// -----------------------------------------------------------------------------
//
// Purpose and Method:
// Inputs:
// Outputs:
// Dependencies:
// Restrictions and Caveats:
//
// -----------------------------------------------------------------------------
cv::Mat
Affine2DFactorizedProblem::computeSigmaMatrix
(
cv::Mat& params
)
{
assert(params.rows == 6);
assert(params.cols == 1);
MAT_TYPE tx = params.at<MAT_TYPE>(0, 0);
MAT_TYPE ty = params.at<MAT_TYPE>(1, 0);
MAT_TYPE a = params.at<MAT_TYPE>(2, 0);
MAT_TYPE b = params.at<MAT_TYPE>(3, 0);
MAT_TYPE c = params.at<MAT_TYPE>(4, 0);
MAT_TYPE d = params.at<MAT_TYPE>(5, 0);
cv::Mat A = (cv::Mat_<MAT_TYPE>(2,2) << a, c,
b, d);
cv::Mat invA = A.inv();
cv::Mat Sigma = cv::Mat::zeros(6, 6, cv::DataType<MAT_TYPE>::type);
cv::Mat Sigma_roi = Sigma(cv::Range(0,2), cv::Range(0,2));
invA.copyTo(Sigma_roi);
Sigma_roi = Sigma(cv::Range(2,4), cv::Range(2,4));
invA.copyTo(Sigma_roi);
Sigma_roi = Sigma(cv::Range(4,6), cv::Range(4,6));
invA.copyTo(Sigma_roi);
return Sigma;
};
int main(int argc, char *argv[]) {
std::cout << std::endl;
std::cout << " Abstract Binary Search Algorithm " << std::endl;
std::cout << "Copyright (c) 2015+ Oscar Riveros. All rights reserved." << std::endl;
std::cout << std::endl;
if (argc != 3) {
std::cerr << "Usage: " << argv[0] << " universe<path> log<0|1>" << std::endl;
return EXIT_FAILURE;
}
nInt log = nInt(std::atoi(argv[2]));
std::chrono::time_point<std::chrono::system_clock> start, end;
Int t;
Int u;
std::vector<Int> U;
try {
std::ifstream file(argv[1], std::ifstream::in);
while (file.good()) {
file >> t;
while (file >> u) {
U.push_back(u);
}
}
std::sort(U.begin(), U.end());
} catch (...) {
std::cout << "Something is wrong..." << std::endl;
exit(EXIT_FAILURE);
}
std::cout << std::endl;
std::cout << "(<" << t << ", " << Sigma(U) - t << ">, [ ";
for (auto u : U) std::cout << u << " ";
std::cout << "])" << std::endl;
std::cout << std::endl;
start = std::chrono::system_clock::now();
auto S = ABS(U, t, log);
end = std::chrono::system_clock::now();
std::cout << std::endl;
std::cout << "(<" << Sigma(S) << ", " << Sigma(U) - Sigma(S) << ">, [ ";
for (auto u : S) std::cout << u << " ";
std::cout << "])" << std::endl;
std::cout << std::endl;
std::chrono::duration<double> seconds = end - start;
std::cout << "Time : " << seconds.count() << std::endl;
std::cout << "Cycles : " << cycles << std::endl;
std::cout << std::endl;
return EXIT_SUCCESS;
}
// Normalized score
static double Sigma_N(const MSA &msa, unsigned SeqIndex1, unsigned SeqIndex2)
{
unsigned Length = UINT_MAX;
double Score = Sigma(msa, SeqIndex1, SeqIndex2, &Length);
double RandomScore = Length*BLOSUM62_Expected;
return Score - RandomScore;
}
matrix network::sigma_vector(const matrix &Z_vector,int layer) const
{
int nraws=layers_sizes[layer];
matrix Sigma(1,nraws);
for (int j=0; j<nraws; j++)
Sigma.put_val(j,0)=sigmoid(Z_vector.get_val(j,0));
return Sigma;
}
double ZMMCIS::logpri()const {
const SpdMatrix & Sigma(model_->Sigma());
double ans = 0;
for (int i = 0; i < Sigma.nrow(); ++i) {
if (samplers_[i].sigma_max() > 0) {
ans += priors_[i]->logp(1.0 / Sigma(i, i));
}
}
return ans;
}
std::vector<Int> ABS(std::vector<Int> U, Int t, nInt log) {
std::vector<Int> S(U);
std::sort(U.begin(), U.end());
t = iMin(t, Sigma(U) - t);
if (!t) return U;
if (std::binary_search(U.begin(), U.end(), t)) return std::vector<Int>({t});
for (nInt k = 0; k < U.size(); k++) {
if (U.at(k) > t) {
U.erase(U.begin() + k, U.end());
}
}
if (U.size() == 1) return U;
Int L = iMin(iPow(U.size(), 4), iPow(2, U.size() - 1));
Int i, j, s, d, n, e = 0;
Int k = 0;
Int l = iPow(2, U.size() - 1);
while (k < L) {
i = k;
j = l;
e = (i + j) / 2;
while (i < j) {
cycles++;
n = (i + j) / 2;
S = Phi(U, n + e);
s = Sigma(S);
d = s - t;
if (log) {
std::cout << "(<" << Sigma(S) << ", " << Sigma(U) - Sigma(S) << ">, [ ";
for (auto u : S) std::cout << u << " ";
std::cout << "])" << std::endl;
}
if (d == 0) return S;
if (d < 0) i = n + 1;
if (d > 0) j = n;
e++;
}
k++;
l++;
}
return std::vector<Int>();
}
void MHMM_EM::CheckMatrixSize(const MatrixXf& data) {
size_t O = (size_t)data.rows();
size_t Q = (size_t)Transmat.rows();
size_t M = (size_t)Mixmat.cols();
//check mu
if (isempty<MultiD>(Mu))
throw std::runtime_error("Mu matrix must be not empty");
if((size_t)Mu.rows() != M )
throw std::runtime_error("Wrong size Mu matrix");
//check sigma
if (isempty<MultiD>(Sigma))
throw std::runtime_error("Mu matrix must be not empty");
if((size_t)Sigma.cols() != M || (size_t)Sigma.rows() != Q)
throw std::runtime_error("Wrong size Mu matrix");
if((size_t)Sigma(0,0).cols() != O || (size_t)Sigma(0,0).rows() != O)
throw std::runtime_error("Wrong size Sigma matrix");
//check prior
if (isempty<MatrixXf>(Prior))
throw std::runtime_error("Prior matrix must be not empty");
/*
O - Dimension of the data point
M - Number of mixture components used to model the data point distribution
Q - Number of states in an HMM
prior0 has size Q x 1
transmat0 has size Q x Q
mu0 has size O x Q x M
Sigma0 has size O x O x Q x M
mixmat0 has size Q x M; */
}
double DiskSCFPotentialEval(double R,double Z, double phi,
double t,
struct potentialArg * potentialArgs){
double * args= potentialArgs->args;
//Get args
int nsigma_args= (int) *args;
double * Sigma_args= args+1;
double * hz_args= args+1+nsigma_args;
//Calculate Rforce
double r= sqrt( R * R + Z * Z );
return Sigma(r,Sigma_args) * Hz(Z,hz_args);
}
TEST(ProbDistributionsLkjCorr,testHalf) {
unsigned int K = 4;
Eigen::MatrixXd Sigma(K,K);
Sigma.setConstant(0.5);
Sigma.diagonal().setOnes();
double eta = rand() / double(RAND_MAX) + 0.5;
double f = stan::prob::do_lkj_constant(eta, K);
EXPECT_FLOAT_EQ(f + (eta - 1.0) * log(0.3125), stan::prob::lkj_corr_log(Sigma, eta));
eta = 1.0;
f = stan::prob::do_lkj_constant(eta, K);
EXPECT_FLOAT_EQ(f, stan::prob::lkj_corr_log(Sigma, eta));
}
Vec ArModel::simulate(int n) const {
int p = number_of_lags();
Vec acf = autocovariance(p);
Spd Sigma(p);
Sigma.diag() = acf[0];
for(int i = 1; i < p; ++i) {
Sigma.subdiag(i) = acf[i];
Sigma.superdiag(i) = acf[i];
}
Vec zero(p, 0.0);
Vec y0 = rmvn(zero, Sigma);
return simulate(n, y0);
}
// -----------------------------------------------------------------------------
//
// Purpose and Method:
// Inputs:
// Outputs:
// Dependencies:
// Restrictions and Caveats:
//
// -----------------------------------------------------------------------------
cv::Mat
Homography2DFactorizedProblem::computeSigmaMatrix
(
cv::Mat& params
)
{
assert(params.rows == 9);
assert(params.cols == 1);
cv::Mat H = params.reshape(1, 3).t();
cv::Mat invH = H.inv();
cv::Mat Sigma = cv::Mat::zeros(9, 9, cv::DataType<MAT_TYPE>::type);
cv::Mat Sigma_roi = Sigma(cv::Range(0,3), cv::Range(0,3));
invH.copyTo(Sigma_roi);
Sigma_roi = Sigma(cv::Range(3,6), cv::Range(3,6));
invH.copyTo(Sigma_roi);
Sigma_roi = Sigma(cv::Range(6,9), cv::Range(6,9));
invH.copyTo(Sigma_roi);
return Sigma;
};
Vector ArModel::simulate(int n, RNG &rng) const {
int p = number_of_lags();
Vector acf = autocovariance(p);
SpdMatrix Sigma(p);
Sigma.diag() = acf[0];
for (int i = 1; i < p; ++i) {
Sigma.subdiag(i) = acf[i];
Sigma.superdiag(i) = acf[i];
}
Vector zero(p, 0.0);
Vector y0 = rmvn(zero, Sigma);
return simulate(n, y0, rng);
}
void plot()
{
gROOT->LoadMacro("tdrstyle.C");
gROOT->LoadMacro("Sigma.C++");
setTDRStyle();
version=8;
TString str("SigmaNew_v");
str+=version;
Sigma()->Print(str+".png");
//Sigma()->Print(str+".eps");
//Sigma()->Print(str+".pdf");
return;
}
TEST(ProbDistributionsLkjCorr,testIdentity) {
unsigned int K = 4;
Eigen::MatrixXd Sigma(K,K);
Sigma.setZero();
Sigma.diagonal().setOnes();
srand(time(0));
double eta = rand() / double(RAND_MAX) + 0.5;
double f = stan::prob::do_lkj_constant(eta, K);
EXPECT_FLOAT_EQ(f, stan::prob::lkj_corr_log(Sigma, eta));
eta = 1.0;
f = stan::prob::do_lkj_constant(eta, K);
