// Reference: http://nehe.gamedev.net/data/lessons/lesson.asp?lesson=30
GLint testIntPlane(const Plane &pl, const Vec &pos, const Vec &dir, GLdouble &lamda, const GLdouble offset){
if ( (fabs(pl.position.getX() - pos.getX()) > offset) ||
(fabs(pl.position.getZ() - pos.getZ()) > offset))
{
return 0;
}
/* Dot Product between the plane normal and ray direction */
GLdouble theDotProd = dotProd(dir, pl.normal);
GLdouble lam;
// Determine If Ray Parallel To Plane
if ((theDotProd < ZERO) && (theDotProd > -ZERO))
return 0;
/* Find the distance to the collision point */
lam = dotProd(pl.position-pos, pl.normal);
lam /= theDotProd;
if (lam < -ZERO) // Test If Collision Behind Start
return 0;
lamda=lam;
return 1;
}
double ModelMFWt::objective(const Data& data, std::unordered_set<int>& invalidUsers,
std::unordered_set<int>& invalidItems) {
int u, ii, item;
float itemRat;
double rmse = 0, uRegErr = 0, iRegErr = 0, obj = 0, diff = 0;
gk_csr_t *trainMat = data.trainMat;
std::unordered_set<int> headItems = getHeadItems(trainMat, 0.5);
std::unordered_set<int> headUsers = getHeadUsers(trainMat, 0.5);
double lambda0 = 0.8;
double lambda1 = 1.0 - lambda0;
for (u = 0; u < nUsers; u++) {
//skip if invalid user
auto search = invalidUsers.find(u);
if (search != invalidUsers.end()) {
//found and skip
continue;
}
for (ii = trainMat->rowptr[u]; ii < trainMat->rowptr[u+1]; ii++) {
item = trainMat->rowind[ii];
//skip if invalid item
search = invalidItems.find(item);
if (search != invalidItems.end()) {
//found and skip
continue;
}
itemRat = trainMat->rowval[ii];
diff = itemRat - estRating(u, item);
if (headItems.find(item) != headItems.end()
&& headUsers.find(u) != headUsers.end()) {
rmse += diff*diff*lambda0;
} else {
rmse += diff*diff*(lambda0+lambda1);
}
}
uRegErr += dotProd(uFac[u], uFac[u], facDim);
}
uRegErr = uRegErr*uReg;
for (item = 0; item < nItems; item++) {
//skip if invalid item
auto search = invalidItems.find(item);
if (search != invalidItems.end()) {
//found and skip
continue;
}
iRegErr += dotProd(iFac[item], iFac[item], facDim);
}
iRegErr = iRegErr*iReg;
obj = rmse + uRegErr + iRegErr;
//std::cout <<"\nrmse: " << std::scientific << rmse << " uReg: " << uRegErr << " iReg: " << iRegErr ;
return obj;
}
/* returns the projection vector of u on v inside v
same transformation is applied to v_id */
void proj(double *u, double *v, double *u_id, double *v_id, int dim)
{
double u_v = dotProd(u,v,dim);
double u_u = dotProd(u,u,dim);
int i;
for(i = 0; i < dim; i++)
{
v[i] -= (u[i] * u_v) / u_u;
v_id[i] -= (u_id[i] * u_v) / u_u;
}
}
void CSysSolve::modGramSchmidt(int i, vector<vector<double> > & Hsbg, vector<CSysVector> & w) {
/*--- Parameter for reorthonormalization ---*/
static const double reorth = 0.98;
/*--- get the norm of the vector being orthogonalized, and find the
threshold for re-orthogonalization ---*/
double nrm = dotProd(w[i+1],w[i+1]);
double thr = nrm*reorth;
if (nrm <= 0.0) {
/*--- The norm of w[i+1] < 0.0 ---*/
cerr << "CSysSolve::modGramSchmidt: dotProd(w[i+1],w[i+1]) < 0.0" << endl;
throw(-1);
}
else if (nrm != nrm) {
/*--- This is intended to catch if nrm = NaN, but some optimizations
may mess it up (according to posts on stackoverflow.com) ---*/
cerr << "CSysSolve::modGramSchmidt: w[i+1] = NaN" << endl;
throw(-1);
}
/*--- Begin main Gram-Schmidt loop ---*/
for (int k = 0; k < i+1; k++) {
double prod = dotProd(w[i+1],w[k]);
Hsbg[k][i] = prod;
w[i+1].Plus_AX(-prod, w[k]);
/*--- Check if reorthogonalization is necessary ---*/
if (prod*prod > thr) {
prod = dotProd(w[i+1],w[k]);
Hsbg[k][i] += prod;
w[i+1].Plus_AX(-prod, w[k]);
}
/*--- Update the norm and check its size ---*/
nrm -= Hsbg[k][i]*Hsbg[k][i];
if (nrm < 0.0) nrm = 0.0;
thr = nrm*reorth;
}
/*--- Test the resulting vector ---*/
nrm = w[i+1].norm();
Hsbg[i+1][i] = nrm;
if (nrm <= 0.0) {
/*--- w[i+1] is a linear combination of the w[0:i] ---*/
cerr << "CSysSolve::modGramSchmidt: w[i+1] linearly dependent on w[0:i]" << endl;
throw(-1);
}
/*--- Scale the resulting vector ---*/
w[i+1] /= nrm;
}
/**
* @param x coordinates of point to be tested
* @param t coordinates of apex point of cone
* @param b coordinates of center of basement circle
* @param aperture in radians
Code copied from http://stackoverflow.com/questions/10768142/verify-if-point-is-inside-a-cone-in-3d-space
credit to: furikuretsu
altered to suit this purpose
*/
static bool isLyingInCone(float x[], float t[], float b[], float radius, float height)
{
float aperture = 2.f * atan(radius/height);
// This is for our convenience
float halfAperture = aperture/2.f;
// Vector pointing to X point from apex
float apexToXVect[] = {t[0]-x[0],t[1]-x[1],t[2]-x[2]};
// Vector pointing from apex to circle-center point.
float axisVect[] = {t[0]-b[0],t[1]-b[1],t[2]-b[2]};
// X is lying in cone only if it's lying in
// infinite version of its cone -- that is,
// not limited by "round basement".
