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📄 classifier2d.h

📁 Gaussian Mixture Algorithm
💻 H
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/*************************************************************************** *   Copyright (C) 2008 by Yann LeCun and Pierre Sermanet * *   yann@cs.nyu.edu, pierre.sermanet@gmail.com * * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: *     * Redistributions of source code must retain the above copyright *       notice, this list of conditions and the following disclaimer. *     * Redistributions in binary form must reproduce the above copyright *       notice, this list of conditions and the following disclaimer in the *       documentation and/or other materials provided with the distribution. *     * Redistribution under a license not approved by the Open Source *       Initiative (http://www.opensource.org) must display the *       following acknowledgement in all advertising material: *        This product includes software developed at the Courant *        Institute of Mathematical Sciences (http://cims.nyu.edu). *     * The names of the authors may not be used to endorse or promote products *       derived from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESS OR IMPLIED * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. IN NO EVENT SHALL ThE AUTHORS BE LIABLE FOR ANY * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ***************************************************************************/#ifndef CLASSIFIER2D_H_#define CLASSIFIER2D_H_#include "Net.h"#include "libidx.h"namespace ebl {class Classifier2D {public:	parameter 	*theparam;	lenet7			*thenet;	int					height;	int					width;	Idx<ubyte>	grabbed;	Idx<ubyte>	grabbed2;	double			contrast;	double			brightness;	double			coeff;	double			bias;	Idx<int>		sizes;	Idx<void*>	inputs;		//! state_idx*	Idx<void*>	outputs;	//! state_idx*	Idx<void*>  results;		//! Idx<double>*	Idx<double> smoothing_kernel;	Idx<const char*> labels;	  Classifier2D(const char *paramfile, Idx<int> &sz, Idx<const char*> &lbls,  		double b, double c, int h, int w);  virtual ~Classifier2D();  Idx<double> fprop(ubyte *img, float zoom, double threshold = 1.8, int objsize = 60);    // Sub functions  Idx<ubyte> multi_res_prep(ubyte *img, float zoom);  Idx<double> multi_res_fprop(double threshold, int objsize);	Idx<double> postprocess_output(double threshold, int objsize);	//! mark local maxima (in space and feature) of in r.	//! Put winning class in (r i j 0) and score (normalized	//! to 0 1) in (r i j 1).	void mark_maxima(Idx<double> &in, Idx<double> &inc, 			Idx<double> &r, double threshold);	Idx<double> prune(Idx<double> &res);};////////////////////////////////////////////////////////////////class Classifier2DBinoc : public Classifier2D {public:	  Classifier2DBinoc(const char *paramfile, Idx<int> &sz, Idx<const char*> &lbls,  		double b, double c, int h, int w);  virtual ~Classifier2DBinoc();    //! Compute multi-resolution inputs and fprop through each.  Idx<double> fprop(ubyte *left, ubyte *right,   		float zoom, int dx, int dy, double threshold = 1.8, int objsize = 60);    // Sub functions  void multi_res_prep(ubyte *left, ubyte *right,   		int dx, int dy, float zoom);};////////////////////////////////////////////////////////////////} // end namespace ebl#endif /* CLASSIFIER2D_H_ */

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