[mlpack-svn] r17091 - mlpack/trunk/src/mlpack/tests
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Wed Aug 20 17:12:08 EDT 2014
Author: rcurtin
Date: Wed Aug 20 17:12:07 2014
New Revision: 17091
Log:
Convert tabs to spaces.
Modified:
mlpack/trunk/src/mlpack/tests/gmm_test.cpp
Modified: mlpack/trunk/src/mlpack/tests/gmm_test.cpp
==============================================================================
--- mlpack/trunk/src/mlpack/tests/gmm_test.cpp (original)
+++ mlpack/trunk/src/mlpack/tests/gmm_test.cpp Wed Aug 20 17:12:07 2014
@@ -28,8 +28,8 @@
{
// Create a GMM.
GMM<> gmm(2, 2);
- gmm.Component(0) = distribution::GaussianDistribution("0 0", "1 0; 0 1");
- gmm.Component(1) = distribution::GaussianDistribution("3 3", "2 1; 1 2");
+ gmm.Component(0) = distribution::GaussianDistribution("0 0", "1 0; 0 1");
+ gmm.Component(1) = distribution::GaussianDistribution("3 3", "2 1; 1 2");
gmm.Weights() = "0.3 0.7";
// Now test a couple observations. These comparisons are calculated by hand.
@@ -49,8 +49,8 @@
{
// Create a GMM (same as the last test).
GMM<> gmm(2, 2);
- gmm.Component(0) = distribution::GaussianDistribution("0 0", "1 0; 0 1");
- gmm.Component(1) = distribution::GaussianDistribution("3 3", "2 1; 1 2");
+ gmm.Component(0) = distribution::GaussianDistribution("0 0", "1 0; 0 1");
+ gmm.Component(1) = distribution::GaussianDistribution("3 3", "2 1; 1 2");
gmm.Weights() = "0.3 0.7";
// Now test a couple observations. These comparisons are calculated by hand.
@@ -106,17 +106,17 @@
arma::mat actualCovar = ccov(data, 1 /* biased estimator */);
// Check the model to see that it is correct.
- BOOST_REQUIRE_CLOSE(gmm.Component(0).Mean()[0], actualMean(0), 1e-5);
- BOOST_REQUIRE_CLOSE(gmm.Component(0).Mean()[1], actualMean(1), 1e-5);
-
- BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(0, 0),
- actualCovar(0, 0), 1e-5);
- BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(0, 1),
- actualCovar(0, 1), 1e-5);
- BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(1, 0),
- actualCovar(1, 0), 1e-5);
- BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(1, 1),
- actualCovar(1, 1), 1e-5);
+ BOOST_REQUIRE_CLOSE(gmm.Component(0).Mean()[0], actualMean(0), 1e-5);
+ BOOST_REQUIRE_CLOSE(gmm.Component(0).Mean()[1], actualMean(1), 1e-5);
+
+ BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(0, 0),
+ actualCovar(0, 0), 1e-5);
+ BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(0, 1),
+ actualCovar(0, 1), 1e-5);
+ BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(1, 0),
+ actualCovar(1, 0), 1e-5);
+ BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(1, 1),
+ actualCovar(1, 1), 1e-5);
BOOST_REQUIRE_CLOSE(gmm.Weights()[0], 1.0, 1e-5);
}
@@ -326,48 +326,48 @@
for (size_t i = 0; i < 3; i++)
BOOST_REQUIRE_SMALL((g.Component(sortedIndices[0]).Mean()[i]
- - d4.Mean()[i]), 0.4);
+ - d4.Mean()[i]), 0.4);
for (size_t row = 0; row < 3; row++)
for (size_t col = 0; col < 3; col++)
BOOST_REQUIRE_SMALL((g.Component(sortedIndices[0]).Covariance()(row, col)
- - d4.Covariance()(row, col)), 0.60); // Big tolerance! Lots of noise.
+ - d4.Covariance()(row, col)), 0.60); // Big tolerance! Lots of noise.
// Second Gaussian (d1).
BOOST_REQUIRE_SMALL(g.Weights()[sortedIndices[1]] - 0.2, 0.1);
for (size_t i = 0; i < 3; i++)
BOOST_REQUIRE_SMALL((g.Component(sortedIndices[1]).Mean()[i]
- - d1.Mean()[i]), 0.4);
+ - d1.Mean()[i]), 0.4);
for (size_t row = 0; row < 3; row++)
for (size_t col = 0; col < 3; col++)
BOOST_REQUIRE_SMALL((g.Component(sortedIndices[1]).Covariance()(row, col)
- - d1.Covariance()(row, col)), 0.55); // Big tolerance! Lots of noise.
