void view_turn(view *V, const double *w)
{
#if 0
double M[16], T[16], q[4], a[3], b[3], c[3], t[3];
assert(V);
/* Transform the mark and drag vectors from eye space into the world */
/* space current at the time of the mark. */
state_matrix(M, &V->mark);
mtranspose(T, M);
vnormalize(t, V->a);
vtransform(a, T, t);
vnormalize(t, w);
vtransform(b, T, t);
/* These determine the axis and angle of rotation. Find the quaternion. */
vcrs(t, a, b);
vnormalize(c, t);
qrotate(q, c, acos(vdot(a, b)));
/* Accumulate this quaternion with the view state, rotating the view. */
qmultiply(view_curr(V)->q, V->mark.q, q);
#endif
}
void
diffuse_cn(float4 *ret, float4 N)
{
float4 L;
float NdotL;
float4 rr;
L = rsl_getL();
vnormalize(&L);
NdotL = vdot(N, L);
if (NdotL < 0.0f) {
NdotL = 0.0f;
}
//printf("diffuse. L = %f, %f, %f\n", L[0], L[1], L[2]);
//printf("diffuse. N = %f, %f, %f\n", N[0], N[1], N[2]);
//printf("diffuse. NdotL = %f\n", NdotL);
rr[0] = NdotL;
rr[1] = NdotL;
rr[2] = NdotL;
rr[3] = NdotL;
(*ret) = rr;
}
void view_mark(view *V, const double *m)
{
assert(V);
vnormalize(V->m, m);
memcpy(&V->mark, view_curr(V), sizeof (state));
}
void
ray_sphere_intersect(Isect *isect, const Ray *ray, const Sphere *sphere)
{
vec rs;
rs.x = ray->org.x - sphere->center.x;
rs.y = ray->org.y - sphere->center.y;
rs.z = ray->org.z - sphere->center.z;
aobfloat B = vdot(rs, ray->dir);
aobfloat C = vdot(rs, rs) - sphere->radius * sphere->radius;
aobfloat D = B * B - C;
if (D > 0.0) {
aobfloat t = -B - sqrt(D);
if ((t > 0.0) && (t < isect->t)) {
isect->t = t;
isect->hit = 1;
isect->p.x = ray->org.x + ray->dir.x * t;
isect->p.y = ray->org.y + ray->dir.y * t;
isect->p.z = ray->org.z + ray->dir.z * t;
isect->n.x = isect->p.x - sphere->center.x;
isect->n.y = isect->p.y - sphere->center.y;
isect->n.z = isect->p.z - sphere->center.z;
vnormalize(&(isect->n));
}
}
}
// -------------------------------------------------------------------------- //
//
void Test_Mathutils::testVectorNormalization()
{
// Setup.
std::vector<double> vec0(4);
vec0[0] = 1.1;
vec0[1] = 2.2;
vec0[2] = 3.3;
vec0[3] = 7.7;
const double norm0 = vec0[0] + vec0[1] + vec0[2] + vec0[3];
std::vector<double> vec1(4);
vec1[0] = 2.1;
vec1[1] = 3.2;
vec1[2] = 4.3;
vec1[3] = 11.7;
const double norm1 = vec1[0] + vec1[1] + vec1[2] + vec1[3];
// Take a reference.
const std::vector<double> ref = vec0;
const std::vector<double> ref1 = vec1;
// Call.
vnormalize(vec0, ref1);
// Check.
CPPUNIT_ASSERT_EQUAL(static_cast<int>(vec0.size()), static_cast<int>(ref.size()));
CPPUNIT_ASSERT_DOUBLES_EQUAL( vec0[0], ref[0]*norm1/norm0, EPS );
CPPUNIT_ASSERT_DOUBLES_EQUAL( vec0[1], ref[1]*norm1/norm0, EPS );
CPPUNIT_ASSERT_DOUBLES_EQUAL( vec0[2], ref[2]*norm1/norm0, EPS );
CPPUNIT_ASSERT_DOUBLES_EQUAL( vec0[3], ref[3]*norm1/norm0, EPS );
}
static void
render_tile(unsigned char *img, int comps, float *fimg, int w, int h, int nsubsamples, int tilex, int tiley, int tilew, int tileh)
{
int endx = tilex + min_i(tilew, w - tilex);
int endy = tiley + min_i(tileh, h - tiley);
int x, y;
int u, v;
for (y = tiley; y < endy; y++) {
for (x = tilex; x < endx; x++) {
for (v = 0; v < nsubsamples; v++) {
for (u = 0; u < nsubsamples; u++) {
float px = (x + (u / (float)nsubsamples) - (w / 2.0)) / (w / 2.0);
float py = -(y + (v / (float)nsubsamples) - (h / 2.0)) / (h / 2.0);
Ray ray;
ray.org.x = 0.0;
ray.org.y = 0.0;
ray.org.z = 0.0;
ray.dir.x = px;
ray.dir.y = py;
ray.dir.z = -1.0;
vnormalize(&(ray.dir));
Isect isect;
isect.t = 1.0e+17;
isect.hit = 0;
ray_sphere_intersect(&isect, &ray, &spheres[0]);
ray_sphere_intersect(&isect, &ray, &spheres[1]);
ray_sphere_intersect(&isect, &ray, &spheres[2]);
ray_plane_intersect (&isect, &ray, &plane);
if (isect.hit) {
vec col;
ambient_occlusion(&col, &isect);
fimg[3 * (y * w + x) + 0] += col.x;
fimg[3 * (y * w + x) + 1] += col.y;
fimg[3 * (y * w + x) + 2] += col.z;
}
}
}
fimg[3 * (y * w + x) + 0] /= (float)(nsubsamples * nsubsamples);
fimg[3 * (y * w + x) + 1] /= (float)(nsubsamples * nsubsamples);
fimg[3 * (y * w + x) + 2] /= (float)(nsubsamples * nsubsamples);
img[comps * (y * w + x) + 0] = clamp(fimg[3 *(y * w + x) + 0]);
img[comps * (y * w + x) + 1] = clamp(fimg[3 *(y * w + x) + 1]);
img[comps * (y * w + x) + 2] = clamp(fimg[3 *(y * w + x) + 2]);
}
}
}
static void calcTriNormal(const float* v0, const float* v1, const float* v2, float* norm)
{
float e0[3], e1[3];
vsub(e0, v1, v0);
vsub(e1, v2, v0);
vcross(norm, e0, e1);
vnormalize(norm);
}
t_vector count_triangle_normale(t_vector a[3])
{
t_vector ab;
t_vector bc;
ab = vsub(a[0], a[1]);
bc = vsub(a[1], a[2]);
return (vnormalize(vmultiple(ab, bc)));
}
/* Compute the image for the scanlines from [y0,y1), for an overall image
of width w and height h.
