From 9331cef2de5c397c7394156a39295eea2f8e8bc3 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Wed, 28 Jan 2015 11:12:52 +0100
Subject: [PATCH] Binded JFABase
---
.../misc/include/bob.learn.misc/JFAMachine.h | 924 +-----------------
bob/learn/misc/jfa_base.cpp | 568 +++++++++++
bob/learn/misc/main.cpp | 1 +
bob/learn/misc/main.h | 13 +
4 files changed, 596 insertions(+), 910 deletions(-)
create mode 100644 bob/learn/misc/jfa_base.cpp
diff --git a/bob/learn/misc/include/bob.learn.misc/JFAMachine.h b/bob/learn/misc/include/bob.learn.misc/JFAMachine.h
index a1fc17c..48d1f4a 100644
--- a/bob/learn/misc/include/bob.learn.misc/JFAMachine.h
+++ b/bob/learn/misc/include/bob.learn.misc/JFAMachine.h
@@ -1,18 +1,19 @@
/**
- * @date Sat Jul 23 21:41:15 2011 +0200
+ * @date Tue Jan 27 16:47:00 2015 +0200
* @author Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
+ * @author Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
*
* @brief A base class for Joint Factor Analysis-like machines
*
* Copyright (C) Idiap Research Institute, Martigny, Switzerland
*/
-#ifndef BOB_LEARN_MISC_FABASE_H
-#define BOB_LEARN_MISC_FABASE_H
+#ifndef BOB_LEARN_MISC_JFAMACHINE_H
+#define BOB_LEARN_MISC_JFAMACHINE_H
#include <stdexcept>
-#include <bob.learn.misc/Machine.h>
+#include <bob.learn.misc/JFABase.h>
#include <bob.learn.misc/GMMMachine.h>
#include <bob.learn.misc/LinearScoring.h>
@@ -21,701 +22,6 @@
namespace bob { namespace learn { namespace misc {
-/**
- * @brief A FA Base class which contains U, V and D matrices
- * TODO: add a reference to the journal articles
- */
-class FABase
-{
- public:
- /**
- * @brief Default constructor. Builds an otherwise invalid 0 x 0 FABase
- * The Universal Background Model and the matrices U, V and diag(d) are
- * not initialized.
- */
- FABase();
-
- /**
- * @brief Constructor. Builds a new FABase.
- * The Universal Background Model and the matrices U, V and diag(d) are
- * not initialized.
- *
- * @param ubm The Universal Background Model
- * @param ru size of U (CD x ru)
- * @param rv size of U (CD x rv)
- * @warning ru and rv SHOULD BE >= 1. Just set U/V/D to zero if you want
- * to ignore one subspace. This is the case for ISV.
- */
- FABase(const boost::shared_ptr<bob::learn::misc::GMMMachine> ubm, const size_t ru=1, const size_t rv=1);
-
- /**
- * @brief Copy constructor
- */
- FABase(const FABase& other);
-
- /**
- * @brief Just to virtualise the destructor
- */
- virtual ~FABase();
-
- /**
- * @brief Assigns from a different JFA machine
- */
- FABase& operator=(const FABase &other);
-
- /**
- * @brief Equal to
- */
- bool operator==(const FABase& b) const;
-
- /**
- * @brief Not equal to
- */
- bool operator!=(const FABase& b) const;
-
- /**
- * @brief Similar to
- */
- bool is_similar_to(const FABase& b, const double r_epsilon=1e-5,
- const double a_epsilon=1e-8) const;
-
- /**
- * @brief Returns the UBM
- */
- const boost::shared_ptr<bob::learn::misc::GMMMachine> getUbm() const
- { return m_ubm; }
-
- /**
- * @brief Returns the U matrix
- */
- const blitz::Array<double,2>& getU() const
- { return m_U; }
-
- /**
- * @brief Returns the V matrix
- */
- const blitz::Array<double,2>& getV() const
- { return m_V; }
-
- /**
- * @brief Returns the diagonal matrix diag(d) (as a 1D vector)
- */
- const blitz::Array<double,1>& getD() const
- { return m_d; }
-
- /**
- * @brief Returns the UBM mean supervector (as a 1D vector)
- */
- const blitz::Array<double,1>& getUbmMean() const
- { return m_cache_mean; }
-
- /**
- * @brief Returns the UBM variance supervector (as a 1D vector)
- */
- const blitz::Array<double,1>& getUbmVariance() const
- { return m_cache_sigma; }
-
- /**
- * @brief Returns the number of Gaussian components C
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimC() const
- { if(!m_ubm) throw std::runtime_error("No UBM was set in the JFA machine.");
- return m_ubm->getNGaussians(); }
-
- /**
- * @brief Returns the feature dimensionality D
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimD() const
- { if(!m_ubm) throw std::runtime_error("No UBM was set in the JFA machine.");
- return m_ubm->getNInputs(); }
-
- /**
- * @brief Returns the supervector length CD
- * (CxD: Number of Gaussian components by the feature dimensionality)
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimCD() const
- { if(!m_ubm) throw std::runtime_error("No UBM was set in the JFA machine.");
- return m_ubm->getNInputs()*m_ubm->getNGaussians(); }
-
- /**
- * @brief Returns the size/rank ru of the U matrix
- */
- const size_t getDimRu() const
- { return m_ru; }
-
- /**
- * @brief Returns the size/rank rv of the V matrix
- */
- const size_t getDimRv() const
- { return m_rv; }
-
- /**
- * @brief Resets the dimensionality of the subspace U and V
- * U and V are hence uninitialized.
- */
- void resize(const size_t ru, const size_t rv);
-
- /**
- * @brief Resets the dimensionality of the subspace U and V,
- * assuming that no UBM has yet been set
- * U and V are hence uninitialized.
- */
- void resize(const size_t ru, const size_t rv, const size_t cd);
-
- /**
- * @brief Returns the U matrix in order to update it
- * @warning Should only be used by the trainer for efficiency reason,
- * or for testing purpose.
- */
- blitz::Array<double,2>& updateU()
- { return m_U; }
-
- /**
- * @brief Returns the V matrix in order to update it
- * @warning Should only be used by the trainer for efficiency reason,
- * or for testing purpose.
- */
- blitz::Array<double,2>& updateV()
- { return m_V; }
-
- /**
- * @brief Returns the diagonal matrix diag(d) (as a 1D vector) in order
- * to update it
- * @warning Should only be used by the trainer for efficiency reason,
- * or for testing purpose.
