#!/usr/local/bin/perl
use Inline C;
use Mojolicious::Lite;
use Mojo::JSON qw(decode_json encode_json to_json);
use Encode qw(decode encode);
use Getopt::Std;
use Mojo::Server::Daemon;
plugin 'Log::Access';

our $opt_i = 0; # latin1-input?
our $opt_l = undef;
our $opt_p = 5676;
our $opt_n = undef;

getopt('il:p:n:'); 

# -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 40 -binary 1 -iter 15
if(!$ARGV[0]) {
  init_net("vectors15.bin", $opt_n, ($opt_i? 1 : 0));
} else {
  init_net($ARGV[0], $opt_n, ($opt_i? 1 : 0));
}

my $daemon = Mojo::Server::Daemon->new(
    app    => app,
    listen => ['http://'.($opt_l ? $opt_l : '*').":$opt_p"]
);

get '/' => sub {
  my $c    = shift;
	my $word=$c->param('word');
  my $no_nbs=$c->param('n') || 100;
  my $no_iterations=$c->param('N') || 2000;
  my $perplexity=$c->param('perplexity') || 20;
  my $epsilon=$c->param('epsilon') || 5;
  my $som=$c->param('som') || 0;
  my $res;
	my @lists;
	my @collocations;
	if(defined($word) && $word !~ /^\s*$/) {
		$c->inactivity_timeout(300);
		$word =~ s/\s+/ /g;
    for my $w (split(' *\| *', $word)) {
			$c->app->log->debug('Looking for neighbours of '.$w);
      if($opt_i) {
        $res = get_neighbours(encode("iso-8859-1", $w), $no_nbs);
      } else {
        $res = get_neighbours($w, $no_nbs);
      }
      push(@lists, $res->{paradigmatic});
		}
	}
	$word =~ s/ *\| */ | /g;
  $c->render(template=>"index", word=>$word, no_nbs=>$no_nbs, no_iterations => $no_iterations, epsilon=> $epsilon, perplexity=> $perplexity, show_som=>$som, lists=> \@lists, collocators=> $res->{syntagmatic});
};

$daemon->run; # app->start;

exit;

__END__

__C__
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <malloc.h>
#include <stdlib.h>    //strlen
#include <sys/mman.h>
#include <pthread.h>

#define max_size 2000
#define max_w 50
#define MAX_NEIGHBOURS 1000
#define MAX_WORDS -1
#define MAX_THREADS 100
#define MAX_CC 50
#define EXP_TABLE_SIZE 1000
#define MAX_EXP 6

//the thread function
void *connection_handler(void *);

typedef struct {
	long long *index;
	float *dist;
  long long *pos;
	unsigned int length;
} knn;


typedef struct {
  long long wordi[MAX_NEIGHBOURS];
  char sep[MAX_NEIGHBOURS];
  int length;
} wordlist;

typedef struct {
  wordlist *wl;
	char *token;
	int N;
	long from;
	unsigned long upto;
} knnpars;

float *M, *syn1neg_window, *expTable;
char *vocab;

long long words, size;
int num_threads=20;
int latin_enc=0;
int window;

int init_net(char *file_name, char *net_name, int latin) {
  FILE *f, *binvecs, *binwords;
	int binwords_fd, binvecs_fd, net_fd, i;
	long long a, b, c, d, cn;
	float len;

	char binvecs_fname[256], binwords_fname[256];
	strcpy(binwords_fname, file_name);
	strcat(binwords_fname, ".words");
	strcpy(binvecs_fname, file_name);
	strcat(binvecs_fname, ".vecs");

