Moved the new multiclass svm trainer into dlib. Still need to clean up the code

and setup the abstract file.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404195

dlib/svm.h

@@ -38,6 +38,7 @@
 #include "svm/one_vs_all_trainer.h"
 
 #include "svm/structural_svm_problem.h"
+#include "svm/svm_multiclass_linear_trainer.h"
 
 #endif // DLIB_SVm_HEADER
 

dlib/svm/svm_multiclass_linear_trainer.h

+// Copyright (C) 2011  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+#ifndef DLIB_SVm_MULTICLASS_LINEAR_TRAINER_H__ 
+#define DLIB_SVm_MULTICLASS_LINEAR_TRAINER_H__
+
+#include "svm_multiclass_linear_trainer_abstract.h"
+#include <vector>
+#include "../optimization/optimization_oca.h"
+#include "../matrix.h"
+#include "sparse_vector.h"
+
+namespace dlib
+{
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename matrix_type,
+        typename sample_type,
+        typename label_type
+        >
+    class multiclass_svm_problem : public structural_svm_problem<matrix_type,
+                                                                 std::vector<std::pair<unsigned long,typename matrix_type::type> > > 
+    {
+    public:
+        typedef typename matrix_type::type scalar_type;
+        typedef std::vector<std::pair<unsigned long,scalar_type> > feature_vector_type;
+
+        multiclass_svm_problem (
+            const std::vector<sample_type>& samples_,
+            const std::vector<label_type>& labels_
+        ) :
+            samples(samples_),
+            labels(labels_),
+            distinct_labels(select_all_distinct_labels(labels_)),
+            dims(sparse_vector::max_index_plus_one(samples_)+1) // +1 for the bias
+        {}
+
+        virtual long get_num_dimensions (
+        ) const
+        {
+            return dims*distinct_labels.size();
+        }
+
+        virtual long get_num_samples (
+        ) const 
+        {
+            return static_cast<long>(samples.size());
+        }
+
+        virtual void get_truth_joint_feature_vector (
+            long idx,
+            feature_vector_type& psi
+        ) const 
+        {
+            sparse_vector::assign(psi, samples[idx]);
+            // Add a constant -1 to account for the bias term.
+            psi.push_back(std::make_pair(dims-1,-1));
+
+            // Find which distinct label goes with this psi.
+            const long label_idx = index_of_max(vector_to_matrix(distinct_labels) == labels[idx]);
+
+            offset_feature_vector(psi, dims*label_idx);
+        }
+
+        virtual void separation_oracle (
+            const long idx,
+            const matrix_type& current_solution,
+            scalar_type& loss,
+            feature_vector_type& psi
+        ) const 
+        {
+            scalar_type best_val = -std::numeric_limits<scalar_type>::infinity();
+            unsigned long best_idx = 0;
+
+            // figure out which label is the best
+            for (unsigned long i = 0; i < distinct_labels.size(); ++i)
+            {
+                using sparse_vector::dot;
+                // perform: temp == dot(relevant part of current solution, samples[idx]) - current_bias
+                scalar_type temp = dot(rowm(current_solution, range(i*dims, (i+1)*dims-2)), samples[idx]) - current_solution((i+1)*dims-1);
+
+                if (labels[idx] != distinct_labels[i])
+                    temp += 1;
+
+                if (temp > best_val)
+                {
+                    best_val = temp;
+                    best_idx = i;
+                }
+            }
+
+            sparse_vector::assign(psi, samples[idx]);
+            // add a constant -1 to account for the bias term
+            psi.push_back(std::make_pair(dims-1,-1));
+
+            offset_feature_vector(psi, dims*best_idx);
+
+            if (distinct_labels[best_idx] == labels[idx])
+                loss = 0;
+            else
+                loss = 1;
+        }
+
+    private:
+
+        void offset_feature_vector (
+            feature_vector_type& sample,
+            const unsigned long val
+        ) const
+        {
+            if (val != 0)
+            {
+                for (typename feature_vector_type::iterator i = sample.begin(); i != sample.end(); ++i)
+                {
+                    i->first += val;
+                }
+            }
+        }
+
+
+        const std::vector<sample_type>& samples;
+        const std::vector<label_type>& labels;
+        const std::vector<label_type> distinct_labels;
+        const long dims;
+    };
+
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename K,
+        typename label_type_ = typename K::scalar_type 
+        >
+    class svm_multiclass_linear_trainer
+    {
+    public:
+        typedef label_type_ label_type;
+        typedef K kernel_type;
+        typedef typename kernel_type::scalar_type scalar_type;
+        typedef typename kernel_type::sample_type sample_type;
+        typedef typename kernel_type::mem_manager_type mem_manager_type;
+
+        typedef multiclass_linear_decision_function<kernel_type, label_type> trained_function_type;
+
+
+        trained_function_type train (
+            const std::vector<sample_type>& all_samples,
+            const std::vector<label_type>& all_labels
+        ) const
+        {
+            oca solver;
+            typedef matrix<scalar_type,0,1> w_type;
+            w_type weights;
+            multiclass_svm_problem<w_type, sample_type, label_type> problem(all_samples, all_labels);
+            problem.be_verbose();
+            problem.set_max_cache_size(0);
+            problem.set_c(100);
+
+            solver(problem, weights);
+
+            trained_function_type df;
+
+            const long dims = sparse_vector::max_index_plus_one(all_samples);
+            df.labels  = select_all_distinct_labels(all_labels);
+            df.weights = colm(reshape(weights, df.labels.size(), dims+1), range(0,dims-1));
+            df.b       = colm(reshape(weights, df.labels.size(), dims+1), dims);
+            return df;
+        }
+    };
+
+// ----------------------------------------------------------------------------------------
+
+}
+
+
+#endif // DLIB_SVm_MULTICLASS_LINEAR_TRAINER_H__
+