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diff --git a/contrib/mira/Optimiser.h b/contrib/mira/Optimiser.h
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+/***********************************************************************
+Moses - factored phrase-based language decoder
+Copyright (C) 2010 University of Edinburgh
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library 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
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
+***********************************************************************/
+#ifndef _MIRA_OPTIMISER_H_
+#define _MIRA_OPTIMISER_H_
+
+#include <vector>
+
+#include "moses/ScoreComponentCollection.h"
+
+
+namespace Mira
+{
+
+class Optimiser
+{
+public:
+  Optimiser() {}
+
+  virtual size_t updateWeightsHopeFear(
+    Moses::ScoreComponentCollection& weightUpdate,
+    const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
+    const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
+    const std::vector<std::vector<float> >& bleuScoresHope,
+    const std::vector<std::vector<float> >& bleuScoresFear,
+    const std::vector<std::vector<float> >& modelScoresHope,
+    const std::vector<std::vector<float> >& modelScoresFear,
+    float learning_rate,
+    size_t rank,
+    size_t epoch,
+    int updatePosition = -1) = 0;
+};
+
+class Perceptron : public Optimiser
+{
+public:
+  virtual size_t updateWeightsHopeFear(
+    Moses::ScoreComponentCollection& weightUpdate,
+    const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
+    const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
+    const std::vector<std::vector<float> >& bleuScoresHope,
+    const std::vector<std::vector<float> >& bleuScoresFear,
+    const std::vector<std::vector<float> >& modelScoresHope,
+    const std::vector<std::vector<float> >& modelScoresFear,
+    float learning_rate,
+    size_t rank,
+    size_t epoch,
+    int updatePosition = -1);
+};
+
+class MiraOptimiser : public Optimiser
+{
+public:
+  MiraOptimiser() :
+    Optimiser() { }
+
+  MiraOptimiser(float slack) :
+    Optimiser(),
+    m_slack(slack),
+    m_scale_margin(false),
+    m_scale_update(false),
+    m_boost(false),
+    m_normaliseMargin(false),
+    m_sigmoidParam(1.0) { }
+
+  MiraOptimiser(float slack, bool scale_margin, bool scale_update,
+                bool boost, bool normaliseMargin, float sigmoidParam) :
+    Optimiser(),
+    m_slack(slack),
+    m_scale_margin(scale_margin),
+    m_scale_update(scale_update),
+    m_boost(boost),
+    m_normaliseMargin(normaliseMargin),
+    m_sigmoidParam(sigmoidParam) { }
+
+  size_t updateWeights(
+    Moses::ScoreComponentCollection& weightUpdate,
+    const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValues,
+    const std::vector<std::vector<float> >& losses,
+    const std::vector<std::vector<float> >& bleuScores,
+    const std::vector<std::vector<float> >& modelScores,
+    const std::vector< Moses::ScoreComponentCollection>& oracleFeatureValues,
+    const std::vector< float> oracleBleuScores,
+    const std::vector< float> oracleModelScores,
+    float learning_rate,
+    size_t rank,
+    size_t epoch);
+  virtual size_t updateWeightsHopeFear(
+    Moses::ScoreComponentCollection& weightUpdate,
+    const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesHope,
+    const std::vector<std::vector<Moses::ScoreComponentCollection> >& featureValuesFear,
+    const std::vector<std::vector<float> >& bleuScoresHope,
+    const std::vector<std::vector<float> >& bleuScoresFear,
+    const std::vector<std::vector<float> >& modelScoresHope,
+    const std::vector<std::vector<float> >& modelScoresFear,
+    float learning_rate,
+    size_t rank,
+    size_t epoch,
+    int updatePosition = -1);
+  size_t updateWeightsAnalytically(
+    Moses::ScoreComponentCollection& weightUpdate,
+    Moses::ScoreComponentCollection& featureValuesHope,
+    Moses::ScoreComponentCollection& featureValuesFear,
+    float bleuScoreHope,
+    float bleuScoreFear,
+    float modelScoreHope,
+    float modelScoreFear,
+    float learning_rate,
+    size_t rank,
+    size_t epoch);
+
+  void setSlack(float slack) {
+    m_slack = slack;
+  }
+
+private:
+  // regularise Hildreth updates
+  float m_slack;
+
+
+  // scale margin with BLEU score
+  bool m_scale_margin;
+
+  // scale update with oracle BLEU score
+  bool m_scale_update;
+
+  // boosting of updates on misranked candidates
+  bool m_boost;
+
+  // squash margin between 0 and 1 (or depending on m_sigmoidParam)
+  bool m_normaliseMargin;
+
+  // y=sigmoidParam is the axis that this sigmoid approaches
+  float m_sigmoidParam ;
+};
+}
+
+#endif