  latin_enc = latin;
  f = fopen(file_name, "rb");
  if (f == NULL) {
    printf("Input file %s not found\n", file_name);
    return -1;
  }
  fscanf(f, "%lld", &words);
	if(MAX_WORDS > 0 && words > MAX_WORDS) words = MAX_WORDS;
  fscanf(f, "%lld", &size);
	if( (binvecs_fd = open(binvecs_fname, O_RDONLY)) < 0  || (binwords_fd = open(binwords_fname, O_RDONLY)) < 0) {
    printf("Converting %s to memory mappable structures\n", file_name);
		vocab = (char *)malloc((long long)words * max_w * sizeof(char));
		M = (float *)malloc((long long)words * (long long)size * sizeof(float));
		if (M == NULL) {
			printf("Cannot allocate memory: %lld MB    %lld  %lld\n", (long long)words * size * sizeof(float) / 1048576, words, size);
			return -1;
		}
		for (b = 0; b < words; b++) {
			a = 0;
			while (1) {
				vocab[b * max_w + a] = fgetc(f);
				if (feof(f) || (vocab[b * max_w + a] == ' ')) break;
				if ((a < max_w) && (vocab[b * max_w + a] != '\n')) a++;
			}
			vocab[b * max_w + a] = 0;
			fread(&M[b * size], sizeof(float), size, f);
			len = 0;
			for (a = 0; a < size; a++) len += M[a + b * size] * M[a + b * size];
			len = sqrt(len);
			for (a = 0; a < size; a++) M[a + b * size] /= len;
		}
		if( (binvecs = fopen(binvecs_fname, "wb")) != NULL && (binwords = fopen(binwords_fname, "wb")) != NULL) {
			fwrite(M, sizeof(float), (long long)words * (long long)size, binvecs);
			fclose(binvecs);
			fwrite(vocab, sizeof(char), (long long)words * max_w, binwords);
			fclose(binwords);
		}
  }
	if( (binvecs_fd = open(binvecs_fname, O_RDONLY)) >= 0 && (binwords_fd = open(binwords_fname, O_RDONLY)) >= 0) {
		M = mmap(0, sizeof(float) * (long long)words * (long long)size, PROT_READ, MAP_SHARED, binvecs_fd, 0);
		vocab = mmap(0,  sizeof(char) * (long long)words * max_w, PROT_READ, MAP_SHARED, binwords_fd, 0);
    if (M == MAP_FAILED || vocab == MAP_FAILED) {
			close(binvecs_fd);
			close(binwords_fd);
			fprintf(stderr, "Cannot mmap %s or %s\n", binwords_fname, binvecs_fname);
			exit(-1);
    }
	} else {
    fprintf(stderr, "Cannot open %s or %s\n", binwords_fname, binvecs_fname);
    exit(-1);
	}
  fclose(f);

  if(net_name) {
    if( (net_fd = open(net_name, O_RDONLY)) >= 0) {
      window = (lseek(net_fd, 0, SEEK_END) -  sizeof(float) * words * size) / words / size / sizeof(float) / 2;
      //      lseek(net_fd, sizeof(float) * words * size, SEEK_SET);
      munmap(M,  sizeof(float) * words * size);
      M = mmap(0, sizeof(float) * words * size + sizeof(float) * 2 * window * size * words, PROT_READ, MAP_SHARED, net_fd, 0);
      if (M == MAP_FAILED) {
        close(net_fd);
        fprintf(stderr, "Cannot mmap %s\n", net_name);
        exit(-1);
      }
      syn1neg_window =  M + words * size;
    } else {
      fprintf(stderr, "Cannot open %s\n", net_name);
      exit(-1);
    }
    fprintf(stderr, "Successfully memmaped %s. Determined window size: %d\n", net_name, window);
  }    

	expTable = (float *) malloc((EXP_TABLE_SIZE + 1) * sizeof(float));
	for (i = 0; i < EXP_TABLE_SIZE; i++) {
		expTable[i] = exp((i / (float) EXP_TABLE_SIZE * 2 - 1) * MAX_EXP); // Precompute the exp() table
		expTable[i] = expTable[i] / (expTable[i] + 1); // Precompute f(x) = x / (x + 1)
	}
	return 0;
}

knn *getCollocators(int cc, int N) {
	knn *nbs = NULL;
  long window_layer_size = size * window * 2;
	long a, b, c, d, e, window_offset, target, max_target=0, maxmax_target;
	float f, max_f, maxmax_f;
	float *target_sums, *bestf, worstbest;
	long long *besti, *bestp;

  if(cc == -1)
    return NULL;