EXPECT_FLOAT_EQ(f, stan::prob::lkj_corr_log(Sigma, eta));
}
virtual std::ostream& Print (std::ostream& os) const {
Operator<T>::Print(os);
os << " image size: rank(" << Rank() << ") side(" <<
ImageSize() << ") nodes(" << KSpaceSize() << ")" << std::endl;
os << " nfft: maxit(" << Maxit() << ") eps(" << Epsilon() << ") alpha("
<< Alpha() << ") m("<< m_m <<") sigma(" << Sigma() << ")" << std::endl;
os << " have_kspace(" << m_have_kspace << ") have_weights(" <<
m_have_weights << ") have_b0(" << m_have_b0 << ")" << std::endl;
os << " ft-threads(" << m_np << ")";
if (m_3rd_dim_cart)
os << " 3rd dimension (" << m_ncart << ") is Cartesian.";
return os;
}
std::vector<Int> ABS(std::vector<Int> U, Int t, nInt log) {
Int z = iPow(t, U.size() / 2);
std::vector<Int> S(U);
std::sort(U.begin(), U.end());
Int L = iMin(iPow(U.size(), 4), iPow(2, U.size() - 1));
Int i, j, s, d, n, e = 0;
Int k = 0;
Int l = iPow(2, U.size() - 1);
while (k < L) {
i = k;
j = l;
e = (i + j) / 2;
while (i < j) {
cycles++;
n = (i + j) / 2;
S = Phi(U, n + e);
s = iPow(Sigma(S), S.size());
d = s - z;
if (log) {
std::vector<Int> C;
std::set_difference(U.begin(), U.end(), S.begin(), S.end(), std::back_inserter(C));
std::cout << "(<" << Sigma(S) << ", " << Sigma(U) - Sigma(S) << ">, [ ";
for (auto u : S) std::cout << u << " ";
std::cout << "] [";
for (auto u : C) std::cout << u << " ";
std::cout << "])" << std::endl;
}
if (d == 0) return S;
if (d < 0) i = n + 1;
if (d > 0) j = n;
e++;
}
k++;
l++;
}
return std::vector<Int>();
}
TEST(ProbDistributionsLkjCorr,Sigma) {
unsigned int K = 4;
Eigen::MatrixXd Sigma(K,K);
Sigma.setZero();
Sigma.diagonal().setOnes();
double eta = rand() / double(RAND_MAX) + 0.5;
EXPECT_NO_THROW (stan::prob::lkj_corr_log(Sigma, eta));
EXPECT_THROW (stan::prob::lkj_corr_log(Sigma, -eta), std::domain_error);
Sigma = Sigma * -1.0;
EXPECT_THROW (stan::prob::lkj_corr_log(Sigma, eta), std::domain_error);
Sigma = Sigma * (0.0 / 0.0);
EXPECT_THROW (stan::prob::lkj_corr_log(Sigma, eta), std::domain_error);
}
static void myF(myu64 *state, const myu64 *w, int k_index)
{
myu64 t, t1, t2;
unsigned char i;
Ch(&t, state+4, state+5, state+6);
Sigma(&t1, state+4, 14, 18, 23);
bigint_add64(t1.v, t1.v, (state+7)->v);
bigint_add64(t1.v, t1.v, t.v);
for(i=0;i<8;i++)
t.v[i] = pgm_read_byte((unsigned char *)roundconstants_pgm+k_index*8+i);
bigint_add64(t1.v, t1.v, t.v);
bigint_add64(t1.v, t1.v, w->v);
Sigma(&t2, state+0, 28, 34, 5);
Maj(&t,state+0,state+1,state+2);
bigint_add64(t2.v, t2.v, t.v);
*(state+7) = *(state+6);
*(state+6) = *(state+5);
*(state+5) = *(state+4);
bigint_add64((state+4)->v, (state+3)->v, t1.v);
*(state+3) = *(state+2);
*(state+2) = *(state+1);
*(state+1) = *(state+0);
bigint_add64((state+0)->v, t1.v, t2.v);
}
//======================================================================
SpdMatrix ArStateModel::initial_state_variance()const{
if (initial_state_variance_.nrow() != state_dimension()) {
report_error("Sigma_.nrow() != state_dimension() in "
"ArStateModel::initial_state_mean()");
}
SpdMatrix & Sigma(const_cast<SpdMatrix &>(initial_state_variance_));
if (stationary_initial_distribution_) {
Vector gamma = autocovariance(state_dimension());
Sigma.diag() = gamma[0];
for(int i = 1; i < state_dimension(); ++i){
Sigma.superdiag(i) = gamma[i];
}
Sigma.reflect();
}
return initial_state_variance_;
}
Type objective_function<Type>::operator() ()
{
DATA_STRING(distr);
DATA_INTEGER(n);
Type ans = 0;
if (distr == "norm") {
PARAMETER(mu);
PARAMETER(sd);
vector<Type> x = rnorm(n, mu, sd);
ans -= dnorm(x, mu, sd, true).sum();
}
else if (distr == "gamma") {
PARAMETER(shape);
PARAMETER(scale);
vector<Type> x = rgamma(n, shape, scale);
ans -= dgamma(x, shape, scale, true).sum();
}
else if (distr == "pois") {
PARAMETER(lambda);
vector<Type> x = rpois(n, lambda);
ans -= dpois(x, lambda, true).sum();
}
else if (distr == "compois") {
PARAMETER(mode);
PARAMETER(nu);
vector<Type> x = rcompois(n, mode, nu);
ans -= dcompois(x, mode, nu, true).sum();
}
else if (distr == "compois2") {
PARAMETER(mean);
PARAMETER(nu);
vector<Type> x = rcompois2(n, mean, nu);
ans -= dcompois2(x, mean, nu, true).sum();
}
else if (distr == "nbinom") {
PARAMETER(size);
PARAMETER(prob);
vector<Type> x = rnbinom(n, size, prob);
ans -= dnbinom(x, size, prob, true).sum();
}
else if (distr == "nbinom2") {
PARAMETER(mu);
PARAMETER(var);
vector<Type> x = rnbinom2(n, mu, var);
ans -= dnbinom2(x, mu, var, true).sum();
}
else if (distr == "exp") {
PARAMETER(rate);
vector<Type> x = rexp(n, rate);
ans -= dexp(x, rate, true).sum();
}
else if (distr == "beta") {
PARAMETER(shape1);
PARAMETER(shape2);
vector<Type> x = rbeta(n, shape1, shape2);
ans -= dbeta(x, shape1, shape2, true).sum();
}
else if (distr == "f") {
PARAMETER(df1);
PARAMETER(df2);
vector<Type> x = rf(n, df1, df2);
ans -= df(x, df1, df2, true).sum();
}
else if (distr == "logis") {
PARAMETER(location);
PARAMETER(scale);
vector<Type> x = rlogis(n, location, scale);
ans -= dlogis(x, location, scale, true).sum();
}
else if (distr == "t") {
PARAMETER(df);
vector<Type> x = rt(n, df);
ans -= dt(x, df, true).sum();
}
else if (distr == "weibull") {
PARAMETER(shape);
PARAMETER(scale);
vector<Type> x = rweibull(n, shape, scale);
ans -= dweibull(x, shape, scale, true).sum();
}
else if (distr == "AR1") {
PARAMETER(phi);
vector<Type> x(n);
density::AR1(phi).simulate(x);
ans += density::AR1(phi)(x);
}
else if (distr == "ARk") {
PARAMETER_VECTOR(phi);
vector<Type> x(n);
density::ARk(phi).simulate(x);
ans += density::ARk(phi)(x);
}
else if (distr == "MVNORM") {
PARAMETER(phi);
matrix<Type> Sigma(5,5);
for(int i=0; i<Sigma.rows(); i++)
for(int j=0; j<Sigma.rows(); j++)
Sigma(i,j) = exp( -phi * abs(i - j) );
density::MVNORM_t<Type> nldens = density::MVNORM(Sigma);
for(int i = 0; i<n; i++) {
vector<Type> x = nldens.simulate();
ans += nldens(x);
}
}
else if (distr == "SEPARABLE") {
PARAMETER(phi1);
PARAMETER_VECTOR(phi2);
array<Type> x(100, 200);
SEPARABLE( density::ARk(phi2), density::AR1(phi1) ).simulate(x);
ans += SEPARABLE( density::ARk(phi2), density::AR1(phi1) )(x);
}
else if (distr == "GMRF") {
PARAMETER(delta);
matrix<Type> Q0(5, 5);
Q0 <<
1,-1, 0, 0, 0,
-1, 2,-1, 0, 0,
0,-1, 2,-1, 0,
0, 0,-1, 2,-1,
0, 0, 0,-1, 1;
Q0.diagonal().array() += delta;
Eigen::SparseMatrix<Type> Q = asSparseMatrix(Q0);
vector<Type> x(5);
for(int i = 0; i<n; i++) {
density::GMRF(Q).simulate(x);
ans += density::GMRF(Q)(x);
}
}
else if (distr == "SEPARABLE_NESTED") {
PARAMETER(phi1);
PARAMETER(phi2);
PARAMETER(delta);
matrix<Type> Q0(5, 5);
Q0 <<
1,-1, 0, 0, 0,
-1, 2,-1, 0, 0,
0,-1, 2,-1, 0,
0, 0,-1, 2,-1,
0, 0, 0,-1, 1;
Q0.diagonal().array() += delta;
Eigen::SparseMatrix<Type> Q = asSparseMatrix(Q0);
array<Type> x(5, 6, 7);
for(int i = 0; i<n; i++) {
SEPARABLE(density::AR1(phi2),
SEPARABLE(density::AR1(phi1),
density::GMRF(Q) ) ).simulate(x);
ans += SEPARABLE(density::AR1(phi2),
SEPARABLE(density::AR1(phi1),
density::GMRF(Q) ) )(x);
}
}
else error( ("Invalid distribution '" + distr + "'").c_str() );
return ans;
}
int main(int argc, char *argv[]) {
ub4 tots=0;
ub4 ts, r, bts, div;
ub4 q1 = 24;
ub4 q2 = 28;
double totSigma=0.0;
double totRange=0.0;
double sigma,range,mSigma,mRange;
// timer
time_t a,z;
// rounds
qi = pow(2,q1);
// check the command line
if (argc<=1) { usage(); exit(0); }
if (argc>=2) q1 = atoi(argv[1]);
if (argc>=3) q2 = atoi(argv[2]);
if (argc>=4) rng= atoi(argv[3]) % 9;
if (argc>=5) strcpy(s,argv[4]);
#ifdef TEST
#ifdef __TINYC__
puts("Tiny C");
#endif
#ifdef __WATCOMC__
puts("Open Watcom C");
#endif
#ifdef _MSC_VER
puts("Microsoft Visual C");
#endif
#ifdef __GNUC__
puts("GNU C");
#endif
#endif
#if __STDC_VERSION__ >= 199901L
puts("C99 supported.");
#endif
#ifdef MOD
printf("MOD: ");
#endif
#ifdef LIM
printf("LIM: ");
#endif
#ifdef SAM
printf("SAM: ");
#endif
div = q2-q1+1;
