void removeUV()
{
int u,f;
for(u=0;u<NUSERS;u++) {
int base0=useridx[u][0];
int d012=UNALL(u);
int i;
int dall=UNALL(u);
double NuS = 1.0/sqrt(dall);
double lNuSY[NFEATURES];
double sumY[NFEATURES];
ZERO(sumY);
ZERO(lNuSY);
int j;
for(j=0;j<dall;j++) {
int mm=userent[base0+j]&USER_MOVIEMASK;
for(f=0;f<NFEATURES;f++)
sumY[f]+=sY[mm][f];
}
int d0=UNTRAIN(u);
for(f=0;f<NFEATURES;f++)
lNuSY[f] = NuS * sumY[f];
double bUu=bU[u];
for(i=0; i<d012;i++) {
int m=userent[base0+i]&USER_MOVIEMASK;
err[base0+i]-=(bU[u] + bV[m]);
for (f=0; f<NFEATURES; f++)
err[base0+i]-=((sU[u][f] + lNuSY[f]) * sV[m][f]);
}
}
}
void removeUV()
{
int u,f;
for(u=0;u<NUSERS;u++) {
int base0=useridx[u][0];
int d012=UNALL(u);
int i;
int dall=UNALL(u);
double NuS = 1.0/sqrt(dall);
double lNuSY[NFEATURES];
double sumY[NFEATURES];
ZERO(sumY);
ZERO(lNuSY);
int j;
for(j=0;j<dall;j++) {
int mm=userent[base0+j]&USER_MOVIEMASK;
for(f=0;f<NFEATURES;f++)
sumY[f]+=sY[mm][f];
}
int d0=UNTRAIN(u);
for(f=0;f<NFEATURES;f++)
lNuSY[f] = NuS * sumY[f];
//double bUu=bU[u];
//for(i=0; i<d012;i++) {
//int m=userent[base0+i]&USER_MOVIEMASK;
//err[base0+i]-=(bU[u] + bV[m]);
//for (f=0; f<NFEATURES; f++)
//err[base0+i]-=((sU[u][f] + lNuSY[f]) * sV[m][f]);
//}
// For all rated movies
for(i=0;i<d012;i++) {
int entloc = base0+i;
unsigned int sdloc = sdbin[entloc];
int m=userent[entloc]&USER_MOVIEMASK;
int day=userent[entloc]>>(USER_LMOVIEMASK+3);
double devuhat = DEVuHat[entloc];
// Figure out the current error
err[entloc] -= (bU[u] + bV[m] + bVbin[m][dbin(day)] + sdbU[sdloc] + alphabU[u] * devuhat);
for (f=0; f<NFEATURES; f++)
err[entloc] -= (( sU[u][f] + sdsU[sdloc+f*NENTRIES] + alphasU[u][f] * devuhat + lNuSY[f]) * sV[m][f]);
}
}
}
void usertimemovie()
{
lg("User Time(Movie)\n");
int day0[NMOVIES];
ZERO(day0);
// It is OK to look on all data for day0 because it is always known
int i;
for(i=0;i<NENTRIES;i++) {
int m=userent[i]&USER_MOVIEMASK;
int day=userent[i]>>(USER_LMOVIEMASK+3);
if(!day0[m] || day0[m]>day) day0[m]=day;
}
int u;
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d012=UNALL(u);
int d0=UNTRAIN(u);
// compute explanatory variable
double usertime[NMOVIES];
int j;
for(j=0;j<d012;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
int day=userent[base+j]>>(USER_LMOVIEMASK+3);
usertime[j]=DTIME(day-day0[m]);
}
userXX(usertime,&err[base],d0,d012,USERTIMEMOVIE_ALPHA);
}
}
void movieXuser(double *xuser, double alpha)
{
// Remove average but only use training data
double avg[NMOVIES];
int moviecount[NMOVIES];
ZERO(avg);
ZERO(moviecount);
int u;
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d0=UNTRAIN(u);
int j;
double xu=xuser[u];
for(j=0;j<d0;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
avg[m]+=xu;
moviecount[m]++;
}
}
int m;
for(m=0;m<NMOVIES;m++) avg[m]/=moviecount[m];
// compute unbiased estimator
double theta[NMOVIES];
double var[NMOVIES];
ZERO(theta);
ZERO(var);
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d0=UNTRAIN(u);
int j;
double xu=xuser[u];
for(j=0;j<d0;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
// compute explanatory variable
double x=xu-avg[m];
theta[m]+=err[base+j]*x;
var[m]+=x*x;
}
}
for(m=0; m<NMOVIES; m++)
theta[m]=(theta[m]/(var[m]+1.e-20))*moviecount[m]/(moviecount[m]+alpha);
//predict
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d012=UNALL(u);
int j;
double xu=xuser[u];
for(j=0;j<d012;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
double x=xu-avg[m];
err[base+j]-=theta[m]*x;
}
}
}
void removeUV() {
int u,f;
for(u=0;u<NUSERS;u++) {
int base0=useridx[u][0];
int d012=UNALL(u);
int i;
int dall=UNALL(u);
int j,j2;
for(i=0; i<d012;i++) {
int m=userent[base0+i]&USER_MOVIEMASK;
int r=(userent[base0+i]>>USER_LMOVIEMASK)&7;
r++;
err[base0+i] = r - (GLOBAL_MEAN + wbU[u] + wbV[m]);
}
}
}
void removeUV()
{
int u;
for(u=0;u<NUSERS;u++) {
int base0=useridx[u][0];
int d012=UNALL(u);
int i;
for(i=0; i<d012;i++) {
int m=userent[base0+i]&USER_MOVIEMASK;
err[base0+i]-=sU[u]*sV[m];
}
}
}
void removeUV()
{
computeU();
int u;
for(u=0; u<NUSERS; u++) {
int base0=useridx[u][0];
unsigned int *ent=&userent[base0];
int d012=UNALL(u);
int i;
double sUu=sU[u];
for(i=0; i<d012; i++) {
err[base0+i]-=sUu*sV[ent[i]&USER_MOVIEMASK];
}
}
}
double rmseprobe()
{
int k=2;
int u, f;
int n=0;
double s=0.;
int i;
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int base0=useridx[u][0];
int dall=UNALL(u);
double NuS = 1.0/sqrt(dall);
double lNuSY[NFEATURES];
double sumY[NFEATURES];
ZERO(sumY);
ZERO(lNuSY);
int j;
for(j=0;j<dall;j++) {
int mm=userent[base0+j]&USER_MOVIEMASK;
for(f=0;f<NFEATURES;f++)
sumY[f]+=sY[mm][f];
}
int d0=UNTRAIN(u);
for(j=0;j<d0;j++) {
int mm=userent[base0+j]&USER_MOVIEMASK;
for(f=0;f<NFEATURES;f++)
lNuSY[f] = NuS * sumY[f];
}
for(i=1;i<k;i++) base+=useridx[u][i];
int d=useridx[u][k];
//s+=fvsqr(&err[base],d);
for(i=0; i<d;i++) {
int m=userent[base+i]&USER_MOVIEMASK;
double e=err[base+i];
for (f=0; f<NFEATURES; f++) {
e-=(bU[u][f] + bV[m][f]);
e-=((sU[u][f] + lNuSY[f]) * sV[m][f]);
}
s+=e*e;
}
n+=d;
}
return sqrt(s/n);
}
void userXmovie(double *xmovie, double alpha)
{
int u;
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d012=UNALL(u);
int d0=UNTRAIN(u);
// compute avg explanatory variable
double x[NMOVIES];
int j;
for(j=0;j<d012;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
x[j]=xmovie[m];
}
userXX(x,&err[base],d0,d012,alpha);
}
}
void usertime()
{
lg("User Time\n");
int u;
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d012=UNALL(u);
int d0=UNTRAIN(u);
// It is OK to look on all data for day0 because it is always known
int day0=uivmin(&userent[base],UNTOTAL(u))>>(USER_LMOVIEMASK+3);
// compute explanatory variable
double usertime[NMOVIES];
int j;
for(j=0;j<d012;j++) {
int day=userent[base+j]>>(USER_LMOVIEMASK+3);
usertime[j]=DTIME(day-day0);
}
userXX(usertime,&err[base],d0,d012,USERTIME_ALPHA);
}
}
void uavg()
{
lg("User avg centering\n");
ZERO(useravg);
int u;
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d0=UNTRAIN(u);
int i;
for(i=0; i<d0;i++)
useravg[u]+=err[base+i];
useravg[u]/=d0+USERAVG_ALPHA;
}
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d012=UNALL(u);
int i;
for(i=0; i<d012;i++) {
err[base++]-=useravg[u];
}
}
}
void score_setup()
{
int i,u;
//weight_time_setup();
if(load_model) {
fpV=fopen(fnameV,"rb");
fpU=fopen(fnameU,"rb");
if(fpV || fpU) {
lg("Loading %s and %s\n",fnameV,fnameU);
if(!fpV || !fpU)
error("Cant open both files");
}
}
int day0[NMOVIES];
ZERO(day0);
// It is OK to look on all data for day0 because it is always known
for(i=0;i<NENTRIES;i++) {
int m=userent[i]&USER_MOVIEMASK;
int day=userent[i]>>(USER_LMOVIEMASK+3);
if(!day0[m] || day0[m]>day) day0[m]=day;
}
DEVuHat = (float *) malloc(NENTRIES*sizeof(float));
sdbU = (float *) malloc(NENTRIES*sizeof(float));
sdsU = (float *) malloc(((unsigned int)NENTRIES)*((unsigned int)NFEATURES)*sizeof(float));
memset(DEVuHat,0,NENTRIES*sizeof(float));
memset(sdbU,0,NENTRIES*sizeof(float));
memset(sdsU,0,((unsigned int)NENTRIES)*((unsigned int)NFEATURES)*sizeof(float));
ZERO(sdbin);
int tcount[100000];
ZERO(tcount);
ZERO(avgdate);
ZERO(avgdevu);
ZERO(ucnt);
int j;
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d012=UNALL(u);
int d0=UNTRAIN(u);
// compute explanatory variable
for(j=0;j<d012;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
int day=userent[base+j]>>(USER_LMOVIEMASK+3);
if ( day < minday )
minday = day;
if ( day > maxday )
maxday = day;
//usertime[j]=DTIME(day-day0[m]);
if ( day < 0 )
;//toosmall++;
else if ( day < 100000-1 )
tcount[day]++;
else
;//toobig++;
ucnt[u]++;
avgdate[u] += day;
}
}
printf("minday: %d, maxday: %d\n", minday, maxday);
fflush(stdout);
for(u=0;u<NUSERS;u++) {
// 1) Find the average date of rating for every customer. In this step I include the probe dates also in the calculation of the average.