	a = posix_memalign((void **) &target_sums, 128, words * sizeof(float));
	besti = malloc(N * sizeof(long long));
	bestp = malloc(N * sizeof(long long));
	bestf = malloc(N * sizeof(float));
  for (b = 0; b < words; b++)
			target_sums[b]=0;
  for (b = 0; b < N; b++)
			bestf[b]=-1;
  worstbest = -1;
  d = cc;
  maxmax_f = -1;
  maxmax_target = 0;

  besti[0]=d;
  bestf[0]=1.0;
  bestp[0]=0;
  
  for (a = window * 2 + 1; a >=0; a--) {
    printf("window pos: %ld\n", a);
    if (a != window) {
      max_f = -1;
      window_offset = a * size;
      if (a > window)
        window_offset -= size;
      for(target = 0; target < words / 2; target ++) {
        if(target == d)
          continue;
        f = 0;
        for (c = 0; c < size; c++)
          f += M[d* size + c]	* syn1neg_window[target * window_layer_size	+ window_offset + c];
        if (f < -MAX_EXP)
          continue;
        else if (f > MAX_EXP)
          continue;
        else
          f = expTable[(int) ((f + MAX_EXP)	* (EXP_TABLE_SIZE / MAX_EXP / 2))];
        if(f > max_f) {
          max_f = f;
          max_target = target;
        }
        target_sums[target] += (1-target_sums[target]) * f;
        if(f > worstbest) {
          for (b = 0; b < N; b++) {
            if (f > bestf[b]) {
              for (e = N - 1; e > b; e--) {
                bestf[e] = bestf[e - 1];
                besti[e] = besti[e - 1];
                bestp[e] = bestp[e - 1];
              }
              bestf[b] = f;
              besti[b] = target;
              bestp[b] = window-a;
              break;
            }
          }
          worstbest = bestf[N-1];
        }
      }
      printf("%d %.2f\n", max_target, max_f);
      printf("%s (%.2f) ", &vocab[max_target * max_w], max_f);
      if(max_f > maxmax_f) {
        maxmax_f = max_f;
        maxmax_target = max_target;
      }
    } else {
      printf("\x1b[1m%s\x1b[0m ", &vocab[d*max_w]);
    }
  }
  max_f = -1;
  for (b = 0; b < words; b++) {
    if(target_sums[b] > max_f) {
      max_f = target_sums[b];
      max_target = b;
    }
  }
  printf(" -- max sum: %s (%.2f), max resp.: \x1b[1m%s\x1b[0m (%.2f)\n",
         &vocab[max_target * max_w], max_f,
         &vocab[maxmax_target * max_w], maxmax_f);
  for(b=0; b<N; b++) 
    printf("%-32s %.2f %d\n", &vocab[besti[b]*max_w], bestf[b], bestp[b]);
  printf("\n");
	free(target_sums);
  nbs = malloc(sizeof(knn));
	nbs->index = besti;
	nbs->dist = bestf;
	nbs->pos = bestp;
	nbs->length = N;
  return(nbs);
}

wordlist *getTargetWords(char *st1) {
  wordlist *wl = malloc(sizeof(wordlist));
  char st[100][max_size], sep[100];
	long a, b=0, c=0, cn=0;
  
	while (1) {
		st[cn][b] = st1[c];
		b++;
		c++;
		st[cn][b] = 0;
		if (st1[c] == 0) break;
		if (st1[c] == ' ' || st1[c] == '-') {
      sep[cn++] = st1[c];
			b = 0;
			c++;
		}
	}
	cn++;
	for (a = 0; a < cn; a++) {
		for (b = 0; b < words; b++) if (!strcmp(&vocab[b * max_w], st[a])) break;
		if (b == words) b = -1;
		wl->wordi[a] = b;
		fprintf(stderr, "Word: \"%s\"  Position in vocabulary: %lld\n", st[a], wl->wordi[a]);
		if (b == -1) {
			fprintf(stderr, "Out of dictionary word!\n");
			cn--;
			break;
		}
	}
  wl->length=cn;
  return(wl);
}
 
void *_get_neighbours(knnpars *pars) {
	char *st1 = pars->token;
	int N = pars->N;
	long from = pars -> from;
	unsigned long upto = pars -> upto;
	char file_name[max_size], st[100][max_size], *sep;
	float dist, len, *bestd, vec[max_size];
	long long a, b, c, d, cn, *bi, *besti;
	char ch;
	knn *nbs = NULL;
  wordlist *wl = pars->wl;
  
