(3) = isinf(a(2));
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:6
|
||||
|
||||
%@test:7
|
||||
%$ scale = 1;
|
||||
%$ shape = 2;
|
||||
%$ mode = scale*((shape-1)/shape)^(1/shape);
|
||||
%$
|
||||
%$ try
|
||||
%$ [a, b, c] = lpdfgweibull(mode, shape, scale);
|
||||
%$ p = rand(1000,1)*4;
|
||||
%$ am = lpdfgweibull(p, shape*ones(size(p)), scale*ones(size(p)));
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$
|
||||
%$ t(2) = abs(b)<1e-8;
|
||||
%$ t(3) = c<0;
|
||||
%$ t(4) = all(am<a);
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
scale = 1;
|
||||
shape = 2;
|
||||
mode = scale*((shape-1)/shape)^(1/shape);
|
||||
|
||||
try
|
||||
[a, b, c] = lpdfgweibull(mode, shape, scale);
|
||||
p = rand(1000,1)*4;
|
||||
am = lpdfgweibull(p, shape*ones(size(p)), scale*ones(size(p)));
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
|
||||
t(2) = abs(b)<1e-8;
|
||||
t(3) = c<0;
|
||||
t(4) = all(am<a);
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:7
|
||||
|
||||
%@test:8
|
||||
%$ scale = 1;
|
||||
%$ shape = 2;
|
||||
%$ density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
%$
|
||||
%$ try
|
||||
%$ if isoctave
|
||||
%$ s = quadl(density, .0000000001, 100000, 1e-10);
|
||||
%$ else
|
||||
%$ s = integral(density, 0, 100000);
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(s-1)<1e-6;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
scale = 1;
|
||||
shape = 2;
|
||||
density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
|
||||
try
|
||||
if isoctave
|
||||
s = quadl(density, .0000000001, 100000, 1e-10);
|
||||
else
|
||||
s = integral(density, 0, 100000);
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(s-1)<1e-6;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:8
|
||||
|
||||
%@test:9
|
||||
%$ scale = 1;
|
||||
%$ shape = 1;
|
||||
%$ density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
%$
|
||||
%$ try
|
||||
%$ if isoctave
|
||||
%$ s = quadl(density, .0000000001, 100000, 1e-10);
|
||||
%$ else
|
||||
%$ s = integral(density, 0, 100000);
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(s-1)<1e-6;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
scale = 1;
|
||||
shape = 1;
|
||||
density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
|
||||
try
|
||||
if isoctave
|
||||
s = quadl(density, .0000000001, 100000, 1e-10);
|
||||
else
|
||||
s = integral(density, 0, 100000);
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(s-1)<1e-6;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:9
|
||||
|
||||
%@test:10
|
||||
%$ scale = 1;
|
||||
%$ shape = .5;
|
||||
%$ density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
%$
|
||||
%$ try
|
||||
%$ if isoctave
|
||||
%$ s = quadl(density, .0000000001, 100000, 1e-10);
|
||||
%$ else
|
||||
%$ s = integral(density, 0, 100000);
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ if isoctave
|
||||
%$ t(2) = abs(s-1)<5e-5;
|
||||
%$ else
|
||||
%$ t(2) = abs(s-1)<1e-6;
|
||||
%$ end
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
scale = 1;
|
||||
shape = .5;
|
||||
density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
|
||||
try
|
||||
if isoctave
|
||||
s = quadl(density, .0000000001, 100000, 1e-10);
|
||||
else
|
||||
s = integral(density, 0, 100000);
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
if isoctave
|
||||
t(2) = abs(s-1)<5e-5;
|
||||
else
|
||||
t(2) = abs(s-1)<1e-6;
|
||||
end
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:10
|
||||
|
||||
%@test:11
|
||||
%$ scale = 1;
|
||||
%$ shape = 2;
|
||||
%$ xdens = @(x) x.*exp(lpdfgweibull(x,shape,scale));
|
||||
%$
|
||||
%$ try
|
||||
%$ if isoctave
|
||||
%$ s = quadgk(xdens, .0000000001, 100000, 1e-10);
|
||||
%$ else
|
||||
%$ s = integral(xdens, 0, 100000);
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(s-scale*gamma(1+1/shape))<1e-6;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
scale = 1;
|
||||
shape = 2;
|
||||
xdens = @(x) x.*exp(lpdfgweibull(x,shape,scale));
|
||||
|
||||
try
|
||||
if isoctave
|
||||
s = quadgk(xdens, .0000000001, 100000, 1e-10);
|
||||
else
|
||||
s = integral(xdens, 0, 100000);
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(s-scale*gamma(1+1/shape))<1e-6;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:11
|
||||
|
||||
%@test:12
|
||||
%$ scale = 1;
|
||||
%$ shape = 1;
|
||||
%$ xdens = @(x) x.*exp(lpdfgweibull(x,shape,scale));
|
||||
%$
|
||||
%$ try
|
||||
%$ if isoctave
|
||||
%$ s = quadl(xdens, .0000000001, 100000, 1e-10);
|
||||
%$ else
|
||||
%$ s = integral(xdens, 0, 100000);
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(s-scale*gamma(1+1/shape))<1e-6;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
scale = 1;
|
||||
shape = 1;
|
||||
xdens = @(x) x.*exp(lpdfgweibull(x,shape,scale));
|
||||
|
||||
try
|
||||
if isoctave
|
||||
s = quadl(xdens, .0000000001, 100000, 1e-10);
|
||||
else
|
||||
s = integral(xdens, 0, 100000);
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(s-scale*gamma(1+1/shape))<1e-6;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:12
|
||||
|
||||
%@test:13
|
||||
%$ scale = 1;
|
||||
%$ shape = .5;
|
||||
%$ xdens = @(x) x.*exp(lpdfgweibull(x,shape,scale));
|
||||
%$
|
||||
%$ try
|
||||
%$ if isoctave
|
||||
%$ s = quadl(xdens, .0000000001, 100000, 1e-10);
|
||||
%$ else
|
||||
%$ s = integral(xdens, 0, 100000);
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(s-scale*gamma(1+1/shape))<1e-6;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
scale = 1;
|
||||
shape = .5;
|
||||
xdens = @(x) x.*exp(lpdfgweibull(x,shape,scale));
|
||||
|
||||
try
|
||||
if isoctave
|
||||
s = quadl(xdens, .0000000001, 100000, 1e-10);
|
||||
else
|
||||
s = integral(xdens, 0, 100000);
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = abs(s-scale*gamma(1+1/shape))<1e-6;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:13
|
||||
|
||||
%@test:14
|
||||
%$ scale = 1;
|
||||
%$ shape = 2;
|
||||
%$ density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
%$ n = 200;
|
||||
%$
|
||||
%$ try
|
||||
%$ s = NaN(n, 1);
|
||||
%$ for i=1:n
|
||||
%$ if isoctave
|
||||
%$ s(i) = quadl(density, .0000000001, .1*i, 1e-10);
|
||||
%$ else
|
||||
%$ s(i) = integral(density, 0, .1*i);
|
||||
%$ end
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ for i=1:n
|
||||
%$ x = .1*i;
|
||||
%$ q = 1-exp(-(x/scale)^shape);
|
||||
%$ t(i+1) = abs(s(i)-q)<1e-6;
|
||||
%$ end
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
scale = 1;
|
||||
shape = 2;
|
||||
density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
n = 200;
|
||||
|
||||
try
|
||||
s = NaN(n, 1);
|
||||
for i=1:n
|
||||
if isoctave
|
||||
s(i) = quadl(density, .0000000001, .1*i, 1e-10);
|
||||
else
|
||||
s(i) = integral(density, 0, .1*i);
|
||||
end
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
for i=1:n
|
||||
x = .1*i;
|
||||
q = 1-exp(-(x/scale)^shape);
|
||||
t(i+1) = abs(s(i)-q)<1e-6;
|
||||
end
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:14
|
||||
|
||||
%@test:15
|
||||
%$ scale = 1;
|
||||
%$ shape = 1;
|
||||
%$ density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
%$ n = 200;
|
||||
%$
|
||||
%$ try
|
||||
%$ s = NaN(n, 1);
|
||||
%$ for i=1:n
|
||||
%$ if isoctave
|
||||
%$ s(i) = quadl(density, .0000000001, .1*i, 1e-10);
|
||||
%$ else
|
||||
%$ s(i) = integral(density, 0, .1*i);
|
||||
%$ end
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ for i=1:n
|
||||
%$ x = .1*i;
|
||||
%$ q = 1-exp(-(x/scale)^shape);
|
||||
%$ t(i+1) = abs(s(i)-q)<1e-6;
|
||||
%$ end
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
scale = 1;
|
||||
shape = 1;
|
||||
density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
n = 200;
|
||||
|
||||
try
|
||||
s = NaN(n, 1);
|
||||
for i=1:n
|
||||
if isoctave
|
||||
s(i) = quadl(density, .0000000001, .1*i, 1e-10);
|
||||
else
|
||||
s(i) = integral(density, 0, .1*i);
|
||||
end
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
for i=1:n
|
||||
x = .1*i;
|
||||
q = 1-exp(-(x/scale)^shape);
|
||||
t(i+1) = abs(s(i)-q)<1e-6;
|
||||
end
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:15
|
||||
|
||||
%@test:16
|
||||
%$ scale = 1;
|
||||
%$ shape = .5;
|
||||
%$ density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
%$ n = 200;
|
||||
%$
|
||||
%$ try
|
||||
%$ s = NaN(n, 1);
|
||||
%$ for i=1:n
|
||||
%$ if isoctave
|
||||
%$ s(i) = quadl(density, .0000000001, .1*i, 1e-10);
|
||||
%$ else
|
||||
%$ s(i) = integral(density, 0, .1*i);
|
||||
%$ end
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ for i=1:n
|
||||
%$ x = .1*i;
|
||||
%$ q = 1-exp(-(x/scale)^shape);
|
||||
%$ if isoctave
|
||||
%$ t(i+1) = abs(s(i)-q)<5e-5;
|
||||
%$ else
|
||||
%$ t(i+1) = abs(s(i)-q)<1e-6;
|
||||
%$ end
|
||||
%$ end
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
scale = 1;
|
||||
shape = .5;
|
||||
density = @(x) exp(lpdfgweibull(x,shape,scale));
|
||||
n = 200;
|
||||
|
||||
try
|
||||
s = NaN(n, 1);
|
||||
for i=1:n
|
||||
if isoctave
|
||||
s(i) = quadl(density, .0000000001, .1*i, 1e-10);
|
||||
else
|
||||
s(i) = integral(density, 0, .1*i);
|
||||
end
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
for i=1:n
|
||||
x = .1*i;
|
||||
q = 1-exp(-(x/scale)^shape);
|
||||
if isoctave
|
||||
t(i+1) = abs(s(i)-q)<5e-5;
|
||||
else
|
||||
t(i+1) = abs(s(i)-q)<1e-6;
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:16
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function [shape, scale] = weibull_specification(mu, sigma2, lb, name) % --*-- Unitary tests --*--
|
||||
function [shape, scale] = weibull_specification(mu, sigma2, lb, name)
|
||||
|
||||
% Returns the hyperparameters of the Weibull distribution given the expectation and variance.