*/
static void ao_scanlines(int y0, int y1, int w, int h, int nsubsamples,
float image[]) {
static Plane plane = { vec(0.0f, -0.5f, 0.0f), vec(0.f, 1.f, 0.f) };
static Sphere spheres[3] = {
{ vec(-2.0f, 0.0f, -3.5f), 0.5f },
{ vec(-0.5f, 0.0f, -3.0f), 0.5f },
{ vec(1.0f, 0.0f, -2.2f), 0.5f } };
srand48(y0);
for (int y = y0; y < y1; ++y) {
for (int x = 0; x < w; ++x) {
int offset = 3 * (y * w + x);
for (int u = 0; u < nsubsamples; ++u) {
for (int v = 0; v < nsubsamples; ++v) {
float px = (x + (u / (float)nsubsamples) - (w / 2.0f)) / (w / 2.0f);
float py = -(y + (v / (float)nsubsamples) - (h / 2.0f)) / (h / 2.0f);
// Scale NDC based on width/height ratio, supporting non-square image output
px *= (float)w / (float)h;
float ret = 0.f;
Ray ray;
Isect isect;
ray.org = vec(0.f, 0.f, 0.f);
ray.dir.x = px;
ray.dir.y = py;
ray.dir.z = -1.0f;
vnormalize(ray.dir);
isect.t = 1.0e+17f;
isect.hit = 0;
for (int snum = 0; snum < 3; ++snum)
ray_sphere_intersect(isect, ray, spheres[snum]);
ray_plane_intersect(isect, ray, plane);
if (isect.hit)
ret = ambient_occlusion(isect, plane, spheres);
// Update image for AO for this ray
image[offset+0] += ret;
image[offset+1] += ret;
image[offset+2] += ret;
}
}
// Normalize image pixels by number of samples taken per pixel
image[offset+0] /= nsubsamples * nsubsamples;
image[offset+1] /= nsubsamples * nsubsamples;
image[offset+2] /= nsubsamples * nsubsamples;
}
}
}
void appendArrowHead(struct duDebugDraw* dd, const float* p, const float* q,
const float s, unsigned int col)
{
if (!dd) return;
float ax[3], ay[3] = {0,1,0}, az[3];
vsub(az, q, p);
vnormalize(az);
vcross(ax, ay, az);
vcross(ay, az, ax);
vnormalize(ay);
dd->vertex(p, col);
// dd->vertex(p[0]+az[0]*s+ay[0]*s/2, p[1]+az[1]*s+ay[1]*s/2, p[2]+az[2]*s+ay[2]*s/2, col);
dd->vertex(p[0]+az[0]*s+ax[0]*s/3, p[1]+az[1]*s+ax[1]*s/3, p[2]+az[2]*s+ax[2]*s/3, col);
dd->vertex(p, col);
// dd->vertex(p[0]+az[0]*s-ay[0]*s/2, p[1]+az[1]*s-ay[1]*s/2, p[2]+az[2]*s-ay[2]*s/2, col);
dd->vertex(p[0]+az[0]*s-ax[0]*s/3, p[1]+az[1]*s-ax[1]*s/3, p[2]+az[2]*s-ax[2]*s/3, col);
}
t_vector get_cone_normale(t_vector p, t_icone *cone)
{
double m;
t_vector res;
m = pow(vlen(vsub(p, cone->vertex)), 2) / vscalar_multiple(vsub(p,
cone->vertex), cone->vector);
res = vsum(cone->vertex, vk_multiple(cone->vector, m));
res = vnormalize(vsub(p, res));
return (res);
}
// modified to only render one row (specified by parameter y) per function call
void
aobench_render(unsigned char *img, int w, int h, int nsubsamples, int y)
{
int x, u, v;
for (x = 0; x < w; x++) {
aobfloat r = 0.0, g = 0.0, b = 0.0;
for (v = 0; v < nsubsamples; v++) {
for (u = 0; u < nsubsamples; u++) {
aobfloat px = (x + (u / (aobfloat)nsubsamples) - (w / 2.0)) / (w / 2.0);
aobfloat py = -(y + (v / (aobfloat)nsubsamples) - (h / 2.0)) / (h / 2.0);
Ray ray;
ray.org.x = 0.0;
ray.org.y = 0.0;
ray.org.z = 0.0;
ray.dir.x = px;
ray.dir.y = py;
ray.dir.z = -1.0;
vnormalize(&(ray.dir));
Isect isect;
isect.t = 1.0e+17;
isect.hit = 0;
ray_sphere_intersect(&isect, &ray, &spheres[0]);
ray_sphere_intersect(&isect, &ray, &spheres[1]);
ray_sphere_intersect(&isect, &ray, &spheres[2]);
ray_plane_intersect (&isect, &ray, &plane);
if (isect.hit) {
vec col;
ambient_occlusion(&col, &isect);
r += col.x;
g += col.y;
b += col.z;
}
}
}
r /= (aobfloat)(nsubsamples * nsubsamples);
g /= (aobfloat)(nsubsamples * nsubsamples);
b /= (aobfloat)(nsubsamples * nsubsamples);
img[3 * (y * w + x) + 0] = clamp(r);
img[3 * (y * w + x) + 1] = clamp(g);
img[3 * (y * w + x) + 2] = clamp(b);
}
}
void sph_model::zoom(double *w, const double *v)
{
double d = vdot(v, zoomv);
if (-1 < d && d < 1)
{
double b = scale(zoomk, acos(d) / M_PI) * M_PI;
double y[3];
double x[3];
vcrs(y, v, zoomv);
vnormalize(y, y);
vcrs(x, zoomv, y);
vnormalize(x, x);
vmul(w, zoomv, cos(b));
vmad(w, w, x, sin(b));
}
else vcpy(w, v);
}
static inline void
orthoBasis(vec basis[3], const vec &n) {
basis[2] = n;
basis[1].x = 0.0; basis[1].y = 0.0; basis[1].z = 0.0;
if ((n.x < 0.6f) && (n.x > -0.6f)) {
basis[1].x = 1.0;
} else if ((n.y < 0.6f) && (n.y > -0.6f)) {
basis[1].y = 1.0;
} else if ((n.z < 0.6f) && (n.z > -0.6f)) {
basis[1].z = 1.0;
} else {
basis[1].x = 1.0;
}
basis[0] = vcross(basis[1], basis[2]);
vnormalize(basis[0]);
