void Camera::preHandle ()
{
_dx = standardize (_dx);
_dy = standardize (_dy);
_normal = standardize (_normal);
_r2 = _radius * _radius;
_apertureDis = std::uniform_real_distribution<> (-_radius, _radius);
}
void Request::addHeader(const char* key, const char* value) {
std::string header_type(key);
std::string header_value(value);
if(!header_type.compare("If-Modified-Since")){
headers[header_type] = header_value;}
else{
header_type = standardize(header_type);
header_value = standardize(header_value);
headers[header_type] = header_value;
}
}
calendar &calendar::operator +=(int rhs)
{
turn_number += rhs;
second += rhs * 6;
standardize();
return *this;
}
std::string calendar::textify_period()
{
standardize();
std::stringstream ret;
int am;
std::string tx;
// Describe the biggest time period, as "<am> <tx>s", am = amount, tx = name
if (year > 0) {
am = year;
tx = "year";
} else if (season > 0) {
am = season;
tx = "season";
} else if (day > 0) {
am = day;
tx = "day";
} else if (hour > 0) {
am = hour;
tx = "hour";
} else if (minute >= 5) {
am = minute;
tx = "minute";
} else {
am = second / 6 + minute * 10;
tx = "turn";
}
ret << am << " " << tx << (am > 1 ? "s" : "");
return ret.str();
}
PUBLIC char *
pure_lambda_action (char *A, char *B, char *Law, interpreter * L)
{
char *r, *result;
char buffer[BUFSIZE];
/* (A)B -> result */
if ((strlen (A) + strlen (B)) >= (BUFSIZE-200))
return NULL;
strcpy (buffer, "eval ((");
strcat (buffer, Law);
strcat (buffer, ")");
strcat (buffer, A);
strcat (buffer, ")");
strcat (buffer, B);
strcat (buffer, ";");
/* reduce to normal form */
r = reduce_lambda (buffer, L);
result = standardize (r, L);
free (r);
return result;
}
std::string calendar::textify_period()
{
standardize();
int am;
const char* tx;
// Describe the biggest time period, as "<am> <tx>s", am = amount, tx = name
if (year > 0) {
am = year;
tx = ngettext("%d year", "%d years", am);
} else if (season > 0) {
am = season;
tx = ngettext("%d season", "%d seasons", am);
} else if (day > 0) {
am = day;
tx = ngettext("%d day", "%d days", am);
} else if (hour > 0) {
am = hour;
tx = ngettext("%d hour", "%d hours", am);
} else if (minute >= 5) {
am = minute;
tx = ngettext("%d minute", "%d minutes", am);
} else {
am = second / 6 + minute * 10;
tx = ngettext("%d turn", "%d turns", am);
}
return string_format(tx, am);
}
void calendar::increment()
{
turn_number++;
second += 6;
if (second >= 60) {
standardize();
}
}
bool c_CurveClustering::standardize(void)
{
/*!< for each curve, convert to the standard form */
size_t n_curves = m_p_curve_set->size();
for (size_t i = 0; i < n_curves; ++i)
{
standardize((*m_p_curve_set)[i], m_standard_curve_set[i]);
}
return true;
}
Metavalue::Metavalue(const std::string& _keyword, std::type_index _datatype,
const std::string& _value, const std::string& _comment)
: keyword(_keyword), datatype(_datatype), value(_value),
comment(_comment) {
if (datatype != std::type_index(typeid(void))) {
standardize();
} else {
debug(LOG_DEBUG, DEBUG_LOG, 0, "don't standardize comments");
}
}
/**
* \brief parse a buffer into a property triple
*/
property_triple::property_triple(const std::string& buffer) {
debug(LOG_DEBUG, DEBUG_LOG, 0, "creating property from '%s'",
buffer.c_str());
std::string b = standardize(buffer);
size_t equalsign = b.rfind('=');
