wcluster.cc

/*
Hierarchically clusters phrases. Extension using dependency parses.
Comments indicating any changes are introduced by "dlm"
Running time: O(N*C^2).

We want to cluster the phrases so that the pairwise mututal information between
clusters is maximized.  This mututal information is a sum over terms between
each pair of clusters: q2[a, b] for clusters a and b.  The trick is to compute
quickly the loss of mututal information when two clusters a and b are merged.

The four structures p1, p2, q2, L2 allow this quick computation.
  p1[a] = probability of of cluster a.
  p2[a, b] = probability of cluster a followed by cluster b.
  q2[a, b] = contribution to the mutual information from clusters a and b (computed from p2[a, b]).
  L2[a, b] = the loss of mutual information if clusters a and b were merged.

Changes:
 * Removed hash tables for efficiency.
 * Notation: a is an phrase (sequence of words), c is a cluster, s is a slot.

To cut down memory usage:
 * Change double to float.
Ideas:
 * Hashing vectors is really slow.
 * Find intuition behind algorithm based on simple cases
 * Test clustering algorithm on artificial generated data.  Generate a text
   with a class-based ngram model.
*/

#include "basic/std.h"
#include "basic/stl-basic.h"
#include "basic/stl-utils.h"
#include "basic/str.h"
#include "basic/strdb.h"
#include "basic/union-set.h"
#include "basic/mem-tracker.h"
#include "basic/opt.h"
#include <unistd.h>

vector< OptInfo<bool> > bool_opts;
vector< OptInfo<int> > int_opts;
vector< OptInfo<double> > double_opts;
vector< OptInfo<string> > string_opts;

opt_define_string(output_dir,    "output_dir", "",         "Output everything to this directory.");
opt_define_string(text_file,     "text", "",               "Text file with corpora (input).");
opt_define_string(restrict_file, "restrict", "",           "Only consider words that appear in this text (input).");
opt_define_string(paths_file,    "paths", "",              "File containing root-to-node paths in the clustering tree (input/output).");
opt_define_string(map_file,      "map", "",                "File containing lots of good information about each phrase, more general than paths (output)");
opt_define_string(collocs_file,  "collocs", "",            "Collocations with most mutual information (output).");
opt_define_string(featvec_file,  "featvec", "",            "Feature vectors (output).");
opt_define_string(comment,       "comment", "",            "Description of this run.");

opt_define_int(ncollocs,     "ncollocs", 500,              "Collocations with most mutual information (output).");
opt_define_int(initC,        "c", 1000,                    "Number of clusters.");
opt_define_int(plen,         "plen", 1,                    "Maximum length of a phrase to consider.");
opt_define_int(min_occur,    "min-occur", 1,               "Keep phrases that occur at least this many times.");
opt_define_int(rand_seed,    "rand", time(NULL)*getpid(),  "Number to call srand with.");

opt_define_bool(chk,         "chk", false,                 "Check data structures are valid (expensive).");
opt_define_bool(print_stats, "stats", false,               "Just print out stats.");
opt_define_bool(paths2map,   "paths2map", false,           "Take the paths file and generate a map file.");

#define use_restrict (!restrict_file.empty())
const char *delim_str = "$#$";

typedef IntPair _;

StrDB db;                // word database
IntVec phrase_freqs;     //dlm: phrase a < N -> number of times heads and dependents appear in the text
IntVecVec head_phrases;  //dlm: phrase a < N -> list of phrases that appear as heads of a in the text
IntVecVec dep_phrases; //dlm: phrase a < N -> list of phrases that appear as dependents a in the text
IntIntPairMap cluster_tree; // cluster c -> the 2 sub-clusters that merged to create c
int delim_word;

IntVec freq_order_phrases; // List of phrases in decreasing order of frequency.

// Allows for very quick (inverse Ackermann) lookup of clusters and merging
// of clusters.  Each phrase points to an arbitrary representative phrase of
// the cluster.
UnionSet phrase2rep;   // phrase a -> the rep phrase in the same cluster as a
IntIntMap rep2cluster; // rep phrase a -> the cluster that contains a
IntIntMap cluster2rep; // cluster a -> the rep phrase in cluster a

// Store all the phrases efficiently.  Just for printing out.
// For each phrase length, we store a flattened list of words.
IntVecVec phrases; // length of phrase -> flattened list of words

// Each cluster will occupy a slot.  There will always be two extra slots
// as intermediate scratch space.
IntVec slot2cluster; // slot index -> cluster (-1 if none exists)
IntIntMap cluster2slot; // cluster -> slot index
int free_slot1, free_slot2; // two free slots
int nslots;

// Partial results that allow quick computation and update of mutual information.
// Mutual information is the sum of all the q2 terms.
// Update p1, p2, q2 for 0..N-1, but L2 only for 0..initC-1.
DoubleVec p1;        // slot s (containing cluster a) -> probability Pr(a)
DoubleVecVec p2;     // slots s, t (containing clusters a, b) -> probability Pr(a, b)
DoubleVecVec q2;     // slots s, t (contianing clusters a, b) -> contribution to mutual information
DoubleVecVec L2;     // slots s, t (containing clusters a, b) -> loss of mutual information if merge a and b

int curr_cluster_id; // ID to assign to a new cluster
int stage2_cluster_offset; // start of the IDs of clusters created in stage 2

double curr_minfo; // Mutual info, should be sum of all q2's

// Map phrase to the KL divergence to its cluster
DoubleVec kl_map[2];