printf("%d %s trial sets in [2**%d..2**%d]\n",div,RNGs[rng],q1,q2);
puts("Trial Range Sigma Time");
puts("------------------------------------------");
for (j=q1; j<=q2; j++) {
for (i=0; i<MODU; i++) totals[i]=0;
probtot = 0.0;
qi = pow(2,j);
rSeed(rng,s,rStateSize(rng)*7);
time(&a);
for (q=0; q<qi; q++) {
#ifdef MOD
r=rRandom(rng) % MODU;
#else
#ifdef LIM
r=Lim(rng);
#endif
r=Sam(rng);
#endif
totals[r]++;
}
time(&z);
ts=(size_t)(z-a);
tots+=ts;
for (i=0; i<MODU; i++) {
// expected probabilities
expect[i] = (double)1/MODU;
// actual probabilities
prob[i]=(double)totals[i]/qi;
// probtot holds total of probabilities - it should converge to 1.0
probtot=probtot+prob[i];
// collect value-names & decide output format
values[i] = i + 'A';
}
sigma = Sigma(0,MODM1);
range = prob[MaxP(0,MODM1)]-prob[MinP(0,MODM1)];
totSigma+=sigma; totRange+=range;
printf("2**%d %1.7f %1.7f %3d s\n",j,range,sigma,ts);
}
puts("------------------------------------------");
mSigma = (double)totSigma/div; mRange = (double)totRange/div;
printf("Mean: %1.7f %1.7f %4d s\n",mRange,mSigma,tots);
#ifdef TEST
puts(""); for (i=0; i<MODU; i++) printf("%2c) %1.7f\n",values[i],prob[i]);
#endif
return 0;
}
MvRegData *MvReg::simdat(const Vector &x, RNG &rng) const {
Vector mu = predict(x);
Vector y = rmvn_mt(rng, mu, Sigma());
return new MvRegData(y, x);
}
AlgorithmGaussNoise::AlgorithmGaussNoise(double s):SimpleAlgorithm()
{
Sigma(s);
}
int main(int argc, char** argv){
ros::init(argc, argv, "wkmeans_test");
ros::NodeHandle node;
ros::Rate rate(100);
std::size_t K = 10;
arma::vec pi = arma::randu<arma::vec>(3);
pi = arma::normalise(pi);
std::vector<arma::vec> Mu(K);
std::vector<arma::mat> Sigma(K);
float a = -2;
float b = 2;
for(std::size_t k = 0; k < K;k++){
Mu[k] = (b - a) * arma::randu<arma::vec>(3) + a;
Sigma[k].zeros(3,3);
arma::vec tmp = 0.3 * arma::randu<arma::vec>(3) + 0.2;
Sigma[k](0,0) =tmp(0);
Sigma[k](1,1) =tmp(1);
Sigma[k](2,2) =tmp(2);
}
GMM gmm;
gmm.setParam(pi,Mu,Sigma);
std::size_t nb_samples = 10000;
arma::colvec weights;
arma::mat X(nb_samples,3);
gmm.sample(X);
arma::vec L(nb_samples);
gmm.P(X,L);
L = L / arma::max(L);
// L.elem(q1).zeros();
unsigned char rgb[3];
std::vector<std::array<float,3> > colors(nb_samples);
for(std::size_t i = 0 ; i < L.n_elem;i++){
ColorMap::jetColorMap(rgb,L(i),0,1);
colors[i][0] = ((float)rgb[0])/255;
colors[i][1] = ((float)rgb[1])/255;
colors[i][2] = ((float)rgb[2])/255;
}
arma::uvec q1 = arma::find(L > 0.6*arma::max(L));
std::vector<std::array<float,3> > colors2(q1.n_elem);
arma::mat X_W(q1.n_elem,3);
for(std::size_t i = 0; i < q1.n_elem;i++){
assert(q1(i) < colors.size());
assert(q1(i) < X.n_rows);
colors2[i] = colors[q1(i)];
X_W.row(i) = X.row(q1(i));
}
Weighted_Kmeans<double> weighted_kmeans;
weighted_kmeans.cluster(X_W.st(),L);
weighted_kmeans.centroids.print("centroids");
opti_rviz::Vis_points points(node,"centroids");
points.scale = 0.1;
points.r = 1;
points.b = 1;
arma::fmat c = arma::conv_to<arma::fmat>::from(weighted_kmeans.centroids.st());
points.initialise("world",c);
opti_rviz::Vis_point_cloud vis_point(node,"samples");
vis_point.set_display_type(opti_rviz::Vis_point_cloud::DEFAULT);
vis_point.initialise("world",X_W);
opti_rviz::Vis_gmm vis_gmm(node,"gmm");
vis_gmm.initialise("world",pi,Mu,Sigma);
while(node.ok()){
vis_gmm.update(pi,Mu,Sigma);
vis_gmm.publish();
vis_point.update(X_W,colors2,weights);
vis_point.publish();
points.update(c);
points.publish();
ros::spinOnce();
rate.sleep();
}
}
main()
{
/******************************************************************************/
/* BUILDING DISK POSITION DISTRIBUTION */
/******************************************************************************/
std::cout << "Building disk\n"; // printing disk parameters
std::cout << " Mdisk = " << Md << "\n";
std::cout << " Ndisk = " << Ndisk << "\n";
std::cout << " Radial Scale Length h = " << h << "\n";
std::cout << " Vertical Scale Length z0 = " << z0 << "\n";
std::cout << " Radial Cutoff = " << diskcut << "\n";
std::cout << " Vertical Cutoff = " << thickcut << "\n";
std::vector<Vector> disk(Ndisk, Vector(3)); // array of disk positions
min = 0.0, max = diskcut; // setting max and min radii
topbound = findtopbound(surfacedist, min, max); // finding topbound
for (int i = 0; i < Ndisk; i++) // for each particle in disk
{
r = rejectionsample(surfacedist, min, max, topbound); // choose
// random radius
disk[i][0] = r; // assigning as such
disk[i][1] = randomreal(0.0, 2.0*PI); // randomize azimuthal angle
}
min = -thickcut; // setting min and max
max = thickcut; // heights
topbound = findtopbound(diskthick, min, max); // finding topbound
for (int i = 0; i < Ndisk; i++) // for each disk particle
{
disk[i][2] = rejectionsample(diskthick, min, max, topbound); //
// choosing height randomly
bodies[i].mass = Md / ((double)Ndisk); // assigning masses such that
// total mass is correct
bodies[i].pos = cyltocart(disk[i]); // transforming to cartesian coords
bodies[i].id = i; // assigning particle id
bodies[i].DM = false; // these are not dark
}
/******************************************************************************/
/* BUILDING HALO POSITION DISTRIBUTION */
/******************************************************************************/
std::cout << "Building Halo\n"; // printing parameters
std::cout << " Mhalo = " << Mh << "\n";
std::cout << " Nhalo = " << Nhalo << "\n";
std::cout << " Scale Length rc = " << rc << "\n";
std::cout << " Scale Length gamma = " << g << "\n";
std::cout << " Cutoff Radius = " << halocut << "\n";
std::vector<Vector> halo(Nhalo, Vector(3)); // array of halo positions
min = 0.0, max = halocut; // max and min for distribution
topbound = findtopbound(hernquisthalo, min, max); // finding topbound
for (int i = 0; i < Nhalo; i++) // for each bulge particle
{
r = rejectionsample(hernquisthalo, min, max, topbound); // select r from
// distribution
bodies[Ndisk + i].pos = randomsphere(r); // randomize position
// on sphere
bodies[Ndisk + i].mass = Mh / ((double)Nhalo); // normalizing mass
bodies[Ndisk + i].id = Ndisk + i; // setting appropriate ID
bodies[Ndisk + i].DM = true; // these are dark
halo[i] = carttosphere(bodies[Ndisk + i].pos); // saving copy in
// spherical coords for later
}
/******************************************************************************/
/* BUILDING BULGE POSITION DISTRIBUTION */
/******************************************************************************/
std::cout << "Building Bulge\n";
std::cout << " Mbulge = " << Mb << "\n";
std::cout << " Nbulge = " << Nbulge << "\n";
std::cout << " Scale Length a = " << a << "\n";
std::cout << " Cutoff Radius = " << bulgecut << "\n";
std::vector<Vector> bulge(Nbulge, Vector(3)); // array of bulge positions
min = 0.0; max = bulgecut; // distribution max and min
topbound = findtopbound(bulgedist, min, max); // finding topbound
for (int i = 0; i < Nbulge; i++) // for each particle
{
r = rejectionsample(bulgedist, min, max, topbound); // select r from
// distribution
bodies[Ndisk + Nhalo + i].pos = randomsphere(r); // randomize sphere
// position
bodies[Ndisk + Nhalo + i].mass = Mb / (double)Nbulge; // setting mass
bodies[Ndisk + Nhalo + i].id = Ndisk + Nhalo + i; // setting IDs
bulge[i] = carttosphere(bodies[Ndisk + Nhalo + i].pos); // saving copy
// in spherical coordinates
}
/******************************************************************************/
/* Approximating Cumulative Mass Distribution M(r) */
/******************************************************************************/
dr = halocut / ((double) massbins); // setting separation between mass
// bins
std::cout << "Approximating Cumulative Mass Distribution M(r)\n";
std::cout << " Number of bins = " << massbins << "\n";
std::cout << " dr = " << dr << "\n";
std::vector <Vector> Massatr(massbins, Vector(2)); // Array to hold
// radius and value of cumulative
