diff options
Diffstat (limited to 'report/pyp_clustering/acl09-short/code')
31 files changed, 0 insertions, 4891 deletions
diff --git a/report/pyp_clustering/acl09-short/code/antoniakpred.m b/report/pyp_clustering/acl09-short/code/antoniakpred.m deleted file mode 100644 index c4153c04..00000000 --- a/report/pyp_clustering/acl09-short/code/antoniakpred.m +++ /dev/null @@ -1,12 +0,0 @@ -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 deleted file mode 100644 index 67857497..00000000 --- a/report/pyp_clustering/acl09-short/code/approximations.eps +++ /dev/null @@ -1,897 +0,0 @@ -%!PS-Adobe-3.0 EPSF-3.0 -%%Creator: MATLAB, The Mathworks, Inc. Version 7.7.0.471 (R2008b). 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rad rad scale 0 0 1 180 360 arc tMatrix setmatrix lrx rad sub lry rad - sub translate rad rad scale 0 0 1 0 180 arc tMatrix setmatrix - closepath} bdef -/FtbRR {MtbRR stroke } bdef -/PtbRR {MtbRR fill } bdef -/stri 6 array def /dtri 6 array def -/smat 6 array def /dmat 6 array def -/tmat1 6 array def /tmat2 6 array def /dif 3 array def -/asub {/ind2 exch def /ind1 exch def dup dup - ind1 get exch ind2 get sub exch } bdef -/tri_to_matrix { - 2 0 asub 3 1 asub 4 0 asub 5 1 asub - dup 0 get exch 1 get 7 -1 roll astore } bdef -/compute_transform { - dmat dtri tri_to_matrix tmat1 invertmatrix - smat stri tri_to_matrix tmat2 concatmatrix } bdef -/ds {stri astore pop} bdef -/dt {dtri astore pop} bdef -/db {2 copy /cols xdef /rows xdef mul dup 3 mul string - currentfile - 3 index 0 eq {/ASCIIHexDecode filter} - {/ASCII85Decode filter 3 index 2 eq {/RunLengthDecode filter} if } - ifelse exch readstring pop - dup 0 3 index getinterval /rbmap xdef - dup 2 index dup getinterval /gbmap xdef - 1 index dup 2 mul exch getinterval /bbmap xdef pop pop}bdef -/it {gs np dtri aload pop moveto lineto lineto cp c - 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36 36 4070 3771 FO - 36 36 4335 3742 FO -gs 647 460 3857 5001 rc -18 w -0.7 sg -265 -33 266 -34 265 -36 265 -38 265 -42 266 -44 265 -49 265 -52 -266 -60 265 -66 265 -62 265 -96 266 -158 886 3932 14 MP stroke -DA -265 -40 266 -43 265 -46 265 -50 265 -55 266 -60 265 -67 265 -75 -266 -87 265 -100 265 -96 265 -148 266 -242 886 4398 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 3774 mt 1188 3774 L -1152 3738 mt 1152 3810 L -1381 3678 mt 1453 3678 L -1417 3642 mt 1417 3714 L -1646 3616 mt 1718 3616 L -1682 3580 mt 1682 3652 L -1911 3549 mt 1983 3549 L -1947 3513 mt 1947 3585 L -2177 3490 mt 2249 3490 L -2213 3454 mt 2213 3526 L -2442 3438 mt 2514 3438 L -2478 3402 mt 2478 3474 L -2707 3390 mt 2779 3390 L -2743 3354 mt 2743 3426 L -2973 3344 mt 3045 3344 L -3009 3308 mt 3009 3380 L -3238 3303 mt 3310 3303 L -3274 3267 mt 3274 3339 L -3503 3267 mt 3575 3267 L -3539 3231 mt 3539 3303 L -3768 3228 mt 3840 3228 L -3804 3192 mt 3804 3264 L -4034 3193 mt 4106 3193 L -4070 3157 mt 4070 3229 L -4299 3160 mt 4371 3160 L -4335 3124 mt 4335 3196 L - 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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 deleted file mode 100644 index 17f22652..00000000 --- a/report/pyp_clustering/acl09-short/code/crppred.m +++ /dev/null @@ -1,12 +0,0 @@ -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 deleted file mode 100644 index e6869e4f..00000000 --- a/report/pyp_clustering/acl09-short/code/crppred_geom.m +++ /dev/null @@ -1,12 +0,0 @@ -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 deleted file mode 100644 index 23dbb0ac..00000000 --- a/report/pyp_clustering/acl09-short/code/logbinmean.m +++ /dev/null @@ -1,38 +0,0 @@ -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 deleted file mode 100644 index f72f1432..00000000 --- a/report/pyp_clustering/acl09-short/code/noP0pred.m +++ /dev/null @@ -1,11 +0,0 @@ -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 deleted file mode 100644 index 6094346a..00000000 --- a/report/pyp_clustering/acl09-short/code/plot0.eps +++ /dev/null @@ -1,633 +0,0 @@ -%!PS-Adobe-3.0 EPSF-3.0 -%%Creator: MATLAB, The Mathworks, Inc. 