- */
- blitz::Array<double,1>& updateD()
- { return m_d; }
-
-
- /**
- * @brief Sets (the mean supervector of) the Universal Background Model
- * U, V and d are uninitialized in case of dimensions update (C or D)
- */
- void setUbm(const boost::shared_ptr<bob::learn::misc::GMMMachine> ubm);
-
- /**
- * @brief Sets the U matrix
- */
- void setU(const blitz::Array<double,2>& U);
-
- /**
- * @brief Sets the V matrix
- */
- void setV(const blitz::Array<double,2>& V);
-
- /**
- * @brief Sets the diagonal matrix diag(d)
- * (a 1D vector is expected as an argument)
- */
- void setD(const blitz::Array<double,1>& d);
-
-
- /**
- * @brief Estimates x from the GMM statistics considering the LPT
- * assumption, that is the latent session variable x is approximated
- * using the UBM
- */
- void estimateX(const bob::learn::misc::GMMStats& gmm_stats, blitz::Array<double,1>& x) const;
-
- /**
- * @brief Compute and put U^{T}.Sigma^{-1} matrix in cache
- * @warning Should only be used by the trainer for efficiency reason,
- * or for testing purpose.
- */
- void updateCacheUbmUVD();
-
-
- private:
- /**
- * @brief Update cache arrays/variables
- */
- void updateCache();
- /**
- * @brief Put GMM mean/variance supervector in cache
- */
- void updateCacheUbm();
- /**
- * @brief Resize working arrays
- */
- void resizeTmp();
- /**
- * @brief Computes (Id + U^T.Sigma^-1.U.N_{i,h}.U)^-1 =
- * (Id + sum_{c=1..C} N_{i,h}.U_{c}^T.Sigma_{c}^-1.U_{c})^-1
- */
- void computeIdPlusUSProdInv(const bob::learn::misc::GMMStats& gmm_stats,
- blitz::Array<double,2>& out) const;
- /**
- * @brief Computes Fn_x = sum_{sessions h}(N*(o - m))
- * (Normalised first order statistics)
- */
- void computeFn_x(const bob::learn::misc::GMMStats& gmm_stats,
- blitz::Array<double,1>& out) const;
- /**
- * @brief Estimates the value of x from the passed arguments
- * (IdPlusUSProdInv and Fn_x), considering the LPT assumption
- */
- void estimateX(const blitz::Array<double,2>& IdPlusUSProdInv,
- const blitz::Array<double,1>& Fn_x, blitz::Array<double,1>& x) const;
-
-
- // UBM
- boost::shared_ptr<bob::learn::misc::GMMMachine> m_ubm;
-
- // dimensionality
- size_t m_ru; // size of U (CD x ru)
- size_t m_rv; // size of V (CD x rv)
-
- // U, V, D matrices
- // D is assumed to be diagonal, and only the diagonal is stored
- blitz::Array<double,2> m_U;
- blitz::Array<double,2> m_V;
- blitz::Array<double,1> m_d;
-
- // Vectors/Matrices precomputed in cache
- blitz::Array<double,1> m_cache_mean;
- blitz::Array<double,1> m_cache_sigma;
- blitz::Array<double,2> m_cache_UtSigmaInv;
-
- mutable blitz::Array<double,2> m_tmp_IdPlusUSProdInv;
- mutable blitz::Array<double,1> m_tmp_Fn_x;
- mutable blitz::Array<double,1> m_tmp_ru;
- mutable blitz::Array<double,2> m_tmp_ruD;
- mutable blitz::Array<double,2> m_tmp_ruru;
-};
-
-
-/**
- * @brief A JFA Base class which contains U, V and D matrices
- * TODO: add a reference to the journal articles
- */
-class JFABase
-{
- public:
- /**
- * @brief Default constructor. Builds a 1 x 1 JFABase
- * The Universal Background Model and the matrices U, V and diag(d) are
- * not initialized.
- */
- JFABase();
-
- /**
- * @brief Constructor. Builds a new JFABase.
- * The Universal Background Model and the matrices U, V and diag(d) are
- * not initialized.
- *
- * @param ubm The Universal Background Model
- * @param ru size of U (CD x ru)
- * @param rv size of U (CD x rv)
- * @warning ru and rv SHOULD BE >= 1.
- */
- JFABase(const boost::shared_ptr<bob::learn::misc::GMMMachine> ubm, const size_t ru=1, const size_t rv=1);
-
- /**
- * @brief Copy constructor
- */
- JFABase(const JFABase& other);
-
- /**
- * @deprecated Starts a new JFAMachine from an existing Configuration object.
- */
- JFABase(bob::io::base::HDF5File& config);
-
- /**
- * @brief Just to virtualise the destructor
- */
- virtual ~JFABase();
-
- /**
- * @brief Assigns from a different JFA machine
- */
- JFABase& operator=(const JFABase &other);
-
- /**
- * @brief Equal to
- */
- bool operator==(const JFABase& b) const
- { return m_base.operator==(b.m_base); }
-
- /**
- * @brief Not equal to
- */
- bool operator!=(const JFABase& b) const
- { return m_base.operator!=(b.m_base); }
-
- /**
- * @brief Similar to
- */
- bool is_similar_to(const JFABase& b, const double r_epsilon=1e-5,
- const double a_epsilon=1e-8) const
- { return m_base.is_similar_to(b.m_base, r_epsilon, a_epsilon); }
-
- /**
- * @brief Saves model to an HDF5 file
- */
- void save(bob::io::base::HDF5File& config) const;
-
- /**
- * @brief Loads data from an existing configuration object. Resets
- * the current state.
- */
- void load(bob::io::base::HDF5File& config);
-
- /**
- * @brief Returns the UBM
- */
- const boost::shared_ptr<bob::learn::misc::GMMMachine> getUbm() const
- { return m_base.getUbm(); }
-
- /**
- * @brief Returns the U matrix
- */
- const blitz::Array<double,2>& getU() const
- { return m_base.getU(); }
-
- /**
- * @brief Returns the V matrix
- */
- const blitz::Array<double,2>& getV() const
- { return m_base.getV(); }
-
- /**
- * @brief Returns the diagonal matrix diag(d) (as a 1D vector)
- */
- const blitz::Array<double,1>& getD() const
- { return m_base.getD(); }
-
- /**
- * @brief Returns the number of Gaussian components C
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimC() const
- { return m_base.getDimC(); }
-
- /**
- * @brief Returns the feature dimensionality D
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimD() const
- { return m_base.getDimD(); }
-
- /**
- * @brief Returns the supervector length CD
- * (CxD: Number of Gaussian components by the feature dimensionality)
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimCD() const
- { return m_base.getDimCD(); }
-
- /**
- * @brief Returns the size/rank ru of the U matrix
- */
- const size_t getDimRu() const
- { return m_base.getDimRu(); }
-
- /**
- * @brief Returns the size/rank rv of the V matrix
- */
- const size_t getDimRv() const
- { return m_base.getDimRv(); }
-
- /**
- * @brief Resets the dimensionality of the subspace U and V
- * U and V are hence uninitialized.