// We'll use dotProd() to
// determine angle between apexToXVect and axis.
bool isInInfiniteCone = dotProd(apexToXVect,axisVect)/magn(apexToXVect)/magn(axisVect) > cos(halfAperture);
// We can safely compare cos() of angles
// between vectors instead of bare angles.
return isInInfiniteCone;
}
/* -------------------------------------
* Logistic Activation
* -------------------------------------
* Theta = array of K x N. K = Númber of
* classes. N = Dimension of each
* observation.
*/
double logSumExp(double* theta, int i, int length){
if(!stocMode){
if(-log(1 + exp(pow(-1, logistic_labels[i])*
dotProd(logistic_values[i], theta, length))) < -1e30){
return -1e10; // Numerical stability...
}
return -log(1 + exp(pow(-1, logistic_labels[i])*
dotProd(logistic_values[i], theta, length)));
}
if(-log(1 + exp(pow(-1, sample_logistic_labels[i])*
dotProd(sample_logistic_values[i], theta, length))) < -1e30){
return -1e10; // Numerical stability...
}
return -log(1 + exp(pow(-1, sample_logistic_labels[i])*
dotProd(sample_logistic_values[i], theta, length)));
}
int32_t dotProdVec(vector *a, vector *b){
int32_t *x = vectorToArray(a);
int32_t *y = vectorToArray(b);
int32_t result = dotProd(x, y, LENGTH);
free(x);
free(y);
return result;
}
double CSysVector::norm() const {
/*--- just call dotProd on this*, then sqrt ---*/
double val = dotProd(*this,*this);
if (val < 0.0) {
cerr << "CSysVector::norm(): " << "inner product of CSysVector is negative";
throw(-1);
}
return sqrt(val);
}
double GradientProjection::computeCost(
valarray<double> const &b,
valarray<double> const &x) const {
// computes cost = 2 b x - x A x
double cost = 2. * dotProd(b,x);
valarray<double> Ax(x.size());
for (unsigned i=0; i<denseSize; i++) {
Ax[i] = 0;
for (unsigned j=0; j<denseSize; j++) {
Ax[i] += (*denseQ)[i*denseSize+j]*x[j];
}
}
if(sparseQ) {
valarray<double> r(x.size());
sparseQ->rightMultiply(x,r);
Ax+=r;
}
return cost - dotProd(x,Ax);
}
void SteerLib::GJK_EPA::getClosestEdge(std::vector<Util::Vector> simplex, float& distance, Util::Vector& normal, int& index)
{
for(int i = 0; i < simplex.size(); i++){
int j = (i + 1 == simplex.size()) ? 0 : i + 1;
Util::Vector a = simplex[i];
Util::Vector b = simplex[j];
Util::Vector e = b - a;
Util::Vector n = (a*dotProd(e, e) - e*dotProd(e,a));
n = Util::normalize(n);
float d = dotProd(n, a);
if(d < distance)
{
distance = d;
normal = n;
index = j;
}
}
}
/* -------------------------------------
* Binary Logistic
* -------------------------------------
* Theta = array of K x N. K = Númber of
* classes. N = Dimension of each
* observation.
*/
double logistic(double* theta, int length){
double loss = 0;
int i;
if(!stocMode){
SAMPLE = MAX_FILE_ROWS;
for(i = 0; i < SAMPLE; i++){
// printf("logistic_label[%d] = %d | logSumExp[%d] = %lf\n", i, logistic_labels[i], i, logSumExp(theta, i, length));
loss = loss + logistic_labels[i]*logSumExp(theta, i, length) +
(1 - logistic_labels[i])*logSumExp(theta, i, length) +
regularization*dotProd(theta, theta, length);
}
}else{
for(i = 0; i < SAMPLE; i++){
loss = loss + sample_logistic_labels[i]*logSumExp(theta, i, length) +
(1 - sample_logistic_labels[i])*logSumExp(theta, i, length) +
regularization*dotProd(theta, theta, length);
}
}
return -loss;
}
bool RaySphere(Real3 p1,Real3 p2,Real3 sc, real r,real& u1,real& u2)
{
real a,b,c;
real bb4ac;
Real3 dp = p2-p1;
Real3 l = p1-sc;
a = dotProd(dp,dp);
b = 2*dotProd(dp,l);
c = dotProd(sc,sc)+dotProd(p1,p1)-2*dotProd(sc,p1)-r*r;
bb4ac = b * b - 4 * a * c;
if (fabs(a) < EPSILON6 || bb4ac < 0) {
u1 = u2 = 0;
return false;
}
real disc =sqrt(bb4ac);
u1 = (-b + disc) / (2 * a);
u2 = (-b - disc) / (2 * a);
return true;
}
LinRegResult linear_regression(DataSet theData) {
LinRegResult result;
int n = theData.n; // number of data points
double sumx = DESCALE(sum(theData.x, n)); // sum of x
double sumxx = DESCALE(dotProd(theData.x, theData.x, n)); // sum of each x squared
double sumy = DESCALE(sum(theData.y, n)); // sum of y
double sumyy = DESCALE(dotProd(theData.y, theData.y, n)); // sum of each y squared
double sumxy = DESCALE(dotProd(theData.x, theData.y, n)); // sum of each x * y
double m, b, r;
// Compute least-squares best fit straight line
m = (n * sumxy - sumx * sumy) / (n * sumxx - sqr(sumx)); // slope
b = (sumy * sumxx - (sumx * sumxy)) / (n * sumxx - sqr(sumx)); // y-intercept
r = (sumxy - sumx * sumy / n) / sqrt((sumxx - sqr(sumx) / n) * (sumyy - sqr(sumy)/ n)); // correlation