// mass distribution
for (int i = 0; i < massbins; i++) // for each mass bin
{
Massatr[i][1] = ((double)(i+1))*dr; // setting radius
Massatr[i][0] = 0.0; // clearing total mass
for (int j = 0; j < Ndisk; j++) // for each disk mass
{
r = sqrt(disk[j][0]*disk[j][0] + disk[j][2]*disk[j][2]); // radius
// in spherical coordinates
if((r < (double)(i+1)*dr)) // if radius less than bin radius
{
Massatr[i][0] += Md / (double)Ndisk; // add mass
}
}
for (int j = 0; j < Nhalo; j++) // for each halo mass
{
r = halo[j][0]; // radius
if((r < (double)(i+1)*dr)) // if radius less than bin radius
{
Massatr[i][0] += Mh / (double)Nhalo; // add mass
}
}
for (int j = 0; j < Nbulge; j++) // for each bulge mass
{
r = bulge[j][0]; // radius
if((r < (double)(i+1)*dr)) // if radius less than bin radius
{
Massatr[i][0] += Mb / (double)Nbulge; // add mass
}
}
}
/******************************************************************************/
/* Setting Halo Velocities */
/******************************************************************************/
std::cout << "Setting Halo Velocities\n";
double v; // variable to hold speed
double vesc; // variable to hold escape speed
std::vector<Vector> halovels(Nhalo, Vector(3)); // Vector to hold halo
// velocities
for (int i = 0; i < Nhalo; i++) // for each halo mass
{
r = halo[i][0]; // radius
startbin = floor(r/dr); // starting index is floor of r/dr
vesc = sqrt(2.0*Massatr[startbin][0]/r); // escape velocity
vr2 = 0.0; // clearing radial dispersion
for (int j = startbin; j < massbins; j++) // for each mass bin
{
vr2 += hernquisthalo(Massatr[j][1])*dr*Massatr[j][0]; // add
} // contribution
vr2 /= (hernquisthalo(r)/(r*r)); // dividing by halo density at r
min = 0.0; // distribution min
max = 0.95*vesc; // distribution max is 0.95 of
// escape velocity
topbound = vr2 / 2.71828; // topbound is vr^2 / e
v = rejectionsample(halospeeddist, min, max, topbound); // selecting
// absolute speed
halovels[i] = randomsphere(v); // randomizing cartesian velocities
// on a sphere of radius v
bodies[Ndisk + i].vel = halovels[i];// assigning velocities as such
}
/******************************************************************************/
/* Setting Bulge Velocities */
/******************************************************************************/
std::cout << "Setting Bulge Velocities\n";
std::vector<Vector> bulgevels(Nbulge, Vector(3)); // Vector to hold bulge
// velocities
for (int i = 0; i < Nbulge; i++) // for each particle
{
r = bulge[i][0]; // radius
startbin = floor(r / dr); // starting index
vesc = sqrt(2.0*Massatr[startbin][0]/r); // escape velocity
vr2 = 0.0; // clearing radial dispersion
for (int j = startbin; j < massbins; j++) // for each mass bin
{
vr2 += bulgedist(Massatr[j][1])*dr*Massatr[j][0]; // add
// contribution
}
vr2 /= bulgedist(r)/(r*r); // dividing by halo density at r
min = 0.0; // distribution min
max = 0.95*vesc; // max is 0.95 of escape velocity
topbound = vr2 / 2.71828; // topbound is vr^2 /e
v = rejectionsample(bulgedist, min, max, topbound); // selecting absolute
// absolute speed
bulgevels[i] = randomsphere(v); // randomizing constrained cartesian
// velocities
bodies[Ndisk + Nhalo + i].vel = bulgevels[i]; // assining data as such
}
/******************************************************************************/
/* Setting Disk Velocities */
/******************************************************************************/
std::cout << "Setting Disk Velocities\n";
std::cout << " Q = " << Q << "\n";
std::cout << " Reference radius = " << rref << "\n";
std::vector<Vector> diskvels(Ndisk, Vector(3)); // Vector to hold disk
// velocities
double vz2; // vertical dispersion
double vc; // circular speed
double ar; // radial acceleration
double k; // epicyclic frequency
double sigmaz2; // azimuthal velocity dispersion
double sigmaavg; // average dispersion
double Omega; // angular frequency
double A; // radial dispersion constant
int count; // count variable for averaging
Vector acc(3); // vector to store acceleration
double as = 0.25*h;
maketree(bodies, Ntot, root); // making tree
// NORMALIZING RADIAL DISPERSION
std::cout << " Normalizing Radial Distribution\n";
dr = diskcut / 1000.0; // width of annulus in which
// to average dispersion
sigmaavg = 0.0; // zeroing average
for (int i = 0; i < Ndisk; i++) // for each disk particle
{
r = disk[i][0]; // radius
if (fabs(r - rref) < dr) // if radius in annulus
{ // calculate epicylclic frequency
k = epicyclicfrequency(bodies, Ntot, i, root, eps, theta, 0.05*dr);
sigmaavg += 3.36*Sigma(r)/k; // calculate dispersion and add to
// average
count += 1; // up count
}
}
sigmaavg /= (double)count; // divide total by count
sigmaavg *= Q; // adjust by Q
A = sigmaavg*sigmaavg / Sigma(rref); // setting norm constant
// ASSIGNING VELOCITIES
std::cout << " Setting particle velocities\n";
for (int i = 0; i < Ndisk; i++) // for every particle
{
r = disk[i][0]; // radius
vz2 = PI*z0*Sigma(sqrt(r*r + 2.0*as*as)); // vertical dispersion
diskvels[i][2] = gaussianrandom(sqrt(vz2)); // randomizing vertical
// with this dispersion
vr2 = A*Sigma(sqrt(r*r + 2.0*as*as)); // assigning radial dispersion
diskvels[i][0] = gaussianrandom(sqrt(vr2)); // randomizing radial
// dispersion
acc = treeforce(&bodies[i], root, eps, theta); // acceleration
ar = (acc[0]*bodies[i].pos[0] + acc[1]*bodies[i].pos[1])/r; //
// radial acceleration
Omega = sqrt(fabs(ar)/r); // angular frequency
k = epicyclicfrequency(bodies, Ntot, i, root, eps, theta, dr); //
// epicyclic frequency
vc = Omega*r; // circular speed
v = sqrt(fabs(vc*vc + vr2*(1.0 - (k*k)/(4.0*Omega*Omega) - 2.0*r/h))); //
// azimuthal streaming velocity
sigmaz2 = vr2*k*k/(4.0*Omega*Omega);// azimuthal dispersion
v += gaussianrandom(sqrt(sigmaz2)); // adding random azimuthal component
diskvels[i][1] = v; // assigning azimuthal velocity
// transforming to cartesian coords
bodies[i].vel[0] = diskvels[i][0]*cos(disk[i][1])
- diskvels[i][1]*sin(disk[i][1]);
bodies[i].vel[1] = diskvels[i][0]*sin(disk[i][1])
+ diskvels[i][1]*cos(disk[i][1]);
bodies[i].vel[2] = diskvels[i][2];
}
/******************************************************************************/
/* Reporting Average Disk Speed */
/******************************************************************************/
v = 0.0;
for (int i = 0; i < Ndisk; i++)
{
v += bodies[i].vel.norm();
}
v /= (double)Ndisk;
std::cout << "Average Disk Particle Speed: " << v << "\n";
std::cout << "Disk Size: " << diskcut << "\n";
std::cout << "Disk Crossing Time: " << diskcut / v << "\n";
writeinitfile(Ntot, Nhalo, bodies, "data/initfile.txt");
}
int main()
{
// Set all ranges
Range<double> X(Xfrom,Xto);
Range<double> T(Yfrom,Yto);
// Declare all TwoVarDFunctions
TwoVarDFunction<double,double,double> Sigma(*sigma);
TwoVarDFunction<double,double,double> Mu(*mu);
TwoVarDFunction<double,double,double> Forcing(*forcing);
TwoVarDFunction<double,double,double> B(*b);
// Declare all AtomicDFunctions
AtomicDFunction<double,double> Ic(*IC); // Change from Call<->Put
// AtomicDFunction<double,double> Bcr(*BCR_Topper_p11);
AtomicDFunction<double,double> Bcr(*BCR);// Change from Call<->Put
AtomicDFunction<double,double> Bcl(*BCL);// Change from Call<->Put
// Declare the pde
ParabolicPDE<double,double,double> pde(X,T,Sigma,Mu,B,Forcing,Ic,Bcl,Bcr);
// Declare the finite difference scheme
// ParabolicFDM<double,double,double> FDM(pde,XINTERVALS,YINTERVALS,THETA); // V1
int choice = 3;
cout << "1) Explicit Euler 2) Implicit Euler 3) Crank Nicolson ";
cin >> choice;
//OptionType type = AmericanCallType;
OptionType type = EuropeanCallType;
ParabolicFDM<double,double,double> FDM(pde,XINTERVALS,YINTERVALS,choice,
type);
// compute option prices
FDM.start();
// Retrieve and store option prices
Vector <double,long> result = FDM.line(); // Does include ENDS!!