- */
- void resize(const size_t ru, const size_t rv)
- { m_base.resize(ru, rv); }
-
- /**
- * @brief Returns the U matrix in order to update it
- * @warning Should only be used by the trainer for efficiency reason,
- * or for testing purpose.
- */
- blitz::Array<double,2>& updateU()
- { return m_base.updateU(); }
-
- /**
- * @brief Returns the V matrix in order to update it
- * @warning Should only be used by the trainer for efficiency reason,
- * or for testing purpose.
- */
- blitz::Array<double,2>& updateV()
- { return m_base.updateV(); }
-
- /**
- * @brief Returns the diagonal matrix diag(d) (as a 1D vector) in order
- * to update it
- * @warning Should only be used by the trainer for efficiency reason,
- * or for testing purpose.
- */
- blitz::Array<double,1>& updateD()
- { return m_base.updateD(); }
-
-
- /**
- * @brief Sets (the mean supervector of) the Universal Background Model
- * U, V and d are uninitialized in case of dimensions update (C or D)
- */
- void setUbm(const boost::shared_ptr<bob::learn::misc::GMMMachine> ubm)
- { m_base.setUbm(ubm); }
-
- /**
- * @brief Sets the U matrix
- */
- void setU(const blitz::Array<double,2>& U)
- { m_base.setU(U); }
-
- /**
- * @brief Sets the V matrix
- */
- void setV(const blitz::Array<double,2>& V)
- { m_base.setV(V); }
-
- /**
- * @brief Sets the diagonal matrix diag(d)
- * (a 1D vector is expected as an argument)
- */
- void setD(const blitz::Array<double,1>& d)
- { m_base.setD(d); }
-
- /**
- * @brief Estimates x from the GMM statistics considering the LPT
- * assumption, that is the latent session variable x is approximated
- * using the UBM
- */
- void estimateX(const bob::learn::misc::GMMStats& gmm_stats, blitz::Array<double,1>& x) const
- { m_base.estimateX(gmm_stats, x); }
-
- /**
- * @brief Precompute (put U^{T}.Sigma^{-1} matrix in cache)
- * @warning Should only be used by the trainer for efficiency reason,
- * or for testing purpose.
- */
- void precompute()
- { m_base.updateCacheUbmUVD(); }
-
- /**
- * @brief Returns the FABase member
- */
- const bob::learn::misc::FABase& getBase() const
- { return m_base; }
-
-
- private:
- // FABase
- bob::learn::misc::FABase m_base;
-};
-
-
-/**
- * @brief An ISV Base class which contains U and D matrices
- * TODO: add a reference to the journal articles
- */
-class ISVBase
-{
- public:
- /**
- * @brief Default constructor. Builds an otherwise invalid 0 x 0 ISVBase
- * The Universal Background Model and the matrices U, V and diag(d) are
- * not initialized.
- */
- ISVBase();
-
- /**
- * @brief Constructor. Builds a new ISVBase.
- * The Universal Background Model and the matrices U, V and diag(d) are
- * not initialized.
- *
- * @param ubm The Universal Background Model
- * @param ru size of U (CD x ru)
- * @warning ru SHOULD BE >= 1.
- */
- ISVBase(const boost::shared_ptr<bob::learn::misc::GMMMachine> ubm, const size_t ru=1);
-
- /**
- * @brief Copy constructor
- */
- ISVBase(const ISVBase& other);
-
- /**
- * @deprecated Starts a new JFAMachine from an existing Configuration object.
- */
- ISVBase(bob::io::base::HDF5File& config);
-
- /**
- * @brief Just to virtualise the destructor
- */
- virtual ~ISVBase();
-
- /**
- * @brief Assigns from a different JFA machine
- */
- ISVBase& operator=(const ISVBase &other);
-
- /**
- * @brief Equal to
- */
- bool operator==(const ISVBase& b) const
- { return m_base.operator==(b.m_base); }
-
- /**
- * @brief Not equal to
- */
- bool operator!=(const ISVBase& b) const
- { return m_base.operator!=(b.m_base); }
-
- /**
- * @brief Similar to
- */
- bool is_similar_to(const ISVBase& b, const double r_epsilon=1e-5,
- const double a_epsilon=1e-8) const
- { return m_base.is_similar_to(b.m_base, r_epsilon, a_epsilon); }
-
- /**
- * @brief Saves machine to an HDF5 file
- */
- void save(bob::io::base::HDF5File& config) const;
-
- /**
- * @brief Loads data from an existing configuration object. Resets
- * the current state.
- */
- void load(bob::io::base::HDF5File& config);
-
- /**
- * @brief Returns the UBM
- */
- const boost::shared_ptr<bob::learn::misc::GMMMachine> getUbm() const
- { return m_base.getUbm(); }
-
- /**
- * @brief Returns the U matrix
- */
- const blitz::Array<double,2>& getU() const
- { return m_base.getU(); }
-
- /**
- * @brief Returns the diagonal matrix diag(d) (as a 1D vector)
- */
- const blitz::Array<double,1>& getD() const
- { return m_base.getD(); }
-
- /**
- * @brief Returns the number of Gaussian components C
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimC() const
- { return m_base.getDimC(); }
-
- /**
- * @brief Returns the feature dimensionality D
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimD() const
- { return m_base.getDimD(); }
-
- /**
- * @brief Returns the supervector length CD
- * (CxD: Number of Gaussian components by the feature dimensionality)
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimCD() const
- { return m_base.getDimCD(); }
-
- /**
- * @brief Returns the size/rank ru of the U matrix
- */
- const size_t getDimRu() const
- { return m_base.getDimRu(); }
-
- /**
- * @brief Resets the dimensionality of the subspace U and V
- * U and V are hence uninitialized.
- */
- void resize(const size_t ru)
- { m_base.resize(ru, 1);
- blitz::Array<double,2>& V = m_base.updateV();
- V = 0;
- }
-
- /**
- * @brief Returns the U matrix in order to update it
- * @warning Should only be used by the trainer for efficiency reason,
- * or for testing purpose.
- */
- blitz::Array<double,2>& updateU()
- { return m_base.updateU(); }
-
- /**
- * @brief Returns the diagonal matrix diag(d) (as a 1D vector) in order
- * to update it
- * @warning Should only be used by the trainer for efficiency reason,
- * or for testing purpose.