avgdate[u] /= ucnt[u];
//printf("U: %d, avgdate: %f, ucnt: %d\n", u, avgdate[u], ucnt[u]);
//fflush(stdout);
}
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d012=UNALL(u);
int d0=UNTRAIN(u);
int j;
for(j=0;j<d012;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
int day=userent[base+j]>>(USER_LMOVIEMASK+3);
//2) For every rating [i] in the data set (including probe) I calculate DEVu[i]:
// DEVu[i] = sign(t[i] - t_mean_for_customer) * powf(abs(t[i] - t_mean_for_customer), 0.4);
double DEVu = sign(day - avgdate[u]) * powf(abs(day - avgdate[u]), 0.4);
avgdevu[u] += DEVu;
//printf("U: %d, M: %d, day: %d, uavg: %f, DEVu: %f\n", u, m, day, avgdate[u], DEVu);
//fflush(stdout);
}
}
for(u=0;u<NUSERS;u++) {
//3) Find the average DEVu[i] for every customer. His/hers probe DEVu[i] values are also included.
avgdevu[u] /= ucnt[u];
//printf("U: %d, avgdevu: %f, avgdate: %f, ucnt: %d\n", u, avgdevu[u], avgdate[u], ucnt[u]);
//fflush(stdout);
}
ZERO(maxDEVuHat);
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d012=UNALL(u);
int d0=UNTRAIN(u);
int j;
for(j=0;j<d012;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
int day=userent[base+j]>>(USER_LMOVIEMASK+3);
// 2) For every rating [i] in the data set (including probe) I calculate DEVu[i]:
// DEVu[i] = sign(t[i] - t_mean_for_customer) * powf(abs(t[i] - t_mean_for_customer), 0.4);
double DEVu = sign(day - avgdate[u]) * powf(abs(day - avgdate[u]), 0.4);
// 4) Subtract every customer's average DEVu_avg value from every time deviation:
// DEVu_hat[i] = DEVu[i] - DEVu_avg_for_customer;
double DEVuHat = DEVu - avgdevu[u];
//printf("U: %d, M: %d, ndevu: %f, day: %d, uavg: %f, DEVu: %f\n", u, m, DEVuHat, day, avgdate[u], DEVu);
//fflush(stdout);
// Get the max absolute value of a user's devu_hat values...maxDevu_hat...
double tDEVu = fabs(DEVuHat);
if ( tDEVu > maxDEVuHat[u] )
maxDEVuHat[u] = tDEVu;
}
}
// Compute and store DEVuHats and create single day bin numbering per user
int daysBinValue[maxday+1];
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d012=UNALL(u);
int d0=UNTRAIN(u);
int j;
ZERO(daysBinValue);
int dcount=0;
for(j=0;j<d012;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
int day=userent[base+j]>>(USER_LMOVIEMASK+3);
DEVuHat[base+j] = devuHat(day,u);
if ( daysBinValue[day] == 0 ) {
sdbin[base+j] = base+j;
daysBinValue[day] = base+j;
if ( daysBinValue[day] > NENTRIES ) {
printf("Days bin v: %d\n", daysBinValue[day]);
fflush(stdout);
}
dcount++;
} else {
if ( daysBinValue[day] > NENTRIES ) {
printf("Days bin v: %d\n", daysBinValue[day]);
fflush(stdout);
}
sdbin[base+j] = daysBinValue[day];
}
}
}
//for (i=minday; i < maxday; i++ ) {
//printf("day: %d, count: %d\n", i, tcount[i]);
//fflush(stdout);
//}
}
int doAllFeatures()
{
/* Initial biases */
{
int u,m;
for(u=0;u<NUSERS;u++) {
bU[u]=0.0;
}
for(m=0;m<NMOVIES;m++) {
bV[m]=0.0;
}
}
/* Initial estimation for current feature */
{
int u,m,f;
double uvInit = sqrt(GLOBAL_MEAN/NFEATURES);
for(u=0;u<NUSERS;u++) {
for(f=0;f<NFEATURES;f++) {
sU[u][f]= uvInit * (rand()%14000 + 2000) * 0.000001235f;
}
}
for(m=0;m<NMOVIES;m++) {
for(f=0;f<NFEATURES;f++) {
sV[m][f]= uvInit * (rand()%14000 + 2000) * -0.000001235f;
sY[m][f]=0.0;
}
}
}
/* Optimize current feature */
double nrmse=2., last_rmse=10.;
double prmse = 0, last_prmse=0;
double thr=sqrt(1.-E);
int loopcount=0;
double Gamma1 = G1;
double Gamma2 = G2;
while( ((nrmse < (last_rmse-E) && prmse<last_prmse) || loopcount < 15) && loopcount < 40 ) {
last_rmse=nrmse;
last_prmse=prmse;
clock_t t0=clock();
loopcount++;
double aErrAvg=0;
double astepSuAvg=0;
double astepSvAvg=0;
double astepSyAvg=0;
double abU=0, abV=0, asU=0, asV=0, asY=0;
int n1=0, n2=0, n3=0;
int u,m, f;
for(u=0;u<NUSERS;u++) {
// Calculate sumY and NuSY for each factor
double sumY[NFEATURES];
ZERO(sumY);
double lNuSY[NFEATURES];
ZERO(lNuSY);
int base0=useridx[u][0];
int d0=UNTRAIN(u);
int j;
int f;
int dall=UNALL(u);
double NuS = 1.0/sqrt(dall);
for(j=0;j<dall;j++) {
int mm=userent[base0+j]&USER_MOVIEMASK;
for(f=0;f<NFEATURES;f++)
sumY[f]+=sY[mm][f];
}
for(f=0;f<NFEATURES;f++) {
lNuSY[f] = NuS * sumY[f];
}
double ycontrib[NFEATURES];
ZERO(ycontrib);
// For all rated movies
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
// Figure out the current error
double ee=err[base0+j];
double e2 = ee;
e2 -= (bU[u] + bV[m]);
for (f=0; f<NFEATURES; f++)
e2 -= ((sU[u][f]+lNuSY[f])*sV[m][f]);
//int r=(userent[base0+j]>>USER_LMOVIEMASK)&7;
//r++;
//double rui = r - e2;
//if ( rui > 5.00 )
//e2 += (rui-5.0);
//else if (rui < 1.0)
//e2 -= (1.0 - rui);
// Train the biases
double bUu = bU[u];
double bVm = bV[m];
//bU[u] += Gamma1 * (e2 - bUu * L6);
//bV[m] += Gamma1 * (e2 - bVm * L6);
bU[u] += Gamma1 * (e2 - bUu * LbU);
bV[m] += Gamma1 * (e2 - bVm * LbV);
aErrAvg+=fabs(e2);
abU += fabs(bU[u]);
abV += fabs(bV[m]);
n1++;
// update U V and slope component of Y
double yfactor = NuS;
for (f=0; f<NFEATURES; f++) {
double sUu = sU[u][f];
double sVm = sV[m][f];
//sU[u][f] += ((Gamma2) * ((e2 * sVm) - L7 * sUu));
//sV[m][f] += ((Gamma2) * ((e2 * (sUu + lNuSY[f])) - L7 * sVm));
sU[u][f] += ((Gamma2) * ((e2 * sVm) - LsU * sUu));
sV[m][f] += ((Gamma2) * ((e2 * (sUu + lNuSY[f])) - LsV * sVm));
asU += fabs(sU[u][f]);
asV += fabs(sV[m][f]);
astepSuAvg+=fabs(e2 * sV[m][f]);
astepSvAvg+=fabs(e2 * sU[u][f]);
n2++;
ycontrib[f] += e2 * sVm * yfactor;
}
}
// Train Ys over all known movies for user
for(j=0;j<dall;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
for (f=0; f<NFEATURES; f++) {
double sYm = sY[m][f];
sY[m][f] += Gamma2 * (ycontrib[f] - LsY * sYm);
asY += fabs(sY[m][f]);
astepSyAvg+=fabs(ycontrib[f]);
n3++;
}
}
}
aErrAvg/=n1;
astepSuAvg/=n2;
astepSvAvg/=n2;
astepSyAvg/=n3;
abU/=n1, abV/=n1, asU/=n2, asV/=n2, asY/=n2;
double bUREG = 1.9074 / 100.0 * aErrAvg / abU;
double bVREG = 1.9074 / 100.0 * aErrAvg / abV;
double sUREG = 1.9074 / 100.0 * astepSuAvg / asU;
double sVREG = 1.9074 / 100.0 * astepSvAvg / asV;
double sYREG = 1.9074 / 100.0 * astepSyAvg / asY;
printf("NREG - bU: %f bV: %f, sU: %f, sV: %f, sY: %f\n", bUREG, bVREG, sUREG, sVREG, sYREG);
// Report rmse for main loop
nrmse=0.;
int ntrain=0;
int elcnt=0;
int k=2;
int n=0;
double s=0.;
for(u=0;u<NUSERS;u++) {
int base0=useridx[u][0];
int d0=UNTRAIN(u);
int j;
// Setup the Ys again
double sumY[NFEATURES];
ZERO(sumY);
double lNuSY[NFEATURES];
ZERO(lNuSY);
int dall=UNALL(u);
double NuS = 1.0/sqrt(dall);
for(j=0;j<dall;j++) {
int mm=userent[base0+j]&USER_MOVIEMASK;
for(f=0;f<NFEATURES;f++)
sumY[f]+=sY[mm][f];
}
for(f=0;f<NFEATURES;f++)
lNuSY[f] = NuS * sumY[f];
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
double ee = err[base0+j];
double e2 = ee;
e2 -= (bU[u] + bV[m]);
for (f=0; f<NFEATURES; f++)
e2 -= ( (sU[u][f] + lNuSY[f]) * sV[m][f]);
//int r=(userent[base0+j]>>USER_LMOVIEMASK)&7;
//r++;
//double rui = r - e2;
//if ( rui > 5.00 )
//e2 += (rui-5.0);
//else if (rui < 1.0)
//e2 -= (1.0 - rui);
if( elcnt++ == 5000 ) {
printf("0 E: %f \t NE: %f\tNuSY: %f\tsV: %f\tsU: %f\tbU: %f\tbV: %f\tsY: %f\tU: %d\tM: %d\n", ee, e2, lNuSY[0], sV[m][0], sU[u][0], bU[u], bV[m], sY[m][0],u, m);
printf("1 E: %f \t NE: %f\tNuSY: %f\tsV: %f\tsU: %f\tbU: %f\tbV: %f\tsY: %f\tU: %d\tM: %d\n", ee, e2, lNuSY[1], sV[m][1], sU[u][1], bU[u], bV[m], sY[m][1],u, m);
printf("2 E: %f \t NE: %f\tNuSY: %f\tsV: %f\tsU: %f\tbU: %f\tbV: %f\tsY: %f\tU: %d\tM: %d\n", ee, e2, lNuSY[2], sV[m][2], sU[u][2], bU[u], bV[m], sY[m][2],u, m);