if (std::string::npos == equalsign) {
throw badproperty();
}
value = trim(b.substr(equalsign + 1));
std::string key = trim(b.substr(0, equalsign));
size_t position = key.find_last_of(" \t");
if (std::string::npos == position) {
throw badproperty();
}
devicename = standardize(key.substr(0, position));
property = standardize(key.substr(position + 1));
debug(LOG_DEBUG, DEBUG_LOG, 0, "found triple: device = '%s', "
"property = '%s', value = '%s'",
devicename.c_str(), property.c_str(), value.c_str());
}
bool URL::isEqual(wchar_t* urlString) {
if (!urlString) {
return !isValid();
}
std::wstring string(urlString);
if (!standardize(&string)) {
return false;
}
return wcscmp(m_value, string.c_str()) == 0;
}
bool c_CurveClustering::solve(void)
{
/*!< step 1: standardize curve set for further process */
standardize();
/*!< step 2: calculate similarities */
calculate_similarities(m_similarities);
// show_similarity_matrix();
/*!< step 3: cluster standardized curve set */
cluster(m_similarities);
return true;
}
Population SUS::do_selection(const Population& population, const vector<float>& fitness) {
vector<float> standard_fitness = standardize(fitness);
Population new_population;
float flindex = random_number_in_01();
int picked_index = 1;
for (int i = 0; i < int(population.size()) && picked_index <= int(population.size()); i++) {
flindex += standard_fitness[i];
while (picked_index < flindex) {
if (picked_index <= int(population.size()))
new_population.push_back(population[picked_index - 1]);
picked_index++;
}
}
return new_population;
}
bool URL::setString(wchar_t* value) {
if (m_value) {
delete[] m_value;
m_value = NULL;
}
if (value) {
std::wstring string(value);
if (!standardize(&string)) {
return false;
}
m_value = _wcsdup(string.c_str());
}
return true;
}
calendar& calendar::operator +=(calendar &rhs)
{
second += rhs.second;
minute += rhs.minute;
hour += rhs.hour;
day += rhs.day;
int tmpseason = int(season) + int(rhs.season);
while (tmpseason >= 4) {
year++;
tmpseason -= 4;
}
season = season_type(tmpseason);
year += rhs.year;
standardize();
return *this;
}
calendar& calendar::operator -=(calendar &rhs)
{
calendar tmp(rhs);
tmp.standardize();
second -= tmp.second;
minute -= tmp.minute;
hour -= tmp.hour;
day -= tmp.day;
int tmpseason = int(season) - int(tmp.season);
while (tmpseason < 0) {
year--;
tmpseason += 4;
}
season = season_type(tmpseason);
year -= tmp.year;
standardize();
return *this;
}
float detect_lm(SPH &searchsph, SPH &testsph, SHORTIM testim, int lmcm[], SPH &refsph, int lm[])
{
float ccmax=-1.0;
for(int n=0; n<searchsph.n; n++)
{
testsph.set(testim, lmcm[0]+searchsph.i[n], lmcm[1]+searchsph.j[n], lmcm[2]+searchsph.k[n]);
standardize(testsph.v, testsph.n);
searchsph.v[n] = dot(testsph.v, refsph.v, testsph.n);
if( searchsph.v[n] > ccmax )
{
ccmax = searchsph.v[n];
lm[0] = lmcm[0]+searchsph.i[n];
lm[1] = lmcm[1]+searchsph.j[n];
lm[2] = lmcm[2]+searchsph.k[n];
}
}
return(ccmax);
}
int main(int narg, char *arg[10])
{
struct edge *color[maxrank];
int i,j,rank,vert;
int graph[maxvert][maxvert];
long time; float t;
long start_time,end_time;
f=fopen(arg[1],"r"); /* char */
if (f == NULL) { printf("\n file does not exist\n"); exit(0);}
fscanf (f,"%d%d",&rank,&vert);
for (i=0;i<vert;i++) for(j=0;j<vert;j++) fscanf (f, "%d", &graph[i][j]);