- */
- blitz::Array<double,1>& updateD()
- { return m_base.updateD(); }
-
-
- /**
- * @brief Sets (the mean supervector of) the Universal Background Model
- * U, V and d are uninitialized in case of dimensions update (C or D)
- */
- void setUbm(const boost::shared_ptr<bob::learn::misc::GMMMachine> ubm)
- { m_base.setUbm(ubm); }
-
- /**
- * @brief Sets the U matrix
- */
- void setU(const blitz::Array<double,2>& U)
- { m_base.setU(U); }
-
- /**
- * @brief Sets the diagonal matrix diag(d)
- * (a 1D vector is expected as an argument)
- */
- void setD(const blitz::Array<double,1>& d)
- { m_base.setD(d); }
-
- /**
- * @brief Estimates x from the GMM statistics considering the LPT
- * assumption, that is the latent session variable x is approximated
- * using the UBM
- */
- void estimateX(const bob::learn::misc::GMMStats& gmm_stats, blitz::Array<double,1>& x) const
- { m_base.estimateX(gmm_stats, x); }
-
- /**
- * @brief Precompute (put U^{T}.Sigma^{-1} matrix in cache)
- * @warning Should only be used by the trainer for efficiency reason,
- * or for testing purpose.
- */
- void precompute()
- { m_base.updateCacheUbmUVD(); }
-
- /**
- * @brief Returns the FABase member
- */
- const bob::learn::misc::FABase& getBase() const
- { return m_base; }
-
-
- private:
- // FABase
- bob::learn::misc::FABase m_base;
-};
-
/**
* @brief A JFAMachine which is associated to a JFABase that contains
@@ -723,7 +29,7 @@ class ISVBase
* (latent variables y and z)
* TODO: add a reference to the journal articles
*/
-class JFAMachine: public Machine<bob::learn::misc::GMMStats, double>
+class JFAMachine
{
public:
/**
@@ -792,16 +98,16 @@ class JFAMachine: public Machine<bob::learn::misc::GMMStats, double>
* @warning An exception is thrown if no Universal Background Model has
* been set yet.
*/
- const size_t getDimC() const
- { return m_jfa_base->getDimC(); }
+ const size_t getNGaussians() const
+ { return m_jfa_base->getNGaussians(); }
/**
* @brief Returns the feature dimensionality D
* @warning An exception is thrown if no Universal Background Model has
* been set yet.
*/
- const size_t getDimD() const
- { return m_jfa_base->getDimD(); }
+ const size_t getNInputs() const
+ { return m_jfa_base->getNInputs(); }
/**
* @brief Returns the supervector length CD
@@ -809,8 +115,8 @@ class JFAMachine: public Machine<bob::learn::misc::GMMStats, double>
* @warning An exception is thrown if no Universal Background Model has
* been set yet.
*/
- const size_t getDimCD() const
- { return m_jfa_base->getDimCD(); }
+ const size_t getSupervectorLength() const
+ { return m_jfa_base->getSupervectorLength(); }
/**
* @brief Returns the size/rank ru of the U matrix
@@ -914,7 +220,7 @@ class JFAMachine: public Machine<bob::learn::misc::GMMStats, double>
*/
void forward_(const bob::learn::misc::GMMStats& input, double& score) const;
- protected:
+ private:
/**
* @brief Resize latent variable according to the JFABase
*/
@@ -943,208 +249,6 @@ class JFAMachine: public Machine<bob::learn::misc::GMMStats, double>
mutable blitz::Array<double,1> m_tmp_Ux;
};
-/**
- * @brief A ISVMachine which is associated to a ISVBase that contains
- * U D matrices.
- * TODO: add a reference to the journal articles
- */
-class ISVMachine: public Machine<bob::learn::misc::GMMStats, double>
-{
- public:
- /**
- * @brief Default constructor. Builds an otherwise invalid 0 x 0 ISVMachine
- * The Universal Background Model and the matrices U, V and diag(d) are
- * not initialized.
- */
- ISVMachine();
-
- /**
- * @brief Constructor. Builds a new ISVMachine.
- *
- * @param isv_base The ISVBase associated with this machine
- */
- ISVMachine(const boost::shared_ptr<bob::learn::misc::ISVBase> isv_base);
-
- /**
- * @brief Copy constructor
- */
- ISVMachine(const ISVMachine& other);
-
- /**
- * @brief Starts a new ISVMachine from an existing Configuration object.
- */
- ISVMachine(bob::io::base::HDF5File& config);
-
- /**
- * @brief Just to virtualise the destructor
- */
- virtual ~ISVMachine();
-
- /**
- * @brief Assigns from a different ISV machine
- */
- ISVMachine& operator=(const ISVMachine &other);
-
- /**
- * @brief Equal to
- */
- bool operator==(const ISVMachine& b) const;
-
- /**
- * @brief Not equal to
- */
- bool operator!=(const ISVMachine& b) const;
-
- /**
- * @brief Similar to
- */
- bool is_similar_to(const ISVMachine& b, const double r_epsilon=1e-5,
- const double a_epsilon=1e-8) const;
-
- /**
- * @brief Saves machine to an HDF5 file
- */
- void save(bob::io::base::HDF5File& config) const;
-
- /**
- * @brief Loads data from an existing configuration object. Resets
- * the current state.
- */
- void load(bob::io::base::HDF5File& config);
-
-
- /**
- * @brief Returns the number of Gaussian components C
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimC() const
- { return m_isv_base->getDimC(); }
-
- /**
- * @brief Returns the feature dimensionality D
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimD() const
- { return m_isv_base->getDimD(); }
-
- /**
- * @brief Returns the supervector length CD
- * (CxD: Number of Gaussian components by the feature dimensionality)
- * @warning An exception is thrown if no Universal Background Model has
- * been set yet.