printf("3 E: %f \t NE: %f\tNuSY: %f\tsV: %f\tsU: %f\tbU: %f\tbV: %f\tsY: %f\tU: %d\tM: %d\n", ee, e2, lNuSY[3], sV[m][3], sU[u][3], bU[u], bV[m], sY[m][3],u, m);
fflush(stdout);
}
nrmse+=e2*e2;
}
ntrain+=d0;
// Sum up probe rmse
int i;
int base=useridx[u][0];
for(i=1;i<k;i++) base+=useridx[u][i];
int d=useridx[u][k];
for(i=0; i<d;i++) {
int m=userent[base+i]&USER_MOVIEMASK;
double e=err[base+i];
e-=(bU[u] + bV[m]);
for (f=0; f<NFEATURES; f++)
e-=((sU[u][f] + lNuSY[f]) * sV[m][f]);
//int r=(userent[base+i]>>USER_LMOVIEMASK)&7;
//r++;
//double rui = r - e;
//if ( rui > 5.00 )
//e += (rui-5.0);
//else if (rui < 1.0)
//e -= (1.0 - rui);
s+=e*e;
}
n+=d;
}
nrmse=sqrt(nrmse/ntrain);
prmse = sqrt(s/n);
lg("%f\t%f\t%f\n",nrmse,prmse,(clock()-t0)/(double)CLOCKS_PER_SEC);
Gamma1 *= 0.90;
Gamma2 *= 0.90;
}
/* Perform a final iteration in which the errors are clipped and stored */
removeUV();
//if(save_model) {
//dappend_bin(fnameV,sV,NMOVIES);
//dappend_bin(fnameU,sU,NUSERS);
//}
return 1;
}
int doAllFeatures()
{
int cloop=0;
/* Initial weight factors */
int i, j, h, c, r;
for (i=0; i < NMOVIES; i++) {
for (r=0; r<5; r++) {
for (c=0; c < NFACTORS; c++) {
Aic[i][r][c] = 0.02 * randn() - 0.01; // Normal Distribution
}
}
}
for (c=0; c < NFACTORS; c++) {
for (j=0; j < TOTAL_FEATURES; j++) {
//vishid[j][0][i] = 0.02 * randn() - 0.01; // Normal Distribution
//vishid[j][1][i] = 0.02 * randn() - 0.01; // Normal Distribution
//vishid[j][2][i] = 0.02 * randn() - 0.01; // Normal Distribution
//vishid[j][3][i] = 0.02 * randn() - 0.01; // Normal Distribution
//vishid[j][4][i] = 0.02 * randn() - 0.01; // Normal Distribution
Bcj[c][j] = 0.2/3.0 * randn() - 0.1/3.0; // Normal Distribution
}
}
/* Initial biases */
for(i=0;i<TOTAL_FEATURES;i++) {
hidbiases[i]=0.0;
}
for (j=0; j<NMOVIES; j++) {
unsigned int mtot = moviercount[j*5+0] + moviercount[j*5+1] + moviercount[j*5+2] + moviercount[j*5+3] + moviercount[j*5+4];
for (i=0; i<5; i++) {
visbiases[j][i] = log( ((double)moviercount[j*5+i]) / ((double) mtot) );
//printf("mrc: %d, mc %d, log:%f frac: %f\n", moviercount[j*5+i], moviecount[j] , log( moviercount[j*5+i] /(double) moviecount[j]),
//(moviercount[j*5+i] /(double) moviecount[j]) );
}
}
/* Optimize current feature */
double nrmse=2., last_rmse=10.;
double prmse = 0, last_prmse=0;
double s;
//double s2;
int n;
int loopcount=0;
double EpsilonW = epsilonw;
double EpsilonVB = epsilonvb;
double EpsilonHB = epsilonhb;
double Momentum = momentum;
ZERO(Ainc);
ZERO(Binc);
ZERO(visbiasinc);
ZERO(hidbiasinc);
int tSteps = 1;
//while ( ((nrmse < (last_rmse-E) && prmse<last_prmse) || loopcount < 14) && loopcount < 80 ) {
while ( ((nrmse < (last_rmse-E) ) || loopcount < 14) && loopcount < 80 ) {
//if ( loopcount >= 10 )
//tSteps = 1 + loopcount / 5;
last_rmse=nrmse;
last_prmse=prmse;
clock_t t0=clock();
loopcount++;
int ntrain = 0;
nrmse = 0.0;
s = 0.0;
//s2 = 0.0;
n = 0;
if ( loopcount > 5 )
Momentum = finalmomentum;
//* CDpos =0, CDneg=0 (matrices)
ZERO(Apos);
ZERO(Aneg);
ZERO(Bpos);
ZERO(Bneg);
ZERO(poshidact);
ZERO(neghidact);
ZERO(posvisact);
ZERO(negvisact);
ZERO(moviecount);
int u,m, f;
for(u=0;u<NUSERS;u++) {
//* CDpos =0, CDneg=0 (matrices)
ZERO(negvisprobs);
ZERO(nvp2);
//* perform steps 1 to 8
int base0=useridx[u][0];
int d0=UNTRAIN(u);
int dall=UNALL(u);
// For all rated movies, accumulate contributions to hidden units
double sumW[TOTAL_FEATURES];
ZERO(sumW);
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
moviecount[m]++;
// 1. get one data point from data set.
// 2. use values of this data point to set state of visible neurons Si
int r=(userent[base0+j]>>USER_LMOVIEMASK)&7;
// Add to the bias contribution for set visible units
posvisact[m][r] += 1.0;
// for all hidden units h:
for(h=0;h<TOTAL_FEATURES;h++) {
// sum_j(W[i][j] * v[0][j]))
//sumW[h] += vishid[m][r][h];
sumW[h] += Wij(m,r,h);
}
}
// Sample the hidden units state after computing probabilities
for(h=0;h<TOTAL_FEATURES;h++) {
// 3. compute Sj for each hidden neuron based on formula above and states of visible neurons Si
// poshidprobs[h] = 1./(1 + exp(-V*vishid - hidbiases);
// compute Q(h[0][i] = 1 | v[0]) # for binomial units, sigmoid(b[i] + sum_j(W[i][j] * v[0][j]))
poshidprobs[h] = 1.0/(1.0 + exp(-sumW[h] - hidbiases[h]));
// sample h[0][i] from Q(h[0][i] = 1 | v[0])
if ( poshidprobs[h] > (rand()/(double)(RAND_MAX)) ) {
poshidstates[h]=1;
poshidact[h] += 1.0;
} else {
poshidstates[h]=0;
}
//poshidact[h] += poshidprobs[h];
}
// Load up a copy of poshidstates for use in loop
for ( h=0; h < TOTAL_FEATURES; h++ )
curposhidstates[h] = poshidstates[h];
// Make T Contrastive Divergence steps
int stepT = 0;
do {
// Determine if this is the last pass through this loop
int finalTStep = (stepT+1 >= tSteps);
// 5. on visible neurons compute Si using the Sj computed in step3. This is known as reconstruction
// for all visible units j:
int r;
int count = d0;
count += useridx[u][1]; // too compute probe errors
for(j=0;j<count;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
for(h=0;h<TOTAL_FEATURES;h++) {
if ( curposhidstates[h] == 1 ) {
for(r=0;r<5;r++) {
//negvisprobs[m][r] += vishid[m][r][h];
negvisprobs[m][r] += Wij(m,r,h);
}
}
//for(r=0;r<5;r++)
//negvisprobs[m][r] += poshidprobs[h] * vishid[m][r][h];
if ( loopcount >= 10 ) {
for(r=0;r<5;r++)
//nvp2[m][r] += poshidprobs[h] * vishid[m][r][h];
nvp2[m][r] += poshidprobs[h] * Wij(m,r,h);
}
}
// compute P(v[1][j] = 1 | h[0]) # for binomial units, sigmoid(c[j] + sum_i(W[i][j] * h[0][i]))
negvisprobs[m][0] = 1./(1 + exp(-negvisprobs[m][0] - visbiases[m][0]));
negvisprobs[m][1] = 1./(1 + exp(-negvisprobs[m][1] - visbiases[m][1]));
negvisprobs[m][2] = 1./(1 + exp(-negvisprobs[m][2] - visbiases[m][2]));
negvisprobs[m][3] = 1./(1 + exp(-negvisprobs[m][3] - visbiases[m][3]));
negvisprobs[m][4] = 1./(1 + exp(-negvisprobs[m][4] - visbiases[m][4]));
// Normalize probabilities
double tsum =
negvisprobs[m][0] +
negvisprobs[m][1] +
negvisprobs[m][2] +
negvisprobs[m][3] +
negvisprobs[m][4];
if ( tsum != 0 ) {
negvisprobs[m][0] /= tsum;
negvisprobs[m][1] /= tsum;
negvisprobs[m][2] /= tsum;
negvisprobs[m][3] /= tsum;
negvisprobs[m][4] /= tsum;
}
if ( loopcount >= 10 ) {
nvp2[m][0] = 1./(1 + exp(-nvp2[m][0] - visbiases[m][0]));
nvp2[m][1] = 1./(1 + exp(-nvp2[m][1] - visbiases[m][1]));
nvp2[m][2] = 1./(1 + exp(-nvp2[m][2] - visbiases[m][2]));
nvp2[m][3] = 1./(1 + exp(-nvp2[m][3] - visbiases[m][3]));
nvp2[m][4] = 1./(1 + exp(-nvp2[m][4] - visbiases[m][4]));
double tsum2 =
nvp2[m][0] +
nvp2[m][1] +
nvp2[m][2] +
nvp2[m][3] +
nvp2[m][4];
if ( tsum2 != 0 ) {
nvp2[m][0] /= tsum2;
nvp2[m][1] /= tsum2;
nvp2[m][2] /= tsum2;
nvp2[m][3] /= tsum2;
nvp2[m][4] /= tsum2;
}
}
// sample v[1][j] from P(v[1][j] = 1 | h[0])
double randval = (rand()/(double)(RAND_MAX));
if ( (randval -= negvisprobs[m][0]) <= 0.0 )
negvissoftmax[m] = 0;
else if ( (randval -= negvisprobs[m][1]) <= 0.0 )
negvissoftmax[m] = 1;
else if ( (randval -= negvisprobs[m][2]) <= 0.0 )
negvissoftmax[m] = 2;
else if ( (randval -= negvisprobs[m][3]) <= 0.0 )
negvissoftmax[m] = 3;
else //if ( (randval -= negvisprobs[m][4]) <= 0.0 )
negvissoftmax[m] = 4;
//negvisact[m*5+0] += negvisprobs[m*5+0];
//negvisact[m*5+1] += negvisprobs[m*5+1];
//negvisact[m*5+2] += negvisprobs[m*5+2];
//negvisact[m*5+3] += negvisprobs[m*5+3];
//negvisact[m*5+4] += negvisprobs[m*5+4];
// if in training data then train on it
if ( j < d0 && finalTStep )
negvisact[m][negvissoftmax[m]] += 1.0;
}
// 6. compute state of hidden neurons Sj again using Si from 5 step.
