57 lines
1.5 KiB
C++
57 lines
1.5 KiB
C++
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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TEST(ML_NBAYES, regression_5911)
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{
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int N=12;
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Ptr<ml::NormalBayesClassifier> nb = cv::ml::NormalBayesClassifier::create();
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// data:
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float X_data[] = {
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1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4,
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5,5,5,5, 5,5,5,5, 5,5,5,5, 5,5,5,5,
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4,3,2,1, 4,3,2,1, 4,3,2,1, 4,3,2,1
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};
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Mat_<float> X(N, 4, X_data);
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// labels:
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int Y_data[] = { 0,0,0,0, 1,1,1,1, 2,2,2,2 };
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Mat_<int> Y(N, 1, Y_data);
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nb->train(X, ml::ROW_SAMPLE, Y);
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// single prediction:
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Mat R1,P1;
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for (int i=0; i<N; i++)
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{
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Mat r,p;
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nb->predictProb(X.row(i), r, p);
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R1.push_back(r);
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P1.push_back(p);
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}
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// bulk prediction (continuous memory):
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Mat R2,P2;
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nb->predictProb(X, R2, P2);
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EXPECT_EQ(255 * R2.total(), sum(R1 == R2)[0]);
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EXPECT_EQ(255 * P2.total(), sum(P1 == P2)[0]);
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// bulk prediction, with non-continuous memory storage
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Mat R3_(N, 1+1, CV_32S),
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P3_(N, 3+1, CV_32F);
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nb->predictProb(X, R3_.col(0), P3_.colRange(0,3));
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Mat R3 = R3_.col(0).clone(),
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P3 = P3_.colRange(0,3).clone();
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EXPECT_EQ(255 * R3.total(), sum(R1 == R3)[0]);
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EXPECT_EQ(255 * P3.total(), sum(P1 == P3)[0]);
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}
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}} // namespace
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