|
||||
%
|
||||
|
|
@ -9,7 +9,7 @@ function [shape, scale] = weibull_specification(mu, sigma2, lb, name) % --*--
|
|||
%
|
||||
%
|
||||
|
||||
% Copyright © 2015-2017 Dynare Team
|
||||
% Copyright © 2015-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -65,78 +65,80 @@ end
|
|||
|
||||
scale = mu/gamma(1+1/shape);
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ debug = false;
|
||||
%$ scales = 1:.01:5;
|
||||
%$ shapes = .5:.01:2;
|
||||
%$ n_scales = length(scales);
|
||||
%$ n_shapes = length(shapes);
|
||||
%$ mu = NaN(n_scales, n_shapes);
|
||||
%$ s2 = NaN(n_scales, n_shapes);
|
||||
%$ for i=1:n_shapes
|
||||
%$ g1 = gamma(1+1/shapes(i));
|
||||
%$ g2 = gamma(1+2/shapes(i));
|
||||
%$ g3 = g1*g1;
|
||||
%$ for j=1:n_scales
|
||||
%$ mu(j, i) = scales(j)*g1;
|
||||
%$ s2(j, i) = scales(j)*scales(j)*(g2-g3);
|
||||
%$ end
|
||||
%$ end
|
||||
%$ if debug
|
||||
%$ success = [];
|
||||
%$ failed1 = [];
|
||||
%$ failed1_ = [];
|
||||
%$ failed2 = [];
|
||||
%$ end
|
||||
%$ try
|
||||
%$ for i=1:n_shapes
|
||||
%$ for j=1:n_scales
|
||||
%$ if debug
|
||||
%$ disp(sprintf('... mu=%s and s2=%s', num2str(mu(j,i)),num2str(s2(j,i))))
|
||||
%$ end
|
||||
%$ if ~isnan(mu(j,i)) && ~isnan(s2(j,i)) && ~isinf(mu(j,i)) && ~isinf(s2(j,i))
|
||||
%$ [shape, scale] = weibull_specification(mu(j,i), s2(j,i));
|
||||
%$ if isnan(scale)
|
||||
%$ t = false;
|
||||
%$ else
|
||||
%$ if abs(scales(j)-scale)<1e-9 && abs(shapes(i)-shape)<1e-9
|
||||
%$ t = true;
|
||||
%$ else
|
||||
%$ t = false;
|
||||
%$ end
|
||||
%$ end
|
||||
%$ if ~t && debug
|
||||
%$ failed1 = [failed1; mu(j,i) s2(j,i)];
|
||||
%$ failed1_ = [failed1_; shapes(i) scales(j)];
|
||||
%$ error('UnitTest','Cannot compute scale and shape hyperparameters for mu=%s and s2=%s', num2str(mu(j,i)), num2str(s2(j,i)))
|
||||
%$ end
|
||||
%$ if debug
|
||||
%$ success = [success; mu(j,i) s2(j,i)];
|
||||
%$ end
|
||||
%$ else
|
||||
%$ failed2 = [failed2; shapes(i) scales(j)];
|
||||
%$ continue % Pass this test
|
||||
%$ end
|
||||
%$ end
|
||||
%$ end
|
||||
%$ catch
|
||||
%$ t = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if debug
|
||||
%$ figure(1)
|
||||
%$ plot(success(:,1),success(:,2),'ok');
|
||||
%$ if ~isempty(failed1)
|
||||
%$ hold on
|
||||
%$ plot(failed1(:,1),failed1(:,2),'or');
|
||||
%$ hold off
|
||||
%$ figure(2)
|
||||
%$ plot(failed1_(:,1),failed1_(:,2),'or')
|
||||
%$ end
|
||||
%$ if ~isempty(failed2)
|
||||
%$ figure(2)
|
||||
%$ plot(failed2(:,1),failed2(:,2),'or');
|
||||
%$ end
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
debug = false;
|
||||
scales = 1:.01:5;
|
||||
shapes = .5:.01:2;
|
||||
n_scales = length(scales);
|
||||
n_shapes = length(shapes);
|
||||
mu = NaN(n_scales, n_shapes);
|
||||
s2 = NaN(n_scales, n_shapes);
|
||||
for i=1:n_shapes
|
||||
g1 = gamma(1+1/shapes(i));
|
||||
g2 = gamma(1+2/shapes(i));
|
||||
g3 = g1*g1;
|
||||
for j=1:n_scales
|
||||
mu(j, i) = scales(j)*g1;
|
||||
s2(j, i) = scales(j)*scales(j)*(g2-g3);
|
||||
end
|
||||
end
|
||||
if debug
|
||||
success = [];
|
||||
failed1 = [];
|
||||
failed1_ = [];
|
||||
failed2 = [];
|
||||
end
|
||||
try
|
||||
for i=1:n_shapes
|
||||
for j=1:n_scales
|
||||
if debug
|
||||
disp(sprintf('... mu=%s and s2=%s', num2str(mu(j,i)),num2str(s2(j,i))))
|
||||
end
|
||||
if ~isnan(mu(j,i)) && ~isnan(s2(j,i)) && ~isinf(mu(j,i)) && ~isinf(s2(j,i))
|
||||
[shape, scale] = weibull_specification(mu(j,i), s2(j,i));
|
||||
if isnan(scale)
|
||||
t = false;
|
||||
else
|
||||
if abs(scales(j)-scale)<1e-9 && abs(shapes(i)-shape)<1e-9
|
||||
t = true;
|
||||
else
|
||||
t = false;
|
||||
end
|
||||
end
|
||||
if ~t && debug
|
||||
failed1 = [failed1; mu(j,i) s2(j,i)];
|
||||
failed1_ = [failed1_; shapes(i) scales(j)];
|
||||
error('UnitTest','Cannot compute scale and shape hyperparameters for mu=%s and s2=%s', num2str(mu(j,i)), num2str(s2(j,i)))
|
||||
end
|
||||
if debug
|
||||
success = [success; mu(j,i) s2(j,i)];
|
||||
end
|
||||
else
|
||||
failed2 = [failed2; shapes(i) scales(j)];
|
||||
continue % Pass this test
|
||||
end
|
||||
end
|
||||
end
|
||||
catch
|
||||
t = false;
|
||||
end
|
||||
|
||||
if debug
|
||||
figure(1)
|
||||
plot(success(:,1),success(:,2),'ok');
|
||||
if ~isempty(failed1)
|
||||
hold on
|
||||
plot(failed1(:,1),failed1(:,2),'or');
|
||||
hold off
|
||||
figure(2)
|
||||
plot(failed1_(:,1),failed1_(:,2),'or')
|
||||
end
|
||||
if ~isempty(failed2)
|
||||
figure(2)
|
||||
plot(failed2(:,1),failed2(:,2),'or');
|
||||
end
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function hessian_mat = hessian(func,x, gstep, varargin) % --*-- Unitary tests --*--
|
||||
function hessian_mat = hessian(func,x, gstep, varargin)
|
||||
|
||||
% Computes second order partial derivatives
|
||||
%
|
||||
|
|
@ -26,7 +26,7 @@ function hessian_mat = hessian(func,x, gstep, varargin) % --*-- Unitary tests --
|
|||
% none
|
||||
%
|
||||
|
||||
% Copyright © 2001-2017 Dynare Team
|
||||
% Copyright © 2001-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -98,53 +98,54 @@ for i=1:n
|
|||
end
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ % Create a function.
|
||||
%$ fid = fopen('exfun.m','w+');
|
||||
%$ fprintf(fid,'function [f,g,H] = exfun(xvar)\\n');
|
||||
%$ fprintf(fid,'x = xvar(1);\\n');
|
||||
%$ fprintf(fid,'y = xvar(2);\\n');
|
||||
%$ fprintf(fid,'f = x^2* log(y);\\n');
|
||||
%$ fprintf(fid,'if nargout>1\\n');
|
||||
%$ fprintf(fid,' g = zeros(2,1);\\n');
|
||||
%$ fprintf(fid,' g(1) = 2*x*log(y);\\n');
|
||||
%$ fprintf(fid,' g(2) = x*x/y;\\n');
|
||||
%$ fprintf(fid,'end\\n');
|
||||
%$ fprintf(fid,'if nargout>2\\n');
|
||||
%$ fprintf(fid,' H = zeros(2,2);\\n');
|
||||
%$ fprintf(fid,' H(1,1) = 2*log(y);\\n');
|
||||
%$ fprintf(fid,' H(1,2) = 2*x/y;\\n');
|
||||
%$ fprintf(fid,' H(2,1) = H(1,2);\\n');
|
||||
%$ fprintf(fid,' H(2,2) = -x*x/(y*y);\\n');
|
||||
%$ fprintf(fid,' H = H(:);\\n');
|
||||
%$ fprintf(fid,'end\\n');
|
||||
%$ fclose(fid);
|
||||
%$
|
||||
%$ rehash;
|
||||
%$
|
||||
%$ t = zeros(5,1);
|
||||
%$
|
||||
%$ % Evaluate the Hessian at (1,e)
|
||||
%$ try
|
||||
%$ H = hessian('exfun',[1; exp(1)],[1e-2; 1]);
|
||||
%$ t(1) = 1;
|
||||
%$ catch
|
||||
%$ t(1) = 0;
|
||||
%$ end
|
||||
%$
|
||||
%$ % Compute the true Hessian matrix
|
||||
%$ [f, g, Htrue] = exfun([1 exp(1)]);
|
||||
%$
|
||||
%$ % Delete exfun routine from disk.
|
||||
%$ delete('exfun.m');
|
||||
%$
|
||||
%$ % Compare the values in H and Htrue
|
||||
%$ if t(1)
|
||||
%$ t(2) = dassert(abs(H(1)-Htrue(1))<1e-6,true);
|
||||
%$ t(3) = dassert(abs(H(2)-Htrue(2))<1e-6,true);
|
||||
%$ t(4) = dassert(abs(H(3)-Htrue(3))<1e-6,true);
|
||||
%$ t(5) = dassert(abs(H(4)-Htrue(4))<1e-6,true);
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
% Create a function.
|
||||
fid = fopen('exfun.m','w+');
|
||||
fprintf(fid,'function [f,g,H] = exfun(xvar)\\n');
|
||||
fprintf(fid,'x = xvar(1);\\n');
|
||||
fprintf(fid,'y = xvar(2);\\n');
|
||||
fprintf(fid,'f = x^2* log(y);\\n');
|
||||
fprintf(fid,'if nargout>1\\n');
|
||||
fprintf(fid,' g = zeros(2,1);\\n');
|
||||
fprintf(fid,' g(1) = 2*x*log(y);\\n');
|
||||
fprintf(fid,' g(2) = x*x/y;\\n');
|
||||
fprintf(fid,'end\\n');
|
||||
fprintf(fid,'if nargout>2\\n');
|
||||
fprintf(fid,' H = zeros(2,2);\\n');
|
||||
fprintf(fid,' H(1,1) = 2*log(y);\\n');
|
||||
fprintf(fid,' H(1,2) = 2*x/y;\\n');
|
||||
fprintf(fid,' H(2,1) = H(1,2);\\n');
|
||||
fprintf(fid,' H(2,2) = -x*x/(y*y);\\n');
|
||||
fprintf(fid,' H = H(:);\\n');
|
||||
fprintf(fid,'end\\n');
|
||||
fclose(fid);
|
||||
|
||||
rehash;
|
||||
|
||||
t = zeros(5,1);
|
||||
|
||||
% Evaluate the Hessian at (1,e)
|
||||
try
|
||||
H = hessian('exfun',[1; exp(1)],[1e-2; 1]);
|
||||
t(1) = 1;
|
||||
catch
|
||||
t(1) = 0;
|
||||
end
|
||||
|
||||
% Compute the true Hessian matrix
|
||||
[f, g, Htrue] = exfun([1 exp(1)]);
|
||||
|
||||
% Delete exfun routine from disk.
|
||||
delete('exfun.m');
|
||||
|
||||
% Compare the values in H and Htrue
|
||||
if t(1)
|
||||
t(2) = dassert(abs(H(1)-Htrue(1))<1e-6,true);
|
||||
t(3) = dassert(abs(H(2)-Htrue(2))<1e-6,true);
|
||||
t(4) = dassert(abs(H(3)-Htrue(3))<1e-6,true);
|
||||
t(5) = dassert(abs(H(4)-Htrue(4))<1e-6,true);
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
|
|||
|
|
@ -4,13 +4,13 @@ function internals(flag, varargin)
|
|||
%! @deftypefn {Function File} internals (@var{flag},@var{a},@var{b}, ...)
|
||||
%! @anchor{internals}
|
||||
%! @sp 1
|
||||
%! This command provides internal documentation and unitary tests for the matlab routines.