basis[1] = vcross(basis[2], basis[0]);
vnormalize(basis[1]);
}
static void bislerp(double *p, const double *a, const double *b,
const double *c, const double *d,
double x, double y)
{
double t[3];
double u[3];
vslerp(t, a, b, x);
vslerp(u, c, d, x);
vslerp(p, t, u, y);
vnormalize(p, p);
}
static void
orthoBasis(vec *basis, vec n)
{
basis[2] = n;
basis[1].x = 0.0; basis[1].y = 0.0; basis[1].z = 0.0;
if ((n.x < 0.6) && (n.x > -0.6)) {
basis[1].x = 1.0;
} else if ((n.y < 0.6) && (n.y > -0.6)) {
basis[1].y = 1.0;
} else if ((n.z < 0.6) && (n.z > -0.6)) {
basis[1].z = 1.0;
} else {
basis[1].x = 1.0;
}
vcross(&basis[0], basis[1], basis[2]);
vnormalize(&basis[0]);
vcross(&basis[1], basis[2], basis[0]);
vnormalize(&basis[1]);
}
void c_ctr::read_init_information(const char* theta_init_path,
const char* beta_init_path,
const c_corpus* c,
double alpha_smooth) {
int num_topics = m_num_factors;
m_theta = gsl_matrix_alloc(c->m_num_docs, num_topics);
printf("\nreading theta initialization from %s\n", theta_init_path);
FILE * f = fopen(theta_init_path, "r");
mtx_fscanf(f, m_theta);
fclose(f);
//smoothing
gsl_matrix_add_constant(m_theta, alpha_smooth);
//normalize m_theta, in case it's not
for (size_t j = 0; j < m_theta->size1; j ++) {
gsl_vector_view theta_v = gsl_matrix_row(m_theta, j);
vnormalize(&theta_v.vector);
}
m_beta = gsl_matrix_alloc(num_topics, c->m_size_vocab);
printf("reading beta initialization from %s\n", beta_init_path);
f = fopen(beta_init_path, "r");
mtx_fscanf(f, m_beta);
fclose(f);
// exponentiate if it's not
if (mget(m_beta, 0, 0) < 0) {
mtx_exp(m_beta);
}
else {
gsl_matrix_add_constant(m_beta, beta_smooth);
for (size_t j = 0; j < m_beta->size1; j ++) {
gsl_vector_view beta_v = gsl_matrix_row(m_beta, j);
vnormalize(&beta_v.vector);
}
}
}
double c_ctr::doc_inference(const c_document* doc, const gsl_vector* theta_v,
const gsl_matrix* log_beta, gsl_matrix* phi,
gsl_vector* gamma, gsl_matrix* word_ss,
bool update_word_ss) {
double pseudo_count = 1.0;
double likelihood = 0;
gsl_vector* log_theta_v = gsl_vector_alloc(theta_v->size);
gsl_vector_memcpy(log_theta_v, theta_v);
vct_log(log_theta_v);
int n, k, w;
double x;
for (n = 0; n < doc->m_length; n ++) {
w = doc->m_words[n];
for (k = 0; k < m_num_factors; k ++)
mset(phi, n, k, vget(theta_v, k) * mget(m_beta, k, w));
gsl_vector_view row = gsl_matrix_row(phi, n);
vnormalize(&row.vector);
for (k = 0; k < m_num_factors; k ++) {
x = mget(phi, n, k);
if (x > 0)
likelihood += x*(vget(log_theta_v, k) + mget(log_beta, k, w) - log(x));
}
}
if (pseudo_count > 0) {
likelihood += pseudo_count * vsum(log_theta_v);
}
gsl_vector_set_all(gamma, pseudo_count); // smoothing with small pseudo counts
for (n = 0; n < doc->m_length; n ++) {
for (k = 0; k < m_num_factors; k ++) {
x = doc->m_counts[n] * mget(phi, n, k);
vinc(gamma, k, x);
if (update_word_ss) minc(word_ss, k, doc->m_words[n], x);
}
}
gsl_vector_free(log_theta_v);
return likelihood;
}
static inline void ray_sphere_intersect(Isect &isect, Ray &ray, Sphere &sphere) {
vec rs = ray.org - sphere.center;
float B = dot(rs, ray.dir);
float C = dot(rs, rs) - sphere.radius * sphere.radius;
float D = B * B - C;
if (D > 0.) {
float t = -B - sqrtf(D);
if ((t > 0.0) && (t < isect.t)) {
isect.t = t;
isect.hit = 1;
isect.p = ray.org + t * ray.dir;
isect.n = isect.p - sphere.center;
vnormalize(isect.n);
}
}
}
t_figure *cone_init(t_ray *axis, double k, int color,
double reflection)
{
t_figure *new_figure;
t_icone *cone;
new_figure = (t_figure*)malloc(sizeof(t_figure));
new_figure->type = InfiniteCone;
cone = (t_icone*)malloc(sizeof(t_icone));
new_figure->figure = cone;
cone->vertex = axis->o;
cone->radius = k;
new_figure->color = color;
new_figure->reflection = reflection;
cone->vector = vnormalize(axis->v);
free(axis);
new_figure->next = NULL;
return (new_figure);
}
void
specularbrdf_cvnvf(float4 *ret, float4 L, float4 N, float4 V, float roughness)
{
float4 r;
float4 H = L + V;
vnormalize(&H);
float NdotH = vdot(N, H);
if (NdotH < 0.0f) NdotH = 0.0f;
float val = powf(NdotH, 1.0f / roughness);
r.x = val;
r.y = val;
r.z = val;
(*ret) = r;
}
void
specular_cnvf(float4 *ret, float4 N, float4 V, float roughness)
{
// TODO: Calculate contribution from lights in the scene.
