preso

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@435 ec762483-ff6d-05da-a07a-a48fb63a330f

31 files changed, 4891 insertions, 0 deletions

diff --git a/report/pyp_clustering/acl09-short/code/antoniakpred.m b/report/pyp_clustering/acl09-short/code/antoniakpred.m
new file mode 100644
index 00000000..c4153c04
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/antoniakpred.m
@@ -0,0 +1,12 @@
+function output = antoniakpred(input,b)
+
+uniqin = unique(input);
+prediction = zeros(max(input),1);
+
+p0=1/30114;
+for i = 1:length(uniqin)
+  prediction(uniqin(i)) = b*p0*log((b*p0+uniqin(i))/(b*p0));
+end
+
+output = prediction(input);
+
diff --git a/report/pyp_clustering/acl09-short/code/approximations.eps b/report/pyp_clustering/acl09-short/code/approximations.eps
new file mode 100644
index 00000000..67857497
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/approximations.eps
@@ -0,0 +1,897 @@
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+4335 3124 mt 4335 3196 L
+ 861 3907 mt  911 3957 L
+ 911 3907 mt  861 3957 L
+1127 3749 mt 1177 3799 L
+1177 3749 mt 1127 3799 L
+1392 3653 mt 1442 3703 L
+1442 3653 mt 1392 3703 L
+1657 3591 mt 1707 3641 L
+1707 3591 mt 1657 3641 L
+1922 3524 mt 1972 3574 L
+1972 3524 mt 1922 3574 L
+2188 3465 mt 2238 3515 L
+2238 3465 mt 2188 3515 L
+2453 3413 mt 2503 3463 L
+2503 3413 mt 2453 3463 L
+2718 3365 mt 2768 3415 L
+2768 3365 mt 2718 3415 L
+2984 3319 mt 3034 3369 L
+3034 3319 mt 2984 3369 L
+3249 3278 mt 3299 3328 L
+3299 3278 mt 3249 3328 L
+3514 3242 mt 3564 3292 L
+3564 3242 mt 3514 3292 L
+3779 3203 mt 3829 3253 L
+3829 3203 mt 3779 3253 L
+4045 3168 mt 4095 3218 L
+4095 3168 mt 4045 3218 L
+4310 3135 mt 4360 3185 L
+4360 3135 mt 4310 3185 L
+gs 647 460 3857 5001 rc
+gr
+
+  36   36  886 3932 FO
+  36   36 1152 3825 FO
+  36   36 1417 3737 FO
+  36   36 1682 3663 FO
+  36   36 1947 3567 FO
+  36   36 2213 3455 FO
+  36   36 2478 3330 FO
+  36   36 2743 3183 FO
+  36   36 3009 3003 FO
+  36   36 3274 2790 FO
+  36   36 3539 2539 FO
+  36   36 3804 2234 FO
+  36   36 4070 1938 FO
+  36   36 4335 1575 FO
+gs 647 460 3857 5001 rc
+18 w
+0.7 sg
+265 -54 266 -58 265 -65 265 -72 265 -82 266 -95 265 -109 265 -127 
+266 -155 265 -182 265 -180 265 -268 266 -415 886 3932 14 MP stroke
+DA
+265 -56 266 -59 265 -68 265 -74 265 -85 266 -99 265 -113 265 -133 
+266 -162 265 -191 265 -187 265 -277 266 -425 886 4013 14 MP stroke
+SO
+6 w
+gr
+
+0.7 sg
+0 sg
+ 850 3932 mt  922 3932 L
+ 886 3896 mt  886 3968 L
+1116 3517 mt 1188 3517 L
+1152 3481 mt 1152 3553 L
+1381 3249 mt 1453 3249 L
+1417 3213 mt 1417 3285 L
+1646 3069 mt 1718 3069 L
+1682 3033 mt 1682 3105 L
+1911 2887 mt 1983 2887 L
+1947 2851 mt 1947 2923 L
+2177 2732 mt 2249 2732 L
+2213 2696 mt 2213 2768 L
+2442 2605 mt 2514 2605 L
+2478 2569 mt 2478 2641 L
+2707 2496 mt 2779 2496 L
+2743 2460 mt 2743 2532 L
+2973 2401 mt 3045 2401 L
+3009 2365 mt 3009 2437 L
+3238 2319 mt 3310 2319 L
+3274 2283 mt 3274 2355 L
+3503 2247 mt 3575 2247 L
+3539 2211 mt 3539 2283 L
+3768 2181 mt 3840 2181 L
+3804 2145 mt 3804 2217 L
+4034 2124 mt 4106 2124 L
+4070 2088 mt 4070 2160 L
+4299 2069 mt 4371 2069 L
+4335 2033 mt 4335 2105 L
+ 861 3907 mt  911 3957 L
+ 911 3907 mt  861 3957 L
+1127 3492 mt 1177 3542 L
+1177 3492 mt 1127 3542 L
+1392 3224 mt 1442 3274 L
+1442 3224 mt 1392 3274 L
+1657 3044 mt 1707 3094 L
+1707 3044 mt 1657 3094 L
+1922 2862 mt 1972 2912 L
+1972 2862 mt 1922 2912 L
+2188 2707 mt 2238 2757 L
+2238 2707 mt 2188 2757 L
+2453 2580 mt 2503 2630 L
+2503 2580 mt 2453 2630 L
+2718 2471 mt 2768 2521 L
+2768 2471 mt 2718 2521 L
+2984 2376 mt 3034 2426 L
+3034 2376 mt 2984 2426 L
+3249 2294 mt 3299 2344 L
+3299 2294 mt 3249 2344 L
+3514 2222 mt 3564 2272 L
+3564 2222 mt 3514 2272 L
+3779 2156 mt 3829 2206 L
+3829 2156 mt 3779 2206 L
+4045 2099 mt 4095 2149 L
+4095 2099 mt 4045 2149 L
+4310 2044 mt 4360 2094 L
+4360 2044 mt 4310 2094 L
+gs 647 460 3857 5001 rc
+gr
+
+  36   36  886 3932 FO
+  36   36 1152 3713 FO
+  36   36 1417 3510 FO
+  36   36 1682 3318 FO
+  36   36 1947 3048 FO
+  36   36 2213 2733 FO
+  36   36 2478 2401 FO
+  36   36 2743 2061 FO
+  36   36 3009 1720 FO
+  36   36 3274 1380 FO
+  36   36 3539 1045 FO
+  36   36 3804  746 FO
+gs 647 460 3857 5001 rc
+gr
+
+ 240 4103 mt  -90 rotate
+(Mean number of lexical entries) s
+90 rotate
+1812 5794 mt 
+(Word frequency \(n) s
+%%IncludeResource: font Helvetica
+/Helvetica /ISOLatin1Encoding 132 FMSR
+
+3188 5878 mt 
+(w) s
+%%IncludeResource: font Helvetica
+/Helvetica /ISOLatin1Encoding 168 FMSR
+
+3283 5794 mt 
+(\)) s
+%%IncludeResource: font Helvetica
+/Helvetica /ISOLatin1Encoding 120 FMSR
+
+ 630 5503 mt 
+( ) s
+4487  502 mt 
+( ) s
+1 sg
+0 846 2267 0 0 -846 707 1366 4 MP
+PP
+-2267 0 0 846 2267 0 0 -846 707 1366 5 MP stroke
+4 w
+DO
+SO
+6 w
+0 sg
+ 707 1366 mt 2974 1366 L
+ 707  520 mt 2974  520 L
+ 707 1366 mt  707  520 L
+2974 1366 mt 2974  520 L
+ 707 1366 mt 2974 1366 L
+ 707 1366 mt  707  520 L
+ 707 1366 mt 2974 1366 L
+ 707  520 mt 2974  520 L
+ 707 1366 mt  707  520 L
+2974 1366 mt 2974  520 L
+%%IncludeResource: font Helvetica
+/Helvetica /ISOLatin1Encoding 168 FMSR
+
+1183  698 mt 
+(Expectation) s
+gs 707 520 2268 847 rc
+18 w
+0.7 sg
+365 0 780 637 2 MP stroke
+gr
+
+18 w
+0.7 sg
+0 sg
+1183  902 mt 
+(Antoniak approx.) s
+gs 707 520 2268 847 rc
+DA
+0.7 sg
+365 0 780 841 2 MP stroke
+SO
+gr
+
+0.7 sg
+0 sg
+1183 1105 mt 
+(Empirical, fixed base) s
+gs 707 520 2268 847 rc
+6 w
+gs 889 971 147 147 rc
+ 926 1044 mt  998 1044 L
+ 962 1008 mt  962 1080 L
+ 937 1019 mt  987 1069 L
+ 987 1019 mt  937 1069 L
+gr
+
+gr
+
+6 w
+1183 1309 mt 
+(Empirical, inferred base) s
+gs 707 520 2268 847 rc
+gs 889 1175 147 147 rc
+  36   36  962 1248 FO
+gr
+
+gr
+
+
+end %%Color Dict
+
+eplot
+%%EndObject
+
+epage
+end
+
+showpage
+
+%%Trailer
+%%EOF
diff --git a/report/pyp_clustering/acl09-short/code/cohnpred.m b/report/pyp_clustering/acl09-short/code/cohnpred.m
new file mode 100644
index 00000000..35a49605
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/cohnpred.m
@@ -0,0 +1,12 @@
+function output = cohnpred(input,b)
+
+uniqin = unique(input);
+prediction = zeros(max(input),1);
+
+p0=1/30114;
+for i = 1:length(uniqin)
+  prediction(uniqin(i)) = b*p0*(psi(b*p0+uniqin(i)) - psi(b*p0));
+end
+
+output = prediction(input);
+
diff --git a/report/pyp_clustering/acl09-short/code/cokus.c b/report/pyp_clustering/acl09-short/code/cokus.c
new file mode 100644
index 00000000..3a959c0f
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/cokus.c
@@ -0,0 +1,167 @@
+// This is the ``Mersenne Twister'' random number generator MT19937, which
+// generates pseudorandom integers uniformly distributed in 0..(2^32 - 1)
+// starting from any odd seed in 0..(2^32 - 1).  This version is a recode
+// by Shawn Cokus (Cokus@math.washington.edu) on March 8, 1998 of a version by
+// Takuji Nishimura (who had suggestions from Topher Cooper and Marc Rieffel in
+// July-August 1997).
