Adding upstream version 1.44.3.upstream/1.44.3upstream

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 827 insertions, 0 deletions

diff --git a/ml/dlib/tools/python/src/sequence_segmenter.cpp b/ml/dlib/tools/python/src/sequence_segmenter.cpp
new file mode 100644
index 00000000..9fde1e77
--- /dev/null
+++ b/ml/dlib/tools/python/src/sequence_segmenter.cpp
@@ -0,0 +1,827 @@
+// Copyright (C) 2013  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+
+#include "opaque_types.h"
+#include <dlib/python.h>
+#include <dlib/matrix.h>
+#include <dlib/svm_threaded.h>
+
+using namespace dlib;
+using namespace std;
+namespace py = pybind11;
+
+typedef matrix<double,0,1> dense_vect; 
+typedef std::vector<std::pair<unsigned long,double> > sparse_vect;
+typedef std::vector<std::pair<unsigned long, unsigned long> > ranges;
+
+// ----------------------------------------------------------------------------------------
+
+template <typename samp_type, bool BIO, bool high_order, bool nonnegative>
+class segmenter_feature_extractor
+{
+
+public:
+    typedef std::vector<samp_type> sequence_type;
+    const static bool use_BIO_model = BIO;
+    const static bool use_high_order_features = high_order;
+    const static bool allow_negative_weights = nonnegative;
+
+
+    unsigned long _num_features;
+    unsigned long _window_size;
+
+    segmenter_feature_extractor(
+    ) : _num_features(1), _window_size(1) {}
+
+    segmenter_feature_extractor(
+        unsigned long _num_features_,
+        unsigned long _window_size_
+    ) : _num_features(_num_features_), _window_size(_window_size_) {}
+
+    unsigned long num_features(
+    ) const { return _num_features; }
+
+    unsigned long window_size(
+    ) const {return _window_size; }
+
+    template <typename feature_setter>
+    void get_features (
+        feature_setter& set_feature,
+        const std::vector<dense_vect>& x,
+        unsigned long position
+    ) const
+    {
+        for (long i = 0; i < x[position].size(); ++i)
+        {
+            set_feature(i, x[position](i));
+        }
+    }
+
+    template <typename feature_setter>
+    void get_features (
+        feature_setter& set_feature,
+        const std::vector<sparse_vect>& x,
+        unsigned long position
+    ) const
+    {
+        for (unsigned long i = 0; i < x[position].size(); ++i)
+        {
+            set_feature(x[position][i].first, x[position][i].second);
+        }
+    }
+
+    friend void serialize(const segmenter_feature_extractor& item, std::ostream& out)
+    {
+        dlib::serialize(item._num_features, out);
+        dlib::serialize(item._window_size, out);
+    }
+    friend void deserialize(segmenter_feature_extractor& item, std::istream& in)
+    {
+        dlib::deserialize(item._num_features, in);
+        dlib::deserialize(item._window_size, in);
+    }
+};
+
+// ----------------------------------------------------------------------------------------
+
+struct segmenter_type
+{
+    /*!
+        WHAT THIS OBJECT REPRESENTS
+            This the object that python will use directly to represent a
+            sequence_segmenter.  All it does is contain all the possible template
+            instantiations of a sequence_segmenter and invoke the right one depending on
+            the mode variable. 