// For all rated movies accumulate contributions to hidden units from sampled visible units
ZERO(sumW);
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
// for all hidden units h:
for(h=0;h<TOTAL_FEATURES;h++) {
//sumW[h] += vishid[m][negvissoftmax[m]][h];
sumW[h] += Wij(m,negvissoftmax[m],h);
//sumW[h] += vishid[m][0][h] * negvisprobs[m*5+0];
//sumW[h] += vishid[m][1][h] * negvisprobs[m*5+1];
//sumW[h] += vishid[m][2][h] * negvisprobs[m*5+2];
//sumW[h] += vishid[m][3][h] * negvisprobs[m*5+3];
//sumW[h] += vishid[m][4][h] * negvisprobs[m*5+4];
}
}
// for all hidden units h:
for(h=0;h<TOTAL_FEATURES;h++) {
// compute Q(h[1][i] = 1 | v[1]) # for binomial units, sigmoid(b[i] + sum_j(W[i][j] * v[1][j]))
neghidprobs[h] = 1./(1 + exp(-sumW[h] - hidbiases[h]));
// Experimentally sample the hidden units state again TODO: What is best?
if ( neghidprobs[h] > (rand()/(double)(RAND_MAX)) ) {
neghidstates[h]=1;
if ( finalTStep )
neghidact[h] += 1.0;
} else {
neghidstates[h]=0;
}
//if ( finalTStep )
//neghidact[h] += neghidprobs[h];
}
// Compute error rmse and prmse before we start iterating on T
if ( stepT == 0 ) {
// Compute rmse on training data
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
int r=(userent[base0+j]>>USER_LMOVIEMASK)&7;
//# Compute some error function like sum of squared difference between Si in 1) and Si in 5)
if ( loopcount < 10 ) {
double expectedV = negvisprobs[m][1] + 2.0 * negvisprobs[m][2] + 3.0 * negvisprobs[m][3] + 4.0 * negvisprobs[m][4];
double vdelta = (((double)r)-expectedV);
nrmse += (vdelta * vdelta);
} else {
double expectedV = nvp2[m][1] + 2.0 * nvp2[m][2] + 3.0 * nvp2[m][3] + 4.0 * nvp2[m][4];
double vdelta = (((double)r)-expectedV);
nrmse += (vdelta * vdelta);
}
}
ntrain+=d0;
// Sum up probe rmse
int base=useridx[u][0];
for(i=1;i<2;i++) base+=useridx[u][i];
int d=useridx[u][2];
for(i=0; i<d;i++) {
int m=userent[base+i]&USER_MOVIEMASK;
int r=(userent[base+i]>>USER_LMOVIEMASK)&7;
//# Compute some error function like sum of squared difference between Si in 1) and Si in 5)
if ( loopcount < 10 ) {
double expectedV = negvisprobs[m][1] + 2.0 * negvisprobs[m][2] + 3.0 * negvisprobs[m][3] + 4.0 * negvisprobs[m][4];
double vdelta = (((double)r)-expectedV);
s+=vdelta*vdelta;
} else {
double expectedV = nvp2[m][1] + 2.0 * nvp2[m][2] + 3.0 * nvp2[m][3] + 4.0 * nvp2[m][4];
double vdelta = (((double)r)-expectedV);
s+=vdelta*vdelta;
}
}
n+=d;
}
// If looping again, load the curposvisstates
if ( !finalTStep ) {
for ( h=0; h < TOTAL_FEATURES; h++ )
curposhidstates[h] = neghidstates[h];
ZERO(negvisprobs);
}
// 8. repeating multiple times steps 5,6 and 7 compute (Si.Sj)n. Where n is small number and can
// increase with learning steps to achieve better accuracy.
} while ( ++stepT < tSteps );
// Accumulate contrastive divergence contributions for (Si.Sj)0 and (Si.Sj)T
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
int r=(userent[base0+j]>>USER_LMOVIEMASK)&7;
// for all hidden units h:
for(h=0;h<TOTAL_FEATURES;h++) {
if ( poshidstates[h] == 1 ) {
// 4. now Si and Sj values can be used to compute (Si.Sj)0 here () means just values not average
//* accumulate CDpos = CDpos + (Si.Sj)0
//CDpos[m][r][h] += 1.0;
for (c=0; c < NFACTORS; c++) {
Apos[m][r][c] += Bcj[c][h];
Bpos[c][h] += Aic[m][r][c];
}
}
//CDpos[m][r][h] += poshidprobs[h];
// 7. now use Si and Sj to compute (Si.Sj)1 (fig.3)
//TODO - This is experimental!!!!!!!
//CDneg[m][negvissoftmax[m]][h] += neghidprobs[h];
//CDneg[m][negvissoftmax[m]][h] += (double)neghidstates[h];
if ( neghidstates[h] == 1 ) {
for (c=0; c < NFACTORS; c++) {
Aneg[m][negvissoftmax[m]][c] += Bcj[c][h];
Bneg[c][h] += Aic[m][negvissoftmax[m]][c];
}
}
}
}
// Update weights and biases after batch
//
//int bsize = 1000;
int bsize = 100;
if ( ((u+1) % bsize) == 0 || (u+1) == NUSERS ) {
int numcases = u % bsize;
numcases++;
cloop++;
//if ( numcases != bsize ) printf("u: %d, numcases: %d\n", u, numcases);
// Update A factors
for(m=0;m < NMOVIES;m++) {
if ( moviecount[m] == 0 ) continue;
// for all c factors
for(c=0;c < NFACTORS; c++) {
// for all softmax
int rr;
for(rr=0;rr<5;rr++) {
//# At the end compute average of CDpos and CDneg by dividing them by number of data points.
//# Compute CD = < Si.Sj >0 < Si.Sj >n = CDpos CDneg
double Ap = Apos[m][rr][c];
double An = Aneg[m][rr][c];
if ( Ap != 0.0 || An != 0.0 ) {
Ap /= ((double)moviecount[m]);
An /= ((double)moviecount[m]);
// W += epsilon * (h[0] * v[0]' - Q(h[1][.] = 1 | v[1]) * v[1]')
//# Update weights and biases W = W + alpha*CD (biases are just weights to neurons that stay always 1.0)
//e.g between data and reconstruction.
//double preW = vishid[m][rr][h];
Ainc[m][rr][c] = Momentum * Ainc[m][rr][c] + EpsilonW * ((Ap - An) - weightcost * Aic[m][rr][c]);
Aic[m][rr][c] += Ainc[m][rr][c];
//if ( cloop % 50 == 0 && c == 7 )
//printf("Aic: %f\t m: %d\t r: %d\t c: %d\n", Aic[m][rr][c], m, rr, c);
//printf("W: %f preW: %f, CDp: %f, CDn: %f, m: %d, r: %d, h: %d, nhp: %f, nvp: %f\n", vishid[m][rr][h], preW, CDp, CDn, m, rr, h,
//neghidprobs[h],negvisprobs[m*5+rr]
//);
}
}
}
// Update visible softmax biases
// c += epsilon * (v[0] - v[1])$
// for all softmax
int rr;
for(rr=0;rr<5;rr++) {
if ( posvisact[m][rr] != 0.0 || negvisact[m][rr] != 0.0 ) {
posvisact[m][rr] /= ((double)moviecount[m]);
negvisact[m][rr] /= ((double)moviecount[m]);
visbiasinc[m][rr] = Momentum * visbiasinc[m][rr] + EpsilonVB * ((posvisact[m][rr] - negvisact[m][rr]));
//visbiasinc[m][rr] = Momentum * visbiasinc[m][rr] + EpsilonVB * ((posvisact[m][rr] - negvisact[m][rr]) - weightcost * visbiases[m][rr]);
visbiases[m][rr] += visbiasinc[m][rr];
//printf("vb: %f, pa: %f, na: %f\n", visbiases[(m*5+rr)], posvisact[(m*5+rr)], negvisact[(m*5+rr)]);
}
}
}
// Update B factors
for(c=0;c<NFACTORS;c++) {
// for all hidden units h:
for(h=0;h<TOTAL_FEATURES;h++) {
//# At the end compute average of CDpos and CDneg by dividing them by number of data points.
//# Compute CD = < Si.Sj >0 < Si.Sj >n = CDpos CDneg
double Bp = Bpos[c][h];
double Bn = Bneg[c][h];
if ( Bp != 0.0 || Bn != 0.0 ) {
Bp /= ((double)numcases);
Bn /= ((double)numcases);
// W += epsilon * (h[0] * v[0]' - Q(h[1][.] = 1 | v[1]) * v[1]')
//# Update weights and biases W = W + alpha*CD (biases are just weights to neurons that stay always 1.0)
//e.g between data and reconstruction.