+//
+// Effectiveness of the recoding (on Goedel2.math.washington.edu, a DEC Alpha
+// running OSF/1) using GCC -O3 as a compiler: before recoding: 51.6 sec. to
+// generate 300 million random numbers; after recoding: 24.0 sec. for the same
+// (i.e., 46.5% of original time), so speed is now about 12.5 million random
+// number generations per second on this machine.
+//
+// According to the URL <http://www.math.keio.ac.jp/~matumoto/emt.html>
+// (and paraphrasing a bit in places), the Mersenne Twister is ``designed
+// with consideration of the flaws of various existing generators,'' has
+// a period of 2^19937 - 1, gives a sequence that is 623-dimensionally
+// equidistributed, and ``has passed many stringent tests, including the
+// die-hard test of G. Marsaglia and the load test of P. Hellekalek and
+// S. Wegenkittl.''  It is efficient in memory usage (typically using 2506
+// to 5012 bytes of static data, depending on data type sizes, and the code
+// is quite short as well).  It generates random numbers in batches of 624
+// at a time, so the caching and pipelining of modern systems is exploited.
+// It is also divide- and mod-free.
+//
+// This library is free software; you can redistribute it and/or modify it
+// under the terms of the GNU Library General Public License as published by
+// the Free Software Foundation (either version 2 of the License or, at your
+// option, any later version).  This library is distributed in the hope that
+// it will be useful, but WITHOUT ANY WARRANTY, without even the implied
+// warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See
+// the GNU Library General Public License for more details.  You should have
+// received a copy of the GNU Library General Public License along with this
+// library; if not, write to the Free Software Foundation, Inc., 59 Temple
+// Place, Suite 330, Boston, MA 02111-1307, USA.
+//
+// The code as Shawn received it included the following notice:
+//
+//   Copyright (C) 1997 Makoto Matsumoto and Takuji Nishimura.  When
+//   you use this, send an e-mail to <matumoto@math.keio.ac.jp> with
+//   an appropriate reference to your work.
+//
+// It would be nice to CC: <Cokus@math.washington.edu> when you write.
+//
+
+#include <stdio.h>
+#include <stdlib.h>
+
+//
+// uint32 must be an unsigned integer type capable of holding at least 32
+// bits; exactly 32 should be fastest, but 64 is better on an Alpha with
+// GCC at -O3 optimization so try your options and see what's best for you
+//
+
+typedef unsigned long uint32;
+
+#define N              (624)                 // length of state vector
+#define M              (397)                 // a period parameter
+#define K              (0x9908B0DFU)         // a magic constant
+#define hiBit(u)       ((u) & 0x80000000U)   // mask all but highest   bit of u
+#define loBit(u)       ((u) & 0x00000001U)   // mask all but lowest    bit of u
+#define loBits(u)      ((u) & 0x7FFFFFFFU)   // mask     the highest   bit of u
+#define mixBits(u, v)  (hiBit(u)|loBits(v))  // move hi bit of u to hi bit of v
+
+static uint32   state[N+1];     // state vector + 1 extra to not violate ANSI C
+static uint32   *next;          // next random value is computed from here
+static int      left = -1;      // can *next++ this many times before reloading
+
+
+void seedMT(uint32 seed)
+ {
+    //
+    // We initialize state[0..(N-1)] via the generator
+    //
+    //   x_new = (69069 * x_old) mod 2^32
+    //
+    // from Line 15 of Table 1, p. 106, Sec. 3.3.4 of Knuth's
+    // _The Art of Computer Programming_, Volume 2, 3rd ed.
+    //
+    // Notes (SJC): I do not know what the initial state requirements
+    // of the Mersenne Twister are, but it seems this seeding generator
+    // could be better.  It achieves the maximum period for its modulus
+    // (2^30) iff x_initial is odd (p. 20-21, Sec. 3.2.1.2, Knuth); if
+    // x_initial can be even, you have sequences like 0, 0, 0, ...;
+    // 2^31, 2^31, 2^31, ...; 2^30, 2^30, 2^30, ...; 2^29, 2^29 + 2^31,
+    // 2^29, 2^29 + 2^31, ..., etc. so I force seed to be odd below.
+    //
+    // Even if x_initial is odd, if x_initial is 1 mod 4 then
+    //
+    //   the          lowest bit of x is always 1,
+    //   the  next-to-lowest bit of x is always 0,
+    //   the 2nd-from-lowest bit of x alternates      ... 0 1 0 1 0 1 0 1 ... ,
+    //   the 3rd-from-lowest bit of x 4-cycles        ... 0 1 1 0 0 1 1 0 ... ,
+    //   the 4th-from-lowest bit of x has the 8-cycle ... 0 0 0 1 1 1 1 0 ... ,
+    //    ...
+    //
+    // and if x_initial is 3 mod 4 then
+    //
+    //   the          lowest bit of x is always 1,
+    //   the  next-to-lowest bit of x is always 1,
+    //   the 2nd-from-lowest bit of x alternates      ... 0 1 0 1 0 1 0 1 ... ,
+    //   the 3rd-from-lowest bit of x 4-cycles        ... 0 0 1 1 0 0 1 1 ... ,
+    //   the 4th-from-lowest bit of x has the 8-cycle ... 0 0 1 1 1 1 0 0 ... ,
+    //    ...
+    //
+    // The generator's potency (min. s>=0 with (69069-1)^s = 0 mod 2^32) is
+    // 16, which seems to be alright by p. 25, Sec. 3.2.1.3 of Knuth.  It
+    // also does well in the dimension 2..5 spectral tests, but it could be
+    // better in dimension 6 (Line 15, Table 1, p. 106, Sec. 3.3.4, Knuth).
+    //
+    // Note that the random number user does not see the values generated
+    // here directly since reloadMT() will always munge them first, so maybe
+    // none of all of this matters.  In fact, the seed values made here could
+    // even be extra-special desirable if the Mersenne Twister theory says
+    // so-- that's why the only change I made is to restrict to odd seeds.
