summaryrefslogtreecommitdiff
path: root/training
diff options
context:
space:
mode:
Diffstat (limited to 'training')
-rw-r--r--training/Makefile.am39
-rw-r--r--training/atools.cc207
-rwxr-xr-xtraining/cluster-em.pl110
-rwxr-xr-xtraining/cluster-ptrain.pl144
-rw-r--r--training/grammar_convert.cc316
-rw-r--r--training/lbfgs.h1459
-rw-r--r--training/lbfgs_test.cc112
-rwxr-xr-xtraining/make-lexcrf-grammar.pl236
-rw-r--r--training/model1.cc103
-rw-r--r--training/mr_em_train.cc270
-rw-r--r--training/mr_optimize_reduce.cc243
-rw-r--r--training/optimize.cc114
-rw-r--r--training/optimize.h104
-rw-r--r--training/optimize_test.cc105
-rw-r--r--training/plftools.cc93
15 files changed, 3655 insertions, 0 deletions
diff --git a/training/Makefile.am b/training/Makefile.am
new file mode 100644
index 00000000..a2888f7a
--- /dev/null
+++ b/training/Makefile.am
@@ -0,0 +1,39 @@
+bin_PROGRAMS = \
+ model1 \
+ mr_optimize_reduce \
+ grammar_convert \
+ atools \
+ plftools \
+ lbfgs_test \
+ mr_em_train \
+ collapse_weights \
+ optimize_test
+
+atools_SOURCES = atools.cc
+
+model1_SOURCES = model1.cc
+model1_LDADD = libhg.a
+
+grammar_convert_SOURCES = grammar_convert.cc
+
+optimize_test_SOURCES = optimize_test.cc
+
+collapse_weights_SOURCES = collapse_weights.cc
+
+lbfgs_test_SOURCES = lbfgs_test.cc
+
+mr_optimize_reduce_SOURCES = mr_optimize_reduce.cc
+mr_optimize_reduce_LDADD = libhg.a
+
+mr_em_train_SOURCES = mr_em_train.cc
+mr_em_train_LDADD = libhg.a
+
+plftools_SOURCES = plftools.cc
+plftools_LDADD = libhg.a
+
+LDADD = libhg.a
+
+AM_CPPFLAGS = -W -Wall -Wno-sign-compare $(GTEST_CPPFLAGS)
+AM_LDFLAGS = $(BOOST_LDFLAGS) $(BOOST_PROGRAM_OPTIONS_LIB) -lz
+
+noinst_LIBRARIES = libhg.a
diff --git a/training/atools.cc b/training/atools.cc
new file mode 100644
index 00000000..bac73859
--- /dev/null
+++ b/training/atools.cc
@@ -0,0 +1,207 @@
+#include <iostream>
+#include <sstream>
+#include <vector>
+
+#include <map>
+#include <boost/program_options.hpp>
+#include <boost/shared_ptr.hpp>
+
+#include "filelib.h"
+#include "aligner.h"
+
+namespace po = boost::program_options;
+using namespace std;
+using boost::shared_ptr;
+
+struct Command {
+ virtual ~Command() {}
+ virtual string Name() const = 0;
+
+ // returns 1 for alignment grid output [default]
+ // returns 2 if Summary() should be called [for AER, etc]
+ virtual int Result() const { return 1; }
+
+ virtual bool RequiresTwoOperands() const { return true; }
+ virtual void Apply(const Array2D<bool>& a, const Array2D<bool>& b, Array2D<bool>* x) = 0;
+ void EnsureSize(const Array2D<bool>& a, const Array2D<bool>& b, Array2D<bool>* x) {
+ x->resize(max(a.width(), b.width()), max(a.height(), b.width()));
+ }
+ bool Safe(const Array2D<bool>& a, int i, int j) const {
+ if (i < a.width() && j < a.height())
+ return a(i,j);
+ else
+ return false;
+ }
+ virtual void Summary() { assert(!"Summary should have been overridden"); }
+};
+
+// compute fmeasure, second alignment is reference, first is hyp
+struct FMeasureCommand : public Command {
+ FMeasureCommand() : matches(), num_predicted(), num_in_ref() {}
+ int Result() const { return 2; }
+ string Name() const { return "f"; }
+ bool RequiresTwoOperands() const { return true; }
+ void Apply(const Array2D<bool>& hyp, const Array2D<bool>& ref, Array2D<bool>* x) {
+ int i_len = ref.width();
+ int j_len = ref.height();
+ for (int i = 0; i < i_len; ++i) {
+ for (int j = 0; j < j_len; ++j) {
+ if (ref(i,j)) {
+ ++num_in_ref;
+ if (Safe(hyp, i, j)) ++matches;
+ }
+ }
+ }
+ for (int i = 0; i < hyp.width(); ++i)
+ for (int j = 0; j < hyp.height(); ++j)
+ if (hyp(i,j)) ++num_predicted;
+ }
+ void Summary() {
+ if (num_predicted == 0 || num_in_ref == 0) {
+ cerr << "Insufficient statistics to compute f-measure!\n";
+ abort();
+ }
+ const double prec = static_cast<double>(matches) / num_predicted;
+ const double rec = static_cast<double>(matches) / num_in_ref;
+ cout << "P: " << prec << endl;
+ cout << "R: " << rec << endl;
+ const double f = (2.0 * prec * rec) / (rec + prec);
+ cout << "F: " << f << endl;
+ }
+ int matches;
+ int num_predicted;
+ int num_in_ref;
+};
+
+struct ConvertCommand : public Command {
+ string Name() const { return "convert"; }
+ bool RequiresTwoOperands() const { return false; }
+ void Apply(const Array2D<bool>& in, const Array2D<bool>&not_used, Array2D<bool>* x) {
+ *x = in;
+ }
+};
+
+struct InvertCommand : public Command {
+ string Name() const { return "invert"; }
+ bool RequiresTwoOperands() const { return false; }
+ void Apply(const Array2D<bool>& in, const Array2D<bool>&not_used, Array2D<bool>* x) {
+ Array2D<bool>& res = *x;
+ res.resize(in.height(), in.width());
+ for (int i = 0; i < in.height(); ++i)
+ for (int j = 0; j < in.width(); ++j)
+ res(i, j) = in(j, i);
+ }
+};
+
+struct IntersectCommand : public Command {
+ string Name() const { return "intersect"; }
+ bool RequiresTwoOperands() const { return true; }
+ void Apply(const Array2D<bool>& a, const Array2D<bool>& b, Array2D<bool>* x) {
+ EnsureSize(a, b, x);
+ Array2D<bool>& res = *x;
+ for (int i = 0; i < a.width(); ++i)
+ for (int j = 0; j < a.height(); ++j)
+ res(i, j) = Safe(a, i, j) && Safe(b, i, j);
+ }
+};
+
+map<string, boost::shared_ptr<Command> > commands;
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+ po::options_description opts("Configuration options");
+ ostringstream os;
+ os << "[REQ] Operation to perform:";
+ for (map<string, boost::shared_ptr<Command> >::iterator it = commands.begin();
+ it != commands.end(); ++it) {
+ os << ' ' << it->first;
+ }
+ string cstr = os.str();
+ opts.add_options()
+ ("input_1,i", po::value<string>(), "[REQ] Alignment 1 file, - for STDIN")
+ ("input_2,j", po::value<string>(), "[OPT] Alignment 2 file, - for STDIN")
+ ("command,c", po::value<string>()->default_value("convert"), cstr.c_str())
+ ("help,h", "Print this help message and exit");
+ po::options_description clo("Command line options");
+ po::options_description dcmdline_options;
+ dcmdline_options.add(opts);
+
+ po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+ po::notify(*conf);
+
+ if (conf->count("help") || conf->count("input_1") == 0 || conf->count("command") == 0) {
+ cerr << dcmdline_options << endl;
+ exit(1);
+ }
+ const string cmd = (*conf)["command"].as<string>();
+ if (commands.count(cmd) == 0) {
+ cerr << "Don't understand command: " << cmd << endl;
+ exit(1);
+ }
+ if (commands[cmd]->RequiresTwoOperands()) {
+ if (conf->count("input_2") == 0) {
+ cerr << "Command '" << cmd << "' requires two alignment files\n";
+ exit(1);
+ }
+ if ((*conf)["input_1"].as<string>() == "-" && (*conf)["input_2"].as<string>() == "-") {
+ cerr << "Both inputs cannot be STDIN\n";
+ exit(1);
+ }
+ } else {
+ if (conf->count("input_2") != 0) {
+ cerr << "Command '" << cmd << "' requires only one alignment file\n";
+ exit(1);
+ }
+ }
+}
+
+template<class C> static void AddCommand() {
+ C* c = new C;
+ commands[c->Name()].reset(c);
+}
+
+int main(int argc, char **argv) {
+ AddCommand<ConvertCommand>();
+ AddCommand<InvertCommand>();
+ AddCommand<IntersectCommand>();
+ AddCommand<FMeasureCommand>();
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+ Command& cmd = *commands[conf["command"].as<string>()];
+ boost::shared_ptr<ReadFile> rf1(new ReadFile(conf["input_1"].as<string>()));
+ boost::shared_ptr<ReadFile> rf2;
+ if (cmd.RequiresTwoOperands())
+ rf2.reset(new ReadFile(conf["input_2"].as<string>()));
+ istream* in1 = rf1->stream();
+ istream* in2 = NULL;
+ if (rf2) in2 = rf2->stream();
+ while(*in1) {
+ string line1;
+ string line2;
+ getline(*in1, line1);
+ if (in2) {
+ getline(*in2, line2);
+ if ((*in1 && !*in2) || (*in2 && !*in1)) {
+ cerr << "Mismatched number of lines!\n";
+ exit(1);
+ }
+ }
+ if (line1.empty() && !*in1) break;
+ shared_ptr<Array2D<bool> > out(new Array2D<bool>);
+ shared_ptr<Array2D<bool> > a1 = AlignerTools::ReadPharaohAlignmentGrid(line1);
+ if (in2) {
+ shared_ptr<Array2D<bool> > a2 = AlignerTools::ReadPharaohAlignmentGrid(line2);
+ cmd.Apply(*a1, *a2, out.get());
+ } else {
+ Array2D<bool> dummy;
+ cmd.Apply(*a1, dummy, out.get());
+ }
+
+ if (cmd.Result() == 1) {
+ AlignerTools::SerializePharaohFormat(*out, &cout);
+ }
+ }
+ if (cmd.Result() == 2)
+ cmd.Summary();
+ return 0;
+}
+
diff --git a/training/cluster-em.pl b/training/cluster-em.pl
new file mode 100755
index 00000000..175870da
--- /dev/null
+++ b/training/cluster-em.pl
@@ -0,0 +1,110 @@
+#!/usr/bin/perl -w
+
+use strict;
+my $SCRIPT_DIR; BEGIN { use Cwd qw/ abs_path /; use File::Basename; $SCRIPT_DIR = dirname(abs_path($0)); push @INC, $SCRIPT_DIR; }
+use Getopt::Long;
+my $parallel = 1;
+
+my $CWD=`pwd`; chomp $CWD;
+my $BIN_DIR = "/chomes/redpony/cdyer-svn-repo/cdec/src";
+my $OPTIMIZER = "$BIN_DIR/mr_em_train";
+my $DECODER = "$BIN_DIR/cdec";
+my $COMBINER_CACHE_SIZE = 150;
+my $PARALLEL = "/chomes/redpony/svn-trunk/sa-utils/parallelize.pl";
+die "Can't find $OPTIMIZER" unless -f $OPTIMIZER;
+die "Can't execute $OPTIMIZER" unless -x $OPTIMIZER;
+die "Can't find $DECODER" unless -f $DECODER;
+die "Can't execute $DECODER" unless -x $DECODER;
+die "Can't find $PARALLEL" unless -f $PARALLEL;
+die "Can't execute $PARALLEL" unless -x $PARALLEL;
+my $restart = '';
+if ($ARGV[0] && $ARGV[0] eq '--restart') { shift @ARGV; $restart = 1; }
+
+die "Usage: $0 [--restart] training.corpus weights.init grammar.file [grammar2.file] ...\n" unless (scalar @ARGV >= 3);
+
+my $training_corpus = shift @ARGV;
+my $initial_weights = shift @ARGV;
+my @in_grammar_files = @ARGV;
+my $pmem="2500mb";
+my $nodes = 40;
+my $max_iteration = 1000;
+my $CFLAG = "-C 1";
+unless ($parallel) { $CFLAG = "-C 500"; }
+my @grammar_files;
+for my $g (@in_grammar_files) {
+ unless ($g =~ /^\//) { $g = $CWD . '/' . $g; }
+ die "Can't find $g" unless -f $g;
+ push @grammar_files, $g;
+}
+
+print STDERR <<EOT;
+EM TRAIN CONFIGURATION INFORMATION
+
+ Grammar file(s): @grammar_files
+ Training corpus: $training_corpus
+ Initial weights: $initial_weights
+ Decoder memory: $pmem
+ Nodes requested: $nodes
+ Max iterations: $max_iteration
+ restart: $restart
+EOT
+
+my $nodelist="1";
+for (my $i=1; $i<$nodes; $i++) { $nodelist .= " 1"; }
+my $iter = 1;
+
+my $dir = "$CWD/emtrain";
+if ($restart) {
+ die "$dir doesn't exist, but --restart specified!\n" unless -d $dir;
+ my $o = `ls -t $dir/weights.*`;
+ my ($a, @x) = split /\n/, $o;
+ if ($a =~ /weights.(\d+)\.gz$/) {
+ $iter = $1;
+ } else {
+ die "Unexpected file: $a!\n";
+ }
+ print STDERR "Restarting at iteration $iter\n";
+} else {
+ die "$dir already exists!\n" if -e $dir;
+ mkdir $dir or die "Can't create $dir: $!";
+
+ unless ($initial_weights =~ /\.gz$/) {
+ `cp $initial_weights $dir/weights.1`;
+ `gzip -9 $dir/weights.1`;
+ } else {
+ `cp $initial_weights $dir/weights.1.gz`;
+ }
+}
+
+while ($iter < $max_iteration) {
+ my $cur_time = `date`; chomp $cur_time;
+ print STDERR "\nStarting iteration $iter...\n";
+ print STDERR " time: $cur_time\n";
+ my $start = time;
+ my $next_iter = $iter + 1;
+ my $gfile = '-g' . (join ' -g ', @grammar_files);
+ my $dec_cmd="$DECODER --feature_expectations -S 999 $CFLAG $gfile -n -w $dir/weights.$iter.gz < $training_corpus 2> $dir/deco.log.$iter";
+ my $opt_cmd = "$OPTIMIZER $gfile -o $dir/weights.$next_iter.gz";
+ my $pcmd = "$PARALLEL -e $dir/err -p $pmem --nodelist \"$nodelist\" -- ";
+ my $cmd = "";
+ if ($parallel) { $cmd = $pcmd; }
+ $cmd .= "$dec_cmd | $opt_cmd";
+
+ print STDERR "EXECUTING: $cmd\n";
+ my $result = `$cmd`;
+ if ($? != 0) {
+ die "Error running iteration $iter: $!";
+ }
+ chomp $result;
+ my $end = time;
+ my $diff = ($end - $start);
+ print STDERR " ITERATION $iter TOOK $diff SECONDS\n";
+ $iter = $next_iter;
+ if ($result =~ /1$/) {
+ print STDERR "Training converged.\n";
+ last;
+ }
+}
+
+print "FINAL WEIGHTS: $dir/weights.$iter\n";
+
diff --git a/training/cluster-ptrain.pl b/training/cluster-ptrain.pl
new file mode 100755
index 00000000..99369cdc
--- /dev/null
+++ b/training/cluster-ptrain.pl
@@ -0,0 +1,144 @@
+#!/usr/bin/perl -w
+
+use strict;
+my $SCRIPT_DIR; BEGIN { use Cwd qw/ abs_path /; use File::Basename; $SCRIPT_DIR = dirname(abs_path($0)); push @INC, $SCRIPT_DIR; }
+use Getopt::Long;
+
+my $MAX_ITER_ATTEMPTS = 5; # number of times to retry a failed function evaluation
+my $CWD=`pwd`; chomp $CWD;
+my $BIN_DIR = $SCRIPT_DIR;
+my $OPTIMIZER = "$BIN_DIR/mr_optimize_reduce";
+my $DECODER = "$BIN_DIR/cdec";
+my $COMBINER_CACHE_SIZE = 150;
+my $PARALLEL = "/chomes/redpony/svn-trunk/sa-utils/parallelize.pl";
+die "Can't find $OPTIMIZER" unless -f $OPTIMIZER;
+die "Can't execute $OPTIMIZER" unless -x $OPTIMIZER;
+my $restart = '';
+if ($ARGV[0] && $ARGV[0] eq '--restart') { shift @ARGV; $restart = 1; }
+
+my $pmem="2500mb";
+my $nodes = 36;
+my $max_iteration = 1000;
+my $PRIOR_FLAG = "";
+my $parallel = 1;
+my $CFLAG = "-C 1";
+my $LOCAL;
+my $PRIOR;
+my $OALG = "lbfgs";
+my $sigsq = 1;
+my $means_file;
+GetOptions("decoder=s" => \$DECODER,
+ "run_locally" => \$LOCAL,
+ "gaussian_prior" => \$PRIOR,
+ "sigma_squared=f" => \$sigsq,
+ "means=s" => \$means_file,
+ "optimizer=s" => \$OALG,
+ "pmem=s" => \$pmem
+ ) or usage();
+usage() unless scalar @ARGV==3;
+my $config_file = shift @ARGV;
+my $training_corpus = shift @ARGV;
+my $initial_weights = shift @ARGV;
+die "Can't find $config_file" unless -f $config_file;
+die "Can't find $DECODER" unless -f $DECODER;
+die "Can't execute $DECODER" unless -x $DECODER;
+if ($LOCAL) { print STDERR "Will running LOCALLY.\n"; $parallel = 0; }
+if ($PRIOR) {
+ $PRIOR_FLAG="-p --sigma_squared $sigsq";
