/* 
 * PROJECT: NyARToolkit
 * --------------------------------------------------------------------------------
 * This work is based on the original ARToolKit developed by
 *   Hirokazu Kato
 *   Mark Billinghurst
 *   HITLab, University of Washington, Seattle
 * http://www.hitl.washington.edu/artoolkit/
 *
 * The NyARToolkit is Java edition ARToolKit class library.
 * Copyright (C)2008-2009 Ryo Iizuka
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 * 
 * For further information please contact.
 *	http://nyatla.jp/nyatoolkit/
 *	<airmail(at)ebony.plala.or.jp> or <nyatla(at)nyatla.jp>
 * 
 */
package jp.nyatla.nyartoolkit.core.pca2d;

import jp.nyatla.nyartoolkit.NyARException;
import jp.nyatla.nyartoolkit.core.NyARMat;
import jp.nyatla.nyartoolkit.core.NyARVec;
import jp.nyatla.nyartoolkit.core.types.matrix.NyARDoubleMatrix22;
/**
 * NyARMatrixを利用した主成分分析
 * ARToolKitと同じ処理をします。
 */
public class NyARPca2d_MatrixPCA implements INyARPca2d
{
	private final NyARMat __pca_input = new NyARMat(1, 2);
	private final NyARMat __pca_evec = new NyARMat(2, 2);
	private final NyARVec __pca_ev = new NyARVec(2);
	private final NyARVec __pca_mean = new NyARVec(2);	
	
	public void pca(double[] i_v1,double[] i_v2,int i_number_of_point,NyARDoubleMatrix22 o_evec, double[] o_ev,double[] o_mean) throws NyARException
	{
		final NyARMat input = this.__pca_input;// 次処理で初期化される。		
		// pcaの準備
		input.realloc(i_number_of_point, 2);
		final double[][] input_array=input.getArray();
		for(int i=0;i<i_number_of_point;i++){
			input_array[i][0]=i_v1[i];
			input_array[i][1]=i_v2[i];
		}
		// 主成分分析
		input.pca(this.__pca_evec, this.__pca_ev, this.__pca_mean);
		final double[] mean_array = this.__pca_mean.getArray();
		final double[][] evec_array = this.__pca_evec.getArray();
		final double[] ev_array=this.__pca_ev.getArray();
		o_evec.m00=evec_array[0][0];
		o_evec.m01=evec_array[0][1];
		o_evec.m10=evec_array[1][0];
		o_evec.m11=evec_array[1][1];
		o_ev[0]=ev_array[0];
		o_ev[1]=ev_array[1];
		o_mean[0]=mean_array[0];
		o_mean[1]=mean_array[1];
		return;
	}
}