+    //
+
+    register uint32 x = (seed | 1U) & 0xFFFFFFFFU, *s = state;
+    register int    j;
+
+    for(left=0, *s++=x, j=N; --j;
+        *s++ = (x*=69069U) & 0xFFFFFFFFU);
+ }
+
+
+uint32 reloadMT(void)
+ {
+    register uint32 *p0=state, *p2=state+2, *pM=state+M, s0, s1;
+    register int    j;
+
+    if(left < -1)
+        seedMT(4357U);
+
+    left=N-1, next=state+1;
+
+    for(s0=state[0], s1=state[1], j=N-M+1; --j; s0=s1, s1=*p2++)
+        *p0++ = *pM++ ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
+
+    for(pM=state, j=M; --j; s0=s1, s1=*p2++)
+        *p0++ = *pM++ ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
+
+    s1=state[0], *p0 = *pM ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
+    s1 ^= (s1 >> 11);
+    s1 ^= (s1 <<  7) & 0x9D2C5680U;
+    s1 ^= (s1 << 15) & 0xEFC60000U;
+    return(s1 ^ (s1 >> 18));
+ }
+
+
+inline uint32 randomMT(void)
+ {
+    uint32 y;
+
+    if(--left < 0)
+        return(reloadMT());
+
+    y  = *next++;
+    y ^= (y >> 11);
+    y ^= (y <<  7) & 0x9D2C5680U;
+    y ^= (y << 15) & 0xEFC60000U;
+    y ^= (y >> 18);
+    return(y);
+ }
+
+
+
+
diff --git a/report/pyp_clustering/acl09-short/code/crppred.m b/report/pyp_clustering/acl09-short/code/crppred.m
new file mode 100644
index 00000000..17f22652
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/crppred.m
@@ -0,0 +1,12 @@
+function output = crppred(input,b)
+
+uniqin = unique(input);
+prediction = zeros(max(input),1);
+
+p0=1/30114;
+for i = 1:length(uniqin)
+  prediction(uniqin(i)) = b*p0*sum(1./((1:uniqin(i))+b*p0-1));
+end
+
+output = prediction(input);
+
diff --git a/report/pyp_clustering/acl09-short/code/crppred_geom.m b/report/pyp_clustering/acl09-short/code/crppred_geom.m
new file mode 100644
index 00000000..e6869e4f
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/crppred_geom.m
@@ -0,0 +1,12 @@
+function output = crppred_geom(input,lengths,b)
+
+
+output = zeros(length(input),1);
+
+p0=(1/52).^lengths;
+a=b*p0;
+for i = 1:length(input)
+  output(i) = a(i)*sum(1./((1:input(i))+a(i)-1));
+end
+
+
diff --git a/report/pyp_clustering/acl09-short/code/logbinmean.m b/report/pyp_clustering/acl09-short/code/logbinmean.m
new file mode 100644
index 00000000..23dbb0ac
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/logbinmean.m
@@ -0,0 +1,38 @@
+function [ logbinsvalid , meanval, seval ] = logbinmean( frequency, typecount, NBINS , MinCounts );
+
+% calculate distribution of frequency
+Maxfrequency = max( frequency );
+meanK  = mean( frequency );
+linbins = linspace( log10(1) , log10( Maxfrequency ) , NBINS );
+stepb   = linbins( 2 ) - linbins( 1 );
+
+logbins  = 10.^linbins;
+
+% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+LL       = length( linbins ) - 1;
+for i=1:LL
+  lowb  = linbins( i   );
+  highb = linbins( i+1 );
+  linbinsout( i ) = (highb + lowb) / 2;
+  
+  lowb  = logbins( i   );
+  highb = logbins( i+1 );
+  step  = highb - lowb;
+  logbinsout( i ) = 10^linbinsout( i );
+  
+  indices = find( frequency >= lowb & frequency < highb);
+  
+  meanval(i) = mean(typecount(indices));
+  rawcounts(i) = length(indices);
+  seval(i) = std(typecount(indices))./sqrt(rawcounts(i));
+  
+end
+
+valid = 1:LL;
+valid( find( rawcounts <= MinCounts )) = [];
+
+linbinsvalid   = linbinsout( valid );
+logbinsvalid   = logbinsout( valid );
+
+meanval     =  meanval( valid );
+seval       =  seval( valid );
diff --git a/report/pyp_clustering/acl09-short/code/noP0pred.m b/report/pyp_clustering/acl09-short/code/noP0pred.m
new file mode 100644
index 00000000..f72f1432
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/noP0pred.m
@@ -0,0 +1,11 @@
+function output = antoniakpred(input,b)
+
+uniqin = unique(input);
+prediction = zeros(max(input),1);
+
+for i = 1:length(uniqin)
+  prediction(uniqin(i)) = b*log((b+uniqin(i))/b);
+end
+
+output = prediction(input);
+
diff --git a/report/pyp_clustering/acl09-short/code/plot0.eps b/report/pyp_clustering/acl09-short/code/plot0.eps
new file mode 100644
index 00000000..6094346a
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/plot0.eps
@@ -0,0 +1,633 @@
+%!PS-Adobe-3.0 EPSF-3.0
+%%Creator: MATLAB, The Mathworks, Inc. Version 7.7.0.471 (R2008b). Operating System: Linux 2.6.18-128.1.6.el5.inf.1PAE #1 SMP Wed Apr 15 10:23:41 BST 2009 i686.
+%%Title: /afs/inf.ed.ac.uk/user/s/sgwater/research/papers/2009/acl09-short/code/plot0.eps
+%%CreationDate: 07/23/2009  17:36:19
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+-55 95 -55 -95 110 0 6678 5182 4 MP
+DP
+-55 95 -55 -95 110 0 7116 5187 4 MP
+DP
+gs 1070 452 6380 4909 rc
+/c8 { 1.000000 0.400000 0.200000 sr} bdef
+c8
+438 -12 439 -12 439 -13 439 -13 439 -13 439 -13 439 -14 439 -13 
+438 -15 439 -15 439 -14 439 -19 439 -31 1466 5171 14 MP stroke
+gr
+
+c8
+  48   48 1466 5171 FO
+  48   48 1905 5140 FO
+  48   48 2344 5121 FO
+  48   48 2783 5108 FO
+  48   48 3222 5092 FO
+  48   48 3660 5077 FO
+  48   48 4099 5065 FO
+  48   48 4538 5050 FO
+  48   48 4977 5036 FO
+  48   48 5416 5022 FO
+  48   48 5855 5013 FO
+  48   48 6294 4999 FO
+  48   48 6733 4995 FO
+  48   48 7171 4967 FO
+gs 1070 452 6380 4909 rc
+gr
+
+-55 95 -55 -95 110 0 1411 5203 4 MP
+DP
+-55 95 -55 -95 110 0 1850 5173 4 MP
+DP
+-55 95 -55 -95 110 0 2289 5153 4 MP
+DP
+-55 95 -55 -95 110 0 2728 5139 4 MP
+DP
+-55 95 -55 -95 110 0 3167 5122 4 MP
+DP
+-55 95 -55 -95 110 0 3605 5105 4 MP
+DP
+-55 95 -55 -95 110 0 4044 5089 4 MP
+DP
+-55 95 -55 -95 110 0 4483 5070 4 MP
+DP
+-55 95 -55 -95 110 0 4922 5053 4 MP
+DP
+-55 95 -55 -95 110 0 5361 5036 4 MP
+DP
+-55 95 -55 -95 110 0 5800 5021 4 MP
+DP
+-55 95 -55 -95 110 0 6239 5007 4 MP
+DP
+-55 95 -55 -95 110 0 6678 4984 4 MP
+DP
+-55 95 -55 -95 110 0 7116 4944 4 MP
+DP
+gs 1070 452 6380 4909 rc
+/c9 { 0.400000 0.400000 1.000000 sr} bdef
+c9
+438 -44 439 -46 439 -50 439 -51 439 -57 439 -61 439 -65 439 -72 
+438 -81 439 -89 439 -85 439 -130 439 -215 1466 5171 14 MP stroke
+gr
+
+c9
+  48   48 1466 5171 FO
+  48   48 1905 4956 FO
+  48   48 2344 4826 FO
+  48   48 2783 4741 FO
+  48   48 3222 4651 FO
+  48   48 3660 4571 FO
+  48   48 4099 4500 FO
+  48   48 4538 4435 FO
+  48   48 4977 4372 FO
+  48   48 5416 4316 FO
+  48   48 5855 4268 FO
+  48   48 6294 4214 FO
+  48   48 6733 4167 FO
+  48   48 7171 4121 FO
+gs 1070 452 6380 4909 rc
+gr
+
+-55 95 -55 -95 110 0 1411 5203 4 MP
+DP
+-55 95 -55 -95 110 0 1850 5057 4 MP
+DP
+-55 95 -55 -95 110 0 2289 4938 4 MP
+DP
+-55 95 -55 -95 110 0 2728 4838 4 MP
+DP
+-55 95 -55 -95 110 0 3167 4706 4 MP
+DP
+-55 95 -55 -95 110 0 3605 4555 4 MP
+DP
+-55 95 -55 -95 110 0 4044 4385 4 MP
+DP
+-55 95 -55 -95 110 0 4483 4185 4 MP
+DP
+-55 95 -55 -95 110 0 4922 3940 4 MP