|
||||
%! This command provides internal documentation and unit tests for the matlab routines.
|
||||
%! @sp 2
|
||||
%! @strong{Inputs}
|
||||
%! @sp 1
|
||||
%! @table @ @var
|
||||
%! @item flag
|
||||
%! Mandatory argument: --doc (for displaying internal documentation) or --test (for performing unitary tests).
|
||||
%! Mandatory argument: --doc (for displaying internal documentation) or --test (for performing unit tests).
|
||||
%! @item b
|
||||
%! Name of the routine to be tested or for which internal documentation is needed.
|
||||
%! @item c
|
||||
|
|
@ -36,13 +36,13 @@ function internals(flag, varargin)
|
|||
%! @example
|
||||
%! internals --test particle/local_state_iteration
|
||||
%! @end example
|
||||
%! will execute the unitary tests associated the routine local_state_iteration.
|
||||
%! will execute the unit tests associated the routine local_state_iteration.
|
||||
%! @sp 2
|
||||
%! @strong{Remarks}
|
||||
%! @sp 1
|
||||
%! [1] It is not possible to display the internal documentation of more than one routine.
|
||||
%! @sp 1
|
||||
%! [2] It is possible to perform unitary tests on a list of routines.
|
||||
%! [2] It is possible to perform unit tests on a list of routines.
|
||||
%! @sp 1
|
||||
%! [3] For displaying the internal documentation, matlab calls texinfo which has to be installed.
|
||||
%! @sp 2
|
||||
|
|
@ -56,7 +56,7 @@ function internals(flag, varargin)
|
|||
%! @end deftypefn
|
||||
%@eod:
|
||||
|
||||
% Copyright © 2011-2014 Dynare Team
|
||||
% Copyright © 2011-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function b = isolder(f, F) % --*-- Unitary tests --*--
|
||||
function b = isolder(f, F)
|
||||
|
||||
% Returns true if f is older than any file in folder (and subfolders) F.
|
||||
%
|
||||
|
|
@ -9,7 +9,7 @@ function b = isolder(f, F) % --*-- Unitary tests --*--
|
|||
% OUTPUT
|
||||
% - b [logical]
|
||||
|
||||
% Copyright © 2015-2017 Dynare Team
|
||||
% Copyright © 2015-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -56,38 +56,40 @@ for i=1:length(files)
|
|||
end
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ t = false(3,1);
|
||||
%$ mkdir toto;
|
||||
%$ cd toto
|
||||
%$ mkdir titi;
|
||||
%$ mkdir tata;
|
||||
%$ cd tata
|
||||
%$ mkdir tutu;
|
||||
%$ cd tutu
|
||||
%$ system('touch a.m');
|
||||
%$ cd ..
|
||||
%$ system('touch b.m');
|
||||
%$ system('touch c.m');
|
||||
%$ cd ../titi
|
||||
%$ system('touch d.m');
|
||||
%$ cd ..
|
||||
%$ pause(1)
|
||||
%$ system('touch e.m');
|
||||
%$ t(1) = isequal(isolder('e.m'), false);
|
||||
%$ pause(1)
|
||||
%$ system('touch tata/tutu/a.m');
|
||||
%$ system('touch tata/b.m');
|
||||
%$ system('touch tata/c.m');
|
||||
%$ system('touch titi/d.m');
|
||||
%$ t(2) = isequal(isolder('e.m'), true);
|
||||
%$ pause(1)
|
||||
%$ system('touch e.m');
|
||||
%$ t(3) = isequal(isolder('e.m'), false);
|
||||
%$ cd ..
|
||||
%$ if isoctave()
|
||||
%$ confirm_recursive_rmdir(false, 'local');
|
||||
%$ end
|
||||
%$ rmdir('toto','s');
|
||||
%$ T = all(t);
|
||||
t = false(3,1);
|
||||
mkdir toto;
|
||||
cd toto
|
||||
mkdir titi;
|
||||
mkdir tata;
|
||||
cd tata
|
||||
mkdir tutu;
|
||||
cd tutu
|
||||
system('touch a.m');
|
||||
cd ..
|
||||
system('touch b.m');
|
||||
system('touch c.m');
|
||||
cd ../titi
|
||||
system('touch d.m');
|
||||
cd ..
|
||||
pause(1)
|
||||
system('touch e.m');
|
||||
t(1) = isequal(isolder('e.m'), false);
|
||||
pause(1)
|
||||
system('touch tata/tutu/a.m');
|
||||
system('touch tata/b.m');
|
||||
system('touch tata/c.m');
|
||||
system('touch titi/d.m');
|
||||
t(2) = isequal(isolder('e.m'), true);
|
||||
pause(1)
|
||||
system('touch e.m');
|
||||
t(3) = isequal(isolder('e.m'), false);
|
||||
cd ..
|
||||
if isoctave()
|
||||
confirm_recursive_rmdir(false, 'local');
|
||||
end
|
||||
rmdir('toto','s');
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
function o2WysrOISH = load_m_file_data_legacy(datafile, U7ORsJ0vy3) % --*-- Unitary tests --*--
|
||||
function o2WysrOISH = load_m_file_data_legacy(datafile, U7ORsJ0vy3)
|
||||
|
||||
% Copyright © 2014-2017 Dynare Team
|
||||
% Copyright © 2014-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -78,7 +78,7 @@ JSmvfqTSXT = repmat(' vec(%s) ', 1, length(U7ORsJ0vy3));
|
|||
VbO4y7zOlh = sprintf('[%s]', JSmvfqTSXT);
|
||||
o2WysrOISH = dseries(eval(sprintf(VbO4y7zOlh, U7ORsJ0vy3{:})), [], U7ORsJ0vy3);
|
||||
|
||||
return
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
% Write a data file
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function P=lyapunov_solver(T,R,Q,DynareOptions) % --*-- Unitary tests --*--
|
||||
function P=lyapunov_solver(T,R,Q,DynareOptions)
|
||||
% function P=lyapunov_solver(T,R,Q,DynareOptions)
|
||||
% Solves the Lyapunov equation P-T*P*T' = R*Q*R' arising in a state-space
|
||||
% system, where P is the variance of the states
|
||||
|
|
@ -23,7 +23,7 @@ function P=lyapunov_solver(T,R,Q,DynareOptions) % --*-- Unitary tests --*--
|
|||
% Square-root solver for discrete-time Lyapunov equations (requires Matlab System Control toolbox
|
||||
% or Octave control package)
|
||||
|
||||
% Copyright © 2016-2022 Dynare Team
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -65,122 +65,124 @@ else
|
|||
P = lyapunov_symm(T,R*Q*R',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold, [], DynareOptions.debug);
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ t = NaN(13,1);
|
||||
%$ options_.lyapunov_complex_threshold = 1e-15;
|
||||
%$ options_.qz_zero_threshold = 1e-6;
|
||||
%$ options_.qz_criterium=1-options_.qz_zero_threshold;
|
||||
%$ options_.lyapunov_fixed_point_tol = 1e-10;
|
||||
%$ options_.lyapunov_doubling_tol = 1e-16;
|
||||
%$ options_.debug=false;
|
||||
%$
|
||||
%$ n_small=8;
|
||||
%$ m_small=10;
|
||||
%$ T_small=randn(n_small,n_small);
|
||||
%$ T_small=0.99*T_small/max(abs(eigs(T_small)));
|
||||
%$ tmp2=randn(m_small,m_small);
|
||||
%$ Q_small=tmp2*tmp2';
|
||||
%$ R_small=randn(n_small,m_small);
|
||||
%$
|
||||
%$ n_large=9;
|
||||
%$ m_large=11;
|
||||
%$ T_large=randn(n_large,n_large);
|
||||
%$ T_large=0.99*T_large/max(abs(eigs(T_large)));
|
||||
%$ tmp2=randn(m_large,m_large);
|
||||
%$ Q_large=tmp2*tmp2';
|
||||
%$ R_large=randn(n_large,m_large);
|
||||
%$
|
||||
%$ % DynareOptions.lyapunov_fp == 1
|
||||
%$ options_.lyapunov_fp = true;
|
||||
%$ try
|
||||
%$ Pstar1_small = lyapunov_solver(T_small,R_small,Q_small,options_);
|
||||
%$ Pstar1_large = lyapunov_solver(T_large,R_large,Q_large,options_);
|
||||
%$ t(1) = 1;
|
||||
%$ catch
|
||||
%$ t(1) = 0;
|
||||
%$ end
|
||||
%$
|
||||
%$ % Dynareoptions.lyapunov_db == 1
|
||||
%$ options_.lyapunov_fp = false;
|
||||
%$ options_.lyapunov_db = true;
|
||||
%$ try
|
||||
%$ Pstar2_small = lyapunov_solver(T_small,R_small,Q_small,options_);
|
||||
%$ Pstar2_large = lyapunov_solver(T_large,R_large,Q_large,options_);
|
||||
%$ t(2) = 1;
|
||||
%$ catch
|
||||
%$ t(2) = 0;
|
||||
%$ end
|
||||
%$
|
||||
%$ % Dynareoptions.lyapunov_srs == 1
|
||||
%$ if (isoctave && user_has_octave_forge_package('control')) || (~isoctave && user_has_matlab_license('control_toolbox'))
|
||||
%$ options_.lyapunov_db = false;
|
||||
%$ options_.lyapunov_srs = true;
|
||||
%$ try
|
||||
%$ Pstar3_small = lyapunov_solver(T_small,R_small,Q_small,options_);
|
||||
%$ Pstar3_large = lyapunov_solver(T_large,R_large,Q_large,options_);
|
||||
%$ t(3) = 1;
|
||||
%$ catch
|
||||
%$ t(3) = 0;
|
||||
%$ end
|
||||
%$ else
|
||||
%$ t(3) = 1;
|
||||
%$ end
|
||||
%$
|
||||
%$ % Standard
|
||||
%$ options_.lyapunov_srs = false;
|
||||
%$ try
|
||||
%$ Pstar4_small = lyapunov_solver(T_small,R_small,Q_small,options_);
|
||||
%$ Pstar4_large = lyapunov_solver(T_large,R_large,Q_large,options_);
|
||||
%$ t(4) = 1;
|
||||
%$ catch
|
||||
%$ t(4) = 0;
|
||||
%$ end
|
||||
%$
|
||||
%$ % Test the results.