//double preW = vishid[m][rr][h];
Binc[c][h] = Momentum * Binc[c][h] + EpsilonW * ((Bp - Bn) - weightcost * Bcj[c][h]);
Bcj[c][h] += Binc[c][h];
//if ( cloop % 50 == 0 && h == 7 )
//printf("Bcj: %f\t c: %d\t h: %d\n", Bcj[c][h], c, h);
//printf("W: %f preW: %f, CDp: %f, CDn: %f, m: %d, r: %d, h: %d, nhp: %f, nvp: %f\n", vishid[m][rr][h], preW, CDp, CDn, m, rr, h,
//neghidprobs[h],negvisprobs[m*5+rr]
//);
}
}
}
// Update hidden biases
// b += epsilon * (h[0] - Q(h[1][.] = 1 | v[1]))
for(h=0;h<TOTAL_FEATURES;h++) {
if ( poshidact[h] != 0.0 || neghidact[h] != 0.0 ) {
//poshidact[h] /= ((double)(numcases*ntrain*5));
//neghidact[h] /= ((double)(numcases*ntrain*5));
poshidact[h] /= ((double)(numcases));
neghidact[h] /= ((double)(numcases));
//poshidact[h] /= ((double)(mcount));
//neghidact[h] /= ((double)(mcount));
hidbiasinc[h] = Momentum * hidbiasinc[h] + EpsilonHB * ((poshidact[h] - neghidact[h]));
//hidbiasinc[h] = Momentum * hidbiasinc[h] + EpsilonHB * ((poshidact[h] - neghidact[h]) - weightcost * hidbiases[h]);
hidbiases[h] += hidbiasinc[h];
//printf("hb: %f, pa: %f, na: %f, d0:%d\n", hidbiases[h], poshidact[h], neghidact[h], d0);
}
}
ZERO(Apos);
ZERO(Aneg);
ZERO(Bpos);
ZERO(Bneg);
ZERO(poshidact);
ZERO(neghidact);
ZERO(posvisact);
ZERO(negvisact);
//ZERO(poscnt);
//ZERO(negcnt);
ZERO(moviecount);
//mcount = 0;
}
}
nrmse=sqrt(nrmse/ntrain);
prmse = sqrt(s/n);
//double prmse2 = sqrt(s2/n);
//lg("%f\t%f\t%f\t%f\n",nrmse,prmse,prmse2,(clock()-t0)/(double)CLOCKS_PER_SEC);
lg("%f\t%f\t%f\n",nrmse,prmse,(clock()-t0)/(double)CLOCKS_PER_SEC);
if ( loopcount > 6 ) {
EpsilonW *= 0.90;
EpsilonVB *= 0.90;
EpsilonHB *= 0.90;
} //else if ( loopcount > 5 ) {
//EpsilonW *= 0.82;
//EpsilonVB *= 0.82;
//EpsilonHB *= 0.82;
//}
//printf("dd: %d %d %d %d %d\n", dd[0], dd[1], dd[2], dd[3], dd[4]);
}
/* Perform a final iteration in which the errors are clipped and stored */
recordErrors();
//if(save_model) {
//dappend_bin(fnameV,sV,NMOVIES);
//dappend_bin(fnameU,sU,NUSERS);
//}
return 1;
}
void recordErrors()
{
int u,h,f, j, i;
for(u=0;u<NUSERS;u++) {
//* CDpos =0, CDneg=0 (matrices)
ZERO(negvisprobs);
//* perform steps 1 to 8
int base0=useridx[u][0];
int d0=UNTRAIN(u);
int dall=UNALL(u);
// For all rated movies, accumulate contributions to hidden units
double sumW[TOTAL_FEATURES];
ZERO(sumW);
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
// 1. get one data point from data set.
// 2. use values of this data point to set state of visible neurons Si
int r=(userent[base0+j]>>USER_LMOVIEMASK)&7;
// for all hidden units h:
for(h=0;h<TOTAL_FEATURES;h++) {
// sum_j(W[i][j] * v[0][j]))
//sumW[h] += vishid[m][r][h];
sumW[h] += Wij(m,r,h);
}
}
// Compute the hidden probabilities
for(h=0;h<TOTAL_FEATURES;h++) {
// 3. compute Sj for each hidden neuron based on formula above and states of visible neurons Si
// compute Q(h[0][i] = 1 | v[0]) # for binomial units, sigmoid(b[i] + sum_j(W[i][j] * v[0][j]))
poshidprobs[h] = 1.0/(1.0 + exp(-sumW[h] - hidbiases[h]));
}
// 5. on visible neurons compute Si using the Sj computed in step3. This is known as reconstruction
// for all visible units j:
int r;
int count = dall;
for(j=0;j<count;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
for(h=0;h<TOTAL_FEATURES;h++) {
for(r=0;r<5;r++)
//negvisprobs[m][r] += poshidprobs[h] * vishid[m][r][h];
negvisprobs[m][r] += poshidprobs[h] * Wij(m,r,h);
}
// compute P(v[1][j] = 1 | h[0]) # for binomial units, sigmoid(c[j] + sum_i(W[i][j] * h[0][i]))
negvisprobs[m][0] = 1./(1 + exp(-negvisprobs[m][0] - visbiases[m][0]));
negvisprobs[m][1] = 1./(1 + exp(-negvisprobs[m][1] - visbiases[m][1]));
negvisprobs[m][2] = 1./(1 + exp(-negvisprobs[m][2] - visbiases[m][2]));
negvisprobs[m][3] = 1./(1 + exp(-negvisprobs[m][3] - visbiases[m][3]));
negvisprobs[m][4] = 1./(1 + exp(-negvisprobs[m][4] - visbiases[m][4]));
// Normalize probabilities
double tsum =
negvisprobs[m][0] +
negvisprobs[m][1] +
negvisprobs[m][2] +
negvisprobs[m][3] +
negvisprobs[m][4];
if ( tsum != 0 ) {
negvisprobs[m][0] /= tsum;
negvisprobs[m][1] /= tsum;
negvisprobs[m][2] /= tsum;
negvisprobs[m][3] /= tsum;
negvisprobs[m][4] /= tsum;
}
}
// Compute and save error residuals
for(i=0; i<dall;i++) {
int m=userent[base0+i]&USER_MOVIEMASK;
int r=(userent[base0+i]>>USER_LMOVIEMASK)&7;
double expectedV = negvisprobs[m][1] + 2.0 * negvisprobs[m][2] + 3.0 * negvisprobs[m][3] + 4.0 * negvisprobs[m][4];
double vdelta = (((double)r)-expectedV);
err[base0+i] = vdelta;
}
}
}
int doAllFeatures() {
/* Initial biases */
{
int u,m;
for(u=0;u<NUSERS;u++) {
wbU[u]=0.0;
}
for(m=0;m<NMOVIES;m++) {
wbV[m]=0.0;
}
}
float swbU[NUSERS];
float swbV[NMOVIES];
/* Optimize current feature */
float nrmse=2., last_rmse=10.;
float prmse = 0, last_prmse=0;
int loopcount=0;
float Gamma0 = G0;
while( ( (prmse<=last_prmse) || loopcount < 6) ) {
last_rmse=nrmse;
last_prmse=prmse;
nrmse=0.;
clock_t t0=clock();
loopcount++;
int u,m,j;
// Save the prior wbU and wbV for when the RMSE gets worse
for(u=0;u<NUSERS;u++) {
swbU[u] = wbU[u];
}
for(m=0;m<NMOVIES;m++) {
swbV[m]=wbV[m];
}
// Train
for(u=0;u<NUSERS;u++) {
//if (u%10000 == 0) {
//printf("On user: %d\n", u);
//fflush(stdout);
//}
int d0 = UNTRAIN(u);
int base0=useridx[u][0];
// For all rated movies
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
// Figure out the current error
int r=(userent[base0+j]>>USER_LMOVIEMASK)&7;
r++;
//float ee=err[base0+j];
//float e2 = ee;
float e2;
e2 = r - (GLOBAL_MEAN + wbU[u] + wbV[m]);
// Train the biases
float wbUu = wbU[u];
float wbVm = wbV[m];
wbU[u] += Gamma0 * (e2 - wbUu * L4);
wbV[m] += Gamma0 * (e2 - wbVm * L4);
}
}
// Report rmse for main loop
nrmse=0.;
int ntrain=0;
int elcnt=0;
int k=2;
int n=0;
float s=0.;
int i;
for(u=0;u<NUSERS;u++) {
int base0=useridx[u][0];
int d012=UNALL(u);
int i;
int dall=UNALL(u);
int d0 = UNTRAIN(u);
for(i=0; i<d0;i++) {
int m=userent[base0+i]&USER_MOVIEMASK;
int r=(userent[base0+i]>>USER_LMOVIEMASK)&7;
r++;