+    !*/
+
+    segmenter_type() : mode(-1)
+    { }
+
+    ranges segment_sequence_dense (
+        const std::vector<dense_vect>& x
+    ) const 
+    {
+        switch (mode)
+        {
+            case 0: return segmenter0(x);
+            case 1: return segmenter1(x);
+            case 2: return segmenter2(x);
+            case 3: return segmenter3(x);
+            case 4: return segmenter4(x);
+            case 5: return segmenter5(x);
+            case 6: return segmenter6(x);
+            case 7: return segmenter7(x);
+            default: throw dlib::error("Invalid mode");
+        }
+    }
+
+    ranges segment_sequence_sparse (
+        const std::vector<sparse_vect>& x
+    ) const 
+    {
+        switch (mode)
+        {
+            case 8: return segmenter8(x);
+            case 9: return segmenter9(x);
+            case 10: return segmenter10(x);
+            case 11: return segmenter11(x);
+            case 12: return segmenter12(x);
+            case 13: return segmenter13(x);
+            case 14: return segmenter14(x);
+            case 15: return segmenter15(x);
+            default: throw dlib::error("Invalid mode");
+        }
+    }
+
+    const matrix<double,0,1> get_weights() 
+    { 
+        switch(mode)
+        {
+            case 0: return segmenter0.get_weights(); 
+            case 1: return segmenter1.get_weights(); 
+            case 2: return segmenter2.get_weights(); 
+            case 3: return segmenter3.get_weights(); 
+            case 4: return segmenter4.get_weights(); 
+            case 5: return segmenter5.get_weights(); 
+            case 6: return segmenter6.get_weights(); 
+            case 7: return segmenter7.get_weights(); 
+
+            case 8: return segmenter8.get_weights(); 
+            case 9: return segmenter9.get_weights(); 
+            case 10: return segmenter10.get_weights(); 
+            case 11: return segmenter11.get_weights(); 
+            case 12: return segmenter12.get_weights(); 
+            case 13: return segmenter13.get_weights(); 
+            case 14: return segmenter14.get_weights(); 
+            case 15: return segmenter15.get_weights(); 
+
+            default: throw dlib::error("Invalid mode");
+        }
+    }
+
+    friend void serialize (const segmenter_type& item, std::ostream& out) 
+    {
+        serialize(item.mode, out);
+        switch(item.mode)
+        {
+            case 0: serialize(item.segmenter0, out); break;
+            case 1: serialize(item.segmenter1, out); break;
+            case 2: serialize(item.segmenter2, out); break;
+            case 3: serialize(item.segmenter3, out); break;
+            case 4: serialize(item.segmenter4, out); break;
+            case 5: serialize(item.segmenter5, out); break;
+            case 6: serialize(item.segmenter6, out); break;
+            case 7: serialize(item.segmenter7, out); break;
+
+            case 8: serialize(item.segmenter8, out); break;
+            case 9: serialize(item.segmenter9, out); break;
+            case 10: serialize(item.segmenter10, out); break;
+            case 11: serialize(item.segmenter11, out); break;
+            case 12: serialize(item.segmenter12, out); break;
+            case 13: serialize(item.segmenter13, out); break;
+            case 14: serialize(item.segmenter14, out); break;
+            case 15: serialize(item.segmenter15, out); break;
+            default: throw dlib::error("Invalid mode");
+        }
+    }
+    friend void deserialize (segmenter_type& item, std::istream& in)
+    {
+        deserialize(item.mode, in);
+        switch(item.mode)
+        {
+            case 0: deserialize(item.segmenter0, in); break;
+            case 1: deserialize(item.segmenter1, in); break;
+            case 2: deserialize(item.segmenter2, in); break;
+            case 3: deserialize(item.segmenter3, in); break;
+            case 4: deserialize(item.segmenter4, in); break;
+            case 5: deserialize(item.segmenter5, in); break;
+            case 6: deserialize(item.segmenter6, in); break;
+            case 7: deserialize(item.segmenter7, in); break;
+
+            case 8: deserialize(item.segmenter8, in); break;
+            case 9: deserialize(item.segmenter9, in); break;
+            case 10: deserialize(item.segmenter10, in); break;
+            case 11: deserialize(item.segmenter11, in); break;
+            case 12: deserialize(item.segmenter12, in); break;
+            case 13: deserialize(item.segmenter13, in); break;