+ if ($means_file) { $PRIOR_FLAG .= " -u $means_file"; }
+}
+
+if ($parallel) {
+ die "Can't find $PARALLEL" unless -f $PARALLEL;
+ die "Can't execute $PARALLEL" unless -x $PARALLEL;
+}
+unless ($parallel) { $CFLAG = "-C 500"; }
+unless ($config_file =~ /^\//) { $config_file = $CWD . '/' . $config_file; }
+
+print STDERR <<EOT;
+PTRAIN CONFIGURATION INFORMATION
+
+ Config file: $config_file
+ Training corpus: $training_corpus
+ Initial weights: $initial_weights
+ Decoder memory: $pmem
+ Nodes requested: $nodes
+ Max iterations: $max_iteration
+ Optimizer: $OALG
+ PRIOR: $PRIOR_FLAG
+ restart: $restart
+EOT
+if ($OALG) { $OALG="-m $OALG"; }
+
+my $nodelist="1";
+for (my $i=1; $i<$nodes; $i++) { $nodelist .= " 1"; }
+my $iter = 1;
+
+my $dir = "$CWD/ptrain";
+if ($restart) {
+ die "$dir doesn't exist, but --restart specified!\n" unless -d $dir;
+ my $o = `ls -t $dir/weights.*`;
+ my ($a, @x) = split /\n/, $o;
+ if ($a =~ /weights.(\d+)\.gz$/) {
+ $iter = $1;
+ } else {
+ die "Unexpected file: $a!\n";
+ }
+ print STDERR "Restarting at iteration $iter\n";
+} else {
+ die "$dir already exists!\n" if -e $dir;
+ mkdir $dir or die "Can't create $dir: $!";
+
+ unless ($initial_weights =~ /\.gz$/) {
+ `cp $initial_weights $dir/weights.1`;
+ `gzip -9 $dir/weights.1`;
+ } else {
+ `cp $initial_weights $dir/weights.1.gz`;
+ }
+}
+
+my $iter_attempts = 1;
+while ($iter < $max_iteration) {
+ my $cur_time = `date`; chomp $cur_time;
+ print STDERR "\nStarting iteration $iter...\n";
+ print STDERR " time: $cur_time\n";
+ my $start = time;
+ my $next_iter = $iter + 1;
+ my $dec_cmd="$DECODER -G $CFLAG -c $config_file -w $dir/weights.$iter.gz < $training_corpus 2> $dir/deco.log.$iter";
+ my $opt_cmd = "$OPTIMIZER $PRIOR_FLAG -M 50 $OALG -s $dir/opt.state -i $dir/weights.$iter.gz -o $dir/weights.$next_iter.gz";
+ my $pcmd = "$PARALLEL -e $dir/err -p $pmem --nodelist \"$nodelist\" -- ";
+ my $cmd = "";
+ if ($parallel) { $cmd = $pcmd; }
+ $cmd .= "$dec_cmd | $opt_cmd";
+
+ print STDERR "EXECUTING: $cmd\n";
+ my $result = `$cmd`;
+ my $exit_code = $? >> 8;
+ if ($exit_code == 99) {
+ $iter_attempts++;
+ if ($iter_attempts > $MAX_ITER_ATTEMPTS) {
+ die "Received restart request $iter_attempts times from optimizer, giving up\n";
+ }
+ print STDERR "Function evaluation failed, retrying (attempt $iter_attempts)\n";
+ next;
+ }
+ if ($? != 0) {
+ die "Error running iteration $iter: $!";
+ }
+ chomp $result;
+ my $end = time;
+ my $diff = ($end - $start);
+ print STDERR " ITERATION $iter TOOK $diff SECONDS\n";
+ $iter = $next_iter;
+ if ($result =~ /1$/) {
+ print STDERR "Training converged.\n";
+ last;
+ }
+ $iter_attempts = 1;
+}
+
+print "FINAL WEIGHTS: $dir/weights.$iter\n";
+
+sub usage {
+ die "Usage: $0 [OPTIONS] cdec.ini training.corpus weights.init\n";
+}
diff --git a/training/grammar_convert.cc b/training/grammar_convert.cc
new file mode 100644
index 00000000..22ba0f46
--- /dev/null
+++ b/training/grammar_convert.cc
@@ -0,0 +1,316 @@
+#include <iostream>
+#include <algorithm>
+#include <sstream>
+
+#include <boost/lexical_cast.hpp>
+#include <boost/program_options.hpp>
+
+#include "tdict.h"
+#include "filelib.h"
+#include "hg.h"
+#include "hg_io.h"
+#include "kbest.h"
+#include "viterbi.h"
+#include "weights.h"
+
+namespace po = boost::program_options;
+using namespace std;
+
+WordID kSTART;
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+ po::options_description opts("Configuration options");
+ opts.add_options()
+ ("input,i", po::value<string>()->default_value("-"), "Input file")
+ ("format,f", po::value<string>()->default_value("cfg"), "Input format. Values: cfg, json, split")
+ ("output,o", po::value<string>()->default_value("json"), "Output command. Values: json, 1best")
+ ("reorder,r", "Add Yamada & Knight (2002) reorderings")
+ ("weights,w", po::value<string>(), "Feature weights for k-best derivations [optional]")
+ ("collapse_weights,C", "Collapse order features into a single feature whose value is all of the locally applying feature weights")
+ ("k_derivations,k", po::value<int>(), "Show k derivations and their features")
+ ("max_reorder,m", po::value<int>()->default_value(999), "Move a constituent at most this far")
+ ("help,h", "Print this help message and exit");
+ po::options_description clo("Command line options");
+ po::options_description dcmdline_options;
+ dcmdline_options.add(opts);
+
+ po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+ po::notify(*conf);
+
+ if (conf->count("help") || conf->count("input") == 0) {
+ cerr << "\nUsage: grammar_convert [-options]\n\nConverts a grammar file (in Hiero format) into JSON hypergraph.\n";
+ cerr << dcmdline_options << endl;
+ exit(1);
+ }
+}
+
+int GetOrCreateNode(const WordID& lhs, map<WordID, int>* lhs2node, Hypergraph* hg) {
+ int& node_id = (*lhs2node)[lhs];
+ if (!node_id)
+ node_id = hg->AddNode(lhs)->id_ + 1;
+ return node_id - 1;
+}
+
+void FilterAndCheckCorrectness(int goal, Hypergraph* hg) {
+ if (goal < 0) {
+ cerr << "Error! [S] not found in grammar!\n";
+ exit(1);
+ }
+ if (hg->nodes_[goal].in_edges_.size() != 1) {
+ cerr << "Error! [S] has more than one rewrite!\n";
+ exit(1);
+ }
+ int old_size = hg->nodes_.size();
+ hg->TopologicallySortNodesAndEdges(goal);
+ if (hg->nodes_.size() != old_size) {
+ cerr << "Warning! During sorting " << (old_size - hg->nodes_.size()) << " disappeared!\n";
+ }
+}
+
+void CreateEdge(const TRulePtr& r, const Hypergraph::TailNodeVector& tail, Hypergraph::Node* head_node, Hypergraph* hg) {
+ Hypergraph::Edge* new_edge = hg->AddEdge(r, tail);
+ hg->ConnectEdgeToHeadNode(new_edge, head_node);
+ new_edge->feature_values_ = r->scores_;
+}
+
+// from a category label like "NP_2", return "NP"
+string PureCategory(WordID cat) {
+ assert(cat < 0);
+ string c = TD::Convert(cat*-1);
+ size_t p = c.find("_");
+ if (p == string::npos) return c;
+ return c.substr(0, p);
+};
+
+string ConstituentOrderFeature(const TRule& rule, const vector<int>& pi) {
+ const static string kTERM_VAR = "x";
+ const vector<WordID>& f = rule.f();
+ map<string, int> used;
+ vector<string> terms(f.size());
+ for (int i = 0; i < f.size(); ++i) {
+ const string term = (f[i] < 0 ? PureCategory(f[i]) : kTERM_VAR);
+ int& count = used[term];
+ if (!count) {
+ terms[i] = term;
+ } else {
+ ostringstream os;
+ os << term << count;
+ terms[i] = os.str();
+ }
+ ++count;
+ }
+ ostringstream os;
+ os << PureCategory(rule.GetLHS()) << ':';
+ for (int i = 0; i < f.size(); ++i) {
+ if (i > 0) os << '_';
+ os << terms[pi[i]];
+ }
+ return os.str();
+}
+
+bool CheckPermutationMask(const vector<int>& mask, const vector<int>& pi) {
+ assert(mask.size() == pi.size());
+
+ int req_min = -1;
+ int cur_max = 0;
+ int cur_mask = -1;
+ for (int i = 0; i < mask.size(); ++i) {
+ if (mask[i] != cur_mask) {
+ cur_mask = mask[i];
+ req_min = cur_max - 1;
+ }
+ if (pi[i] > req_min) {
+ if (pi[i] > cur_max) cur_max = pi[i];
+ } else {
+ return false;
+ }
+ }
+
+ return true;
+}
+
+void PermuteYKRecursive(int nodeid, const WordID& parent, const int max_reorder, Hypergraph* hg) {
+ Hypergraph::Node* node = &hg->nodes_[nodeid];
+ if (node->in_edges_.size() != 1) {
+ cerr << "Multiple rewrites of [" << TD::Convert(node->cat_ * -1) << "] (parent is [" << TD::Convert(parent*-1) << "])\n";
+ cerr << " not recursing!\n";
+ return;
+ }
+ const int oe_index = node->in_edges_.front();
+ const TRule& rule = *hg->edges_[oe_index].rule_;
+ const Hypergraph::TailNodeVector orig_tail = hg->edges_[oe_index].tail_nodes_;
+ const int tail_size = orig_tail.size();
+ for (int i = 0; i < tail_size; ++i) {
+ PermuteYKRecursive(hg->edges_[oe_index].tail_nodes_[i], node->cat_, max_reorder, hg);
+ }
+ const vector<WordID>& of = rule.f_;
+ if (of.size() == 1) return;
+// cerr << "Permuting [" << TD::Convert(node->cat_ * -1) << "]\n";
+// cerr << "ORIG: " << rule.AsString() << endl;
+ vector<WordID> pi(of.size(), 0);
+ for (int i = 0; i < pi.size(); ++i) pi[i] = i;
+
+ vector<int> permutation_mask(of.size(), 0);
+ const bool dont_reorder_across_PU = true; // TODO add configuration
+ if (dont_reorder_across_PU) {
+ int cur = 0;
+ for (int i = 0; i < pi.size(); ++i) {
+ if (of[i] >= 0) continue;
+ const string cat = PureCategory(of[i]);
+ if (cat == "PU" || cat == "PU!H" || cat == "PUNC" || cat == "PUNC!H" || cat == "CC") {
+ ++cur;
+ permutation_mask[i] = cur;
+ ++cur;
+ } else {
+ permutation_mask[i] = cur;
+ }
+ }
+ }
+ int fid = FD::Convert(ConstituentOrderFeature(rule, pi));
+ hg->edges_[oe_index].feature_values_.set_value(fid, 1.0);
+ while (next_permutation(pi.begin(), pi.end())) {
+ if (!CheckPermutationMask(permutation_mask, pi))
+ continue;
+ vector<WordID> nf(pi.size(), 0);
+ Hypergraph::TailNodeVector tail(pi.size(), 0);
+ bool skip = false;
+ for (int i = 0; i < pi.size(); ++i) {
+ int dist = pi[i] - i; if (dist < 0) dist *= -1;
+ if (dist > max_reorder) { skip = true; break; }
+ nf[i] = of[pi[i]];
+ tail[i] = orig_tail[pi[i]];
+ }
+ if (skip) continue;
+ TRulePtr nr(new TRule(rule));
+ nr->f_ = nf;
+ int fid = FD::Convert(ConstituentOrderFeature(rule, pi));
+ nr->scores_.set_value(fid, 1.0);
+// cerr << "PERM: " << nr->AsString() << endl;
+ CreateEdge(nr, tail, node, hg);
+ }
+}
+
+void PermuteYamadaAndKnight(Hypergraph* hg, int max_reorder) {
+ assert(hg->nodes_.back().cat_ == kSTART);
+ assert(hg->nodes_.back().in_edges_.size() == 1);
+ PermuteYKRecursive(hg->nodes_.size() - 1, kSTART, max_reorder, hg);
+}
+
+void CollapseWeights(Hypergraph* hg) {
+ int fid = FD::Convert("Reordering");
+ for (int i = 0; i < hg->edges_.size(); ++i) {
+ Hypergraph::Edge& edge = hg->edges_[i];
+ edge.feature_values_.clear();
+ if (edge.edge_prob_ != prob_t::Zero()) {
+ edge.feature_values_.set_value(fid, log(edge.edge_prob_));
+ }
+ }
+}
+
+void ProcessHypergraph(const vector<double>& w, const po::variables_map& conf, const string& ref, Hypergraph* hg) {
+ if (conf.count("reorder"))
+ PermuteYamadaAndKnight(hg, conf["max_reorder"].as<int>());
+ if (w.size() > 0) { hg->Reweight(w); }
+ if (conf.count("collapse_weights")) CollapseWeights(hg);
+ if (conf["output"].as<string>() == "json") {
+ HypergraphIO::WriteToJSON(*hg, false, &cout);
+ if (!ref.empty()) { cerr << "REF: " << ref << endl; }
+ } else {
+ vector<WordID> onebest;
+ ViterbiESentence(*hg, &onebest);
+ if (ref.empty()) {
+ cout << TD::GetString(onebest) << endl;
+ } else {
+ cout << TD::GetString(onebest) << " ||| " << ref << endl;
+ }
+ }
+ if (conf.count("k_derivations")) {
+ const int k = conf["k_derivations"].as<int>();
+ KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(*hg, k);
+ for (int i = 0; i < k; ++i) {
+ const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
+ kbest.LazyKthBest(hg->nodes_.size() - 1, i);
+ if (!d) break;
+ cerr << log(d->score) << " ||| " << TD::GetString(d->yield) << " ||| " << d->feature_values << endl;
+ }
+ }
+}
+
+int main(int argc, char **argv) {
+ kSTART = TD::Convert("S") * -1;
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+ string infile = conf["input"].as<string>();
+ const bool is_split_input = (conf["format"].as<string>() == "split");
+ const bool is_json_input = is_split_input || (conf["format"].as<string>() == "json");
+ const bool collapse_weights = conf.count("collapse_weights");
+ Weights wts;
+ vector<double> w;
+ if (conf.count("weights")) {
+ wts.InitFromFile(conf["weights"].as<string>());
+ wts.InitVector(&w);
+ }
+ if (collapse_weights && !w.size()) {
+ cerr << "--collapse_weights requires a weights file to be specified!\n";
+ exit(1);
+ }
+ ReadFile rf(infile);
+ istream* in = rf.stream();
+ assert(*in);
+ int lc = 0;
+ Hypergraph hg;
+ map<WordID, int> lhs2node;
+ while(*in) {
+ string line;
+ ++lc;
+ getline(*in, line);
+ if (is_json_input) {
+ if (line.empty() || line[0] == '#') continue;
+ string ref;
+ if (is_split_input) {
+ size_t pos = line.rfind("}}");
+ assert(pos != string::npos);
+ size_t rstart = line.find("||| ", pos);
+ assert(rstart != string::npos);
+ ref = line.substr(rstart + 4);
+ line = line.substr(0, pos + 2);
+ }
+ istringstream is(line);
+ if (HypergraphIO::ReadFromJSON(&is, &hg)) {
+ ProcessHypergraph(w, conf, ref, &hg);
+ hg.clear();
+ } else {
+ cerr << "Error reading grammar from JSON: line " << lc << endl;
+ exit(1);
+ }
+ } else {
+ if (line.empty()) {
+ int goal = lhs2node[kSTART] - 1;
+ FilterAndCheckCorrectness(goal, &hg);
+ ProcessHypergraph(w, conf, "", &hg);
+ hg.clear();
+ lhs2node.clear();
+ continue;
+ }
+ if (line[0] == '#') continue;
+ if (line[0] != '[') {
+ cerr << "Line " << lc << ": bad format\n";
+ exit(1);
+ }
+ TRulePtr tr(TRule::CreateRuleMonolingual(line));
+ Hypergraph::TailNodeVector tail;
+ for (int i = 0; i < tr->f_.size(); ++i) {
+ WordID var_cat = tr->f_[i];
+ if (var_cat < 0)
+ tail.push_back(GetOrCreateNode(var_cat, &lhs2node, &hg));
+ }
+ const WordID lhs = tr->GetLHS();
+ int head = GetOrCreateNode(lhs, &lhs2node, &hg);
+ Hypergraph::Edge* edge = hg.AddEdge(tr, tail);
+ edge->feature_values_ = tr->scores_;
+ Hypergraph::Node* node = &hg.nodes_[head];
+ hg.ConnectEdgeToHeadNode(edge, node);
+ }
+ }
+}
+
diff --git a/training/lbfgs.h b/training/lbfgs.h
new file mode 100644
index 00000000..e8baecab
--- /dev/null
+++ b/training/lbfgs.h
@@ -0,0 +1,1459 @@
+#ifndef SCITBX_LBFGS_H
+#define SCITBX_LBFGS_H
+
+#include <cstdio>
+#include <cstddef>
+#include <cmath>
+#include <stdexcept>
+#include <algorithm>
+#include <vector>
+#include <string>
+#include <iostream>
+#include <sstream>
+
+namespace scitbx {
+
+//! Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) %minimizer.