//float ee=err[base0+j];
float e2;
e2 = r - (GLOBAL_MEAN + wbU[u] + wbV[m]);
nrmse+=e2*e2;
ntrain++;
}
// Attempt to compute probe RMSE
int base=useridx[u][0];
for(i=1;i<k;i++) base+=useridx[u][i];
int d=useridx[u][k];
int boffset = base-base0;
for(i=0; i<d;i++) {
int m=userent[base+i]&USER_MOVIEMASK;
//float ee = err[base+i];
float e;
int r=(userent[base+i]>>USER_LMOVIEMASK)&7;
r++;
e = r - (GLOBAL_MEAN + wbU[u] + wbV[m]);
s+=e*e;
}
n+=d;
}
nrmse=sqrt(nrmse/ntrain);
prmse = sqrt(s/n);
lg("%f\t%f\t%f\n",nrmse,prmse,(clock()-t0)/(double)CLOCKS_PER_SEC);
Gamma0 *= 0.90;
}
// Restore the best parameters
int u, m;
for(u=0;u<NUSERS;u++) {
wbU[u] = swbU[u];
}
for(m=0;m<NMOVIES;m++) {
wbV[m] = swbV[m];
}
/* Perform a final iteration in which the errors are clipped and stored */
removeUV();
return 1;
}
computemix(char *fnames[], int nscores, double *xty)
{
#ifdef HOLDOUT
lg("With holdout\n");
#endif
int ns2=nscores+2;
int ns1=nscores+1;
FILE *fp[NSCORES];
openfiles(fp,fnames,nscores);
double xtx[NSCORES+2][NSCORES+2];
ZERO(xtx);
int u;
for(u=0; u<NUSERS; u++) {
PROGRESS(u,NUSERS);
int base=useridx[u][0];
#ifdef HOLDOUT
if(aopt) error("cant do holdout with -a");
int d0=UNTRAIN(u);
int d1=UNALL(u)-d0;
#else
int d0=0;
int d1=UNTRAIN(u);
#endif
seekfiles(fp,nscores, d0);
base+=d0;
int j;
for(j=0;j<d1;j++) {
unsigned int dd=userent[base+j];
int r = (dd>>USER_LMOVIEMASK)&7;
float s[NSCORES+2];
readfiles(fp,s,nscores);
int f;
for(f=0;f<nscores;f++)
s[f]=r-s[f];
s[nscores]=1.;
s[nscores+1]=r;
int ff;
for(f=0;f<ns2;f++) {
for(ff=0;ff<ns2;ff++)
xtx[f][ff] +=s[f]*s[ff];
}
}
int d2=UNTOTAL(u)-(d1+d0);
seekfiles(fp,nscores, d2);
}
closefiles(fp,nscores);
int count=xtx[nscores][nscores];
int j1,j2;
for(j1=0;j1<nscores;j1++)
lg("File %d RMSE %f\n",j1,sqrt((xtx[j1][j1]+xtx[ns1][ns1]-2*xtx[ns1][j1])/count));
double avgs[NSCORES+2],std[NSCORES+2];
for(j1=0;j1<ns2;j1++) {
avgs[j1]=xtx[nscores][j1]/count;
std[j1]=sqrt(xtx[j1][j1]/count-avgs[j1]*avgs[j1]);
}
for(j1=0;j1<ns2;j1++)
lg("%f\t",avgs[j1]);
lg("\n");
for(j1=0;j1<ns2;j1++)
lg("%f\t",std[j1]);
lg("\n");
lg("-------------------------------------------------\n");
for(j1=0;j1<ns2;j1++) {
for(j2=0;j2<ns2;j2++) {
lg("%f\t",(xtx[j1][j2]/count-avgs[j1]*avgs[j2])/(std[j1]*std[j2]+1.e-6));
}
lg("\n");
}
lg("-------------------------------------------------\n");
double eavgs[NSCORES],estd[NSCORES];
for(j1=0;j1<nscores;j1++) {
eavgs[j1]=avgs[ns1]-avgs[j1];
estd[j1]=sqrt((xtx[ns1][ns1]+ xtx[j1][j1]-2*xtx[j1][ns1])/count);
}
for(j1=0;j1<nscores;j1++)
lg("%f\t",eavgs[j1]);
lg("\n");
for(j1=0;j1<nscores;j1++)
lg("%f\t",estd[j1]);
lg("\n");
lg("-------------------------------------------------\n");
for(j1=0;j1<nscores;j1++) {
for(j2=0;j2<nscores;j2++) {
lg("%f\t",((xtx[j1][j2]+xtx[ns1][ns1]-xtx[ns1][j1]-xtx[ns1][j2])/count-eavgs[j1]*eavgs[j2])/(estd[j1]*estd[j2]+1.e-6));
}
lg("\n");
}
char TRANS='N';
char UFLO='U';
int M=ns1;
int N=ns1;
int NRHS=1;
double A[NSCORES+1][NSCORES+1];
int LDA=NSCORES+1;
double B[NSCORES+1];
int LDB=NSCORES+1;
double S[NSCORES+1];
double RCOND=0.00001; // singular values below this are treated as zero.
int RANK;
double WORK[1000];
int LWORK=1000;
int INFO;
for(j1=0;j1<ns1;j1++) {
B[j1]=xtx[ns1][j1];
for(j2=0;j2<ns1;j2++)
A[j1][j2]=xtx[j1][j2];
}
for(j1=0;j1<ns1;j1++) A[j1][j1]+=LAMBDA;
/*dgesv_(&N,&NRHS,A,&LDA,IPIV,B,&LDB,&INFO);*/
/*dgels_(&TRANS,&M,&N,&NRHS,A,&LDA,B,&LDB,WORK,&LWORK,&INFO);*/
/*dgelss_( &M, &N, &NRHS, A, &LDA, B, &LDB, S, &RCOND, &RANK, WORK, &LWORK, &INFO );*/
dposv_(&UFLO,&N,&NRHS,A,&LDA,B,&LDB,&INFO);
if(INFO) error("failed %d\n",INFO);
for(j1=0;j1<=nscores;j1++)
xty[j1]=B[j1];
lg("Check that the matrix inversion worked:\n");
for(j1=0;j1<=nscores;j1++) {
double sum=LAMBDA*B[j1];
for(j2=0;j2<=nscores;j2++)
sum+=xtx[j1][j2]*B[j2];
lg("%f\t%f\n",sum,xtx[nscores+1][j1]);
}
}
int doAllFeatures() {
/* Initial weights */
int i, j, h;
for (j=0; j<NMOVIES; j++) {
for (i=0; i<TOTAL_FEATURES; i++) {
vishid[j][0][i] = 0.02 * randn() - 0.01; // Normal Distribution
vishid[j][1][i] = 0.02 * randn() - 0.01; // Normal Distribution
vishid[j][2][i] = 0.02 * randn() - 0.01; // Normal Distribution
vishid[j][3][i] = 0.02 * randn() - 0.01; // Normal Distribution
vishid[j][4][i] = 0.02 * randn() - 0.01; // Normal Distribution
}
}
/* Initial biases */
for(i=0;i<TOTAL_FEATURES;i++) {
hidbiases[i]=0.0;
}
for (j=0; j<NMOVIES; j++) {
unsigned int mtot = moviercount[j*SOFTMAX+0] + moviercount[j*SOFTMAX+1] + moviercount[j*SOFTMAX+2] + moviercount[j*SOFTMAX+3] + moviercount[j*SOFTMAX+4];
for (i=0; i<SOFTMAX; i++) {
visbiases[j][i] = log( ((double)moviercount[j*SOFTMAX+i]) / ((double) mtot) );
}
}
/* Optimize current feature */
double nrmse=2., last_rmse=10.;
double prmse = 0, last_prmse=0;
double s;
int n;
int loopcount=0;
double EpsilonW = epsilonw;
double EpsilonVB = epsilonvb;
double EpsilonHB = epsilonhb;
double Momentum = momentum;
ZERO(CDinc);
ZERO(visbiasinc);
ZERO(hidbiasinc);
int tSteps = 1;
// Iterate through the model while the RMSE is decreasing
//while ( ((nrmse < (last_rmse-E) && prmse<last_prmse) || loopcount < 14) && loopcount < 80 ) {
while ( ((nrmse < (last_rmse-E) ) || loopcount < 14) && loopcount < 80 ) {
if ( loopcount >= 10 )
tSteps = 3 + (loopcount - 10)/5;
last_rmse=nrmse;
last_prmse=prmse;
clock_t t0=clock();
loopcount++;
int ntrain = 0;
nrmse = 0.0;
s = 0.0;
n = 0;
if ( loopcount > 5 )
Momentum = finalmomentum;
//* CDpos =0, CDneg=0 (matrices)
ZERO(CDpos);
ZERO(CDneg);
ZERO(poshidact);
ZERO(neghidact);
ZERO(posvisact);
ZERO(negvisact);
ZERO(moviecount);
int u,m, f;
for(u=0;u<NUSERS;u++) {
//* Clear summations for probabilities
ZERO(negvisprobs);
ZERO(nvp2);
//* perform steps 1 to 8
int base0=useridx[u][0];
int d0=UNTRAIN(u);
int dall=UNALL(u);
// For all rated movies, accumulate contributions to hidden units
double sumW[TOTAL_FEATURES];
ZERO(sumW);
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
moviecount[m]++;
// 1. get one data point from data set.