|
||||
%$
|
||||
%$ if max(max(abs(Pstar1_small-Pstar2_small)))>1e-8
|
||||
%$ t(5) = 0;
|
||||
%$ else
|
||||
%$ t(5) = 1;
|
||||
%$ end
|
||||
%$
|
||||
%$ if (isoctave && user_has_octave_forge_package('control')) || (~isoctave && user_has_matlab_license('control_toolbox'))
|
||||
%$ if max(max(abs(Pstar1_small-Pstar3_small)))>1e-8
|
||||
%$ t(6) = 0;
|
||||
%$ else
|
||||
%$ t(6) = 1;
|
||||
%$ end
|
||||
%$ else
|
||||
%$ t(6) = 1;
|
||||
%$ end
|
||||
%$
|
||||
%$ if max(max(abs(Pstar1_small-Pstar4_small)))>1e-8
|
||||
%$ t(7) = 0;
|
||||
%$ else
|
||||
%$ t(7) = 1;
|
||||
%$ end
|
||||
%$
|
||||
%$ if max(max(abs(Pstar1_large-Pstar2_large)))>2e-8
|
||||
%$ t(8) = 0;
|
||||
%$ else
|
||||
%$ t(8) = 1;
|
||||
%$ end
|
||||
%$
|
||||
%$ if (isoctave && user_has_octave_forge_package('control')) || (~isoctave && user_has_matlab_license('control_toolbox'))
|
||||
%$ if max(max(abs(Pstar1_large-Pstar3_large)))>1e-8
|
||||
%$ t(9) = 0;
|
||||
%$ else
|
||||
%$ t(9) = 1;
|
||||
%$ end
|
||||
%$ else
|
||||
%$ t(9) = 1;
|
||||
%$ end
|
||||
%$
|
||||
%$ if max(max(abs(Pstar1_large-Pstar4_large)))>2e-8
|
||||
%$ t(10) = 0;
|
||||
%$ else
|
||||
%$ t(10) = 1;
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
t = NaN(13,1);
|
||||
options_.lyapunov_complex_threshold = 1e-15;
|
||||
options_.qz_zero_threshold = 1e-6;
|
||||
options_.qz_criterium=1-options_.qz_zero_threshold;
|
||||
options_.lyapunov_fixed_point_tol = 1e-10;
|
||||
options_.lyapunov_doubling_tol = 1e-16;
|
||||
options_.debug=false;
|
||||
|
||||
n_small=8;
|
||||
m_small=10;
|
||||
T_small=randn(n_small,n_small);
|
||||
T_small=0.99*T_small/max(abs(eigs(T_small)));
|
||||
tmp2=randn(m_small,m_small);
|
||||
Q_small=tmp2*tmp2';
|
||||
R_small=randn(n_small,m_small);
|
||||
|
||||
n_large=9;
|
||||
m_large=11;
|
||||
T_large=randn(n_large,n_large);
|
||||
T_large=0.99*T_large/max(abs(eigs(T_large)));
|
||||
tmp2=randn(m_large,m_large);
|
||||
Q_large=tmp2*tmp2';
|
||||
R_large=randn(n_large,m_large);
|
||||
|
||||
% DynareOptions.lyapunov_fp == 1
|
||||
options_.lyapunov_fp = true;
|
||||
try
|
||||
Pstar1_small = lyapunov_solver(T_small,R_small,Q_small,options_);
|
||||
Pstar1_large = lyapunov_solver(T_large,R_large,Q_large,options_);
|
||||
t(1) = 1;
|
||||
catch
|
||||
t(1) = 0;
|
||||
end
|
||||
|
||||
% Dynareoptions.lyapunov_db == 1
|
||||
options_.lyapunov_fp = false;
|
||||
options_.lyapunov_db = true;
|
||||
try
|
||||
Pstar2_small = lyapunov_solver(T_small,R_small,Q_small,options_);
|
||||
Pstar2_large = lyapunov_solver(T_large,R_large,Q_large,options_);
|
||||
t(2) = 1;
|
||||
catch
|
||||
t(2) = 0;
|
||||
end
|
||||
|
||||
% Dynareoptions.lyapunov_srs == 1
|
||||
if (isoctave && user_has_octave_forge_package('control')) || (~isoctave && user_has_matlab_license('control_toolbox'))
|
||||
options_.lyapunov_db = false;
|
||||
options_.lyapunov_srs = true;
|
||||
try
|
||||
Pstar3_small = lyapunov_solver(T_small,R_small,Q_small,options_);
|
||||
Pstar3_large = lyapunov_solver(T_large,R_large,Q_large,options_);
|
||||
t(3) = 1;
|
||||
catch
|
||||
t(3) = 0;
|
||||
end
|
||||
else
|
||||
t(3) = 1;
|
||||
end
|
||||
|
||||
% Standard
|
||||
options_.lyapunov_srs = false;
|
||||
try
|
||||
Pstar4_small = lyapunov_solver(T_small,R_small,Q_small,options_);
|
||||
Pstar4_large = lyapunov_solver(T_large,R_large,Q_large,options_);
|
||||
t(4) = 1;
|
||||
catch
|
||||
t(4) = 0;
|
||||
end
|
||||
|
||||
% Test the results.
|
||||
|
||||
if max(max(abs(Pstar1_small-Pstar2_small)))>1e-8
|
||||
t(5) = 0;
|
||||
else
|
||||
t(5) = 1;
|
||||
end
|
||||
|
||||
if (isoctave && user_has_octave_forge_package('control')) || (~isoctave && user_has_matlab_license('control_toolbox'))
|
||||
if max(max(abs(Pstar1_small-Pstar3_small)))>1e-8
|
||||
t(6) = 0;
|
||||
else
|
||||
t(6) = 1;
|
||||
end
|
||||
else
|
||||
t(6) = 1;
|
||||
end
|
||||
|
||||
if max(max(abs(Pstar1_small-Pstar4_small)))>1e-8
|
||||
t(7) = 0;
|
||||
else
|
||||
t(7) = 1;
|
||||
end
|
||||
|
||||
if max(max(abs(Pstar1_large-Pstar2_large)))>2e-8
|
||||
t(8) = 0;
|
||||
else
|
||||
t(8) = 1;
|
||||
end
|
||||
|
||||
if (isoctave && user_has_octave_forge_package('control')) || (~isoctave && user_has_matlab_license('control_toolbox'))
|
||||
if max(max(abs(Pstar1_large-Pstar3_large)))>1e-8
|
||||
t(9) = 0;
|
||||
else
|
||||
t(9) = 1;
|
||||
end
|
||||
else
|
||||
t(9) = 1;
|
||||
end
|
||||
|
||||
if max(max(abs(Pstar1_large-Pstar4_large)))>2e-8
|
||||
t(10) = 0;
|
||||
else
|
||||
t(10) = 1;
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function [x,u] = lyapunov_symm(a,b,lyapunov_fixed_point_tol,qz_criterium,lyapunov_complex_threshold,method,debug) % --*-- Unitary tests --*--
|
||||
function [x,u] = lyapunov_symm(a,b,lyapunov_fixed_point_tol,qz_criterium,lyapunov_complex_threshold,method,debug)
|
||||
% Solves the Lyapunov equation x-a*x*a' = b, for b and x symmetric matrices.
|
||||
% If a has some unit roots, the function computes only the solution of the stable subsystem.
|
||||
%
|
||||
|
|
@ -25,7 +25,7 @@ function [x,u] = lyapunov_symm(a,b,lyapunov_fixed_point_tol,qz_criterium,lyapuno
|
|||
% SPECIAL REQUIREMENTS
|
||||
% None
|
||||
|
||||
% Copyright © 2006-2017 Dynare Team
|
||||
% Copyright © 2006-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -163,4 +163,4 @@ if i == 1
|
|||
x(1,1) = (B(1,1)+c)/(1-T(1,1)*T(1,1));
|
||||
end
|
||||
x = U(:,k+1:end)*x*U(:,k+1:end)';
|
||||
u = U(:,1:k);
|
||||
u = U(:,1:k);
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function [X, info] = cycle_reduction(A0, A1, A2, cvg_tol, ch) % --*-- Unitary tests --*--
|
||||
function [X, info] = cycle_reduction(A0, A1, A2, cvg_tol, ch)
|
||||
|
||||
%@info:
|
||||
%! @deftypefn {Function File} {[@var{X}, @var{info}] =} cycle_reduction (@var{A0},@var{A1},@var{A2},@var{cvg_tol},@var{ch})
|
||||
|
|
@ -43,7 +43,7 @@ function [X, info] = cycle_reduction(A0, A1, A2, cvg_tol, ch) % --*-- Unitary te
|
|||
%! @end deftypefn
|
||||
%@eod:
|
||||
|
||||
% Copyright © 2013-2017 Dynare Team
|
||||
% Copyright © 2013-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -112,40 +112,42 @@ if (nargin == 5 && ~isempty(ch) )
|
|||
end
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$
|
||||
%$ t = zeros(3,1);
|
||||
%$
|
||||
%$ % Set the dimension of the problem to be solved.
|
||||
%$ n = 500;
|
||||
%$
|
||||
%$ % Set the equation to be solved
|
||||
%$ A = eye(n);
|
||||
%$ B = diag(30*ones(n,1)); B(1,1) = 20; B(end,end) = 20; B = B - diag(10*ones(n-1,1),-1); B = B - diag(10*ones(n-1,1),1);
|
||||
%$ C = diag(15*ones(n,1)); C = C - diag(5*ones(n-1,1),-1); C = C - diag(5*ones(n-1,1),1);
|
||||
%$
|
||||
%$ % Solve the equation with the cycle reduction algorithm
|
||||
%$ try
|
||||
%$ tic; X1 = cycle_reduction(C,B,A,1e-7); elapsedtime = toc;
|
||||
%$ disp(['Elapsed time for cycle reduction algorithm is: ' num2str(elapsedtime) ' (n=' int2str(n) ').'])
|
||||
%$ t(1) = 1;
|
||||
%$ catch
|
||||
%$ % nothing to do here.
|
||||
%$ end
|
||||
%$
|
||||
%$ % Solve the equation with the logarithmic reduction algorithm
|
||||
%$ try
|
||||
%$ tic; X2 = logarithmic_reduction(A,B,C,1e-16,100); elapsedtime = toc;
|
||||
%$ disp(['Elapsed time for logarithmic reduction algorithm is: ' num2str(elapsedtime) ' (n=' int2str(n) ').'])
|
||||
%$ t(2) = 1;
|
||||
%$ catch
|
||||
%$ % nothing to do here.
|
||||
%$ end
|
||||
%$
|
||||
%$ % Check the results.
|
||||
%$ if t(1) && t(2)
|
||||
%$ t(3) = dassert(X1,X2,1e-12);
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
|
||||
t = zeros(3,1);
|
||||
|
||||
% Set the dimension of the problem to be solved.
|
||||
n = 500;
|
||||
|
||||
% Set the equation to be solved
|
||||
A = eye(n);
|
||||
B = diag(30*ones(n,1)); B(1,1) = 20; B(end,end) = 20; B = B - diag(10*ones(n-1,1),-1); B = B - diag(10*ones(n-1,1),1);
|
||||
C = diag(15*ones(n,1)); C = C - diag(5*ones(n-1,1),-1); C = C - diag(5*ones(n-1,1),1);
|
||||
|
||||
% Solve the equation with the cycle reduction algorithm
|
||||
try
|
||||
tic; X1 = cycle_reduction(C,B,A,1e-7); elapsedtime = toc;
|
||||
disp(['Elapsed time for cycle reduction algorithm is: ' num2str(elapsedtime) ' (n=' int2str(n) ').'])
|
||||
t(1) = 1;
|
||||
catch
|
||||
% nothing to do here.
|
||||
end
|
||||
|
||||
% Solve the equation with the logarithmic reduction algorithm
|
||||
try
|
||||
tic; X2 = logarithmic_reduction(A,B,C,1e-16,100); elapsedtime = toc;
|
||||
disp(['Elapsed time for logarithmic reduction algorithm is: ' num2str(elapsedtime) ' (n=' int2str(n) ').'])
|
||||
t(2) = 1;
|
||||
catch
|
||||
% nothing to do here.
|
||||
end
|
||||
|
||||
% Check the results.
|
||||
if t(1) && t(2)
|
||||
t(3) = dassert(X1,X2,1e-12);
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
function [y, y_] = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, a, b, c) % --*-- Unitary tests --*--
|
||||
function [y, y_] = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, a, b, c)
|
||||
|
||||
% Given the demeaned states (yhat) and structural innovations (epsilon), this routine computes the level of selected endogenous variables when the
|
||||
% model is approximated by an order two taylor expansion around the deterministic steady state. Depending on the number of input/output
|
||||
|
|
@ -26,7 +26,7 @@ function [y, y_] = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, consta
|
|||
% 1. If the function has more than nine input arguments (pruning) then it must have two output arguments (otherwise only one input).
|
||||
% 2. Ninth input argument is not used, it is here only to have a common interface with the mex version.
|
||||
|
||||
% Copyright © 2011-2022 Dynare Team
|
||||
% Copyright © 2011-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -77,7 +77,7 @@ else
|
|||
end
|
||||
end
|
||||
|
||||
return
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
n = 2;
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function [ss, tt, zz, sdim, eigval, info] = mjdgges(e, d, qz_criterium, zhreshold) % --*-- Unitary tests --*--
|
||||
function [ss, tt, zz, sdim, eigval, info] = mjdgges(e, d, qz_criterium, zhreshold)
|
||||
%
|
||||
% INPUTS
|
||||
% e [double] real square (n*n) matrix.
|
||||
|
|
@ -18,7 +18,7 @@ function [ss, tt, zz, sdim, eigval, info] = mjdgges(e, d, qz_criterium, zhreshol
|
|||
% SPECIAL REQUIREMENTS
|
||||
% none.
|
||||
|
||||
% Copyright © 1996-2022 Dynare Team
|
||||
% Copyright © 1996-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -66,18 +66,20 @@ catch
|
|||
info = 1; % Not as precise as lapack's info!
