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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_NBAYES, regression_5911)
{
int N=12;
Ptr<ml::NormalBayesClassifier> nb = cv::ml::NormalBayesClassifier::create();
// data:
float X_data[] = {
1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4,
5,5,5,5, 5,5,5,5, 5,5,5,5, 5,5,5,5,
4,3,2,1, 4,3,2,1, 4,3,2,1, 4,3,2,1
};
Mat_<float> X(N, 4, X_data);
// labels:
int Y_data[] = { 0,0,0,0, 1,1,1,1, 2,2,2,2 };
Mat_<int> Y(N, 1, Y_data);
nb->train(X, ml::ROW_SAMPLE, Y);
// single prediction:
Mat R1,P1;
for (int i=0; i<N; i++)
{
Mat r,p;
nb->predictProb(X.row(i), r, p);
R1.push_back(r);
P1.push_back(p);
}
// bulk prediction (continuous memory):
Mat R2,P2;
nb->predictProb(X, R2, P2);
EXPECT_EQ(255 * R2.total(), sum(R1 == R2)[0]);
EXPECT_EQ(255 * P2.total(), sum(P1 == P2)[0]);
// bulk prediction, with non-continuous memory storage
Mat R3_(N, 1+1, CV_32S),
P3_(N, 3+1, CV_32F);
nb->predictProb(X, R3_.col(0), P3_.colRange(0,3));
Mat R3 = R3_.col(0).clone(),
P3 = P3_.colRange(0,3).clone();
EXPECT_EQ(255 * R3.total(), sum(R1 == R3)[0]);
EXPECT_EQ(255 * P3.total(), sum(P1 == P3)[0]);
}
}} // namespace
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