+/*! Implementation of the
+ Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS)
+ algorithm for large-scale multidimensional minimization
+ problems.
+
+ This code was manually derived from Java code which was
+ in turn derived from the Fortran program
+ <code>lbfgs.f</code>. The Java translation was
+ effected mostly mechanically, with some manual
+ clean-up; in particular, array indices start at 0
+ instead of 1. Most of the comments from the Fortran
+ code have been pasted in.
+
+ Information on the original LBFGS Fortran source code is
+ available at
+ http://www.netlib.org/opt/lbfgs_um.shar . The following
+ information is taken verbatim from the Netlib documentation
+ for the Fortran source.
+
+ <pre>
+ file opt/lbfgs_um.shar
+ for unconstrained optimization problems
+ alg limited memory BFGS method
+ by J. Nocedal
+ contact nocedal@eecs.nwu.edu
+ ref D. C. Liu and J. Nocedal, ``On the limited memory BFGS method for
+ , large scale optimization methods'' Mathematical Programming 45
+ , (1989), pp. 503-528.
+ , (Postscript file of this paper is available via anonymous ftp
+ , to eecs.nwu.edu in the directory pub/%lbfgs/lbfgs_um.)
+ </pre>
+
+ @author Jorge Nocedal: original Fortran version, including comments
+ (July 1990).<br>
+ Robert Dodier: Java translation, August 1997.<br>
+ Ralf W. Grosse-Kunstleve: C++ port, March 2002.<br>
+ Chris Dyer: serialize/deserialize functionality
+ */
+namespace lbfgs {
+
+ //! Generic exception class for %lbfgs %error messages.
+ /*! All exceptions thrown by the minimizer are derived from this class.
+ */
+ class error : public std::exception {
+ public:
+ //! Constructor.
+ error(std::string const& msg) throw()
+ : msg_("lbfgs error: " + msg)
+ {}
+ //! Access to error message.
+ virtual const char* what() const throw() { return msg_.c_str(); }
+ protected:
+ virtual ~error() throw() {}
+ std::string msg_;
+ public:
+ static std::string itoa(unsigned long i) {
+ std::ostringstream os;
+ os << i;
+ return os.str();
+ }
+ };
+
+ //! Specific exception class.
+ class error_internal_error : public error {
+ public:
+ //! Constructor.
+ error_internal_error(const char* file, unsigned long line) throw()
+ : error(
+ "Internal Error: " + std::string(file) + "(" + itoa(line) + ")")
+ {}
+ };
+
+ //! Specific exception class.
+ class error_improper_input_parameter : public error {
+ public:
+ //! Constructor.
+ error_improper_input_parameter(std::string const& msg) throw()
+ : error("Improper input parameter: " + msg)
+ {}
+ };
+
+ //! Specific exception class.
+ class error_improper_input_data : public error {
+ public:
+ //! Constructor.
+ error_improper_input_data(std::string const& msg) throw()
+ : error("Improper input data: " + msg)
+ {}
+ };
+
+ //! Specific exception class.
+ class error_search_direction_not_descent : public error {
+ public:
+ //! Constructor.
+ error_search_direction_not_descent() throw()
+ : error("The search direction is not a descent direction.")
+ {}
+ };
+
+ //! Specific exception class.
+ class error_line_search_failed : public error {
+ public:
+ //! Constructor.
+ error_line_search_failed(std::string const& msg) throw()
+ : error("Line search failed: " + msg)
+ {}
+ };
+
+ //! Specific exception class.
+ class error_line_search_failed_rounding_errors
+ : public error_line_search_failed {
+ public:
+ //! Constructor.
+ error_line_search_failed_rounding_errors(std::string const& msg) throw()
+ : error_line_search_failed(msg)
+ {}
+ };
+
+ namespace detail {
+
+ template <typename NumType>
+ inline
+ NumType
+ pow2(NumType const& x) { return x * x; }
+
+ template <typename NumType>
+ inline
+ NumType
+ abs(NumType const& x) {
+ if (x < NumType(0)) return -x;
+ return x;
+ }
+
+ // This class implements an algorithm for multi-dimensional line search.
+ template <typename FloatType, typename SizeType = std::size_t>
+ class mcsrch
+ {
+ protected:
+ int infoc;
+ FloatType dginit;
+ bool brackt;
+ bool stage1;
+ FloatType finit;
+ FloatType dgtest;
+ FloatType width;
+ FloatType width1;
+ FloatType stx;
+ FloatType fx;
+ FloatType dgx;
+ FloatType sty;
+ FloatType fy;
+ FloatType dgy;
+ FloatType stmin;
+ FloatType stmax;
+
+ static FloatType const& max3(
+ FloatType const& x,
+ FloatType const& y,
+ FloatType const& z)
+ {
+ return x < y ? (y < z ? z : y ) : (x < z ? z : x );
+ }
+
+ public:
+ /* Minimize a function along a search direction. This code is
+ a Java translation of the function <code>MCSRCH</code> from
+ <code>lbfgs.f</code>, which in turn is a slight modification
+ of the subroutine <code>CSRCH</code> of More' and Thuente.
+ The changes are to allow reverse communication, and do not
+ affect the performance of the routine. This function, in turn,
+ calls <code>mcstep</code>.<p>
+
+ The Java translation was effected mostly mechanically, with
+ some manual clean-up; in particular, array indices start at 0
+ instead of 1. Most of the comments from the Fortran code have
+ been pasted in here as well.<p>
+
+ The purpose of <code>mcsrch</code> is to find a step which
+ satisfies a sufficient decrease condition and a curvature
+ condition.<p>
+
+ At each stage this function updates an interval of uncertainty
+ with endpoints <code>stx</code> and <code>sty</code>. The
+ interval of uncertainty is initially chosen so that it
+ contains a minimizer of the modified function
+ <pre>
+ f(x+stp*s) - f(x) - ftol*stp*(gradf(x)'s).
+ </pre>
+ If a step is obtained for which the modified function has a
+ nonpositive function value and nonnegative derivative, then
+ the interval of uncertainty is chosen so that it contains a
+ minimizer of <code>f(x+stp*s)</code>.<p>
+
+ The algorithm is designed to find a step which satisfies
+ the sufficient decrease condition
+ <pre>
+ f(x+stp*s) &lt;= f(X) + ftol*stp*(gradf(x)'s),
+ </pre>
+ and the curvature condition
+ <pre>
+ abs(gradf(x+stp*s)'s)) &lt;= gtol*abs(gradf(x)'s).
+ </pre>
+ If <code>ftol</code> is less than <code>gtol</code> and if,
+ for example, the function is bounded below, then there is
+ always a step which satisfies both conditions. If no step can
+ be found which satisfies both conditions, then the algorithm
+ usually stops when rounding errors prevent further progress.
+ In this case <code>stp</code> only satisfies the sufficient
+ decrease condition.<p>
+
+ @author Original Fortran version by Jorge J. More' and
+ David J. Thuente as part of the Minpack project, June 1983,
+ Argonne National Laboratory. Java translation by Robert
+ Dodier, August 1997.
+
+ @param n The number of variables.
+
+ @param x On entry this contains the base point for the line
+ search. On exit it contains <code>x + stp*s</code>.
+
+ @param f On entry this contains the value of the objective
+ function at <code>x</code>. On exit it contains the value
+ of the objective function at <code>x + stp*s</code>.
+
+ @param g On entry this contains the gradient of the objective
+ function at <code>x</code>. On exit it contains the gradient
+ at <code>x + stp*s</code>.
+
+ @param s The search direction.
+
+ @param stp On entry this contains an initial estimate of a
+ satifactory step length. On exit <code>stp</code> contains
+ the final estimate.
+
+ @param ftol Tolerance for the sufficient decrease condition.
+
+ @param xtol Termination occurs when the relative width of the
+ interval of uncertainty is at most <code>xtol</code>.
+
+ @param maxfev Termination occurs when the number of evaluations
+ of the objective function is at least <code>maxfev</code> by
+ the end of an iteration.
+
+ @param info This is an output variable, which can have these
+ values:
+ <ul>
+ <li><code>info = -1</code> A return is made to compute
+ the function and gradient.
+ <li><code>info = 1</code> The sufficient decrease condition
+ and the directional derivative condition hold.
+ </ul>
+
+ @param nfev On exit, this is set to the number of function
+ evaluations.
+
+ @param wa Temporary storage array, of length <code>n</code>.
+ */
+ void run(
+ FloatType const& gtol,
+ FloatType const& stpmin,
+ FloatType const& stpmax,
+ SizeType n,
+ FloatType* x,
+ FloatType f,
+ const FloatType* g,
+ FloatType* s,
+ SizeType is0,
+ FloatType& stp,
+ FloatType ftol,
+ FloatType xtol,
+ SizeType maxfev,
+ int& info,
+ SizeType& nfev,
+ FloatType* wa);
+
+ /* The purpose of this function is to compute a safeguarded step
+ for a linesearch and to update an interval of uncertainty for
+ a minimizer of the function.<p>
+
+ The parameter <code>stx</code> contains the step with the
+ least function value. The parameter <code>stp</code> contains
+ the current step. It is assumed that the derivative at
+ <code>stx</code> is negative in the direction of the step. If
+ <code>brackt</code> is <code>true</code> when
+ <code>mcstep</code> returns then a minimizer has been
+ bracketed in an interval of uncertainty with endpoints
+ <code>stx</code> and <code>sty</code>.<p>
+
+ Variables that must be modified by <code>mcstep</code> are
+ implemented as 1-element arrays.
+
+ @param stx Step at the best step obtained so far.
+ This variable is modified by <code>mcstep</code>.
+ @param fx Function value at the best step obtained so far.
+ This variable is modified by <code>mcstep</code>.
+ @param dx Derivative at the best step obtained so far.
+ The derivative must be negative in the direction of the
+ step, that is, <code>dx</code> and <code>stp-stx</code> must
+ have opposite signs. This variable is modified by
+ <code>mcstep</code>.
+
+ @param sty Step at the other endpoint of the interval of
+ uncertainty. This variable is modified by <code>mcstep</code>.
+ @param fy Function value at the other endpoint of the interval
+ of uncertainty. This variable is modified by
+ <code>mcstep</code>.
+
+ @param dy Derivative at the other endpoint of the interval of
+ uncertainty. This variable is modified by <code>mcstep</code>.
+
+ @param stp Step at the current step. If <code>brackt</code> is set
+ then on input <code>stp</code> must be between <code>stx</code>
+ and <code>sty</code>. On output <code>stp</code> is set to the
+ new step.
+ @param fp Function value at the current step.
+ @param dp Derivative at the current step.
+
+ @param brackt Tells whether a minimizer has been bracketed.
+ If the minimizer has not been bracketed, then on input this
+ variable must be set <code>false</code>. If the minimizer has
+ been bracketed, then on output this variable is
+ <code>true</code>.
+
+ @param stpmin Lower bound for the step.
+ @param stpmax Upper bound for the step.
+
+ If the return value is 1, 2, 3, or 4, then the step has
+ been computed successfully. A return value of 0 indicates
+ improper input parameters.
+
+ @author Jorge J. More, David J. Thuente: original Fortran version,
+ as part of Minpack project. Argonne Nat'l Laboratory, June 1983.
+ Robert Dodier: Java translation, August 1997.
+ */
+ static int mcstep(
+ FloatType& stx,
+ FloatType& fx,
+ FloatType& dx,
+ FloatType& sty,
+ FloatType& fy,
+ FloatType& dy,
+ FloatType& stp,
+ FloatType fp,
+ FloatType dp,
+ bool& brackt,
+ FloatType stpmin,
+ FloatType stpmax);
+
+ void serialize(std::ostream* out) const {
+ out->write((const char*)&infoc,sizeof(infoc));
+ out->write((const char*)&dginit,sizeof(dginit));
+ out->write((const char*)&brackt,sizeof(brackt));
+ out->write((const char*)&stage1,sizeof(stage1));
+ out->write((const char*)&finit,sizeof(finit));
+ out->write((const char*)&dgtest,sizeof(dgtest));
+ out->write((const char*)&width,sizeof(width));
+ out->write((const char*)&width1,sizeof(width1));
+ out->write((const char*)&stx,sizeof(stx));
+ out->write((const char*)&fx,sizeof(fx));
+ out->write((const char*)&dgx,sizeof(dgx));
+ out->write((const char*)&sty,sizeof(sty));
+ out->write((const char*)&fy,sizeof(fy));
+ out->write((const char*)&dgy,sizeof(dgy));
+ out->write((const char*)&stmin,sizeof(stmin));
+ out->write((const char*)&stmax,sizeof(stmax));
+ }
+
+ void deserialize(std::istream* in) const {
+ in->read((char*)&infoc, sizeof(infoc));
+ in->read((char*)&dginit, sizeof(dginit));
+ in->read((char*)&brackt, sizeof(brackt));
+ in->read((char*)&stage1, sizeof(stage1));
+ in->read((char*)&finit, sizeof(finit));
+ in->read((char*)&dgtest, sizeof(dgtest));
+ in->read((char*)&width, sizeof(width));
+ in->read((char*)&width1, sizeof(width1));
+ in->read((char*)&stx, sizeof(stx));
+ in->read((char*)&fx, sizeof(fx));
+ in->read((char*)&dgx, sizeof(dgx));
+ in->read((char*)&sty, sizeof(sty));
+ in->read((char*)&fy, sizeof(fy));
+ in->read((char*)&dgy, sizeof(dgy));
+ in->read((char*)&stmin, sizeof(stmin));
+ in->read((char*)&stmax, sizeof(stmax));
+ }
+ };
+
+ template <typename FloatType, typename SizeType>
+ void mcsrch<FloatType, SizeType>::run(
+ FloatType const& gtol,
+ FloatType const& stpmin,
+ FloatType const& stpmax,
+ SizeType n,
+ FloatType* x,
+ FloatType f,
+ const FloatType* g,
+ FloatType* s,
+ SizeType is0,
+ FloatType& stp,
+ FloatType ftol,
+ FloatType xtol,
+ SizeType maxfev,
+ int& info,
+ SizeType& nfev,
+ FloatType* wa)
+ {
+ if (info != -1) {
+ infoc = 1;
+ if ( n == 0
+ || maxfev == 0
+ || gtol < FloatType(0)
+ || xtol < FloatType(0)
+ || stpmin < FloatType(0)
+ || stpmax < stpmin) {
+ throw error_internal_error(__FILE__, __LINE__);
+ }
+ if (stp <= FloatType(0) || ftol < FloatType(0)) {
+ throw error_internal_error(__FILE__, __LINE__);
+ }
+ // Compute the initial gradient in the search direction
+ // and check that s is a descent direction.
+ dginit = FloatType(0);
+ for (SizeType j = 0; j < n; j++) {
+ dginit += g[j] * s[is0+j];
+ }
+ if (dginit >= FloatType(0)) {
+ throw error_search_direction_not_descent();
+ }
+ brackt = false;
+ stage1 = true;
+ nfev = 0;
+ finit = f;
+ dgtest = ftol*dginit;
+ width = stpmax - stpmin;
+ width1 = FloatType(2) * width;
+ std::copy(x, x+n, wa);
+ // The variables stx, fx, dgx contain the values of the step,
+ // function, and directional derivative at the best step.
+ // The variables sty, fy, dgy contain the value of the step,
+ // function, and derivative at the other endpoint of
+ // the interval of uncertainty.
+ // The variables stp, f, dg contain the values of the step,
+ // function, and derivative at the current step.
+ stx = FloatType(0);
+ fx = finit;
+ dgx = dginit;
+ sty = FloatType(0);
+ fy = finit;
+ dgy = dginit;
+ }
+ for (;;) {
+ if (info != -1) {
+ // Set the minimum and maximum steps to correspond
+ // to the present interval of uncertainty.
+ if (brackt) {
+ stmin = std::min(stx, sty);
+ stmax = std::max(stx, sty);
+ }
+ else {
+ stmin = stx;
+ stmax = stp + FloatType(4) * (stp - stx);
+ }
+ // Force the step to be within the bounds stpmax and stpmin.
+ stp = std::max(stp, stpmin);
+ stp = std::min(stp, stpmax);
+ // If an unusual termination is to occur then let
+ // stp be the lowest point obtained so far.
+ if ( (brackt && (stp <= stmin || stp >= stmax))
+ || nfev >= maxfev - 1 || infoc == 0
+ || (brackt && stmax - stmin <= xtol * stmax)) {
+ stp = stx;
+ }
+ // Evaluate the function and gradient at stp
+ // and compute the directional derivative.
+ // We return to main program to obtain F and G.
+ for (SizeType j = 0; j < n; j++) {
+ x[j] = wa[j] + stp * s[is0+j];
+ }
+ info=-1;
+ break;
+ }
+ info = 0;
+ nfev++;
+ FloatType dg(0);
+ for (SizeType j = 0; j < n; j++) {
+ dg += g[j] * s[is0+j];
+ }
+ FloatType ftest1 = finit + stp*dgtest;
+ // Test for convergence.
