f4334277
 Hu Chunming
提交3rdparty
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
TEST(ML_RTrees, getVotes)
{
int n = 12;
int count, i;
int label_size = 3;
int predicted_class = 0;
int max_votes = -1;
int val;
// RTrees for classification
Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
//data
Mat data(n, 4, CV_32F);
randu(data, 0, 10);
//labels
Mat labels = (Mat_<int>(n,1) << 0,0,0,0, 1,1,1,1, 2,2,2,2);
rt->train(data, ml::ROW_SAMPLE, labels);
//run function
Mat test(1, 4, CV_32F);
Mat result;
randu(test, 0, 10);
rt->getVotes(test, result, 0);
//count vote amount and find highest vote
count = 0;
const int* result_row = result.ptr<int>(1);
for( i = 0; i < label_size; i++ )
{
val = result_row[i];
//predicted_class = max_votes < val? i;
if( max_votes < val )
{
max_votes = val;
predicted_class = i;
}
count += val;
}
EXPECT_EQ(count, (int)rt->getRoots().size());
EXPECT_EQ(result.at<float>(0, predicted_class), rt->predict(test));
}
TEST(ML_RTrees, 11142_sample_weights_regression)
{
int n = 3;
// RTrees for regression
Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
//simple regression problem of x -> 2x
Mat data = (Mat_<float>(n,1) << 1, 2, 3);
Mat values = (Mat_<float>(n,1) << 2, 4, 6);
Mat weights = (Mat_<float>(n, 1) << 10, 10, 10);
Ptr<TrainData> trainData = TrainData::create(data, ml::ROW_SAMPLE, values);
rt->train(trainData);
double error_without_weights = round(rt->getOOBError());
rt->clear();
Ptr<TrainData> trainDataWithWeights = TrainData::create(data, ml::ROW_SAMPLE, values, Mat(), Mat(), weights );
rt->train(trainDataWithWeights);
double error_with_weights = round(rt->getOOBError());
// error with weights should be larger than error without weights
EXPECT_GE(error_with_weights, error_without_weights);
}
TEST(ML_RTrees, 11142_sample_weights_classification)
{
int n = 12;
// RTrees for classification
Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
Mat data(n, 4, CV_32F);
randu(data, 0, 10);
Mat labels = (Mat_<int>(n,1) << 0,0,0,0, 1,1,1,1, 2,2,2,2);
Mat weights = (Mat_<float>(n, 1) << 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10);
rt->train(data, ml::ROW_SAMPLE, labels);
rt->clear();
double error_without_weights = round(rt->getOOBError());
Ptr<TrainData> trainDataWithWeights = TrainData::create(data, ml::ROW_SAMPLE, labels, Mat(), Mat(), weights );
rt->train(data, ml::ROW_SAMPLE, labels);
double error_with_weights = round(rt->getOOBError());
std::cout << error_without_weights << std::endl;
std::cout << error_with_weights << std::endl;
// error with weights should be larger than error without weights
EXPECT_GE(error_with_weights, error_without_weights);
}
TEST(ML_RTrees, bug_12974_throw_exception_when_predict_different_feature_count)
{
int numFeatures = 5;
// create a 5 feature dataset and train the model
cv::Ptr<RTrees> model = RTrees::create();
Mat samples(10, numFeatures, CV_32F);
randu(samples, 0, 10);
Mat labels = (Mat_<int>(10,1) << 0,0,0,0,0,1,1,1,1,1);
cv::Ptr<TrainData> trainData = TrainData::create(samples, cv::ml::ROW_SAMPLE, labels);
model->train(trainData);
// try to predict on data which have fewer features - this should throw an exception
for(int i = 1; i < numFeatures - 1; ++i) {
Mat test(1, i, CV_32FC1);
ASSERT_THROW(model->predict(test), Exception);
}
// try to predict on data which have more features - this should also throw an exception
Mat test(1, numFeatures + 1, CV_32FC1);
ASSERT_THROW(model->predict(test), Exception);
}
}} // namespace
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