antisymmetrize(graph, vert);
rank=standardize(graph,vert);
i=edgepack (graph,rank,vert,color);
if(i==0) {printf("please check your input!\n"); return;}
printf ("\n\n number of colors: ");
start_time=clock()/1000;
stabil (&rank,vert,graph,color);
end_time=clock()/1000-start_time;
printf("\b\b\b\b\b\b%6d",rank);
for(i=0; i<rank; i++) color[i]->row=-1; j=0;
for(i=0; i<vert; ++i) {
if(color[graph[i][i]]->row<0) color[graph[i][i]]->row=j++;};
printf ("\n\n number of cells: %6d", j);
for(i=0; i<rank; ++i) if(color[i]->row<0) color[i]->row=j++;
printf("\n\n adjacency matrix of the cellular algebra:\n\n");
for(i=0; i<vert; ++i) {
for(j=0; j<vert; ++j) {
printf("%4d ",graph[i][j]);
};
printf("\n");
};
/* printf("\n\n%ld msec \n\n",end_time); */
}
void RandomPCA::pca(MatrixXd &X, int method, bool transpose,
unsigned int ndim, unsigned int nextra, unsigned int maxiter, double tol,
long seed, int kernel, double sigma, bool rbf_center,
unsigned int rbf_sample, bool save_kernel, bool do_orth, bool do_loadings)
{
unsigned int N;
if(kernel != KERNEL_LINEAR)
{
transpose = false;
verbose && std::cout << timestamp()
<< " Kernel not linear, can't transpose" << std::endl;
}
verbose && std::cout << timestamp() << " Transpose: "
<< (transpose ? "yes" : "no") << std::endl;
if(transpose)
{
if(stand_method != STANDARDIZE_NONE)
X_meansd = standardize_transpose(X, stand_method, verbose);
N = X.cols();
}
else
{
if(stand_method != STANDARDIZE_NONE)
X_meansd = standardize(X, stand_method, verbose);
N = X.rows();
}
unsigned int total_dim = ndim + nextra;
MatrixXd R = make_gaussian(X.cols(), total_dim, seed);
MatrixXd Y = X * R;
verbose && std::cout << timestamp() << " dim(Y): " << dim(Y) << std::endl;
normalize(Y);
MatrixXd Yn;
verbose && std::cout << timestamp() << " dim(X): " << dim(X) << std::endl;
MatrixXd K;
if(kernel == KERNEL_RBF)
{
if(sigma == 0)
{
unsigned int med_samples = fminl(rbf_sample, N);
double med = median_dist(X, med_samples, seed, verbose);
sigma = sqrt(med);
}
verbose && std::cout << timestamp() << " Using RBF kernel with sigma="
<< sigma << std::endl;
K.noalias() = rbf_kernel(X, sigma, rbf_center, verbose);
}
else
{
verbose && std::cout << timestamp() << " Using linear kernel" << std::endl;
K.noalias() = X * X.transpose() / (N - 1);
}
//trace = K.diagonal().array().sum() / (N - 1);
trace = K.diagonal().array().sum();
verbose && std::cout << timestamp() << " Trace(K): " << trace
<< " (N: " << N << ")" << std::endl;
verbose && std::cout << timestamp() << " dim(K): " << dim(K) << std::endl;
if(save_kernel)
{
verbose && std::cout << timestamp() << " saving K" << std::endl;
save_text("kernel.txt", K);
}
for(unsigned int iter = 0 ; iter < maxiter ; iter++)
{
verbose && std::cout << timestamp() << " iter " << iter;
Yn.noalias() = K * Y;
if(do_orth)
{
verbose && std::cout << " (orthogonalising)";
ColPivHouseholderQR<MatrixXd> qr(Yn);
MatrixXd I = MatrixXd::Identity(Yn.rows(), Yn.cols());
Yn = qr.householderQ() * I;
Yn.conservativeResize(NoChange, Yn.cols());
}
else
normalize(Yn);
double diff = (Y - Yn).array().square().sum() / Y.size();
verbose && std::cout << " " << diff << std::endl;
Y.noalias() = Yn;
if(diff < tol)
break;
}
verbose && std::cout << timestamp() << " QR begin" << std::endl;
ColPivHouseholderQR<MatrixXd> qr(Y);
MatrixXd Q = MatrixXd::Identity(Y.rows(), Y.cols());