+            case 14: deserialize(item.segmenter14, in); break;
+            case 15: deserialize(item.segmenter15, in); break;
+            default: throw dlib::error("Invalid mode");
+        }
+    }
+
+    int mode;
+
+    typedef segmenter_feature_extractor<dense_vect, false,false,false> fe0;
+    typedef segmenter_feature_extractor<dense_vect, false,false,true>  fe1;
+    typedef segmenter_feature_extractor<dense_vect, false,true, false> fe2;
+    typedef segmenter_feature_extractor<dense_vect, false,true, true>  fe3;
+    typedef segmenter_feature_extractor<dense_vect, true, false,false> fe4;
+    typedef segmenter_feature_extractor<dense_vect, true, false,true>  fe5;
+    typedef segmenter_feature_extractor<dense_vect, true, true, false> fe6;
+    typedef segmenter_feature_extractor<dense_vect, true, true, true>  fe7;
+    sequence_segmenter<fe0> segmenter0;
+    sequence_segmenter<fe1> segmenter1;
+    sequence_segmenter<fe2> segmenter2;
+    sequence_segmenter<fe3> segmenter3;
+    sequence_segmenter<fe4> segmenter4;
+    sequence_segmenter<fe5> segmenter5;
+    sequence_segmenter<fe6> segmenter6;
+    sequence_segmenter<fe7> segmenter7;
+
+    typedef segmenter_feature_extractor<sparse_vect, false,false,false> fe8;
+    typedef segmenter_feature_extractor<sparse_vect, false,false,true>  fe9;
+    typedef segmenter_feature_extractor<sparse_vect, false,true, false> fe10;
+    typedef segmenter_feature_extractor<sparse_vect, false,true, true>  fe11;
+    typedef segmenter_feature_extractor<sparse_vect, true, false,false> fe12;
+    typedef segmenter_feature_extractor<sparse_vect, true, false,true>  fe13;
+    typedef segmenter_feature_extractor<sparse_vect, true, true, false> fe14;
+    typedef segmenter_feature_extractor<sparse_vect, true, true, true>  fe15;
+    sequence_segmenter<fe8> segmenter8;
+    sequence_segmenter<fe9> segmenter9;
+    sequence_segmenter<fe10> segmenter10;
+    sequence_segmenter<fe11> segmenter11;
+    sequence_segmenter<fe12> segmenter12;
+    sequence_segmenter<fe13> segmenter13;
+    sequence_segmenter<fe14> segmenter14;
+    sequence_segmenter<fe15> segmenter15;
+};
+
+
+// ----------------------------------------------------------------------------------------
+
+struct segmenter_params
+{
+    segmenter_params()
+    {
+        use_BIO_model = true;
+        use_high_order_features = true;
+        allow_negative_weights = true;
+        window_size = 5;
+        num_threads = 4;
+        epsilon = 0.1;
+        max_cache_size = 40;
+        be_verbose = false;
+        C = 100;
+    }
+
+    bool use_BIO_model;
+    bool use_high_order_features;
+    bool allow_negative_weights;
+    unsigned long window_size;
+    unsigned long num_threads;
+    double epsilon;
+    unsigned long max_cache_size;
+    bool be_verbose;
+    double C;
+};
+
+
+string segmenter_params__str__(const segmenter_params& p)
+{
+    ostringstream sout;
+    if (p.use_BIO_model)
+        sout << "BIO,";
+    else
+        sout << "BILOU,";
+
+    if (p.use_high_order_features)
+        sout << "highFeats,";
+    else
+        sout << "lowFeats,";
+
+    if (p.allow_negative_weights)
+        sout << "signed,";
+    else
+        sout << "non-negative,";
+
+    sout << "win="<<p.window_size << ",";
+    sout << "threads="<<p.num_threads << ",";
+    sout << "eps="<<p.epsilon << ",";
+    sout << "cache="<<p.max_cache_size << ",";
+    if (p.be_verbose)
+        sout << "verbose,";
+    else
+        sout << "non-verbose,";
+    sout << "C="<<p.C;
+    return trim(sout.str());
+}
+
+string segmenter_params__repr__(const segmenter_params& p)
+{
+    ostringstream sout;
+    sout << "<";
+    sout << segmenter_params__str__(p);
+    sout << ">";
+    return sout.str();
+}
+
+void serialize ( const segmenter_params& item, std::ostream& out)
+{
+    serialize(item.use_BIO_model, out);
+    serialize(item.use_high_order_features, out);
+    serialize(item.allow_negative_weights, out);
+    serialize(item.window_size, out);
+    serialize(item.num_threads, out);
+    serialize(item.epsilon, out);
+    serialize(item.max_cache_size, out);
+    serialize(item.be_verbose, out);
+    serialize(item.C, out);
+}
+
+void deserialize (segmenter_params& item, std::istream& in)
+{
+    deserialize(item.use_BIO_model, in);
+    deserialize(item.use_high_order_features, in);
+    deserialize(item.allow_negative_weights, in);
+    deserialize(item.window_size, in);