float4 Cl = rsl_getCl();
float4 L = rsl_getL();
vnormalize(&L);
float4 r;
float4 specular_col;
specularbrdf_cvnvf(&specular_col, L, N, V, roughness);
r.x = Cl.x * specular_col.x;
r.y = Cl.y * specular_col.y;
r.z = Cl.z * specular_col.z;
(*ret) = r;
}
static void
ray_sphere_intersect(Isect *isect, const Ray *ray, const Sphere *sphere)
{
vec rs;
vsub(&rs, ray->org, sphere->center);
/*
rs.x = ray->org.x - sphere->center.x;
rs.y = ray->org.y - sphere->center.y;
rs.z = ray->org.z - sphere->center.z;
*/
float B = vdot(rs, ray->dir);
float C = vdot(rs, rs) - sphere->radius * sphere->radius;
float D = B * B - C;
if (D > 0.0) {
float t = -B - sqrt(D);
if ((t > 0.0) && (t < isect->t)) {
isect->t = t;
isect->hit = 1;
vmultsadd(&(isect->p), ray->dir, t, ray->org);
vsub(&(isect->n), isect->p, sphere->center);
/*
isect->p.x = ray->org.x + ray->dir.x * t;
isect->p.y = ray->org.y + ray->dir.y * t;
isect->p.z = ray->org.z + ray->dir.z * t;
isect->n.x = isect->p.x - sphere->center.x;
isect->n.y = isect->p.y - sphere->center.y;
isect->n.z = isect->p.z - sphere->center.z;
*/
vnormalize(&(isect->n));
}
}
}
int intersect_disk(t_ray *r, t_prim *o, double *t)
{
t_vector point;
double denominator;
double numerator;
double t0;
if ((denominator = vdot(r->dir, o->normal)) == 0)
return (0);
numerator = vdot(o->loc, o->normal) - vdot(r->loc, o->normal);
t0 = numerator / denominator;
if (t0 > EPSILON)
{
point = vadd(r->loc, vmult(r->dir, t0));
if (vnormalize(vsub(point, o->loc)) <= o->radius)
{
*t = t0;
return (1);
}
return (0);
}
return (0);
}
void c_ctr::learn_map_estimate(const c_data* users, const c_data* items,
const c_corpus* c, const ctr_hyperparameter* param,
const char* directory) {
// init model parameters
printf("\ninitializing the model ...\n");
init_model(param->ctr_run);
// filename
char name[500];
// start time
time_t start, current;
time(&start);
int elapsed = 0;
int iter = 0;
double likelihood = -exp(50), likelihood_old;
double converge = 1.0;
/// create the state log file
sprintf(name, "%s/state.log", directory);
FILE* file = fopen(name, "w");
fprintf(file, "iter time likelihood converge\n");
/* alloc auxiliary variables */
gsl_matrix* XX = gsl_matrix_alloc(m_num_factors, m_num_factors);
gsl_matrix* A = gsl_matrix_alloc(m_num_factors, m_num_factors);
gsl_matrix* B = gsl_matrix_alloc(m_num_factors, m_num_factors);
gsl_vector* x = gsl_vector_alloc(m_num_factors);
gsl_matrix* phi = NULL;
gsl_matrix* word_ss = NULL;
gsl_matrix* log_beta = NULL;
gsl_vector* gamma = NULL;
if (param->ctr_run && param->theta_opt) {
int max_len = c->max_corpus_length();
phi = gsl_matrix_calloc(max_len, m_num_factors);
word_ss = gsl_matrix_calloc(m_num_factors, c->m_size_vocab);
log_beta = gsl_matrix_calloc(m_num_factors, c->m_size_vocab);
gsl_matrix_memcpy(log_beta, m_beta);
mtx_log(log_beta);
gamma = gsl_vector_alloc(m_num_factors);
}
/* tmp variables for indexes */
int i, j, m, n, l, k;
int* item_ids;
int* user_ids;
double result;
/// confidence parameters
double a_minus_b = param->a - param->b;
while ((iter < param->max_iter and converge > 1e-4 ) or iter < min_iter) {
likelihood_old = likelihood;
likelihood = 0.0;
// update U
gsl_matrix_set_zero(XX);
for (j = 0; j < m_num_items; j ++) {
m = items->m_vec_len[j];
if (m>0) {
gsl_vector_const_view v = gsl_matrix_const_row(m_V, j);
gsl_blas_dger(1.0, &v.vector, &v.vector, XX);
}
}
gsl_matrix_scale(XX, param->b);
// this is only for U
gsl_matrix_add_diagonal(XX, param->lambda_u);
for (i = 0; i < m_num_users; i ++) {
item_ids = users->m_vec_data[i];
n = users->m_vec_len[i];
if (n > 0) {
// this user has rated some articles
gsl_matrix_memcpy(A, XX);
gsl_vector_set_zero(x);
