mirror of
https://github.com/opencv/opencv.git
synced 2024-12-15 09:49:13 +08:00
57 lines
1.5 KiB
C++
57 lines
1.5 KiB
C++
|
// 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
|