+ if ((brackt && (stp <= stmin || stp >= stmax)) || infoc == 0) {
+ throw error_line_search_failed_rounding_errors(
+ "Rounding errors prevent further progress."
+ " There may not be a step which satisfies the"
+ " sufficient decrease and curvature conditions."
+ " Tolerances may be too small.");
+ }
+ if (stp == stpmax && f <= ftest1 && dg <= dgtest) {
+ throw error_line_search_failed(
+ "The step is at the upper bound stpmax().");
+ }
+ if (stp == stpmin && (f > ftest1 || dg >= dgtest)) {
+ throw error_line_search_failed(
+ "The step is at the lower bound stpmin().");
+ }
+ if (nfev >= maxfev) {
+ throw error_line_search_failed(
+ "Number of function evaluations has reached maxfev().");
+ }
+ if (brackt && stmax - stmin <= xtol * stmax) {
+ throw error_line_search_failed(
+ "Relative width of the interval of uncertainty"
+ " is at most xtol().");
+ }
+ // Check for termination.
+ if (f <= ftest1 && abs(dg) <= gtol * (-dginit)) {
+ info = 1;
+ break;
+ }
+ // In the first stage we seek a step for which the modified
+ // function has a nonpositive value and nonnegative derivative.
+ if ( stage1 && f <= ftest1
+ && dg >= std::min(ftol, gtol) * dginit) {
+ stage1 = false;
+ }
+ // A modified function is used to predict the step only if
+ // we have not obtained a step for which the modified
+ // function has a nonpositive function value and nonnegative
+ // derivative, and if a lower function value has been
+ // obtained but the decrease is not sufficient.
+ if (stage1 && f <= fx && f > ftest1) {
+ // Define the modified function and derivative values.
+ FloatType fm = f - stp*dgtest;
+ FloatType fxm = fx - stx*dgtest;
+ FloatType fym = fy - sty*dgtest;
+ FloatType dgm = dg - dgtest;
+ FloatType dgxm = dgx - dgtest;
+ FloatType dgym = dgy - dgtest;
+ // Call cstep to update the interval of uncertainty
+ // and to compute the new step.
+ infoc = mcstep(stx, fxm, dgxm, sty, fym, dgym, stp, fm, dgm,
+ brackt, stmin, stmax);
+ // Reset the function and gradient values for f.
+ fx = fxm + stx*dgtest;
+ fy = fym + sty*dgtest;
+ dgx = dgxm + dgtest;
+ dgy = dgym + dgtest;
+ }
+ else {
+ // Call mcstep to update the interval of uncertainty
+ // and to compute the new step.
+ infoc = mcstep(stx, fx, dgx, sty, fy, dgy, stp, f, dg,
+ brackt, stmin, stmax);
+ }
+ // Force a sufficient decrease in the size of the
+ // interval of uncertainty.
+ if (brackt) {
+ if (abs(sty - stx) >= FloatType(0.66) * width1) {
+ stp = stx + FloatType(0.5) * (sty - stx);
+ }
+ width1 = width;
+ width = abs(sty - stx);
+ }
+ }
+ }
+
+ template <typename FloatType, typename SizeType>
+ int mcsrch<FloatType, SizeType>::mcstep(
+ FloatType& stx,
+ FloatType& fx,
+ FloatType& dx,
+ FloatType& sty,
+ FloatType& fy,
+ FloatType& dy,
+ FloatType& stp,
+ FloatType fp,
+ FloatType dp,
+ bool& brackt,
+ FloatType stpmin,
+ FloatType stpmax)
+ {
+ bool bound;
+ FloatType gamma, p, q, r, s, sgnd, stpc, stpf, stpq, theta;
+ int info = 0;
+ if ( ( brackt && (stp <= std::min(stx, sty)
+ || stp >= std::max(stx, sty)))
+ || dx * (stp - stx) >= FloatType(0) || stpmax < stpmin) {
+ return 0;
+ }
+ // Determine if the derivatives have opposite sign.
+ sgnd = dp * (dx / abs(dx));
+ if (fp > fx) {
+ // First case. A higher function value.
+ // The minimum is bracketed. If the cubic step is closer
+ // to stx than the quadratic step, the cubic step is taken,
+ // else the average of the cubic and quadratic steps is taken.
+ info = 1;
+ bound = true;
+ theta = FloatType(3) * (fx - fp) / (stp - stx) + dx + dp;
+ s = max3(abs(theta), abs(dx), abs(dp));
+ gamma = s * std::sqrt(pow2(theta / s) - (dx / s) * (dp / s));
+ if (stp < stx) gamma = - gamma;
+ p = (gamma - dx) + theta;
+ q = ((gamma - dx) + gamma) + dp;
+ r = p/q;
+ stpc = stx + r * (stp - stx);
+ stpq = stx
+ + ((dx / ((fx - fp) / (stp - stx) + dx)) / FloatType(2))
+ * (stp - stx);
+ if (abs(stpc - stx) < abs(stpq - stx)) {
+ stpf = stpc;
+ }
+ else {
+ stpf = stpc + (stpq - stpc) / FloatType(2);
+ }
+ brackt = true;
+ }
+ else if (sgnd < FloatType(0)) {
+ // Second case. A lower function value and derivatives of
+ // opposite sign. The minimum is bracketed. If the cubic
+ // step is closer to stx than the quadratic (secant) step,
+ // the cubic step is taken, else the quadratic step is taken.
+ info = 2;
+ bound = false;
+ theta = FloatType(3) * (fx - fp) / (stp - stx) + dx + dp;
+ s = max3(abs(theta), abs(dx), abs(dp));
+ gamma = s * std::sqrt(pow2(theta / s) - (dx / s) * (dp / s));
+ if (stp > stx) gamma = - gamma;
+ p = (gamma - dp) + theta;
+ q = ((gamma - dp) + gamma) + dx;
+ r = p/q;
+ stpc = stp + r * (stx - stp);
+ stpq = stp + (dp / (dp - dx)) * (stx - stp);
+ if (abs(stpc - stp) > abs(stpq - stp)) {
+ stpf = stpc;
+ }
+ else {
+ stpf = stpq;
+ }
+ brackt = true;
+ }
+ else if (abs(dp) < abs(dx)) {
+ // Third case. A lower function value, derivatives of the
+ // same sign, and the magnitude of the derivative decreases.
+ // The cubic step is only used if the cubic tends to infinity
+ // in the direction of the step or if the minimum of the cubic
+ // is beyond stp. Otherwise the cubic step is defined to be
+ // either stpmin or stpmax. The quadratic (secant) step is also
+ // computed and if the minimum is bracketed then the the step
+ // closest to stx is taken, else the step farthest away is taken.
+ info = 3;
+ bound = true;
+ theta = FloatType(3) * (fx - fp) / (stp - stx) + dx + dp;
+ s = max3(abs(theta), abs(dx), abs(dp));
+ gamma = s * std::sqrt(
+ std::max(FloatType(0), pow2(theta / s) - (dx / s) * (dp / s)));
+ if (stp > stx) gamma = -gamma;
+ p = (gamma - dp) + theta;
+ q = (gamma + (dx - dp)) + gamma;
+ r = p/q;
+ if (r < FloatType(0) && gamma != FloatType(0)) {
+ stpc = stp + r * (stx - stp);
+ }
+ else if (stp > stx) {
+ stpc = stpmax;
+ }
+ else {
+ stpc = stpmin;
+ }
+ stpq = stp + (dp / (dp - dx)) * (stx - stp);
+ if (brackt) {
+ if (abs(stp - stpc) < abs(stp - stpq)) {
+ stpf = stpc;
+ }
+ else {
+ stpf = stpq;
+ }
+ }
+ else {
+ if (abs(stp - stpc) > abs(stp - stpq)) {
+ stpf = stpc;
+ }
+ else {
+ stpf = stpq;
+ }
+ }
+ }
+ else {
+ // Fourth case. A lower function value, derivatives of the
+ // same sign, and the magnitude of the derivative does
+ // not decrease. If the minimum is not bracketed, the step
+ // is either stpmin or stpmax, else the cubic step is taken.
+ info = 4;
+ bound = false;
+ if (brackt) {
+ theta = FloatType(3) * (fp - fy) / (sty - stp) + dy + dp;
+ s = max3(abs(theta), abs(dy), abs(dp));
+ gamma = s * std::sqrt(pow2(theta / s) - (dy / s) * (dp / s));
+ if (stp > sty) gamma = -gamma;
+ p = (gamma - dp) + theta;
+ q = ((gamma - dp) + gamma) + dy;
+ r = p/q;
+ stpc = stp + r * (sty - stp);
+ stpf = stpc;
+ }
+ else if (stp > stx) {
+ stpf = stpmax;
+ }
+ else {
+ stpf = stpmin;
+ }
+ }
+ // Update the interval of uncertainty. This update does not
+ // depend on the new step or the case analysis above.
+ if (fp > fx) {
+ sty = stp;
+ fy = fp;
+ dy = dp;
+ }
+ else {
+ if (sgnd < FloatType(0)) {
+ sty = stx;
+ fy = fx;
+ dy = dx;
+ }
+ stx = stp;
+ fx = fp;
+ dx = dp;
+ }
+ // Compute the new step and safeguard it.
+ stpf = std::min(stpmax, stpf);
+ stpf = std::max(stpmin, stpf);
+ stp = stpf;
+ if (brackt && bound) {
+ if (sty > stx) {
+ stp = std::min(stx + FloatType(0.66) * (sty - stx), stp);
+ }
+ else {
+ stp = std::max(stx + FloatType(0.66) * (sty - stx), stp);
+ }
+ }
+ return info;
+ }
+
+ /* Compute the sum of a vector times a scalar plus another vector.
+ Adapted from the subroutine <code>daxpy</code> in
+ <code>lbfgs.f</code>.
+ */
+ template <typename FloatType, typename SizeType>
+ void daxpy(
+ SizeType n,
+ FloatType da,
+ const FloatType* dx,
+ SizeType ix0,
+ SizeType incx,
+ FloatType* dy,
+ SizeType iy0,
+ SizeType incy)
+ {
+ SizeType i, ix, iy, m;
+ if (n == 0) return;
+ if (da == FloatType(0)) return;
+ if (!(incx == 1 && incy == 1)) {
+ ix = 0;
+ iy = 0;
+ for (i = 0; i < n; i++) {
+ dy[iy0+iy] += da * dx[ix0+ix];
+ ix += incx;
+ iy += incy;
+ }
+ return;
+ }
+ m = n % 4;
+ for (i = 0; i < m; i++) {
+ dy[iy0+i] += da * dx[ix0+i];
+ }
+ for (; i < n;) {
+ dy[iy0+i] += da * dx[ix0+i]; i++;
+ dy[iy0+i] += da * dx[ix0+i]; i++;
+ dy[iy0+i] += da * dx[ix0+i]; i++;
+ dy[iy0+i] += da * dx[ix0+i]; i++;
+ }
+ }
+
+ template <typename FloatType, typename SizeType>
+ inline
+ void daxpy(
+ SizeType n,
+ FloatType da,
+ const FloatType* dx,
+ SizeType ix0,
+ FloatType* dy)
+ {
+ daxpy(n, da, dx, ix0, SizeType(1), dy, SizeType(0), SizeType(1));
+ }
+
+ /* Compute the dot product of two vectors.
+ Adapted from the subroutine <code>ddot</code>
+ in <code>lbfgs.f</code>.
+ */
+ template <typename FloatType, typename SizeType>
+ FloatType ddot(
+ SizeType n,
+ const FloatType* dx,
+ SizeType ix0,
+ SizeType incx,
+ const FloatType* dy,
+ SizeType iy0,
+ SizeType incy)
+ {
+ SizeType i, ix, iy, m;
+ FloatType dtemp(0);
+ if (n == 0) return FloatType(0);
+ if (!(incx == 1 && incy == 1)) {
+ ix = 0;
+ iy = 0;
+ for (i = 0; i < n; i++) {
+ dtemp += dx[ix0+ix] * dy[iy0+iy];
+ ix += incx;
+ iy += incy;
+ }
+ return dtemp;
+ }
+ m = n % 5;
+ for (i = 0; i < m; i++) {
+ dtemp += dx[ix0+i] * dy[iy0+i];
+ }
+ for (; i < n;) {
+ dtemp += dx[ix0+i] * dy[iy0+i]; i++;
+ dtemp += dx[ix0+i] * dy[iy0+i]; i++;
+ dtemp += dx[ix0+i] * dy[iy0+i]; i++;
+ dtemp += dx[ix0+i] * dy[iy0+i]; i++;
+ dtemp += dx[ix0+i] * dy[iy0+i]; i++;
+ }
+ return dtemp;
+ }
+
+ template <typename FloatType, typename SizeType>
+ inline
+ FloatType ddot(
+ SizeType n,
+ const FloatType* dx,
+ const FloatType* dy)
+ {
+ return ddot(
+ n, dx, SizeType(0), SizeType(1), dy, SizeType(0), SizeType(1));
+ }
+
+ } // namespace detail
+
+ //! Interface to the LBFGS %minimizer.
+ /*! This class solves the unconstrained minimization problem
+ <pre>
+ min f(x), x = (x1,x2,...,x_n),
+ </pre>
+ using the limited-memory BFGS method. The routine is
+ especially effective on problems involving a large number of
+ variables. In a typical iteration of this method an
+ approximation Hk to the inverse of the Hessian
+ is obtained by applying <code>m</code> BFGS updates to a
+ diagonal matrix Hk0, using information from the
+ previous <code>m</code> steps. The user specifies the number
+ <code>m</code>, which determines the amount of storage
+ required by the routine. The user may also provide the
+ diagonal matrices Hk0 (parameter <code>diag</code> in the run()
+ function) if not satisfied with the default choice. The
+ algorithm is described in "On the limited memory BFGS method for
+ large scale optimization", by D. Liu and J. Nocedal, Mathematical
+ Programming B 45 (1989) 503-528.
+
+ The user is required to calculate the function value
+ <code>f</code> and its gradient <code>g</code>. In order to
+ allow the user complete control over these computations,
+ reverse communication is used. The routine must be called
+ repeatedly under the control of the member functions
+ <code>requests_f_and_g()</code>,
+ <code>requests_diag()</code>.
+ If neither requests_f_and_g() nor requests_diag() is
+ <code>true</code> the user should check for convergence
+ (using class traditional_convergence_test or any
+ other custom test). If the convergence test is negative,
+ the minimizer may be called again for the next iteration.
+
+ The steplength (stp()) is determined at each iteration
+ by means of the line search routine <code>mcsrch</code>, which is
+ a slight modification of the routine <code>CSRCH</code> written
+ by More' and Thuente.
+
+ The only variables that are machine-dependent are
+ <code>xtol</code>,
+ <code>stpmin</code> and
+ <code>stpmax</code>.
+
+ Fatal errors cause <code>error</code> exceptions to be thrown.
+ The generic class <code>error</code> is sub-classed (e.g.
+ class <code>error_line_search_failed</code>) to facilitate
+ granular %error handling.
+
+ A note on performance: Using Compaq Fortran V5.4 and
+ Compaq C++ V6.5, the C++ implementation is about 15% slower
+ than the Fortran implementation.
+ */
+ template <typename FloatType, typename SizeType = std::size_t>
+ class minimizer
+ {
+ public:
+ //! Default constructor. Some members are not initialized!
+ minimizer()
+ : n_(0), m_(0), maxfev_(0),
+ gtol_(0), xtol_(0),
+ stpmin_(0), stpmax_(0),
+ ispt(0), iypt(0)
+ {}
+
+ //! Constructor.
+ /*! @param n The number of variables in the minimization problem.
+ Restriction: <code>n &gt; 0</code>.
+
+ @param m The number of corrections used in the BFGS update.
+ Values of <code>m</code> less than 3 are not recommended;
+ large values of <code>m</code> will result in excessive
+ computing time. <code>3 &lt;= m &lt;= 7</code> is
+ recommended.
+ Restriction: <code>m &gt; 0</code>.
+
+ @param maxfev Maximum number of function evaluations
+ <b>per line search</b>.
+ Termination occurs when the number of evaluations
+ of the objective function is at least <code>maxfev</code> by
+ the end of an iteration.
+
+ @param gtol Controls the accuracy of the line search.
+ If the function and gradient evaluations are inexpensive with
+ respect to the cost of the iteration (which is sometimes the
+ case when solving very large problems) it may be advantageous
+ to set <code>gtol</code> to a small value. A typical small
+ value is 0.1.
+ Restriction: <code>gtol</code> should be greater than 1e-4.
+
+ @param xtol An estimate of the machine precision (e.g. 10e-16
+ on a SUN station 3/60). The line search routine will
+ terminate if the relative width of the interval of
+ uncertainty is less than <code>xtol</code>.
+
+ @param stpmin Specifies the lower bound for the step
+ in the line search.
+ The default value is 1e-20. This value need not be modified
+ unless the exponent is too large for the machine being used,
+ or unless the problem is extremely badly scaled (in which
+ case the exponent should be increased).
+
+ @param stpmax specifies the upper bound for the step
+ in the line search.
+ The default value is 1e20. This value need not be modified
+ unless the exponent is too large for the machine being used,
+ or unless the problem is extremely badly scaled (in which
+ case the exponent should be increased).