for (l=0; l < n; l ++) {
j = item_ids[l];
gsl_vector_const_view v = gsl_matrix_const_row(m_V, j);
gsl_blas_dger(a_minus_b, &v.vector, &v.vector, A);
gsl_blas_daxpy(param->a, &v.vector, x);
}
gsl_vector_view u = gsl_matrix_row(m_U, i);
matrix_vector_solve(A, x, &(u.vector));
// update the likelihood
gsl_blas_ddot(&u.vector, &u.vector, &result);
likelihood += -0.5 * param->lambda_u * result;
}
}
if (param->lda_regression) break; // one iteration is enough for lda-regression
// update V
if (param->ctr_run && param->theta_opt) gsl_matrix_set_zero(word_ss);
gsl_matrix_set_zero(XX);
for (i = 0; i < m_num_users; i ++) {
n = users->m_vec_len[i];
if (n>0) {
gsl_vector_const_view u = gsl_matrix_const_row(m_U, i);
gsl_blas_dger(1.0, &u.vector, &u.vector, XX);
}
}
gsl_matrix_scale(XX, param->b);
for (j = 0; j < m_num_items; j ++) {
gsl_vector_view v = gsl_matrix_row(m_V, j);
gsl_vector_view theta_v = gsl_matrix_row(m_theta, j);
user_ids = items->m_vec_data[j];
m = items->m_vec_len[j];
if (m>0) {
// m > 0, some users have rated this article
gsl_matrix_memcpy(A, XX);
gsl_vector_set_zero(x);
for (l = 0; l < m; l ++) {
i = user_ids[l];
gsl_vector_const_view u = gsl_matrix_const_row(m_U, i);
gsl_blas_dger(a_minus_b, &u.vector, &u.vector, A);
gsl_blas_daxpy(param->a, &u.vector, x);
}
// adding the topic vector
// even when ctr_run=0, m_theta=0
gsl_blas_daxpy(param->lambda_v, &theta_v.vector, x);
gsl_matrix_memcpy(B, A); // save for computing likelihood
// here different from U update
gsl_matrix_add_diagonal(A, param->lambda_v);
matrix_vector_solve(A, x, &v.vector);
// update the likelihood for the relevant part
likelihood += -0.5 * m * param->a;
for (l = 0; l < m; l ++) {
i = user_ids[l];
gsl_vector_const_view u = gsl_matrix_const_row(m_U, i);
gsl_blas_ddot(&u.vector, &v.vector, &result);
likelihood += param->a * result;
}
likelihood += -0.5 * mahalanobis_prod(B, &v.vector, &v.vector);
// likelihood part of theta, even when theta=0, which is a
// special case
gsl_vector_memcpy(x, &v.vector);
gsl_vector_sub(x, &theta_v.vector);
gsl_blas_ddot(x, x, &result);
likelihood += -0.5 * param->lambda_v * result;
if (param->ctr_run && param->theta_opt) {
const c_document* doc = c->m_docs[j];
likelihood += doc_inference(doc, &theta_v.vector, log_beta, phi, gamma, word_ss, true);
optimize_simplex(gamma, &v.vector, param->lambda_v, &theta_v.vector);
}
}
else {
// m=0, this article has never been rated
if (param->ctr_run && param->theta_opt) {
const c_document* doc = c->m_docs[j];
doc_inference(doc, &theta_v.vector, log_beta, phi, gamma, word_ss, false);
vnormalize(gamma);
gsl_vector_memcpy(&theta_v.vector, gamma);
}
}
}
// update beta if needed
if (param->ctr_run && param->theta_opt) {
gsl_matrix_memcpy(m_beta, word_ss);
for (k = 0; k < m_num_factors; k ++) {
gsl_vector_view row = gsl_matrix_row(m_beta, k);
vnormalize(&row.vector);
}
gsl_matrix_memcpy(log_beta, m_beta);
mtx_log(log_beta);
}
time(¤t);
elapsed = (int)difftime(current, start);
iter++;
converge = fabs((likelihood-likelihood_old)/likelihood_old);
if (likelihood < likelihood_old) printf("likelihood is decreasing!\n");
fprintf(file, "%04d %06d %10.5f %.10f\n", iter, elapsed, likelihood, converge);
fflush(file);
printf("iter=%04d, time=%06d, likelihood=%.5f, converge=%.10f\n", iter, elapsed, likelihood, converge);
// save intermediate results
if (iter % param->save_lag == 0) {
sprintf(name, "%s/%04d-U.dat", directory, iter);
FILE * file_U = fopen(name, "w");
mtx_fprintf(file_U, m_U);
fclose(file_U);
sprintf(name, "%s/%04d-V.dat", directory, iter);
FILE * file_V = fopen(name, "w");
mtx_fprintf(file_V, m_V);
fclose(file_V);
if (param->ctr_run) {