#define FOR_SLOT(s) \
  for(int s = 0; s < len(slot2cluster); s++) \
    for(bool _tmp = true; slot2cluster[s] != -1 && _tmp; _tmp = false)

// We store only L2[s, t] for which the cluster ID in slot s is smaller
// than the one in slot t.
#define ORDER_VALID(s, t) (slot2cluster[s] < slot2cluster[t])

#define num_phrases(l) (len(phrases[l])/(l))

int N; // number of phrases
int T; // length of text

// Output a phrase.
struct Phrase { Phrase(int a) : a(a) { } int a; };
ostream &operator<<(ostream &out, const Phrase &phrase) {
  // Decode the phrase ID into the length and the offset in phrases.
  int a = phrase.a;
  int l; for(l = 1; a >= num_phrases(l); a -= num_phrases(l), l++);

  foridx(i, l) {
    if(i > 0) out << ' ';
    out << db[phrases[l][a*l+i]];
  }
  return out;
}

// For pretty-printing of clusters.
struct Cluster { Cluster(int c) : c(c) { } int c; };
ostream &operator<<(ostream &out, const Cluster &cluster) {
  int c = cluster.c;
  out << c;

  int a;
  bool more;
  if(c < N)
    a = c, more = false;
  else {
    assert(contains(cluster2rep, c));
    a = cluster2rep[c], more = true;
  }

  out << '(' << Phrase(a);
  if(more) out << "|...";
  out << ')';
  return out;
}

#define Slot(s) Cluster(slot2cluster[s])

////////////////////////////////////////////////////////////

// p2[s, t] + p2[t, s].
inline double bi_p2(int s, int t) {
  if(s == t) return p2[s][s];
  return p2[s][t] + p2[t][s];
}

// q2[s, t] + q2[t, s].
inline double bi_q2(int s, int t) {
  if(s == t) return q2[s][s];
  return q2[s][t] + q2[t][s];
}

// Hypothetical p1[st] = p1[s] + p1[t].
inline double hyp_p1(int s, int t) {
  return p1[s] + p1[t];
}

//// hyp_p2

// Hypothetical p2[st, u] = p2[s, u] + p2[t, u].
inline double hyp_p2(const IntPair &st, int u) {
  return p2[st.first][u] + p2[st.second][u];
}

// Hypothetical p2[u, st] = p2[u, s] + p2[u, t].
inline double hyp_p2(int u, const IntPair &st) {
  return p2[u][st.first] + p2[u][st.second];
}

inline double bi_hyp_p2(const IntPair &st, int u) {
  return hyp_p2(st, u) + hyp_p2(u, st);
}

// Hypothetical p2[st, st] = p2[s, s] + p2[s, t] + p2[t, s] + p2[t, t].
inline double hyp_p2(const IntPair &st) {
  return p2[st.first][st.first] + p2[st.first][st.second] +
         p2[st.second][st.first] + p2[st.second][st.second];
}

//// hyp_q2

inline double p2q(double pst, double ps, double pt) {
  if(feq(pst, 0.0)) return 0.0;
  return pst * log2(pst / (ps*pt));
}

// Hypothetical q2[st, u].
inline double hyp_q2(const IntPair &st, int u) {
  return p2q(hyp_p2(st, u), hyp_p1(st.first, st.second), p1[u]);
}

// Hypothetical q2[u, st].
inline double hyp_q2(int u, const IntPair &st) {
  return p2q(hyp_p2(u, st), hyp_p1(st.first, st.second), p1[u]);
}

inline double bi_hyp_q2(const IntPair &st, int u) {
  return hyp_q2(st, u) + hyp_q2(u, st);
}

// Hypothetical q2[st, st].
inline double hyp_q2(const IntPair &st) {
  double p = hyp_p2(_(st.first, st.second)); // p2[st,st]
  double P = hyp_p1(st.first, st.second);
  return p2q(p, P, P);
}

////////////////////////////////////////////////////////////

// Return slot.
void put_cluster_in_slot(int a, int s) {
  cluster2slot[a] = s;
  slot2cluster[s] = a;
}
inline int put_cluster_in_free_slot(int a) {
  int s = -1;

  // Find available slot.
  if(free_slot1 != -1)      s = free_slot1, free_slot1 = -1;
  else if(free_slot2 != -1) s = free_slot2, free_slot2 = -1;
  assert(s != -1);

  put_cluster_in_slot(a, s);
  return s;
}

inline void free_up_slots(int s, int t) {
  free_slot1 = s;
  free_slot2 = t;
  cluster2slot.erase(slot2cluster[s]);
  cluster2slot.erase(slot2cluster[t]);
  slot2cluster[s] = slot2cluster[t] = -1;
}

void init_slot(int s) {
  // Clear any entries that relates to s.
  // The p1 and L2 will be filled in densely, so they
  // will be overwritten anyway.
  FOR_SLOT(t)
    p2[s][t] = q2[s][t] = p2[t][s] = q2[t][s] = 0;
}

void add_to_set(const IntVec &phrases, IntIntMap &phrase_counts, int offset) {
  forvec(_, int, a, phrases)
    phrase_counts[a+offset]++;
}

bool is_good_phrase(const IntVec &phrase) {
  if(len(phrase) == 1) return phrase[0] != delim_word && phrase[0] != -1; // Can't be delimiter or an invalid word