+    deserialize(item.num_threads, in);
+    deserialize(item.epsilon, in);
+    deserialize(item.max_cache_size, in);
+    deserialize(item.be_verbose, in);
+    deserialize(item.C, in);
+}
+
+// ----------------------------------------------------------------------------------------
+
+template <typename T>
+void configure_trainer (
+    const std::vector<std::vector<dense_vect> >& samples,
+    structural_sequence_segmentation_trainer<T>& trainer,
+    const segmenter_params& params
+)
+{
+    pyassert(samples.size() != 0, "Invalid arguments.  You must give some training sequences.");
+    pyassert(samples[0].size() != 0, "Invalid arguments. You can't have zero length training sequences.");
+    pyassert(params.window_size != 0, "Invalid window_size parameter, it must be > 0.");
+    pyassert(params.epsilon > 0, "Invalid epsilon parameter, it must be > 0.");
+    pyassert(params.C > 0, "Invalid C parameter, it must be > 0.");
+    const long dims = samples[0][0].size();
+
+    trainer = structural_sequence_segmentation_trainer<T>(T(dims, params.window_size));
+    trainer.set_num_threads(params.num_threads);
+    trainer.set_epsilon(params.epsilon);
+    trainer.set_max_cache_size(params.max_cache_size);
+    trainer.set_c(params.C);
+    if (params.be_verbose)
+        trainer.be_verbose();
+}
+
+// ----------------------------------------------------------------------------------------
+
+template <typename T>
+void configure_trainer (
+    const std::vector<std::vector<sparse_vect> >& samples,
+    structural_sequence_segmentation_trainer<T>& trainer,
+    const segmenter_params& params
+)
+{
+    pyassert(samples.size() != 0, "Invalid arguments.  You must give some training sequences.");
+    pyassert(samples[0].size() != 0, "Invalid arguments. You can't have zero length training sequences.");
+
+    unsigned long dims = 0;
+    for (unsigned long i = 0; i < samples.size(); ++i)
+    {
+        dims = std::max(dims, max_index_plus_one(samples[i]));
+    }
+
+    trainer = structural_sequence_segmentation_trainer<T>(T(dims, params.window_size));
+    trainer.set_num_threads(params.num_threads);
+    trainer.set_epsilon(params.epsilon);
+    trainer.set_max_cache_size(params.max_cache_size);
+    trainer.set_c(params.C);
+    if (params.be_verbose)
+        trainer.be_verbose();
+}
+
+// ----------------------------------------------------------------------------------------
+
+segmenter_type train_dense (
+    const std::vector<std::vector<dense_vect> >& samples,
+    const std::vector<ranges>& segments,
+    segmenter_params params
+)
+{
+    pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs");
+
+    int mode = 0;
+    if (params.use_BIO_model)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+    if (params.use_high_order_features)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+    if (params.allow_negative_weights)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+
+
+    segmenter_type res;
+    res.mode = mode;
+    switch(mode)
+    {
+        case 0: { structural_sequence_segmentation_trainer<segmenter_type::fe0> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter0 = trainer.train(samples, segments);
+                } break;
+        case 1: { structural_sequence_segmentation_trainer<segmenter_type::fe1> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter1 = trainer.train(samples, segments);
+                } break;
+        case 2: { structural_sequence_segmentation_trainer<segmenter_type::fe2> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter2 = trainer.train(samples, segments);
+                } break;
+        case 3: { structural_sequence_segmentation_trainer<segmenter_type::fe3> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter3 = trainer.train(samples, segments);
+                } break;
+        case 4: { structural_sequence_segmentation_trainer<segmenter_type::fe4> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter4 = trainer.train(samples, segments);
+                } break;
+        case 5: { structural_sequence_segmentation_trainer<segmenter_type::fe5> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter5 = trainer.train(samples, segments);
+                } break;
+        case 6: { structural_sequence_segmentation_trainer<segmenter_type::fe6> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter6 = trainer.train(samples, segments);
+                } break;