///////////////////////////////////////////////////////////
Vector<double, long> xArr = FDM.xarr();
Vector<double, long> tArr = FDM.tarr();
double h = xArr[2] - xArr[1];
double k = tArr[2] - tArr[1];
cout << "h " << h << endl;
// Create and fill Delta vector
Vector <double,long> DeltaMesh(xArr.Size()-2, xArr.MinIndex());
for (long kk = DeltaMesh.MinIndex(); kk <= DeltaMesh.MaxIndex(); kk++)
{
DeltaMesh[kk] = xArr[kk+1];
}
Vector <double,long> Delta(result.Size()-2,result.MinIndex());
for (long i = Delta.MinIndex(); i <= Delta.MaxIndex(); i++)
{
Delta[i] = (result[i+1] - result[i])/(h);
}
print(result);
print(Delta);
// Create and fill Gamma vector
Vector <double,long> GammaMesh(DeltaMesh.Size()-2, DeltaMesh.MinIndex());
for (long p = GammaMesh.MinIndex(); p <= GammaMesh.MaxIndex(); p++)
{
GammaMesh[p] = DeltaMesh[p+1];
}
Vector <double,long> Gamma(Delta.Size()-2, Delta.MinIndex());
for (long n = Gamma.MinIndex(); n <= Gamma.MaxIndex(); n++)
{
Gamma[n] = (Delta[n+1] - Delta[n])/(h);
}
/*// Create and fill Theta vector
Vector <double,long> ThetaMesh(tArr.Size()-1, tArr.MinIndex());
for (long m = ThetaMesh.MinIndex(); m <= ThetaMesh.MaxIndex(); m++)
{
ThetaMesh[m] = tArr[m+1];
}*/
long NP1 = FDM.result().MaxRowIndex();
long NP = FDM.result().MaxRowIndex() -1;
Vector <double,long> Theta(result.Size(), result.MinIndex());
for (long ii = Theta.MinIndex(); ii <= Theta.MaxIndex(); ii++)
{
Theta[ii] = -(FDM.result()(NP1, ii) -FDM.result()(NP, ii) )/k;
}
try
{
printOneExcel(FDM.xarr(), result, string("Price"));
printOneExcel(DeltaMesh, Delta, string("Delta"));
printOneExcel(GammaMesh, Gamma, string("Gamma"));
printOneExcel(FDM.xarr(), Theta, string("Theta"));
}
catch(DatasimException& e)
{
e.print();
ExcelDriver& excel = ExcelDriver::Instance();
excel.MakeVisible(true);
long y = 1;
excel.printStringInExcel(e.Message(), y, y, string("Err"));
list<string> dump;
dump.push_back(e.MessageDump()[0]);
dump.push_back(e.MessageDump()[1]);
dump.push_back(e.MessageDump()[2]);
excel.printStringInExcel(dump, 1, 1, string("Err"));
return 0;
}
return 0;
}
int main(int argc, const char** argv){
bool ReDoCuts=false;
TCut TwelveCut = "gamma_CL>0.1&&BDT_response>0.36&&piplus_MC12TuneV3_ProbNNpi>0.2&&piminus_MC12TuneV3_ProbNNpi>0.2&&Kaon_MC12TuneV3_ProbNNk>0.4";
TCut ElevenCut = "gamma_CL>0.1&&BDT_response>0.30&&piplus_MC12TuneV3_ProbNNpi>0.2&&piminus_MC12TuneV3_ProbNNpi>0.2&&Kaon_MC12TuneV3_ProbNNk>0.4";
//______________________________MAKE CUT FILE FOR 2012___________________________________
if(ReDoCuts){
DataFile MCA(std::getenv("BUKETAPMCBDTRESPROOT"),MC,Twel,MagAll,buketap,"BDTApplied_SampleA");
DataFile MCB(std::getenv("BUKETAPMCBDTRESPROOT"),MC,Twel,MagAll,buketap,"BDTApplied_SampleB");
TreeReader* MC12Reader= new TreeReader("DecayTree");
MC12Reader->AddFile(MCA);
MC12Reader->AddFile(MCB);
MC12Reader->Initialize();
TFile* MC12Cut = new TFile("CutFile12.root","RECREATE");
TTree* MC12CutTree=MC12Reader->CopyTree(TwelveCut,-1,"DecayTree");
TRandom3 *MCRand = new TRandom3(224);
TH1I * MCnCands12= new TH1I("MCnCands12","MCnCands12",10,0,10);
TTree*MC12SingleTree=HandyFunctions::GetSingleTree(MCRand,MC12CutTree,MCnCands12,NULL);
MCnCands12->Write();
MC12SingleTree->Write();
MC12Cut->Close();
//________________________________MAKE CUT FILE FOR 2011__________________________________
DataFile MC11A(std::getenv("BUKETAPMCBDTRESPROOT"),MC,Elev,MagAll,buketap,"BDTApplied_SampleA");
DataFile MC11B(std::getenv("BUKETAPMCBDTRESPROOT"),MC,Elev,MagAll,buketap,"BDTApplied_SampleB");
TreeReader* MC11Reader= new TreeReader("DecayTree");
MC11Reader->AddFile(MC11A);
MC11Reader->AddFile(MC11B);
MC11Reader->Initialize();
TFile* MC11Cut = new TFile("CutFile11.root","RECREATE");
TTree* MC11CutTree=MC11Reader->CopyTree(ElevenCut,-1,"DecayTree");
TH1I * MCnCands11= new TH1I("MCnCands11","MCnCands11",10,0,10);
TTree* MC11SingleTree=HandyFunctions::GetSingleTree(MCRand,MC11CutTree,MCnCands11,NULL);
MCnCands11->Write();
MC11SingleTree->Write();
MC11Cut->Close();
//_________________________________ MAKE FLAT TREES ____________________________________
TFile* MC12Input = new TFile("CutFile12.root");
TTree* MC12InputTree=(TTree*)MC12Input->Get("DecayTree");
Float_t MCEta_Mass12[20]; MC12InputTree->SetBranchAddress("Bu_DTFNoFix_eta_prime_M",&MCEta_Mass12);
Int_t isSingle12; MC12InputTree->SetBranchAddress("isSingle",&isSingle12);
TFile* MC12FlatOut = new TFile("MCMinimalFile12.root","RECREATE");
TTree* MC12FlatTree = MC12InputTree->CloneTree(0);
Double_t MCBu_DTFNoFix_eta_Prime_MF12; MC12FlatTree->Branch("Bu_DTFNoFix_eta_prime_MF",&MCBu_DTFNoFix_eta_Prime_MF12,"Bu_DTFNoFix_eta_prime_MF/D");
Long64_t Entries12=MC12InputTree->GetEntries();
for(int i=0;i<Entries12;++i){
MC12InputTree->GetEntry(i);
if(isSingle12==0)continue;
MCBu_DTFNoFix_eta_Prime_MF12=MCEta_Mass12[0];
MC12FlatTree->Fill();
}
MC12FlatTree->Write();
MC12FlatOut->Close();
TFile* MC11Input = new TFile("CutFile11.root");
TTree* MC11InputTree=(TTree*)MC11Input->Get("DecayTree");
Float_t MCEta_Mass11[20]; MC11InputTree->SetBranchAddress("Bu_DTFNoFix_eta_prime_M",&MCEta_Mass11);
Int_t isSingle11; MC11InputTree->SetBranchAddress("isSingle",&isSingle11);
TFile* MC11FlatOut = new TFile("MCMinimalFile11.root","RECREATE");
TTree* MC11FlatTree = MC11InputTree->CloneTree(0);
Double_t MCBu_DTFNoFix_eta_Prime_MF11; MC11FlatTree->Branch("Bu_DTFNoFix_eta_prime_MF",&MCBu_DTFNoFix_eta_Prime_MF11,"Bu_DTFNoFix_eta_prime_MF/D");
Long64_t Entries11=MC11InputTree->GetEntries();
for(int i=0;i<Entries11;++i){
MC11InputTree->GetEntry(i);
if(isSingle11==0)continue;
MCBu_DTFNoFix_eta_Prime_MF11=MCEta_Mass11[0];
MC11FlatTree->Fill();
}
MC11FlatTree->Write();
MC11FlatOut->Close();
}
//_____________________________________________LOAD ROODATASETS___________________________________
TFile* MCFlatInput12= new TFile("MCMinimalFile12.root");
TTree* MCFlatInputTree12=(TTree*)MCFlatInput12->Get("DecayTree");
TFile* MCFlatInput11= new TFile("MCMinimalFile11.root");
TTree* MCFlatInputTree11=(TTree*)MCFlatInput11->Get("DecayTree");
RooRealVar MCBMass("Bu_DTF_MF","Bu_DTF_MF",5000.0,5600.0);
RooRealVar MCEtaMass("eta_prime_MM","eta_prime_MM",700.0,1200.0);
RooRealVar BDT_response("BDT_response","BDT_response",-1.0,1.0);
RooRealVar gamma_CL("gamma_CL","gamma_CL",0.1,1.0);
RooArgSet Args(MCBMass,MCEtaMass,BDT_response,gamma_CL);
RooDataSet* MCData12 = new RooDataSet("MCData12","MCData12",Args,Import(*MCFlatInputTree12));
std::cout <<" Data File 12 Loaded"<<std::endl;
RooDataSet* MCData11 = new RooDataSet("MCData11","MCData11",Args,Import(*MCFlatInputTree11));
std::cout<<" Data File 11 loaded"<<std::endl;
RooDataSet* MCDataAll= new RooDataSet("MCDataAll","MCDataAll",Args);
MCDataAll->append(*MCData12);
MCDataAll->append(*MCData11);
RooPlot* massFrame = MCBMass.frame(Title("Data Import Check"),Bins(50));
MCDataAll->plotOn(massFrame);
RooPlot *BDTFrame = BDT_response.frame(Title("BDT Cut Check"),Bins(50));
MCDataAll->plotOn(BDTFrame);
TCanvas C;
C.Divide(2,1);
C.cd(1);
massFrame->Draw();
C.cd(2);
BDTFrame->Draw();
C.SaveAs("ImportChecks.eps");
//________________________________MAKE MCROODATACATEGORIES__________________________________
RooDataSet* MCBData=(RooDataSet*)MCDataAll->reduce(RooArgSet(MCBMass));
