[mlpack-git] master: Fix for new DatasetInfo API. (1b32dab)
gitdub at big.cc.gt.atl.ga.us
gitdub at big.cc.gt.atl.ga.us
Wed Dec 23 11:44:58 EST 2015
Repository : https://github.com/mlpack/mlpack
On branch : master
Link : https://github.com/mlpack/mlpack/compare/de9cc4b05069e1fa4793d9355f2f595af5ff45d2...6070527af14296cd99739de6c62666cc5d2a2125
>---------------------------------------------------------------
commit 1b32dab9e5182de7cb13b775695652b49bbd0cda
Author: Ryan Curtin <ryan at ratml.org>
Date: Wed Oct 21 10:18:12 2015 -0400
Fix for new DatasetInfo API.
>---------------------------------------------------------------
1b32dab9e5182de7cb13b775695652b49bbd0cda
src/mlpack/tests/serialization_test.cpp | 31 +++++++++++++++++--------------
1 file changed, 17 insertions(+), 14 deletions(-)
diff --git a/src/mlpack/tests/serialization_test.cpp b/src/mlpack/tests/serialization_test.cpp
index be35e46..ca2470d 100644
--- a/src/mlpack/tests/serialization_test.cpp
+++ b/src/mlpack/tests/serialization_test.cpp
@@ -847,7 +847,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingCategoricalSplitTest)
*/
BOOST_AUTO_TEST_CASE(HoeffdingSplitTest)
{
- data::DatasetInfo info;
+ data::DatasetInfo info(5);
info.MapString("0", 2); // Dimension 1 is categorical.
info.MapString("1", 2);
HoeffdingSplit<> split(5, 2, info, 0.99, 15000, 1);
@@ -856,12 +856,12 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitTest)
split.Train(arma::vec("0.3 0.4 1 0.6 0.7"), 0);
split.Train(arma::vec("-0.3 0.0 0 0.7 0.8"), 1);
- data::DatasetInfo wrongInfo;
+ data::DatasetInfo wrongInfo(3);
wrongInfo.MapString("1", 1);
HoeffdingSplit<> xmlSplit(3, 7, wrongInfo, 0.1, 10, 1);
// Force the binarySplit to split.
- data::DatasetInfo binaryInfo;
+ data::DatasetInfo binaryInfo(2);
binaryInfo.MapString("cat0", 0);
binaryInfo.MapString("cat1", 0);
binaryInfo.MapString("cat0", 1);
@@ -875,7 +875,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitTest)
binarySplit.Train(arma::Col<size_t>("1 0"), 1);
}
- HoeffdingSplit<> textSplit(10, 11, wrongInfo, 0.75, 1000, 1);
+ HoeffdingSplit<> textSplit(3, 11, wrongInfo, 0.75, 1000, 1);
SerializeObjectAll(split, xmlSplit, textSplit, binarySplit);
@@ -899,7 +899,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitTest)
BOOST_AUTO_TEST_CASE(HoeffdingSplitAfterSplitTest)
{
// Force the split to split.
- data::DatasetInfo info;
+ data::DatasetInfo info(2);
info.MapString("cat0", 0);
info.MapString("cat1", 0);
info.MapString("cat0", 1);
@@ -914,12 +914,12 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitAfterSplitTest)
}
BOOST_REQUIRE_EQUAL(split.SplitCheck(), 2);
- data::DatasetInfo wrongInfo;
+ data::DatasetInfo wrongInfo(3);
wrongInfo.MapString("1", 1);
HoeffdingSplit<> xmlSplit(3, 7, wrongInfo, 0.1, 10, 1);
- data::DatasetInfo binaryInfo;
- binaryInfo.MapString("0", 2); // Dimension 1 is categorical.
+ data::DatasetInfo binaryInfo(5);
+ binaryInfo.MapString("0", 2); // Dimension 2 is categorical.
binaryInfo.MapString("1", 2);
HoeffdingSplit<> binarySplit(5, 2, binaryInfo, 0.99, 15000, 1);
@@ -927,7 +927,7 @@ BOOST_AUTO_TEST_CASE(HoeffdingSplitAfterSplitTest)
binarySplit.Train(arma::vec("0.3 0.4 1 0.6 0.7"), 0);
binarySplit.Train(arma::vec("-0.3 0.0 0 0.7 0.8"), 1);
- HoeffdingSplit<> textSplit(10, 11, wrongInfo, 0.75, 1000, 1);
+ HoeffdingSplit<> textSplit(3, 11, wrongInfo, 0.75, 1000, 1);
SerializeObjectAll(split, xmlSplit, textSplit, binarySplit);
@@ -957,7 +957,7 @@ BOOST_AUTO_TEST_CASE(EmptyStreamingDecisionTreeTest)
{
using namespace mlpack::tree;
- data::DatasetInfo info;
+ data::DatasetInfo info(6);
StreamingDecisionTree<HoeffdingSplit<>> tree(info, 2, 2);
StreamingDecisionTree<HoeffdingSplit<>> xmlTree(info, 3, 3);
StreamingDecisionTree<HoeffdingSplit<>> binaryTree(info, 4, 4);
@@ -991,7 +991,7 @@ BOOST_AUTO_TEST_CASE(StreamingDecisionTreeTest)
labels[2 * i + 1] = 1;
}
// Make the features categorical.
- data::DatasetInfo info;
+ data::DatasetInfo info(2);
info.MapString("a", 0);
info.MapString("b", 0);
info.MapString("c", 0);
@@ -1003,9 +1003,12 @@ BOOST_AUTO_TEST_CASE(StreamingDecisionTreeTest)
StreamingDecisionTree<HoeffdingSplit<>> tree(dataset, info, labels, 2);
- StreamingDecisionTree<HoeffdingSplit<>> xmlTree(info, 1, 1);
- StreamingDecisionTree<HoeffdingSplit<>> binaryTree(info, 5, 6);
- StreamingDecisionTree<HoeffdingSplit<>> textTree(info, 7, 100);
+ data::DatasetInfo xmlInfo(1);
+ StreamingDecisionTree<HoeffdingSplit<>> xmlTree(xmlInfo, 1, 1);
+ data::DatasetInfo binaryInfo(5);
+ StreamingDecisionTree<HoeffdingSplit<>> binaryTree(binaryInfo, 5, 6);
+ data::DatasetInfo textInfo(7);
+ StreamingDecisionTree<HoeffdingSplit<>> textTree(textInfo, 7, 100);
SerializeObjectAll(tree, xmlTree, textTree, binaryTree);
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