	besti = malloc(N * sizeof(long long));
	bestd = malloc(N * sizeof(float));

	float worstbest=-1;

	for (a = 0; a < N; a++) bestd[a] = 0;
	a = 0;
  bi = wl->wordi;
	cn = wl->length;
  sep = wl->sep;
	b = bi[0];
	c = 0;

  if(from < 0) {
    nbs = getCollocators(b, pars->N);
    pthread_exit(nbs);
  }
	if (b == -1) {
    N = 0;
	  goto end;
  }
	for (a = 0; a < size; a++) vec[a] = 0;
	for (b = 0; b < cn; b++) {
		if (bi[b] == -1) continue;
    if(b>0 && sep[b-1] == '-')
      for (a = 0; a < size; a++) vec[a] -= M[a + bi[b] * size];
    else
      for (a = 0; a < size; a++) vec[a] += M[a + bi[b] * size];
	}
	len = 0;
	for (a = 0; a < size; a++) len += vec[a] * vec[a];
	len = sqrt(len);
	for (a = 0; a < size; a++) vec[a] /= len;
	for (a = 0; a < N; a++) bestd[a] = -1;
	for (c = from; c < upto; c++) {
		a = 0;
// do not skip taget word
//		for (b = 0; b < cn; b++) if (bi[b] == c) a = 1;
//		if (a == 1) continue;
		dist = 0;
		for (a = 0; a < size; a++) dist += vec[a] * M[a + c * size];
		if(dist > worstbest) {
			for (a = 0; a < N; a++) {
				if (dist > bestd[a]) {
					for (d = N - 1; d > a; d--) {
						bestd[d] = bestd[d - 1];
						besti[d] = besti[d - 1];
					}
					bestd[a] = dist;
					besti[a] = c;
					break;
				}
			}
			worstbest = bestd[N-1];
		}
	}

	nbs = malloc(sizeof(knn));
	nbs->index = besti;
	nbs->dist = bestd;
	nbs->length = N;
end:
	pthread_exit(nbs);
}


SV *get_neighbours(char *st1, int N) {
  HV *result = newHV();
	float bestd[MAX_NEIGHBOURS], vec[max_size];
	long long besti[MAX_NEIGHBOURS], bestp[MAX_NEIGHBOURS], a, b, c, d, slice;
	char *bestw[MAX_NEIGHBOURS];
	knn *nbs[MAX_THREADS];
	knnpars pars[MAX_THREADS];
  pthread_t *pt = (pthread_t *)malloc((num_threads+1) * sizeof(pthread_t));
  wordlist *wl;
  
  if(N>MAX_NEIGHBOURS) N=MAX_NEIGHBOURS;
	
	slice = words / num_threads;

  wl = getTargetWords(st1);

  a = num_threads;
  pars[a].token = st1;
  pars[a].wl = wl;
  pars[a].N = N;
  pars[a].from = -1;
  pthread_create(&pt[a], NULL, _get_neighbours, (void *) &pars[a]);

	for(a=0; a < num_threads; a++) {
		pars[a].token = st1;
		pars[a].wl = wl;
		pars[a].N = N;
		pars[a].from = a*slice;
		pars[a].upto = ((a+1)*slice > words? words:(a+1)*slice);
		pthread_create(&pt[a], NULL, _get_neighbours, (void *) &pars[a]);
	}
  for (a = 0; a < num_threads; a++) pthread_join(pt[a], &nbs[a]);

  pthread_join(pt[a], &nbs[a]);

	if(!nbs[0])
		goto end;

	for(b=0; b < N; b++) {
		besti[b] = nbs[0]->index[b];
		bestd[b] = nbs[0]->dist[b];
	}

	for(a=1; a < num_threads; a++) {
		for(b=0; b < N; b++) {
			for(c=0; c < N; c++) {
				if(nbs[a]->dist[b] > bestd[c]) {
					for(d=N-1; d>c; d--) {
						bestd[d] = bestd[d-1];
						besti[d] = besti[d-1];
					}
					besti[c] = nbs[a]->index[b];
					bestd[c] = nbs[a]->dist[b];
					break;
				}
			}
		}
	}