  // HACK HACK HACK - pick out some phrases
  // Can't be too many delim words.
  int di = index_of(phrase, delim_word, 1);
  if(di > 0 && di < len(phrase)-1) return false; // Delimiter must occur at the ends
  if(phrase[0] == delim_word && phrase[len(phrase)-1] == delim_word) return false; // Only one delimiter allowed

  // If every word is capitalized with the exception of some function
  // words which must go in the middle
  forvec(i, int, a, phrase) {
    bool at_end = i == 0 || i == len(phrase)-1;
    const string &word = db[a];
    bool is_upper = isupper(word[0]);

    if(at_end && !is_upper) return false; // Ends must be uppercase
    if(is_upper) continue; // Ok
    if(word[0] == '\'' || word == "of" || word == "and") continue; // Ok
    return false;
  }
  return true;
}

void read_restrict_text() {
  // Read the words from the text file that restricts what words we will cluster
  if(restrict_file.empty()) return;
  track("read_restrict_text()", restrict_file, false);
  read_text(restrict_file.c_str(), NULL, db, false, false, true);
}

IntVecIntMap vec2phrase;
IntVecVec tuples; //dep: instead of intvec text

void read_text_process_word(IntVec tuple) { //dlm: pushing back tuples instead of words
  tuples.push_back(tuple); //dlm
}
void read_text() {
  track("read_text()", "", false);
  read_text(text_file.c_str(), read_text_process_word, db, !use_restrict, !use_restrict, !use_restrict);
  //forvec(, IntVec, l, tuples) {
  
  int tuples_len = len(tuples); //dlm: not the same as T

  //dlm: count tokens using counts from input file
  foridx(i, tuples_len){ //dlm
    T += tuples[i].back(); //dlm
  }

  delim_word = db.lookup(delim_str, false, -1);

  if(!paths2map) db.destroy_s2i(); // Conserve memory.

  // Count the phrases that we care about so we can map them all to integers.
  
  track_block("Counting phrases", "", false) {
    phrases.resize(plen+1);
    for(int l = 1; l <= plen; l++) {
      // Count.
      IntVecIntMap freqs; // phrase vector -> number of occurrences
      for(int i = 0; i < tuples_len-l+1; i++) { //dlm: read lines
	IntVec a_vec; //dlm: head
	IntVec b_vec; //dlm: dep
	a_vec.push_back(tuples[i][0]); //dlm
	b_vec.push_back(tuples[i][1]); //dlm
       	int count = tuples[i][2]; //dlm

	if(is_good_phrase(a_vec)) freqs[a_vec] += count;//dlm
	if(is_good_phrase(b_vec)) freqs[b_vec] += count;//dlm
      }

      forcmap(const IntVec &, a_vec, int, count, IntVecIntMap, freqs) {
        if(count < min_occur) continue;

        int a = len(phrase_freqs);
        phrase_freqs.push_back(count);
        vec2phrase[a_vec] = a;
        forvec(_, int, w, a_vec) phrases[l].push_back(w);
      }

      logs(len(freqs) << " distinct phrases of length " << l << ", keeping " << num_phrases(l) << " which occur at least " << min_occur << " times");
    }
  }

  N = len(phrase_freqs); // number of phrases
  
  track_block("Finding head/dep phrases", "", false) { //dlm: "left" is "head", "right" is "dep"
    head_phrases.resize(N);//dlm: not really the same in dlm
    dep_phrases.resize(N); //dlm
    for(int l = 1; l <= plen; l++) {
      for(int i = 0; i < tuples_len-l+1; i++) { //dlm: i over tuples
	IntVec a_vec; //dlm
	IntVec b_vec; //dlm
	a_vec.push_back(tuples[i][0]); //dlm
	b_vec.push_back(tuples[i][1]); //dlm
       	int count = tuples[i][2]; //dlm

	if(!contains(vec2phrase, a_vec) || !contains(vec2phrase, b_vec)) continue; //dlm
        int a = vec2phrase[a_vec];//dlm: head
	int b = vec2phrase[b_vec];//dlm: dep
	//dlm: deps of a
	for(int c = 0; c < count; c++) //dlm: lazy solution
	  dep_phrases[a].push_back(b);//dlm

        //dlm: heads of b
	for(int c = 0; c < count; c++)//dlm
	  head_phrases[b].push_back(a);//dlm
	  
      }
    }
  }

#if 1
  if(!featvec_file.empty()) {
    ofstream out(featvec_file.c_str());
    out << N << ' ' << 2*N << endl;
    foridx(a, N) {
      IntIntMap phrase_counts;
      add_to_set(head_phrases[a], phrase_counts, 0); //dlm
      add_to_set(dep_phrases[a], phrase_counts, N); //dlm
      out << Phrase(a) << ' ' << len(phrase_counts);
      forcmap(int, b, int, count, IntIntMap, phrase_counts)
        out << '\t' << b << ' ' << count;
      out << endl;
    }
  }
#endif

