[mlpack-git] master, mlpack-1.0.x: Minor formatting changes according to the style guide (mostly, I think?). (29fc8c4)
gitdub at big.cc.gt.atl.ga.us
gitdub at big.cc.gt.atl.ga.us
Thu Mar 5 21:51:33 EST 2015
Repository : https://github.com/mlpack/mlpack
On branches: master,mlpack-1.0.x
Link : https://github.com/mlpack/mlpack/compare/904762495c039e345beba14c1142fd719b3bd50e...f94823c800ad6f7266995c700b1b630d5ffdcf40
>---------------------------------------------------------------
commit 29fc8c49df015330acc5256cc242f0b0763957c4
Author: Ryan Curtin <ryan at ratml.org>
Date: Mon Jul 7 14:08:12 2014 +0000
Minor formatting changes according to the style guide (mostly, I think?).
>---------------------------------------------------------------
29fc8c49df015330acc5256cc242f0b0763957c4
src/mlpack/tests/perceptron_test.cpp | 102 +++++++++++++++++------------------
1 file changed, 51 insertions(+), 51 deletions(-)
diff --git a/src/mlpack/tests/perceptron_test.cpp b/src/mlpack/tests/perceptron_test.cpp
index 70b368e..7570e30 100644
--- a/src/mlpack/tests/perceptron_test.cpp
+++ b/src/mlpack/tests/perceptron_test.cpp
@@ -1,6 +1,6 @@
-/*
- * @file: perceptron_test.cpp
- * @author: Udit Saxena
+/**
+ * @file perceptron_test.cpp
+ * @author Udit Saxena
*
* Tests for perceptron.
*/
@@ -14,12 +14,12 @@ using namespace mlpack;
using namespace arma;
using namespace mlpack::perceptron;
-BOOST_AUTO_TEST_SUITE(PERCEPTRONTEST);
-/*
-This test tests whether the perceptron converges for the
-AND gate classifier.
-*/
-BOOST_AUTO_TEST_CASE(AND)
+BOOST_AUTO_TEST_SUITE(PerceptronTest);
+
+/**
+ * This test tests whether the perceptron converges for the AND gate classifier.
+ */
+BOOST_AUTO_TEST_CASE(And)
{
mat trainData;
trainData << 0 << 1 << 1 << 0 << endr
@@ -35,18 +35,16 @@ BOOST_AUTO_TEST_CASE(AND)
Row<size_t> predictedLabels(testData.n_cols);
p.Classify(testData, predictedLabels);
- BOOST_CHECK_EQUAL(predictedLabels(0,0),0);
- BOOST_CHECK_EQUAL(predictedLabels(0,1),0);
- BOOST_CHECK_EQUAL(predictedLabels(0,2),1);
- BOOST_CHECK_EQUAL(predictedLabels(0,3),0);
-
+ BOOST_CHECK_EQUAL(predictedLabels(0, 0), 0);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 1), 0);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 2), 1);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 3), 0);
}
-/*
-This test tests whether the perceptron converges for the
-OR gate classifier.
-*/
-BOOST_AUTO_TEST_CASE(OR)
+/**
+ * This test tests whether the perceptron converges for the OR gate classifier.
+ */
+BOOST_AUTO_TEST_CASE(Or)
{
mat trainData;
trainData << 0 << 1 << 1 << 0 << endr
@@ -59,25 +57,25 @@ BOOST_AUTO_TEST_CASE(OR)
mat testData;
testData << 0 << 1 << 1 << 0 << endr
- << 1 << 0 << 1 << 0 << endr;
+ << 1 << 0 << 1 << 0 << endr;
Row<size_t> predictedLabels(testData.n_cols);
p.Classify(testData, predictedLabels);
- BOOST_CHECK_EQUAL(predictedLabels(0,0),1);
- BOOST_CHECK_EQUAL(predictedLabels(0,1),1);
- BOOST_CHECK_EQUAL(predictedLabels(0,2),1);
- BOOST_CHECK_EQUAL(predictedLabels(0,3),0);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 0), 1);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 1), 1);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 2), 1);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 3), 0);
}
-/*
-This tests the convergence on a set of linearly
-separable data with 3 classes.