+ */
+ explicit
+ minimizer(
+ SizeType n,
+ SizeType m = 5,
+ SizeType maxfev = 20,
+ FloatType gtol = FloatType(0.9),
+ FloatType xtol = FloatType(1.e-16),
+ FloatType stpmin = FloatType(1.e-20),
+ FloatType stpmax = FloatType(1.e20))
+ : n_(n), m_(m), maxfev_(maxfev),
+ gtol_(gtol), xtol_(xtol),
+ stpmin_(stpmin), stpmax_(stpmax),
+ iflag_(0), requests_f_and_g_(false), requests_diag_(false),
+ iter_(0), nfun_(0), stp_(0),
+ stp1(0), ftol(0.0001), ys(0), point(0), npt(0),
+ ispt(n+2*m), iypt((n+2*m)+n*m),
+ info(0), bound(0), nfev(0)
+ {
+ if (n_ == 0) {
+ throw error_improper_input_parameter("n = 0.");
+ }
+ if (m_ == 0) {
+ throw error_improper_input_parameter("m = 0.");
+ }
+ if (maxfev_ == 0) {
+ throw error_improper_input_parameter("maxfev = 0.");
+ }
+ if (gtol_ <= FloatType(1.e-4)) {
+ throw error_improper_input_parameter("gtol <= 1.e-4.");
+ }
+ if (xtol_ < FloatType(0)) {
+ throw error_improper_input_parameter("xtol < 0.");
+ }
+ if (stpmin_ < FloatType(0)) {
+ throw error_improper_input_parameter("stpmin < 0.");
+ }
+ if (stpmax_ < stpmin) {
+ throw error_improper_input_parameter("stpmax < stpmin");
+ }
+ w_.resize(n_*(2*m_+1)+2*m_);
+ scratch_array_.resize(n_);
+ }
+
+ //! Number of free parameters (as passed to the constructor).
+ SizeType n() const { return n_; }
+
+ //! Number of corrections kept (as passed to the constructor).
+ SizeType m() const { return m_; }
+
+ /*! \brief Maximum number of evaluations of the objective function
+ per line search (as passed to the constructor).
+ */
+ SizeType maxfev() const { return maxfev_; }
+
+ /*! \brief Control of the accuracy of the line search.
+ (as passed to the constructor).
+ */
+ FloatType gtol() const { return gtol_; }
+
+ //! Estimate of the machine precision (as passed to the constructor).
+ FloatType xtol() const { return xtol_; }
+
+ /*! \brief Lower bound for the step in the line search.
+ (as passed to the constructor).
+ */
+ FloatType stpmin() const { return stpmin_; }
+
+ /*! \brief Upper bound for the step in the line search.
+ (as passed to the constructor).
+ */
+ FloatType stpmax() const { return stpmax_; }
+
+ //! Status indicator for reverse communication.
+ /*! <code>true</code> if the run() function returns to request
+ evaluation of the objective function (<code>f</code>) and
+ gradients (<code>g</code>) for the current point
+ (<code>x</code>). To continue the minimization the
+ run() function is called again with the updated values for
+ <code>f</code> and <code>g</code>.
+ <p>
+ See also: requests_diag()
+ */
+ bool requests_f_and_g() const { return requests_f_and_g_; }
+
+ //! Status indicator for reverse communication.
+ /*! <code>true</code> if the run() function returns to request
+ evaluation of the diagonal matrix (<code>diag</code>)
+ for the current point (<code>x</code>).
+ To continue the minimization the run() function is called
+ again with the updated values for <code>diag</code>.
+ <p>
+ See also: requests_f_and_g()
+ */
+ bool requests_diag() const { return requests_diag_; }
+
+ //! Number of iterations so far.
+ /*! Note that one iteration may involve multiple evaluations
+ of the objective function.
+ <p>
+ See also: nfun()
+ */
+ SizeType iter() const { return iter_; }
+
+ //! Total number of evaluations of the objective function so far.
+ /*! The total number of function evaluations increases by the
+ number of evaluations required for the line search. The total
+ is only increased after a successful line search.
+ <p>
+ See also: iter()
+ */
+ SizeType nfun() const { return nfun_; }
+
+ //! Norm of gradient given gradient array of length n().
+ FloatType euclidean_norm(const FloatType* a) const {
+ return std::sqrt(detail::ddot(n_, a, a));
+ }
+
+ //! Current stepsize.
+ FloatType stp() const { return stp_; }
+
+ //! Execution of one step of the minimization.
+ /*! @param x On initial entry this must be set by the user to
+ the values of the initial estimate of the solution vector.
+
+ @param f Before initial entry or on re-entry under the
+ control of requests_f_and_g(), <code>f</code> must be set
+ by the user to contain the value of the objective function
+ at the current point <code>x</code>.
+
+ @param g Before initial entry or on re-entry under the
+ control of requests_f_and_g(), <code>g</code> must be set
+ by the user to contain the components of the gradient at
+ the current point <code>x</code>.
+
+ The return value is <code>true</code> if either
+ requests_f_and_g() or requests_diag() is <code>true</code>.
+ Otherwise the user should check for convergence
+ (e.g. using class traditional_convergence_test) and
+ call the run() function again to continue the minimization.
+ If the return value is <code>false</code> the user
+ should <b>not</b> update <code>f</code>, <code>g</code> or
+ <code>diag</code> (other overload) before calling
+ the run() function again.
+
+ Note that <code>x</code> is always modified by the run()
+ function. Depending on the situation it can therefore be
+ necessary to evaluate the objective function one more time
+ after the minimization is terminated.
+ */
+ bool run(
+ FloatType* x,
+ FloatType f,
+ const FloatType* g)
+ {
+ return generic_run(x, f, g, false, 0);
+ }
+
+ //! Execution of one step of the minimization.
+ /*! @param x See other overload.
+
+ @param f See other overload.
+
+ @param g See other overload.
+
+ @param diag On initial entry or on re-entry under the
+ control of requests_diag(), <code>diag</code> must be set by
+ the user to contain the values of the diagonal matrix Hk0.
+ The routine will return at each iteration of the algorithm
+ with requests_diag() set to <code>true</code>.
+ <p>
+ Restriction: all elements of <code>diag</code> must be
+ positive.
+ */
+ bool run(
+ FloatType* x,
+ FloatType f,
+ const FloatType* g,
+ const FloatType* diag)
+ {
+ return generic_run(x, f, g, true, diag);
+ }
+
+ void serialize(std::ostream* out) const {
+ out->write((const char*)&n_, sizeof(n_)); // sanity check
+ out->write((const char*)&m_, sizeof(m_)); // sanity check
+ SizeType fs = sizeof(FloatType);
+ out->write((const char*)&fs, sizeof(fs)); // sanity check
+
+ mcsrch_instance.serialize(out);
+ out->write((const char*)&iflag_, sizeof(iflag_));
+ out->write((const char*)&requests_f_and_g_, sizeof(requests_f_and_g_));
+ out->write((const char*)&requests_diag_, sizeof(requests_diag_));
+ out->write((const char*)&iter_, sizeof(iter_));
+ out->write((const char*)&nfun_, sizeof(nfun_));
+ out->write((const char*)&stp_, sizeof(stp_));
+ out->write((const char*)&stp1, sizeof(stp1));
+ out->write((const char*)&ftol, sizeof(ftol));
+ out->write((const char*)&ys, sizeof(ys));
+ out->write((const char*)&point, sizeof(point));
+ out->write((const char*)&npt, sizeof(npt));
+ out->write((const char*)&info, sizeof(info));
+ out->write((const char*)&bound, sizeof(bound));
+ out->write((const char*)&nfev, sizeof(nfev));
+ out->write((const char*)&w_[0], sizeof(FloatType) * w_.size());
+ out->write((const char*)&scratch_array_[0], sizeof(FloatType) * scratch_array_.size());
+ }
+
+ void deserialize(std::istream* in) {
+ SizeType n, m, fs;
+ in->read((char*)&n, sizeof(n));
+ in->read((char*)&m, sizeof(m));
+ in->read((char*)&fs, sizeof(fs));
+ assert(n == n_);
+ assert(m == m_);
+ assert(fs == sizeof(FloatType));
+
+ mcsrch_instance.deserialize(in);
+ in->read((char*)&iflag_, sizeof(iflag_));
+ in->read((char*)&requests_f_and_g_, sizeof(requests_f_and_g_));
+ in->read((char*)&requests_diag_, sizeof(requests_diag_));
+ in->read((char*)&iter_, sizeof(iter_));
+ in->read((char*)&nfun_, sizeof(nfun_));
+ in->read((char*)&stp_, sizeof(stp_));
+ in->read((char*)&stp1, sizeof(stp1));
+ in->read((char*)&ftol, sizeof(ftol));
+ in->read((char*)&ys, sizeof(ys));
+ in->read((char*)&point, sizeof(point));
+ in->read((char*)&npt, sizeof(npt));
+ in->read((char*)&info, sizeof(info));
+ in->read((char*)&bound, sizeof(bound));
+ in->read((char*)&nfev, sizeof(nfev));
+ in->read((char*)&w_[0], sizeof(FloatType) * w_.size());
+ in->read((char*)&scratch_array_[0], sizeof(FloatType) * scratch_array_.size());
+ }
+
+ protected:
+ static void throw_diagonal_element_not_positive(SizeType i) {
+ throw error_improper_input_data(
+ "The " + error::itoa(i) + ". diagonal element of the"
+ " inverse Hessian approximation is not positive.");
+ }
+
+ bool generic_run(
+ FloatType* x,
+ FloatType f,
+ const FloatType* g,
+ bool diagco,
+ const FloatType* diag);
+
+ detail::mcsrch<FloatType, SizeType> mcsrch_instance;
+ const SizeType n_;
+ const SizeType m_;
+ const SizeType maxfev_;
+ const FloatType gtol_;
+ const FloatType xtol_;
+ const FloatType stpmin_;
+ const FloatType stpmax_;
+ int iflag_;
+ bool requests_f_and_g_;
+ bool requests_diag_;
+ SizeType iter_;
+ SizeType nfun_;
+ FloatType stp_;
+ FloatType stp1;
+ FloatType ftol;
+ FloatType ys;
+ SizeType point;
+ SizeType npt;
+ const SizeType ispt;
+ const SizeType iypt;
+ int info;
+ SizeType bound;
+ SizeType nfev;
+ std::vector<FloatType> w_;
+ std::vector<FloatType> scratch_array_;
+ };
+
+ template <typename FloatType, typename SizeType>
+ bool minimizer<FloatType, SizeType>::generic_run(
+ FloatType* x,
+ FloatType f,
+ const FloatType* g,
+ bool diagco,
+ const FloatType* diag)
+ {
+ bool execute_entire_while_loop = false;
+ if (!(requests_f_and_g_ || requests_diag_)) {
+ execute_entire_while_loop = true;
+ }
+ requests_f_and_g_ = false;
+ requests_diag_ = false;
+ FloatType* w = &(*(w_.begin()));
+ if (iflag_ == 0) { // Initialize.
+ nfun_ = 1;
+ if (diagco) {
+ for (SizeType i = 0; i < n_; i++) {
+ if (diag[i] <= FloatType(0)) {
+ throw_diagonal_element_not_positive(i);
+ }
+ }
+ }
+ else {
+ std::fill_n(scratch_array_.begin(), n_, FloatType(1));
+ diag = &(*(scratch_array_.begin()));
+ }
+ for (SizeType i = 0; i < n_; i++) {
+ w[ispt + i] = -g[i] * diag[i];
+ }
+ FloatType gnorm = std::sqrt(detail::ddot(n_, g, g));
+ if (gnorm == FloatType(0)) return false;
+ stp1 = FloatType(1) / gnorm;
+ execute_entire_while_loop = true;
+ }
+ if (execute_entire_while_loop) {
+ bound = iter_;
+ iter_++;
+ info = 0;
+ if (iter_ != 1) {
+ if (iter_ > m_) bound = m_;
+ ys = detail::ddot(
+ n_, w, iypt + npt, SizeType(1), w, ispt + npt, SizeType(1));
+ if (!diagco) {
+ FloatType yy = detail::ddot(
+ n_, w, iypt + npt, SizeType(1), w, iypt + npt, SizeType(1));
+ std::fill_n(scratch_array_.begin(), n_, ys / yy);
+ diag = &(*(scratch_array_.begin()));
+ }
+ else {
+ iflag_ = 2;
+ requests_diag_ = true;
+ return true;
+ }
+ }
+ }
+ if (execute_entire_while_loop || iflag_ == 2) {
+ if (iter_ != 1) {
+ if (diag == 0) {
+ throw error_internal_error(__FILE__, __LINE__);
+ }
+ if (diagco) {
+ for (SizeType i = 0; i < n_; i++) {
+ if (diag[i] <= FloatType(0)) {
+ throw_diagonal_element_not_positive(i);
+ }
+ }
+ }
+ SizeType cp = point;
+ if (point == 0) cp = m_;
+ w[n_ + cp -1] = 1 / ys;
+ SizeType i;
+ for (i = 0; i < n_; i++) {
+ w[i] = -g[i];
+ }
+ cp = point;
+ for (i = 0; i < bound; i++) {
+ if (cp == 0) cp = m_;
+ cp--;
+ FloatType sq = detail::ddot(
+ n_, w, ispt + cp * n_, SizeType(1), w, SizeType(0), SizeType(1));
+ SizeType inmc=n_+m_+cp;
+ SizeType iycn=iypt+cp*n_;
+ w[inmc] = w[n_ + cp] * sq;
+ detail::daxpy(n_, -w[inmc], w, iycn, w);
+ }
+ for (i = 0; i < n_; i++) {
+ w[i] *= diag[i];
+ }
+ for (i = 0; i < bound; i++) {
+ FloatType yr = detail::ddot(
+ n_, w, iypt + cp * n_, SizeType(1), w, SizeType(0), SizeType(1));
+ FloatType beta = w[n_ + cp] * yr;
+ SizeType inmc=n_+m_+cp;
+ beta = w[inmc] - beta;
+ SizeType iscn=ispt+cp*n_;
+ detail::daxpy(n_, beta, w, iscn, w);
+ cp++;
+ if (cp == m_) cp = 0;
+ }
+ std::copy(w, w+n_, w+(ispt + point * n_));
+ }
+ stp_ = FloatType(1);
+ if (iter_ == 1) stp_ = stp1;
+ std::copy(g, g+n_, w);
+ }
+ mcsrch_instance.run(
+ gtol_, stpmin_, stpmax_, n_, x, f, g, w, ispt + point * n_,
+ stp_, ftol, xtol_, maxfev_, info, nfev, &(*(scratch_array_.begin())));
+ if (info == -1) {
+ iflag_ = 1;
+ requests_f_and_g_ = true;
+ return true;
+ }
+ if (info != 1) {
+ throw error_internal_error(__FILE__, __LINE__);
+ }
+ nfun_ += nfev;
+ npt = point*n_;
+ for (SizeType i = 0; i < n_; i++) {
+ w[ispt + npt + i] = stp_ * w[ispt + npt + i];
+ w[iypt + npt + i] = g[i] - w[i];
+ }
+ point++;
+ if (point == m_) point = 0;
+ return false;
+ }
+
+ //! Traditional LBFGS convergence test.
+ /*! This convergence test is equivalent to the test embedded
+ in the <code>lbfgs.f</code> Fortran code. The test assumes that
+ there is a meaningful relation between the Euclidean norm of the
+ parameter vector <code>x</code> and the norm of the gradient
+ vector <code>g</code>. Therefore this test should not be used if
+ this assumption is not correct for a given problem.
+ */
+ template <typename FloatType, typename SizeType = std::size_t>
+ class traditional_convergence_test
+ {
+ public:
+ //! Default constructor.
+ traditional_convergence_test()
+ : n_(0), eps_(0)
+ {}
+
+ //! Constructor.
+ /*! @param n The number of variables in the minimization problem.
+ Restriction: <code>n &gt; 0</code>.
+
+ @param eps Determines the accuracy with which the solution
+ is to be found.
+ */
+ explicit
+ traditional_convergence_test(
+ SizeType n,
+ FloatType eps = FloatType(1.e-5))
+ : n_(n), eps_(eps)
+ {
+ if (n_ == 0) {
+ throw error_improper_input_parameter("n = 0.");
+ }
+ if (eps_ < FloatType(0)) {
+ throw error_improper_input_parameter("eps < 0.");
+ }
+ }
+
+ //! Number of free parameters (as passed to the constructor).
+ SizeType n() const { return n_; }
+
+ /*! \brief Accuracy with which the solution is to be found
+ (as passed to the constructor).
+ */
+ FloatType eps() const { return eps_; }
+
+ //! Execution of the convergence test for the given parameters.
+ /*! Returns <code>true</code> if
+ <pre>
+ ||g|| &lt; eps * max(1,||x||),
+ </pre>
+ where <code>||.||</code> denotes the Euclidean norm.
+
+ @param x Current solution vector.
+
+ @param g Components of the gradient at the current
+ point <code>x</code>.
