Added the ability to use a kernel cache to the batch_trainer object. I also

changed it so that it always calls clear() on the trainer it uses before it
begins training.  This way it always forgets the results of previous trainings.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403143

dlib/svm/pegasos.h

@@ -10,6 +10,7 @@
 #include "kernel.h"
 #include "kcentroid.h"
 #include <iostream>
+#include "../smart_pointers.h"
 
 namespace dlib
 {
@@ -30,6 +31,11 @@ namespace dlib
         typedef typename kernel_type::mem_manager_type mem_manager_type;
         typedef decision_function<kernel_type> trained_function_type;
 
+        template <typename K_>
+        struct rebind {
+            typedef svm_pegasos<K_> other;
+        };
+
         svm_pegasos (
         ) :
             max_sv(40),
@@ -255,6 +261,22 @@ namespace dlib
         svm_pegasos<K>& b
     ) { a.swap(b); }
 
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename T,
+        typename U
+        >
+    void replicate_settings (
+        const svm_pegasos<T>& source,
+        svm_pegasos<U>& dest
+    )
+    {
+        dest.set_tolerance(source.get_tolerance());
+        dest.set_lambda(source.get_lambda());
+        dest.set_max_num_sv(source.get_max_num_sv());
+    }
+
 // ----------------------------------------------------------------------------------------
 // ----------------------------------------------------------------------------------------
 // ----------------------------------------------------------------------------------------
@@ -264,6 +286,134 @@ namespace dlib
         >
     class batch_trainer 
     {
+
+    // ------------------------------------------------------------------------------------
+
+        template <
+            typename K,
+            typename sample_vector_type
+            >
+        class caching_kernel 
+        {
+        public:
+            typedef typename K::scalar_type scalar_type;
+            typedef long sample_type;
+            //typedef typename K::sample_type sample_type;
+            typedef typename K::mem_manager_type mem_manager_type;
+
+            caching_kernel () : samples(0), counter(0), counter_threshold(0) {}
+
+            caching_kernel (
+                const K& kern,
+                const sample_vector_type& samps,
+                long cache_size_
+            ) : real_kernel(kern), samples(&samps), counter(0)  
+            {
+                cache_size = std::min<long>(cache_size_, samps.size());
+
+                cache.reset(new cache_type);
+                cache->frequency_of_use.resize(samps.size());
+                for (unsigned long i = 0; i < samps.size(); ++i)
+                    cache->frequency_of_use[i] = std::make_pair(0, i);
+
+                // Set the cache build/rebuild threshold so that we have to have
+                // as many cache misses as there are entries in the cache before
+                // we build/rebuild.
+                counter_threshold = samps.size()*cache_size;
+                cache->sample_location.assign(samples->size(), -1);
+            }
+
+            scalar_type operator() (
+                const sample_type& a,
+                const sample_type& b
+            )  const
+            { 
+                const long a_loc = cache->sample_location[a];
+                const long b_loc = cache->sample_location[b];
+
+                cache->frequency_of_use[a].first += 1;
+                cache->frequency_of_use[b].first += 1;
+
+                // rebuild the cache every so often
+                if (counter > counter_threshold )
+                {
+                    build_cache();
+                }
+
+                if (a_loc != -1)
+                {
+                    return cache->kernel(a_loc, b);
+                }
+                else if (b_loc != -1)
+                {
+                    return cache->kernel(b_loc, a);
+                }
+                else
+                {
+                    ++counter;
+                    return real_kernel((*samples)(a), (*samples)(b));
+                }
+            }
+
+            bool operator== (
+                const caching_kernel& item
+            ) const
+            {
+                return item.real_kernel == real_kernel &&
+                    item.samples == samples;
+            }
+
+        private:
+            K real_kernel;
+
+            void build_cache (
+            ) const
+            {
+                std::sort(cache->frequency_of_use.rbegin(), cache->frequency_of_use.rend());
+                counter = 0;
+
+
+                cache->kernel.set_size(cache_size, samples->size());
+                cache->sample_location.assign(samples->size(), -1);
+
+                // loop over all the samples in the cache
+                for (unsigned long i = 0; i < cache_size; ++i)
+                {
+                    const long cur = cache->frequency_of_use[i].second;
+                    cache->sample_location[cur] = i;
+
+                    // now populate all possible kernel products with the current sample
+                    for (unsigned long j = 0; j < samples->size(); ++j)
+                    {
+                        cache->kernel(i, j) = real_kernel((*samples)(cur), (*samples)(j));
+                    }
+
+                }
+
+                // reset the frequency of use metrics
+                for (unsigned long i = 0; i < samples->size(); ++i)
+                    cache->frequency_of_use[i] = std::make_pair(0, i);
+            }
+
+
+            struct cache_type
+            {
+                matrix<float> kernel;  
+
+                std::vector<long> sample_location; // where in the cache a sample is.  -1 means not in cache
+                std::vector<std::pair<long,long> > frequency_of_use;  
+            };
+
+            const sample_vector_type* samples;
+
+            shared_ptr<cache_type> cache;
+            mutable unsigned long counter;
+            unsigned long counter_threshold;
+            long cache_size;
+        };
+
+    // ------------------------------------------------------------------------------------
+
     public:
         typedef typename trainer_type::kernel_type kernel_type;
         typedef typename trainer_type::scalar_type scalar_type;
@@ -274,25 +424,35 @@ namespace dlib
 