sprintf(name, "%s/%04d-theta.dat", directory, iter);
FILE * file_theta = fopen(name, "w");
mtx_fprintf(file_theta, m_theta);
fclose(file_theta);
sprintf(name, "%s/%04d-beta.dat", directory, iter);
FILE * file_beta = fopen(name, "w");
mtx_fprintf(file_beta, m_beta);
fclose(file_beta);
}
}
}
// save final results
sprintf(name, "%s/final-U.dat", directory);
FILE * file_U = fopen(name, "w");
mtx_fprintf(file_U, m_U);
fclose(file_U);
sprintf(name, "%s/final-V.dat", directory);
FILE * file_V = fopen(name, "w");
mtx_fprintf(file_V, m_V);
fclose(file_V);
if (param->ctr_run) {
sprintf(name, "%s/final-theta.dat", directory);
FILE * file_theta = fopen(name, "w");
mtx_fprintf(file_theta, m_theta);
fclose(file_theta);
sprintf(name, "%s/final-beta.dat", directory);
FILE * file_beta = fopen(name, "w");
mtx_fprintf(file_beta, m_beta);
fclose(file_beta);
}
// free memory
gsl_matrix_free(XX);
gsl_matrix_free(A);
gsl_matrix_free(B);
gsl_vector_free(x);
if (param->ctr_run && param->theta_opt) {
gsl_matrix_free(phi);
gsl_matrix_free(log_beta);
gsl_matrix_free(word_ss);
gsl_vector_free(gamma);
}
}
line(vec2 pt_a, vec2 pt_b, int flags)
: thing((pt_a+pt_b)*0.5f, LINE | flags),
endpoint_a(pt_a), endpoint_b(pt_b), normal(vperp(vnormalize(endpoint_b - endpoint_a)))
{ }
static void
render(unsigned char *img, int comps, int w, int h, int nsubsamples)
{
int x, y;
int u, v;
//float *fimg = (float *)malloc(sizeof(float) * w * h * 3);
vec *fimg = (vec *)malloc(sizeof(vec) * w * h);
memset((void *)fimg, 0, sizeof(vec) * w * h);
for (y = 0; y < h; y++) {
for (x = 0; x < w; x++) {
for (v = 0; v < nsubsamples; v++) {
for (u = 0; u < nsubsamples; u++) {
float px = (x + (u / (float)nsubsamples) - (w / 2.0)) / (w / 2.0);
float py = -(y + (v / (float)nsubsamples) - (h / 2.0)) / (h / 2.0);
Ray ray;
ray.org.x = 0.0;
ray.org.y = 0.0;
ray.org.z = 0.0;
ray.dir.x = px;
ray.dir.y = py;
ray.dir.z = -1.0;
vnormalize(&(ray.dir));
Isect isect;
isect.t = 1.0e+17;
isect.hit = 0;
ray_sphere_intersect(&isect, &ray, &spheres[0]);
ray_sphere_intersect(&isect, &ray, &spheres[1]);
ray_sphere_intersect(&isect, &ray, &spheres[2]);
ray_plane_intersect (&isect, &ray, &plane);
if (isect.hit) {
vec col;
ambient_occlusion(&col, &isect);
vadd(&fimg[y * w + x], fimg[y * w + x], col);
/*
fimg[y * w + x].x += col.x;
fimg[y * w + x].y += col.y;
fimg[y * w + x].z += col.z;
*/
}
}
}
vdivs(&fimg[y * w + x], fimg[y * w + x], (float)(nsubsamples * nsubsamples));
/*
fimg[y * w + x].x /= (float)(nsubsamples * nsubsamples);
fimg[y * w + x].y /= (float)(nsubsamples * nsubsamples);
fimg[y * w + x].z /= (float)(nsubsamples * nsubsamples);
*/
img[comps * (y * w + x) + 0] = clamp(fimg[y * w + x].x);
img[comps * (y * w + x) + 1] = clamp(fimg[y * w + x].y);
img[comps * (y * w + x) + 2] = clamp(fimg[y * w + x].z);
}
}
}
line(int flags, FILE *bin)
: thing(LINE | flags, bin)
{
BATTLEMINTS_READ_SLOTS(*this, endpoint_a, endpoint_b, bin);
normal = vperp(vnormalize(endpoint_b - endpoint_a));
}
void sph_model::draw(const double *P, const double *V, const int *fv, int fc,
const int *pv, int pc, float alpha)
{
double M[16];
mmultiply(M, P, V);
glMatrixMode(GL_PROJECTION);
glLoadMatrixd(P);
glMatrixMode(GL_MODELVIEW);
glLoadMatrixd(V);
glEnable(GL_CULL_FACE);
glEnable(GL_DEPTH_TEST);
glEnable(GL_BLEND);
glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);
glBindBuffer(GL_ARRAY_BUFFER, vertices);
glEnableClientState(GL_VERTEX_ARRAY);
glVertexPointer(2, GL_FLOAT, 0, 0);
for (int i = 15; i >= 0; --i)
{
glActiveTexture(GL_TEXTURE0 + i);
glBindTexture(GL_TEXTURE_2D, cache.get_fill());
}
// This is a hack that ensures that the root pages of all files are touched.