MCBData->Print("v");
RooDataSet* MCEtaData=(RooDataSet*)MCDataAll->reduce(RooArgSet(MCEtaMass));
MCEtaData->Print("v");
RooCategory MCMassType("MCMassType","MCMassType") ;
MCMassType.defineType("B") ;
MCMassType.defineType("Eta") ;
// Construct combined dataset in (x,sample)
RooDataSet MCcombData("MCcombData","MC combined data",Args,Index(MCMassType),Import("B",*MCBData),Import("Eta",*MCEtaData));
//=============================================== MC FIT MODEL===================================
RooRealVar Mean("Mean","Mean",5279.29,5276.0,5284.00);
RooRealVar Sigma("Sigma","Sigma",20.54,17.0,24.8);
RooRealVar LAlpha("LAlpha","LAlpha",-1.064,-2.5,0.0);
RooRealVar RAlpha("RAlpha","RAlpha",1.88,0.0,5.0);
RooRealVar LN("LN","LN",13.0,0.0,40.0);
RooRealVar RN("RN","RN",2.56,0.0,6.0);
RooCBShape CBLeft("CBLeft","CBLeft",MCBMass,Mean,Sigma,LAlpha,LN);
RooCBShape CBRight("CBRight","CBRight",MCBMass,Mean,Sigma,RAlpha,RN);
RooRealVar FitFraction("FitFraction","FitFraction",0.5,0.0,1.0);
RooAddPdf DCB("DCB","DCB",RooArgList(CBRight,CBLeft),FitFraction);
RooRealVar SignalYield("SignalYield","SignalYield",4338.0,500.0,10000.0);
// RooExtendPdf ExtDCB("ExtDCB","ExtDCB",DCB,SignalYield);
//==============================ETA DCB ++++++++++++++++++++++++++++++
RooRealVar MCEtamean("MCEtamean","MCEtamean",958.0,955.0,960.0);
RooRealVar MCEtasigma("MCEtasigma","MCEtasigma",9.16,8.0,14.0);
RooRealVar EtaLAlpha("EtaLAlpha","EtaLAlpha",-1.45,-5.0,1.0);
RooRealVar EtaRAlpha("EtaRAlpha","EtaRAlpha",1.76,0.0,4.0);
RooRealVar EtaLN("EtaLN","EtaLN",0.1,0.0,20.0);
RooRealVar EtaRN("EtaRN","EtaRN",0.1,0.0,20.0);
RooCBShape EtaCBLeft("EtaCBLeft","EtaCBLeft",MCEtaMass,MCEtamean,MCEtasigma,EtaLAlpha,EtaLN);
RooCBShape EtaCBRight("EtaCBRight","EtaCBRight",MCEtaMass,MCEtamean,MCEtasigma,EtaRAlpha,EtaRN);
RooRealVar EtaFitFraction("EtaFitFraction","EtaFitFraction",0.22,0.1,1.0);
RooAddPdf EtaDCB("EteaDCB","EtaDCB",RooArgList(EtaCBRight,EtaCBLeft),EtaFitFraction);
RooProdPdf MCSignalPdf("MCSignalPdf","MCSignalPdf",RooArgSet(EtaDCB,DCB));
RooExtendPdf ExtendedMCSignalPdf("ExtendedMCSignalPdf","ExtendedMCSignalPdf",MCSignalPdf,SignalYield);
RooSimultaneous MCsimPdf("MCsimPdf","MC simultaneous pdf",MCMassType) ;
// MCsimPdf.addPdf(ExtDCB,"B");
// MCsimPdf.addPdf(ExtendedMCEtaDCB,"Eta");
//============================== DO the MC FIT =======================================
//MCsimPdf.fitTo(MCcombData,Extended(kTRUE),Minos(kTRUE));
//ExtendedMCEtaDCB.fitTo(*MCEtaData,Extended(kTRUE),Minos(kTRUE));
//ExtDCB.fitTo(*MCBData,Extended(
ExtendedMCSignalPdf.fitTo(*MCDataAll,Extended(kTRUE),Minos(kTRUE));
RooPlot* MCframe1 = MCBMass.frame(Range(5100.0,5500.0),Bins(50),Title("B mass projection"));
MCDataAll->plotOn(MCframe1);
ExtendedMCSignalPdf.plotOn(MCframe1);
ExtendedMCSignalPdf.paramOn(MCframe1);
RooPlot* MCframe2 = MCEtaMass.frame(Range(880.0,1020.0),Bins(50),Title("Eta mass projection")) ;
MCDataAll->plotOn(MCframe2);
ExtendedMCSignalPdf.plotOn(MCframe2);
ExtendedMCSignalPdf.paramOn(MCframe2);
TCanvas* MCc = new TCanvas("rf501_simultaneouspdf","rf403_simultaneouspdf",1200,1000) ;
gPad->SetLeftMargin(0.15) ; MCframe1->GetYaxis()->SetTitleOffset(1.4) ; MCframe1->Draw() ;
MCc->SaveAs("MCSimulCanvas.pdf");
TCanvas* MCcEta = new TCanvas(" Eta Canvas","Eta Canvas",1200,1000);
gPad->SetLeftMargin(0.15) ; MCframe2->GetYaxis()->SetTitleOffset(1.4) ; MCframe2->Draw() ;
MCcEta->SaveAs("MCEtaCanvas.pdf");
TFile* MCFits= new TFile("MCFitResult.root","RECREATE");
// TCanvas* DecMCB=HandyFunctions::DecoratePlot(MCframe1);
// TCanvas* DecMCEta=HandyFunctions::DecoratePlot(MCframe2);
//DecMCEta->Write();
// DecMCB->Write();
MCc->Write();
MCcEta->Write();
std::cout<<"MC Eta Chi2 = "<<MCframe2->chiSquare()<<std::endl;
std::cout<<"MC B Chi2 = "<<MCframe1->chiSquare()<<std::endl;
//___________________________________ CUT DOWN COLLISION DATA ______________________________
if(ReDoCuts){
DataFile TwelveA(std::getenv("BUKETAPDATABDTRESPROOT"),Data,Twel,MagAll,buketap,"BDTApplied_SampleA");
DataFile TwelveB(std::getenv("BUKETAPDATABDTRESPROOT"),Data,Twel,MagAll,buketap,"BDTApplied_SampleB");
DataFile ElevenA(std::getenv("BUKETAPDATABDTRESPROOT"),Data,Elev,MagAll,buketap,"BDTApplied_SampleA");
DataFile ElevenB(std::getenv("BUKETAPDATABDTRESPROOT"),Data,Elev,MagAll,buketap,"BDTApplied_SampleB");
TRandom3* DataRand= new TRandom3(224);
TH1I* DataNCand12= new TH1I("DataNCand12","DataNCand12",10,0,10);
TH1I* DataNCand11= new TH1I("DataNCand11","DataNCand11",10,0,10);
TreeReader* UncutDataReader12= new TreeReader("DecayTree");
UncutDataReader12->AddFile(TwelveA);
UncutDataReader12->AddFile(TwelveB);
UncutDataReader12->Initialize();
TFile* CutDataFile12 = new TFile("CutDataFile12.root","RECREATE");
TTree* CutDataTree12 = UncutDataReader12->CopyTree(TwelveCut,-1,"DecayTree");
TTree* SingleCutDataTree12=HandyFunctions::GetSingleTree(DataRand,CutDataTree12,DataNCand12,NULL);
SingleCutDataTree12->Write();
CutDataFile12->Close();
TreeReader* UncutDataReader11= new TreeReader("DecayTree");
UncutDataReader11->AddFile(ElevenB);
UncutDataReader11->AddFile(ElevenA);
UncutDataReader11->Initialize();
TFile* CutDataFile11 = new TFile("CutDataFile11.root","RECREATE");
TTree* CutDataTree11 = UncutDataReader11->CopyTree(ElevenCut,-1,"DecayTree");
TTree* SingleCutDataTree11=HandyFunctions::GetSingleTree(DataRand,CutDataTree11,DataNCand11,NULL);
SingleCutDataTree11->Write();
CutDataFile11->Close();
TFile* DataInput12 = new TFile("CutDataFile12.root");
TTree* DataInputTree12=(TTree*)DataInput12->Get("DecayTree");
DataInputTree12->SetBranchStatus("*",0);
DataInputTree12->SetBranchStatus("Bu_DTF_MF",1);
DataInputTree12->SetBranchStatus("Bu_DTFNoFix_eta_prime_M",1);
DataInputTree12->SetBranchStatus("eta_prime_MM",1);
DataInputTree12->SetBranchStatus("isSingle",1);
Float_t Eta_Mass12[20]; DataInputTree12->SetBranchAddress("Bu_DTFNoFix_eta_prime_M",&Eta_Mass12);
Int_t isSingle12; DataInputTree12->SetBranchAddress("isSingle",&isSingle12);
TFile* MinimalDataFile12 = new TFile("MinimalDataFile12.root","RECREATE");
TTree* MinimalDataTree12= DataInputTree12->CloneTree(0);
Double_t Bu_DTFNoFix_eta_prime_MF12; MinimalDataTree12->Branch("Bu_DTFNoFix_eta_prime_MF",&Bu_DTFNoFix_eta_prime_MF12,"Bu_DTFNoFix_eta_prime_MF/D");
Long64_t Entries12=DataInputTree12->GetEntries();
for(int i=0;i<Entries12;++i){
DataInputTree12->GetEntry(i);
if(isSingle12==0)continue;
Bu_DTFNoFix_eta_prime_MF12=Eta_Mass12[0];
MinimalDataTree12->Fill();
}
MinimalDataTree12->Write();
MinimalDataFile12->Close();
TFile* DataInput11 = new TFile("CutDataFile11.root");
TTree* DataInputTree11=(TTree*)DataInput11->Get("DecayTree");
DataInputTree11->SetBranchStatus("*",0);
DataInputTree11->SetBranchStatus("Bu_DTF_MF",1);
DataInputTree11->SetBranchStatus("Bu_DTFNoFix_eta_prime_M",1);
DataInputTree11->SetBranchStatus("eta_prime_MM",1);
DataInputTree11->SetBranchStatus("isSingle",1);
Float_t Eta_Mass11[20]; DataInputTree11->SetBranchAddress("Bu_DTFNoFix_eta_prime_M",&Eta_Mass11);
Int_t isSingle11; DataInputTree11->SetBranchAddress("isSingle",&isSingle11);
TFile* MinimalDataFile11 = new TFile("MinimalDataFile11.root","RECREATE");
TTree* MinimalDataTree11= DataInputTree11->CloneTree(0);