-*/
-BOOST_AUTO_TEST_CASE(RANDOM3)
+/**
+ * This tests the convergence on a set of linearly separable data with 3
+ * classes.
+ */
+BOOST_AUTO_TEST_CASE(Random3)
{
mat trainData;
trainData << 0 << 1 << 1 << 4 << 5 << 4 << 1 << 2 << 1 << endr
- << 1 << 0 << 1 << 1 << 1 << 2 << 4 << 5 << 4 << endr;
+ << 1 << 0 << 1 << 1 << 1 << 2 << 4 << 5 << 4 << endr;
Mat<size_t> labels;
labels << 0 << 0 << 0 << 1 << 1 << 1 << 2 << 2 << 2;
@@ -90,23 +88,23 @@ BOOST_AUTO_TEST_CASE(RANDOM3)
Row<size_t> predictedLabels(testData.n_cols);
p.Classify(testData, predictedLabels);
- for (size_t i = 0; i<predictedLabels.n_cols; i++)
- BOOST_CHECK_EQUAL(predictedLabels(0,i),0);
+ for (size_t i = 0; i < predictedLabels.n_cols; i++)
+ BOOST_CHECK_EQUAL(predictedLabels(0, i), 0);
}
-/*
-This tests the convergence of the perceptron on a dataset
-which has only TWO points which belong to different classes.
-*/
-BOOST_AUTO_TEST_CASE(TWOPOINTS)
+/**
+ * This tests the convergence of the perceptron on a dataset
+ * which has only TWO points which belong to different classes.
+ */
+BOOST_AUTO_TEST_CASE(TwoPoints)
{
mat trainData;
trainData << 0 << 1 << endr
- << 1 << 0 << endr;
+ << 1 << 0 << endr;
Mat<size_t> labels;
- labels << 0 << 1 ;
+ labels << 0 << 1;
Perceptron<> p(trainData, labels.row(0), 1000);
@@ -116,14 +114,15 @@ BOOST_AUTO_TEST_CASE(TWOPOINTS)
Row<size_t> predictedLabels(testData.n_cols);
p.Classify(testData, predictedLabels);
- BOOST_CHECK_EQUAL(predictedLabels(0,0),0);
- BOOST_CHECK_EQUAL(predictedLabels(0,1),1);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 0), 0);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 1), 1);
}
-/*
-This tests the convergence of the perceptron on a dataset
-which has a non-linearly separable dataset.
-*/
-BOOST_AUTO_TEST_CASE(NONLINSEPDS)
+
+/**
+ * This tests the convergence of the perceptron on a dataset
+ * which has a non-linearly separable dataset.
+ */
+BOOST_AUTO_TEST_CASE(NonLinearlySeparableDataset)
{
mat trainData;
trainData << 1 << 2 << 3 << 4 << 5 << 6 << 7 << 8
@@ -137,14 +136,15 @@ BOOST_AUTO_TEST_CASE(NONLINSEPDS)
Perceptron<> p(trainData, labels.row(0), 1000);
mat testData;
- testData << 3 << 4 << 5 << 6 << endr
+ testData << 3 << 4 << 5 << 6 << endr
<< 3 << 2.3 << 1.7 << 1.5 << endr;
Row<size_t> predictedLabels(testData.n_cols);
p.Classify(testData, predictedLabels);
- BOOST_CHECK_EQUAL(predictedLabels(0,0),0);
- BOOST_CHECK_EQUAL(predictedLabels(0,1),0);
- BOOST_CHECK_EQUAL(predictedLabels(0,2),1);
- BOOST_CHECK_EQUAL(predictedLabels(0,3),1);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 0), 0);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 1), 0);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 2), 1);
+ BOOST_CHECK_EQUAL(predictedLabels(0, 3), 1);
}
+
BOOST_AUTO_TEST_SUITE_END();
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