#if 0
  foridx(a, N) {
    track("", Cluster(a), true);
    forvec(_, int, b, head_phrases[a]) //dlm
      logs("LEFT " << Cluster(b));
    forvec(_, int, b, dep_phrases[a]) //dlm
      logs("RIGHT " << Cluster(b));
  }
#endif

  destroy(tuples);//dlm
  initC = min(initC, N);

  logs("Text length: " << T << ", " << N << " phrases, " << len(db) << " words");
}

// O(C) time.
double compute_s1(int s) { // compute s1[s]
  double q = 0.0;
  FOR_SLOT(t) q += bi_q2(s, t);
  return q;
}

// O(C) time.
double compute_L2(int s, int t) { // compute L2[s, t]
  assert(ORDER_VALID(s, t));
  // st is the hypothetical new cluster that combines s and t

  // Lose old associations with s and t
  double l = compute_s1(s) + compute_s1(t) - bi_q2(s, t);

  // Form new associations with st
  FOR_SLOT(u) {
    if(u == s || u == t) continue;
    l -= bi_hyp_q2(_(s, t), u);
  }
  l -= hyp_q2(_(s, t)); // q2[st, st]
  return l;
}

void repcheck() {
  if(!chk) return;
  double sum;

  assert_eq(len(rep2cluster), len(cluster2rep));
  assert_eq(len(rep2cluster), len(cluster2slot));
  
  assert(free_slot1 == -1 || slot2cluster[free_slot1] == -1);
  assert(free_slot2 == -1 || slot2cluster[free_slot2] == -1);
  FOR_SLOT(s) {
    assert(contains(cluster2slot, slot2cluster[s]));
    assert(cluster2slot[slot2cluster[s]] == s);
  }

  sum = 0.0;
  FOR_SLOT(s) FOR_SLOT(t) {
    double q = q2[s][t];
    //logs(s << ' ' << t << ' ' << p2[s][t] << ' ' << p1[s] << ' ' << p1[t]);
    assert_feq(q, p2q(p2[s][t], p1[s], p1[t]));
    sum += q;
  }
  assert_feq(sum, curr_minfo);

  FOR_SLOT(s) FOR_SLOT(t) {
    if(!ORDER_VALID(s, t)) continue;
    double l = L2[s][t];
    assert(l + TOL >= 0);
    assert_feq(l, compute_L2(s, t));
  }
}

void dump() {
  track("dump()", "", true);
  FOR_SLOT(s) logs("p1[" << Slot(s) << "] = " << p1[s]);
  FOR_SLOT(s) FOR_SLOT(t) logs("p2[" << Slot(s) << ", " << Slot(t) << "] = " << p2[s][t]);
  FOR_SLOT(s) FOR_SLOT(t) logs("q2[" << Slot(s) << ", " << Slot(t) << "] = " << q2[s][t]);
  FOR_SLOT(s) FOR_SLOT(t) logs("L2[" << Slot(s) << ", " << Slot(t) << "] = " << L2[s][t]);
  logs("curr_minfo = " << curr_minfo);
}


// c is new cluster that has been just formed from a and b
// Want to compute L2[d, e]
// O(1) time.
double compute_L2_using_old(int s, int t, int u, int v, int w) {
  assert(ORDER_VALID(v, w));
  assert(v != u && w != u);

  double l = L2[v][w];
  
  // Remove old associations between v and w with s and t
  l -= bi_q2(v, s) + bi_q2(w, s) + bi_q2(v, t) + bi_q2(w, t);
  l += bi_hyp_q2(_(v, w), s) + bi_hyp_q2(_(v, w), t);

  // Add new associations between v and w with u (ab)
  l += bi_q2(v, u) + bi_q2(w, u);
  l -= bi_hyp_q2(_(v, w), u);

  return l;
}

// return q2
double set_p2_q2_from_count(int s, int t, int count) {
  double pst = (double)count / (T-1); // p2[s,t]
  double ps = p1[s];
  double pt = p1[t];
  double qst = p2q(pst, ps, pt); // q2[s,t]
  p2[s][t] = pst;
  q2[s][t] = qst;
  return qst;
}

// O(N lg N) time.
// Sort the phrases by decreasing frequency and then set the initC most frequent
// phrases to be in the initial cluster.
bool phrase_freq_greater(int a, int b) {
  return phrase_freqs[a] > phrase_freqs[b];
}
void create_initial_clusters() {
  track("create_initial_clusters()", "", true);

  freq_order_phrases.resize(N);
  foridx(a, N) freq_order_phrases[a] = a;

  logs("Sorting " << N << " phrases by frequency");
  sort(freq_order_phrases.begin(), freq_order_phrases.end(), phrase_freq_greater);

  // Initialize slots
  logs("Selecting top " << initC << " phrases to be initial clusters");
  nslots = initC+2;
  slot2cluster.resize(nslots);
  free_up_slots(initC, initC+1);