Double_t Bu_DTFNoFix_eta_prime_MF11; MinimalDataTree11->Branch("Bu_DTFNoFix_eta_prime_MF",&Bu_DTFNoFix_eta_prime_MF11,"Bu_DTFNoFix_eta_prime_MF/D");
Long64_t Entries11=DataInputTree11->GetEntries();
for(int i=0;i<Entries11;++i){
DataInputTree11->GetEntry(i);
if(isSingle11==0)continue;
Bu_DTFNoFix_eta_prime_MF11=Eta_Mass11[0];
MinimalDataTree11->Fill();
}
MinimalDataTree11->Write();
MinimalDataFile11->Close();
}
//___________________________________ LOAD DATA TO ROODATASET____________________________________
RooRealVar BMass("Bu_DTF_MF","Bu_DTF_MF",5000.0,5600.0);
RooRealVar EtaMass("eta_prime_MM","eta_prime_MM",870.0,1050.0);
RooArgSet MassArgs(BMass,EtaMass);
TFile* Data12File = new TFile("MinimalDataFile12.root");
TTree* DataTree12=(TTree*)Data12File->Get("DecayTree");
RooDataSet* Data12 = new RooDataSet("Data12","Data12",MassArgs,Import(*DataTree12));
TFile* Data11File = new TFile("MinimalDataFile11.root");
TTree* DataTree11=(TTree*)Data11File->Get("DecayTree");
RooDataSet* Data11 = new RooDataSet("Data11","Data11",MassArgs,Import(*DataTree11));
RooDataSet* AllData = new RooDataSet("AllData","AllData",MassArgs);
AllData->append(*Data12);
AllData->append(*Data11);
TCanvas ImportC;
RooPlot* ImportCheck = BMass.frame(Title("ImportCheck"),Bins(50));
AllData->plotOn(ImportCheck);
ImportCheck->Draw();
ImportC.SaveAs("Alldataimport.pdf");
std::cout<<" Data Loaded, Total Entries = "<<AllData->numEntries()<<std::endl;
AllData->Print("v");
RooDataSet* BData=(RooDataSet*)AllData->reduce(RooArgSet(BMass));
BData->Print("v");
RooDataSet* EtaData=(RooDataSet*)AllData->reduce(RooArgSet(EtaMass));
EtaData->Print("v");
//___________________________________Fit to Eta_Prime in BMass Sidebands______________________
RooDataSet* BSidebands=(RooDataSet*)AllData->reduce(Cut("(Bu_DTF_MF>5000.0&&Bu_DTF_MF<5179.0)||(Bu_DTF_MF>5379.0&&Bu_DTF_MF<5800.0)"));
TCanvas BSidebandCanvas;
RooPlot* BSidebandPlot = EtaMass.frame(Title("B sidebands"),Bins(30));
BSidebands->plotOn(BSidebandPlot);
BSidebandPlot->Draw();
BSidebandCanvas.SaveAs("BSidebandDataCheck.pdf");
RooRealVar BsbMean(" Mean","BsbMean",958.0,900.0,1020.0);
RooRealVar BsbSigma(" Sigma","BsbSigma",19.8,10.0,40.8);
RooRealVar BsbLAlpha(" Alpha","BsbLAlpha",-1.63,-10.0,0.0);
// RooRealVar BsbRAlpha("BsbRAlpha","BsbRAlpha",1.47,0.0,10.0);
RooRealVar BsbLN(" N","BsbLN",0.1,0.0,20.0);
// RooRealVar BsbRN("BsbRN","BsbRN",0.1,0.0,20.0);
RooCBShape BsbCBLeft("BsbCBLeft","BsbCBLeft",EtaMass,BsbMean,BsbSigma,BsbLAlpha,BsbLN);
// RooCBShape BsbCBRight("BsbCBRight","BsbCBRight",EtaMass,BsbMean,BsbSigma,BsbRAlpha,BsbRN);
// RooRealVar BsbFitFraction("BsbFitFraction","BsbFitFraction",0.5,0.0,1.0);
// RooAddPdf BsbDCB("BsbDCB","BsbDCB",RooArgList(BsbCBRight,BsbCBLeft),BsbFitFraction);
RooRealVar Bsbslope("Bsbslope","Bsbslope",0.5,0.0,1.0);
RooRealVar BsbP2("BsbP2","BsbP2",-0.5,-1.0,0.0);
RooChebychev BsbLinear("BsbLinear","BsbLinear",EtaMass,RooArgSet(Bsbslope,BsbP2));
RooRealVar BsbFitFraction("BsbFitFraction","BsbFitFraction",0.2,0.0,1.0);
RooAddPdf BsbBackground("BsbBackground","BsbBackground",RooArgList(BsbLinear,BsbCBLeft),BsbFitFraction);
RooRealVar BsbYield(" Yield","BsbYield",500.0,0.0,1000.0);
RooExtendPdf BsbExtDCB("BsbExtDCB","BsbExtDCB",BsbCBLeft,BsbYield);
BsbExtDCB.fitTo(*BSidebands,Extended(kTRUE),Minos(kTRUE));
TCanvas BSBFitCanvas;
RooPlot* BSBFitPlot = EtaMass.frame(Title("Eta fit in B Sidebands"),Bins(30));
BSidebands->plotOn(BSBFitPlot);
BsbExtDCB.plotOn(BSBFitPlot);
BsbExtDCB.paramOn(BSBFitPlot);
BSBFitPlot->Draw();
BSBFitCanvas.SaveAs("BSidebandFit.pdf");
TFile * SidebandFitFile= new TFile("SidebandFit.root","RECREATE");
BSBFitCanvas.Write();
SidebandFitFile->Close();
//___________________________________DO THE 2D FIT TO DATA___________________________________
const double PDGBMass= 5279.26;
BMass.setRange("SignalWindow",PDGBMass-(3*Sigma.getVal()),PDGBMass+(3*Sigma.getVal()));
RooRealVar DSignalYield("DSignalYield","DSignalYield",4000.0,0.0,10000.0);
//================================= B MASS SIGNAL PDF==============================
RooRealVar DMean("Mean","DMean",5279.29,5270.0,5290.00);
RooRealVar DSigma("Sigma","DSigma",19.8,10.0,40.8);
RooRealVar DLAlpha("DLAlpha","DLAlpha",LAlpha.getVal());
RooRealVar DRAlpha("DRAlpha","DRAlpha",RAlpha.getVal());
RooRealVar DLN("DLN","DLN",LN.getVal());
RooRealVar DRN("DRN","DRN",RN.getVal());
RooCBShape DCBLeft("DCBLeft","DCBLeft",BMass,DMean,DSigma,DLAlpha,DLN);
RooCBShape DCBRight("DCBRight","DCBRight",BMass,DMean,DSigma,DRAlpha,DRN);
RooRealVar DFitFraction("FitFraction","DFitFraction",0.5,0.0,1.0);
RooAddPdf DDCB("DDCB","DDCB",RooArgList(DCBRight,DCBLeft),DFitFraction);
//==============================B MASS BKG PDF==============================
RooRealVar slope("slope","slope",-0.5,-1.0,0.0);
RooChebychev bkg("bkg","Background",BMass,RooArgSet(slope));
//==============================Eta mass signal pdf================================
RooRealVar DEtamean("Etamean","DEtamean",958.0,945.0,980.0) ;
RooRealVar DEtasigma("Etasigma","DEtasigma",15.0,5.0,65.0) ;
RooRealVar DEtaLAlpha("DEtaLAlpha","DEtaLAlpha",EtaLAlpha.getVal());
RooRealVar DEtaRAlpha("DEtaRAlpha","DEtaRAlpha",EtaRAlpha.getVal());
RooRealVar DEtaLN("DEtaLN","DEtaLN",EtaLN.getVal());
RooRealVar DEtaRN("DEtaRN","DEtaRN",EtaRN.getVal());
RooCBShape EtaDCBLeft("EtaDCBLeft","EtaDCBLeft",EtaMass,DEtamean,DEtasigma,DEtaLAlpha,DEtaLN);
RooCBShape EtaDCBRight("EtaDCBRight","EtaDCBRight",EtaMass,DEtamean,DEtasigma,DEtaRAlpha,DEtaRN);
RooRealVar DEtaFitFraction("EtaFitFraction","DEtaFitFraction",0.5,0.0,1.0);
RooAddPdf EtaDDCB("EtaDDCB","EtaDDCB",RooArgList(EtaDCBRight,EtaDCBLeft),DEtaFitFraction);
RooProdPdf DSignalPdf("DSignalPdf","DSignalPdf",RooArgList(EtaDDCB,DDCB));
RooExtendPdf DExtSignalPdf("DExtSignalPdf","DExtSignalPdf",DSignalPdf,DSignalYield);
//=============================== Eta mass bkg pdf==================================
RooRealVar EtaBkgMean("EtaBkgMean","EtaBkgMean",958.0,900.0,1020.0);
RooRealVar EtaBkgSigma("EtaBkgSigma","EtaBkgSigma",19.8,10.0,40.8);
RooRealVar EtaBkgLAlpha("EtaBkgLAlpha","EtaBkgLAlpha",BsbLAlpha.getVal());
// RooRealVar EtaBkgRAlpha("EtaBkgRAlpha","EtaBkgRAlpha",BsbRAlpha.getVal());
RooRealVar EtaBkgLN("EtaBkgLN","EtaBkgLN",BsbLN.getVal());
// RooRealVar EtaBkgRN("EtaBkgRN","EtaBkgRN",BsbRN.getVal());
RooCBShape EtaBkgCBLeft("EtaBkgCBLeft","EtaBkgCBLeft",EtaMass,DEtamean,EtaBkgSigma,EtaBkgLAlpha,EtaBkgLN);
// RooCBShape EtaBkgCBRight("EtaBkgCBRight","EtaBkgCBRight",EtaMass,DEtamean,EtaBkgSigma,EtaBkgRAlpha,EtaBkgRN);
// RooRealVar EtaBkgFitFraction("EtaBkgFitFraction","EtaBkgFitFraction",0.5,0.0,1.0);
// RooAddPdf EtaBkgDCB("EtaBkgDCB","EtaBkgDCB",RooArgList(EtaBkgCBRight,EtaBkgCBLeft),EtaBkgFitFraction);
RooProdPdf DataBackgroundPDF("DataBackgroundPDF","DataBackgroundPDF",RooArgList(EtaBkgCBLeft,bkg));
RooRealVar DataBackgroundYield("BackgroundYield","DataBackgroundYield",500.0,0.0,10000.0);
RooExtendPdf ExtDataBackgroundPDF("ExtDataBackgroundPDF","ExtDataBackgroundPDF",DataBackgroundPDF,DataBackgroundYield);
RooAddPdf TotalPDF("TotalPDF","TotalPDF",RooArgList(ExtDataBackgroundPDF,DExtSignalPdf));
std::cout<<"Dependents = "<<std::endl;