  
	if(nbs) {
    AV* array = newAV();
		for (a = 0; a < N; a++) {
			bestw[a] = (char *)malloc(max_size * sizeof(char));
		}
		for (a = 0; a < N; a++) {
			strcpy(bestw[a], &vocab[besti[a] * max_w]);
			HV* hash = newHV();
      SV* word = newSVpvf(bestw[a], 0);
      if(latin_enc == 0) SvUTF8_on(word);
			hv_store(hash, "word", strlen("word"), word , 0);
			hv_store(hash, "dist", strlen("dist"), newSVnv(bestd[a]), 0);
			hv_store(hash, "rank", strlen("rank"), newSVuv(besti[a]), 0);
			AV *vector = newAV();
			for (b = 0; b < size; b++) {
				av_push(vector, newSVnv(M[b + besti[a] * size]));
			}
			hv_store(hash, "vector", strlen("vector"), newRV_noinc((SV*)vector), 0);
			av_push(array, newRV_noinc((SV*)hash));
		}
    hv_store(result, "paradigmatic", strlen("paradigmatic"), newRV_noinc((SV*)array), 0);

    for(b=0; b < nbs[num_threads]->length; b++) {
      besti[b] = nbs[num_threads]->index[b];
      bestd[b] = nbs[num_threads]->dist[b];
      bestp[b] = nbs[num_threads]->pos[b];
    }
    array = newAV();
		for (a = 0; a < nbs[num_threads]->length; a++) {
			strcpy(bestw[a], &vocab[besti[a] * max_w]);
			HV* hash = newHV();
      SV* word = newSVpvf(bestw[a], 0);
      if(latin_enc == 0) SvUTF8_on(word);
			hv_store(hash, "word", strlen("word"), word , 0);
			hv_store(hash, "dist", strlen("dist"), newSVnv(bestd[a]), 0);
			hv_store(hash, "pos", strlen("pos"), newSVnv(bestp[a]), 0);
			av_push(array, newRV_noinc((SV*)hash));
		}
    hv_store(result, "syntagmatic", strlen("syntagmatic"), newRV_noinc((SV*)array), 0);
	}
end:
	return newRV_noinc((SV*)result);
}


__DATA__

@@ index.html.ep
<!DOCTYPE html>
<html>
<head>
	<title>DeReKo-Word-Vector-Distances</title>
	<link rel="stylesheet" href="//code.jquery.com/ui/1.11.4/themes/smoothness/jquery-ui.css">
	<script src="http://code.jquery.com/jquery-latest.min.js"></script>
  <script src="//code.jquery.com/ui/1.11.4/jquery-ui.js"></script>
	<script>
 $(function() {
    $( document ).tooltip({
			content: function() {
				return $(this).attr('title');
			}}
		)
	 })
  </script>
	<script src="//d3js.org/d3.v3.min.js" charset="utf-8"></script>
	<script src="http://klinux10/word2vec/tsne.js"></script>
	<script src="http://klinux10/word2vec/som.js"></script>
	<script src="http://klinux10/word2vec/labeler.js"></script>
<style>
body, input {
	font-family: Arial, sans-serif;
	font-size: 11pt;
}

.ui-tooltip-content {
	font-size: 9pt;
	colour: #222222;
}

svg > .ui-tooltip-content {
	font-size: 8pt;
	colour: #222222;
}

#collocators {
    margin-bottom: 15px;
}
  
#wrapper {
    width: 100%;
//   border: 1px solid red; 
    overflow: hidden; /* will contain if #first is longer than #second */
}
#first {
    margin-right: 20px;
    float: left;
 //    border: 1px solid green;
}
#second {
    border: 1px solid #333;
    overflow: hidden; /* if you don't want #second to wrap below #first */
}
#som2 svg {
    border: 1px solid #333;
}

#cost {
	  font-size: 8pt;
	  color: #222222;
    margin-top: 4px;
    margin-bottom: 12px;
}

#sominfo1, #sominfo {
	  font-size: 8pt;
	  color: #222222;
    margin-top: 0px;
}

#somcolor1, #somcolor2, #somcolor3 {
  display: inline-block;
  height: 10px;
  width: 10px;
}