+ - d1.Covariance()(row, col)), 0.55); // Big tolerance! Lots of noise.
// Third Gaussian (d2).
BOOST_REQUIRE_SMALL(g.Weights()[sortedIndices[2]] - 0.3, 0.1);
for (size_t i = 0; i < 3; i++)
BOOST_REQUIRE_SMALL((g.Component(sortedIndices[2]).Mean()[i]
- - d2.Mean()[i]), 0.4);
+ - d2.Mean()[i]), 0.4);
for (size_t row = 0; row < 3; row++)
for (size_t col = 0; col < 3; col++)
BOOST_REQUIRE_SMALL((g.Component(sortedIndices[2]).Covariance()(row, col)
- - d2.Covariance()(row, col)), 0.50); // Big tolerance! Lots of noise.
+ - d2.Covariance()(row, col)), 0.50); // Big tolerance! Lots of noise.
// Fourth gaussian (d3).
BOOST_REQUIRE_SMALL(g.Weights()[sortedIndices[3]] - 0.4, 0.1);
for (size_t i = 0; i < 3; ++i)
BOOST_REQUIRE_SMALL((g.Component(sortedIndices[3]).Mean()[i]
- - d3.Mean()[i]), 0.4);
+ - d3.Mean()[i]), 0.4);
for (size_t row = 0; row < 3; ++row)
for (size_t col = 0; col < 3; ++col)
BOOST_REQUIRE_SMALL((g.Component(sortedIndices[3]).Covariance()(row, col)
- - d3.Covariance()(row, col)), 0.50);
+ - d3.Covariance()(row, col)), 0.50);
}
/**
@@ -382,13 +382,13 @@
gmm.Weights() = arma::vec("0.40 0.60");
// N([2.25 3.10], [1.00 0.20; 0.20 0.89])
- gmm.Component(0) = distribution::GaussianDistribution("2.25 3.10",
- "1.00 0.60; 0.60 0.89");
+ gmm.Component(0) = distribution::GaussianDistribution("2.25 3.10",
+ "1.00 0.60; 0.60 0.89");
// N([4.10 1.01], [1.00 0.00; 0.00 1.01])
- gmm.Component(1) = distribution::GaussianDistribution("4.10 1.01",
- "1.00 0.70; 0.70 1.01");
+ gmm.Component(1) = distribution::GaussianDistribution("4.10 1.01",
+ "1.00 0.70; 0.70 1.01");
// Now generate a bunch of observations.
arma::mat observations(2, 4000);
@@ -409,32 +409,32 @@
BOOST_REQUIRE_CLOSE(gmm.Weights()[1], gmm2.Weights()[sortedIndices[1]], 7.0);
BOOST_REQUIRE_CLOSE(gmm.Component(0).Mean()[0],
- gmm2.Component(sortedIndices[0]).Mean()[0], 6.5);
+ gmm2.Component(sortedIndices[0]).Mean()[0], 6.5);
BOOST_REQUIRE_CLOSE(gmm.Component(0).Mean()[1],
- gmm2.Component(sortedIndices[0]).Mean()[1], 6.5);
+ gmm2.Component(sortedIndices[0]).Mean()[1], 6.5);
BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(0, 0),
- gmm2.Component(sortedIndices[0]).Covariance()(0, 0), 13.0);
+ gmm2.Component(sortedIndices[0]).Covariance()(0, 0), 13.0);
BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(0, 1),
- gmm2.Component(sortedIndices[0]).Covariance()(0, 1), 22.0);
+ gmm2.Component(sortedIndices[0]).Covariance()(0, 1), 22.0);
BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(1, 0),
- gmm2.Component(sortedIndices[0]).Covariance()(1, 0), 22.0);
+ gmm2.Component(sortedIndices[0]).Covariance()(1, 0), 22.0);
BOOST_REQUIRE_CLOSE(gmm.Component(0).Covariance()(1, 1),
- gmm2.Component(sortedIndices[0]).Covariance()(1, 1), 13.0);
-
+ gmm2.Component(sortedIndices[0]).Covariance()(1, 1), 13.0);
+
BOOST_REQUIRE_CLOSE(gmm.Component(1).Mean()[0],
- gmm2.Component(sortedIndices[1]).Mean()[0], 6.5);
+ gmm2.Component(sortedIndices[1]).Mean()[0], 6.5);
BOOST_REQUIRE_CLOSE(gmm.Component(1).Mean()[1],
- gmm2.Component(sortedIndices[1]).Mean()[1], 6.5);
+ gmm2.Component(sortedIndices[1]).Mean()[1], 6.5);
BOOST_REQUIRE_CLOSE(gmm.Component(1).Covariance()(0, 0),
- gmm2.Component(sortedIndices[1]).Covariance()(0, 0), 13.0);
+ gmm2.Component(sortedIndices[1]).Covariance()(0, 0), 13.0);
BOOST_REQUIRE_CLOSE(gmm.Component(1).Covariance()(0, 1),
- gmm2.Component(sortedIndices[1]).Covariance()(0, 1), 22.0);
+ gmm2.Component(sortedIndices[1]).Covariance()(0, 1), 22.0);
BOOST_REQUIRE_CLOSE(gmm.Component(1).Covariance()(1, 0),
- gmm2.Component(sortedIndices[1]).Covariance()(1, 0), 22.0);
+ gmm2.Component(sortedIndices[1]).Covariance()(1, 0), 22.0);
BOOST_REQUIRE_CLOSE(gmm.Component(1).Covariance()(1, 1),
- gmm2.Component(sortedIndices[1]).Covariance()(1, 1), 13.0);
+ gmm2.Component(sortedIndices[1]).Covariance()(1, 1), 13.0);
}
/**
@@ -444,11 +444,11 @@
{
// First create a Gaussian with a few components.