         batch_trainer (
         ) :
-            min_learning_rate(0.1)
+            min_learning_rate(0.1),
+            use_cache(false),
+            cache_size(100)
         {
         }
 
         batch_trainer (
             const trainer_type& trainer_, 
             const scalar_type min_learning_rate_,
-            bool verbose_
+            bool verbose_,
+            bool use_cache_,
+            long cache_size_ = 100
         ) :
             trainer(trainer_),
             min_learning_rate(min_learning_rate_),
-            verbose(verbose_)
+            verbose(verbose_),
+            use_cache(use_cache_),
+            cache_size(cache_size_)
         {
             // make sure requires clause is not broken
-            DLIB_ASSERT(0 < min_learning_rate_,
+            DLIB_ASSERT(0 < min_learning_rate_ &&
+                        cache_size_ > 0,
                         "\tbatch_trainer::batch_trainer()"
                         << "\n\t invalid inputs were given to this function"
                         << "\n\t min_learning_rate_: " << min_learning_rate_ 
+                        << "\n\t cache_size_: " << cache_size_ 
             );
+            
+            trainer.clear();
         }
 
         const scalar_type get_min_learning_rate (
@@ -310,7 +470,10 @@ namespace dlib
             const in_scalar_vector_type& y
         ) const
         {
-            return do_train(vector_to_matrix(x), vector_to_matrix(y));
+            if (use_cache)
+                return do_train_cached(vector_to_matrix(x), vector_to_matrix(y));
+            else
+                return do_train(vector_to_matrix(x), vector_to_matrix(y));
         }
 
     private:
@@ -344,7 +507,7 @@ namespace dlib
                 {
                     if ( (count&0x7FF) == 0)
                     {
-                        std::cout << "\rrbatch_trainer(): Percent complete: " 
+                        std::cout << "\rbatch_trainer(): Percent complete: " 
                                   << 100*min_learning_rate/cur_learning_rate << "             " << std::flush;
                     }
                     ++count;
@@ -365,10 +528,84 @@ namespace dlib
             }
         }
 