result.m = m * SCALE;
result.b = b * SCALE;
result.r = r * SCALE;
return result;
}
std::pair<double, double> getMeanVar(std::vector<std::vector<double>> uFac,
std::vector<std::vector<double>> iFac, int facDim, int nUsers, int nItems) {
double mean = 0, var = 0, diff = 0;
for (int u = 0; u < nUsers; u++) {
for (int item = 0; item < nItems; item++) {
mean += dotProd(uFac[u], iFac[item], facDim);
}
}
mean = mean/(nItems*nUsers);
for(int u = 0; u < nUsers; u++) {
for (int item = 0; item < nItems; item++) {
diff = dotProd(uFac[u], iFac[item], facDim) - mean;
var += diff*diff;
}
}
var = var/((nItems*nUsers) - 1);
return std::make_pair(mean, var);
}
int main(void){
/*Dot prod test*/
int32_t a[LENGTH] = {1, 3, -5};
int32_t b[LENGTH] = {4, -2, -1};
printf("%d\n", dotProd(a, b, LENGTH));
/*cross prod test*/
vector x = {2, 1, -1};
vector y = {-3, 4, 1};
vector c = crossProd(&x, &y);
printVector(&c);
}
/* Function: AreaOfTri
* Description: Computes the area of a triangle
* Input: inputModel - three vertices of the triangle
* Output: Area
*/
double AreaOfTri(point A, point B, point C)
{
double mside1, mside2;
point side1, side2;
double dot = 0.0;
pDIFFERENCE(A, B, side1);
pDIFFERENCE(C, B, side2);
mside1 = vecLeng(A, B);
mside2 = vecLeng(C, B);
dot = dotProd(side1, side2);
if(mside1 > mside2)
return (sqrt(mside2 * mside2 - dot * dot) * mside1) / 2.0;
else
return (sqrt(mside1 * mside1 - dot * dot) * mside2) / 2.0;
}
/*populate the correlation matrix*/
matrix * populateCorrMatrix(matrix *V, matrix *W){
matrix * correlations = init_matrix(W->cols, V->cols);
double a[V->rows];
double b[V->rows];
int x, y, i;
//find correlations for each sample against each class
for(x = 0; x < correlations->cols; x++){ //for each sample
for(y = 0; y < correlations->rows; y++){ //for each class
for(i = 0; i < W->rows; i++){
a[i] = W->graph[i][y]; //set a as the predictor column
b[i] = V->graph[i][x]; //set b as the sample's column
}
correlations->graph[y][x] = dotProd(a,b,V->rows);
}
}
return correlations;
}
bool SteerLib::GJK_EPA::gjk(const std::vector<Util::Vector>& _shapeA, const std::vector<Util::Vector>& _shapeB, std::vector<Util::Vector>& simplex) {
Util::Vector dir(1,0,0);
simplex.push_back((getFarPoint(_shapeA, dir) - getFarPoint(_shapeB, -dir)));
dir = -dir;
while(true){
float dotProduct = 0;
simplex.push_back((getFarPoint(_shapeA, dir) - getFarPoint(_shapeB, -dir)));
if(dotProd(simplex.back(), dir) <= 0)
return false;
else if (checkOrigin(simplex, dir)){
simplex.push_back((getFarPoint(_shapeA, dir) - getFarPoint(_shapeB, -dir)));
return true;
}
}
}
Util::Vector SteerLib::GJK_EPA::getFarPoint(const std::vector<Util::Vector>& shape, const Util::Vector& dir){
Util::Vector farPoint(0,0,0);
float farDistance = 0;
float farIndex = 0;
for(int i = 0; i < shape.size(); i++){
float checkFar = dotProd(shape[i], dir);
if (checkFar > farDistance){
farDistance = checkFar;
farIndex = i;
}
}
farPoint[0] = shape[farIndex][0];
farPoint[1] = shape[farIndex][1];
farPoint[2] = shape[farIndex][2];
return farPoint;
}
// puts result in 'ret'
void Quaternion::slerp(Quaternion const &a, Quaternion b, float t, Quaternion &ret){
// reverse sign if dot prod < 0
if (dotProd(a, b) < 0) { b *= -1; }
float angle = getAngle(a, b);
float sc1, sc2;
// like suggested in the book, for small angles we use the sin(a) = a appr.
if (angle > 0.00001) {
sc1 = sin( (1-t)*angle ) / sin( angle );
sc2 = sin( t*angle ) / sin( angle );
} else {
sc1 = 1 - t;
sc2 = t;
}
ret.w = sc1*a.w + sc2*b.w;
ret.w = sc1*a.w + sc2*b.w;
ret.x = sc1*a.x + sc2*b.x;
ret.y = sc1*a.y + sc2*b.y;
ret.z = sc1*a.z + sc2*b.z;
}
// compute optimal step size along descent vector d relative to
// a gradient related vector g
// stepsize = ( g' d ) / ( d' A d )
double GradientProjection::computeStepSize(
valarray<double> const & g, valarray<double> const & d) const {
COLA_ASSERT(g.size()==d.size());
valarray<double> Ad;
if(sparseQ) {
Ad.resize(g.size());
sparseQ->rightMultiply(d,Ad);
}
double const numerator = dotProd(g, d);
double denominator = 0;
for (unsigned i=0; i<g.size(); i++) {
double r = sparseQ ? Ad[i] : 0;
if(i<denseSize) { for (unsigned j=0; j<denseSize; j++) {
r += (*denseQ)[i*denseSize+j] * d[j];
} }
denominator += r * d[i];
}
if(denominator==0) {
return 0;
}
return numerator/(2.*denominator);
}
/* Function: penaltyForce
* Description: Computes the penalty force between two points.