+ */
+ bool
+ operator()(const FloatType* x, const FloatType* g) const
+ {
+ FloatType xnorm = std::sqrt(detail::ddot(n_, x, x));
+ FloatType gnorm = std::sqrt(detail::ddot(n_, g, g));
+ if (gnorm <= eps_ * std::max(FloatType(1), xnorm)) return true;
+ return false;
+ }
+ protected:
+ const SizeType n_;
+ const FloatType eps_;
+ };
+
+}} // namespace scitbx::lbfgs
+
+template <typename T>
+std::ostream& operator<<(std::ostream& os, const scitbx::lbfgs::minimizer<T>& min) {
+ return os << "ITER=" << min.iter() << "\tNFUN=" << min.nfun() << "\tSTP=" << min.stp() << "\tDIAG=" << min.requests_diag() << "\tF&G=" << min.requests_f_and_g();
+}
+
+
+#endif // SCITBX_LBFGS_H
diff --git a/training/lbfgs_test.cc b/training/lbfgs_test.cc
new file mode 100644
index 00000000..4171c118
--- /dev/null
+++ b/training/lbfgs_test.cc
@@ -0,0 +1,112 @@
+#include <cassert>
+#include <iostream>
+#include <sstream>
+#include "lbfgs.h"
+#include "sparse_vector.h"
+#include "fdict.h"
+
+using namespace std;
+
+double TestOptimizer() {
+ cerr << "TESTING NON-PERSISTENT OPTIMIZER\n";
+
+ // f(x,y) = 4x1^2 + x1*x2 + x2^2 + x3^2 + 6x3 + 5
+ // df/dx1 = 8*x1 + x2
+ // df/dx2 = 2*x2 + x1
+ // df/dx3 = 2*x3 + 6
+ double x[3];
+ double g[3];
+ scitbx::lbfgs::minimizer<double> opt(3);
+ scitbx::lbfgs::traditional_convergence_test<double> converged(3);
+ x[0] = 8;
+ x[1] = 8;
+ x[2] = 8;
+ double obj = 0;
+ do {
+ g[0] = 8 * x[0] + x[1];
+ g[1] = 2 * x[1] + x[0];
+ g[2] = 2 * x[2] + 6;
+ obj = 4 * x[0]*x[0] + x[0] * x[1] + x[1]*x[1] + x[2]*x[2] + 6 * x[2] + 5;
+ opt.run(x, obj, g);
+
+ cerr << x[0] << " " << x[1] << " " << x[2] << endl;
+ cerr << " obj=" << obj << "\td/dx1=" << g[0] << " d/dx2=" << g[1] << " d/dx3=" << g[2] << endl;
+ cerr << opt << endl;
+ } while (!converged(x, g));
+ return obj;
+}
+
+double TestPersistentOptimizer() {
+ cerr << "\nTESTING PERSISTENT OPTIMIZER\n";
+ // f(x,y) = 4x1^2 + x1*x2 + x2^2 + x3^2 + 6x3 + 5
+ // df/dx1 = 8*x1 + x2
+ // df/dx2 = 2*x2 + x1
+ // df/dx3 = 2*x3 + 6
+ double x[3];
+ double g[3];
+ scitbx::lbfgs::traditional_convergence_test<double> converged(3);
+ x[0] = 8;
+ x[1] = 8;
+ x[2] = 8;
+ double obj = 0;
+ string state;
+ do {
+ g[0] = 8 * x[0] + x[1];
+ g[1] = 2 * x[1] + x[0];
+ g[2] = 2 * x[2] + 6;
+ obj = 4 * x[0]*x[0] + x[0] * x[1] + x[1]*x[1] + x[2]*x[2] + 6 * x[2] + 5;
+
+ {
+ scitbx::lbfgs::minimizer<double> opt(3);
+ if (state.size() > 0) {
+ istringstream is(state, ios::binary);
+ opt.deserialize(&is);
+ }
+ opt.run(x, obj, g);
+ ostringstream os(ios::binary); opt.serialize(&os); state = os.str();
+ }
+
+ cerr << x[0] << " " << x[1] << " " << x[2] << endl;
+ cerr << " obj=" << obj << "\td/dx1=" << g[0] << " d/dx2=" << g[1] << " d/dx3=" << g[2] << endl;
+ } while (!converged(x, g));
+ return obj;
+}
+
+void TestSparseVector() {
+ cerr << "Testing SparseVector<double> serialization.\n";
+ int f1 = FD::Convert("Feature_1");
+ int f2 = FD::Convert("Feature_2");
+ FD::Convert("LanguageModel");
+ int f4 = FD::Convert("SomeFeature");
+ int f5 = FD::Convert("SomeOtherFeature");
+ SparseVector<double> g;
+ g.set_value(f2, log(0.5));
+ g.set_value(f4, log(0.125));
+ g.set_value(f1, 0);
+ g.set_value(f5, 23.777);
+ ostringstream os;
+ double iobj = 1.5;
+ B64::Encode(iobj, g, &os);
+ cerr << iobj << "\t" << g << endl;
+ string data = os.str();
+ cout << data << endl;
+ SparseVector<double> v;
+ double obj;
+ assert(B64::Decode(&obj, &v, &data[0], data.size()));
+ cerr << obj << "\t" << v << endl;
+ assert(obj == iobj);
+ assert(g.num_active() == v.num_active());
+}
+
+int main() {
+ double o1 = TestOptimizer();
+ double o2 = TestPersistentOptimizer();
+ if (o1 != o2) {
+ cerr << "OPTIMIZERS PERFORMED DIFFERENTLY!\n" << o1 << " vs. " << o2 << endl;
+ return 1;
+ }
+ TestSparseVector();
+ cerr << "SUCCESS\n";
+ return 0;
+}
+
diff --git a/training/make-lexcrf-grammar.pl b/training/make-lexcrf-grammar.pl
new file mode 100755
index 00000000..0e290492
--- /dev/null
+++ b/training/make-lexcrf-grammar.pl
@@ -0,0 +1,236 @@
+#!/usr/bin/perl -w
+use utf8;
+use strict;
+my ($effile, $model1) = @ARGV;
+die "Usage: $0 corpus.fr-en corpus.model1\n" unless $effile && -f $effile && $model1 && -f $model1;
+
+open EF, "<$effile" or die;
+open M1, "<$model1" or die;
+binmode(EF,":utf8");
+binmode(M1,":utf8");
+binmode(STDOUT,":utf8");
+my %model1;
+while(<M1>) {
+ chomp;
+ my ($f, $e, $lp) = split /\s+/;
+ $model1{$f}->{$e} = $lp;
+}
+
+my $ADD_MODEL1 = 0; # found that model1 hurts performance
+my $IS_FRENCH_F = 0; # indicates that the f language is french
+my $IS_ARABIC_F = 1; # indicates that the f language is arabic
+my $ADD_PREFIX_ID = 0;
+my $ADD_LEN = 1;
+my $ADD_LD = 0;
+my $ADD_DICE = 1;
+my $ADD_111 = 1;
+my $ADD_ID = 1;
+my $ADD_PUNC = 1;
+my $ADD_NUM_MM = 1;
+my $ADD_NULL = 1;
+my $BEAM_RATIO = 50;
+
+my %fdict;
+my %fcounts;
+my %ecounts;
+
+while(<EF>) {
+ chomp;
+ my ($f, $e) = split /\s*\|\|\|\s*/;
+ my @es = split /\s+/, $e;
+ my @fs = split /\s+/, $f;
+ for my $ew (@es){ $ecounts{$ew}++; }
+ push @fs, '<eps>' if $ADD_NULL;
+ for my $fw (@fs){ $fcounts{$fw}++; }
+ for my $fw (@fs){
+ for my $ew (@es){
+ $fdict{$fw}->{$ew}++;
+ }
+ }
+}
+
+print STDERR "Dice 0\n" if $ADD_DICE;
+print STDERR "OneOneOne 0\nId_OneOneOne 0\n" if $ADD_111;
+print STDERR "Identical 0\n" if $ADD_ID;
+print STDERR "PuncMiss 0\n" if $ADD_PUNC;
+print STDERR "IsNull 0\n" if $ADD_NULL;
+print STDERR "Model1 0\n" if $ADD_MODEL1;
+print STDERR "DLen 0\n" if $ADD_LEN;
+print STDERR "NumMM 0\n" if $ADD_NUM_MM;
+print STDERR "Level 0\n" if $ADD_LD;
+print STDERR "PfxIdentical 0\n" if ($ADD_PREFIX_ID);
+my $fc = 1000000;
+for my $f (sort keys %fdict) {
+ my $re = $fdict{$f};
+ my $max;
+ for my $e (sort {$re->{$b} <=> $re->{$a}} keys %$re) {
+ my $efcount = $re->{$e};
+ unless (defined $max) { $max = $efcount; }
+ my $m1 = $model1{$f}->{$e};
+ unless (defined $m1) { next; }
+ $fc++;
+ my $dice = 2 * $efcount / ($ecounts{$e} + $fcounts{$f});
+ my $feats = "F$fc=1";
+ my $oe = $e;
+ my $len_e = length($oe);
+ my $of = $f; # normalized form
+ if ($IS_FRENCH_F) {
+ # see http://en.wikipedia.org/wiki/Use_of_the_circumflex_in_French
+ $of =~ s/â/as/g;
+ $of =~ s/ê/es/g;
+ $of =~ s/î/is/g;
+ $of =~ s/ô/os/g;
+ $of =~ s/û/us/g;
+ } elsif ($IS_ARABIC_F) {
+ if (length($of) > 1 && !($of =~ /\d/)) {
+ $of =~ s/\$/sh/g;
+ }
+ }
+ my $len_f = length($of);
+ $feats .= " Model1=$m1" if ($ADD_MODEL1);
+ $feats .= " Dice=$dice" if $ADD_DICE;
+ my $is_null = undef;
+ if ($ADD_NULL && $f eq '<eps>') {
+ $feats .= " IsNull=1";
+ $is_null = 1;
+ }
+ if ($ADD_LEN) {
+ if (!$is_null) {
+ my $dlen = abs($len_e - $len_f);
+ $feats .= " DLen=$dlen";
+ }
+ }
+ my $f_num = ($of =~ /^-?\d[0-9\.\,]+%?$/); # this matches *two digit* and more numbers
+ my $e_num = ($oe =~ /^-?\d[0-9\.\,]+%?$/);
+ my $both_non_numeric = (!$e_num && !$f_num);
+ if ($ADD_NUM_MM && (($f_num && !$e_num) || ($e_num && !$f_num))) {
+ $feats .= " NumMM=1";
+ }
+ if ($ADD_PREFIX_ID) {
+ if ($len_e > 3 && $len_f > 3 && $both_non_numeric) {
+ my $pe = substr $oe, 0, 3;
+ my $pf = substr $of, 0, 3;
+ if ($pe eq $pf) { $feats .= " PfxIdentical=1"; }
+ }
+ }
+ if ($ADD_LD) {
+ my $ld = 0;
+ if ($is_null) { $ld = length($e); } else {
+ $ld = levenshtein($e, $f);
+ }
+ $feats .= " Leven=$ld";
+ }
+ my $ident = ($e eq $f);
+ if ($ident && $ADD_ID) { $feats .= " Identical=1"; }
+ if ($ADD_111 && ($efcount == 1 && $ecounts{$e} == 1 && $fcounts{$f} == 1)) {
+ if ($ident && $ADD_ID) {
+ $feats .= " Id_OneOneOne=1";
+ }
+ $feats .= " OneOneOne=1";
+ }
+ if ($ADD_PUNC) {
+ if (($f =~ /^[0-9!\$%,\-\/"':;=+?.()«»]+$/ && $e =~ /[a-z]+/) ||
+ ($e =~ /^[0-9!\$%,\-\/"':;=+?.()«»]+$/ && $f =~ /[a-z]+/)) {
+ $feats .= " PuncMiss=1";
+ }
+ }
+ my $r = (0.5 - rand)/5;
+ print STDERR "F$fc $r\n";
+ print "$f ||| $e ||| $feats\n";
+ }
+}
+
+sub levenshtein
+{
+ # $s1 and $s2 are the two strings
+ # $len1 and $len2 are their respective lengths
+ #
+ my ($s1, $s2) = @_;
+ my ($len1, $len2) = (length $s1, length $s2);
+
+ # If one of the strings is empty, the distance is the length
+ # of the other string
+ #
+ return $len2 if ($len1 == 0);
+ return $len1 if ($len2 == 0);
+
+ my %mat;
+
+ # Init the distance matrix
+ #
+ # The first row to 0..$len1
+ # The first column to 0..$len2
+ # The rest to 0
+ #
+ # The first row and column are initialized so to denote distance
+ # from the empty string
+ #
+ for (my $i = 0; $i <= $len1; ++$i)
+ {
+ for (my $j = 0; $j <= $len2; ++$j)
+ {
+ $mat{$i}{$j} = 0;
+ $mat{0}{$j} = $j;
+ }
+
+ $mat{$i}{0} = $i;
+ }
+
+ # Some char-by-char processing is ahead, so prepare
+ # array of chars from the strings
+ #
+ my @ar1 = split(//, $s1);
+ my @ar2 = split(//, $s2);
+
+ for (my $i = 1; $i <= $len1; ++$i)
+ {
+ for (my $j = 1; $j <= $len2; ++$j)
+ {
+ # Set the cost to 1 iff the ith char of $s1
+ # equals the jth of $s2
+ #
+ # Denotes a substitution cost. When the char are equal
+ # there is no need to substitute, so the cost is 0
+ #
+ my $cost = ($ar1[$i-1] eq $ar2[$j-1]) ? 0 : 1;
+
+ # Cell $mat{$i}{$j} equals the minimum of:
+ #
+ # - The cell immediately above plus 1
+ # - The cell immediately to the left plus 1
+ # - The cell diagonally above and to the left plus the cost
+ #
+ # We can either insert a new char, delete a char or
+ # substitute an existing char (with an associated cost)
+ #
+ $mat{$i}{$j} = min([$mat{$i-1}{$j} + 1,
+ $mat{$i}{$j-1} + 1,
+ $mat{$i-1}{$j-1} + $cost]);
+ }
+ }
+
+ # Finally, the Levenshtein distance equals the rightmost bottom cell
+ # of the matrix
+ #
+ # Note that $mat{$x}{$y} denotes the distance between the substrings
+ # 1..$x and 1..$y
+ #
+ return $mat{$len1}{$len2};
+}
+
+
+# minimal element of a list
+#
+sub min
+{
+ my @list = @{$_[0]};
+ my $min = $list[0];
+
+ foreach my $i (@list)
+ {
+ $min = $i if ($i < $min);
+ }
+
+ return $min;
+}
+
diff --git a/training/model1.cc b/training/model1.cc
new file mode 100644
index 00000000..f571700f
--- /dev/null
+++ b/training/model1.cc
@@ -0,0 +1,103 @@
+#include <iostream>
+
+#include "lattice.h"
+#include "stringlib.h"
+#include "filelib.h"
+#include "ttables.h"
+#include "tdict.h"
+
+using namespace std;
+
+int main(int argc, char** argv) {
+ if (argc != 2) {
+ cerr << "Usage: " << argv[0] << " corpus.fr-en\n";
+ return 1;
+ }
+ const int ITERATIONS = 5;
+ const prob_t BEAM_THRESHOLD(0.0001);
+ TTable tt;
+ const WordID kNULL = TD::Convert("<eps>");
+ bool use_null = true;
+ TTable::Word2Word2Double was_viterbi;
+ for (int iter = 0; iter < ITERATIONS; ++iter) {
+ const bool final_iteration = (iter == (ITERATIONS - 1));
+ cerr << "ITERATION " << (iter + 1) << (final_iteration ? " (FINAL)" : "") << endl;
+ ReadFile rf(argv[1]);
+ istream& in = *rf.stream();
+ prob_t likelihood = prob_t::One();
+ double denom = 0.0;
+ int lc = 0;
+ bool flag = false;
+ while(true) {
+ string line;
+ getline(in, line);
+ if (!in) break;
+ ++lc;
+ if (lc % 1000 == 0) { cerr << '.'; flag = true; }
+ if (lc %50000 == 0) { cerr << " [" << lc << "]\n" << flush; flag = false; }
+ string ssrc, strg;
+ ParseTranslatorInput(line, &ssrc, &strg);
+ Lattice src, trg;
+ LatticeTools::ConvertTextToLattice(ssrc, &src);
+ LatticeTools::ConvertTextToLattice(strg, &trg);
+ assert(src.size() > 0);
+ assert(trg.size() > 0);
+ denom += 1.0;
+ vector<prob_t> probs(src.size() + 1);
+ for (int j = 0; j < trg.size(); ++j) {
+ const WordID& f_j = trg[j][0].label;
+ prob_t sum = prob_t::Zero();
+ if (use_null) {
+ probs[0] = tt.prob(kNULL, f_j);
+ sum += probs[0];
+ }
+ for (int i = 1; i <= src.size(); ++i) {
+ probs[i] = tt.prob(src[i-1][0].label, f_j);
+ sum += probs[i];
+ }
+ if (final_iteration) {
+ WordID max_i = 0;
+ prob_t max_p = prob_t::Zero();
+ if (use_null) {
+ max_i = kNULL;
+ max_p = probs[0];
+ }
+ for (int i = 1; i <= src.size(); ++i) {
+ if (probs[i] > max_p) {
+ max_p = probs[i];
+ max_i = src[i-1][0].label;
+ }
+ }
+ was_viterbi[max_i][f_j] = 1.0;
+ } else {
+ if (use_null)
+ tt.Increment(kNULL, f_j, probs[0] / sum);
+ for (int i = 1; i <= src.size(); ++i)
+ tt.Increment(src[i-1][0].label, f_j, probs[i] / sum);
+ }
+ likelihood *= sum;
+ }
+ }
+ if (flag) { cerr << endl; }
+ cerr << " log likelihood: " << log(likelihood) << endl;
+ cerr << " cross entopy: " << (-log(likelihood) / denom) << endl;
+ cerr << " perplexity: " << pow(2.0, -log(likelihood) / denom) << endl;
+ if (!final_iteration) tt.Normalize();
+ }
+ for (TTable::Word2Word2Double::iterator ei = tt.ttable.begin(); ei != tt.ttable.end(); ++ei) {
+ const TTable::Word2Double& cpd = ei->second;
+ const TTable::Word2Double& vit = was_viterbi[ei->first];
+ const string& esym = TD::Convert(ei->first);
+ prob_t max_p = prob_t::Zero();
+ for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi)
+ if (fi->second > max_p) max_p = prob_t(fi->second);
+ const prob_t threshold = max_p * BEAM_THRESHOLD;
+ for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi) {
+ if (fi->second > threshold || (vit.count(fi->first) > 0)) {
+ cout << esym << ' ' << TD::Convert(fi->first) << ' ' << log(fi->second) << endl;
+ }
+ }
+ }
+ return 0;
+}
+
diff --git a/training/mr_em_train.cc b/training/mr_em_train.cc
new file mode 100644
index 00000000..a15fbe4c
--- /dev/null
+++ b/training/mr_em_train.cc
@@ -0,0 +1,270 @@
+#include <iostream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "config.h"
+#ifdef HAVE_BOOST_DIGAMMA