- */
- const size_t getDimCD() const
- { return m_isv_base->getDimCD(); }
-
- /**
- * @brief Returns the size/rank ru of the U matrix
- */
- const size_t getDimRu() const
- { return m_isv_base->getDimRu(); }
-
- /**
- * @brief Returns the x session factor
- */
- const blitz::Array<double,1>& getX() const
- { return m_cache_x; }
-
- /**
- * @brief Returns the z speaker factor
- */
- const blitz::Array<double,1>& getZ() const
- { return m_z; }
-
- /**
- * @brief Returns the z speaker factors in order to update it
- */
- blitz::Array<double,1>& updateZ()
- { return m_z; }
-
- /**
- * @brief Returns the V matrix
- */
- void setZ(const blitz::Array<double,1>& z);
-
- /**
- * @brief Returns the ISVBase
- */
- const boost::shared_ptr<bob::learn::misc::ISVBase> getISVBase() const
- { return m_isv_base; }
-
- /**
- * @brief Sets the ISVBase
- */
- void setISVBase(const boost::shared_ptr<bob::learn::misc::ISVBase> isv_base);
-
-
- /**
- * @brief Estimates x from the GMM statistics considering the LPT
- * assumption, that is the latent session variable x is approximated
- * using the UBM
- */
- void estimateX(const bob::learn::misc::GMMStats& gmm_stats, blitz::Array<double,1>& x) const
- { m_isv_base->estimateX(gmm_stats, x); }
- /**
- * @brief Estimates Ux from the GMM statistics considering the LPT
- * assumption, that is the latent session variable x is approximated
- * using the UBM
- */
- void estimateUx(const bob::learn::misc::GMMStats& gmm_stats, blitz::Array<double,1>& Ux);
-
- /**
- * @brief Execute the machine
- *
- * @param input input data used by the machine
- * @param score value computed by the machine
- * @warning Inputs are checked
- */
- void forward(const bob::learn::misc::GMMStats& input, double& score) const;
- /**
- * @brief Computes a score for the given UBM statistics and given the
- * Ux vector
- */
- void forward(const bob::learn::misc::GMMStats& gmm_stats,
- const blitz::Array<double,1>& Ux, double& score) const;
-
- /**
- * @brief Execute the machine
- *
- * @param input input data used by the machine
- * @param score value computed by the machine
- * @warning Inputs are NOT checked
- */
- void forward_(const bob::learn::misc::GMMStats& input, double& score) const;
-
- protected:
- /**
- * @brief Resize latent variable according to the ISVBase
- */
- void resize();
- /**
- * @ Update cache
- */
- void updateCache();
- /**
- * @brief Resize working arrays
- */
- void resizeTmp();
-
- // UBM
- boost::shared_ptr<bob::learn::misc::ISVBase> m_isv_base;
-
- // y and z vectors/factors learned during the enrolment procedure
- blitz::Array<double,1> m_z;
-
- // cache
- blitz::Array<double,1> m_cache_mDz;
- mutable blitz::Array<double,1> m_cache_x;
-
- // x vector/factor in cache when computing scores
- mutable blitz::Array<double,1> m_tmp_Ux;
-};
-
} } } // namespaces
-#endif // BOB_LEARN_MISC_FABASE_H
+#endif // BOB_LEARN_MISC_JFAMACHINE_H
diff --git a/bob/learn/misc/jfa_base.cpp b/bob/learn/misc/jfa_base.cpp
new file mode 100644
index 0000000..7fa60b5
--- /dev/null
+++ b/bob/learn/misc/jfa_base.cpp
@@ -0,0 +1,568 @@
+/**
+ * @date Tue Jan 27 17:03:15 2015 +0200
+ * @author Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+ *
+ * @brief Python API for bob::learn::em
+ *
+ * Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
+ */
+
+#include "main.h"
+
+/******************************************************************/
+/************ Constructor Section *********************************/
+/******************************************************************/
+
+static auto JFABase_doc = bob::extension::ClassDoc(
+ BOB_EXT_MODULE_PREFIX ".JFABase",
+
+ "Constructor. Builds a new JFABase. "
+ "TODO: add a reference to the journal articles",
+ ""
+).add_constructor(
+ bob::extension::FunctionDoc(
+ "__init__",
+ "Creates a FABase",
+ "",
+ true
+ )
+ .add_prototype("gmm,ru,rv","")
+ .add_prototype("other","")
+ .add_prototype("hdf5","")
+ .add_prototype("","")
+
+ .add_parameter("gmm", ":py:class:`bob.learn.misc.GMMMachine`", "The Universal Background Model.")
+ .add_parameter("ru", "int", "Size of U (Within client variation matrix). In the end the U matrix will have (number_of_gaussians * feature_dimension x ru)")
+ .add_parameter("rv", "int", "Size of V (Between client variation matrix). In the end the U matrix will have (number_of_gaussians * feature_dimension x rv)")
+ .add_parameter("other", ":py:class:`bob.learn.misc.JFABase`", "A JFABase object to be copied.")
+ .add_parameter("hdf5", ":py:class:`bob.io.base.HDF5File`", "An HDF5 file open for reading")
+
+);
+
+
+static int PyBobLearnMiscJFABase_init_copy(PyBobLearnMiscJFABaseObject* self, PyObject* args, PyObject* kwargs) {
+
+ char** kwlist = JFABase_doc.kwlist(1);
+ PyBobLearnMiscJFABaseObject* o;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!", kwlist, &PyBobLearnMiscJFABase_Type, &o)){
+ JFABase_doc.print_usage();
+ return -1;
+ }
+
+ self->cxx.reset(new bob::learn::misc::JFABase(*o->cxx));
+ return 0;
+}
+
+
+static int PyBobLearnMiscJFABase_init_hdf5(PyBobLearnMiscJFABaseObject* self, PyObject* args, PyObject* kwargs) {
+
+ char** kwlist = JFABase_doc.kwlist(2);
+
+ PyBobIoHDF5FileObject* config = 0;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, &PyBobIoHDF5File_Converter, &config)){
+ JFABase_doc.print_usage();
+ return -1;
+ }
+
+ self->cxx.reset(new bob::learn::misc::JFABase(*(config->f)));
+
+ return 0;
+}
+
+
+static int PyBobLearnMiscJFABase_init_ubm(PyBobLearnMiscJFABaseObject* self, PyObject* args, PyObject* kwargs) {