* Input: p - Coordinates of first point
* pV - Velocity of first point
* I - Intersection point
* V - Velocity of Intersection point
* kH - K value for Hook's Law
* kD - K value for damping force
* Output: Penalty force vector
*/
point penaltyForce(point p, point pV, point I, point V, double kH, double kD)
{
double mag, length, dot;
point dist, hForce, dForce, pVel, vDiff, pForce;
// Initialize force computation variables
pDIFFERENCE(p, I, dist);
pDIFFERENCE(pV, V, vDiff);
dot = dotProd(vDiff, dist);
// Compute Hooks Force
pNORMALIZE(dist);
pMULTIPLY(dist, -(kH * length), hForce);
// Compute Damping Forces
mag = length;
pNORMALIZE(pV);
pMULTIPLY(pV, (kD * (dot/length)), dForce);
// Compute Penalty Force
pSUM(hForce, dForce, pForce);
return pForce;
} //end penaltyForce
void collideWithSphere(Particles& particles, real sphereRadius)
{
Particles::Positions& pos = particles.pos_;
Particles::Velocities& dv = particles.dv_;
Particles::Velocities& vel = particles.vel_;
const unsigned size = pos.size();
// collision with glass
for (unsigned i=0; i<size; ++i) {
Real3 distance = pos[i]+dv[i];
if (distance.sqrnorm() > sphereRadius*sphereRadius) {
Real3 d = normalize(distance);
//pos[i]= sphereRadius*d;
dv[i]=sphereRadius*d-pos[i];
// perfect bounce == 2 slip walls = 1
real k = 1.8;
vel[i]+= -k*dotProd(vel[i],d)*d;
// friction
vel[i]*=0.95;
}
}
}
/*!
* @brief Classify objects according to their size and color.
*
* @param pImgRaw Pointer to the image captured from the camera. Used to measure the color of images.
* @param pObj Pointer to the first object of the list of objects to be classified.
* @param thresholdWeight Minimum weight of an object to be considered.
* @param spotSize Size of the spot to debayer for measuring the color.
*/
void classifyObjects(uint8 const * const pImgRaw, struct object * const pObj, uint32 const thresholdWeight, t_index const spotSize)
{
inline int32 dotProd(uint8 const * const vec1, int32 const * const vec2)
{
return vec1[0] * vec2[0] + vec1[1] * vec2[1] + vec1[2] * vec2[2];
}
struct object * obj;
for (obj = pObj; obj != NULL; obj = obj->pNext)
{
obj->posWghtX /= obj->weight;
obj->posWghtY /= obj->weight;
if (obj->weight < thresholdWeight)
{
obj->classification = e_classification_tooSmall;
}
else
{
uint8 color[3];
int16 posX, posY;
int32 planes[3][3] = {
{ -3288, 6429, -4160 }, /* Between green and yellow and orange and red. */
{ -141, 7330, -7662 }, /* Between green and yellow. */
{ -782105, 575153, -64151 } /* Between orange and red. */
};
posX = 2 * obj->posWghtX - spotSize / 2;
posY = 2 * obj->posWghtY - spotSize / 2;
/* Move the spot inside the picture. */
if (posX < 0)
posX = 0;
else if (posX + spotSize >= WIDTH_CAPTURE)
posX = WIDTH_CAPTURE - spotSize;
if (posY < 0)
posY = 0;
else if (posY + spotSize >= HEIGHT_CAPTURE)
posY = HEIGHT_CAPTURE - spotSize;
OscVisDebayerSpot(pImgRaw, WIDTH_CAPTURE, HEIGHT_CAPTURE, data.enBayerOrder, posX, posY, spotSize, color);
obj->color.red = color[2];
obj->color.green = color[1];
obj->color.blue = color[0];
if (dotProd(color, planes[0]) > 255)
if (dotProd(color, planes[1]) > 255)
obj->classification = e_classification_sugusGreen;
else
obj->classification = e_classification_sugusYellow;
else
if (dotProd(color, planes[2]) > 255)
obj->classification = e_classification_sugusOrange;
else
obj->classification = e_classification_sugusRed;
// obj->classification = e_classification_unknown;
}
}
}
void CSysSolve::ModGramSchmidt(int i, vector<vector<su2double> > & Hsbg, vector<CSysVector> & w) {
bool Convergence = true;
int rank = MASTER_NODE;
#ifdef HAVE_MPI
int size;
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &size);
#endif
/*--- Parameter for reorthonormalization ---*/
static const su2double reorth = 0.98;
/*--- Get the norm of the vector being orthogonalized, and find the
threshold for re-orthogonalization ---*/
su2double nrm = dotProd(w[i+1], w[i+1]);
su2double thr = nrm*reorth;
/*--- The norm of w[i+1] < 0.0 or w[i+1] = NaN ---*/
if ((nrm <= 0.0) || (nrm != nrm)) Convergence = false;
/*--- Synchronization point to check the convergence of the solver ---*/