+#include <boost/math/special_functions/digamma.hpp>
+using boost::math::digamma;
+#endif
+
+#include "tdict.h"
+#include "filelib.h"
+#include "trule.h"
+#include "fdict.h"
+#include "weights.h"
+#include "sparse_vector.h"
+
+using namespace std;
+using boost::shared_ptr;
+namespace po = boost::program_options;
+
+#ifndef HAVE_BOOST_DIGAMMA
+#warning Using Mark Johnson's digamma()
+double digamma(double x) {
+ double result = 0, xx, xx2, xx4;
+ assert(x > 0);
+ for ( ; x < 7; ++x)
+ result -= 1/x;
+ x -= 1.0/2.0;
+ xx = 1.0/x;
+ xx2 = xx*xx;
+ xx4 = xx2*xx2;
+ result += log(x)+(1./24.)*xx2-(7.0/960.0)*xx4+(31.0/8064.0)*xx4*xx2-(127.0/30720.0)*xx4*xx4;
+ return result;
+}
+#endif
+
+void SanityCheck(const vector<double>& w) {
+ for (int i = 0; i < w.size(); ++i) {
+ assert(!isnan(w[i]));
+ }
+}
+
+struct FComp {
+ const vector<double>& w_;
+ FComp(const vector<double>& w) : w_(w) {}
+ bool operator()(int a, int b) const {
+ return w_[a] > w_[b];
+ }
+};
+
+void ShowLargestFeatures(const vector<double>& w) {
+ vector<int> fnums(w.size() - 1);
+ for (int i = 1; i < w.size(); ++i)
+ fnums[i-1] = i;
+ vector<int>::iterator mid = fnums.begin();
+ mid += (w.size() > 10 ? 10 : w.size()) - 1;
+ partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
+ cerr << "MOST PROBABLE:";
+ for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
+ cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
+ }
+ cerr << endl;
+}
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+ po::options_description opts("Configuration options");
+ opts.add_options()
+ ("output,o",po::value<string>()->default_value("-"),"Output log probs file")
+ ("grammar,g",po::value<vector<string> >()->composing(),"SCFG grammar file(s)")
+ ("optimization_method,m", po::value<string>()->default_value("em"), "Optimization method (em, vb)")
+ ("input_format,f",po::value<string>()->default_value("b64"),"Encoding of the input (b64 or text)");
+ po::options_description clo("Command line options");
+ clo.add_options()
+ ("config", po::value<string>(), "Configuration file")
+ ("help,h", "Print this help message and exit");
+ po::options_description dconfig_options, dcmdline_options;
+ dconfig_options.add(opts);
+ dcmdline_options.add(opts).add(clo);
+
+ po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+ if (conf->count("config")) {
+ ifstream config((*conf)["config"].as<string>().c_str());
+ po::store(po::parse_config_file(config, dconfig_options), *conf);
+ }
+ po::notify(*conf);
+
+ if (conf->count("help") || !conf->count("grammar")) {
+ cerr << dcmdline_options << endl;
+ exit(1);
+ }
+}
+
+// describes a multinomial or multinomial with a prior
+// does not contain the parameters- just the list of events
+// and any hyperparameters
+struct MultinomialInfo {
+ MultinomialInfo() : alpha(1.0) {}
+ vector<int> events; // the events that this multinomial generates
+ double alpha; // hyperparameter for (optional) Dirichlet prior
+};
+
+typedef map<WordID, MultinomialInfo> ModelDefinition;
+
+void LoadModelEvents(const po::variables_map& conf, ModelDefinition* pm) {
+ ModelDefinition& m = *pm;
+ m.clear();
+ vector<string> gfiles = conf["grammar"].as<vector<string> >();
+ for (int i = 0; i < gfiles.size(); ++i) {
+ ReadFile rf(gfiles[i]);
+ istream& in = *rf.stream();
+ int lc = 0;
+ while(in) {
+ string line;
+ getline(in, line);
+ if (line.empty()) continue;
+ ++lc;
+ TRule r(line, true);
+ const SparseVector<double>& f = r.GetFeatureValues();
+ if (f.num_active() == 0) {
+ cerr << "[WARNING] no feature found in " << gfiles[i] << ':' << lc << endl;
+ continue;
+ }
+ if (f.num_active() > 1) {
+ cerr << "[ERROR] more than one feature found in " << gfiles[i] << ':' << lc << endl;
+ exit(1);
+ }
+ SparseVector<double>::const_iterator it = f.begin();
+ if (it->second != 1.0) {
+ cerr << "[ERROR] feature with value != 1 found in " << gfiles[i] << ':' << lc << endl;
+ exit(1);
+ }
+ m[r.GetLHS()].events.push_back(it->first);
+ }
+ }
+ for (ModelDefinition::iterator it = m.begin(); it != m.end(); ++it) {
+ const vector<int>& v = it->second.events;
+ cerr << "Multinomial [" << TD::Convert(it->first*-1) << "]\n";
+ if (v.size() < 1000) {
+ cerr << " generates:";
+ for (int i = 0; i < v.size(); ++i) {
+ cerr << " " << FD::Convert(v[i]);
+ }
+ cerr << endl;
+ }
+ }
+}
+
+void Maximize(const ModelDefinition& m, const bool use_vb, vector<double>* counts) {
+ for (ModelDefinition::const_iterator it = m.begin(); it != m.end(); ++it) {
+ const MultinomialInfo& mult_info = it->second;
+ const vector<int>& events = mult_info.events;
+ cerr << "Multinomial [" << TD::Convert(it->first*-1) << "]";
+ double tot = 0;
+ for (int i = 0; i < events.size(); ++i)
+ tot += (*counts)[events[i]];
+ cerr << " = " << tot << endl;
+ assert(tot > 0.0);
+ double ltot = log(tot);
+ if (use_vb)
+ ltot = digamma(tot + events.size() * mult_info.alpha);
+ for (int i = 0; i < events.size(); ++i) {
+ if (use_vb) {
+ (*counts)[events[i]] = digamma((*counts)[events[i]] + mult_info.alpha) - ltot;
+ } else {
+ (*counts)[events[i]] = log((*counts)[events[i]]) - ltot;
+ }
+ }
+ if (events.size() < 50) {
+ for (int i = 0; i < events.size(); ++i) {
+ cerr << " p(" << FD::Convert(events[i]) << ")=" << exp((*counts)[events[i]]);
+ }
+ cerr << endl;
+ }
+ }
+}
+
+int main(int argc, char** argv) {
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+
+ const bool use_b64 = conf["input_format"].as<string>() == "b64";
+ const bool use_vb = conf["optimization_method"].as<string>() == "vb";
+ if (use_vb)
+ cerr << "Using variational Bayes, make sure alphas are set\n";
+
+ ModelDefinition model_def;
+ LoadModelEvents(conf, &model_def);
+
+ const string s_obj = "**OBJ**";
+ int num_feats = FD::NumFeats();
+ cerr << "Number of features: " << num_feats << endl;
+
+ vector<double> counts(num_feats, 0);
+ double logprob = 0;
+ // 0<TAB>**OBJ**=12.2;Feat1=2.3;Feat2=-0.2;
+ // 0<TAB>**OBJ**=1.1;Feat1=1.0;
+
+ // E-step
+ while(cin) {
+ string line;
+ getline(cin, line);
+ if (line.empty()) continue;
+ int feat;
+ double val;
+ size_t i = line.find("\t");
+ assert(i != string::npos);
+ ++i;
+ if (use_b64) {
+ SparseVector<double> g;
+ double obj;
+ if (!B64::Decode(&obj, &g, &line[i], line.size() - i)) {
+ cerr << "B64 decoder returned error, skipping!\n";
+ continue;
+ }
+ logprob += obj;
+ const SparseVector<double>& cg = g;
+ for (SparseVector<double>::const_iterator it = cg.begin(); it != cg.end(); ++it) {
+ if (it->first >= num_feats) {
+ cerr << "Unexpected feature: " << FD::Convert(it->first) << endl;
+ abort();
+ }
+ counts[it->first] += it->second;
+ }
+ } else { // text encoding - your counts will not be accurate!
+ while (i < line.size()) {
+ size_t start = i;
+ while (line[i] != '=' && i < line.size()) ++i;
+ if (i == line.size()) { cerr << "FORMAT ERROR\n"; break; }
+ string fname = line.substr(start, i - start);
+ if (fname == s_obj) {
+ feat = -1;
+ } else {
+ feat = FD::Convert(line.substr(start, i - start));
+ if (feat >= num_feats) {
+ cerr << "Unexpected feature: " << line.substr(start, i - start) << endl;
+ abort();
+ }
+ }
+ ++i;
+ start = i;
+ while (line[i] != ';' && i < line.size()) ++i;
+ if (i - start == 0) continue;
+ val = atof(line.substr(start, i - start).c_str());
+ ++i;
+ if (feat == -1) {
+ logprob += val;
+ } else {
+ counts[feat] += val;
+ }
+ }
+ }
+ }
+
+ cerr << "LOGPROB: " << logprob << endl;
+ // M-step
+ Maximize(model_def, use_vb, &counts);
+
+ SanityCheck(counts);
+ ShowLargestFeatures(counts);
+ Weights weights;
+ weights.InitFromVector(counts);
+ weights.WriteToFile(conf["output"].as<string>(), false);
+
+ return 0;
+}
diff --git a/training/mr_optimize_reduce.cc b/training/mr_optimize_reduce.cc
new file mode 100644
index 00000000..56b73c30
--- /dev/null
+++ b/training/mr_optimize_reduce.cc
@@ -0,0 +1,243 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "optimize.h"
+#include "fdict.h"
+#include "weights.h"
+#include "sparse_vector.h"
+
+using namespace std;
+using boost::shared_ptr;
+namespace po = boost::program_options;
+
+void SanityCheck(const vector<double>& w) {
+ for (int i = 0; i < w.size(); ++i) {
+ assert(!isnan(w[i]));
+ assert(!isinf(w[i]));
+ }
+}
+
+struct FComp {
+ const vector<double>& w_;
+ FComp(const vector<double>& w) : w_(w) {}
+ bool operator()(int a, int b) const {
+ return fabs(w_[a]) > fabs(w_[b]);
+ }
+};
+
+void ShowLargestFeatures(const vector<double>& w) {
+ vector<int> fnums(w.size());
+ for (int i = 0; i < w.size(); ++i)
+ fnums[i] = i;
+ vector<int>::iterator mid = fnums.begin();
+ mid += (w.size() > 10 ? 10 : w.size());
+ partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
+ cerr << "TOP FEATURES:";
+ for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
+ cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
+ }
+ cerr << endl;
+}
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+ po::options_description opts("Configuration options");
+ opts.add_options()
+ ("input_weights,i",po::value<string>(),"Input feature weights file")
+ ("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
+ ("optimization_method,m", po::value<string>()->default_value("lbfgs"), "Optimization method (sgd, lbfgs, rprop)")
+ ("state,s",po::value<string>(),"Read (and write if output_state is not set) optimizer state from this state file. In the first iteration, the file should not exist.")
+ ("input_format,f",po::value<string>()->default_value("b64"),"Encoding of the input (b64 or text)")
+ ("output_state,S", po::value<string>(), "Output state file (optional override)")
+ ("correction_buffers,M", po::value<int>()->default_value(10), "Number of gradients for LBFGS to maintain in memory")
+ ("eta,e", po::value<double>()->default_value(0.1), "Learning rate for SGD (eta)")
+ ("gaussian_prior,p","Use a Gaussian prior on the weights")
+ ("means,u", po::value<string>(), "File containing the means for Gaussian prior")
+ ("sigma_squared", po::value<double>()->default_value(1.0), "Sigma squared term for spherical Gaussian prior");
+ po::options_description clo("Command line options");
+ clo.add_options()
+ ("config", po::value<string>(), "Configuration file")
+ ("help,h", "Print this help message and exit");
+ po::options_description dconfig_options, dcmdline_options;
+ dconfig_options.add(opts);
+ dcmdline_options.add(opts).add(clo);
+
+ po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+ if (conf->count("config")) {
+ ifstream config((*conf)["config"].as<string>().c_str());
+ po::store(po::parse_config_file(config, dconfig_options), *conf);
+ }
+ po::notify(*conf);
+
+ if (conf->count("help") || !conf->count("input_weights") || !conf->count("state")) {
+ cerr << dcmdline_options << endl;
+ exit(1);
+ }
+}
+
+int main(int argc, char** argv) {
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+
+ const bool use_b64 = conf["input_format"].as<string>() == "b64";
+
+ Weights weights;
+ weights.InitFromFile(conf["input_weights"].as<string>());
+ const string s_obj = "**OBJ**";
+ int num_feats = FD::NumFeats();
+ cerr << "Number of features: " << num_feats << endl;
+ const bool gaussian_prior = conf.count("gaussian_prior");
+ vector<double> means(num_feats, 0);
+ if (conf.count("means")) {
+ if (!gaussian_prior) {
+ cerr << "Don't use --means without --gaussian_prior!\n";
+ exit(1);
+ }
+ Weights wm;
+ wm.InitFromFile(conf["means"].as<string>());
+ if (num_feats != FD::NumFeats()) {
+ cerr << "[ERROR] Means file had unexpected features!\n";
+ exit(1);
+ }
+ wm.InitVector(&means);
+ }
+ shared_ptr<Optimizer> o;
+ const string omethod = conf["optimization_method"].as<string>();
+ if (omethod == "sgd")
+ o.reset(new SGDOptimizer(conf["eta"].as<double>()));
+ else if (omethod == "rprop")
+ o.reset(new RPropOptimizer(num_feats)); // TODO add configuration
+ else
+ o.reset(new LBFGSOptimizer(num_feats, conf["correction_buffers"].as<int>()));
+ cerr << "Optimizer: " << o->Name() << endl;
+ string state_file = conf["state"].as<string>();
+ {
+ ifstream in(state_file.c_str(), ios::binary);
+ if (in)
+ o->Load(&in);
+ else
+ cerr << "No state file found, assuming ITERATION 1\n";
+ }
+
+ vector<double> lambdas(num_feats, 0);
+ weights.InitVector(&lambdas);
+ double objective = 0;
+ vector<double> gradient(num_feats, 0);
+ // 0<TAB>**OBJ**=12.2;Feat1=2.3;Feat2=-0.2;
+ // 0<TAB>**OBJ**=1.1;Feat1=1.0;
+ int total_lines = 0; // TODO - this should be a count of the
+ // training instances!!
+ while(cin) {
+ string line;
+ getline(cin, line);
+ if (line.empty()) continue;
+ ++total_lines;
+ int feat;
+ double val;
+ size_t i = line.find("\t");
+ assert(i != string::npos);
+ ++i;
+ if (use_b64) {
+ SparseVector<double> g;
+ double obj;
+ if (!B64::Decode(&obj, &g, &line[i], line.size() - i)) {
+ cerr << "B64 decoder returned error, skipping gradient!\n";
+ cerr << " START: " << line.substr(0,min(200ul, line.size())) << endl;
+ if (line.size() > 200)
+ cerr << " END: " << line.substr(line.size() - 200, 200) << endl;
+ cout << "-1\tRESTART\n";
+ exit(99);
+ }
+ objective += obj;
+ const SparseVector<double>& cg = g;
+ for (SparseVector<double>::const_iterator it = cg.begin(); it != cg.end(); ++it) {
+ if (it->first >= num_feats) {
+ cerr << "Unexpected feature in gradient: " << FD::Convert(it->first) << endl;
+ abort();
+ }
+ gradient[it->first] -= it->second;
+ }
+ } else { // text encoding - your gradients will not be accurate!