+
+ char** kwlist = JFABase_doc.kwlist(0);
+
+ PyBobLearnMiscGMMMachineObject* ubm;
+ int ru = 1;
+ int rv = 1;
+
+ //Here we have to select which keyword argument to read
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O!ii", kwlist, &PyBobLearnMiscGMMMachine_Type, &ubm,
+ &ru, &rv)){
+ JFABase_doc.print_usage();
+ return -1;
+ }
+
+ self->cxx.reset(new bob::learn::misc::JFABase(ubm->cxx, ru, rv));
+ return 0;
+}
+
+
+static int PyBobLearnMiscJFABase_init(PyBobLearnMiscJFABaseObject* self, PyObject* args, PyObject* kwargs) {
+ BOB_TRY
+
+ // get the number of command line arguments
+ int nargs = (args?PyTuple_Size(args):0) + (kwargs?PyDict_Size(kwargs):0);
+
+ switch (nargs) {
+
+ case 1:{
+ //Reading the input argument
+ PyObject* arg = 0;
+ if (PyTuple_Size(args))
+ arg = PyTuple_GET_ITEM(args, 0);
+ else {
+ PyObject* tmp = PyDict_Values(kwargs);
+ auto tmp_ = make_safe(tmp);
+ arg = PyList_GET_ITEM(tmp, 0);
+ }
+
+ // If the constructor input is Gaussian object
+ if (PyBobLearnMiscJFABase_Check(arg))
+ return PyBobLearnMiscJFABase_init_copy(self, args, kwargs);
+ // If the constructor input is a HDF5
+ else if (PyBobIoHDF5File_Check(arg))
+ return PyBobLearnMiscJFABase_init_hdf5(self, args, kwargs);
+ }
+ case 3:
+ return PyBobLearnMiscJFABase_init_ubm(self, args, kwargs);
+ default:
+ PyErr_Format(PyExc_RuntimeError, "number of arguments mismatch - %s requires 1 or 3 arguments, but you provided %d (see help)", Py_TYPE(self)->tp_name, nargs);
+ JFABase_doc.print_usage();
+ return -1;
+ }
+ BOB_CATCH_MEMBER("cannot create JFABase", 0)
+ return 0;
+}
+
+
+
+static void PyBobLearnMiscJFABase_delete(PyBobLearnMiscJFABaseObject* self) {
+ self->cxx.reset();
+ Py_TYPE(self)->tp_free((PyObject*)self);
+}
+
+static PyObject* PyBobLearnMiscJFABase_RichCompare(PyBobLearnMiscJFABaseObject* self, PyObject* other, int op) {
+ BOB_TRY
+
+ if (!PyBobLearnMiscJFABase_Check(other)) {
+ PyErr_Format(PyExc_TypeError, "cannot compare `%s' with `%s'", Py_TYPE(self)->tp_name, Py_TYPE(other)->tp_name);
+ return 0;
+ }
+ auto other_ = reinterpret_cast<PyBobLearnMiscJFABaseObject*>(other);
+ switch (op) {
+ case Py_EQ:
+ if (*self->cxx==*other_->cxx) Py_RETURN_TRUE; else Py_RETURN_FALSE;
+ case Py_NE:
+ if (*self->cxx==*other_->cxx) Py_RETURN_FALSE; else Py_RETURN_TRUE;
+ default:
+ Py_INCREF(Py_NotImplemented);
+ return Py_NotImplemented;
+ }
+ BOB_CATCH_MEMBER("cannot compare JFABase objects", 0)
+}
+
+int PyBobLearnMiscJFABase_Check(PyObject* o) {
+ return PyObject_IsInstance(o, reinterpret_cast<PyObject*>(&PyBobLearnMiscJFABase_Type));
+}
+
+
+/******************************************************************/
+/************ Variables Section ***********************************/
+/******************************************************************/
+
+/***** shape *****/
+static auto shape = bob::extension::VariableDoc(
+ "shape",
+ "(int,int, int, int)",
+ "A tuple that represents the number of gaussians, dimensionality of each Gaussian, dimensionality of the rU (within client variability matrix) and dimensionality of the rV (between client variability matrix) ``(#Gaussians, #Inputs, #rU, #rV)``.",
+ ""
+);
+PyObject* PyBobLearnMiscJFABase_getShape(PyBobLearnMiscJFABaseObject* self, void*) {
+ BOB_TRY
+ return Py_BuildValue("(i,i,i,i)", self->cxx->getNGaussians(), self->cxx->getNInputs(), self->cxx->getDimRu(), self->cxx->getDimRv());
+ BOB_CATCH_MEMBER("shape could not be read", 0)
+}
+
+/***** supervector_length *****/
+static auto supervector_length = bob::extension::VariableDoc(
+ "supervector_length",
+ "int",
+
+ "Returns the supervector length."
+ "NGaussians x NInputs: Number of Gaussian components by the feature dimensionality",
+
+ "@warning An exception is thrown if no Universal Background Model has been set yet."
+);
+PyObject* PyBobLearnMiscJFABase_getSupervectorLength(PyBobLearnMiscJFABaseObject* self, void*) {
+ BOB_TRY
+ return Py_BuildValue("i", self->cxx->getSupervectorLength());
+ BOB_CATCH_MEMBER("supervector_length could not be read", 0)
+}
+
+
+/***** u *****/
+static auto U = bob::extension::VariableDoc(
+ "u",
+ "array_like <float, 2D>",
+ "Returns the U matrix (within client variability matrix)",
+ ""
+);
+PyObject* PyBobLearnMiscJFABase_getU(PyBobLearnMiscJFABaseObject* self, void*){
+ BOB_TRY
+ return PyBlitzArrayCxx_AsConstNumpy(self->cxx->getU());
+ BOB_CATCH_MEMBER("``u`` could not be read", 0)
+}
+int PyBobLearnMiscJFABase_setU(PyBobLearnMiscJFABaseObject* self, PyObject* value, void*){
+ BOB_TRY
+ PyBlitzArrayObject* o;
+ if (!PyBlitzArray_Converter(value, &o)){
+ PyErr_Format(PyExc_RuntimeError, "%s %s expects a 2D array of floats", Py_TYPE(self)->tp_name, U.name());
+ return -1;
+ }
+ auto o_ = make_safe(o);
+ auto b = PyBlitzArrayCxx_AsBlitz<double,2>(o, "u");
+ if (!b) return -1;
+ self->cxx->setU(*b);
+ return 0;
+ BOB_CATCH_MEMBER("``u`` matrix could not be set", -1)
+}
+
+/***** v *****/
+static auto V = bob::extension::VariableDoc(
+ "v",
+ "array_like <float, 2D>",
+ "Returns the V matrix (between client variability matrix)",
+ ""
+);
+PyObject* PyBobLearnMiscJFABase_getV(PyBobLearnMiscJFABaseObject* self, void*){
+ BOB_TRY
+ return PyBlitzArrayCxx_AsConstNumpy(self->cxx->getV());
+ BOB_CATCH_MEMBER("``v`` could not be read", 0)
+}
+int PyBobLearnMiscJFABase_setV(PyBobLearnMiscJFABaseObject* self, PyObject* value, void*){
+ BOB_TRY
+ PyBlitzArrayObject* o;
+ if (!PyBlitzArray_Converter(value, &o)){