+DP
+-55 95 -55 -95 110 0 5361 3650 4 MP
+DP
+-55 95 -55 -95 110 0 5800 3310 4 MP
+DP
+-55 95 -55 -95 110 0 6239 2895 4 MP
+DP
+-55 95 -55 -95 110 0 6678 2492 4 MP
+DP
+-55 95 -55 -95 110 0 7116 2000 4 MP
+DP
+gs 1070 452 6380 4909 rc
+/c10 { 0.000000 0.700000 0.500000 sr} bdef
+c10
+438 -74 439 -78 439 -89 439 -98 439 -111 439 -129 439 -148 439 -173 
+438 -211 439 -248 439 -244 439 -364 439 -564 1466 5171 14 MP stroke
+gr
+
+c10
+  48   48 1466 5171 FO
+  48   48 1905 4607 FO
+  48   48 2344 4243 FO
+  48   48 2783 3999 FO
+  48   48 3222 3751 FO
+  48   48 3660 3540 FO
+  48   48 4099 3368 FO
+  48   48 4538 3220 FO
+  48   48 4977 3090 FO
+  48   48 5416 2979 FO
+  48   48 5855 2882 FO
+  48   48 6294 2791 FO
+  48   48 6733 2713 FO
+  48   48 7171 2638 FO
+gs 1070 452 6380 4909 rc
+gr
+
+-55 95 -55 -95 110 0 1411 5203 4 MP
+DP
+-55 95 -55 -95 110 0 1850 4905 4 MP
+DP
+-55 95 -55 -95 110 0 2289 4630 4 MP
+DP
+-55 95 -55 -95 110 0 2728 4368 4 MP
+DP
+-55 95 -55 -95 110 0 3167 4001 4 MP
+DP
+-55 95 -55 -95 110 0 3605 3574 4 MP
+DP
+-55 95 -55 -95 110 0 4044 3123 4 MP
+DP
+-55 95 -55 -95 110 0 4483 2661 4 MP
+DP
+-55 95 -55 -95 110 0 4922 2196 4 MP
+DP
+-55 95 -55 -95 110 0 5361 1735 4 MP
+DP
+-55 95 -55 -95 110 0 5800 1279 4 MP
+DP
+-55 95 -55 -95 110 0 6239 873 4 MP
+DP
+gs 1070 452 6380 4909 rc
+gr
+
+0 sg
+ 617 4557 mt  -90 rotate
+(Mean number of lexical entries \(tables\)) s
+90 rotate
+3390 5724 mt 
+(Word frequency \(n) s
+%%IncludeResource: font Helvetica
+/Helvetica /ISOLatin1Encoding 144 FMSR
+
+4963 5820 mt 
+(w) s
+%%IncludeResource: font Helvetica
+/Helvetica /ISOLatin1Encoding 192 FMSR
+
+5066 5724 mt 
+(\)) s
+%%IncludeResource: font Helvetica
+/Helvetica /ISOLatin1Encoding 120 FMSR
+
+1053 5403 mt 
+( ) s
+7433  494 mt 
+( ) s
+6 w
+1 sg
+0 729 2519 0 0 -729 1129 1241 4 MP
+PP
+-2519 0 0 729 2519 0 0 -729 1129 1241 5 MP stroke
+4 w
+DO
+SO
+6 w
+0 sg
+1129 1241 mt 3648 1241 L
+1129  512 mt 3648  512 L
+1129 1241 mt 1129  512 L
+3648 1241 mt 3648  512 L
+1129 1241 mt 3648 1241 L
+1129 1241 mt 1129  512 L
+1129 1241 mt 3648 1241 L
+1129  512 mt 3648  512 L
+1129 1241 mt 1129  512 L
+3648 1241 mt 3648  512 L
+%%IncludeResource: font Helvetica
+/Helvetica /ISOLatin1Encoding 192 FMSR
+
+1609  712 mt 
+(Expectation) s
+gs 1129 512 2520 730 rc
+24 w
+370 0 1202 644 2 MP stroke
+gr
+
+24 w
+1609  945 mt 
+(Empirical, fixed base) s
+gs 1129 512 2520 730 rc
+gs 1265 754 245 245 rc
+  48   48 1387  876 FO
+gr
+
+gr
+
+1609 1178 mt 
+(Empirical, inferred base) s
+gs 1129 512 2520 730 rc
+gs 1265 986 245 245 rc
+-55 95 -55 -95 110 0 1332 1140 4 MP
+DP
+gr
+
+6 w
+gr
+
+6 w
+
+end %%Color Dict
+
+eplot
+%%EndObject
+
+epage
+end
+
+showpage
+
+%%Trailer
+%%EOF
diff --git a/report/pyp_clustering/acl09-short/code/plot3.pdf b/report/pyp_clustering/acl09-short/code/plot3.pdf
new file mode 100644
index 00000000..a3e81faa
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/plot3.pdf
diff --git a/report/pyp_clustering/acl09-short/code/pygibbs3.c b/report/pyp_clustering/acl09-short/code/pygibbs3.c
new file mode 100644
index 00000000..3c2240a1
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/pygibbs3.c
@@ -0,0 +1,198 @@
+#include <stdio.h>
+#include <math.h>
+
+#define myrand() (double) (((unsigned long) randomMT()) / 4294967296.)
+
+#define W 30114
+#define N 831190 
+#define KWMAX 1000
+
+#define NLOOPS 1000
+#define BURNIN 0
+#define SAMPLEFREQ 1
+
+#define ALPHA 0.0 // PYB a
+//#define GAMMA 1000000000.0
+#define GAMMA .01 // Dirichlet over multinomial P0
+
+double BETA; // CRP alpha (PYB b)
+int w[N], z[N]; // words, table assignments
+int typecount[W], typetot; //# of tables of each type, total # tables
+int usedcount[W];
+double ztot[W][KWMAX];
+double k; // total # tables
+int nactive;
+
+void initialise(void);
+void anderson(void);
+void fileread(void);
+
+void initialise(void)
+{
+  int i,j; 
+
+  for (i = 1; i < W; i++) {
+    typecount[i] = 0;
+    usedcount[i] = 0;
+    for (j = 0; j < KWMAX; j++) {
+      ztot[i][j] = 0;
+    }
+  }
+
+}
+
+void anderson(void)  //stochastic Anderson-style initialisation
+{
+  int i,j, tag;
+  double max, totprob, r, runtot;
+  double probs[KWMAX];
+  int ind, temp;
+  
+  ztot[w[0]][0] = 1;
+  z[0] = 0;
+  typecount[w[0]] = 1;
+  usedcount[w[0]] = 1;
+  k = 1;				       
+  typetot = 1;
+
+  for (i = 1; i < N; i++) {
+    //    printf("%5d\n", w[i]);
+    max = 0; tag = 0; totprob = 0;
+    for (j = 0; j < usedcount[w[i]]; j++) {
+      probs[j] = ztot[w[i]][j] - ALPHA;
+	totprob += probs[j];
+    } 
+    probs[usedcount[w[i]]] = (ALPHA*k+BETA)*((double) typecount[w[i]]+GAMMA)/((double) typetot+W*GAMMA);
+    totprob += probs[usedcount[w[i]]];
+    //    printf("%10.6lf\n",totprob);
+    r = myrand()*totprob;
+    max = probs[0];
+    j = 0;
+    while (r>max) {
+      j++;
+      max += probs[j];
+    }
+    //    printf("%5d\n",j);
+    z[i] = j;
+    ztot[w[i]][j]++;
+    if (ztot[w[i]][j]==1) {
+      typecount[w[i]]++;
+      usedcount[w[i]]++;
+      if (usedcount[w[i]]==KWMAX) {
+	printf("Maximum number of tables exceeded!!!\n");
+      }
+      typetot++;
+      k++;
+    }
+  }
+}
+
+void fileread(void) 
+{
+  int i,j, wt;
+  FILE *fileptr;
+  
+  fileptr = fopen("wsj.dat", "r"); 
+
+  for (i = 1; i < N; i++) {
+    fscanf(fileptr, "%d", &wt);
+    w[i] = wt-1;
+    z[i] = 0;
+  }
+  printf("Total cases: %10d\n", N);
+  fclose(fileptr);
+}
+
+main(int argc, char* argv[])
+{
+  int i,j,loop,run;
+  int temp,ind, tag;
+  double newprob, WBETA;
+  double probs[KWMAX];
+  double max, totprob, r;
+  int sampcount;
+  FILE *fileptr;
+  char filename[30];
+  double score;
+
+  if (argc < 2) {
+    printf("Please provide a value of b\n");
+    exit(0);
+  }
+  BETA = strtol(argv[1]);
+  printf("Basic initialising...\n");
+
+   // you can seed with any uint32, but the best are odds in 0..(2^32 - 1)
+  seedMT(4157U);
+
+  sprintf(filename,"typecountrecordwsjpeak%0.1f.%0.1f.dat",ALPHA,BETA);
+  fileptr = fopen(filename, "w");
+
+  printf("Reading from file...\n");
+  fileread();
+
+  printf("Initialising...\n");
+  initialise();
+    printf("k = %1.0f, typetot = %d\n",k,typetot);
+
+  printf("Finding start state...\n");
+  anderson();
+  printf("Beginning burnin...\n");
+  for (loop = 0; loop < NLOOPS; loop++) {
+    for (i = 0; i < N; i++) {
+      j = z[i];
+      ztot[w[i]][j]--;
+      if (ztot[w[i]][j] == 0) {
+	if (j==usedcount[w[i]]) {
+	  usedcount[w[i]]--;
+	}
+	typecount[w[i]]--;
+	typetot--;
+	k--;
+      }
+      max = 0; tag = 0; totprob = 0;
+      for (j = 0; j <= usedcount[w[i]]; j++) {
+	if (ztot[w[i]][j] > 0) {
+	  probs[j] = ztot[w[i]][j] - ALPHA;
+	} else {
