int main()
{
printf("\n\n\nretfl: %f\n", retfl((float)pi));
printf("retdbl: %f\n", retdbl(pi));
printf("fl2dbl: %f\n", fl2dbl((float)pi));
printf("\nobvod: %f\n", obvod(5, M_PI));
printf("fl2i: %d, %d, %d\n", fl2i(3.14), fl2i(3.5), fl2i(3.51));
float floatnums[] = {1.1, 2.2, 3.9, -2, 1, 9, 10, 88, 9, 10, -6, 12};
printf("\navg: %f\n", prumer(floatnums, sizeof(floatnums)/sizeof(*floatnums)));
printf("\nmax: %f\n", max(floatnums, sizeof(floatnums)/sizeof(*floatnums)));
printf("\narrsum: %f\n", arrsum(floatnums, sizeof(floatnums)/sizeof(*floatnums)));
printf("\n--------------------------------\n");
printf("\nvsphere: %f\n", vsphere(5, M_PI));
printf("pytha(3, 4): %f\n", pytha(3.0, 4.0));
double veca[] = {3.0, 1.0, 5.0};
double vecb[] = {2.0, -2.0, 1.0};
int veclen = 3;
vecsum(veca, vecb, veclen);
printf("vecsum: ");
for(int i=0; i<veclen; i++)
printf("%f ", veca[i]);
printf("\nvecsize: %f\n", vecsize(veca, veclen));
}
static int vecsum_normal_loop(int pass, const struct libhdfs_data *ldata,
const struct options *opts)
{
double sum = 0.0;
while (1) {
int res = hdfsReadFully(ldata->fs, ldata->file, ldata->buf,
NORMAL_READ_CHUNK_SIZE);
if (res == 0) // EOF
break;
if (res < 0) {
int err = errno;
fprintf(stderr, "hdfsRead failed with error %d (%s)\n",
err, strerror(err));
return err;
}
if (res < NORMAL_READ_CHUNK_SIZE) {
fprintf(stderr, "hdfsRead got a partial read of "
"length %d\n", res);
return EINVAL;
}
sum += vecsum(ldata->buf,
NORMAL_READ_CHUNK_SIZE / sizeof(double));
}
printf("finished normal pass %d. sum = %g\n", pass, sum);
return 0;
}
void crf1dc_alpha_score(crf1d_context_t* ctx)
{
int i, t;
floatval_t sum, *cur = NULL;
floatval_t *scale = &ctx->scale_factor[0];
const floatval_t *prev = NULL, *trans = NULL, *state = NULL;
const int T = ctx->num_items;
const int L = ctx->num_labels;
/* Compute the alpha scores on nodes (0, *).
alpha[0][j] = state[0][j]
*/
cur = ALPHA_SCORE(ctx, 0);
state = EXP_STATE_SCORE(ctx, 0);
veccopy(cur, state, L);
sum = vecsum(cur, L);
*scale = (sum != 0.) ? 1. / sum : 1.;
vecscale(cur, *scale, L);
++scale;
/* Compute the alpha scores on nodes (t, *).
alpha[t][j] = state[t][j] * \sum_{i} alpha[t-1][i] * trans[i][j]
*/
for (t = 1;t < T;++t) {
prev = ALPHA_SCORE(ctx, t-1);
cur = ALPHA_SCORE(ctx, t);
state = EXP_STATE_SCORE(ctx, t);
veczero(cur, L);
for (i = 0;i < L;++i) {
trans = EXP_TRANS_SCORE(ctx, i);
vecaadd(cur, prev[i], trans, L);
}
vecmul(cur, state, L);
sum = vecsum(cur, L);
*scale = (sum != 0.) ? 1. / sum : 1.;
vecscale(cur, *scale, L);
++scale;
}
/* Compute the logarithm of the normalization factor here.
norm = 1. / (C[0] * C[1] ... * C[T-1])
log(norm) = - \sum_{t = 0}^{T-1} log(C[t]).
*/
ctx->log_norm = -vecsumlog(ctx->scale_factor, T);
}
static void vecsum_local(struct local_data *cdata, const struct options *opts)
{
int pass;
for (pass = 0; pass < opts->passes; pass++) {
double sum = vecsum(cdata->mmap, cdata->length / sizeof(double));
printf("finished vecsum_local pass %d. sum = %g\n", pass, sum);
}
}
int main(void)
{
int n;
int *v;
int i;
int sum, start, end;
while (scanf("%d", &n) == 1 && n > 0) {
v = (int *)malloc(n*sizeof(int));
assert(v);
for (i = 0; i < n; i++) {
scanf("%d", &(v[i]));
}
sum = vecsum(v, n, &start, &end);
printf("Maximum sum %d from %d to %d.\n", sum, start, end);
free(v);
}
return 0;
}
static int vecsum_zcr_loop(int pass, struct libhdfs_data *ldata,
struct hadoopRzOptions *zopts,
const struct options *opts)
{
int32_t len;
double sum = 0.0;
const double *buf;
struct hadoopRzBuffer *rzbuf = NULL;
int ret;
while (1) {
rzbuf = hadoopReadZero(ldata->file, zopts, ZCR_READ_CHUNK_SIZE);
if (!rzbuf) {
ret = errno;
fprintf(stderr, "hadoopReadZero failed with error "
"code %d (%s)\n", ret, strerror(ret));
goto done;
}
buf = hadoopRzBufferGet(rzbuf);
if (!buf) break;
len = hadoopRzBufferLength(rzbuf);
if (len < ZCR_READ_CHUNK_SIZE) {
fprintf(stderr, "hadoopReadZero got a partial read "
"of length %d\n", len);
ret = EINVAL;
goto done;
}
sum += vecsum(buf,
ZCR_READ_CHUNK_SIZE / sizeof(double));
hadoopRzBufferFree(ldata->file, rzbuf);
}
printf("finished zcr pass %d. sum = %g\n", pass, sum);
ret = 0;
done:
if (rzbuf)
hadoopRzBufferFree(ldata->file, rzbuf);
return ret;
}
void crf1dc_marginal_without_beta(crf1d_context_t* ctx)
{
int i, j, t;
floatval_t *prob = NULL;
floatval_t *row = ctx->row;
const floatval_t *fwd = NULL;
const int T = ctx->num_items;
const int L = ctx->num_labels;
/*
Compute marginal probabilities of states at T-1
p(T-1,j) = fwd'[T-1][j]
*/
fwd = ALPHA_SCORE(ctx, T-1);
prob = STATE_MEXP(ctx, T-1);
veccopy(prob, fwd, L);
/*
Repeat the following computation for t = T-1,T-2, ..., 1.