#third {
  border: 1px solid #333;
}

</style>
<script>

var opt = {epsilon: <%= $epsilon %>, perplexity: <%= $perplexity %>},
		mapWidth = 800, // width map
		mapHeight = 800,
		jitterRadius = 7;

var T = new tsnejs.tSNE(opt); // create a tSNE instance

var Y;

var data;
var labeler;


function applyJitter() {
  svg.selectAll('.tsnet')
    .data(labels)
    .transition()
    .duration(50)
    .attr("transform", function(d, i) {
      T.Y[i][0] = (d.x - mapWidth/2 - tx)/ss/20;
      T.Y[i][1] = (d.y - mapHeight/2 - ty)/ss/20;
      return "translate(" +
        (d.x) + "," +
        (d.y) + ")";
    });
}
    
function updateEmbedding() {
  var Y = T.getSolution();
  svg.selectAll('.tsnet')
     .data(data.words)
     .attr("transform", function(d, i) { 
			 return "translate(" +
         ((Y[i][0]*20*ss + tx) + mapWidth/2) + "," +
         ((Y[i][1]*20*ss + ty) + mapHeight/2) + ")"; });
}

var svg;
var labels = [];
var anchor_array = [];
var text;

function drawEmbedding() {
  $("#embed").empty();
  var div = d3.select("#embed");
	
  // get min and max in each column of Y
  var Y = T.Y;
  
  svg = div.append("svg") // svg is global
					 .attr("width", mapWidth)
					 .attr("height", mapHeight);
	
  var g = svg.selectAll(".b")
						 .data(data.words)
						 .enter().append("g")
						 .attr("class", "tsnet");
	
  g.append("a")
	 .attr("xlink:href", function(word) {return "/?word="+word;})
	 .attr("title", function(d, i) {
		 return "rank: "+i +"  "+"freq. rank: "+data.ranks[i].toString().replace(/\B(?=(\d{3})+(?!\d))/g, ",");
	 })
		.append("text")
   .attr("text-anchor", "top")
   .attr("font-size", 12)
   .attr("fill", function(d) {
			if(data.target.indexOf(" "+d+" ") >= 0) {
				return "red";
			} else {
				return "#333"
			}
	 })
   .text(function(d) { return d; });
	
  var zoomListener = d3.behavior.zoom()
											 .scaleExtent([0.1, 10])
											 .center([0,0])
											 .on("zoom", zoomHandler);
  zoomListener(svg);
}

var tx=0, ty=0;
var ss=1;
var iter_id=-1;

function zoomHandler() {
  tx = d3.event.translate[0];
  ty = d3.event.translate[1];
  ss = d3.event.scale;
  updateEmbedding();
}

var stepnum = 0;

function stopStep() {
  clearInterval(iter_id);
	text = svg.selectAll("text");
	
  // jitter function needs different data and co-ordinate representation
	labels = d3.range(data.words.length).map(function(i) {
    var x = (T.Y[i][0]*20*ss + tx) + mapWidth/2;
    var y = (T.Y[i][1]*20*ss + ty) + mapHeight/2;
		anchor_array.push({x: x, y: y, r: jitterRadius});
		return {
      x: x,
      y: y,
			name: data.words[i]
		};
	});
  
  // get the actual label bounding boxes for the jitter function
	var index = 0;
	text.each(function() {
		labels[index].width = this.getBBox().width;
		labels[index].height = this.getBBox().height;
		index += 1;
	});

	 
//	 setTimeout(updateEmbedding, 1);
//	 setTimeout(
	labeler =	 d3.labeler()
			         .label(labels)
			         .anchor(anchor_array)
			         .width(mapWidth)
			         .height(mapHeight)
			         .update(applyJitter);
  //         .start(1000);

  iter_id = setInterval(jitterStep, 1);
}

var jitter_i=0;

function jitterStep() {
  if(jitter_i++ > 100) {
    clearInterval(iter_id);
  } else {
		labeler.start2(10);
	  applyJitter();
  }
}

var last_cost=1000;

function step() {
  var i = T.iter;
  