  // Create the inital clusters.
  phrase2rep.Init(N); // Init union-set: each phrase starts out in its own cluster
  curr_minfo = 0.0;
  foridx(s, initC) {
    int a = freq_order_phrases[s];
    put_cluster_in_slot(a, s);

    rep2cluster[a] = a;
    cluster2rep[a] = a;
  }

  // Allocate memory
  p1.resize(nslots);
  matrix_resize(p2, nslots, nslots);
  matrix_resize(q2, nslots, nslots);
  matrix_resize(L2, nslots, nslots);

  FOR_SLOT(s) init_slot(s);

  // Compute p1
  FOR_SLOT(s) {
    int a = slot2cluster[s];
    p1[s] = (double)phrase_freqs[a] / T;
  }

  // Compute p2, q2, curr_minfo
  FOR_SLOT(s) {
    int a = slot2cluster[s];
    IntIntMap dep_phrase_freqs; //dlm

    // Find collocations of (a, b), where both are clusters.
    forvec(_, int, b, dep_phrases[a])
      if(contains(cluster2slot, b))
        dep_phrase_freqs[b]++; //dlm

    forcmap(int, b, int, count, IntIntMap, dep_phrase_freqs) { //dlm
      int t = cluster2slot[b];
      curr_minfo += set_p2_q2_from_count(s, t, count);
    }
  }
}

// Output the ncollocs bigrams that have the highest mutual information.
void output_best_collocations() {
  if(collocs_file.empty()) return;
  logs("Writing to " << collocs_file);

  vector< pair<double, IntPair> > collocs;
  FOR_SLOT(s) FOR_SLOT(t) {
    collocs.push_back(pair<double, IntPair>(q2[s][t], _(slot2cluster[s], slot2cluster[t])));
  }
  ncollocs = min(ncollocs, len(collocs));
  partial_sort(collocs.begin(), collocs.begin()+ncollocs, collocs.end(), greater< pair<double, IntPair> >());

  ofstream out(collocs_file.c_str());
  assert(out);
  for(int i = 0; i < ncollocs; i++) {
    const IntPair &ab = collocs[i].second;
    out << collocs[i].first << '\t' << Phrase(ab.first) << '\t' << Phrase(ab.second) << endl;
  }
}

// O(C^3) time.
void compute_L2() {
  track("compute_L2()", "", true);

  track_block("Computing L2", "", false)
  FOR_SLOT(s) {
    track_block("L2", "L2[" << Slot(s) << ", *]", false)
    FOR_SLOT(t) {
      if(!ORDER_VALID(s, t)) continue;
      double l = L2[s][t] = compute_L2(s, t);
      logs("L2[" << Slot(s) << "," << Slot(t) << "] = " << l << ", resulting minfo = " << curr_minfo-l);
    }
  }
}

// Add new phrase as a cluster.
// Compute its L2 between a and all existing clusters.
// O(C^2) time, O(T) time over all calls.
void incorporate_new_phrase(int a) {
  track("incorporate_new_phrase()", Cluster(a), false);

  int s = put_cluster_in_free_slot(a);
  init_slot(s);
  cluster2rep[a] = a;
  rep2cluster[a] = a;

  // Compute p1
  p1[s] = (double)phrase_freqs[a] / T;
  
  // Overall all calls: O(T)
  // Compute p2, q2 between a and everything in clusters
  IntIntMap freqs;
  freqs.clear(); //dlm: deps
  forvec(_, int, b, dep_phrases[a]) { //dlm
    b = phrase2rep.GetRoot(b);
    if(!contains(rep2cluster, b)) continue;
    b = rep2cluster[b];
    if(!contains(cluster2slot, b)) continue;
    freqs[b]++;
  }
  forcmap(int, b, int, count, IntIntMap, freqs) {
    curr_minfo += set_p2_q2_from_count(cluster2slot[a], cluster2slot[b], count);
    logs(Cluster(a) << ' ' << Cluster(b) << ' ' << count << ' ' << set_p2_q2_from_count(cluster2slot[a], cluster2slot[b], count));
  }

  freqs.clear(); //dlm: heads
  forvec(_, int, b, head_phrases[a]) { //dlm
    b = phrase2rep.GetRoot(b);
    if(!contains(rep2cluster, b)) continue;
    b = rep2cluster[b];
    if(!contains(cluster2slot, b)) continue;
    freqs[b]++;
  }
  forcmap(int, b, int, count, IntIntMap, freqs) {
    curr_minfo += set_p2_q2_from_count(cluster2slot[b], cluster2slot[a], count);
    logs(Cluster(b) << ' ' << Cluster(a) << ' ' << count << ' ' << set_p2_q2_from_count(cluster2slot[b], cluster2slot[a], count));
  }

  curr_minfo -= q2[s][s]; // q2[s, s] was double-counted

  // Update L2: O(C^2)
  track_block("Update L2", "", false) {
    FOR_SLOT(t) { // L2[s, *], L2[*, s]
      if(s == t) continue;
      int S, T;
      if(ORDER_VALID(s, t)) S = s, T = t;
      else                  S = t, T = s;
      L2[S][T] = compute_L2(S, T);
      logs("L2[" << Slot(S) << ", " << Slot(T) << "] = " << L2[S][T]);
    }