GMM<> gmm(3, 2);
- gmm.Component(0) = distribution::GaussianDistribution("0 0", "1 0; 0 1");
- gmm.Component(1) = distribution::GaussianDistribution("1 3", "3 2; 2 3");
- gmm.Component(2) = distribution::GaussianDistribution("-2 -2",
+ gmm.Component(0) = distribution::GaussianDistribution("0 0", "1 0; 0 1");
+ gmm.Component(1) = distribution::GaussianDistribution("1 3", "3 2; 2 3");
+ gmm.Component(2) = distribution::GaussianDistribution("-2 -2",
"2.2 1.4; 1.4 5.1");
- gmm.Weights() = "0.6 0.25 0.15";
+ gmm.Weights() = "0.6 0.25 0.15";
arma::mat observations = arma::trans(arma::mat(
" 0 0;"
@@ -515,7 +515,7 @@
{
for (size_t j = 0; j < gmm.Dimensionality(); ++j)
BOOST_REQUIRE_CLOSE(gmm.Component(i).Mean()[j],
- gmm2.Component(i).Mean()[j], 1e-3);
+ gmm2.Component(i).Mean()[j], 1e-3);
for (size_t j = 0; j < gmm.Dimensionality(); ++j)
{
@@ -687,7 +687,7 @@
for (size_t j = 0; j < gmm.Dimensionality(); ++j)
{
BOOST_REQUIRE_CLOSE(gmm.Component(i).Mean()[j],
- oldgmm.Component(i).Mean()[j], 1e-3);
+ oldgmm.Component(i).Mean()[j], 1e-3);
for (size_t k = 0; k < gmm.Dimensionality(); ++k)
BOOST_REQUIRE_CLOSE(gmm.Component(i).Covariance()(j, k),
@@ -709,7 +709,7 @@
for (size_t j = 0; j < gmm.Dimensionality(); ++j)
{
BOOST_REQUIRE_CLOSE(gmm.Component(i).Mean()[j],
- oldgmm.Component(i).Mean()[j], 1e-3);
+ oldgmm.Component(i).Mean()[j], 1e-3);
for (size_t k = 0; k < gmm.Dimensionality(); ++k)
BOOST_REQUIRE_CLOSE(gmm.Component(i).Covariance()(j, k),
@@ -733,7 +733,7 @@
for (size_t j = 0; j < gmm.Dimensionality(); ++j)
{
BOOST_REQUIRE_CLOSE(gmm.Component(i).Mean()[j],
- oldgmm.Component(i).Mean()[j], 1e-3);
+ oldgmm.Component(i).Mean()[j], 1e-3);
for (size_t k = 0; k < gmm.Dimensionality(); ++k)
BOOST_REQUIRE_CLOSE(gmm.Component(i).Covariance()(j, k),
@@ -753,7 +753,7 @@
for (size_t j = 0; j < gmm.Dimensionality(); ++j)
{
BOOST_REQUIRE_CLOSE(gmm.Component(i).Mean()[j],
- oldgmm.Component(i).Mean()[j], 1e-3);
+ oldgmm.Component(i).Mean()[j], 1e-3);
for (size_t k = 0; k < gmm.Dimensionality(); ++k)
BOOST_REQUIRE_CLOSE(gmm.Component(i).Covariance()(j, k),
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