  if(i > <%= $no_iterations %>) {
		stopStep();
  } else {
    var cost = Math.round(T.step() * 100000) / 100000; // do a few steps
    $("#cost").html("tsne iteration " + i + ", cost: " + cost.toFixed(5));
    if(i % 250 == 0 && cost >= last_cost) {
		  stopStep();
    } else {
      last_cost = cost;
      updateEmbedding();
    }
  }
}

function showMap(j) {
	data=j;
  T.iter=0;
  T.initDataRaw(data.vecs); // init embedding
  drawEmbedding(); // draw initial embedding
	
	if(iter_id >= 0) {
		clearInterval(iter_id);
	}
  //T.debugGrad();
  iter_id = setInterval(step, 1);
  if(<%= $show_som %>) {
    makeSOM(j, <%= $no_iterations %>);
  }
}
 
</script>
</head>
<body>
	<form action="<%=url_for('/')->to_abs%>" method="GET">
		word(s): 
    <input type="text" name="word" size="20"  value="<%= $word %>" title="When looking for multiple words use spaces as separators to search around the average vector and | as separator to get the neighbours for each word."> 
		max. neighbours: <input type="text" size="8" name="n" value="<%= $no_nbs %>">
		max. iterations: <input type="text" name="N" size="8" value="<%= $no_iterations %>">
    SOM <input type="checkbox" name="som" value="1" <%= ($show_som ? "checked" : "") %>>
		<span>  </span><input type="submit" value="Show">
	</form>
	<br>
	% if($lists) {
	<div id="wrapper">
		<table id="first">
			<tr>
				<th align="right">#</th><th align="right">cos</th><th align="left">paradigmatic</th><th title="Position in winodw around target word. Absolute value can be too low because of sub-sampling frequent words.">@</th><th align="right" title="&#34;Responsivenes&#34; of the collocator at the relative position @. Approximation of the probability that the combination of the target word and the collocator at the relative position @ come from the corpus.">resp.</th><th align="left">syntagmatic</th>
			</tr>
			% my $j=0; my @words; my @vecs; my @ranks; for my $list (@$lists) {
			% my $i=0; for my $item (@$list) {
      % my $c = (@$collocators)[$i];      
			% if(!grep{$_ eq $item->{word}} @words) {
      %   push @vecs, $item->{vector};
			%   push @words, $item->{word};
			%   push @ranks, $item->{rank};
      % }
			<tr>
				<td align="right">
  				<%= ++$i %>.
				</td>
				<td align="right">
  				<%= sprintf("%.3f", $item->{dist}) %>
				</td>
				<td>
  				<a title="freq. rank: <%= $item->{rank} %>" href="/?word=<%= $item->{word} %>">
						<%= $item->{word} %>
					</a>
				</td>
				<td align="right">
          <%= $c->{pos} %>:
				</td>
				<td align="right">
  				<%= sprintf("%.3f", $c->{dist}) %>
				</td>
				<td align="left">
  				<a href="/?word=<%= $c->{word} %>">
            <%= $c->{word} %>
				</td>
			</tr>
			% }
			% }
		</table>
		<script>
		 % use Mojo::ByteStream 'b';
		 $(window).load(function() {
			 showMap(<%= b(Mojo::JSON::to_json({target => " $word ", words => \@words, vecs => \@vecs, ranks => \@ranks})); %>);
		 });
    </script>
		% }
		<div id="second" style="width:800px; height:800px; font-family: arial;">
			<div id="embed">
			</div>
		</div>
    <div id="cost"></div>
		% if($show_som) {
		<div id="som2">
		</div>
    <div id="sominfo1"><span id="somcolor1">   </span> <span id="somword1"> </span> <span id="somcolor2">   </span> <span id="somword2"> </span> <span id="somcolor3">   </span></div>
    <div id="sominfo">SOM iteration <span id="iterations">0</span></div>
    % }
  </div>
	<p>
		Word vector model based on  DeReKo-2015-II. Trained with <a href="https://code.google.com/p/word2vec/">word2vec</a> using the following parameters:</p>
  <pre>
-cbow 1 -size 300 -window 7 -negative 5 -hs 0 -sample 1e-5 -threads 44 -binary 1 -iter 5
	</pre>
	</p>
</body>
</html>