#ifdef HAVE_MPI
unsigned short *sbuf_conv = NULL, *rbuf_conv = NULL;
sbuf_conv = new unsigned short[1]; sbuf_conv[0] = 0;
rbuf_conv = new unsigned short[1]; rbuf_conv[0] = 0;
/*--- Convergence criteria ---*/
sbuf_conv[0] = Convergence;
SU2_MPI::Reduce(sbuf_conv, rbuf_conv, 1, MPI_UNSIGNED_SHORT, MPI_SUM, MASTER_NODE, MPI_COMM_WORLD);
/*-- Compute global convergence criteria in the master node --*/
sbuf_conv[0] = 0;
if (rank == MASTER_NODE) {
if (rbuf_conv[0] == size) sbuf_conv[0] = 1;
else sbuf_conv[0] = 0;
}
SU2_MPI::Bcast(sbuf_conv, 1, MPI_UNSIGNED_SHORT, MASTER_NODE, MPI_COMM_WORLD);
if (sbuf_conv[0] == 1) Convergence = true;
else Convergence = false;
delete [] sbuf_conv;
delete [] rbuf_conv;
#endif
if (!Convergence) {
if (rank == MASTER_NODE)
cout << "\n !!! Error: SU2 has diverged. Now exiting... !!! \n" << endl;
#ifndef HAVE_MPI
exit(EXIT_DIVERGENCE);
#else
MPI_Abort(MPI_COMM_WORLD,1);
#endif
}
/*--- Begin main Gram-Schmidt loop ---*/
for (int k = 0; k < i+1; k++) {
su2double prod = dotProd(w[i+1], w[k]);
Hsbg[k][i] = prod;
w[i+1].Plus_AX(-prod, w[k]);
/*--- Check if reorthogonalization is necessary ---*/
if (prod*prod > thr) {
prod = dotProd(w[i+1], w[k]);
Hsbg[k][i] += prod;
w[i+1].Plus_AX(-prod, w[k]);
}
/*--- Update the norm and check its size ---*/
nrm -= Hsbg[k][i]*Hsbg[k][i];
if (nrm < 0.0) nrm = 0.0;
thr = nrm*reorth;
}
/*--- Test the resulting vector ---*/
nrm = w[i+1].norm();
Hsbg[i+1][i] = nrm;
// if (nrm <= 0.0) {
//
// /*--- w[i+1] is a linear combination of the w[0:i] ---*/
//
// cerr << "The FGMRES linear solver has diverged" << endl;
//#ifndef HAVE_MPI
// exit(EXIT_DIVERGENCE);
//#else
// MPI_Abort(MPI_COMM_WORLD,1);
// MPI_Finalize();
//#endif
//
// }
/*--- Scale the resulting vector ---*/
w[i+1] /= nrm;
}
unsigned long CSysSolve::BCGSTAB_LinSolver(const CSysVector & b, CSysVector & x, CMatrixVectorProduct & mat_vec,
CPreconditioner & precond, su2double tol, unsigned long m, su2double *residual, bool monitoring) {
int rank = 0;
#ifdef HAVE_MPI
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
#endif
/*--- Check the subspace size ---*/
if (m < 1) {
if (rank == MASTER_NODE) cerr << "CSysSolve::BCGSTAB: illegal value for subspace size, m = " << m << endl;
#ifndef HAVE_MPI
exit(EXIT_FAILURE);
#else
MPI_Abort(MPI_COMM_WORLD,1);
MPI_Finalize();
#endif
}
CSysVector r(b);
CSysVector r_0(b);
CSysVector p(b);
CSysVector v(b);
CSysVector s(b);
CSysVector t(b);
CSysVector phat(b);
CSysVector shat(b);
CSysVector A_x(b);
/*--- Calculate the initial residual, compute norm, and check if system is already solved ---*/
mat_vec(x, A_x);
r -= A_x; r_0 = r; // recall, r holds b initially
su2double norm_r = r.norm();
su2double norm0 = b.norm();
if ( (norm_r < tol*norm0) || (norm_r < eps) ) {
if (rank == MASTER_NODE) cout << "CSysSolve::BCGSTAB(): system solved by initial guess." << endl;
return 0;
}
/*--- Initialization ---*/
su2double alpha = 1.0, beta = 1.0, omega = 1.0, rho = 1.0, rho_prime = 1.0;
/*--- Set the norm to the initial initial residual value ---*/
norm0 = norm_r;
/*--- Output header information including initial residual ---*/
int i = 0;
if ((monitoring) && (rank == MASTER_NODE)) {
WriteHeader("BCGSTAB", tol, norm_r);
WriteHistory(i, norm_r, norm0);
}
/*--- Loop over all search directions ---*/
for (i = 0; i < (int)m; i++) {
/*--- Compute rho_prime ---*/
rho_prime = rho;
/*--- Compute rho_i ---*/
rho = dotProd(r, r_0);
/*--- Compute beta ---*/
beta = (rho / rho_prime) * (alpha /omega);
/*--- p_{i} = r_{i-1} + beta * p_{i-1} - beta * omega * v_{i-1} ---*/
su2double beta_omega = -beta*omega;
p.Equals_AX_Plus_BY(beta, p, beta_omega, v);
p.Plus_AX(1.0, r);
/*--- Preconditioning step ---*/
precond(p, phat);
mat_vec(phat, v);
/*--- Calculate step-length alpha ---*/
su2double r_0_v = dotProd(r_0, v);
alpha = rho / r_0_v;
/*--- s_{i} = r_{i-1} - alpha * v_{i} ---*/
s.Equals_AX_Plus_BY(1.0, r, -alpha, v);
/*--- Preconditioning step ---*/
precond(s, shat);
mat_vec(shat, t);
/*--- Calculate step-length omega ---*/
omega = dotProd(t, s) / dotProd(t, t);
/*--- Update solution and residual: ---*/