+        case 7: { structural_sequence_segmentation_trainer<segmenter_type::fe7> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter7 = trainer.train(samples, segments);
+                } break;
+        default: throw dlib::error("Invalid mode");
+    }
+
+
+    return res;
+}
+
+// ----------------------------------------------------------------------------------------
+
+segmenter_type train_sparse (
+    const std::vector<std::vector<sparse_vect> >& samples,
+    const std::vector<ranges>& segments,
+    segmenter_params params
+)
+{
+    pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs");
+
+    int mode = 0;
+    if (params.use_BIO_model)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+    if (params.use_high_order_features)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+    if (params.allow_negative_weights)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+
+    mode += 8;
+
+    segmenter_type res;
+    res.mode = mode;
+    switch(mode)
+    {
+        case 8: { structural_sequence_segmentation_trainer<segmenter_type::fe8> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter8 = trainer.train(samples, segments);
+                } break;
+        case 9: { structural_sequence_segmentation_trainer<segmenter_type::fe9> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter9 = trainer.train(samples, segments);
+                } break;
+        case 10: { structural_sequence_segmentation_trainer<segmenter_type::fe10> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter10 = trainer.train(samples, segments);
+                } break;
+        case 11: { structural_sequence_segmentation_trainer<segmenter_type::fe11> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter11 = trainer.train(samples, segments);
+                } break;
+        case 12: { structural_sequence_segmentation_trainer<segmenter_type::fe12> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter12 = trainer.train(samples, segments);
+                } break;
+        case 13: { structural_sequence_segmentation_trainer<segmenter_type::fe13> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter13 = trainer.train(samples, segments);
+                } break;
+        case 14: { structural_sequence_segmentation_trainer<segmenter_type::fe14> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter14 = trainer.train(samples, segments);
+                } break;
+        case 15: { structural_sequence_segmentation_trainer<segmenter_type::fe15> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res.segmenter15 = trainer.train(samples, segments);
+                } break;
+        default: throw dlib::error("Invalid mode");
+    }
+
+
+    return res;
+}
+
+// ----------------------------------------------------------------------------------------
+
+
+struct segmenter_test 
+{
+    double precision;
+    double recall;
+    double f1;
+};
+
+void serialize(const segmenter_test& item, std::ostream& out)
+{
+    serialize(item.precision, out);
+    serialize(item.recall, out);
+    serialize(item.f1, out);
+}
+
+void deserialize(segmenter_test& item, std::istream& in)
+{
+    deserialize(item.precision, in);
+    deserialize(item.recall, in);
+    deserialize(item.f1, in);
+}
+
+std::string segmenter_test__str__(const segmenter_test& item)
+{
+    std::ostringstream sout;
+    sout << "precision: "<< item.precision << "  recall: "<< item.recall << "  f1-score: " << item.f1; 
+    return sout.str();
+}
+std::string segmenter_test__repr__(const segmenter_test& item) { return "< " + segmenter_test__str__(item) + " >";}
+
+// ----------------------------------------------------------------------------------------
+
+const segmenter_test test_sequence_segmenter1 (
+    const segmenter_type& segmenter,
+    const std::vector<std::vector<dense_vect> >& samples,
+    const std::vector<ranges>& segments 
+)
+{
+    pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs");
+    matrix<double,1,3> res;
+    
+    switch(segmenter.mode)
+    {
+        case 0: res = test_sequence_segmenter(segmenter.segmenter0, samples, segments); break;
+        case 1: res = test_sequence_segmenter(segmenter.segmenter1, samples, segments); break;
+        case 2: res = test_sequence_segmenter(segmenter.segmenter2, samples, segments); break;
+        case 3: res = test_sequence_segmenter(segmenter.segmenter3, samples, segments); break;
+        case 4: res = test_sequence_segmenter(segmenter.segmenter4, samples, segments); break;