RooArgSet* Dependents=TotalPDF.getDependents(AllData);
Dependents->Print("v");
std::cout<<"parameters= "<<std::endl;
RooArgSet* parameters=TotalPDF.getParameters(AllData);
parameters->Print("v");
RooCategory MassType("MassType","MassType") ;
MassType.defineType("B") ;
MassType.defineType("Eta") ;
// Construct combined dataset in (x,sample)
RooDataSet combData("combData","combined data",MassArgs,Index(MassType),Import("B",*BData),Import("Eta",*EtaData));
RooSimultaneous simPdf("simPdf","simultaneous pdf",MassType) ;
// Associate model with the physics state and model_ctl with the control state
// simPdf.addPdf(WholeFit,"B");
// simPdf.addPdf(WholeEtaFit,"Eta");
// simPdf.fitTo(combData,Extended(kTRUE)/*,Minos(kTRUE)*/);
TotalPDF.fitTo(*AllData,Extended(kTRUE),Minos(kTRUE));
RooPlot* frame1 = BMass.frame(Bins(50),Title("B mass projection"));
AllData->plotOn(frame1);
TotalPDF.plotOn(frame1,Components(ExtDataBackgroundPDF),LineStyle(kDashed),LineColor(kRed));
TotalPDF.plotOn(frame1);
TotalPDF.paramOn(frame1);
// The same plot for the control sample slice
RooPlot* frame2 = EtaMass.frame(Bins(50),Title("Eta mass projection")) ;
AllData->plotOn(frame2);
TotalPDF.plotOn(frame2,Components(ExtDataBackgroundPDF),LineStyle(kDashed),LineColor(kRed));
TotalPDF.plotOn(frame2);
TotalPDF.paramOn(frame2);
TCanvas* DecoratedCanvas =HandyFunctions::DecoratePlot(frame2);
TCanvas* DataBC= new TCanvas("BCanvas","BCanvas",1200,1000) ;
gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
TCanvas* EtaBC= new TCanvas("EtaCanvas","EtaCanvas",1200,1000) ;
gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
DataBC->SaveAs("DataBC.pdf");
EtaBC->SaveAs("EtaBC.pdf");
TFile * DataSimulFit = new TFile("DataSimulFit.root","RECREATE");
DataBC->Write();
EtaBC->Write();
DecoratedCanvas->Write();
}
RcppExport SEXP nniv(SEXP arg1, SEXP arg2, SEXP arg3) {
// 3 arguments
// arg1 for parameters
// arg2 for data
// arg3 for Gibbs
// data
List list2(arg2);
const MatrixXd X=as< Map<MatrixXd> >(list2["X"]),
Z=as< Map<MatrixXd> >(list2["Z"]);
const VectorXd t=as< Map<VectorXd> >(list2["t"]),
y=as< Map<VectorXd> >(list2["y"]);
const int N=X.rows(), p=X.cols(), q=Z.cols(), r=p+q, s=p+r;
// parameters
List list1(arg1),
beta_info=list1["beta"],
Tprec_info=list1["Tprec"],
mu_info=list1["mu"],
theta_info=list1["theta"];
// prior parameters
List beta_prior=beta_info["prior"],
Tprec_prior=Tprec_info["prior"],
mu_prior=mu_info["prior"],
theta_prior=theta_info["prior"];
const double Tprec_prior_nu=as<double>(Tprec_prior["nu"]);
const Matrix2d Tprec_prior_Psi=as< Map<MatrixXd> >(Tprec_prior["Psi"]);
const double beta_prior_mean=as<double>(beta_prior["mean"]);
const double beta_prior_prec=as<double>(beta_prior["prec"]);
const Vector2d mu_prior_mean=as< Map<VectorXd> >(mu_prior["mean"]);
const Matrix2d mu_prior_prec=as< Map<MatrixXd> >(mu_prior["prec"]);
const VectorXd theta_prior_mean=as< Map<VectorXd> >(theta_prior["mean"]);
const MatrixXd theta_prior_prec=as< Map<MatrixXd> >(theta_prior["prec"]);
// initialize parameters
double beta=as<double>(beta_info["init"]);
Matrix2d Tprec=as< Map<MatrixXd> >(Tprec_info["init"]);
Vector2d mu =as< Map<VectorXd> >(mu_info["init"]);
VectorXd theta=as< Map<VectorXd> >(theta_info["init"]);
// Gibbs
List list3(arg3); //, save=list3["save"];
const int burnin=as<int>(list3["burnin"]), M=as<int>(list3["M"]),
thin=as<int>(list3["thin"]), m=7+s;
MatrixXd GS(M, m);
// prior parameter intermediate values
double beta_prior_prod=beta_prior_prec * beta_prior_mean;
VectorXd theta_prior_prod=theta_prior_prec * theta_prior_mean;
Vector2d mu_prior_prod=mu_prior_prec*mu_prior_mean;
// parameter intermediate values
Matrix2d Sigma, B_inverse;
Sigma=Tprec.inverse();
B_inverse.setIdentity();
VectorXd gamma=theta.segment(0, p),
delta=theta.segment(p, q),
eta =theta.segment(r, p);
/*
MatrixXd Theta(2, r);
Theta.row(0)=theta.segment(0, r);
Theta.bottomLeftCorner(1, q)=RowVectorXd::Zero(q);
Theta.bottomRightCorner(1, p)=eta.transpose();
*/
Vector2d eps, eps_sum;
MatrixXd D(N, 2), theta_cond_var_root(s, s), W(2, s);
W.setZero();
MatrixXd theta_cond_prec(s, s);
VectorXd theta_cond_prod(s), w(r);
Matrix2d mu_cond_prec, mu_cond_var_root, E;
Vector2d u, R, mu_cond_prod, mu_u;
double beta_scale, beta_prec, beta_prod, beta_cond_var, beta_cond_mean;
int h=0, i, l;
// Gibbs loop
//for(int l=-burnin; l<=(M-1)*thin; ++l) {
l=-burnin;
do{
// sample beta
D.col(0).setConstant(-mu[0]);
D.col(1).setConstant(-mu[1]);
D.col(0) += (t - X*gamma - Z*delta);
D.col(1) += (y - X*eta);
beta_scale=1./(Sigma(0, 0)*t.dot(t));
beta_prec=1./(beta_scale*Sigma.determinant());
beta_prod=beta_prec*beta_scale
*((Sigma(0, 0)*D.col(1)-Sigma(0, 1)*D.col(0)).array()*t.array()).sum();
beta_cond_var=1./(beta_prec+beta_prior_prec);
beta_cond_mean=beta_cond_var*(beta_prod+beta_prior_prod);
beta=rnorm1d(beta_cond_mean, sqrt(beta_cond_var));
B_inverse(1, 0)=-beta;
// sample theta
theta_cond_prec=theta_prior_prec;
theta_cond_prod=theta_prior_prod;
for(i=0; i<N; ++i) {
/*
W.topLeftCorner(1, p)=X.row(i);
W.block(0, p, 1, q)=Z.row(i);
W.bottomRightCorner(1, p)=X.row(i);
*/
W.block(0, 0, 1, p)=X.row(i);
W.block(0, p, 1, q)=Z.row(i);
W.block(1, r, 1, p)=X.row(i);
theta_cond_prec += (W.transpose() * Tprec * W);
u[0]=t[i];
u[1]=y[i];
R=B_inverse*u-mu;
theta_cond_prod += (W.transpose() * Tprec * R);
}
theta_cond_var_root=inv_root_chol(theta_cond_prec);
// theta_cond_var_root=inv_root_svd(theta_cond_prec); // for validation only
theta=theta_cond_var_root*(rnormXd(s)+theta_cond_var_root.transpose()*theta_cond_prod);
gamma=theta.segment(0, p);
delta=theta.segment(p, q);
eta =theta.segment(r, p);
/*
Theta.topRows(1)=theta.segment(0, r).transpose();
Theta.bottomRightCorner(1, p)=eta.transpose();
*/
// sample mu
eps_sum.setZero();
//W.setZero();
E.setZero();
for(i=0; i<N; ++i) {
/*
W.topLeftCorner(1, p)=X.row(i);
W.block(0, p, 1, q)=Z.row(i);
W.bottomRightCorner(1, p)=X.row(i);
*/
W.block(0, 0, 1, p)=X.row(i);
W.block(0, p, 1, q)=Z.row(i);
W.block(1, r, 1, p)=X.row(i);
/*
w.segment(0, q)=Z.row(i);
w.segment(q, p)=X.row(i);
*/
u[0]=t[i];
u[1]=y[i];
//eps += B_inverse*u - Theta*w;
eps = B_inverse*u - W*theta;
eps_sum += eps;
eps -= mu;
E += eps*eps.transpose();
}
mu_cond_prod=Tprec*eps_sum+mu_prior_prod;
mu_cond_prec=(N*Tprec+mu_prior_prec);
mu_cond_var_root=inv_root_chol(mu_cond_prec);
// mu_cond_var_root=inv_root_svd(mu_cond_prec); // for validation only
mu=mu_cond_var_root*(rnormXd(2)+mu_cond_var_root.transpose()*mu_cond_prod);
// sample Tprec
Tprec = rwishart((E+Tprec_prior_Psi).inverse(), N+Tprec_prior_nu);
Sigma = Tprec.inverse();
if(l>=0 && l%thin == 0) {
h = (l/thin);
GS.block(h, 0, 1, s)=theta.transpose();
GS(h, s)=beta;
GS(h, s+1)=mu[0];
GS(h, s+2)=mu[1];
GS(h, s+3)=Tprec(0, 0);
GS(h, s+4)=Tprec(0, 1);
GS(h, s+5)=Tprec(0, 1);
GS(h, s+6)=Tprec(1, 1);
}
l++;
} while (l<=(M-1)*thin && beta==beta);
if(beta != beta) GS.conservativeResize(h+1, m);
return wrap(GS);
}
MvRegData * MVTR::simdat(const Vec &x)const{
Vec Y = rmvt(predict(x), Sigma(), nu());
return new MvRegData(Y,x);
}
Ptr<MvtIndepProposal> TIM::create_proposal(int dim, double nu){
Vector mu(dim);
SpdMatrix Sigma(dim);
Sigma.set_diag(1.0);
return new MvtIndepProposal(mu, Sigma, nu, rng());
}