+        template <
+            typename in_sample_vector_type,
+            typename in_scalar_vector_type
+            >
+        const decision_function<kernel_type> do_train_cached (
+            const in_sample_vector_type& x,
+            const in_scalar_vector_type& y
+        ) const
+        {
+            typedef typename decision_function<kernel_type>::sample_vector_type sample_vector_type;
+            typedef typename decision_function<kernel_type>::scalar_vector_type scalar_vector_type;
+
+            dlib::rand::kernel_1a rnd;
+
+            // make a caching kernel
+            typedef caching_kernel<kernel_type, in_sample_vector_type> ckernel_type;
+            ckernel_type ck(trainer.get_kernel(), x, cache_size);
+
+            // now rebind the trainer to use the caching kernel
+            typename trainer_type::template rebind<ckernel_type>::other my_trainer;
+            my_trainer.set_kernel(ck);
+            replicate_settings(trainer, my_trainer);
+
+            scalar_type cur_learning_rate = min_learning_rate + 10;
+            unsigned long count = 0;
+
+            while (cur_learning_rate > min_learning_rate)
+            {
+                const long i = rnd.get_random_32bit_number()%x.size();
+                // keep feeding the trainer data until its learning rate goes below our threshold
+                cur_learning_rate = my_trainer.train(i, y(i));
+
+                if (verbose)
+                {
+                    if ( (count&0x7FF) == 0)
+                    {
+                        std::cout << "\rbatch_trainer(): Percent complete: " 
+                                  << 100*min_learning_rate/cur_learning_rate << "             " << std::flush;
+                    }
+                    ++count;
+                }
+            }
+
+            if (verbose)
+            {
+                decision_function<ckernel_type> cached_df;
+                cached_df = my_trainer.get_decision_function();
+
+                std::cout << "\rbatch_trainer(): Percent complete: 100           " << std::endl;
+                std::cout << "    Num sv: " << cached_df.support_vectors.size() << std::endl;
+                std::cout << "    bias:   " << cached_df.b << std::endl;
+
+                return decision_function<kernel_type> (
+                        cached_df.alpha,
+                        cached_df.b,
+                        trainer.get_kernel(),
+                        rowm(x, cached_df.support_vectors)
+                        );
+            }
+            else
+            {
+                decision_function<ckernel_type> cached_df;
+                cached_df = my_trainer.get_decision_function();
+
+                return decision_function<kernel_type> (
+                        cached_df.alpha,
+                        cached_df.b,
+                        trainer.get_kernel(),
+                        rowm(x, cached_df.support_vectors)
+                        );
+            }
+        }
 
         trainer_type trainer;
         scalar_type min_learning_rate;
         bool verbose;
+        bool use_cache;
+        long cache_size;
 
     }; // end of class batch_trainer
 
@@ -380,7 +617,7 @@ namespace dlib
     const batch_trainer<trainer_type> batch (
         const trainer_type& trainer,
         const typename trainer_type::scalar_type min_learning_rate = 0.1
-    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, false); }
+    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, false, false); }
 
 // ----------------------------------------------------------------------------------------
 
@@ -390,7 +627,29 @@ namespace dlib
     const batch_trainer<trainer_type> verbose_batch (
         const trainer_type& trainer,
         const typename trainer_type::scalar_type min_learning_rate = 0.1
-    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, true); }
+    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, true, false); }
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename trainer_type
+        >
+    const batch_trainer<trainer_type> batch_cached (
+        const trainer_type& trainer,
+        const typename trainer_type::scalar_type min_learning_rate = 0.1,
+        long cache_size = 100
+    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, false, true, cache_size); }
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename trainer_type
+        >
+    const batch_trainer<trainer_type> verbose_batch_cached (
+        const trainer_type& trainer,
+        const typename trainer_type::scalar_type min_learning_rate = 0.1,
+        long cache_size = 100
+    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, true, true, cache_size); }
 
 // ----------------------------------------------------------------------------------------
 

dlib/svm/pegasos_abstract.h

@@ -51,6 +51,11 @@ namespace dlib
         typedef typename kernel_type::mem_manager_type mem_manager_type;
         typedef decision_function<kernel_type> trained_function_type;
 
+        template <typename K_>
+        struct rebind {
+            typedef svm_pegasos<K_> other;
+        };
+
         svm_pegasos (
         );
         /*!
@@ -272,6 +277,24 @@ namespace dlib
         provides serialization support for svm_pegasos objects
     !*/
 
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename T,
+        typename U
+        >
+    void replicate_settings (
+        const svm_pegasos<T>& source,
+        svm_pegasos<U>& dest
+    );
+    /*!
+        ensures
+            - copies all the parameters from the source trainer to the dest trainer.
+            - #dest.get_tolerance() == source.get_tolerance()
+            - #dest.get_lambda() == source.get_lambda()
+            - #dest.get_max_num_sv() == source.get_max_num_sv()
+    !*/
+
 // ----------------------------------------------------------------------------------------
 // ----------------------------------------------------------------------------------------
 // ----------------------------------------------------------------------------------------
@@ -284,6 +307,7 @@ namespace dlib
         /*!
             REQUIREMENTS ON trainer_type
                 - trainer_type == some kind of online trainer object (e.g. svm_pegasos)
+                  replicate_settings() must also be defined for the type.
 