x.Plus_AX(alpha, phat); x.Plus_AX(omega, shat);
r.Equals_AX_Plus_BY(1.0, s, -omega, t);
/*--- Check if solution has converged, else output the relative residual if necessary ---*/
norm_r = r.norm();
if (norm_r < tol*norm0) break;
if (((monitoring) && (rank == MASTER_NODE)) && ((i+1) % 50 == 0) && (rank == MASTER_NODE)) WriteHistory(i+1, norm_r, norm0);
}
if ((monitoring) && (rank == MASTER_NODE)) {
cout << "# BCGSTAB final (true) residual:" << endl;
cout << "# Iteration = " << i << ": |res|/|res0| = " << norm_r/norm0 << ".\n" << endl;
}
// /*--- Recalculate final residual (this should be optional) ---*/
// mat_vec(x, A_x);
// r = b; r -= A_x;
// su2double true_res = r.norm();
//
// if ((fabs(true_res - norm_r) > tol*10.0) && (rank == MASTER_NODE)) {
// cout << "# WARNING in CSysSolve::BCGSTAB(): " << endl;
// cout << "# true residual norm and calculated residual norm do not agree." << endl;
// cout << "# true_res - calc_res = " << true_res <<" "<< norm_r << endl;
// }
(*residual) = norm_r;
return i;
}
unsigned long CSysSolve::CG_LinSolver(const CSysVector & b, CSysVector & x, CMatrixVectorProduct & mat_vec,
CPreconditioner & precond, su2double tol, unsigned long m, bool monitoring) {
int rank = 0;
#ifdef HAVE_MPI
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
#endif
/*--- Check the subspace size ---*/
if (m < 1) {
if (rank == MASTER_NODE) cerr << "CSysSolve::ConjugateGradient: illegal value for subspace size, m = " << m << endl;
#ifndef HAVE_MPI
exit(EXIT_FAILURE);
#else
MPI_Abort(MPI_COMM_WORLD,1);
MPI_Finalize();
#endif
}
CSysVector r(b);
CSysVector A_p(b);
/*--- Calculate the initial residual, compute norm, and check if system is already solved ---*/
mat_vec(x, A_p);
r -= A_p; // recall, r holds b initially
su2double norm_r = r.norm();
su2double norm0 = b.norm();
if ( (norm_r < tol*norm0) || (norm_r < eps) ) {
if (rank == MASTER_NODE) cout << "CSysSolve::ConjugateGradient(): system solved by initial guess." << endl;
return 0;
}
su2double alpha, beta, r_dot_z;
CSysVector z(r);
precond(r, z);
CSysVector p(z);
/*--- Set the norm to the initial initial residual value ---*/
norm0 = norm_r;
/*--- Output header information including initial residual ---*/
int i = 0;
if ((monitoring) && (rank == MASTER_NODE)) {
WriteHeader("CG", tol, norm_r);
WriteHistory(i, norm_r, norm0);
}
/*--- Loop over all search directions ---*/
for (i = 0; i < (int)m; i++) {
/*--- Apply matrix to p to build Krylov subspace ---*/
mat_vec(p, A_p);
/*--- Calculate step-length alpha ---*/
r_dot_z = dotProd(r, z);
alpha = dotProd(A_p, p);
alpha = r_dot_z / alpha;
/*--- Update solution and residual: ---*/
x.Plus_AX(alpha, p);
r.Plus_AX(-alpha, A_p);
/*--- Check if solution has converged, else output the relative residual if necessary ---*/
norm_r = r.norm();
if (norm_r < tol*norm0) break;
if (((monitoring) && (rank == MASTER_NODE)) && ((i+1) % 5 == 0)) WriteHistory(i+1, norm_r, norm0);
precond(r, z);
/*--- Calculate Gram-Schmidt coefficient beta,
beta = dotProd(r_{i+1}, z_{i+1}) / dotProd(r_{i}, z_{i}) ---*/
beta = 1.0 / r_dot_z;
r_dot_z = dotProd(r, z);
beta *= r_dot_z;
/*--- Gram-Schmidt orthogonalization; p = beta *p + z ---*/
p.Equals_AX_Plus_BY(beta, p, 1.0, z);
}
if ((monitoring) && (rank == MASTER_NODE)) {
cout << "# Conjugate Gradient final (true) residual:" << endl;
cout << "# Iteration = " << i << ": |res|/|res0| = " << norm_r/norm0 << ".\n" << endl;
}
// /*--- Recalculate final residual (this should be optional) ---*/
// mat_vec(x, A_p);
// r = b;
// r -= A_p;
// su2double true_res = r.norm();
//
// if (fabs(true_res - norm_r) > tol*10.0) {
// if (rank == MASTER_NODE) {
// cout << "# WARNING in CSysSolve::ConjugateGradient(): " << endl;
// cout << "# true residual norm and calculated residual norm do not agree." << endl;
// cout << "# true_res - calc_res = " << true_res - norm_r << endl;
// }
// }
return i;
}
/* -------------------------------------
* FindH
* IN
* func: Function to be minimized.
* nRow: Number of rows (length) of x.
* N_max: Maximum number of CG iterates.
* TOL: Minimum size for gradient of f.
* OUT
* x: Local minimum of func.