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ try
|
||||
%$ E =[0,0,0,0,0,0,0,-1,0,0;0,0,0,0,0,0,0,0,-1,0;0,0,0,0,0,0,0,0,0,-0.990099009900990;0,0,0,0,0,0,0,0,0,0.0990099009900990;0,0,0,-1.01010101010101,0,0.0427672955974843,0,0,0,0;0,0,0,0,0,0.128301886792453,-1,0,0,0;0.800000000000000,0,0,0,0,0,0,0,0,0;0,1,0,0,0,0,1,0,0,0;0,0,0.900000000000000,0,0,0,0,0,0,0;0,0,0,-1.01010101010101,-1,0,2,0,-1,0];
|
||||
%$ D=[0,0,0,0,-1,0,0,-0.792000000000000,0,0;0,0,0,0,0,0,0,0,-0.990000000000000,0;0,0,0,-0.000493818030899887,0,0,0,0,0,-0.882178217821782;0,0,0,-1.00493818030900,0,0,0,0,0,0.0882178217821782;0,0,0,-1,0.128301886792453,0,0,0,0,0;-1,0,0,0,0,0,-0.990000000000000,0,0,0;1,0,0,0,0,0,0,0,0,0;0,1,0,0,0,0,0,0,0,0;0,0,1,0,0,0,0,0,0,0;0,0,0,0,-1,0,0,0,0,0];
|
||||
%$ [ss, tt, w, sdim, dr.eigval, info1]=mjdgges(E, D, 1.000001, 1e-06);
|
||||
%$ if sdim==5
|
||||
%$ t(1) = 1;
|
||||
%$ else
|
||||
%$ t(1) = 0;
|
||||
%$ end
|
||||
%$ catch
|
||||
%$ t(1) = 0;
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
%@eof:1
|
||||
try
|
||||
E =[0,0,0,0,0,0,0,-1,0,0;0,0,0,0,0,0,0,0,-1,0;0,0,0,0,0,0,0,0,0,-0.990099009900990;0,0,0,0,0,0,0,0,0,0.0990099009900990;0,0,0,-1.01010101010101,0,0.0427672955974843,0,0,0,0;0,0,0,0,0,0.128301886792453,-1,0,0,0;0.800000000000000,0,0,0,0,0,0,0,0,0;0,1,0,0,0,0,1,0,0,0;0,0,0.900000000000000,0,0,0,0,0,0,0;0,0,0,-1.01010101010101,-1,0,2,0,-1,0];
|
||||
D=[0,0,0,0,-1,0,0,-0.792000000000000,0,0;0,0,0,0,0,0,0,0,-0.990000000000000,0;0,0,0,-0.000493818030899887,0,0,0,0,0,-0.882178217821782;0,0,0,-1.00493818030900,0,0,0,0,0,0.0882178217821782;0,0,0,-1,0.128301886792453,0,0,0,0,0;-1,0,0,0,0,0,-0.990000000000000,0,0,0;1,0,0,0,0,0,0,0,0,0;0,1,0,0,0,0,0,0,0,0;0,0,1,0,0,0,0,0,0,0;0,0,0,0,-1,0,0,0,0,0];
|
||||
[ss, tt, w, sdim, dr.eigval, info1]=mjdgges(E, D, 1.000001, 1e-06);
|
||||
if sdim==5
|
||||
t(1) = 1;
|
||||
else
|
||||
t(1) = 0;
|
||||
end
|
||||
catch
|
||||
t(1) = 0;
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
function retval = corr(x, y) % --*-- Unitary tests --*--
|
||||
function retval = corr(x, y)
|
||||
%@info:
|
||||
%! @deftypefn {Function File} {} corr (@var{x})
|
||||
%! @deftypefnx {Function File} {} corr (@var{x}, @var{y})
|
||||
|
|
@ -39,7 +39,7 @@ function retval = corr(x, y) % --*-- Unitary tests --*--
|
|||
% Copyright © 1993-1996 Kurt Hornik
|
||||
% Copyright © 1996-2015 John W. Eaton
|
||||
% Copyright © 2013-2015 Julien Bect
|
||||
% Copyright © 2016-2017 Dynare Team
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -110,79 +110,77 @@ c = x'*y;
|
|||
s = sx'*sy;
|
||||
retval = c./(n * s);
|
||||
|
||||
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ x = rand (10);
|
||||
%$ cc1 = corr(x);
|
||||
%$ cc2 = corr(x, x);
|
||||
%$ t(1) = dassert(size(cc1),[10, 10]);
|
||||
%$ t(2) = dassert(size (cc2),[10, 10]);
|
||||
%$ t(3) = dassert(cc1, cc2, sqrt (eps));
|
||||
%$ T = all(t);
|
||||
x = rand (10);
|
||||
cc1 = corr(x);
|
||||
cc2 = corr(x, x);
|
||||
t(1) = dassert(size(cc1),[10, 10]);
|
||||
t(2) = dassert(size (cc2),[10, 10]);
|
||||
t(3) = dassert(cc1, cc2, sqrt (eps));
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
||||
%@test:2
|
||||
%$ x = [1:3]';
|
||||
%$ y = [3:-1:1]';
|
||||
%$ t = zeros(3,1);
|
||||
%$ t(1) = dassert(corr(x, y), -1, 5*eps);
|
||||
%$ t(2) = dassert(corr(x, flipud (y)), 1, 5*eps);
|
||||
%$ t(3) = dassert(corr([x, y]), [1 -1; -1 1], 5*eps);
|
||||
%$ T = all(t);
|
||||
x = [1:3]';
|
||||
y = [3:-1:1]';
|
||||
t = zeros(3,1);
|
||||
t(1) = dassert(corr(x, y), -1, 5*eps);
|
||||
t(2) = dassert(corr(x, flipud (y)), 1, 5*eps);
|
||||
t(3) = dassert(corr([x, y]), [1 -1; -1 1], 5*eps);
|
||||
T = all(t);
|
||||
%@eof:2
|
||||
|
||||
%@test:3
|
||||
%$ if ~isoctave()
|
||||
%$ t = zeros(3,1);
|
||||
%$ t(1) = dassert(corr(5), NaN);
|
||||
%$ t(2) = dassert(corr([1 2 3],5),NaN(3,1));
|
||||
%$ t(3) = dassert(corr(5,[1 2 3]),NaN(1,3));
|
||||
%$ else
|
||||
%$ t = 1;
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
if ~isoctave()
|
||||
t = zeros(3,1);
|
||||
t(1) = dassert(corr(5), NaN);
|
||||
t(2) = dassert(corr([1 2 3],5),NaN(3,1));
|
||||
t(3) = dassert(corr(5,[1 2 3]),NaN(1,3));
|
||||
else
|
||||
t = 1;
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:3
|
||||
|
||||
%@test:4
|
||||
%$ t = zeros(6,1);
|
||||
%$ try
|
||||
%$ corr()
|
||||
%$ t(1) = false;
|
||||
%$ catch
|
||||
%$ t(1) = true;
|
||||
%$ end
|
||||
%$ try
|
||||
%$ corr(1, 2, 3)
|
||||
%$ t(2) = false;
|
||||
%$ catch
|
||||
%$ t(2) = true;
|
||||
%$ end
|
||||
%$ try
|
||||
%$ corr([1; 2], ['A', 'B'])
|
||||
%$ t(3) = false;
|
||||
%$ catch
|
||||
%$ t(3) = true;
|
||||
%$ end
|
||||
%$ try
|
||||
%$ corr([1; 2], ['A', 'B'])
|
||||
%$ t(4) = false;
|
||||
%$ catch
|
||||
%$ t(4) = true;
|
||||
%$ end
|
||||
%$ try
|
||||
%$ corr(ones (2,2,2))
|
||||
%$ t(5) = false;
|
||||
%$ catch
|
||||
%$ t(5) = true;
|
||||
%$ end
|
||||
%$ try
|
||||
%$ corr(ones (2,2), ones (2,2,2))
|
||||
%$ t(6) = false;
|
||||
%$ catch
|
||||
%$ t(6) = true;
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
t = zeros(6,1);
|
||||
try
|
||||
corr()
|
||||
t(1) = false;
|
||||
catch
|
||||
t(1) = true;
|
||||
end
|
||||
try
|
||||
corr(1, 2, 3)
|
||||
t(2) = false;
|
||||
catch
|
||||
t(2) = true;
|
||||
end
|
||||
try
|
||||
corr([1; 2], ['A', 'B'])
|
||||
t(3) = false;
|
||||
catch
|
||||
t(3) = true;
|
||||
end
|
||||
try
|
||||
corr([1; 2], ['A', 'B'])
|
||||
t(4) = false;
|
||||
catch
|
||||
t(4) = true;
|
||||
end
|
||||
try
|
||||
corr(ones (2,2,2))
|
||||
t(5) = false;
|
||||
catch
|
||||
t(5) = true;
|
||||
end
|
||||
try
|
||||
corr(ones (2,2), ones (2,2,2))
|
||||
t(6) = false;
|
||||
catch
|
||||
t(6) = true;
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:4
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
function rnd = gamrnd(a, b, method) % --*-- Unitary tests --*--
|
||||
function rnd = gamrnd(a, b, method)
|
||||
|
||||
% This function produces independent random variates from the Gamma distribution.
|
||||
%
|
||||
|
|
@ -126,7 +126,7 @@ if ~isempty(ddx)
|
|||
end
|
||||
end
|
||||
|
||||
return
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
if ~isoctave && ~user_has_matlab_license('statistics_toolbox')
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function [q,N] = quantile(X, p, dim, method, weights) % --*-- Unitary tests --*--
|
||||
function [q,N] = quantile(X, p, dim, method, weights)
|
||||
|
||||
% Quantiles of a sample via various methods.
|
||||
%
|
||||
|
|
@ -49,7 +49,7 @@ function [q,N] = quantile(X, p, dim, method, weights) % --*-- Unitary tests --*-
|
|||
% http://fr.mathworks.com/matlabcentral/fileexchange/46555-quantile-calculation
|
||||
%
|
||||
% Copyright © 2014-2016 University of Surrey (Christopher Hummersone)
|
||||
% Copyright © 2016-2017 Dynare Team
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -234,24 +234,25 @@ function y = irearrange(x,order,shape)
|
|||
y = reshape(x,shape);
|
||||
y = ipermute(y,order);
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ X = randn(10000000, 1);
|
||||
%$
|
||||
%$ try
|
||||
%$ q = quantile(X, [.25, .5, .75, .95 ]);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ e = [-0.674489750196082, 0, 0.674489750196082, 1.644853626951472];
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ for i=1:4
|
||||
%$ t(i+1) = abs(q(i)-e(i))<2e-3;
|
||||
%$ end
|
||||
%$ end
|
||||
%$
|
||||
%$ T = all(t);
|
||||
%@eof:1
|
||||
X = randn(10000000, 1);
|
||||
|
||||
try
|
||||
q = quantile(X, [.25, .5, .75, .95 ]);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
e = [-0.674489750196082, 0, 0.674489750196082, 1.644853626951472];
|
||||
|
||||
if t(1)
|
||||
for i=1:4
|
||||
t(i+1) = abs(q(i)-e(i))<2e-3;
|
||||
end
|
||||
end
|
||||
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function p = wblcdf(x, scale, shape) % --*-- Unitary tests --*--
|
||||
function p = wblcdf(x, scale, shape)
|
||||
|
||||
% Cumulative distribution function for the Weibull distribution.