+ PyErr_Format(PyExc_RuntimeError, "%s %s expects a 2D array of floats", Py_TYPE(self)->tp_name, V.name());
+ return -1;
+ }
+ auto o_ = make_safe(o);
+ auto b = PyBlitzArrayCxx_AsBlitz<double,2>(o, "v");
+ if (!b) return -1;
+ self->cxx->setV(*b);
+ return 0;
+ BOB_CATCH_MEMBER("``v`` matrix could not be set", -1)
+}
+
+
+/***** d *****/
+static auto D = bob::extension::VariableDoc(
+ "d",
+ "array_like <float, 1D>",
+ "Returns the diagonal matrix diag(d) (as a 1D vector)",
+ ""
+);
+PyObject* PyBobLearnMiscJFABase_getD(PyBobLearnMiscJFABaseObject* self, void*){
+ BOB_TRY
+ return PyBlitzArrayCxx_AsConstNumpy(self->cxx->getD());
+ BOB_CATCH_MEMBER("``d`` could not be read", 0)
+}
+int PyBobLearnMiscJFABase_setD(PyBobLearnMiscJFABaseObject* self, PyObject* value, void*){
+ BOB_TRY
+ PyBlitzArrayObject* o;
+ if (!PyBlitzArray_Converter(value, &o)){
+ PyErr_Format(PyExc_RuntimeError, "%s %s expects a 1D array of floats", Py_TYPE(self)->tp_name, D.name());
+ return -1;
+ }
+ auto o_ = make_safe(o);
+ auto b = PyBlitzArrayCxx_AsBlitz<double,1>(o, "d");
+ if (!b) return -1;
+ self->cxx->setD(*b);
+ return 0;
+ BOB_CATCH_MEMBER("``d`` matrix could not be set", -1)
+}
+
+
+/***** ubm *****/
+static auto ubm = bob::extension::VariableDoc(
+ "ubm",
+ ":py:class:`bob.learn.misc.GMMMachine`",
+ "Returns the UBM (Universal Background Model",
+ ""
+);
+PyObject* PyBobLearnMiscJFABase_getUBM(PyBobLearnMiscJFABaseObject* self, void*){
+ BOB_TRY
+
+ boost::shared_ptr<bob::learn::misc::GMMMachine> ubm_gmmMachine = self->cxx->getUbm();
+
+ //Allocating the correspondent python object
+ PyBobLearnMiscGMMMachineObject* retval =
+ (PyBobLearnMiscGMMMachineObject*)PyBobLearnMiscGMMMachine_Type.tp_alloc(&PyBobLearnMiscGMMMachine_Type, 0);
+ retval->cxx = ubm_gmmMachine;
+
+ return Py_BuildValue("O",retval);
+ BOB_CATCH_MEMBER("ubm could not be read", 0)
+}
+int PyBobLearnMiscJFABase_setUBM(PyBobLearnMiscJFABaseObject* self, PyObject* value, void*){
+ BOB_TRY
+
+ if (!PyBobLearnMiscGMMMachine_Check(value)){
+ PyErr_Format(PyExc_RuntimeError, "%s %s expects a :py:class:`bob.learn.misc.GMMMachine`", Py_TYPE(self)->tp_name, ubm.name());
+ return -1;
+ }
+
+ PyBobLearnMiscGMMMachineObject* ubm_gmmMachine = 0;
+ PyArg_Parse(value, "O!", &PyBobLearnMiscGMMMachine_Type,&ubm_gmmMachine);
+
+ self->cxx->setUbm(ubm_gmmMachine->cxx);
+
+ return 0;
+ BOB_CATCH_MEMBER("ubm could not be set", -1)
+}
+
+
+
+
+static PyGetSetDef PyBobLearnMiscJFABase_getseters[] = {
+ {
+ shape.name(),
+ (getter)PyBobLearnMiscJFABase_getShape,
+ 0,
+ shape.doc(),
+ 0
+ },
+
+ {
+ supervector_length.name(),
+ (getter)PyBobLearnMiscJFABase_getSupervectorLength,
+ 0,
+ supervector_length.doc(),
+ 0
+ },
+
+ {
+ U.name(),
+ (getter)PyBobLearnMiscJFABase_getU,
+ (setter)PyBobLearnMiscJFABase_setU,
+ U.doc(),
+ 0
+ },
+
+ {
+ V.name(),
+ (getter)PyBobLearnMiscJFABase_getV,
+ (setter)PyBobLearnMiscJFABase_setV,
+ V.doc(),
+ 0
+ },
+
+ {
+ D.name(),
+ (getter)PyBobLearnMiscJFABase_getD,
+ (setter)PyBobLearnMiscJFABase_setD,
+ D.doc(),
+ 0
+ },
+
+ {
+ ubm.name(),
+ (getter)PyBobLearnMiscJFABase_getUBM,
+ (setter)PyBobLearnMiscJFABase_setUBM,
+ ubm.doc(),
+ 0
+ },
+
+
+ {0} // Sentinel
+};
+
+
+/******************************************************************/
+/************ Functions Section ***********************************/
+/******************************************************************/
+
+
+/*** save ***/
+static auto save = bob::extension::FunctionDoc(
+ "save",
+ "Save the configuration of the JFABase to a given HDF5 file"
+)
+.add_prototype("hdf5")
+.add_parameter("hdf5", ":py:class:`bob.io.base.HDF5File`", "An HDF5 file open for writing");
+static PyObject* PyBobLearnMiscJFABase_Save(PyBobLearnMiscJFABaseObject* self, PyObject* args, PyObject* kwargs) {
+
+ BOB_TRY
+
+ // get list of arguments
+ char** kwlist = save.kwlist(0);
+ PyBobIoHDF5FileObject* hdf5;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, PyBobIoHDF5File_Converter, &hdf5)) return 0;
+
+ auto hdf5_ = make_safe(hdf5);
+ self->cxx->save(*hdf5->f);
+
+ BOB_CATCH_MEMBER("cannot save the data", 0)
+ Py_RETURN_NONE;
+}
+
+/*** load ***/
+static auto load = bob::extension::FunctionDoc(
+ "load",
+ "Load the configuration of the JFABase to a given HDF5 file"
+)
+.add_prototype("hdf5")
+.add_parameter("hdf5", ":py:class:`bob.io.base.HDF5File`", "An HDF5 file open for reading");
+static PyObject* PyBobLearnMiscJFABase_Load(PyBobLearnMiscJFABaseObject* self, PyObject* args, PyObject* kwargs) {
+ BOB_TRY
+
+ char** kwlist = load.kwlist(0);
+ PyBobIoHDF5FileObject* hdf5;
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O&", kwlist, PyBobIoHDF5File_Converter, &hdf5)) return 0;
+
+ auto hdf5_ = make_safe(hdf5);
+ self->cxx->load(*hdf5->f);
+
+ BOB_CATCH_MEMBER("cannot load the data", 0)
+ Py_RETURN_NONE;
+}
+
+
+/*** is_similar_to ***/
+static auto is_similar_to = bob::extension::FunctionDoc(
+ "is_similar_to",
+
+ "Compares this JFABase with the ``other`` one to be approximately the same.",
+ "The optional values ``r_epsilon`` and ``a_epsilon`` refer to the "
+ "relative and absolute precision for the ``weights``, ``biases`` "
+ "and any other values internal to this machine."
+)
+.add_prototype("other, [r_epsilon], [a_epsilon]","output")
+.add_parameter("other", ":py:class:`bob.learn.misc.JFABase`", "A JFABase object to be compared.")
+.add_parameter("r_epsilon", "float", "Relative precision.")
+.add_parameter("a_epsilon", "float", "Absolute precision.")
