[mlpack-svn] r16428 - mlpack/trunk/src/mlpack/tests
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Tue Apr 15 11:24:09 EDT 2014
Author: rcurtin
Date: Tue Apr 15 11:24:09 2014
New Revision: 16428
Log:
Test incremental variance functionality.
Modified:
mlpack/trunk/src/mlpack/tests/nbc_test.cpp
Modified: mlpack/trunk/src/mlpack/tests/nbc_test.cpp
==============================================================================
--- mlpack/trunk/src/mlpack/tests/nbc_test.cpp (original)
+++ mlpack/trunk/src/mlpack/tests/nbc_test.cpp Tue Apr 15 11:24:09 2014
@@ -67,4 +67,59 @@
BOOST_REQUIRE_EQUAL(testRes(i), calcVec(i));
}
+// The same test, but this one uses the incremental algorithm to calculate
+// variance.
+BOOST_AUTO_TEST_CASE(NaiveBayesClassifierIncrementalTest)
+{
+ const char* trainFilename = "trainSet.csv";
+ const char* testFilename = "testSet.csv";
+ const char* trainResultFilename = "trainRes.csv";
+ const char* testResultFilename = "testRes.csv";
+ size_t classes = 2;
+
+ arma::mat trainData, trainRes, calcMat;
+ data::Load(trainFilename, trainData, true);
+ data::Load(trainResultFilename, trainRes, true);
+
+ // Get the labels out.
+ arma::Col<size_t> labels(trainData.n_cols);
+ for (size_t i = 0; i < trainData.n_cols; ++i)
+ labels[i] = trainData(trainData.n_rows - 1, i);
+ trainData.shed_row(trainData.n_rows - 1);
+
+ NaiveBayesClassifier<> nbcTest(trainData, labels, classes, true);
+
+ size_t dimension = nbcTest.Means().n_rows;
+ calcMat.zeros(2 * dimension + 1, classes);
+
+ for (size_t i = 0; i < dimension; i++)
+ {
+ for (size_t j = 0; j < classes; j++)
+ {
+ calcMat(i, j) = nbcTest.Means()(i, j);
+ calcMat(i + dimension, j) = nbcTest.Variances()(i, j);
+ }
+ }
+
+ for (size_t i = 0; i < classes; i++)
+ calcMat(2 * dimension, i) = nbcTest.Probabilities()(i);
+
+ for (size_t i = 0; i < calcMat.n_rows; i++)
+ for (size_t j = 0; j < classes; j++)
+ BOOST_REQUIRE_CLOSE(trainRes(i, j) + .00001, calcMat(i, j), 0.01);
+
+ arma::mat testData;
+ arma::Mat<size_t> testRes;
+ arma::Col<size_t> calcVec;
+ data::Load(testFilename, testData, true);
+ data::Load(testResultFilename, testRes, true);
+
+ testData.shed_row(testData.n_rows - 1); // Remove the labels.
+
+ nbcTest.Classify(testData, calcVec);
+
+ for (size_t i = 0; i < testData.n_cols; i++)
+ BOOST_REQUIRE_EQUAL(testRes(i), calcVec(i));
+}
+
BOOST_AUTO_TEST_SUITE_END();
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