|
||||
%
|
||||
|
|
@ -10,7 +10,7 @@ function p = wblcdf(x, scale, shape) % --*-- Unitary tests --*--
|
|||
% OUTPUTS
|
||||
% - p [double] Positive scalar between
|
||||
|
||||
% Copyright © 2015-2017 Dynare Team
|
||||
% Copyright © 2015-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -63,86 +63,86 @@ end
|
|||
p = 1-exp(-(x/scale)^shape);
|
||||
|
||||
%@test:1
|
||||
%$ try
|
||||
%$ p = wblcdf(-1, .5, .1);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ % Check the results
|
||||
%$ if t(1)
|
||||
%$ t(2) = isequal(p, 0);
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
try
|
||||
p = wblcdf(-1, .5, .1);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
% Check the results
|
||||
if t(1)
|
||||
t(2) = isequal(p, 0);
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
||||
%@test:2
|
||||
%$ try
|
||||
%$ p = wblcdf(Inf, .5, .1);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ % Check the results
|
||||
%$ if t(1)
|
||||
%$ t(2) = isequal(p, 1);
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
try
|
||||
p = wblcdf(Inf, .5, .1);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
% Check the results
|
||||
if t(1)
|
||||
t(2) = isequal(p, 1);
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:2
|
||||
|
||||
%@test:3
|
||||
%$ % Set the hyperparameters of a Weibull definition.
|
||||
%$ scale = .5;
|
||||
%$ shape = 1.5;
|
||||
%$
|
||||
%$ % Compute the median of the weibull distribution.
|
||||
%$ m = scale*log(2)^(1/shape);
|
||||
%$
|
||||
%$ try
|
||||
%$ p = wblcdf(m, scale, shape);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ % Check the results
|
||||
%$ if t(1)
|
||||
%$ t(2) = abs(p-.5)<1e-12;
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
% Set the hyperparameters of a Weibull definition.
|
||||
scale = .5;
|
||||
shape = 1.5;
|
||||
|
||||
% Compute the median of the weibull distribution.
|
||||
m = scale*log(2)^(1/shape);
|
||||
|
||||
try
|
||||
p = wblcdf(m, scale, shape);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
% Check the results
|
||||
if t(1)
|
||||
t(2) = abs(p-.5)<1e-12;
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:3
|
||||
|
||||
%@test:4
|
||||
%$ % Consistency check between wblinv and wblcdf.
|
||||
%$
|
||||
%$ % Set the hyperparameters of a Weibull definition.
|
||||
%$ scale = .5;
|
||||
%$ shape = 1.5;
|
||||
%$
|
||||
%$ % Compute quatiles of the weibull distribution.
|
||||
%$ q = 0:.05:1;
|
||||
%$ m = zeros(size(q));
|
||||
%$ p = zeros(size(q));
|
||||
%$ for i=1:length(q)
|
||||
%$ m(i) = wblinv(q(i), scale, shape);
|
||||
%$ end
|
||||
%$
|
||||
%$ try
|
||||
%$ for i=1:length(q)
|
||||
%$ p(i) = wblcdf(m(i), scale, shape);
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ % Check the results
|
||||
%$ if t(1)
|
||||
%$ for i=1:length(q)
|
||||
%$ t(i+1) = abs(p(i)-q(i))<1e-12;
|
||||
%$ end
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
% Consistency check between wblinv and wblcdf.
|
||||
|
||||
% Set the hyperparameters of a Weibull definition.
|
||||
scale = .5;
|
||||
shape = 1.5;
|
||||
|
||||
% Compute quatiles of the weibull distribution.
|
||||
q = 0:.05:1;
|
||||
m = zeros(size(q));
|
||||
p = zeros(size(q));
|
||||
for i=1:length(q)
|
||||
m(i) = wblinv(q(i), scale, shape);
|
||||
end
|
||||
|
||||
try
|
||||
for i=1:length(q)
|
||||
p(i) = wblcdf(m(i), scale, shape);
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
% Check the results
|
||||
if t(1)
|
||||
for i=1:length(q)
|
||||
t(i+1) = abs(p(i)-q(i))<1e-12;
|
||||
end
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:4
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function t = wblinv(proba, scale, shape) % --*-- Unitary tests --*--
|
||||
function t = wblinv(proba, scale, shape)
|
||||
|
||||
% Inverse cumulative distribution function.
|
||||
%
|
||||
|
|
@ -10,7 +10,7 @@ function t = wblinv(proba, scale, shape) % --*-- Unitary tests --*--
|
|||
% OUTPUTS
|
||||
% - t [double] scalar such that P(X<=t)=proba
|
||||
|
||||
% Copyright © 2015-2020 Dynare Team
|
||||
% Copyright © 2015-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -58,110 +58,112 @@ end
|
|||
|
||||
t = exp(log(scale)+log(-log(1-proba))/shape);
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ try
|
||||
%$ x = wblinv(0, 1, 2);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = isequal(x, 0);
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
try
|
||||
x = wblinv(0, 1, 2);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = isequal(x, 0);
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
||||
%@test:2
|
||||
%$ try
|
||||
%$ x = wblinv(1, 1, 2);
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = isinf(x);
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
try
|
||||
x = wblinv(1, 1, 2);
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = isinf(x);
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:2
|
||||
|
||||
%@test:3
|
||||
%$ scales = [.5, 1, 5];
|
||||
%$ shapes = [.1, 1, 2];
|
||||
%$ x = NaN(9,1);
|
||||
%$
|
||||
%$ try
|
||||
%$ k = 0;
|
||||
%$ for i=1:3
|
||||
%$ for j=1:3
|
||||
%$ k = k+1;
|
||||
%$ x(k) = wblinv(.5, scales(i), shapes(j));
|
||||
%$ end
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ k = 1;
|
||||
%$ for i=1:3
|
||||
%$ for j=1:3
|
||||
%$ k = k+1;
|
||||
%$ t(k) = abs(x(k-1)-scales(i)*log(2)^(1/shapes(j)))<1e-12;
|
||||
%$ end
|
||||
%$ end
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
scales = [.5, 1, 5];
|
||||
shapes = [.1, 1, 2];
|
||||
x = NaN(9,1);
|
||||
|
||||
try
|
||||
k = 0;
|
||||
for i=1:3
|
||||
for j=1:3
|
||||
k = k+1;
|
||||
x(k) = wblinv(.5, scales(i), shapes(j));
|
||||
end
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
k = 1;
|
||||
for i=1:3
|
||||
for j=1:3
|
||||
k = k+1;
|
||||
t(k) = abs(x(k-1)-scales(i)*log(2)^(1/shapes(j)))<1e-12;
|
||||
end
|
||||
end
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:3
|
||||
|
||||
%@test:4
|
||||
%$ debug = false;
|
||||
%$ scales = [ .5, 1, 5];
|
||||
%$ shapes = [ 1, 2, 3];
|
||||
%$ x = NaN(9,1);
|
||||
%$ p = 1e-1;
|
||||
%$
|
||||
%$ try
|
||||
%$ k = 0;
|
||||
%$ for i=1:3
|
||||
%$ for j=1:3
|
||||
%$ k = k+1;
|
||||
%$ x(k) = wblinv(p, scales(i), shapes(j));
|
||||
%$ end
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ k = 1;
|
||||
%$ for i=1:3
|
||||
%$ for j=1:3
|
||||
%$ k = k+1;
|
||||
%$ shape = shapes(j);
|
||||
%$ scale = scales(i);
|
||||
%$ density = @(z) exp(lpdfgweibull(z,shape,scale));
|
||||
%$ if debug
|
||||
%$ [shape, scale, x(k-1)]
|
||||
%$ end
|
||||
%$ if isoctave
|
||||
%$ s = quadv(density, 0, x(k-1),1e-10);
|
||||
%$ else
|
||||
%$ s = integral(density, 0, x(k-1));
|
||||
%$ end
|
||||
%$ if debug
|
||||
%$ [s, abs(p-s)]
|
||||
%$ end
|
||||
%$ if isoctave
|
||||
%$ t(k) = abs(p-s)<1e-9;
|
||||
%$ else
|
||||
%$ t(k) = abs(p-s)<1e-12;
|
||||
%$ end
|
||||
%$ end
|
||||
%$ end
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
debug = false;
|
||||
scales = [ .5, 1, 5];
|
||||
shapes = [ 1, 2, 3];
|
||||
x = NaN(9,1);
|
||||
p = 1e-1;
|
||||
|
||||
try
|
||||
k = 0;
|
||||
for i=1:3
|
||||
for j=1:3
|
||||
k = k+1;
|
||||
x(k) = wblinv(p, scales(i), shapes(j));
|
||||
end
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
k = 1;
|
||||
for i=1:3
|
||||
for j=1:3
|
||||
k = k+1;
|
||||
shape = shapes(j);
|
||||
scale = scales(i);
|
||||
density = @(z) exp(lpdfgweibull(z,shape,scale));
|
||||
if debug
|
||||
[shape, scale, x(k-1)]
|
||||
end
|
||||
if isoctave
|
||||
s = quadv(density, 0, x(k-1),1e-10);
|
||||
else
|
||||
s = integral(density, 0, x(k-1));
|
||||
end
|
||||
if debug
|
||||
[s, abs(p-s)]
|
||||
end
|
||||
if isoctave
|
||||
t(k) = abs(p-s)<1e-9;
|
||||
else
|
||||
t(k) = abs(p-s)<1e-12;
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:4
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
Subproject commit a519e8a68177e2a5beccff3d04ddcb9e4f64030d
|
||||
Subproject commit f2960bf774113d278eeb43faf9a30c31495e6843
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
function pdraw = prior_draw(BayesInfo, prior_trunc, uniform) % --*-- Unitary tests --*--
|
||||
function pdraw = prior_draw(BayesInfo, prior_trunc, uniform)
|
||||
|
||||
% This function generate one draw from the joint prior distribution and
|
||||
% allows sampling uniformly from the prior support (uniform==1 when called with init==1)
|
||||
|
|
@ -26,7 +26,7 @@ function pdraw = prior_draw(BayesInfo, prior_trunc, uniform) % --*-- Unitary tes
|
|||
% NOTE 3. This code relies on bayestopt_ as created in the base workspace
|
||||
% by the preprocessor (or as updated in subsequent pieces of code and handed to the base workspace)
|
||||
%
|
||||
% Copyright © 2006-2017 Dynare Team
|
||||
% Copyright © 2006-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -174,101 +174,103 @@ if weibull_draws
|
|||
end
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ % Fill global structures with required fields...
|
||||
%$ prior_trunc = 1e-10;
|
||||
%$ p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1); % Prior shape
|
||||
%$ p1 = .4*ones(14,1); % Prior mean
|
||||
%$ p2 = .2*ones(14,1); % Prior std.
|
||||
%$ p3 = NaN(14,1);
|
||||
%$ p4 = NaN(14,1);
|
||||
%$ p5 = NaN(14,1);
|
||||
%$ p6 = NaN(14,1);
|
||||
%$ p7 = NaN(14,1);
|
||||
%$
|
||||
%$ for i=1:14
|
||||
%$ switch p0(i)
|
||||
%$ case 1
|
||||
%$ % Beta distribution
|
||||
%$ p3(i) = 0;
|
||||
%$ p4(i) = 1;
|
||||
%$ [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
|
||||
%$ case 2
|
||||
%$ % Gamma distribution
|
||||
%$ p3(i) = 0;
|
||||
%$ p4(i) = Inf;
|
||||
%$ [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
|
||||
%$ case 3
|
||||
%$ % Normal distribution
|
||||
%$ p3(i) = -Inf;
|
||||
%$ p4(i) = Inf;
|
||||
%$ p6(i) = p1(i);
|
||||
%$ p7(i) = p2(i);
|
||||
%$ p5(i) = p1(i);
|
||||
%$ case 4
|
||||
%$ % Inverse Gamma (type I) distribution
|
||||
%$ p3(i) = 0;
|
||||
%$ p4(i) = Inf;
|
||||
%$ [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
|
||||
%$ case 5
|
||||
%$ % Uniform distribution
|
||||
%$ [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
|
||||
%$ p3(i) = p6(i);
|
||||
%$ p4(i) = p7(i);
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
|
||||
%$ case 6
|
||||
%$ % Inverse Gamma (type II) distribution
|
||||
%$ p3(i) = 0;
|
||||
%$ p4(i) = Inf;
|
||||
%$ [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
|
||||
%$ case 8
|
||||
%$ % Weibull distribution
|
||||
%$ p3(i) = 0;
|
||||
%$ p4(i) = Inf;
|
||||
%$ [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
|
||||
%$ otherwise
|
||||
%$ error('This density is not implemented!')
