// Copyright (C) 2003--2004 Ronan Collobert (collober@idiap.ch)
//                
// This file is part of Torch 3.1.
//
// All rights reserved.
// 
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions
// are met:
// 1. Redistributions of source code must retain the above copyright
//    notice, this list of conditions and the following disclaimer.
// 2. 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.
// 3. The name of the author may not be used to endorse or promote products
//    derived from this software without specific prior written permission.
// 
// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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 TEMPORAL_CONVOLUTION_INC
#define TEMPORAL_CONVOLUTION_INC

#include "GradientMachine.h"

namespace Torch {

/** Class for doing a convolution over a sequence.
    
    For each component of output frames, it computes the convolution
    of the input sequence with a kernel of size #k_w# (over the time).

    Note that, depending of the size of your kernel, several (last) frames
    of the input sequence could be lost.

    Note also that \emph{no} non-linearity is applied in this layer.

    @author Ronan Collobert (collober@idiap.ch)
*/
class TemporalConvolution : public GradientMachine
{
  public:
    /// Kernel size.
    int k_w;

    /// Time translation after one application of the kernel.
    int d_t;
    
    /** #weights[i]# means kernel-weights for the #i#-th component of output frames.
        #weights[i]# contains #input_frame_size# times #k_w# weights.
    */
    real **weights;

    /// Derivatives associated to #weights#.
    real **der_weights;

    /// #biases[i]# is the bias for the #i#-th component of output frames.
    real *biases;

    /// Derivatives associated to #biases#.
    real *der_biases;
    
    /// Create a convolution layer...
    TemporalConvolution(int input_frame_size, int output_frame_size, int k_w_=5, int d_t_=1);

    //-----
    
    void reset_();
    virtual void reset();
    virtual void forward(Sequence *inputs);
    virtual void backward(Sequence *inputs, Sequence *alpha);

    virtual ~TemporalConvolution();
};

}

#endif