+        case 5: res = test_sequence_segmenter(segmenter.segmenter5, samples, segments); break;
+        case 6: res = test_sequence_segmenter(segmenter.segmenter6, samples, segments); break;
+        case 7: res = test_sequence_segmenter(segmenter.segmenter7, samples, segments); break;
+        default: throw dlib::error("Invalid mode");
+    }
+
+
+    segmenter_test temp;
+    temp.precision = res(0);
+    temp.recall = res(1);
+    temp.f1 = res(2);
+    return temp;
+}
+
+const segmenter_test test_sequence_segmenter2 (
+    const segmenter_type& segmenter,
+    const std::vector<std::vector<sparse_vect> >& samples,
+    const std::vector<ranges>& segments 
+)
+{
+    pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs");
+    matrix<double,1,3> res;
+    
+    switch(segmenter.mode)
+    {
+        case 8: res = test_sequence_segmenter(segmenter.segmenter8, samples, segments); break;
+        case 9: res = test_sequence_segmenter(segmenter.segmenter9, samples, segments); break;
+        case 10: res = test_sequence_segmenter(segmenter.segmenter10, samples, segments); break;
+        case 11: res = test_sequence_segmenter(segmenter.segmenter11, samples, segments); break;
+        case 12: res = test_sequence_segmenter(segmenter.segmenter12, samples, segments); break;
+        case 13: res = test_sequence_segmenter(segmenter.segmenter13, samples, segments); break;
+        case 14: res = test_sequence_segmenter(segmenter.segmenter14, samples, segments); break;
+        case 15: res = test_sequence_segmenter(segmenter.segmenter15, samples, segments); break;
+        default: throw dlib::error("Invalid mode");
+    }
+
+
+    segmenter_test temp;
+    temp.precision = res(0);
+    temp.recall = res(1);
+    temp.f1 = res(2);
+    return temp;
+}
+
+// ----------------------------------------------------------------------------------------
+
+const segmenter_test cross_validate_sequence_segmenter1 (
+    const std::vector<std::vector<dense_vect> >& samples,
+    const std::vector<ranges>& segments,
+    long folds,
+    segmenter_params params
+)
+{
+    pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs");
+    pyassert(1 < folds && folds <= static_cast<long>(samples.size()), "folds argument is outside the valid range.");
+
+    matrix<double,1,3> res;
+    
+    int mode = 0;
+    if (params.use_BIO_model)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+    if (params.use_high_order_features)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+    if (params.allow_negative_weights)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+
+
+    switch(mode)
+    {
+        case 0: { structural_sequence_segmentation_trainer<segmenter_type::fe0> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 1: { structural_sequence_segmentation_trainer<segmenter_type::fe1> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 2: { structural_sequence_segmentation_trainer<segmenter_type::fe2> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 3: { structural_sequence_segmentation_trainer<segmenter_type::fe3> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 4: { structural_sequence_segmentation_trainer<segmenter_type::fe4> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 5: { structural_sequence_segmentation_trainer<segmenter_type::fe5> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 6: { structural_sequence_segmentation_trainer<segmenter_type::fe6> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 7: { structural_sequence_segmentation_trainer<segmenter_type::fe7> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        default: throw dlib::error("Invalid mode");
+    }
+
+
+    segmenter_test temp;
+    temp.precision = res(0);
+    temp.recall = res(1);
+    temp.f1 = res(2);
+    return temp;
+}
+
+const segmenter_test cross_validate_sequence_segmenter2 (
+    const std::vector<std::vector<sparse_vect> >& samples,
+    const std::vector<ranges>& segments,
+    long folds,
+    segmenter_params params
+)
+{
+    pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs");
+    pyassert(1 < folds && folds <= static_cast<long>(samples.size()), "folds argument is outside the valid range.");
+
+    matrix<double,1,3> res;
+    
+    int mode = 0;
+    if (params.use_BIO_model)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+    if (params.use_high_order_features)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+    if (params.allow_negative_weights)
+        mode = mode*2 + 1;
+    else 
+        mode = mode*2;