|
||||
%$ end
|
||||
%$ end
|
||||
%$
|
||||
%$ BayesInfo.pshape = p0;
|
||||
%$ BayesInfo.p1 = p1;
|
||||
%$ BayesInfo.p2 = p2;
|
||||
%$ BayesInfo.p3 = p3;
|
||||
%$ BayesInfo.p4 = p4;
|
||||
%$ BayesInfo.p5 = p5;
|
||||
%$ BayesInfo.p6 = p6;
|
||||
%$ BayesInfo.p7 = p7;
|
||||
%$
|
||||
%$ ndraws = 1e5;
|
||||
%$ m0 = BayesInfo.p1; %zeros(14,1);
|
||||
%$ v0 = diag(BayesInfo.p2.^2); %zeros(14);
|
||||
%$
|
||||
%$ % Call the tested routine
|
||||
%$ try
|
||||
%$ % Initialization of prior_draws.
|
||||
%$ prior_draw(BayesInfo, prior_trunc, false);
|
||||
%$ % Do simulations in a loop and estimate recursively the mean and teh variance.
|
||||
%$ for i = 1:ndraws
|
||||
%$ draw = transpose(prior_draw());
|
||||
%$ m1 = m0 + (draw-m0)/i;
|
||||
%$ m2 = m1*m1';
|
||||
%$ v0 = v0 + ((draw*draw'-m2-v0) + (i-1)*(m0*m0'-m2'))/i;
|
||||
%$ m0 = m1;
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = all(abs(m0-BayesInfo.p1)<3e-3);
|
||||
%$ t(3) = all(all(abs(v0-diag(BayesInfo.p2.^2))<5e-3));
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
% Fill global structures with required fields...
|
||||
prior_trunc = 1e-10;
|
||||
p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1); % Prior shape
|
||||
p1 = .4*ones(14,1); % Prior mean
|
||||
p2 = .2*ones(14,1); % Prior std.
|
||||
p3 = NaN(14,1);
|
||||
p4 = NaN(14,1);
|
||||
p5 = NaN(14,1);
|
||||
p6 = NaN(14,1);
|
||||
p7 = NaN(14,1);
|
||||
|
||||
for i=1:14
|
||||
switch p0(i)
|
||||
case 1
|
||||
% Beta distribution
|
||||
p3(i) = 0;
|
||||
p4(i) = 1;
|
||||
[p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
|
||||
case 2
|
||||
% Gamma distribution
|
||||
p3(i) = 0;
|
||||
p4(i) = Inf;
|
||||
[p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
|
||||
case 3
|
||||
% Normal distribution
|
||||
p3(i) = -Inf;
|
||||
p4(i) = Inf;
|
||||
p6(i) = p1(i);
|
||||
p7(i) = p2(i);
|
||||
p5(i) = p1(i);
|
||||
case 4
|
||||
% Inverse Gamma (type I) distribution
|
||||
p3(i) = 0;
|
||||
p4(i) = Inf;
|
||||
[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
|
||||
case 5
|
||||
% Uniform distribution
|
||||
[p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
|
||||
p3(i) = p6(i);
|
||||
p4(i) = p7(i);
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
|
||||
case 6
|
||||
% Inverse Gamma (type II) distribution
|
||||
p3(i) = 0;
|
||||
p4(i) = Inf;
|
||||
[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
|
||||
case 8
|
||||
% Weibull distribution
|
||||
p3(i) = 0;
|
||||
p4(i) = Inf;
|
||||
[p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
|
||||
otherwise
|
||||
error('This density is not implemented!')
|
||||
end
|
||||
end
|
||||
|
||||
BayesInfo.pshape = p0;
|
||||
BayesInfo.p1 = p1;
|
||||
BayesInfo.p2 = p2;
|
||||
BayesInfo.p3 = p3;
|
||||
BayesInfo.p4 = p4;
|
||||
BayesInfo.p5 = p5;
|
||||
BayesInfo.p6 = p6;
|
||||
BayesInfo.p7 = p7;
|
||||
|
||||
ndraws = 1e5;
|
||||
m0 = BayesInfo.p1; %zeros(14,1);
|
||||
v0 = diag(BayesInfo.p2.^2); %zeros(14);
|
||||
|
||||
% Call the tested routine
|
||||
try
|
||||
% Initialization of prior_draws.
|
||||
prior_draw(BayesInfo, prior_trunc, false);
|
||||
% Do simulations in a loop and estimate recursively the mean and teh variance.
|
||||
for i = 1:ndraws
|
||||
draw = transpose(prior_draw());
|
||||
m1 = m0 + (draw-m0)/i;
|
||||
m2 = m1*m1';
|
||||
v0 = v0 + ((draw*draw'-m2-v0) + (i-1)*(m0*m0'-m2'))/i;
|
||||
m0 = m1;
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = all(abs(m0-BayesInfo.p1)<3e-3);
|
||||
t(3) = all(all(abs(v0-diag(BayesInfo.p2.^2))<5e-3));
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function [logged_prior_density, dlprior, d2lprior, info] = priordens(x, pshape, p6, p7, p3, p4, initialization) % --*-- Unitary tests --*--
|
||||
function [logged_prior_density, dlprior, d2lprior, info] = priordens(x, pshape, p6, p7, p3, p4, initialization)
|
||||
% Computes a prior density for the structural parameters of DSGE models
|
||||
%
|
||||
% INPUTS
|
||||
|
|
@ -15,7 +15,7 @@ function [logged_prior_density, dlprior, d2lprior, info] = priordens(x, pshape,
|
|||
% info [double] error code for index of Inf-prior parameter
|
||||
%
|
||||
|
||||
% Copyright © 2003-2017 Dynare Team
|
||||
% Copyright © 2003-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -191,85 +191,87 @@ if nargout==3
|
|||
d2lprior = diag(d2lprior);
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ % Fill global structures with required fields...
|
||||
%$ prior_trunc = 1e-10;
|
||||
%$ p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1); % Prior shape
|
||||
%$ p1 = .4*ones(14,1); % Prior mean
|
||||
%$ p2 = .2*ones(14,1); % Prior std.
|
||||
%$ p3 = NaN(14,1);
|
||||
%$ p4 = NaN(14,1);
|
||||
%$ p5 = NaN(14,1);
|
||||
%$ p6 = NaN(14,1);
|
||||
%$ p7 = NaN(14,1);
|
||||
%$
|
||||
%$ for i=1:14
|
||||
%$ switch p0(i)
|
||||
%$ case 1
|
||||
%$ % Beta distribution
|
||||
%$ p3(i) = 0;
|
||||
%$ p4(i) = 1;
|
||||
%$ [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
|
||||
%$ case 2
|
||||
%$ % Gamma distribution
|
||||
%$ p3(i) = 0;
|
||||
%$ p4(i) = Inf;
|
||||
%$ [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
|
||||
%$ case 3
|
||||
%$ % Normal distribution
|
||||
%$ p3(i) = -Inf;
|
||||
%$ p4(i) = Inf;
|
||||
%$ p6(i) = p1(i);
|
||||
%$ p7(i) = p2(i);
|
||||
%$ p5(i) = p1(i);
|
||||
%$ case 4
|
||||
%$ % Inverse Gamma (type I) distribution
|
||||
%$ p3(i) = 0;
|
||||
%$ p4(i) = Inf;
|
||||
%$ [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
|
||||
%$ case 5
|
||||
%$ % Uniform distribution
|
||||
%$ [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
|
||||
%$ p3(i) = p6(i);
|
||||
%$ p4(i) = p7(i);
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
|
||||
%$ case 6
|
||||
%$ % Inverse Gamma (type II) distribution
|
||||
%$ p3(i) = 0;
|
||||
%$ p4(i) = Inf;
|
||||
%$ [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
|
||||
%$ case 8
|
||||
%$ % Weibull distribution
|
||||
%$ p3(i) = 0;
|
||||
%$ p4(i) = Inf;
|
||||
%$ [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
|
||||
%$ p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
|
||||
%$ otherwise
|
||||
%$ error('This density is not implemented!')
|
||||
%$ end
|
||||
%$ end
|
||||
%$
|
||||
%$ % Call the tested routine
|
||||
%$ try
|
||||
%$ % Initialization of priordens.
|
||||
%$ lpdstar = priordens(p5, p0, p6, p7, p3, p4, true);
|
||||
%$ % Do simulations in a loop and estimate recursively the mean and teh variance.
|
||||
%$ LPD = NaN(10000,1);
|
||||
%$ for i = 1:10000
|
||||
%$ draw = p5+randn(size(p5))*.02;
|
||||
%$ LPD(i) = priordens(p5, p0, p6, p7, p3, p4);
|
||||
%$ end
|
||||
%$ t(1) = true;
|
||||
%$ catch
|
||||
%$ t(1) = false;
|
||||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = all(LPD<=lpdstar);
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
%@eof:1
|
||||
% Fill global structures with required fields...
|
||||
prior_trunc = 1e-10;
|
||||
p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1); % Prior shape
|
||||
p1 = .4*ones(14,1); % Prior mean
|
||||
p2 = .2*ones(14,1); % Prior std.
|
||||
p3 = NaN(14,1);
|
||||
p4 = NaN(14,1);
|
||||
p5 = NaN(14,1);
|
||||
p6 = NaN(14,1);
|
||||
p7 = NaN(14,1);
|
||||
|
||||
for i=1:14
|
||||
switch p0(i)
|
||||
case 1
|
||||
% Beta distribution
|
||||
p3(i) = 0;
|
||||
p4(i) = 1;
|
||||
[p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
|
||||
case 2
|
||||
% Gamma distribution
|
||||
p3(i) = 0;
|
||||
p4(i) = Inf;
|
||||
[p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
|
||||
case 3
|
||||
% Normal distribution
|
||||
p3(i) = -Inf;
|
||||
p4(i) = Inf;
|
||||
p6(i) = p1(i);
|
||||
p7(i) = p2(i);
|
||||
p5(i) = p1(i);
|
||||
case 4
|
||||
% Inverse Gamma (type I) distribution
|
||||
p3(i) = 0;
|
||||
p4(i) = Inf;
|
||||
[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
|
||||
case 5
|
||||
% Uniform distribution
|
||||
[p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
|
||||
p3(i) = p6(i);
|
||||
p4(i) = p7(i);
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
|
||||
case 6
|
||||
% Inverse Gamma (type II) distribution
|
||||
p3(i) = 0;
|
||||
p4(i) = Inf;
|
||||
[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
|
||||
case 8
|
||||
% Weibull distribution
|
||||
p3(i) = 0;
|
||||
p4(i) = Inf;
|
||||
[p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
|
||||
p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
|
||||
otherwise
|
||||
error('This density is not implemented!')
|
||||
end
|
||||
end
|
||||
|
||||
% Call the tested routine
|
||||
try
|
||||
% Initialization of priordens.
|
||||
lpdstar = priordens(p5, p0, p6, p7, p3, p4, true);
|
||||
% Do simulations in a loop and estimate recursively the mean and teh variance.
|
||||
LPD = NaN(10000,1);
|
||||
for i = 1:10000
|
||||
draw = p5+randn(size(p5))*.02;
|
||||
LPD(i) = priordens(p5, p0, p6, p7, p3, p4);
|
||||
end
|
||||
t(1) = true;
|
||||
catch
|
||||
t(1) = false;
|
||||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = all(LPD<=lpdstar);
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function C = compare_vectors(f, A, B) % --*-- Unitary tests --*--
|
||||
function C = compare_vectors(f, A, B)
|
||||
|
||||
% Performs lexicographical comparison of vectors.