GLuint o;
int tock;
for (int i = 0; i < fc; ++i)
{
o = cache.get_page(fv[i], 0, time, tock);
o = cache.get_page(fv[i], 1, time, tock);
o = cache.get_page(fv[i], 2, time, tock);
o = cache.get_page(fv[i], 3, time, tock);
o = cache.get_page(fv[i], 4, time, tock);
o = cache.get_page(fv[i], 5, time, tock);
}
for (int i = 0; i < pc; ++i)
{
o = cache.get_page(pv[i], 0, time, tock);
o = cache.get_page(pv[i], 1, time, tock);
o = cache.get_page(pv[i], 2, time, tock);
o = cache.get_page(pv[i], 3, time, tock);
o = cache.get_page(pv[i], 4, time, tock);
o = cache.get_page(pv[i], 5, time, tock);
}
#if 0
glPolygonMode(GL_FRONT_AND_BACK, GL_LINE);
#endif
glUseProgram(program);
{
glUniform1f(glGetUniformLocation(program, "zoomk"), zoomk);
glUniform1f(glGetUniformLocation(program, "globalAlpha"), alpha);
glUniform3f(glGetUniformLocation(program, "zoomv"),
zoomv[0], zoomv[1], zoomv[2]);
for (int i = 0; i < 6; ++i)
{
double a[3], b[3], c[3], d[3];
vnormalize(a, cube_v[cube_i[i][0]]);
vnormalize(b, cube_v[cube_i[i][1]]);
vnormalize(c, cube_v[cube_i[i][2]]);
vnormalize(d, cube_v[cube_i[i][3]]);
glUniform3f(pos_a, GLfloat(a[0]), GLfloat(a[1]), GLfloat(a[2]));
glUniform3f(pos_b, GLfloat(b[0]), GLfloat(b[1]), GLfloat(b[2]));
glUniform3f(pos_c, GLfloat(c[0]), GLfloat(c[1]), GLfloat(c[2]));
glUniform3f(pos_d, GLfloat(d[0]), GLfloat(d[1]), GLfloat(d[2]));
draw_face(fv, fc, pv, pc, 0, 1, 0, 1, 0, i);
}
}
glUseProgram(0);
glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, 0);
glBindBuffer(GL_ARRAY_BUFFER, 0);
glDisableClientState(GL_VERTEX_ARRAY);
glActiveTexture(GL_TEXTURE0);
}
void c_ctr::stochastic_learn_map_estimate(const c_data* users, const c_data* items,
const c_corpus* c, const ctr_hyperparameter* param,
const char* directory) {
// init model parameters
printf("\nrunning stochastic learning ...\n");
printf("initializing the model ...\n");
init_model(param->ctr_run);
// filename
char name[500];
// start time
time_t start, current;
time(&start);
int elapsed = 0;
int iter = 0;
double likelihood = -exp(50), likelihood_old;
double converge = 1.0;
double learning_rate = param->learning_rate;
/// create the state log file
sprintf(name, "%s/state.log", directory);
FILE* file = fopen(name, "w");
fprintf(file, "iter time likelihood converge\n");
/* alloc auxiliary variables */
gsl_vector* x = gsl_vector_alloc(m_num_factors);
gsl_matrix* phi = NULL;
gsl_matrix* word_ss = NULL;
gsl_matrix* log_beta = NULL;
gsl_vector* gamma = NULL;
if (param->ctr_run && param->theta_opt) {
int max_len = c->max_corpus_length();
phi = gsl_matrix_calloc(max_len, m_num_factors);
word_ss = gsl_matrix_calloc(m_num_factors, c->m_size_vocab);
log_beta = gsl_matrix_calloc(m_num_factors, c->m_size_vocab);
gsl_matrix_memcpy(log_beta, m_beta);
mtx_log(log_beta);
gamma = gsl_vector_alloc(m_num_factors);
}
/* tmp variables for indexes */
int i, j, m, n, l, k, ll, jj;
int* item_ids;
bool positive = true;
double result, inner;
int active_num_items = 0;
for (j = 0; j < m_num_items; ++j) {
if (items->m_vec_len[j] > 0)
active_num_items++;
}
int* idx_base = new int[active_num_items];
l = 0;
for (j = 0; j < m_num_items; ++j) {
if (items->m_vec_len[j] > 0) {
idx_base[l] = j;
++l;
}
}
int* sel = new int[active_num_items];
while (iter < param->max_iter) {
likelihood_old = likelihood;
likelihood = 0.0;
for (i = 0; i < m_num_users; ++i) {
item_ids = users->m_vec_data[i];
n = users->m_vec_len[i];
if (n > 0) {
double lambda_u = param->lambda_u / (2*n);
gsl_vector_view u = gsl_matrix_row(m_U, i);
// this user has rated some articles
// Randomly choose 2*n negative examples
sample_k_from_n(n, active_num_items, sel, idx_base);
qsort(sel, n, sizeof(int), compare);
l = 0; ll = 0;
while (true) {
if (l < n) {
j = item_ids[l]; // positive
} else {
j = -1;
}
if (ll < n) {
jj = sel[ll]; //negative
while (ll < n-1 && jj == sel[ll+1]) ++ll; // skip same values
} else {
jj = -1;
}
if (j == -1) {
if (jj == -1) break;
else {
positive = false; // jj is a negative example
++ll;
}
} else {
if (j < jj) {
positive = true; // j is a positive example
++l;
} else if (j == jj) {
positive = true; // j is a positive example
++l;
++ll;
} else { // j > jj
if (jj == -1) {
positive = true; // j is a positive example
++l;
} else {
positive = false;
++ll; // jj is a negative example
}
}
}
gsl_vector_view v;
gsl_vector_view theta_v;
double lambda_v = 0.0;
if (positive) {
// j is a positive example
lambda_v = param->lambda_v / (2 * items->m_vec_len[j]);
v = gsl_matrix_row(m_V, j);
theta_v = gsl_matrix_row(m_theta, j);
// second-order
// u
gsl_vector_scale(&u.vector, 1 - learning_rate);
gsl_blas_ddot(&v.vector, &v.vector, &inner);
gsl_blas_daxpy(learning_rate / (lambda_u + inner), &v.vector, &u.vector);
// v
if (!param->lda_regression) {
gsl_vector_scale(&v.vector, 1 - learning_rate);
gsl_blas_daxpy(learning_rate, &theta_v.vector, &v.vector);
