apaam/mlpack
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fix random_forest_test

Browse Source
This commit is contained in:
AdarshSantoria
2023-10-01 12:33:15 +05:30
parent 99522e30aa
commit 3d8e9edebf
1 changed files with 8 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/mlpack/tests/random_forest_test.cpp
+8 -8
View File
@@ -172,7 +172,7 @@ TEST_CASE("WeightedNumericLearningTest", "[RandomForestTest]")
noiseLabels[i] = RandInt(3); // Random label.
// Concatenate data matrices.
arma::mat data = arma::join_rows(dataset, noise);
arma::mat fullData = arma::join_rows(dataset, noise);
arma::Row<size_t> fullLabels = arma::join_rows(labels, noiseLabels);
// Now set weights.
@@ -183,8 +183,8 @@ TEST_CASE("WeightedNumericLearningTest", "[RandomForestTest]")
weights[i] = Random(0.0, 0.01); // Low weights for false points.
// Train decision tree and random forest.
RandomForest<> rf(dataset, labels, 3, weights, 20, 1);
DecisionTree<> dt(dataset, labels, 3, weights, 5);
RandomForest<> rf(fullData, fullLabels, 3, weights, 20, 1);
DecisionTree<> dt(fullData, fullLabels, 3, weights, 5);
// Get performance statistics on test data.
arma::mat testDataset;
@@ -396,7 +396,7 @@ TEST_CASE("RandomForestNumericTrainReturnEntropy", "[RandomForestTest]")
noiseLabels[i] = RandInt(3); // Random label.
// Concatenate data matrices.
arma::mat data = arma::join_rows(dataset, noise);
arma::mat fullData = arma::join_rows(dataset, noise);
arma::Row<size_t> fullLabels = arma::join_rows(labels, noiseLabels);
// Now set weights.
@@ -408,13 +408,13 @@ TEST_CASE("RandomForestNumericTrainReturnEntropy", "[RandomForestTest]")
// Test random forest on unweighted numeric dataset.
RandomForest<GiniGain, RandomDimensionSelect> rf;
double entropy = rf.Train(dataset, labels, 3, 10, 1);
double entropy = rf.Train(fullData, fullLabels, 3, 10, 1);
REQUIRE(std::isfinite(entropy) == true);
// Test random forest on weighted numeric dataset.
RandomForest<GiniGain, RandomDimensionSelect> wrf;
entropy = wrf.Train(dataset, labels, 3, weights, 10, 1);
entropy = wrf.Train(fullData, fullLabels, 3, weights, 10, 1);
REQUIRE(std::isfinite(entropy) == true);
}
@@ -577,7 +577,7 @@ TEST_CASE("ExtraTreesAccuracyTest", "[RandomForestTest]")
noiseLabels[i] = RandInt(3); // Random label.
// Concatenate data matrices.
arma::mat data = arma::join_rows(dataset, noise);
arma::mat fullData = arma::join_rows(dataset, noise);
arma::Row<size_t> fullLabels = arma::join_rows(labels, noiseLabels);
// Now set weights.
@@ -588,7 +588,7 @@ TEST_CASE("ExtraTreesAccuracyTest", "[RandomForestTest]")
weights[i] = Random(0.0, 0.01); // Low weights for false points.
// Train extra tree.
ExtraTrees<> et(data, fullLabels, 3, weights, 20, 1);
ExtraTrees<> et(fullData, fullLabels, 3, weights, 20, 1);
// Get performance statistics on test data.
arma::mat testDataset;