    FOR_SLOT(t) { // L2[not s, not s]
      if(t == s) continue;
      FOR_SLOT(u) {
        if(u == s) continue;
        if(!ORDER_VALID(t, u)) continue;
        L2[t][u] += bi_q2(t, s) + bi_q2(u, s) - bi_hyp_q2(_(t, u), s);
      }
    }
  }

  //dump();
}

// O(C^2) time.
// Merge clusters a (in slot s) and b (in slot t) into c (in slot u).
void merge_clusters(int s, int t) {
  assert(ORDER_VALID(s, t));
  int a = slot2cluster[s];
  int b = slot2cluster[t];
  int c = curr_cluster_id++;
  int u = put_cluster_in_free_slot(c);

  free_up_slots(s, t);

  // Record merge in the cluster tree
  cluster_tree[c] = _(a, b);
  curr_minfo -= L2[s][t];

  // Update relationship between clusters and rep phrases
  int A = cluster2rep[a];
  int B = cluster2rep[b];
  phrase2rep.Join(A, B);
  int C = phrase2rep.GetRoot(A); // New rep phrase of cluster c (merged a and b)

  track("Merging clusters", Cluster(a) << " and " << Cluster(b) << " into " << c << ", lost " << L2[s][t], false);

  cluster2rep.erase(a);
  cluster2rep.erase(b);
  rep2cluster.erase(A);
  rep2cluster.erase(B);
  cluster2rep[c] = C;
  rep2cluster[C] = c;

  // Compute p1: O(1)
  p1[u] = p1[s] + p1[t];

  // Compute p2: O(C)
  p2[u][u] = hyp_p2(_(s, t));
  FOR_SLOT(v) {
    if(v == u) continue;
    p2[u][v] = hyp_p2(_(s, t), v);
    p2[v][u] = hyp_p2(v, _(s, t));
  }

  // Compute q2: O(C)
  q2[u][u] = hyp_q2(_(s, t));
  FOR_SLOT(v) {
    if(v == u) continue;
    q2[u][v] = hyp_q2(_(s, t), v);
    q2[v][u] = hyp_q2(v, _(s, t));
  }

  // Compute L2: O(C^2)
  track_block("Compute L2", "", false) {
    FOR_SLOT(v) {
      FOR_SLOT(w) {
        if(!ORDER_VALID(v, w)) continue;
        double l;
        if(v == u || w == u)
          l = compute_L2(v, w);
        else
          l = compute_L2_using_old(s, t, u, v, w);
        L2[v][w] = l;
        logs("L2[" << Slot(v) << "," << Slot(w) << "] = " << l << ", resulting minfo = " << curr_minfo-l);
      }
    }
  }
}
void merge_clusters(const IntPair &st) { merge_clusters(st.first, st.second); }

// MAKE SURE THIS IS NOT DEFINED FOR EFFICIENCY!
//#define PRINT_RANKED

// Merge the optimal pair of clusters that result in the least amount of lost
// mutual information.
// Return the slots.
// O(C^2) time.
IntPair find_opt_clusters_to_merge() {
  track("find_opt_clusters_to_merge()", "", false);
  int best_s = -1, best_t = -1;
  double min_l = 1e30;

  // Pick two clusters to merge
  FOR_SLOT(s) {
    FOR_SLOT(t) {
      if(!ORDER_VALID(s, t)) continue;
      // Consider merging clusters in slots s and t.
      double l = L2[s][t];
#ifndef PRINT_RANKED
      logs("If merge clusters " << Slot(s) << " and " << Slot(t) << ", lose " << l << ", resulting minfo = " << curr_minfo-l);
#endif
      if(l < min_l) {
        min_l = l;
        best_s = s;
        best_t = t;
      }
    }
  }

#ifdef PRINT_RANKED
  vector< pair<double, IntPair> > merges;
  FOR_SLOT(s) {
    FOR_SLOT(t) {
      if(!ORDER_VALID(s, t)) continue;
      merges.push_back(pair<double, IntPair>(L2[s][t], _(s, t)));
    }
  }
  sort(merges.begin(), merges.end());
  for(int i = 0; i < len(merges); i++) {
    const IntPair &st = merges[i].second;
    int s = st.first;
    int t = st.second;
    double l = merges[i].first;
    logs("If merge clusters " << Slot(s) << " and " << Slot(t) << ", lose " << l << ", resulting minfo = " << curr_minfo-l);
  }
#endif

  return IntPair(best_s, best_t);
}

int phrase2cluster(int a) {
  a = phrase2rep.GetRoot(a);
  assert2(contains(rep2cluster, a), a);
  return rep2cluster[a];
}

real kl_divergence(const IntIntMap &a_count2, int a_count1, const IntPairIntMap &count2,
    const IntIntMap &count1, int ca, bool right) {
  real kl = 0;
  forcmap(int, cb, int, count, IntIntMap, a_count2) {
    real p = (real)count/a_count1; // P(cb | a)
    IntPair cab = right ? IntPair(ca, cb) : IntPair(cb, ca);
    real q = (real)(count2.find(cab)->second)/count1.find(ca)->second; // P(cb | ca)
    kl += p * log(p/q);
  }
  return kl;
}

