unsigned long long gauss_next(struct gauss_state *gs)
{
unsigned long long sum = 0;
int i;
for (i = 0; i < GAUSS_ITERS; i++)
sum += __rand(&gs->r) % (gs->nranges + 1);
sum = (sum + GAUSS_ITERS - 1) / GAUSS_ITERS;
if (gs->stddev) {
int dev = gauss_dev(gs);
while (dev + sum >= gs->nranges)
dev /= 2;
sum += dev;
}
return __hash_u64(sum) % gs->nranges;
}
int main(int argc, char *argv[])
{
int i, j;
unsigned char *z;
unsigned char r[100*1000];
double drand48();
unsigned char **threaded_topics, **threaded_docs;
float t0, t1;
float tmp[280];
init_compand();
threaded_docs = calloc(100, sizeof(threaded_docs[0]));
for (i=0;i<100;i++) {
threaded_docs[i] = calloc(280, sizeof(threaded_docs[0][0]));
for (j=0;j<280;j++) {
tmp[j] = gauss_dev(0, 1);
}
scv_normalize(tmp);
compress_vector(threaded_docs[i], tmp, 280);
j = drand48() * (i+1);
z = threaded_docs[i];
threaded_docs[i] = threaded_docs[j];
threaded_docs[j] = z;
}
threaded_topics = calloc(1000, sizeof(threaded_topics[0]));
for (i=0;i<1000;i++) {
threaded_topics[i] = calloc(280, sizeof(threaded_topics[0][0]));
for (j=0;j<280;j++) {
tmp[j] = gauss_dev(0, 1);
}
scv_normalize(tmp);
compress_vector(threaded_topics[i], tmp, 280);
j = drand48() * (i+1);
z = threaded_topics[i];
threaded_topics[i] = threaded_topics[j];
threaded_topics[j] = z;
}
/* convectis style threaded matrix
by vector
by matrix
*/
t0 = millitime();
for (i=0;i<100;i++) {
for (j=0;j<1000;j++) {
r[i*1000+j] = dot8(threaded_topics[j], threaded_docs[i], 280);
}
}
t1 = millitime();
printf("%.3f seconds for %d convectis style categorizations\n",
t1-t0, 100);
t0 = millitime();
for (j=0;j<1000;j++) {
for (i=0;i<100;i++) {
r[i*1000+j] = dot8(threaded_topics[j], threaded_docs[i], 280);
}
}
t1 = millitime();
printf("%.3f seconds for %d convectis matrix categorizations\n",
t1-t0, 100);
return 0;
}