// 2. use values of this data point to set state of visible neurons Si
int r=(userent[base0+j]>>USER_LMOVIEMASK)&7;
// Add to the bias contribution for set visible units
posvisact[m][r] += 1.0;
// for all hidden units h:
for(h=0;h<TOTAL_FEATURES;h++) {
// sum_j(W[i][j] * v[0][j]))
sumW[h] += vishid[m][r][h];
}
}
// Sample the hidden units state after computing probabilities
for(h=0;h<TOTAL_FEATURES;h++) {
// 3. compute Sj for each hidden neuron based on formula above and states of visible neurons Si
// poshidprobs[h] = 1./(1 + exp(-V*vishid - hidbiases);
// compute Q(h[0][i] = 1 | v[0]) # for binomial units, sigmoid(b[i] + sum_j(W[i][j] * v[0][j]))
poshidprobs[h] = 1.0/(1.0 + exp(-sumW[h] - hidbiases[h]));
// sample h[0][i] from Q(h[0][i] = 1 | v[0])
if ( poshidprobs[h] > (rand()/(double)(RAND_MAX)) ) {
poshidstates[h]=1;
poshidact[h] += 1.0;
} else {
poshidstates[h]=0;
}
}
// Load up a copy of poshidstates for use in loop
for ( h=0; h < TOTAL_FEATURES; h++ )
curposhidstates[h] = poshidstates[h];
// Make T Contrastive Divergence steps
int stepT = 0;
do {
// Determine if this is the last pass through this loop
int finalTStep = (stepT+1 >= tSteps);
// 5. on visible neurons compute Si using the Sj computed in step3. This is known as reconstruction
// for all visible units j:
int r;
int count = d0;
count += useridx[u][2]; // too compute probe errors
for(j=0;j<count;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
for(h=0;h<TOTAL_FEATURES;h++) {
// Accumulate Weight values for sampled hidden states == 1
if ( curposhidstates[h] == 1 ) {
for(r=0;r<SOFTMAX;r++) {
negvisprobs[m][r] += vishid[m][r][h];
}
}
// Compute more accurate probabilites for RMSE reporting
if ( stepT == 0 ) {
for(r=0;r<SOFTMAX;r++)
nvp2[m][r] += poshidprobs[h] * vishid[m][r][h];
}
}
// compute P(v[1][j] = 1 | h[0]) # for binomial units, sigmoid(c[j] + sum_i(W[i][j] * h[0][i]))
// Softmax elements are handled individually here
negvisprobs[m][0] = 1./(1 + exp(-negvisprobs[m][0] - visbiases[m][0]));
negvisprobs[m][1] = 1./(1 + exp(-negvisprobs[m][1] - visbiases[m][1]));
negvisprobs[m][2] = 1./(1 + exp(-negvisprobs[m][2] - visbiases[m][2]));
negvisprobs[m][3] = 1./(1 + exp(-negvisprobs[m][3] - visbiases[m][3]));
negvisprobs[m][4] = 1./(1 + exp(-negvisprobs[m][4] - visbiases[m][4]));
// Normalize probabilities
double tsum =
negvisprobs[m][0] +
negvisprobs[m][1] +
negvisprobs[m][2] +
negvisprobs[m][3] +
negvisprobs[m][4];
if ( tsum != 0 ) {
negvisprobs[m][0] /= tsum;
negvisprobs[m][1] /= tsum;
negvisprobs[m][2] /= tsum;
negvisprobs[m][3] /= tsum;
negvisprobs[m][4] /= tsum;
}
// Compute and Normalize more accurate RMSE reporting probabilities
if ( stepT == 0) {
nvp2[m][0] = 1./(1 + exp(-nvp2[m][0] - visbiases[m][0]));
nvp2[m][1] = 1./(1 + exp(-nvp2[m][1] - visbiases[m][1]));
nvp2[m][2] = 1./(1 + exp(-nvp2[m][2] - visbiases[m][2]));
nvp2[m][3] = 1./(1 + exp(-nvp2[m][3] - visbiases[m][3]));
nvp2[m][4] = 1./(1 + exp(-nvp2[m][4] - visbiases[m][4]));
double tsum2 =
nvp2[m][0] +
nvp2[m][1] +
nvp2[m][2] +
nvp2[m][3] +
nvp2[m][4];
if ( tsum2 != 0 ) {
nvp2[m][0] /= tsum2;
nvp2[m][1] /= tsum2;
nvp2[m][2] /= tsum2;
nvp2[m][3] /= tsum2;
nvp2[m][4] /= tsum2;
}
}
// sample v[1][j] from P(v[1][j] = 1 | h[0])
double randval = (rand()/(double)(RAND_MAX));
if ( (randval -= negvisprobs[m][0]) <= 0.0 )
negvissoftmax[m] = 0;
else if ( (randval -= negvisprobs[m][1]) <= 0.0 )
negvissoftmax[m] = 1;
else if ( (randval -= negvisprobs[m][2]) <= 0.0 )
negvissoftmax[m] = 2;
else if ( (randval -= negvisprobs[m][3]) <= 0.0 )
negvissoftmax[m] = 3;
else //if ( (randval -= negvisprobs[m][4]) <= 0.0 )
negvissoftmax[m] = 4;
// if in training data then train on it
if ( j < d0 && finalTStep )
negvisact[m][negvissoftmax[m]] += 1.0;
}
// 6. compute state of hidden neurons Sj again using Si from 5 step.
// For all rated movies accumulate contributions to hidden units from sampled visible units
ZERO(sumW);
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
// for all hidden units h:
for(h=0;h<TOTAL_FEATURES;h++) {
sumW[h] += vishid[m][negvissoftmax[m]][h];
}
}
// for all hidden units h:
for(h=0;h<TOTAL_FEATURES;h++) {
// compute Q(h[1][i] = 1 | v[1]) # for binomial units, sigmoid(b[i] + sum_j(W[i][j] * v[1][j]))
neghidprobs[h] = 1./(1 + exp(-sumW[h] - hidbiases[h]));
// Sample the hidden units state again.
if ( neghidprobs[h] > (rand()/(double)(RAND_MAX)) ) {
neghidstates[h]=1;
if ( finalTStep )
neghidact[h] += 1.0;
} else {
neghidstates[h]=0;
}
}
// Compute error rmse and prmse before we start iterating on T
if ( stepT == 0 ) {
// Compute rmse on training data
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
int r=(userent[base0+j]>>USER_LMOVIEMASK)&7;
//# Compute some error function like sum of squared difference between Si in 1) and Si in 5)
double expectedV = nvp2[m][1] + 2.0 * nvp2[m][2] + 3.0 * nvp2[m][3] + 4.0 * nvp2[m][4];
double vdelta = (((double)r)-expectedV);
nrmse += (vdelta * vdelta);
}
ntrain+=d0;
// Sum up probe rmse
int base=useridx[u][0];
for(i=1;i<2;i++) base+=useridx[u][i];
int d=useridx[u][2];
for(i=0; i<d;i++) {
int m=userent[base+i]&USER_MOVIEMASK;
int r=(userent[base+i]>>USER_LMOVIEMASK)&7;
//# Compute some error function like sum of squared difference between Si in 1) and Si in 5)
double expectedV = nvp2[m][1] + 2.0 * nvp2[m][2] + 3.0 * nvp2[m][3] + 4.0 * nvp2[m][4];
double vdelta = (((double)r)-expectedV);
s+=vdelta*vdelta;
}
n+=d;
}
// If looping again, load the curposvisstates
if ( !finalTStep ) {
for ( h=0; h < TOTAL_FEATURES; h++ )
curposhidstates[h] = neghidstates[h];
ZERO(negvisprobs);
}
// 8. repeating multiple times steps 5,6 and 7 compute (Si.Sj)n. Where n is small number and can
// increase with learning steps to achieve better accuracy.
} while ( ++stepT < tSteps );
// Accumulate contrastive divergence contributions for (Si.Sj)0 and (Si.Sj)T
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
int r=(userent[base0+j]>>USER_LMOVIEMASK)&7;
// for all hidden units h:
for(h=0;h<TOTAL_FEATURES;h++) {
if ( poshidstates[h] == 1 ) {
// 4. now Si and Sj values can be used to compute (Si.Sj)0 here () means just values not average
//* accumulate CDpos = CDpos + (Si.Sj)0
CDpos[m][r][h] += 1.0;
}
// 7. now use Si and Sj to compute (Si.Sj)1 (fig.3)
CDneg[m][negvissoftmax[m]][h] += (double)neghidstates[h];
}
}
// Update weights and biases after batch
//
int bsize = 100;
if ( ((u+1) % bsize) == 0 || (u+1) == NUSERS ) {
int numcases = u % bsize;
numcases++;
// Update weights
for(m=0;m<NMOVIES;m++) {
if ( moviecount[m] == 0 ) continue;
// for all hidden units h:
for(h=0;h<TOTAL_FEATURES;h++) {
// for all softmax
int rr;
for(rr=0;rr<SOFTMAX;rr++) {
//# At the end compute average of CDpos and CDneg by dividing them by number of data points.
//# Compute CD = < Si.Sj >0 < Si.Sj >n = CDpos CDneg
double CDp = CDpos[m][rr][h];
double CDn = CDneg[m][rr][h];
if ( CDp != 0.0 || CDn != 0.0 ) {
CDp /= ((double)moviecount[m]);
CDn /= ((double)moviecount[m]);
// W += epsilon * (h[0] * v[0]' - Q(h[1][.] = 1 | v[1]) * v[1]')
//# Update weights and biases W = W + alpha*CD (biases are just weights to neurons that stay always 1.0)
//e.g between data and reconstruction.
CDinc[m][rr][h] = Momentum * CDinc[m][rr][h] + EpsilonW * ((CDp - CDn) - weightcost * vishid[m][rr][h]);
vishid[m][rr][h] += CDinc[m][rr][h];
}
}
}
// Update visible softmax biases
// c += epsilon * (v[0] - v[1])$
// for all softmax
int rr;
for(rr=0;rr<SOFTMAX;rr++) {
if ( posvisact[m][rr] != 0.0 || negvisact[m][rr] != 0.0 ) {
posvisact[m][rr] /= ((double)moviecount[m]);
negvisact[m][rr] /= ((double)moviecount[m]);
visbiasinc[m][rr] = Momentum * visbiasinc[m][rr] + EpsilonVB * ((posvisact[m][rr] - negvisact[m][rr]));
//visbiasinc[m][rr] = Momentum * visbiasinc[m][rr] + EpsilonVB * ((posvisact[m][rr] - negvisact[m][rr]) - weightcost * visbiases[m][rr]);
visbiases[m][rr] += visbiasinc[m][rr];
}
}
}
// Update hidden biases
// b += epsilon * (h[0] - Q(h[1][.] = 1 | v[1]))
for(h=0;h<TOTAL_FEATURES;h++) {
if ( poshidact[h] != 0.0 || neghidact[h] != 0.0 ) {
poshidact[h] /= ((double)(numcases));
