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diff --git a/gr-digital/lib/digital_lms_dd_equalizer_cc.h b/gr-digital/lib/digital_lms_dd_equalizer_cc.h
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-/* -*- c++ -*- */
-/*
- * Copyright 2011 Free Software Foundation, Inc.
- * 
- * This file is part of GNU Radio
- * 
- * GNU Radio 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, or (at your option)
- * any later version.
- * 
- * GNU Radio 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 GNU Radio; see the file COPYING.  If not, write to
- * the Free Software Foundation, Inc., 51 Franklin Street,
- * Boston, MA 02110-1301, USA.
- */
-
-#ifndef INCLUDED_DIGITAL_LMS_DD_EQUALIZER_CC_H
-#define INCLUDED_DIGITAL_LMS_DD_EQUALIZER_CC_H
-
-#include <gr_adaptive_fir_ccc.h>
-#include <digital_constellation.h>
-
-class digital_lms_dd_equalizer_cc;
-typedef boost::shared_ptr<digital_lms_dd_equalizer_cc> digital_lms_dd_equalizer_cc_sptr;
-
-digital_lms_dd_equalizer_cc_sptr digital_make_lms_dd_equalizer_cc (int num_taps,
-								   float mu, int sps,
-								   digital_constellation_sptr cnst);
-								   
-/*!
- * \brief Least-Mean-Square Decision Directed Equalizer (complex in/out)
- * \ingroup eq_blk
- *
- * This block implements an LMS-based decision-directed equalizer.
- * It uses a set of weights, w, to correlate against the inputs, u,
- * and a decisions is then made from this output. The error
- * in the decision is used to update teh weight vector.
- *
- * y[n] = conj(w[n]) u[n]
- * d[n] = decision(y[n])
- * e[n] = d[n] - y[n]
- * w[n+1] = w[n] + mu u[n] conj(e[n])
- *
- * Where mu is a gain value (between 0 and 1 and usualy small,
- * around 0.001 - 0.01.
- *
- * This block uses the digital_constellation object for making
- * the decision from y[n]. Create the constellation object for
- * whatever constellation is to be used and pass in the object.
- * In Python, you can use something like:
- *    self.constellation = digital.constellation_qpsk()
- * To create a QPSK constellation (see the digital_constellation
- * block for more details as to what constellations are available
- * or how to create your own). You then pass the object to this
- * block as an sptr, or using "self.constellation.base()".
- *
- * The theory for this algorithm can be found in Chapter 9 of:
- * S. Haykin, Adaptive Filter Theory, Upper Saddle River, NJ:
- *    Prentice Hall, 1996.
- *
- */
-class digital_lms_dd_equalizer_cc : public gr_adaptive_fir_ccc
-{
-private:
-  friend digital_lms_dd_equalizer_cc_sptr digital_make_lms_dd_equalizer_cc (int num_taps,
-									    float mu, int sps,
-									    digital_constellation_sptr cnst);
-  
-  float	d_mu;
-  std::vector<gr_complex>  d_taps;
-  digital_constellation_sptr d_cnst;
-
-  digital_lms_dd_equalizer_cc (int num_taps,
-			       float mu, int sps,
-			       digital_constellation_sptr cnst);
-
-protected:
-
-  virtual gr_complex error(const gr_complex &out) 
-  { 
-    gr_complex decision, error;
-    d_cnst->map_to_points(d_cnst->decision_maker(&out), &decision);
-    error = decision - out;
-    return error;
-  }
-
-  virtual void update_tap(gr_complex &tap, const gr_complex &in) 
-  {
-    tap += d_mu*conj(in)*d_error;
-  }
-
-public:
-  float get_gain()
-  {
-    return d_mu;
-  }
-  
-  void set_gain(float mu)
-  {
-    if(mu < 0.0f || mu > 1.0f) {
-      throw std::out_of_range("digital_lms_dd_equalizer::set_mu: Gain value must in [0, 1]");
-    }
-    else {
-      d_mu = mu;
-    }
-  }
-  
-};
-
-#endif