+	  probs[j] = 0; 
+	  if (tag == 0) {
+	    probs[j] = (ALPHA*k+BETA)*(((double) typecount[w[i]])+GAMMA)/(((double) typetot)+((double) W)*GAMMA);
+	    tag = 1;
+	  }
+	}
+	totprob += probs[j];
+      }
+      r = myrand()*totprob;
+      max = probs[0];
+      j = 0;
+      while (r>max) {
+	j++;
+	max += probs[j];
+      }
+      z[i] = j;
+      ztot[w[i]][j]++;
+      if (ztot[w[i]][j]==1) {
+	if (j == usedcount[w[i]]) {
+	  usedcount[w[i]]++;
+	  if (usedcount[w[i]]==KWMAX) {
+	    printf("Maximum number of tables exceeded!!!\n");
+	  }
+	}
+	typecount[w[i]]++;
+	typetot++;
+	k++;
+      }      
+    }
+    printf("Completed sample # %5d\n", loop);
+    if (k != typetot)  printf("k = %1.0f, typetot = %d\n",k,typetot);
+    if (loop >= BURNIN && loop % SAMPLEFREQ == 0) {
+      for (i = 0; i < W; i++) {
+	fprintf(fileptr," %d", typecount[i]); //print (table?) count for each word type
+      }
+      fprintf(fileptr,"\n");
+    }
+  }
+  fclose(fileptr);
+}
+  
diff --git a/report/pyp_clustering/acl09-short/code/pygibbs_geom b/report/pyp_clustering/acl09-short/code/pygibbs_geom
new file mode 100755
index 00000000..14ae82f1
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/pygibbs_geom
diff --git a/report/pyp_clustering/acl09-short/code/pygibbs_geom.c b/report/pyp_clustering/acl09-short/code/pygibbs_geom.c
new file mode 100644
index 00000000..bafa0416
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/pygibbs_geom.c
@@ -0,0 +1,212 @@
+#include <stdio.h>
+#include <math.h>
+
+#define myrand() (double) (((unsigned long) randomMT()) / 4294967296.)
+
+#define W 30114
+#define N 831190 
+#define KWMAX 5000
+
+#define NLOOPS 11000
+#define BURNIN 1000
+#define SAMPLEFREQ 10
+
+#define ALPHA 0.0 // PYB a
+//#define GAMMA 1000000000.0
+#define GAMMA .01 // Dirichlet over multinomial P0
+
+double BETA; // CRP alpha (PYB b)
+int w[N], z[N]; // words, table assignments
+double base[N]; // base prob of word under geometric
+int typecount[W], typetot; //# of tables of each type, total # tables
+int usedcount[W];
+double ztot[W][KWMAX];
+double k; // total # tables
+int nactive;
+
+void initialise(void);
+void anderson(void);
+void fileread(void);
+
+void initialise(void)
+{
+  int i,j; 
+
+  for (i = 1; i < W; i++) {
+    typecount[i] = 0;
+    usedcount[i] = 0;
+    for (j = 0; j < KWMAX; j++) {
+      ztot[i][j] = 0;
+    }
+  }
+
+}
+
+double base_p(int len) {
+  double p = 1.0/26;
+  return pow(p,len)*pow(.5,len); //assume p_# = .5
+}
+
+void anderson(void)  //stochastic Anderson-style initialisation
+{
+  int i,j, tag;
+  double max, totprob, r, runtot;
+  double probs[KWMAX];
+  int ind, temp;
+  
+  ztot[w[0]][0] = 1;
+  z[0] = 0;
+  typecount[w[0]] = 1;
+  usedcount[w[0]] = 1;
+  k = 1;				       
+  typetot = 1;
+
+  for (i = 1; i < N; i++) {
+    //    printf("%5d\n", w[i]);
+    max = 0; tag = 0; totprob = 0;
+    for (j = 0; j < usedcount[w[i]]; j++) {
+      probs[j] = ztot[w[i]][j] - ALPHA;
+	totprob += probs[j];
+    } 
+    probs[usedcount[w[i]]] = (ALPHA*k+BETA)*base[i];
+    totprob += probs[usedcount[w[i]]];
+    //    printf("%10.6lf\n",totprob);
+    r = myrand()*totprob;
+    max = probs[0];
+    j = 0;
+    while (r>max) {
+      j++;
+      max += probs[j];
+    }
+    //    printf("%5d\n",j);
+    z[i] = j;
+    ztot[w[i]][j]++;
+    if (ztot[w[i]][j]==1) {
+      typecount[w[i]]++;
+      usedcount[w[i]]++;
+      if (usedcount[w[i]]==KWMAX) {
+	printf("Maximum number of tables exceeded!!!\n");
+      }
+      typetot++;
+      k++;
+    }
+  }
+}
+
+void fileread(void) 
+{
+  int i,j, wt, len;
+  FILE *fileptr;
+  
+  fileptr = fopen("wsj.dat", "r"); 
+
+  for (i = 1; i < N; i++) {
+    fscanf(fileptr, "%d", &wt);
+    w[i] = wt-1;
+    z[i] = 0;
+  }
+  printf("Total cases: %10d\n", N);
+  fclose(fileptr);
+
+  fileptr = fopen("wsj_lengths.dat", "r"); 
+
+  for (i = 1; i < N; i++) {
+    fscanf(fileptr, "%d", &len);
+    base[i] = base_p(len);
+  }
+  fclose(fileptr);
+}
+
+main(int argc, char* argv[])
+{
+  int i,j,loop,run;
+  int temp,ind, tag;
+  double newprob, WBETA;
+  double probs[KWMAX];
+  double max, totprob, r;
+  int sampcount;
+  FILE *fileptr;
+  char filename[30];
+  double score;
+
+  if (argc < 2) {
+    printf("Please provide a value of b\n");
+    exit(0);
+  }
+  BETA = strtol(argv[1]);
+  printf("Basic initialising...\n");
+
+   // you can seed with any uint32, but the best are odds in 0..(2^32 - 1)
+  seedMT(4157U);
+
+  sprintf(filename,"typecountrecordwsjgeom%0.1f.%0.1f.dat",ALPHA,BETA);
+  fileptr = fopen(filename, "w");
+
+  printf("Reading from file...\n");
+  fileread();
+
+  printf("Initialising...\n");
+  initialise();
+    printf("k = %1.0f, typetot = %d\n",k,typetot);
+
+  printf("Finding start state...\n");
+  anderson();
+  printf("Beginning burnin...\n");
+  for (loop = 0; loop < NLOOPS; loop++) {
+    for (i = 0; i < N; i++) {
+      j = z[i];
+      ztot[w[i]][j]--;
+      if (ztot[w[i]][j] == 0) {
+	if (j==usedcount[w[i]]) {
+	  usedcount[w[i]]--;
+	}
+	typecount[w[i]]--;
+	typetot--;
+	k--;
+      }
+      max = 0; tag = 0; totprob = 0;
+      for (j = 0; j <= usedcount[w[i]]; j++) {
+	if (ztot[w[i]][j] > 0) {
+	  probs[j] = ztot[w[i]][j] - ALPHA;
+	} else {
+	  probs[j] = 0; 
+	  if (tag == 0) {
+	    probs[j] = (ALPHA*k+BETA)*base[i];
+	    tag = 1;
+	  }
+	}
+	totprob += probs[j];
+      }
+      r = myrand()*totprob;
+      max = probs[0];
+      j = 0;
+      while (r>max) {
+	j++;
+	max += probs[j];
+      }
+      z[i] = j;
+      ztot[w[i]][j]++;
+      if (ztot[w[i]][j]==1) {
+	if (j == usedcount[w[i]]) {
+	  usedcount[w[i]]++;
+	  if (usedcount[w[i]]==KWMAX) {
+	    printf("Maximum number of tables exceeded!!!\n");
+	  }
+	}
+	typecount[w[i]]++;
+	typetot++;
+	k++;
+      }      
+    }
+    printf("Completed sample # %5d\n", loop);
+    if (k != typetot)  printf("k = %1.0f, typetot = %d\n",k,typetot);
+    if (loop >= BURNIN && loop % SAMPLEFREQ == 0) {
+      for (i = 0; i < W; i++) {
+	fprintf(fileptr," %d", typecount[i]); //print (table?) count for each word type
+      }
+      fprintf(fileptr,"\n");
+    }
+  }
+  fclose(fileptr);
+}
+  
diff --git a/report/pyp_clustering/acl09-short/code/run-peak.prl b/report/pyp_clustering/acl09-short/code/run-peak.prl
new file mode 100755
index 00000000..fb1e798a
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/run-peak.prl
@@ -0,0 +1,8 @@
+#!/usr/bin/perl
+
+for $i (0..5) {
+$beta = 10**$i;
+$cmd = "pygibbs_peak $beta\n";
+print $cmd;
+`$cmd`;
+}
diff --git a/report/pyp_clustering/acl09-short/code/run.prl b/report/pyp_clustering/acl09-short/code/run.prl
new file mode 100755
index 00000000..ac69559c