+ while (i < line.size()) {
+ size_t start = i;
+ while (line[i] != '=' && i < line.size()) ++i;
+ if (i == line.size()) { cerr << "FORMAT ERROR\n"; break; }
+ string fname = line.substr(start, i - start);
+ if (fname == s_obj) {
+ feat = -1;
+ } else {
+ feat = FD::Convert(line.substr(start, i - start));
+ if (feat >= num_feats) {
+ cerr << "Unexpected feature in gradient: " << line.substr(start, i - start) << endl;
+ abort();
+ }
+ }
+ ++i;
+ start = i;
+ while (line[i] != ';' && i < line.size()) ++i;
+ if (i - start == 0) continue;
+ val = atof(line.substr(start, i - start).c_str());
+ ++i;
+ if (feat == -1) {
+ objective += val;
+ } else {
+ gradient[feat] -= val;
+ }
+ }
+ }
+ }
+
+ if (gaussian_prior) {
+ const double sigsq = conf["sigma_squared"].as<double>();
+ double norm = 0;
+ for (int k = 1; k < lambdas.size(); ++k) {
+ const double& lambda_k = lambdas[k];
+ if (lambda_k) {
+ const double param = (lambda_k - means[k]);
+ norm += param * param;
+ gradient[k] += param / sigsq;
+ }
+ }
+ const double reg = norm / (2.0 * sigsq);
+ cerr << "REGULARIZATION TERM: " << reg << endl;
+ objective += reg;
+ }
+ cerr << "EVALUATION #" << o->EvaluationCount() << " OBJECTIVE: " << objective << endl;
+ double gnorm = 0;
+ for (int i = 0; i < gradient.size(); ++i)
+ gnorm += gradient[i] * gradient[i];
+ cerr << " GNORM=" << sqrt(gnorm) << endl;
+ vector<double> old = lambdas;
+ int c = 0;
+ while (old == lambdas) {
+ ++c;
+ if (c > 1) { cerr << "Same lambdas, repeating optimization\n"; }
+ o->Optimize(objective, gradient, &lambdas);
+ assert(c < 5);
+ }
+ old.clear();
+ SanityCheck(lambdas);
+ ShowLargestFeatures(lambdas);
+ weights.InitFromVector(lambdas);
+ weights.WriteToFile(conf["output_weights"].as<string>(), false);
+
+ const bool conv = o->HasConverged();
+ if (conv) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
+
+ if (conf.count("output_state"))
+ state_file = conf["output_state"].as<string>();
+ ofstream out(state_file.c_str(), ios::binary);
+ cerr << "Writing state to: " << state_file << endl;
+ o->Save(&out);
+ out.close();
+
+ cout << o->EvaluationCount() << "\t" << conv << endl;
+ return 0;
+}
diff --git a/training/optimize.cc b/training/optimize.cc
new file mode 100644
index 00000000..5194752e
--- /dev/null
+++ b/training/optimize.cc
@@ -0,0 +1,114 @@
+#include "optimize.h"
+
+#include <iostream>
+#include <cassert>
+
+#include "lbfgs.h"
+
+using namespace std;
+
+Optimizer::~Optimizer() {}
+
+void Optimizer::Save(ostream* out) const {
+ out->write((const char*)&eval_, sizeof(eval_));
+ out->write((const char*)&has_converged_, sizeof(has_converged_));
+ SaveImpl(out);
+ unsigned int magic = 0xABCDDCBA; // should be uint32_t
+ out->write((const char*)&magic, sizeof(magic));
+}
+
+void Optimizer::Load(istream* in) {
+ in->read((char*)&eval_, sizeof(eval_));
+ ++eval_;
+ in->read((char*)&has_converged_, sizeof(has_converged_));
+ LoadImpl(in);
+ unsigned int magic = 0; // should be uint32_t
+ in->read((char*)&magic, sizeof(magic));
+ assert(magic == 0xABCDDCBA);
+ cerr << Name() << " EVALUATION #" << eval_ << endl;
+}
+
+void Optimizer::SaveImpl(ostream* out) const {
+ (void)out;
+}
+
+void Optimizer::LoadImpl(istream* in) {
+ (void)in;
+}
+
+string RPropOptimizer::Name() const {
+ return "RPropOptimizer";
+}
+
+void RPropOptimizer::OptimizeImpl(const double& obj,
+ const vector<double>& g,
+ vector<double>* x) {
+ for (int i = 0; i < g.size(); ++i) {
+ const double g_i = g[i];
+ const double sign_i = (signbit(g_i) ? -1.0 : 1.0);
+ const double prod = g_i * prev_g_[i];
+ if (prod > 0.0) {
+ const double dij = min(delta_ij_[i] * eta_plus_, delta_max_);
+ (*x)[i] -= dij * sign_i;
+ delta_ij_[i] = dij;
+ prev_g_[i] = g_i;
+ } else if (prod < 0.0) {
+ delta_ij_[i] = max(delta_ij_[i] * eta_minus_, delta_min_);
+ prev_g_[i] = 0.0;
+ } else {
+ (*x)[i] -= delta_ij_[i] * sign_i;
+ prev_g_[i] = g_i;
+ }
+ }
+}
+
+void RPropOptimizer::SaveImpl(ostream* out) const {
+ const size_t n = prev_g_.size();
+ out->write((const char*)&n, sizeof(n));
+ out->write((const char*)&prev_g_[0], sizeof(double) * n);
+ out->write((const char*)&delta_ij_[0], sizeof(double) * n);
+}
+
+void RPropOptimizer::LoadImpl(istream* in) {
+ size_t n;
+ in->read((char*)&n, sizeof(n));
+ assert(n == prev_g_.size());
+ assert(n == delta_ij_.size());
+ in->read((char*)&prev_g_[0], sizeof(double) * n);
+ in->read((char*)&delta_ij_[0], sizeof(double) * n);
+}
+
+string SGDOptimizer::Name() const {
+ return "SGDOptimizer";
+}
+
+void SGDOptimizer::OptimizeImpl(const double& obj,
+ const vector<double>& g,
+ vector<double>* x) {
+ (void)obj;
+ for (int i = 0; i < g.size(); ++i)
+ (*x)[i] -= g[i] * eta_;
+}
+
+string LBFGSOptimizer::Name() const {
+ return "LBFGSOptimizer";
+}
+
+LBFGSOptimizer::LBFGSOptimizer(int num_feats, int memory_buffers) :
+ opt_(num_feats, memory_buffers) {}
+
+void LBFGSOptimizer::SaveImpl(ostream* out) const {
+ opt_.serialize(out);
+}
+
+void LBFGSOptimizer::LoadImpl(istream* in) {
+ opt_.deserialize(in);
+}
+
+void LBFGSOptimizer::OptimizeImpl(const double& obj,
+ const vector<double>& g,
+ vector<double>* x) {
+ opt_.run(&(*x)[0], obj, &g[0]);
+ cerr << opt_ << endl;
+}
+
diff --git a/training/optimize.h b/training/optimize.h
new file mode 100644
index 00000000..eddceaad
--- /dev/null
+++ b/training/optimize.h
@@ -0,0 +1,104 @@
+#ifndef _OPTIMIZE_H_
+#define _OPTIMIZE_H_
+
+#include <iostream>
+#include <vector>
+#include <string>
+#include <cassert>
+
+#include "lbfgs.h"
+
+// abstract base class for first order optimizers
+// order of invocation: new, Load(), Optimize(), Save(), delete
+class Optimizer {
+ public:
+ Optimizer() : eval_(1), has_converged_(false) {}
+ virtual ~Optimizer();
+ virtual std::string Name() const = 0;
+ int EvaluationCount() const { return eval_; }
+ bool HasConverged() const { return has_converged_; }
+
+ void Optimize(const double& obj,
+ const std::vector<double>& g,
+ std::vector<double>* x) {
+ assert(g.size() == x->size());
+ OptimizeImpl(obj, g, x);
+ scitbx::lbfgs::traditional_convergence_test<double> converged(g.size());
+ has_converged_ = converged(&(*x)[0], &g[0]);
+ }
+
+ void Save(std::ostream* out) const;
+ void Load(std::istream* in);
+ protected:
+ virtual void SaveImpl(std::ostream* out) const;
+ virtual void LoadImpl(std::istream* in);
+ virtual void OptimizeImpl(const double& obj,
+ const std::vector<double>& g,
+ std::vector<double>* x) = 0;
+
+ int eval_;
+ private:
+ bool has_converged_;
+};
+
+class RPropOptimizer : public Optimizer {
+ public:
+ explicit RPropOptimizer(int num_vars,
+ double eta_plus = 1.2,
+ double eta_minus = 0.5,
+ double delta_0 = 0.1,
+ double delta_max = 50.0,
+ double delta_min = 1e-6) :
+ prev_g_(num_vars, 0.0),
+ delta_ij_(num_vars, delta_0),
+ eta_plus_(eta_plus),
+ eta_minus_(eta_minus),
+ delta_max_(delta_max),
+ delta_min_(delta_min) {
+ assert(eta_plus > 1.0);
+ assert(eta_minus > 0.0 && eta_minus < 1.0);
+ assert(delta_max > 0.0);
+ assert(delta_min > 0.0);
+ }
+ std::string Name() const;
+ void OptimizeImpl(const double& obj,
+ const std::vector<double>& g,
+ std::vector<double>* x);
+ void SaveImpl(std::ostream* out) const;
+ void LoadImpl(std::istream* in);
+ private:
+ std::vector<double> prev_g_;
+ std::vector<double> delta_ij_;
+ const double eta_plus_;
+ const double eta_minus_;
+ const double delta_max_;
+ const double delta_min_;
+};
+
+class SGDOptimizer : public Optimizer {
+ public:
+ explicit SGDOptimizer(int num_vars, double eta = 0.1) : eta_(eta) {
+ (void) num_vars;
+ }
+ std::string Name() const;
+ void OptimizeImpl(const double& obj,
+ const std::vector<double>& g,
+ std::vector<double>* x);
+ private:
+ const double eta_;
+};
+
+class LBFGSOptimizer : public Optimizer {
+ public:
+ explicit LBFGSOptimizer(int num_vars, int memory_buffers = 10);
+ std::string Name() const;
+ void SaveImpl(std::ostream* out) const;
+ void LoadImpl(std::istream* in);
+ void OptimizeImpl(const double& obj,
+ const std::vector<double>& g,
+ std::vector<double>* x);
+ private:
+ scitbx::lbfgs::minimizer<double> opt_;
+};
+
+#endif
diff --git a/training/optimize_test.cc b/training/optimize_test.cc
new file mode 100644
index 00000000..0ada7cbb
--- /dev/null
+++ b/training/optimize_test.cc
@@ -0,0 +1,105 @@
+#include <cassert>
+#include <iostream>
+#include <sstream>
+#include <boost/program_options/variables_map.hpp>
+#include "optimize.h"
+#include "sparse_vector.h"
+#include "fdict.h"
+
+using namespace std;
+
+double TestOptimizer(Optimizer* opt) {
+ cerr << "TESTING NON-PERSISTENT OPTIMIZER\n";
+
+ // f(x,y) = 4x1^2 + x1*x2 + x2^2 + x3^2 + 6x3 + 5
+ // df/dx1 = 8*x1 + x2
+ // df/dx2 = 2*x2 + x1
+ // df/dx3 = 2*x3 + 6
+ vector<double> x(3);
+ vector<double> g(3);
+ x[0] = 8;
+ x[1] = 8;
+ x[2] = 8;
+ double obj = 0;
+ do {
+ g[0] = 8 * x[0] + x[1];
+ g[1] = 2 * x[1] + x[0];
+ g[2] = 2 * x[2] + 6;
+ obj = 4 * x[0]*x[0] + x[0] * x[1] + x[1]*x[1] + x[2]*x[2] + 6 * x[2] + 5;
+ opt->Optimize(obj, g, &x);
+
+ cerr << x[0] << " " << x[1] << " " << x[2] << endl;
+ cerr << " obj=" << obj << "\td/dx1=" << g[0] << " d/dx2=" << g[1] << " d/dx3=" << g[2] << endl;
+ } while (!opt->HasConverged());
+ return obj;
+}
+
+double TestPersistentOptimizer(Optimizer* opt) {
+ cerr << "\nTESTING PERSISTENT OPTIMIZER\n";
+ // f(x,y) = 4x1^2 + x1*x2 + x2^2 + x3^2 + 6x3 + 5
+ // df/dx1 = 8*x1 + x2
+ // df/dx2 = 2*x2 + x1
+ // df/dx3 = 2*x3 + 6
+ vector<double> x(3);
+ vector<double> g(3);
+ x[0] = 8;
+ x[1] = 8;
+ x[2] = 8;
+ double obj = 0;
+ string state;
+ bool converged = false;
+ while (!converged) {
+ g[0] = 8 * x[0] + x[1];
+ g[1] = 2 * x[1] + x[0];
+ g[2] = 2 * x[2] + 6;
+ obj = 4 * x[0]*x[0] + x[0] * x[1] + x[1]*x[1] + x[2]*x[2] + 6 * x[2] + 5;
+
+ {
+ if (state.size() > 0) {
+ istringstream is(state, ios::binary);
+ opt->Load(&is);
+ }
+ opt->Optimize(obj, g, &x);
+ ostringstream os(ios::binary); opt->Save(&os); state = os.str();
+
+ }
+
+ cerr << x[0] << " " << x[1] << " " << x[2] << endl;
+ cerr << " obj=" << obj << "\td/dx1=" << g[0] << " d/dx2=" << g[1] << " d/dx3=" << g[2] << endl;
+ converged = opt->HasConverged();
+ if (!converged) {
+ // now screw up the state (should be undone by Load)
+ obj += 2.0;
+ g[1] = -g[2];
+ vector<double> x2 = x;
+ try {
+ opt->Optimize(obj, g, &x2);
+ } catch (...) { }
+ }
+ }
+ return obj;
+}
+
+template <class O>
+void TestOptimizerVariants(int num_vars) {
+ O oa(num_vars);
+ cerr << "-------------------------------------------------------------------------\n";
+ cerr << "TESTING: " << oa.Name() << endl;
+ double o1 = TestOptimizer(&oa);
+ O ob(num_vars);
+ double o2 = TestPersistentOptimizer(&ob);
+ if (o1 != o2) {
+ cerr << oa.Name() << " VARIANTS PERFORMED DIFFERENTLY!\n" << o1 << " vs. " << o2 << endl;
+ exit(1);
+ }
+ cerr << oa.Name() << " SUCCESS\n";
+}
+
+int main() {
+ int n = 3;
+ TestOptimizerVariants<SGDOptimizer>(n);
+ TestOptimizerVariants<LBFGSOptimizer>(n);
+ TestOptimizerVariants<RPropOptimizer>(n);
+ return 0;
+}
+
diff --git a/training/plftools.cc b/training/plftools.cc
new file mode 100644
index 00000000..903ec54f
--- /dev/null
+++ b/training/plftools.cc
@@ -0,0 +1,93 @@
+#include <iostream>
+#include <fstream>
+#include <vector>
+
+#include <boost/lexical_cast.hpp>
+#include <boost/program_options.hpp>
+
+#include "filelib.h"
+#include "tdict.h"
+#include "prob.h"
+#include "hg.h"
+#include "hg_io.h"
+#include "viterbi.h"
+#include "kbest.h"
+
+namespace po = boost::program_options;
+using namespace std;
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+ po::options_description opts("Configuration options");
+ opts.add_options()
+ ("input,i", po::value<string>(), "REQ. Lattice input file (PLF), - for STDIN")
+ ("prior_scale,p", po::value<double>()->default_value(1.0), "Scale path probabilities by this amount < 1 flattens, > 1 sharpens")
+ ("weight,w", po::value<vector<double> >(), "Weight(s) for arc features")
+ ("output,o", po::value<string>()->default_value("plf"), "Output format (text, plf)")
+ ("command,c", po::value<string>()->default_value("push"), "Operation to perform: push, graphviz, 1best, 2best ...")
+ ("help,h", "Print this help message and exit");
+ po::options_description clo("Command line options");
+ po::options_description dcmdline_options;
+ dcmdline_options.add(opts);
+
+ po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+ po::notify(*conf);
+
+ if (conf->count("help") || conf->count("input") == 0) {
+ cerr << dcmdline_options << endl;
+ exit(1);
+ }
+}
+
+int main(int argc, char **argv) {
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+ string infile = conf["input"].as<string>();
+ ReadFile rf(infile);
+ istream* in = rf.stream();
+ assert(*in);
+ SparseVector<double> wts;
+ vector<double> wv;
+ if (conf.count("weight") > 0) wv = conf["weight"].as<vector<double> >();
+ if (wv.empty()) wv.push_back(1.0);
+ for (int i = 0; i < wv.size(); ++i) {
+ const string fname = "Feature_" + boost::lexical_cast<string>(i);
+ cerr << "[INFO] Arc weight " << (i+1) << " = " << wv[i] << endl;
+ wts.set_value(FD::Convert(fname), wv[i]);
+ }
+ const string cmd = conf["command"].as<string>();
+ const bool push_weights = cmd == "push";
+ const bool output_plf = cmd == "plf";
+ const bool graphviz = cmd == "graphviz";
+ const bool kbest = cmd.rfind("best") == (cmd.size() - 4) && cmd.size() > 4;
+ int k = 1;
+ if (kbest) {
+ k = boost::lexical_cast<int>(cmd.substr(0, cmd.size() - 4));
+ cerr << "KBEST = " << k << endl;
+ }
+ const double scale = conf["prior_scale"].as<double>();
+ int lc = 0;
+ while(*in) {
+ ++lc;
+ string plf;
+ getline(*in, plf);
+ if (plf.empty()) continue;
+ Hypergraph hg;
+ HypergraphIO::ReadFromPLF(plf, &hg);
+ hg.Reweight(wts);
+ if (graphviz) hg.PrintGraphviz();
+ if (push_weights) hg.PushWeightsToSource(scale);
+ if (output_plf) {
+ cout << HypergraphIO::AsPLF(hg) << endl;
+ } else {
+ KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(hg, k);
+ for (int i = 0; i < k; ++i) {
+ const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
+ kbest.LazyKthBest(hg.nodes_.size() - 1, i);
+ if (!d) break;
+ cout << lc << " ||| " << TD::GetString(d->yield) << " ||| " << d->score << endl;
+ }
+ }
+ }
+ return 0;
+}
+
generated by cgit v1.2.3 (git 2.46.0) at 2025-10-31 18:03:04 +0000