gsl_blas_ddot(&u.vector, &u.vector, &inner);
gsl_blas_ddot(&u.vector, &theta_v.vector, &result);
gsl_blas_daxpy(learning_rate * (1.0 - result) / (lambda_v + inner), &u.vector, &v.vector);
}
gsl_blas_ddot(&u.vector, &v.vector, &result);
likelihood += -0.5 * (1 - result) * (1 - result);
// gsl_blas_ddot(&u.vector, &v.vector, &result);
// result -= 1.0;
} else {
// jj is a negative example
lambda_v = param->lambda_v / (2 * items->m_vec_len[jj]);
v = gsl_matrix_row(m_V, jj);
theta_v = gsl_matrix_row(m_theta, jj);
// second order
// u
gsl_vector_scale(&u.vector, 1 - learning_rate);
// v
if (!param->lda_regression) {
gsl_vector_scale(&v.vector, 1 - learning_rate);
gsl_blas_daxpy(learning_rate, &theta_v.vector, &v.vector);
gsl_blas_ddot(&u.vector, &u.vector, &inner);
gsl_blas_ddot(&u.vector, &theta_v.vector, &result);
gsl_blas_daxpy(-learning_rate * result / (lambda_v + inner), &u.vector, &v.vector);
}
gsl_blas_ddot(&u.vector, &v.vector, &result);
likelihood += -0.5 * result * result;
// gsl_blas_ddot(&u.vector, &v.vector, &result);
}
// update u
// first-order
// gsl_vector_scale(&u.vector, 1 - param->learning_rate * lambda_u);
// gsl_blas_daxpy(-result * param->learning_rate, &v.vector, &u.vector);
// second order
// update v
// gsl_vector_scale(&v.vector, 1 - param->learning_rate * lambda_v);
// gsl_blas_daxpy(-result * param->learning_rate, &u.vector, &v.vector);
// gsl_blas_daxpy(param->learning_rate * lambda_v, &theta_v.vector, &v.vector);
}
assert(n == l && n == l);
//printf("n=%d, l=%d, ll=%d, j=%d, jj=%d\n", n, l, ll, j, jj);
// update the likelihood
gsl_blas_ddot(&u.vector, &u.vector, &result);
likelihood += -0.5 * param->lambda_u * result;
}
}
for (j = 0; j < m_num_items; ++j) {
gsl_vector_view v = gsl_matrix_row(m_V, j);
gsl_vector_view theta_v = gsl_matrix_row(m_theta, j);
gsl_vector_memcpy(x, &v.vector);
gsl_vector_sub(x, &theta_v.vector);
gsl_blas_ddot(x, x, &result);
likelihood += -0.5 * param->lambda_v * result;
}
// update theta
if (param->ctr_run && param->theta_opt) {
gsl_matrix_set_zero(word_ss);
for (j = 0; j < m_num_items; j ++) {
gsl_vector_view v = gsl_matrix_row(m_V, j);
gsl_vector_view theta_v = gsl_matrix_row(m_theta, j);
m = items->m_vec_len[j];
if (m>0) {
// m > 0, some users have rated this article
const c_document* doc = c->m_docs[j];
likelihood += doc_inference(doc, &theta_v.vector, log_beta, phi, gamma, word_ss, true);
optimize_simplex(gamma, &v.vector, param->lambda_v, &theta_v.vector);
}
else {
// m=0, this article has never been rated
const c_document* doc = c->m_docs[j];
doc_inference(doc, &theta_v.vector, log_beta, phi, gamma, word_ss, false);
vnormalize(gamma);
gsl_vector_memcpy(&theta_v.vector, gamma);
}
}
gsl_matrix_memcpy(m_beta, word_ss);
for (k = 0; k < m_num_factors; k ++) {
gsl_vector_view row = gsl_matrix_row(m_beta, k);
vnormalize(&row.vector);
}
gsl_matrix_memcpy(log_beta, m_beta);
mtx_log(log_beta);
}
time(¤t);
elapsed = (int)difftime(current, start);
iter++;
if (iter > 50 && learning_rate > 0.001) learning_rate /= 2.0;
converge = fabs((likelihood-likelihood_old)/likelihood_old);
fprintf(file, "%04d %06d %10.5f %.10f\n", iter, elapsed, likelihood, converge);
fflush(file);
printf("iter=%04d, time=%06d, likelihood=%.5f, converge=%.10f\n", iter, elapsed, likelihood, converge);
// save intermediate results
if (iter % param->save_lag == 0) {
sprintf(name, "%s/%04d-U.dat", directory, iter);
FILE * file_U = fopen(name, "w");
gsl_matrix_fwrite(file_U, m_U);
fclose(file_U);
sprintf(name, "%s/%04d-V.dat", directory, iter);
FILE * file_V = fopen(name, "w");
gsl_matrix_fwrite(file_V, m_V);
fclose(file_V);
if (param->ctr_run && param->theta_opt) {
sprintf(name, "%s/%04d-theta.dat", directory, iter);
FILE * file_theta = fopen(name, "w");
gsl_matrix_fwrite(file_theta, m_theta);
fclose(file_theta);
sprintf(name, "%s/%04d-beta.dat", directory, iter);
FILE * file_beta = fopen(name, "w");
gsl_matrix_fwrite(file_beta, m_beta);
fclose(file_beta);
}
}
}
// save final results
sprintf(name, "%s/final-U.dat", directory);
FILE * file_U = fopen(name, "w");
gsl_matrix_fwrite(file_U, m_U);
fclose(file_U);
sprintf(name, "%s/final-V.dat", directory);
FILE * file_V = fopen(name, "w");
gsl_matrix_fwrite(file_V, m_V);
fclose(file_V);
if (param->ctr_run && param->theta_opt) {
sprintf(name, "%s/final-theta.dat", directory);
FILE * file_theta = fopen(name, "w");
gsl_matrix_fwrite(file_theta, m_theta);
fclose(file_theta);
sprintf(name, "%s/final-beta.dat", directory);
FILE * file_beta = fopen(name, "w");
gsl_matrix_fwrite(file_beta, m_beta);
fclose(file_beta);
}
// free memory
gsl_vector_free(x);
delete [] idx_base;
delete [] sel;
if (param->ctr_run && param->theta_opt) {
gsl_matrix_free(phi);
gsl_matrix_free(log_beta);
gsl_matrix_free(word_ss);
gsl_vector_free(gamma);
}
}
