/** * <p>Predicts a response for an input sample.</p> * * <p>The method runs the sample through the trees in the ensemble and returns the * output class label based on the weighted voting.</p> * * @param sample Input sample. * @param missing Optional mask of missing measurements. To handle missing * measurements, the weak classifiers must include surrogate splits (see * <code>CvDTreeParams.use_surrogates</code>). * @param slice Continuous subset of the sequence of weak classifiers to be used * for prediction. By default, all the weak classifiers are used. * @param rawMode Normally, it should be set to <code>false</code>. * @param returnSum If <code>true</code> then return sum of votes instead of the * class label. * * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-predict">org.opencv.ml.CvBoost.predict</a> */ public float predict(Mat sample, Mat missing, Range slice, boolean rawMode, boolean returnSum) { float retVal = predict_0(nativeObj, sample.nativeObj, missing.nativeObj, slice.start, slice.end, rawMode, returnSum); return retVal; }
/** * <p>Predicts a response for an input sample.</p> * * <p>The method runs the sample through the trees in the ensemble and returns the * output class label based on the weighted voting.</p> * * @param sample Input sample. * @param missing Optional mask of missing measurements. To handle missing * measurements, the weak classifiers must include surrogate splits (see * <code>CvDTreeParams.use_surrogates</code>). * @param slice Continuous subset of the sequence of weak classifiers to be used * for prediction. By default, all the weak classifiers are used. * @param rawMode Normally, it should be set to <code>false</code>. * @param returnSum If <code>true</code> then return sum of votes instead of the * class label. * * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-predict">org.opencv.ml.CvBoost.predict</a> */ public float predict(Mat sample, Mat missing, Range slice, boolean rawMode, boolean returnSum) { float retVal = predict_0(nativeObj, sample.nativeObj, missing.nativeObj, slice.start, slice.end, rawMode, returnSum); return retVal; }
/** * <p>Predicts a response for an input sample.</p> * * <p>The method runs the sample through the trees in the ensemble and returns the * output class label based on the weighted voting.</p> * * @param sample Input sample. * @param missing Optional mask of missing measurements. To handle missing * measurements, the weak classifiers must include surrogate splits (see * <code>CvDTreeParams.use_surrogates</code>). * @param slice Continuous subset of the sequence of weak classifiers to be used * for prediction. By default, all the weak classifiers are used. * @param rawMode Normally, it should be set to <code>false</code>. * @param returnSum If <code>true</code> then return sum of votes instead of the * class label. * * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-predict">org.opencv.ml.CvBoost.predict</a> */ public float predict(Mat sample, Mat missing, Range slice, boolean rawMode, boolean returnSum) { float retVal = predict_0(nativeObj, sample.nativeObj, missing.nativeObj, slice.start, slice.end, rawMode, returnSum); return retVal; }
/** * <p>Predicts a response for an input sample.</p> * * <p>The method runs the sample through the trees in the ensemble and returns the * output class label based on the weighted voting.</p> * * @param sample Input sample. * @param missing Optional mask of missing measurements. To handle missing * measurements, the weak classifiers must include surrogate splits (see * <code>CvDTreeParams.use_surrogates</code>). * @param slice Continuous subset of the sequence of weak classifiers to be used * for prediction. By default, all the weak classifiers are used. * @param rawMode Normally, it should be set to <code>false</code>. * @param returnSum If <code>true</code> then return sum of votes instead of the * class label. * * @see <a href="http://docs.opencv.org/modules/ml/doc/boosting.html#cvboost-predict">org.opencv.ml.CvBoost.predict</a> */ public float predict(Mat sample, Mat missing, Range slice, boolean rawMode, boolean returnSum) { float retVal = predict_0(nativeObj, sample.nativeObj, missing.nativeObj, slice.start, slice.end, rawMode, returnSum); return retVal; }