+
+    mode += 8;
+
+    switch(mode)
+    {
+        case 8: { structural_sequence_segmentation_trainer<segmenter_type::fe8> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 9: { structural_sequence_segmentation_trainer<segmenter_type::fe9> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 10: { structural_sequence_segmentation_trainer<segmenter_type::fe10> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 11: { structural_sequence_segmentation_trainer<segmenter_type::fe11> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 12: { structural_sequence_segmentation_trainer<segmenter_type::fe12> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 13: { structural_sequence_segmentation_trainer<segmenter_type::fe13> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 14: { structural_sequence_segmentation_trainer<segmenter_type::fe14> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        case 15: { structural_sequence_segmentation_trainer<segmenter_type::fe15> trainer;
+                  configure_trainer(samples, trainer, params);
+                  res = cross_validate_sequence_segmenter(trainer, samples, segments, folds);
+                } break;
+        default: throw dlib::error("Invalid mode");
+    }
+
+
+    segmenter_test temp;
+    temp.precision = res(0);
+    temp.recall = res(1);
+    temp.f1 = res(2);
+    return temp;
+}
+
+// ----------------------------------------------------------------------------------------
+
+void bind_sequence_segmenter(py::module& m)
+{
+    py::class_<segmenter_params>(m, "segmenter_params",
+"This class is used to define all the optional parameters to the    \n\
+train_sequence_segmenter() and cross_validate_sequence_segmenter() routines.   ")
+        .def(py::init<>())
+        .def_readwrite("use_BIO_model", &segmenter_params::use_BIO_model)
+        .def_readwrite("use_high_order_features", &segmenter_params::use_high_order_features)
+        .def_readwrite("allow_negative_weights", &segmenter_params::allow_negative_weights)
+        .def_readwrite("window_size", &segmenter_params::window_size)
+        .def_readwrite("num_threads", &segmenter_params::num_threads)
+        .def_readwrite("epsilon", &segmenter_params::epsilon)
+        .def_readwrite("max_cache_size", &segmenter_params::max_cache_size)
+        .def_readwrite("C", &segmenter_params::C, "SVM C parameter")
+        .def_readwrite("be_verbose", &segmenter_params::be_verbose)
+        .def("__repr__",&segmenter_params__repr__)
+        .def("__str__",&segmenter_params__str__)
+        .def(py::pickle(&getstate<segmenter_params>, &setstate<segmenter_params>));
+
+    py::class_<segmenter_type> (m, "segmenter_type", "This object represents a sequence segmenter and is the type of object "
+        "returned by the dlib.train_sequence_segmenter() routine.")
+        .def("__call__", &segmenter_type::segment_sequence_dense)
+        .def("__call__", &segmenter_type::segment_sequence_sparse)
+        .def_property_readonly("weights", &segmenter_type::get_weights)
+        .def(py::pickle(&getstate<segmenter_type>, &setstate<segmenter_type>));
+
+    py::class_<segmenter_test> (m, "segmenter_test", "This object is the output of the dlib.test_sequence_segmenter() and "
+        "dlib.cross_validate_sequence_segmenter() routines.")
+        .def_readwrite("precision", &segmenter_test::precision)
+        .def_readwrite("recall", &segmenter_test::recall)
+        .def_readwrite("f1", &segmenter_test::f1)
+        .def("__repr__",&segmenter_test__repr__)
+        .def("__str__",&segmenter_test__str__)
+        .def(py::pickle(&getstate<segmenter_test>, &setstate<segmenter_test>));
+
+    m.def("train_sequence_segmenter", train_dense, py::arg("samples"), py::arg("segments"), py::arg("params")=segmenter_params());
+    m.def("train_sequence_segmenter", train_sparse, py::arg("samples"), py::arg("segments"), py::arg("params")=segmenter_params());
+
+
+    m.def("test_sequence_segmenter", test_sequence_segmenter1);
+    m.def("test_sequence_segmenter", test_sequence_segmenter2);
+
+    m.def("cross_validate_sequence_segmenter", cross_validate_sequence_segmenter1,
+        py::arg("samples"), py::arg("segments"), py::arg("folds"), py::arg("params")=segmenter_params());
+    m.def("cross_validate_sequence_segmenter", cross_validate_sequence_segmenter2,
+        py::arg("samples"), py::arg("segments"), py::arg("folds"), py::arg("params")=segmenter_params());
+}
+
+
+
+