* -------------------------------------
*/
double * findHSLM(double (*func)(double*, int), double *x,
double** s, double** y,
int nRow, int m, int k, int N_max){
double *q, *r, *alpha, *rho, *Bd, *r_cg, *d, *z, *r_new;
double beta, beta_cg, alpha_cg, epsilon;
int i, state, j;
// Calculate gradient.
r = q = gradCentralDiff(func, x, nRow);
// Initialize variables
alpha = (double*) malloc(nRow * sizeof(double));
rho = (double*) malloc(nRow * sizeof(double));
state = min(k, m);
// Fill in rho
for(i = 0; i < state; i++){
rho[i] = 1 / (dotProd(y[i], s[i], nRow));
}
// First Loop
for(i = (state - 1); i > 0; i--){
alpha[i] = rho[i] * dotProd(s[i], q, nRow);
q = vSum(q, vProd(y[i], -alpha[i], nRow), nRow);
}
/*
* -----------------------------------
* ########### CG Iteration ##########
* -----------------------------------
* Outputs: r
*/
// Initialize: epsilon, d, r_cg, z
epsilon = min(.5, sqrt(norm(q, nRow))) * norm(q, nRow);
d = vProd(q, 1, nRow);
r_cg = vProd(q, 1, nRow);
z = vProd(q, 0, nRow);
for(j = 0; j < N_max; j++){
Bd = hessCentralDiff(func, x, d, nRow);
// Check if d'Bd <= 0 i.e. d is a descent direction.
if(dotProd(d, Bd, nRow) <= 0){
if(j == 0){
r = d;
break;
}else{
r = z;
break;
}
}
// alpha_j = rj'rj/d_j'Bd_j
alpha_cg = dotProd(r_cg, r_cg, nRow) / dotProd(d, Bd, nRow);
// z_{j+1} = z_j + alpha_j*d_j
z = vSum(z, vProd(d, alpha_cg, nRow), nRow);
// r_{j+1} = r_j + alpha_j*B_kd_j
r_new = vSum(r_cg, vProd(Bd, alpha_cg, nRow), nRow);
if(norm(r_new, nRow) < epsilon){
r = z;
break;
}
// Update beta, d, r_cg.
beta_cg = dotProd(r_new, r_new, nRow) / dotProd(r_cg, r_cg, nRow);
d = vSum(vProd(r_new, -1, nRow),
vProd(d, beta_cg, nRow), nRow);
r_cg = r_new;
}
/* -----------------------------------
* ######### CG Iteration End ########
* -----------------------------------
*/
// Second Loop
for(i = 0; i < state; i ++){
beta = rho[i] * dotProd(y[i], r, nRow);
r = vSum(r, vProd(s[i], (alpha[i] - beta), nRow), nRow);
}
// Memory release.
free(alpha);
free(rho);
// Return result.
return r;
}
/* -------------------------------------
* LBFGS
* IN
* func: Function to be minimized.
* nRow: Number of rows (length) of x.
* N_max: Maximum number of CG iterates.
* TOL: Minimum size for gradient of f.
* OUT
* x: Local minimum of func.
* -------------------------------------
*/
double * SLM_LBFGS(double (* func)(double*, int),
int nRow, int m, double TOL, int N_max, int verbose){
// Variable declaration.
double **s, **y;
double *x, *grad, *p, *x_new, *grad_new;
double alpha, norm_grad0;
int i, k, MAX_ITER, exploredDataPoints;
// Space allocation.
x = (double *)malloc(nRow * sizeof(double));
s = (double **)malloc((nRow*m) * sizeof(double));
y = (double **)malloc((nRow*m) * sizeof(double));
// Initialize x.
for(i = 0; i < nRow; i++){
x[i] = ((double) rand() / INT_MAX) ;
}
// Stochastic Mode
if(stocMode){
exploredDataPoints = 0;
//printf("\nRUNNING STOCASTIC MODE\n");
SAMPLE = rand() % (int)(MAX_FILE_ROWS * sampProp);
create_sample(0);
exploredDataPoints += SAMPLE;
}
// Until Convergence or MAX_ITER.
MAX_ITER = 6e6;
grad = gradCentralDiff(func, x, nRow);
// Update s, y.
k = 0;
// Initial norm of gradient.
norm_grad0 = norm(grad, nRow);
while(norm(grad, nRow) > TOL*(1 + norm_grad0) && ((run_logistic*exploredDataPoints + ((1 - run_logistic)*k)) < MAX_ITER)){
if(stocMode){
printf("\nRUNNING STOCASTIC MODE\n");
SAMPLE = rand() % (int)(MAX_FILE_ROWS * sampProp);
create_sample(k);
exploredDataPoints += SAMPLE;
}
// p = -Hgrad(f)
if(k > 0){
p = vProd(findHSLM(func, x, s,
y, nRow, m,
k, N_max),
-1, nRow);
}else{
p = vProd(grad, -1, nRow);
}
// Alpha that statifies Wolfe conditions.
alpha = backTrack(func, x, p, nRow, verbose);
x_new = vSum(x, vProd(p, alpha, nRow), nRow);
//imprimeTit("X_NEW");
//imprimeMatriz(x_new, 1, nRow);
grad_new = gradCentralDiff(func, x_new, nRow);
//imprimeTit("GRAD_NEW");
//imprimeMatriz(grad_new, 1, nRow);
// Update s, y.
updateSY(s, y, vProd(p, alpha, nRow),
vSum(grad_new, vProd(grad, -1, nRow), nRow), m, k);
// ---------------- PRINT ------------------- //
if(verbose){
if(stocMode){
printf("\n ITER = %d; f(x) = %.10e ; "
"||x|| = %.10e ; ||grad|| = %.10e ; "
"||p|| = %.10e ; sTy = %.10e ; "
"alpha = %.10e; explored data points = %d; precision = %fl ",
k,
func(x, nRow),
norm(x, nRow),
norm(grad, nRow),
norm(p, nRow),
dotProd(s[(int)min(k , (m - 1))],
y[(int)min(k , (m - 1))], nRow),
alpha,
exploredDataPoints,
class_precision(x, nRow, 0));
}else{
printf("\n ITER = %d; f(x) = %.10e ; "
"||x|| = %.10e ; ||grad|| = %.10e ; "
"||p|| = %.10e ; sTy = %.10e ; "
"alpha = %.10e",
k,
func(x, nRow),
norm(x, nRow),
norm(grad, nRow),
norm(p, nRow),
dotProd(s[(int)min(k , (m - 1))],
y[(int)min(k , (m - 1))], nRow),
alpha);
}
}
// ---------------- PRINT ------------------- //y
// Update k, x, grad.