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/run.prl
@@ -0,0 +1,8 @@
+#!/usr/bin/perl
+
+for $i (0..5) {
+$beta = 10**$i;
+$cmd = "pygibbs_geom $beta\n";
+print $cmd;
+`$cmd`;
+}
diff --git a/report/pyp_clustering/acl09-short/code/word_lengths.prl b/report/pyp_clustering/acl09-short/code/word_lengths.prl
new file mode 100755
index 00000000..4b4ed03b
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/word_lengths.prl
@@ -0,0 +1,21 @@
+#!/usr/bin/perl -w
+use Getopt::Std;
+use File::Basename;
+use List::Util qw(max maxstr min minstr reduce shuffle sum);
+use lib "$ENV{HOME}/src/perl/";
+use sg_utils;
+use strict;
+use vars qw();
+
+my $usage = "Usage: $0 \n";
+
+getopts('');
+
+die $usage unless (1);
+
+while (<>) {
+chomp;
+print length;
+print "\n";
+}
+
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots2.m b/report/pyp_clustering/acl09-short/code/wsjplots2.m
new file mode 100644
index 00000000..eed41846
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/wsjplots2.m
@@ -0,0 +1,99 @@
+
+load wsj
+
+figure(1)
+clf 
+subplot(1,2,2)
+hold on
+
+for i = 1:9
+  a = i/10;
+  [logbins predicted dummy] = logbinmean(counts,counts.^a,20,20);
+  ph = plot(log10(logbins),log10(predicted),'k');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+end
+
+for i = 1:9
+  a = i/10;  
+  disp(['Loading results for a = ' num2str(a) ]);
+
+  typecountrecord= load([ 'typecountrecordwsjflat' num2str(a) '.1.0.dat']);
+  
+  typecountrecordmean = mean(typecountrecord(500:1000,:));
+  
+  save([ 'typecountrecordmeanwsjflat' num2str(a) '.1.0.mat'],'typecountrecordmean');
+  
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20)
+  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+  drawnow
+end
+
+
+
+
+[logbins meanval seval] = logbinmean(counts,counts,20,20)
+[logbins predicted dummy] = logbinmean(counts,counts,20,20)
+ph = plot(log10(logbins),log10(predicted),'r');
+hold on
+errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+
+set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-0.1 3.5])
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+
+title('Pitman-Yor process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+box on
+
+subplot(1,2,1)
+
+for i = 1:5
+
+  b = 10^(i-1)
+
+  disp(['Loading results for b = ' num2str(b) ]);
+  typecountrecord= load([ 'typecountrecordwsjflat0.0.' num2str(b) '.0.dat']);
+
+  typecountrecordmean = mean(typecountrecord(500:1000,:));
+  save([ 'typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20)
+  [logbins predicted dummy] = logbinmean(counts,crppred(counts,b),20,20)
+%  errorbar(log10(logbins),meanval,seval,'k.');
+  hold on
+  ph = plot(log10(logbins),log10(predicted),'r');
+  %  ph = plot(log10(logbins),predicted,'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-0.1 1.5])
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+box on
+
+
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl.m
new file mode 100644
index 00000000..50582e7f
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/wsjplots_acl.m
@@ -0,0 +1,74 @@
+%wsj_lengths = load([ 'wsj_lengths.dat']);
+%save([ 'wsj_lengths.mat'],'wsj_lengths');
+load wsj
+load wsj_lengths
+
+figure(1)
+clf 
+
+hold on
+
+for i = 3:6
+
+    b = 10^(i-1)
+   
+  % plot lines for CRP exact prediction using summation
+  [logbins predicted dummy] = logbinmean(counts, crppred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+
+  % plot lines for CRP Antoniak prediction
+  [logbins predicted dummy] = logbinmean(counts, antoniakpred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5,'linestyle','--')
+
+  % plot lines for CRP Cohn prediction
+  %[logbins predicted dummy] = logbinmean(counts, cohnpred(counts,b),20,20);
+  %ph = plot(log10(logbins),log10(predicted),'r');
+  %set(ph,'color',[0.2 0.2 1],'linewidth',1.5,'linestyle','.')
+
+  disp(['Loading results for b = ' num2str(b) ]);
+  %%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjflat0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(:,:));
+  %save([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  load([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat']);
+  
+  %plot emprical counts with error bars
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20);
+  plot(log10(logbins),log10(meanval),'k*');
+  %errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+
+  disp(['Loading results for b = ' num2str(b) ]);
+  %%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjpeak0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(:,:));
+  %save([ 'outputs/typecountrecordmeanwsjpeak0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  load([ 'outputs/typecountrecordmeanwsjpeak0.0.' num2str(b) '.0.mat']);
+
+  %plot emprical counts with error bars
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20);
+  plot(log10(logbins),log10(meanval),'ko');
+  %errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'ko');
+
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([.1:.1:1 2:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-1.1 2.5])
+set(gca,'FontSize',14)
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+    '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+%title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+legend('Expectation','Antoniak approx.','Empirical, fixed base','Empirical, inferred base','Location','NorthWest')
+box on
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m
new file mode 100644
index 00000000..33419845
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m
@@ -0,0 +1,164 @@
+%wsj_lengths = load([ 'wsj_lengths.dat']);
+%save([ 'wsj_lengths.mat'],'wsj_lengths');
+load wsj
+load wsj_lengths
+
+figure(1)
+clf 
+
+subplot(1,3,1);
+hold on
+
+for i = 2:6
+
+    b = 10^(i-1)
+
+  disp(['Loading results for b = ' num2str(b) ]);
+%%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjflat0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(500:999,:));
+  %save([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  load([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat']);
+   
+  % plot lines for CRP exact prediction using summation
+  [logbins predicted dummy] = logbinmean(counts, crppred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+
+  % plot lines for CRP Antoniak prediction
+  [logbins predicted dummy] = logbinmean(counts, antoniakpred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5,'linestyle','--')
+
+  % plot lines for incorrect CRP Antoniak prediction (ACL07)
+  %[logbins predicted dummy] = logbinmean(counts, noP0pred(counts,b),20,20);
+  %ph = plot(log10(logbins),log10(predicted),'r');
+  %set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5,'linestyle','-.')
