int main()
{
double a[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0,
14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0,
27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0};
double b[39];
double c[39];
double d[39];
//printf("%f \n", computeScalarSum(a, 1, 38));
// computeSquareVec(a, b, 1, 32);
// computeDiffVec(a, b, c, 1, 32);
scalarDiffVec(&a[1], 1.0, &b[1], 34);
for(int i = 0; i < 39; i++) {
printf(" a %f b%f \n", a[i], b[i]);
}
printf("\nNEXT\n");
divByScalarVec(b, 2.0,25);
initVec(c, 0.0, 39);
initVec(&d[1], 4.0, 38);
initVec(c, 1.0, 5);
for(int i = 0; i < 39; i++) {
printf(" a %f b%f c %f d %f \n", a[i], b[i], c[i], d[i]);
}
}
double ESFinder::calcES(std::shared_ptr<const Arena> arena, vec &psi, const std::vector<vec> &outprj, std::size_t &counter, const bool initVector) {
if (initVector) {
initVec(psi, *arena);
}
const ITP itp(arena);
double diff = 0.;
double energy = 0.;
counter = 0;
do {
counter += 1;
assert(counter < 1e3);
itp.propagate(psi);
if (outprj.size() > 0) {
const double dx = arena->getStepSizeX();
VecTools::projectOut(psi, outprj, dx);
}
double energy2 = itp.calcEnergy(psi);
diff = std::abs(energy - energy2);
energy = energy2;
} while (diff > 1e-10);
return energy;
}
void AdaptiveManifoldFilterN::computeClusters(Mat1b& cluster, Mat1b& cluster_minus, Mat1b& cluster_plus)
{
Mat difEtaSrc;
{
vector<Mat> eta_difCn(jointCnNum);
for (int i = 0; i < jointCnNum; i++)
subtract(jointCn[i], etaFull[i], eta_difCn[i]);
merge(eta_difCn, difEtaSrc);
difEtaSrc = difEtaSrc.reshape(1, (int)difEtaSrc.total());
CV_DbgAssert(difEtaSrc.cols == jointCnNum);
}
Mat1f initVec(1, jointCnNum);
if (useRNG)
{
rnd.fill(initVec, RNG::UNIFORM, -0.5, 0.5);
}
else
{
for (int i = 0; i < (int)initVec.total(); i++)
initVec(0, i) = (i % 2 == 0) ? 0.5f : -0.5f;
}
Mat1f eigenVec(1, jointCnNum);
computeEigenVector(difEtaSrc, cluster, eigenVec, num_pca_iterations_, initVec);
Mat1f difOreientation;
gemm(difEtaSrc, eigenVec, 1, noArray(), 0, difOreientation, GEMM_2_T);
difOreientation = difOreientation.reshape(1, srcSize.height);
CV_DbgAssert(difOreientation.size() == srcSize);
compare(difOreientation, 0, cluster_minus, CMP_LT);
bitwise_and(cluster_minus, cluster, cluster_minus);
compare(difOreientation, 0, cluster_plus, CMP_GE);
bitwise_and(cluster_plus, cluster, cluster_plus);
}
void AdaptiveManifoldFilterN::computeClusters(Mat1b& cluster, Mat1b& cluster_minus, Mat1b& cluster_plus)
{
Mat1f difOreientation;
if (jointCnNum > 1)
{
Mat1f initVec(1, jointCnNum);
if (useRNG)
{
rnd.fill(initVec, RNG::UNIFORM, -0.5, 0.5);
}
else
{
for (int i = 0; i < (int)initVec.total(); i++)
initVec(0, i) = (i % 2 == 0) ? 0.5f : -0.5f;
}
vector<Mat> difEtaSrc(jointCnNum);
for (int i = 0; i < jointCnNum; i++)
subtract(jointCn[i], etaFull[i], difEtaSrc[i]);
Mat1f eigenVec(1, jointCnNum);
computeEigenVector(difEtaSrc, cluster, eigenVec, num_pca_iterations_, initVec);
computeOrientation(difEtaSrc, eigenVec, difOreientation);
CV_DbgAssert(difOreientation.size() == srcSize);
}
else
{
subtract(jointCn[0], etaFull[0], difOreientation);
}
compare(difOreientation, 0, cluster_minus, CMP_LT);
bitwise_and(cluster_minus, cluster, cluster_minus);