x = x_new;
grad = grad_new;
k = k + 1;
}
free(grad);
free(s);
free(y);
return x;
}
void ModelMFWt::hogTrain(const Data &data, Model &bestModel,
std::unordered_set<int>& invalidUsers,
std::unordered_set<int>& invalidItems) {
//copy passed known factors
//uFac = data.origUFac;
//iFac = data.origIFac;
std::cout << "\nModelMFWt::hogTrain trainSeed: " << trainSeed;
int nnz = data.trainNNZ;
std::cout << "\nObj b4 svd: " << objective(data, invalidUsers, invalidItems)
<< " Train RMSE: " << RMSE(data.trainMat)
<< " Train nnz: " << nnz << std::endl;
std::chrono::time_point<std::chrono::system_clock> startSVD, endSVD;
startSVD = std::chrono::system_clock::now();
//initialization with svd of the passed matrix
//svdFrmSvdlibCSR(data.trainMat, facDim, uFac, iFac, false);
endSVD = std::chrono::system_clock::now();
std::chrono::duration<double> durationSVD = (endSVD - startSVD) ;
std::cout << "\nsvd duration: " << durationSVD.count();
int iter, bestIter = -1;
double bestObj, prevObj;
double bestValRMSE, prevValRMSE;
gk_csr_t *trainMat = data.trainMat;
//vector to hold user gradient accumulation
std::vector<std::vector<double>> uGradsAcc (nUsers,
std::vector<double>(facDim,0));
//vector to hold item gradient accumulation
std::vector<std::vector<double>> iGradsAcc (nItems,
std::vector<double>(facDim,0));
//std::cout << "\nNNZ = " << nnz;
prevObj = objective(data, invalidUsers, invalidItems);
bestObj = prevObj;
std::cout << "\nObj aftr svd: " << prevObj << " Train RMSE: " << RMSE(data.trainMat);
std::chrono::time_point<std::chrono::system_clock> start, end;
std::chrono::duration<double> duration;
std::vector<std::unordered_set<int>> uISet(nUsers);
genStats(trainMat, uISet, std::to_string(trainSeed));
getInvalidUsersItems(trainMat, uISet, invalidUsers, invalidItems);
std::unordered_set<int> headItems = getHeadItems(trainMat, 0.5);
std::unordered_set<int> headUsers = getHeadItems(trainMat, 0.5);
double lambda0 = 0.8;
double lambda1 = 1.0 - lambda0;
//random engine
std::mt19937 mt(trainSeed);
//get user-item ratings from training data
auto uiRatings = getUIRatings(trainMat, invalidUsers, invalidItems);
//index to above uiRatings pair
std::vector<size_t> uiRatingInds(uiRatings.size());
std::iota(uiRatingInds.begin(), uiRatingInds.end(), 0);
std::cout << "\nTrain NNZ after removing invalid users and items: "
<< uiRatings.size() << std::endl;
double subIterDuration = 0;
for (iter = 0; iter < maxIter; iter++) {
//shuffle the user item rating indexes
std::shuffle(uiRatingInds.begin(), uiRatingInds.end(), mt);
start = std::chrono::system_clock::now();
const int indsSz = uiRatingInds.size();
#pragma omp parallel for
for (int k = 0; k < indsSz; k++) {
auto ind = uiRatingInds[k];
//get user, item and rating
int u = std::get<0>(uiRatings[ind]);
int item = std::get<1>(uiRatings[ind]);
float itemRat = std::get<2>(uiRatings[ind]);
double r_ui_est = dotProd(uFac[u], iFac[item], facDim);
double diff = itemRat - r_ui_est;
if (headItems.find(item) != headItems.end()) {
diff = diff*lambda0;
} else {
diff = diff*(lambda0 + lambda1);
}
//update user
for (int i = 0; i < facDim; i++) {
uFac[u][i] -= learnRate*(-2.0*diff*iFac[item][i] + 2.0*uReg*uFac[u][i]);
}
r_ui_est = dotProd(uFac[u], iFac[item], facDim);
diff = itemRat - r_ui_est;
if (headItems.find(item) != headItems.end()) {
diff = diff*lambda0;
} else {
diff = diff*(lambda0 + lambda1);
}
//update item
for (int i = 0; i < facDim; i++) {
iFac[item][i] -= learnRate*(-2.0*diff*uFac[u][i] + 2.0*iReg*iFac[item][i]);
}
}
end = std::chrono::system_clock::now();
duration = end - start;
subIterDuration = duration.count();
//check objective
if (iter % OBJ_ITER == 0 || iter == maxIter-1) {
if (isTerminateModel(bestModel, data, iter, bestIter, bestObj, prevObj,
invalidUsers, invalidItems)) {
break;
}
if (iter % 50 == 0) {
std::cout << "ModelMFWt::train trainSeed: " << trainSeed
<< " Iter: " << iter << " Objective: " << std::scientific << prevObj
<< " Train RMSE: " << RMSE(data.trainMat, invalidUsers, invalidItems)
<< " Val RMSE: " << prevValRMSE
<< " sub duration: " << subIterDuration
<< std::endl;
}
if (iter % 500 == 0 || iter == maxIter - 1) {
std::string modelFName = std::string(data.prefix);
bestModel.saveFacs(modelFName);
}
}
}
//save best model found till now
std::string modelFName = std::string(data.prefix);
bestModel.saveFacs(modelFName);
std::cout << "\nBest model validation RMSE: " << bestModel.RMSE(data.valMat,
invalidUsers, invalidItems);
}