+
+  % plot lines for CRP Cohn prediction
+  %[logbins predicted dummy] = logbinmean(counts, cohnpred(counts,b),20,20);
+  %ph = plot(log10(logbins),log10(predicted),'r');
+  %set(ph,'color',[0.2 0.2 1],'linewidth',1.5,'linestyle','.')
+
+   %plot emprical counts with error bars
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20);
+  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([.1:.1:1 2:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-1.1 1.5])
+set(gca,'FontSize',14)
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+    '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+%title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+legend('Expectation','Antoniak approx.','Empirical','Location','NorthWest')
+box on
+
+
+subplot(1,3,2);
+hold on
+
+for i =2:6
+
+  b = 10^(i-1)
+
+  disp(['Loading results for b = ' num2str(b) ]);
+%%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjpeak0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(500:999,:));
+  %save([ 'outputs/typecountrecordmeanwsjpeak0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+   load([ 'outputs/typecountrecordmeanwsjpeak0.0.' num2str(b) '.0.mat']);
+   
+  % plot lines for CRP exact prediction using summation
+  [logbins predicted dummy] = logbinmean(counts, crppred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+
+  %plot emprical counts with error bars
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20);
+  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-.1 2.5])
+set(gca,'FontSize',14)
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {...%'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+    '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+%title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+legend('Expectation','Location','NorthWest')
+box on
+%axis square
+
+
+subplot(1,3,3);
+hold on
+
+for i =2:6
+
+  b = 10^(i-1)
+
+  disp(['Loading results for b = ' num2str(b) ]);
+%%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjgeom0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(500:999,:));
+  %save([ 'outputs/typecountrecordmeanwsjgeom0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  load([ 'outputs/typecountrecordmeanwsjgeom0.0.' num2str(b) '.0.mat']);
+   
+  % plot lines for CRP exact prediction using summation
+%  [logbins meaneval seval] = logbinmean(counts, crppred_geom(counts,wsj_lengths,b),20,20)
+[logbins meaneval seval] = logbinmean(counts, crppred(counts,b),20,20)
+ plot(log10(logbins),log10(meaneval),'r.');
+%errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'r.');
+%  ph = plot(log10(logbins),log10(meaneval),'r');
+%  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+
+  %plot emprical counts with error bars
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20);
+  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-.1 2.5])
+set(gca,'FontSize',14)
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {...%'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+    '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+%title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+legend('Expectation','Location','NorthWest')
+box on
+hold off
+%axis square
+
+
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl_pair.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl_pair.m
new file mode 100644
index 00000000..1d07e54c
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/wsjplots_acl_pair.m
@@ -0,0 +1,117 @@
+%wsj_lengths = load([ 'wsj_lengths.dat']);
+%save([ 'wsj_lengths.mat'],'wsj_lengths');
+load wsj
+load wsj_lengths
+
+figure(1)
+clf 
+
+subplot(1,2,1);
+hold on
+
+for i = 3:6
+
+    b = 10^(i-1)
+
+  disp(['Loading results for b = ' num2str(b) ]);
+%%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjflat0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(:,:));
+  %save([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  load([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat']);
+   
+  % plot lines for CRP exact prediction using summation
+  [logbins predicted dummy] = logbinmean(counts, crppred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+
+  % plot lines for CRP Antoniak prediction
+  [logbins predicted dummy] = logbinmean(counts, antoniakpred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5,'linestyle','--')
+
+  %plot lines for incorrect CRP Antoniak prediction (ACL07)
+  %[logbins predicted dummy] = logbinmean(counts, noP0pred(counts,b),20,20);
+  %ph = plot(log10(logbins),log10(predicted),'r');
+  %set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5,'linestyle',':')
+
+  % plot lines for CRP Cohn prediction
+  %[logbins predicted dummy] = logbinmean(counts, cohnpred(counts,b),20,20);
+  %ph = plot(log10(logbins),log10(predicted),'r');
+  %set(ph,'color',[0.2 0.2 1],'linewidth',1.5,'linestyle','.')
+
+   %plot emprical counts with error bars
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20);
+  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([.1:.1:1 2:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-1.1 1.5])
+set(gca,'FontSize',14)
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+    '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+%title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+legend('Expectation','Antoniak approx.','Empirical','Location','NorthWest')
+box on
+
+
+subplot(1,2,2);
+hold on
+
+for i =3:6
+
+  b = 10^(i-1)
+
+  disp(['Loading results for b = ' num2str(b) ]);
+%%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjpeak0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(:,:));
+  %save([ 'outputs/typecountrecordmeanwsjpeak0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  load([ 'outputs/typecountrecordmeanwsjpeak0.0.' num2str(b) '.0.mat']);
+   
+  % plot lines for CRP exact prediction using summation
+  [logbins predicted dummy] = logbinmean(counts, crppred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+
+   %plot lines for incorrect CRP Antoniak prediction (ACL07)
+  [logbins predicted dummy] = logbinmean(counts, noP0pred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5,'linestyle','-.')
+
+  %plot emprical counts with error bars
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20);
+  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-.1 2.5])
+set(gca,'FontSize',14)
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {...%'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+    '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+%title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+legend('Expectation','GGJ07 approx.','Empirical','Location','NorthWest')
+box on
+%axis square
\ No newline at end of file
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk0.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk0.m
new file mode 100644
index 00000000..dc54dea4
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk0.m
@@ -0,0 +1,54 @@
+%wsj_lengths = load([ 'wsj_lengths.dat']);
+%save([ 'wsj_lengths.mat'],'wsj_lengths');
+load wsj
+load wsj_lengths
+
+figure(1)
+clf 
+
+hold on
+
+%colors = [0 0 0; 0 0 1; 1 0 0; 0 1 0]; %pure black, red, blue, green
+colors = [0 0 0; 1 .4 .2; .4 .4 1; 0 .7 .5]; %same but less garish
+%colors = [0 0 0; .6 .4 .4; .9 .6 .6; 1 .8 .8]; %shades of pink
+%colors = [0 0 0; .3 .3 1; .4 .8 1; .5 1 .8]; %blue/green
+
+for i = 9-[3:6]
+
+  b = 10^(i-1)
+   
+  disp(['Loading results for b = ' num2str(b) ]);
+  %%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjflat0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(:,:));
+  %save([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  load([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat']);
+  
+  %plot emprical counts with error bars
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20);
+  ph = plot(log10(logbins),log10(meanval));
+  set(ph,'color',colors(i-2,:),'linestyle','o','linewidth',2,'markersize',10);
+  %errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([.1:.1:1 2:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-.1 2])
+set(gca,'FontSize',16)
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+    '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+%title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries (tables)')
+xlabel('Word frequency (n_w)')
+labs = {'\alpha = 100000','\alpha = 10000','\alpha = 1000','\alpha = 100'};
+legend(labs,'Location','NorthWest')
+box on
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk1.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk1.m
new file mode 100644
index 00000000..dd3615ac
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk1.m
@@ -0,0 +1,59 @@
+%wsj_lengths = load([ 'wsj_lengths.dat']);
+%save([ 'wsj_lengths.mat'],'wsj_lengths');
+load wsj
+load wsj_lengths
+
+figure(1)
+clf 
+
+hold on
+
+%colors = [0 0 0; 0 0 1; 1 0 0; 0 1 0]; %pure black, red, blue, green
+colors = [0 0 0; 1 .4 .2; .4 .4 1; 0 .7 .5]; %same but less garish
+%colors = [0 0 0; .6 .4 .4; .9 .6 .6; 1 .8 .8]; %shades of pink
+%colors = [0 0 0; .3 .3 1; .4 .8 1; .5 1 .8]; %blue/green
+