|
||||
%
|
||||
|
|
@ -13,7 +13,7 @@ function C = compare_vectors(f, A, B) % --*-- Unitary tests --*--
|
|||
% REMARKS
|
||||
% o It is assumed that vectors A and B have the same number of elements.
|
||||
|
||||
% Copyright © 2013-2017 Dynare Team
|
||||
% Copyright © 2013-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -76,19 +76,21 @@ else
|
|||
end
|
||||
end
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
%$ t(1) = dassert(compare_vectors(@lt, [1990 3], [1991 3]), 1);
|
||||
%$ t(2) = dassert(compare_vectors(@lt, [1990 3], [1990 3]), 0);
|
||||
%$ t(3) = dassert(compare_vectors(@le, [1990 3], [1990 3]), 1);
|
||||
%$ t(4) = dassert(compare_vectors(@lt, [1990 3], [1990 4]), 1);
|
||||
%$ t(5) = dassert(compare_vectors(@le, [1990 3], [1990 4]), 1);
|
||||
%$ t(6) = dassert(compare_vectors(@gt, [1990 3], [1991 3]), 0);
|
||||
%$ t(7) = dassert(compare_vectors(@gt, [1990 3], [1990 3]), 0);
|
||||
%$ t(8) = dassert(compare_vectors(@ge, [1990 3], [1990 3]), 1);
|
||||
%$ t(9) = dassert(compare_vectors(@gt, [1990 3], [1990 4]), 0);
|
||||
%$ t(10) = dassert(compare_vectors(@ge, [1990 3], [1990 4]), 0);
|
||||
%$ t(11) = dassert(compare_vectors(@le, [1991 3], [1990 4]), 0);
|
||||
%$ t(12) = dassert(compare_vectors(@le, [1991 3], [1990 2]), 0);
|
||||
%$ t(13) = dassert(compare_vectors(@le, [1945 2], [1950, 1]),1);
|
||||
%$ T = all(t);
|
||||
%@eof:1
|
||||
t(1) = dassert(compare_vectors(@lt, [1990 3], [1991 3]), 1);
|
||||
t(2) = dassert(compare_vectors(@lt, [1990 3], [1990 3]), 0);
|
||||
t(3) = dassert(compare_vectors(@le, [1990 3], [1990 3]), 1);
|
||||
t(4) = dassert(compare_vectors(@lt, [1990 3], [1990 4]), 1);
|
||||
t(5) = dassert(compare_vectors(@le, [1990 3], [1990 4]), 1);
|
||||
t(6) = dassert(compare_vectors(@gt, [1990 3], [1991 3]), 0);
|
||||
t(7) = dassert(compare_vectors(@gt, [1990 3], [1990 3]), 0);
|
||||
t(8) = dassert(compare_vectors(@ge, [1990 3], [1990 3]), 1);
|
||||
t(9) = dassert(compare_vectors(@gt, [1990 3], [1990 4]), 0);
|
||||
t(10) = dassert(compare_vectors(@ge, [1990 3], [1990 4]), 0);
|
||||
t(11) = dassert(compare_vectors(@le, [1991 3], [1990 4]), 0);
|
||||
t(12) = dassert(compare_vectors(@le, [1991 3], [1990 2]), 0);
|
||||
t(13) = dassert(compare_vectors(@le, [1945 2], [1950, 1]),1);
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
Subproject commit 343d9e13654a07c348fad863f7f79a03896c6bc9
|
||||
Subproject commit 3c4079a8e1e495be50be3cd81855eb37189fceab
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
function tf = ver_greater_than(ver1, ver2) % --*-- Unitary tests --*--
|
||||
function tf = ver_greater_than(ver1, ver2)
|
||||
%function tf = ver_greater_than(ver1, ver2)
|
||||
% ver1 > ver2 ? 1 : 0;
|
||||
%
|
||||
|
|
@ -12,7 +12,7 @@ function tf = ver_greater_than(ver1, ver2) % --*-- Unitary tests --*--
|
|||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
|
||||
% Copyright © 2015-2021 Dynare Team
|
||||
% Copyright © 2015-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -30,7 +30,8 @@ function tf = ver_greater_than(ver1, ver2) % --*-- Unitary tests --*--
|
|||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
tf = ~ver_less_than(ver1, ver2) && ~strcmp(ver1, ver2);
|
||||
return
|
||||
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
ver2='4.4';
|
||||
|
|
@ -38,18 +39,21 @@ ver1='4.5.2';
|
|||
t(1)=dassert(ver_greater_than(ver1,ver2),true);
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
||||
%@test:2
|
||||
ver1='6-unstable-2021-12-15-1737-21a8a579';
|
||||
ver2='4.4';
|
||||
t(1)=dassert(ver_greater_than(ver1,ver2),true);
|
||||
T = all(t);
|
||||
%@eof:2
|
||||
|
||||
%@test:3
|
||||
ver2='5.0';
|
||||
ver1='5.1';
|
||||
t(1)=dassert(ver_greater_than(ver1,ver2),true);
|
||||
T = all(t);
|
||||
%@eof:3
|
||||
|
||||
%@test:4
|
||||
ver2='6-unstable-2021-12-18-1227-c43777f6';
|
||||
ver1='6-unstable-2021-12-19-1953-d841fc7c';
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function tf = ver_less_than(ver1, ver2) % --*-- Unitary tests --*--
|
||||
function tf = ver_less_than(ver1, ver2)
|
||||
%function tf = ver_less_than(ver1, ver2)
|
||||
% ver1 < ver2 ? 1 : 0;
|
||||
%
|
||||
|
|
@ -54,7 +54,7 @@ elseif (maj_ver1 == maj_ver2) && (min_ver1 > min_ver2)
|
|||
end
|
||||
|
||||
%deal with revision in Dynare 4 and unstable versions involved
|
||||
if min(maj_ver1,maj_ver2)<5 %old versioning scheme with three digits
|
||||
if min(maj_ver1,maj_ver2)<5 %old versioning scheme with three digits
|
||||
if (length(ver1) == length(ver2) && length(ver1) == 3)
|
||||
%check if master branch (unstable) or stable
|
||||
ismaster1 = isnan(str2double(ver1{3}));
|
||||
|
|
@ -63,7 +63,7 @@ if min(maj_ver1,maj_ver2)<5 %old versioning scheme with three digits
|
|||
%ver2 is the unstable
|
||||
return
|
||||
end
|
||||
|
||||
|
||||
if ~ismaster1 && ~ismaster2 %both are stable versions
|
||||
rev_ver1 = str2double(ver1{3});
|
||||
rev_ver2 = str2double(ver2{3});
|
||||
|
|
@ -76,7 +76,7 @@ if min(maj_ver1,maj_ver2)<5 %old versioning scheme with three digits
|
|||
%ver1 is an unstable version
|
||||
error('Case is undefined, please contact the developers')
|
||||
end
|
||||
elseif min(maj_ver1,maj_ver2)>=5 %new versioning scheme with three digits
|
||||
elseif min(maj_ver1,maj_ver2)>=5 %new versioning scheme with three digits
|
||||
if strcmp(ver1{2},'x') || strcmp(ver1{2},'unstable')
|
||||
date_number_1=datenum([ver1{3} ver1{4} ver1{5}],'YYYYMMDD');
|
||||
stable_version_indicator_1=0;
|
||||
|
|
@ -102,7 +102,7 @@ elseif min(maj_ver1,maj_ver2)>=5 %new versioning scheme with three digits
|
|||
end
|
||||
tf = false;
|
||||
|
||||
return
|
||||
return % --*-- Unit tests --*--
|
||||
|
||||
%@test:1
|
||||
ver1='4.4';
|
||||
|
|
@ -110,26 +110,24 @@ ver2='4.5.2';
|
|||
t(1)=dassert(ver_less_than(ver1,ver2),true);
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
||||
%@test:2
|
||||
ver1='4.4';
|
||||
ver2='6-unstable-2021-12-15-1737-21a8a579';
|
||||
t(1)=dassert(ver_less_than(ver1,ver2),true);
|
||||
T = all(t);
|
||||
%@eof:2
|
||||
|
||||
%@test:3
|
||||
ver1='5.0';
|
||||
ver2='5.1';
|
||||
t(1)=dassert(ver_less_than(ver1,ver2),true);
|
||||
T = all(t);
|
||||
%@eof:3
|
||||
|
||||
%@test:4
|
||||
ver1='6-unstable-2021-12-18-1227-c43777f6';
|
||||
ver2='6-unstable-2021-12-19-1953-d841fc7c';
|
||||
t(1)=dassert(ver_less_than(ver1,ver2),true);
|
||||
T = all(t);
|
||||
%@eof:4
|
||||
% %@test:5
|
||||
% ver1='5.0';
|
||||
% ver2='5.x-2021-12-14-1101-25c1e0c0';
|
||||
% t(5)=dassert(ver_less_than(ver1,ver2),true)
|
||||
|
||||
|
|
|
|||
|
|
@ -1259,7 +1259,7 @@ M_TRS_FILES = $(patsubst %.mod, %.m.trs, $(MODFILES))
|
|||
M_TRS_FILES += $(patsubst %.mod, %.m.trs, $(PARTICLEFILES))
|
||||
M_TRS_FILES += run_block_byte_tests_matlab.m.trs \
|
||||
run_reporting_test_matlab.m.trs \
|
||||
run_all_unitary_tests.m.trs \
|
||||
run_all_unit_tests.m.trs \
|
||||
histval_initval_file_unit_tests.m.trs \
|
||||
test_aggregate_routine_1_2.m.trs \
|
||||
test_aggregate_routine_1_2_3.m.trs \
|
||||
|
|
@ -1276,7 +1276,7 @@ O_TRS_FILES = $(patsubst %.mod, %.o.trs, $(MODFILES))
|
|||
O_TRS_FILES += $(patsubst %.mod, %.o.trs, $(PARTICLEFILES))
|
||||
O_TRS_FILES += run_block_byte_tests_octave.o.trs \
|
||||
run_reporting_test_octave.o.trs \
|
||||
run_all_unitary_tests.o.trs \
|
||||
run_all_unit_tests.o.trs \
|
||||
histval_initval_file_unit_tests.o.trs \
|
||||
run_kronecker_tests.o.trs \
|
||||
nonlinearsolvers.o.trs \
|
||||
|
|
@ -1306,7 +1306,7 @@ EXTRA_DIST = \
|
|||
run_block_byte_tests_octave.m \
|
||||
run_reporting_test_matlab.m \
|
||||
run_reporting_test_octave.m \
|
||||
run_all_unitary_tests.m \
|
||||
run_all_unit_tests.m \
|
||||
+matlab/+namespace/y_k.m \
|
||||
reporting/AnnualTable.m \
|
||||
reporting/CommResidTablePage.m \
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
% Copyright © 2013-2022 Dynare Team
|
||||
% Copyright © 2013-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
@ -52,7 +52,7 @@ counter = 0;
|
|||
|
||||
for i = 1:length(mlist)
|
||||
f = [top_test_dir filesep mlist{i} ];
|
||||
if is_unitary_test_available(f)
|
||||
if is_unit_test_available(f)
|
||||
[check, info] = mtest(f);
|
||||
for j = 1:size(info, 1)
|
||||
counter = counter + 1;
|
||||
|
|
@ -65,9 +65,9 @@ end
|
|||
|
||||
cd(getenv('TOP_TEST_DIR'));
|
||||
if isoctave
|
||||
fid = fopen('run_all_unitary_tests.o.trs', 'w+');
|
||||
fid = fopen('run_all_unit_tests.o.trs', 'w+');
|
||||
else
|
||||
fid = fopen('run_all_unitary_tests.m.trs', 'w+');
|
||||
fid = fopen('run_all_unit_tests.m.trs', 'w+');
|
||||
end
|
||||
if length(failedtests) > 0
|
||||
fprintf(fid,':test-result: FAIL\n');
|
||||
Loading…
Reference in New Issue