+.add_return("output","bool","True if it is similar, otherwise false.");
+static PyObject* PyBobLearnMiscJFABase_IsSimilarTo(PyBobLearnMiscJFABaseObject* self, PyObject* args, PyObject* kwds) {
+
+ /* Parses input arguments in a single shot */
+ char** kwlist = is_similar_to.kwlist(0);
+
+ //PyObject* other = 0;
+ PyBobLearnMiscJFABaseObject* other = 0;
+ double r_epsilon = 1.e-5;
+ double a_epsilon = 1.e-8;
+
+ if (!PyArg_ParseTupleAndKeywords(args, kwds, "O!|dd", kwlist,
+ &PyBobLearnMiscJFABase_Type, &other,
+ &r_epsilon, &a_epsilon)){
+
+ is_similar_to.print_usage();
+ return 0;
+ }
+
+ if (self->cxx->is_similar_to(*other->cxx, r_epsilon, a_epsilon))
+ Py_RETURN_TRUE;
+ else
+ Py_RETURN_FALSE;
+}
+
+
+/*** resize ***/
+static auto resize = bob::extension::FunctionDoc(
+ "resize",
+ "Allocates space for the statistics and resets to zero.",
+ 0,
+ true
+)
+.add_prototype("n_gaussians,n_inputs")
+.add_parameter("n_gaussians", "int", "Number of gaussians")
+.add_parameter("n_inputs", "int", "Dimensionality of the feature vector");
+static PyObject* PyBobLearnMiscJFABase_resize(PyBobLearnMiscJFABaseObject* self, PyObject* args, PyObject* kwargs) {
+ BOB_TRY
+
+ /* Parses input arguments in a single shot */
+ char** kwlist = resize.kwlist(0);
+
+ int n_gaussians = 0;
+ int n_inputs = 0;
+
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "ii", kwlist, &n_gaussians, &n_inputs)) Py_RETURN_NONE;
+
+ if (n_gaussians <= 0){
+ PyErr_Format(PyExc_TypeError, "n_gaussians must be greater than zero");
+ resize.print_usage();
+ return 0;
+ }
+ if (n_inputs <= 0){
+ PyErr_Format(PyExc_TypeError, "n_inputs must be greater than zero");
+ resize.print_usage();
+ return 0;
+ }
+ self->cxx->resize(n_gaussians, n_inputs);
+
+ BOB_CATCH_MEMBER("cannot perform the resize method", 0)
+
+ Py_RETURN_NONE;
+}
+
+
+
+
+static PyMethodDef PyBobLearnMiscJFABase_methods[] = {
+ {
+ save.name(),
+ (PyCFunction)PyBobLearnMiscJFABase_Save,
+ METH_VARARGS|METH_KEYWORDS,
+ save.doc()
+ },
+ {
+ load.name(),
+ (PyCFunction)PyBobLearnMiscJFABase_Load,
+ METH_VARARGS|METH_KEYWORDS,
+ load.doc()
+ },
+ {
+ is_similar_to.name(),
+ (PyCFunction)PyBobLearnMiscJFABase_IsSimilarTo,
+ METH_VARARGS|METH_KEYWORDS,
+ is_similar_to.doc()
+ },
+ {
+ resize.name(),
+ (PyCFunction)PyBobLearnMiscJFABase_resize,
+ METH_VARARGS|METH_KEYWORDS,
+ resize.doc()
+ },
+
+ {0} /* Sentinel */
+};
+
+
+/******************************************************************/
+/************ Module Section **************************************/
+/******************************************************************/
+
+// Define the JFA type struct; will be initialized later
+PyTypeObject PyBobLearnMiscJFABase_Type = {
+ PyVarObject_HEAD_INIT(0,0)
+ 0
+};
+
+bool init_BobLearnMiscJFABase(PyObject* module)
+{
+ // initialize the type struct
+ PyBobLearnMiscJFABase_Type.tp_name = JFABase_doc.name();
+ PyBobLearnMiscJFABase_Type.tp_basicsize = sizeof(PyBobLearnMiscJFABaseObject);
+ PyBobLearnMiscJFABase_Type.tp_flags = Py_TPFLAGS_DEFAULT;
+ PyBobLearnMiscJFABase_Type.tp_doc = JFABase_doc.doc();
+
+ // set the functions
+ PyBobLearnMiscJFABase_Type.tp_new = PyType_GenericNew;
+ PyBobLearnMiscJFABase_Type.tp_init = reinterpret_cast<initproc>(PyBobLearnMiscJFABase_init);
+ PyBobLearnMiscJFABase_Type.tp_dealloc = reinterpret_cast<destructor>(PyBobLearnMiscJFABase_delete);
+ PyBobLearnMiscJFABase_Type.tp_richcompare = reinterpret_cast<richcmpfunc>(PyBobLearnMiscJFABase_RichCompare);
+ PyBobLearnMiscJFABase_Type.tp_methods = PyBobLearnMiscJFABase_methods;
+ PyBobLearnMiscJFABase_Type.tp_getset = PyBobLearnMiscJFABase_getseters;
+ //PyBobLearnMiscJFABase_Type.tp_call = reinterpret_cast<ternaryfunc>(PyBobLearnMiscJFABase_forward);
+
+
+ // check that everything is fine
+ if (PyType_Ready(&PyBobLearnMiscJFABase_Type) < 0) return false;
+
+ // add the type to the module
+ Py_INCREF(&PyBobLearnMiscJFABase_Type);
+ return PyModule_AddObject(module, "JFABase", (PyObject*)&PyBobLearnMiscJFABase_Type) >= 0;
+}
+
diff --git a/bob/learn/misc/main.cpp b/bob/learn/misc/main.cpp
index 94b84f9..6456cdc 100644
--- a/bob/learn/misc/main.cpp
+++ b/bob/learn/misc/main.cpp
@@ -48,6 +48,7 @@ static PyObject* create_module (void) {
if (!init_BobLearnMiscGMMBaseTrainer(module)) return 0;
if (!init_BobLearnMiscMLGMMTrainer(module)) return 0;
if (!init_BobLearnMiscMAPGMMTrainer(module)) return 0;
+ if (!init_BobLearnMiscJFABase(module)) return 0;
static void* PyBobLearnMisc_API[PyBobLearnMisc_API_pointers];
diff --git a/bob/learn/misc/main.h b/bob/learn/misc/main.h
index a908307..64b501e 100644
--- a/bob/learn/misc/main.h
+++ b/bob/learn/misc/main.h
@@ -27,6 +27,8 @@
#include <bob.learn.misc/ML_GMMTrainer.h>
#include <bob.learn.misc/MAP_GMMTrainer.h>
+#include <bob.learn.misc/JFABase.h>
+
#if PY_VERSION_HEX >= 0x03000000
#define PyInt_Check PyLong_Check
@@ -154,4 +156,15 @@ bool init_BobLearnMiscMAPGMMTrainer(PyObject* module);
int PyBobLearnMiscMAPGMMTrainer_Check(PyObject* o);
+// JFABase
+typedef struct {
+ PyObject_HEAD
+ boost::shared_ptr<bob::learn::misc::JFABase> cxx;
+} PyBobLearnMiscJFABaseObject;
+
+extern PyTypeObject PyBobLearnMiscJFABase_Type;
+bool init_BobLearnMiscJFABase(PyObject* module);
+int PyBobLearnMiscJFABase_Check(PyObject* o);
+
+
#endif // BOB_LEARN_EM_MAIN_H
--
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