compare(difOreientation, 0, cluster_plus, CMP_GE);
bitwise_and(cluster_plus, cluster, cluster_plus);
}
static int start(effect * e)
{
displaywalltv_t * priv = e->priv;
priv->state = 1;
priv->cx = e->video_width / 2;
priv->cy = e->video_height / 2;
priv->vecx = malloc(sizeof(int) * e->video_area);
priv->vecy = malloc(sizeof(int) * e->video_area);
if(priv->vecx == NULL || priv->vecy == NULL)
return -1;
initVec(e);
return 0;
}
const SUCCESS PhysicalObject::collided(PhysicalObject &object, const Radians angle)
{
Vector initVec(getVelocity()-object.getVelocity());
Mag_t<double> initVel(initVec.mag());
Radians initTheta(initVec.theta());
if(fabs(getX(initVel, initTheta-angle)) < REST_THRESHOLD)
{
setVelocity(getVelocity()+initVec/2.0);
object.setVelocity(object.getVelocity()-initVec/2.0);
antigravitate(object);
object.antigravitate(*this);
return SUCCEEDED;
}
// Frame of reference with the object at rest with this approaching it with an angle of 0
Point<double> p2(Vector(Mag_t<double>(2.0*getMass()*getX(initVel, initTheta-angle)/(getMass()+object.getMass())), initTheta-angle)); // Gets the velocity of 2 based on elastic collision equation
Point<double> p1((initVel*getMass()-p2.x()*object.getMass())/getMass(), -p2.y()*object.getMass()/getMass()); // Gets the velocity of 1 based off of elastic collision rulez
if(fabs(p1.x()) < REST_THRESHOLD && fabs(p2.x()) < REST_THRESHOLD)
{
p1.x(0.0);
p2.x(0.0);
}
Vector v1(p1);
Vector v2(p2);
v1.theta(-v1.theta()+initTheta);
v2.theta(angle);
v1 += object.getVelocity();
v2 += object.getVelocity();
Point<double> dif(object.getPosition()-*position);
while(getCollider()->collides(*object.getCollider()))
acceleration.set(*position-dif/pythagoras<double>(dif));
setVelocity(v1);
object.setVelocity(v2);
return SUCCEEDED;
}
void TestClusteringPopulationAnalyzer::testEvaluatePopulationTwoThreads(void)
{
Graph graph;
// Initialize graph
graph.addVertex(Vertex(0));
graph.addVertex(Vertex(1));
graph.addVertex(Vertex(2));
graph.addVertex(Vertex(3));
graph.addVertex(Vertex(4));
graph.addVertex(Vertex(5));
graph.addVertex(Vertex(6));
graph.addVertex(Vertex(7));
graph.addEdge(Vertex(0), Vertex(1), 0.2);
graph.addEdge(Vertex(1), Vertex(2), 0.3);
graph.addEdge(Vertex(1), Vertex(3), 0.5);
graph.addEdge(Vertex(2), Vertex(3), 0.4);
graph.addEdge(Vertex(3), Vertex(4), 0.2);
graph.addEdge(Vertex(4), Vertex(5), 0.5);
graph.addEdge(Vertex(4), Vertex(6), 0.6);
graph.addEdge(Vertex(5), Vertex(6), 0.7);
graph.addEdge(Vertex(6), Vertex(7), 0.1);
IntegerEncodingInitializer initVec(&graph);
std::vector<PopulationMember<IntegerVectorEncoding, double>> vecPop;
for (size_t i = 0; i < populationSize; i++)
{
PopulationMember<IntegerVectorEncoding, double> member;
member.fitnessValue = 0.0;
member.modified = true;
member.populationEncoding = initVec.getRandomSolution();
vecPop.push_back(member);
}
ClusteringPopulationAnalyzer<FitnessAnalyzer, std::vector<PopulationMember<IntegerVectorEncoding, double>>> analyzer(&graph, &vecPop, 2);
analyzer.evaluatePopulation();
FitnessAnalyzer singleAnalyzer;
for (auto& e : vecPop)
{
CPPUNIT_ASSERT(singleAnalyzer.analyze(&e.populationEncoding, &graph) == e.fitnessValue);
}
}