// Motivation: each word has it's own identity (characterized by a
// distribution of its context).  The cluster has a distribution over
// contexts.  We can define an assignment of a word to a cluster by comparing
// this similarity.
// For each cluster, compute the cluster distributions.
void compute_cluster_distribs() {
  track("compute_cluster_distribs()", "", true);

  IntPairIntMap count2; // (cluster a, cluster b) -> number of times a-b appears
  IntIntMap count1; // cluster a -> number of times a appears

  // Compute cluster distributions
  foridx(a, N) {
    int ca = phrase2cluster(a);
    forvec(_, int, b, dep_phrases[a]) {
      int cb = phrase2cluster(b);
      count2[IntPair(ca, cb)]++;
      count1[ca]++;
      count1[cb]++;
    }
  }

  // For each word (phrase), compute its distribution
  kl_map[0].resize(N);
  kl_map[1].resize(N);
  foridx(a, N) {
    int ca = phrase2cluster(a);
    IntIntMap a_count2;
    int a_count1 = 0;
    real kl;

    //dlm: Head distribution
    a_count2.clear(), a_count1 = 0;
    forvec(_, int, b, head_phrases[a]) { //dlm
      int cb = phrase2cluster(b);
      a_count2[cb]++;
      a_count1++;
    }
    kl = kl_map[0][a] = kl_divergence(a_count2, a_count1, count2, count1, ca, false);
    //logs("Left-KL(" << Phrase(a) << " | " << Cluster(ca) << ") = " << kl);

    //dlm: Dep distribution
    a_count2.clear(), a_count1 = 0;
    forvec(_, int, b, dep_phrases[a]) { //dlm
      int cb = phrase2cluster(b);
      a_count2[cb]++;
      a_count1++;
    }
    kl = kl_map[1][a] = kl_divergence(a_count2, a_count1, count2, count1, ca, true);
    //logs("Right-KL(" << Phrase(a) << " | " << Cluster(ca) << ") = " << kl);
  }
}

int word2phrase(int a) {
  IntVecIntMap::const_iterator it = vec2phrase.find(to_vector(1, a));
  return it == vec2phrase.end() ? -1 : it->second;
}

// Read in from paths_file and fill in phrase2rep, rep2cluster
void convert_paths_to_map() {
  track("convert_paths_to_map()", "", false);
  assert(!paths_file.empty() && !map_file.empty());

  // Read clusters
  ifstream in(paths_file.c_str());
  char buf[1024];
  typedef unordered_map<string, StringVec, string_hf, string_eq> SSVMap;
  SSVMap map;
  while(in.getline(buf, sizeof(buf))) {
    char *path = strtok(buf, "\t");
    char *word = strtok(NULL, "\t");
    assert(word && path);
    map[path].push_back(word);
  }

  // Create the inital clusters.
  phrase2rep.Init(N); // Init union-set: each phrase starts out in its own cluster
  foridx(a, N) {
    rep2cluster[a] = a;
    cluster2rep[a] = a;
  }

  // Merge clusters
  curr_cluster_id = N; // New cluster ids will start at N, after all the phrases.
  forcmap(const string &, path, const StringVec &, words, SSVMap, map) {
    int a = -1;
    forvec(i, const string &, word, words) {
      int b = word2phrase(db.lookup(word.c_str(), false, -1));
      if(b == -1) continue;
      if(a != -1) {
        // Record merge in the cluster tree
        int c = curr_cluster_id++;
        cluster_tree[c] = _(a, b);

        // Update relationship between clusters and rep phrases
        int A = cluster2rep[a];
        int B = cluster2rep[b];
        phrase2rep.Join(A, B);
        int C = phrase2rep.GetRoot(A); // New rep phrase of cluster c (merged a and b)

        cluster2rep.erase(a);
        cluster2rep.erase(b);
        rep2cluster.erase(A);
        rep2cluster.erase(B);
        cluster2rep[c] = C;
        rep2cluster[C] = c;
        a = c;
      }
      else 
        a = b;
    }
  }

  compute_cluster_distribs();

  // Merge clusters
  ofstream out(map_file.c_str());
  forcmap(const string &, path, const StringVec &, words, SSVMap, map) {
    forvec(_, const string &, word, words) {
      int a = word2phrase(db.lookup(word.c_str(), false, -1));
      if(a == -1) continue;

      /*cout << a << ' ' << N << endl;
      cout << Phrase(a) << endl;
      cout << kl_map[0][a] << endl;
      cout << kl_map[1][a] << endl;
      cout << phrase_freqs[a] << endl;*/

      out << Phrase(a) << '\t'
          << path << "-L " << kl_map[0][a] << '\t'
          << path << "-R " << kl_map[1][a] << '\t'
          << path << "-freq " << phrase_freqs[a] << endl;
    }
  }
}

void do_clustering() {
  track("do_clustering()", "", true);
  
  compute_L2();
  repcheck();

  curr_cluster_id = N; // New cluster ids will start at N, after all the phrases.

