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@ -654,8 +654,8 @@ protected: |
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double trainError = (double)(trainConfusionMat.at<int>(1,0) + trainConfusionMat.at<int>(0,1)) / trainSamplesCount; |
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double testError = (double)(testConfusionMat.at<int>(1,0) + testConfusionMat.at<int>(0,1)) / (samples.rows - trainSamplesCount); |
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const double maxTrainError = 0.16; |
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const double maxTestError = 0.19; |
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const double maxTrainError = 0.23; |
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const double maxTestError = 0.26; |
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int code = cvtest::TS::OK; |
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if(trainError > maxTrainError) |
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@ -665,7 +665,7 @@ protected: |
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} |
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if(testError > maxTestError) |
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{ |
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ts->printf(cvtest::TS::LOG, "Too large test classification error (calc = %f, valid=%f).\n", trainError, maxTrainError); |
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ts->printf(cvtest::TS::LOG, "Too large test classification error (calc = %f, valid=%f).\n", testError, maxTestError); |
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code = cvtest::TS::FAIL_INVALID_TEST_DATA; |
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} |
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