             WHAT THIS OBJECT REPRESENTS
                 This is a trainer object that is meant to wrap online trainer objects 
@@ -313,11 +337,14 @@ namespace dlib
         batch_trainer (
             const trainer_type& online_trainer, 
             const scalar_type min_learning_rate_,
-            bool verbose_
+            bool verbose_,
+            bool use_cache_,
+            long cache_size_ = 100
         );
         /*!
             requires
                 - min_learning_rate_ > 0
+                - cache_size_ > 0
             ensures
                 - returns a batch trainer object that uses the given online_trainer object
                   to train a decision function.
@@ -325,6 +352,9 @@ namespace dlib
                 - if (verbose_ == true) then
                     - this object will output status messages to standard out while
                       training is under way.
+                - if (use_cache_ == true) then
+                    - this object will cache up to cache_size_ columns of the kernel 
+                      matrix during the training process.
         !*/
 
         const scalar_type get_min_learning_rate (
@@ -364,12 +394,12 @@ namespace dlib
     const batch_trainer<trainer_type> batch (
         const trainer_type& trainer,
         const typename trainer_type::scalar_type min_learning_rate = 0.1
-    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, false); }
+    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, false, false); }
     /*!
         requires
             - min_learning_rate > 0
             - trainer_type == some kind of online trainer object that creates decision_function
-              objects (e.g. svm_pegasos)
+              objects (e.g. svm_pegasos).  replicate_settings() must also be defined for the type.
         ensures
             - returns a batch_trainer object that has been instantiated with the 
               given arguments.
@@ -383,12 +413,12 @@ namespace dlib
     const batch_trainer<trainer_type> verbose_batch (
         const trainer_type& trainer,
         const typename trainer_type::scalar_type min_learning_rate = 0.1
-    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, true); }
+    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, true, false); }
     /*!
         requires
             - min_learning_rate > 0
             - trainer_type == some kind of online trainer object that creates decision_function
-              objects (e.g. svm_pegasos)
+              objects (e.g. svm_pegasos).  replicate_settings() must also be defined for the type.
         ensures
             - returns a batch_trainer object that has been instantiated with the 
               given arguments (and is verbose).
@@ -396,6 +426,49 @@ namespace dlib
 
 // ----------------------------------------------------------------------------------------
 
+    template <
+        typename trainer_type
+        >
+    const batch_trainer<trainer_type> batch_cached (
+        const trainer_type& trainer,
+        const typename trainer_type::scalar_type min_learning_rate = 0.1,
+        long cache_size = 100
+    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, false, true, cache_size); }
+    /*!
+        requires
+            - min_learning_rate > 0
+            - cache_size > 0
+            - trainer_type == some kind of online trainer object that creates decision_function
+              objects (e.g. svm_pegasos).  replicate_settings() must also be defined for the type.
+        ensures
+            - returns a batch_trainer object that has been instantiated with the 
+              given arguments (uses a kernel cache).
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename trainer_type
+        >
+    const batch_trainer<trainer_type> verbose_batch_cached (
+        const trainer_type& trainer,
+        const typename trainer_type::scalar_type min_learning_rate = 0.1,
+        long cache_size = 100
+    ) { return batch_trainer<trainer_type>(trainer, min_learning_rate, true, true, cache_size); }
+    /*!
+        requires
+            - min_learning_rate > 0
+            - cache_size > 0
+            - trainer_type == some kind of online trainer object that creates decision_function
+              objects (e.g. svm_pegasos).  replicate_settings() must also be defined for the type.
+        ensures
+            - returns a batch_trainer object that has been instantiated with the 
+              given arguments (is verbose and uses a kernel cache).
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+
 }
 
 #endif // DLIB_PEGASoS_ABSTRACT_