  // Stage 1: Maintain initC clusters.  For each of the phrases initC..N-1, make
  // it into a new cluster.  Then merge the optimal pair among the initC+1
  // clusters.
  // O(N*C^2) time.
  track_block("Stage 1", "", false) {
    mem_tracker.report_mem_usage();
    for(int i = initC; i < len(freq_order_phrases); i++) { // Merge phrase new_a
      int new_a = freq_order_phrases[i];
      track("Merging phrase", i << '/' << N << ": " << Cluster(new_a), true);
      logs("Mutual info: " << curr_minfo);
      incorporate_new_phrase(new_a);
      repcheck();
      merge_clusters(find_opt_clusters_to_merge());
      repcheck();
    }
  }

  compute_cluster_distribs();

  stage2_cluster_offset = curr_cluster_id;

  // Stage 2: Merge the initC clusters in an hierarchical manner.
  // O(C^3) time.
  track_block("Stage 2", "", false) {
    mem_tracker.report_mem_usage();
    track_foridx(i, initC-1, "Clustering", true) {
      logs("Mutual info of " << len(cluster2slot) << " clusters: " << curr_minfo);
      merge_clusters(find_opt_clusters_to_merge());
      repcheck();
    }
  }

  logs("Done: 1 cluster left: mutual info = " << curr_minfo);
  mem_tracker.report_mem_usage();
  //assert(feq(curr_minfo, 0.0));
}

struct StackItem {
  StackItem(int a, int path_i, char ch) : a(a), path_i(path_i), ch(ch) { }
  int a;
  int path_i;
  char ch;
};

// The cluster tree is composed of the top part, which consists
// of Stage 2 merges, and the bottom part, which consists of stage 1 merges.
// Print out paths from the root only through the stage 2 merges.
void output_cluster_paths() {
  char path[16384];
  vector<StackItem> stack;

  // Figure out what to output
#define out_paths (!paths_file.empty())
#define out_map (!map_file.empty())
  if(!out_paths && !out_map) return;
  ofstream paths_out, map_out;
  if(out_paths) {
    paths_out.open(paths_file.c_str());
    logs("Writing cluster paths to " << paths_file);
  }
  if(out_map) {
    map_out.open(map_file.c_str());
    logs("Writing cluster map to " << map_file);
  }

  stack.push_back(StackItem(cluster2slot.begin()->first, 0, '\0'));

  while(!stack.empty()) {
    // Take off a stack item (a node in the tree).
    StackItem item = stack.back();
    int a = item.a;
    int path_i = item.path_i;
    if(item.ch)
      path[path_i-1] = item.ch;
    stack.pop_back();

    // Look at the node's children (if any).
    IntIntPairMap::const_iterator it = cluster_tree.find(a);
    if(it == cluster_tree.end()) {
      path[path_i] = '\0';
      if(out_paths) paths_out << path << '\t' << Phrase(a) << '\t' << phrase_freqs[a] << endl;
      if(out_map) map_out << Phrase(a) << '\t'
                          << path << "-L " << kl_map[0][a] << '\t'
                          << path << "-R " << kl_map[1][a] << '\t'
                          << path << "-freq " << phrase_freqs[a] << endl;
    }
    else {
      const IntPair &children = it->second;
      // Only print out paths through the part of the tree constructed in stage 2.
      bool extend = a >= stage2_cluster_offset;
      int new_path_i = path_i + extend;

      stack.push_back(StackItem(children.second, new_path_i, extend ? '1' : '\0'));
      stack.push_back(StackItem(children.first, new_path_i, extend ? '0' : '\0'));
    }
  }
}

int main(int argc, char *argv[]) {
  init_opt(argc, argv);

  assert(file_exists(text_file.c_str()));

  // Set output_dir from arguments.
  if(output_dir.empty()) {
    output_dir = file_base(strip_dir(text_file));
    output_dir += str_printf("-c%d", initC);
    output_dir += str_printf("-p%d", plen);
    if(!restrict_file.empty()) output_dir += str_printf("-R%s", file_base(strip_dir(restrict_file)).c_str());
    output_dir += ".out";
  }

  if(system(("mkdir -p " + output_dir).c_str()) != 0)
    assert2(false, "Can't create " << output_dir);
  if(system(("rm -f " + output_dir + "/*").c_str()) != 0)
    assert2(false, "Can't remove things in " << output_dir);

  // Set arguments from the output_dir.
  if(!output_dir.empty()) {
    if(paths_file.empty())               paths_file = output_dir+"/paths";
    if(map_file.empty())                   map_file = output_dir+"/map";
    if(collocs_file.empty())           collocs_file = output_dir+"/collocs";
    if(log_info.log_file.empty()) log_info.log_file = output_dir+"/log";
  }

  init_log;

  track_mem(db);
  track_mem(phrase_freqs);
  track_mem(head_phrases); //dlm
  track_mem(dep_phrases); //dlm
  track_mem(cluster_tree);
  track_mem(freq_order_phrases);
  track_mem(phrase2rep);
  track_mem(rep2cluster);
  track_mem(cluster2rep);
  track_mem(phrases);
  track_mem(slot2cluster);
  track_mem(cluster2slot);
  track_mem(p1);
  track_mem(p2);
  track_mem(q2);
  track_mem(L2);

  read_restrict_text();
  read_text();
  if(featvec_file.empty()) {
    if(paths2map)
      convert_paths_to_map();
    else if(!print_stats) {
      create_initial_clusters();
      output_best_collocations();
      do_clustering();
      output_cluster_paths();
    }
  }

  return 0;
}