+for i = 3:6
+
+  b = 10^(i-1)
+   
+  disp(['Loading results for b = ' num2str(b) ]);
+  %%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjflat0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(:,:));
+  %save([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  load([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat']);
+  
+  %plot emprical counts with error bars
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20);
+  ph = plot(log10(logbins),log10(meanval));
+  set(ph,'color',colors(i-2,:),'linestyle','o','linewidth',2,'markersize',8);
+  %errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+
+    % plot lines for CRP exact prediction using summation
+  [logbins predicted dummy] = logbinmean(counts, crppred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',colors(i-2,:),'linewidth',2);
+
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([.1:.1:1 2:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-.1 2])
+set(gca,'FontSize',16)
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+    '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+%title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries (tables)')
+xlabel('Word frequency (n_w)')
+labs = {'Empirical','Expectation'};
+legend(labs,'Location','NorthWest')
+box on
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk2.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk2.m
new file mode 100644
index 00000000..dd039289
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk2.m
@@ -0,0 +1,58 @@
+%wsj_lengths = load([ 'wsj_lengths.dat']);
+%save([ 'wsj_lengths.mat'],'wsj_lengths');
+load wsj
+load wsj_lengths
+
+figure(1)
+clf 
+
+hold on
+
+%colors = [0 0 0; 0 0 1; 1 0 0; 0 1 0]; %pure black, red, blue, green
+colors = [0 0 0; 1 .4 .2; .4 .4 1; 0 .7 .5]; %same but less garish
+%colors = [0 0 0; .6 .4 .4; .9 .6 .6; 1 .8 .8]; %shades of pink
+%colors = [0 0 0; .3 .3 1; .4 .8 1; .5 1 .8]; %blue/green
+
+for i = 3:6
+
+  b = 10^(i-1)
+   
+  disp(['Loading results for b = ' num2str(b) ]);
+  %%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjflat0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(:,:));
+  %save([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  load([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat']);
+  
+    % plot lines for CRP exact prediction using summation
+  [logbins predicted dummy] = logbinmean(counts, crppred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',colors(i-2,:),'linewidth',2);
+
+  % plot lines for CRP Antoniak prediction
+  [logbins predicted dummy] = logbinmean(counts, antoniakpred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted),'r');
+  set(ph,'color',colors(i-2,:),'linewidth',2,'linestyle','--')
+
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([.1:.1:1 2:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-1.1 2])
+set(gca,'FontSize',16)
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+    '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+%title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries (tables)')
+xlabel('Word frequency (n_w)')
+labs = {'Expectation','Antoniak approximation'};
+legend(labs,'Location','NorthWest')
+box on
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk3.m b/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk3.m
new file mode 100644
index 00000000..8d570b7a
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk3.m
@@ -0,0 +1,74 @@
+%wsj_lengths = load([ 'wsj_lengths.dat']);
+%save([ 'wsj_lengths.mat'],'wsj_lengths');
+load wsj
+load wsj_lengths
+
+figure(1)
+clf 
+
+hold on
+
+%colors = [0 0 0; 0 0 1; 1 0 0; 0 1 0]; %pure black, red, blue, green
+colors = [0 0 0; 1 .4 .2; .4 .4 1; 0 .7 .5]; %similar but less garish
+%colors = [0 0 0; .6 .4 .4; .9 .6 .6; 1 .8 .8]; %shades of pink
+%colors = [0 0 0; .3 .3 1; .4 .8 1; .5 1 .8]; %blue/green
+
+for i = 3:6
+  col = colors(i-2,:);
+  b = 10^(i-1)
+   
+  % plot lines for CRP exact prediction using summation
+  [logbins predicted dummy] = logbinmean(counts, crppred(counts,b),20,20);
+  ph = plot(log10(logbins),log10(predicted));
+  set(ph,'color',col,'linewidth',2);
+
+  disp(['Loading results for b = ' num2str(b) ]);
+  %%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjflat0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(:,:));
+  %save([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  load([ 'outputs/typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat']);
+  
+  %plot emprical counts with error bars
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20);
+  ph = plot(log10(logbins),log10(meanval));
+  %set(ph,'color',col,'linestyle','o','markerfacecolor',col,'markersize',8);
+  set(ph,'color',col,'linestyle','o','linewidth',2,'markersize',8);
+  %errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+
+  disp(['Loading results for b = ' num2str(b) ]);
+  %%%  uncomment these lines if .mat file is not yet generated. %%%
+  %typecountrecord= load([ 'outputs/typecountrecordwsjpeak0.0.' num2str(b) '.0.dat']);
+  %typecountrecordmean = mean(typecountrecord(:,:));
+  %save([ 'outputs/typecountrecordmeanwsjpeak0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  load([ 'outputs/typecountrecordmeanwsjpeak0.0.' num2str(b) '.0.mat']);
+
+  %plot emprical counts with error bars
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20);
+  ph = plot(log10(logbins),log10(meanval));
+  %set(ph,'color',col,'linestyle','^','markerfacecolor',col,'markersize',8);
+  set(ph,'color',col,'linestyle','^','linewidth',2,'markersize',8);
+  %errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'ko');
+
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([.1:.1:1 2:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-.1 2.5])
+set(gca,'FontSize',16)
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'0.1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+    '1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+%title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries (tables)')
+xlabel('Word frequency (n_w)')
+labs = {'Expectation','Empirical, fixed base','Empirical, inferred base'};
+legend(labs,'Location','NorthWest')
+box on
diff --git a/report/pyp_clustering/acl09-short/code/wsjplots_cl.m b/report/pyp_clustering/acl09-short/code/wsjplots_cl.m
new file mode 100644
index 00000000..eed41846
--- /dev/null
+++ b/report/pyp_clustering/acl09-short/code/wsjplots_cl.m
@@ -0,0 +1,99 @@
+
+load wsj
+
+figure(1)
+clf 
+subplot(1,2,2)
+hold on
+
+for i = 1:9
+  a = i/10;
+  [logbins predicted dummy] = logbinmean(counts,counts.^a,20,20);
+  ph = plot(log10(logbins),log10(predicted),'k');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+end
+
+for i = 1:9
+  a = i/10;  
+  disp(['Loading results for a = ' num2str(a) ]);
+
+  typecountrecord= load([ 'typecountrecordwsjflat' num2str(a) '.1.0.dat']);
+  
+  typecountrecordmean = mean(typecountrecord(500:1000,:));
+  
+  save([ 'typecountrecordmeanwsjflat' num2str(a) '.1.0.mat'],'typecountrecordmean');
+  
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20)
+  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+  drawnow
+end
+
+
+
+
+[logbins meanval seval] = logbinmean(counts,counts,20,20)
+[logbins predicted dummy] = logbinmean(counts,counts,20,20)
+ph = plot(log10(logbins),log10(predicted),'r');
+hold on
+errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+
+set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-0.1 3.5])
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+
+title('Pitman-Yor process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+box on
+
+subplot(1,2,1)
+
+for i = 1:5
+
+  b = 10^(i-1)
+
+  disp(['Loading results for b = ' num2str(b) ]);
+  typecountrecord= load([ 'typecountrecordwsjflat0.0.' num2str(b) '.0.dat']);
+
+  typecountrecordmean = mean(typecountrecord(500:1000,:));
+  save([ 'typecountrecordmeanwsjflat0.0.' num2str(b) '.0.mat'],'typecountrecordmean');
+  
+  [logbins meanval seval] = logbinmean(counts,typecountrecordmean,20,20)
+  [logbins predicted dummy] = logbinmean(counts,crppred(counts,b),20,20)
+%  errorbar(log10(logbins),meanval,seval,'k.');
+  hold on
+  ph = plot(log10(logbins),log10(predicted),'r');
+  %  ph = plot(log10(logbins),predicted,'r');
+  set(ph,'color',[0.7 0.7 0.7],'linewidth',1.5)
+  errorbar(log10(logbins),log10(meanval),log10(meanval+seval)-log10(meanval),log10(meanval-seval)-log10(meanval),'k.');
+end
+
+set(gca,'xtick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'ytick',log10([1:10 20:10:100 200:100:1000 2000:1000:5000]))
+set(gca,'xlim',[-0.1 3.5])
+set(gca,'ylim',[-0.1 1.5])
+set(gca,'xticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+set(gca,'yticklabel', {'1',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',  ...
+		    '10',' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '100', ...
+		    ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1000', ...
+		    ' ', ' ', ' ', ' '});
+title('Chinese restaurant process adaptor')
+ylabel('Mean number of lexical entries')
+xlabel('Word frequency (n_w)')
+box on
+
+