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-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 Binary files differdeleted file mode 100644 index a3e81faa..00000000 --- a/report/pyp_clustering/acl09-short/code/plot3.pdf +++ /dev/null diff --git a/report/pyp_clustering/acl09-short/code/pygibbs3.c b/report/pyp_clustering/acl09-short/code/pygibbs3.c deleted file mode 100644 index 3c2240a1..00000000 --- a/report/pyp_clustering/acl09-short/code/pygibbs3.c +++ /dev/null @@ -1,198 +0,0 @@ -#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 Binary files differdeleted file mode 100755 index 14ae82f1..00000000 --- a/report/pyp_clustering/acl09-short/code/pygibbs_geom +++ /dev/null diff --git a/report/pyp_clustering/acl09-short/code/pygibbs_geom.c b/report/pyp_clustering/acl09-short/code/pygibbs_geom.c deleted file mode 100644 index bafa0416..00000000 --- a/report/pyp_clustering/acl09-short/code/pygibbs_geom.c +++ /dev/null @@ -1,212 +0,0 @@ -#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 deleted file mode 100755 index fb1e798a..00000000 --- a/report/pyp_clustering/acl09-short/code/run-peak.prl +++ /dev/null @@ -1,8 +0,0 @@ -#!/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 deleted file mode 100755 index ac69559c..00000000 --- a/report/pyp_clustering/acl09-short/code/run.prl +++ /dev/null @@ -1,8 +0,0 @@ -#!/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 deleted file mode 100755 index 4b4ed03b..00000000 --- a/report/pyp_clustering/acl09-short/code/word_lengths.prl +++ /dev/null @@ -1,21 +0,0 @@ -#!/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 deleted file mode 100644 index eed41846..00000000 --- a/report/pyp_clustering/acl09-short/code/wsjplots2.m +++ /dev/null @@ -1,99 +0,0 @@ - -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 deleted file mode 100644 index 50582e7f..00000000 --- a/report/pyp_clustering/acl09-short/code/wsjplots_acl.m +++ /dev/null @@ -1,74 +0,0 @@ -%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 deleted file mode 100644 index 33419845..00000000 --- a/report/pyp_clustering/acl09-short/code/wsjplots_acl_monkeys.m +++ /dev/null @@ -1,164 +0,0 @@ -%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 deleted file mode 100644 index 1d07e54c..00000000 --- a/report/pyp_clustering/acl09-short/code/wsjplots_acl_pair.m +++ /dev/null @@ -1,117 +0,0 @@ -%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 deleted file mode 100644 index dc54dea4..00000000 --- a/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk0.m +++ /dev/null @@ -1,54 +0,0 @@ -%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 deleted file mode 100644 index dd3615ac..00000000 --- a/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk1.m +++ /dev/null @@ -1,59 +0,0 @@ -%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 deleted file mode 100644 index dd039289..00000000 --- a/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk2.m +++ /dev/null @@ -1,58 +0,0 @@ -%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 deleted file mode 100644 index 8d570b7a..00000000 --- a/report/pyp_clustering/acl09-short/code/wsjplots_acl_talk3.m +++ /dev/null @@ -1,74 +0,0 @@ -%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 deleted file mode 100644 index eed41846..00000000 --- a/report/pyp_clustering/acl09-short/code/wsjplots_cl.m +++ /dev/null @@ -1,99 +0,0 @@ - -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 - - |