neghidact[h] /= ((double)(numcases));
hidbiasinc[h] = Momentum * hidbiasinc[h] + EpsilonHB * ((poshidact[h] - neghidact[h]));
//hidbiasinc[h] = Momentum * hidbiasinc[h] + EpsilonHB * ((poshidact[h] - neghidact[h]) - weightcost * hidbiases[h]);
hidbiases[h] += hidbiasinc[h];
}
}
ZERO(CDpos);
ZERO(CDneg);
ZERO(poshidact);
ZERO(neghidact);
ZERO(posvisact);
ZERO(negvisact);
ZERO(moviecount);
}
}
nrmse=sqrt(nrmse/ntrain);
prmse = sqrt(s/n);
printf("%f\t%f\t%f\n",nrmse,prmse,(clock()-t0)/(double)CLOCKS_PER_SEC);
if ( TOTAL_FEATURES == 200 ) {
if ( loopcount > 6 ) {
EpsilonW *= 0.90;
EpsilonVB *= 0.90;
EpsilonHB *= 0.90;
} else if ( loopcount > 5 ) { // With 200 hidden variables, you need to slow things down a little more
EpsilonW *= 0.50; // This could probably use some more optimization
EpsilonVB *= 0.50;
EpsilonHB *= 0.50;
} else if ( loopcount > 2 ) {
EpsilonW *= 0.70;
EpsilonVB *= 0.70;
EpsilonHB *= 0.70;
}
} else { // The 100 hidden variable case
if ( loopcount > 8 ) {
EpsilonW *= 0.92;
EpsilonVB *= 0.92;
EpsilonHB *= 0.92;
} else if ( loopcount > 6 ) {
EpsilonW *= 0.90;
EpsilonVB *= 0.90;
EpsilonHB *= 0.90;
} else if ( loopcount > 2 ) {
EpsilonW *= 0.78;
EpsilonVB *= 0.78;
EpsilonHB *= 0.78;
}
}
}
/* Perform a final iteration in which the errors are clipped and stored */
recordErrors();
//if(save_model) {
//dappend_bin(fnameV,sV,NMOVIES);
//dappend_bin(fnameU,sU,NUSERS);
//}
return 1;
}
void movietimeuser()
{
lg("Movie Time(User)\n");
ZERO(day0);
// It is OK to look on all data for day0 because it is always known
int u;
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d012=UNTOTAL(u);
int j;
for(j=0;j<d012;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
int day=userent[base+j]>>(USER_LMOVIEMASK+3);
if(!day0[u] || day0[u]>day) day0[u]=day;
}
}
// Remove average but only use training data
double avg[NMOVIES];
int moviecount[NMOVIES];
ZERO(avg);
ZERO(moviecount);
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d0=UNTRAIN(u);
int j;
for(j=0;j<d0;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
int day=userent[base+j]>>(USER_LMOVIEMASK+3);
avg[m]+=DTIME(day-day0[u]);
moviecount[m]++;
}
}
int m;
for(m=0;m<NMOVIES;m++) avg[m]/=moviecount[m];
// compute unbiased estimator
double theta[NMOVIES];
double var[NMOVIES];
ZERO(theta);
ZERO(var);
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d0=UNTRAIN(u);
int j;
for(j=0;j<d0;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
int day=userent[base+j]>>(USER_LMOVIEMASK+3);
// compute explanatory variable
double x=DTIME(day-day0[u])-avg[m];
theta[m]+=err[base+j]*x;
var[m]+=x*x;
}
}
for(m=0; m<NMOVIES; m++)
theta[m]=(theta[m]/(var[m]+1.e-20))*moviecount[m]/(moviecount[m]+MOVIETIME_ALPHA);
//predict
for(u=0;u<NUSERS;u++) {
int base=useridx[u][0];
int d012=UNALL(u);
int j;
for(j=0;j<d012;j++) {
int m=userent[base+j]&USER_MOVIEMASK;
int day=userent[base+j]>>(USER_LMOVIEMASK+3);
double x=DTIME(day-day0[u])-avg[m];
err[base+j]-=theta[m]*x;
}
}
}
int doAllFeatures()
{
/* Initial biases */
{
int u,m,f;
for(u=0;u<NUSERS;u++) {
for(f=0;f<NFEATURES;f++)
bU[u][f]=drand48()*0.01-0.005;
}
for(m=0;m<NMOVIES;m++) {
for(f=0;f<NFEATURES;f++)
bV[m][f]=drand48()*0.01-0.005;
}
}
/* Initial estimation for current feature */
{
int u,m,f;
for(u=0;u<NUSERS;u++) {
for(f=0;f<NFEATURES;f++)
sU[u][f]=drand48()*0.1-0.04;
}
for(m=0;m<NMOVIES;m++) {
for(f=0;f<NFEATURES;f++) {
sV[m][f]=drand48()*0.05-0.025;
sY[m][f]=drand48()*0.02-0.01;
}
}
}
/* Optimize current feature */
double nrmse=2., last_rmse=10.;
double thr=sqrt(1.-E);
int loopcount=0;
//thr=sqrt(1.-E2);
double Gamma2 = G2;
double Gamma0 = G0;
while( ( nrmse < (last_rmse-E) ) || loopcount++ < 20) {
last_rmse=nrmse;
clock_t t0=clock();
int u,m, f;
for(u=0;u<NUSERS;u++) {
// Calculate sumY and NuSY for each factor
double sumY[NFEATURES];
ZERO(sumY);
double lNuSY[NFEATURES];
ZERO(lNuSY);
int base0=useridx[u][0];
int d0=UNTRAIN(u);
int j;
int f;
int dall=UNALL(u);
double NuS = 1.0/sqrt(dall);
for(j=0;j<d0;j++) {
int mm=userent[base0+j]&USER_MOVIEMASK;
for(f=0;f<NFEATURES;f++)
sumY[f]+=sY[mm][f];
}
//if ( loopcount > 1 ) {
//printf("sumY: %f\n", sumY);
//fflush(stdout);
//}
for(j=0;j<d0;j++) {
int mm=userent[base0+j]&USER_MOVIEMASK;
for(f=0;f<NFEATURES;f++) {
lNuSY[f] = NuS * sumY[f];
//if ( loopcount > 1 ) {
//printf("lNuSY: %f\n", lNuSY[f]);
//fflush(stdout);
//}
}
}
double ycontrib[NFEATURES];
ZERO(ycontrib);
// For all rated movies
double bdampen = d0/1.1;
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
// Figure out the current error
double ee=err[base0+j];
double e2 = ee;
for (f=0; f<NFEATURES; f++) {
e2 -= (bU[u][f] + bV[m][f]);
e2 -= ((sU[u][f]+lNuSY[f])*sV[m][f]);
}
// update U V and slope component of Y
//double yfactor = NuS/sqrt(moviecount[m]);
//double yfactor = NuS;
double yfactor = NuS/d0;
for (f=0; f<NFEATURES; f++) {
// Train the biases
double bUu = bU[u][f];
double bVm = bV[m][f];
bU[u][f] += Gamma0 * (e2 - bUu * L4) / bdampen;
bV[m][f] += Gamma0 * (e2 - bVm * L4) / bdampen;
double sUu = sU[u][f];
double sVm = sV[m][f];
sU[u][f] += (Gamma2 * ((e2 * sVm) - L8 * sUu));
sV[m][f] += (Gamma2 * ((e2 * (sUu + lNuSY[f])) - L8 * sVm));
//printf("sU: %f\n", sU[u][f]);
//printf("sV: %f\n", sV[m][f]);
//fflush(stdout);
ycontrib[f] += e2 * sVm * yfactor;
//printf("ycont: %f\n", ycontrib[f]);
//fflush(stdout);
}
}
// Train Ys over all known movies for user
for(j=0;j<dall;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
for (f=0; f<NFEATURES; f++) {
double sYm = sY[m][f];
sY[m][f] += Gamma2 * (ycontrib[f] - L7 * sYm);
//printf("before sY: %f\tycon: %f\tG2*ycon: %f\treg: %f\n", sY[m][f], ycontrib[f], (G2_Y*ycontrib[f]), G2_Y*L7_Y*sYm);
//printf("after sY: %f\tycon: %f\tG2*ycon: %f\treg: %f\n", sY[m][f], ycontrib[f], (G2_Y*ycontrib[f]), G2_Y*L7_Y*sYm);
//printf("sY: %f\tycon: %f\tG2*ycon: %f\n", sY[m][f], ycontrib[f], (G2*ycontrib[f]));
//fflush(stdout);
}
}
}
// Report rmse for main loop
nrmse=0.;
int ntrain=0;
int elcnt=0;
for(u=0;u<NUSERS;u++) {
int base0=useridx[u][0];
int d0=UNTRAIN(u);
int j;
// Setup the Ys again
double sumY[NFEATURES];
ZERO(sumY);
double lNuSY[NFEATURES];
ZERO(lNuSY);
int dall=UNALL(u);
double NuS = 1.0/sqrt(dall);
for(j=0;j<d0;j++) {
int mm=userent[base0+j]&USER_MOVIEMASK;
for(f=0;f<NFEATURES;f++)
sumY[f]+=sY[mm][f];
}
for(j=0;j<d0;j++) {
int mm=userent[base0+j]&USER_MOVIEMASK;
for(f=0;f<NFEATURES;f++)
lNuSY[f] = NuS * sumY[f];
}
for(j=0;j<d0;j++) {
int m=userent[base0+j]&USER_MOVIEMASK;
double ee = err[base0+j];
double e2 = ee;
for (f=0; f<NFEATURES; f++) {
e2 -= (bU[u][f] + bV[m][f]);
e2 -= ( (sU[u][f] + lNuSY[f]) * sV[m][f]);
}
if( elcnt++ == 5000 ) {
printf("0 E: %f \t NE: %f\tNuSY: %f\tsV: %f\tsU: %f\tbU: %f\tbV: %f\tsY: %f\tU: %d\tM: %d\n", ee, e2, lNuSY[0], sV[m][0], sU[u][0], bU[u][0], bV[m][0], sY[m][0],u, m);
printf("1 E: %f \t NE: %f\tNuSY: %f\tsV: %f\tsU: %f\tbU: %f\tbV: %f\tsY: %f\tU: %d\tM: %d\n", ee, e2, lNuSY[1], sV[m][1], sU[u][1], bU[u][1], bV[m][1], sY[m][1],u, m);
printf("2 E: %f \t NE: %f\tNuSY: %f\tsV: %f\tsU: %f\tbU: %f\tbV: %f\tsY: %f\tU: %d\tM: %d\n", ee, e2, lNuSY[2], sV[m][2], sU[u][2], bU[u][2], bV[m][2], sY[m][2],u, m);
printf("3 E: %f \t NE: %f\tNuSY: %f\tsV: %f\tsU: %f\tbU: %f\tbV: %f\tsY: %f\tU: %d\tM: %d\n", ee, e2, lNuSY[3], sV[m][3], sU[u][3], bU[u][3], bV[m][3], sY[m][3],u, m);
fflush(stdout);
}
/*
if( e > 5.0 || e < -5.0 ) {
printf("bad EE: %f\tU: %d\tM: %d\tNuSY: %f\te: %f\t sV: %f\tsU: %f\tbU: %f\tbV: %f\n", ee, u, m, NuSY, e, new_sV[m], new_sU[u], bUu, bVm);
fflush(stdout);
}
*/
nrmse+=e2*e2;
}
ntrain+=d0;
}
nrmse=sqrt(nrmse/ntrain);
double prmse = rmseprobe();
lg("%f\t%f\t%f\n",nrmse,prmse,(clock()-t0)/(double)CLOCKS_PER_SEC);
//rmse_print(0);
if ( loopcount < 6 ) {
Gamma2 *= 0.95;
Gamma0 *= 0.95;
} else if ( loopcount < 14 ) {
Gamma2 *= 0.92;
Gamma0 *= 0.92;
} else {
Gamma2 *= 0.90;
Gamma0 *= 0.90;
}
}
/* Perform a final iteration in which the errors are clipped and stored */
removeUV();
//if(save_model) {
//dappend_bin(fnameV,sV,NMOVIES);
//dappend_bin(fnameU,sU,NUSERS);
//}
return 1;
}