1) Compute p(t-1,i,t,j) using p(t,j)
2) Compute p(t,i) using p(t-1,i,t,j)
*/
for (t = T-1;0 < t;--t) {
fwd = ALPHA_SCORE(ctx, t-1);
prob = STATE_MEXP(ctx, t);
veczero(ctx->adj, L*L);
veczero(row, L);
/*
Compute adj[i][j] and row[j].
adj[i][j] = fwd'[t-1][i] * edge[i][j]
row[j] = \sum_{i} adj[i][j]
*/
for (i = 0;i < L;++i) {
floatval_t *adj = ADJACENCY(ctx, i);
floatval_t *edge = EXP_TRANS_SCORE(ctx, i);
vecaadd(adj, fwd[i], edge, L);
vecadd(row, adj, L);
}
/*
Find z such that z * \sum_{i] adj[i][j] = p(t,j).
Thus, z = p(t,j) / row[j]; we overwrite row with z.
*/
vecinv(row, L);
vecmul(row, prob, L);
/*
Apply the partition factor z (row[j]) to adj[i][j].
*/
for (i = 0;i < L;++i) {
floatval_t *adj = ADJACENCY(ctx, i);
vecmul(adj, row, L);
}
/*
Now that adj[i][j] presents p(t-1,i,t,j),
accumulate model expectations of transitions.
*/
for (i = 0;i < L;++i) {
floatval_t *adj = ADJACENCY(ctx, i);
floatval_t *prob = TRANS_MEXP(ctx, i);
vecadd(prob, adj, L);
}
/*
Compute the marginal probability of states at t-1.
p(t-1,i) = \sum_{j} p(t-1,i,t,j)
*/
prob = STATE_MEXP(ctx, t-1);
for (i = 0;i < L;++i) {
floatval_t *adj = ADJACENCY(ctx, i);
prob[i] = vecsum(adj, L);
}
}
}
void crf1dc_partial_alpha_score(crf1d_context_t* ctx, int *mask)
{
int i, j, t;
int *prev_mask, *curr_mask;
floatval_t sum, *cur = NULL;
floatval_t *scale = &ctx->partial_scale_factor[0];
const floatval_t *prev = NULL, *trans = NULL, *state = NULL;
const int T = ctx->num_items;
const int L = ctx->num_labels;
/* Compute the alpha scores on nodes (0, *).
alpha[0][j] = state[0][j]
*/
cur = PARTIAL_ALPHA_SCORE(ctx, 0);
veczero(cur, L);
state = EXP_STATE_SCORE(ctx, 0);
curr_mask = &mask[0];
for (i = 0; i < L; ++ i) {
if (curr_mask[i]) {
cur[i] = state[i];
}
}
sum = vecsum(cur, L);
/* scale is a temporary structure */
*scale = (sum != 0.) ? 1. / sum : 1.;
vecscale(cur, *scale, L);
++scale;
/* Compute the alpha scores on nodes (t, *).
alpha[t][j] = state[t][j] * \sum_{i} alpha[t-1][i] * trans[i][j]
*/
for (t = 1;t < T;++t) {
prev = PARTIAL_ALPHA_SCORE(ctx, t-1);
cur = PARTIAL_ALPHA_SCORE(ctx, t);
state = EXP_STATE_SCORE(ctx, t);
prev_mask = &mask[(t-1) * L];
curr_mask = &mask[t * L];
veczero(cur, L);
for (i = 0; i < L; ++ i) {
if (prev_mask[i]) {
trans = EXP_TRANS_SCORE(ctx, i);
for (j = 0; j < L; ++ j) {
if (curr_mask[j]) {
cur[j] += prev[i] * trans[j];
}
}
}
}
for (j = 0; j < L; ++ j) {
if (curr_mask[j]) {
cur[j] *= state[j];
}
}
sum = vecsum(cur, L);
*scale = (sum != 0.) ? 1. / sum : 1.;
vecscale(cur, *scale, L);
++scale;
}
/* Compute the logarithm of the normalization factor here.
norm = 1. / (C[0] * C[1] ... * C[T-1])
log(norm) = - \sum_{t = 0}^{T-1} log(C[t]).
*/
ctx->partial_log_norm = -vecsumlog(ctx->partial_scale_factor, T);
}