Q = qr.householderQ() * Q;
Q.conservativeResize(NoChange, Y.cols());
verbose && std::cout << timestamp() << " dim(Q): " << dim(Q) << std::endl;
verbose && std::cout << timestamp() << " QR done" << std::endl;
MatrixXd B = Q.transpose() * X;
verbose && std::cout << timestamp() << " dim(B): " << dim(B) << std::endl;
MatrixXd Et;
pca_small(B, method, Et, d, verbose);
verbose && std::cout << timestamp() << " dim(Et): " << dim(Et) << std::endl;
d = d.array() / (N - 1);
if(transpose)
{
V.noalias() = Q * Et;
// We divide P by sqrt(N - 1) since X has not been divided
// by it (but B has)
P.noalias() = X.transpose() * V;
VectorXd s = 1 / (d.array().sqrt() * sqrt(N - 1));
MatrixXd Dinv = s.asDiagonal();
U = P * Dinv;
}
else
{
// P = U D = X V
U.noalias() = Q * Et;
P.noalias() = U * d.asDiagonal();
if(do_loadings)
{
VectorXd s = 1 / (d.array().sqrt() * sqrt(N - 1));
MatrixXd Dinv = s.asDiagonal();
V = X.transpose() * U * Dinv;
}
}
P.conservativeResize(NoChange, ndim);
U.conservativeResize(NoChange, ndim);
V.conservativeResize(NoChange, ndim);
d.conservativeResize(ndim);
pve = d.array() / trace;
}
PRIVATE char *
_lambda_action (char *A, organization * OA, char *B, organization * OB,
simulation * S, interpreter * L)
{
char *r, *result, *sigma;
char buffer[BUFSIZE];
I_CODE = '+';
/* (A)B -> result */
if ((strlen (A) + strlen (B)) >= (BUFSIZE-500))
return NULL;
if (syntax_sieve (A, OA->Filter.regexp[OPERATOR], S->type_check))
{
if (syntax_sieve (B, OB->Filter.regexp[ARGUMENT], S->type_check))
{
strcpy (buffer, "eval ((");
strcat (buffer, choose_law (OA));
strcat (buffer, ")");
strcat (buffer, A);
strcat (buffer, ")");
strcat (buffer, B);
/* type checking filter */
if (S->type_check)
sigma = type_synthesis (buffer + 5);
else
sigma = buffer; /* something non-NULL */
if (sigma)
{
/* passed filters; now reduce to normal form */
strcat (buffer, ";"); /* terminate phrase */
r = reduce_lambda (buffer, L); REDUCTIONS = L->reductions;
result = standardize (r, L);
free (r);
if (syntax_sieve (result, OA->Filter.regexp[RESULT], S->type_check))
{
return result;
}
else
{
I_CODE = 'R';
free (result);
}
}
else
{
I_CODE = 'T';
S->typeclashes++;
}
}
else
{
I_CODE = 'A';
}
}
else
{
I_CODE = 'O';
}
return NULL;
}
Metavalue::Metavalue(const std::string& _keyword, const unsigned int ui,
const std::string& _comment)
: keyword(_keyword), datatype(typeid(ui)), comment(_comment) {
value = stringprintf("%u", ui);
standardize();
}
double RoomyGraphAlg_degreePrestigeStandardized(RoomyGraph *g, uint64 node) {
return standardize(g, node, RoomyGraphAlg_degreePrestige);
}
/*
C'_d(n_i) = C_d(n_i) / (g-1)
where g is the number of nodes in graph g
*/
double RoomyGraphAlg_degreeCentralityStandardized(RoomyGraph *g, uint64 node) {
return standardize(g, node, RoomyGraphAlg_degreeCentrality);
}
Metavalue::Metavalue(const std::string& _keyword, const long l,
const std::string& _comment)
: keyword(_keyword), datatype(typeid(l)), comment(_comment) {
value = stringprintf("%ld", l);
standardize();
}
int
main (int argc, char** argv)
{
// const char trace = 0;
// const unsigned debug = 0;
const char *me = "main";
// parse and validate the command line options
struct paramsStruct params;
parseCommandLine(argc, argv, ¶ms);
// create results directory in ANALYSIS directory
// permissions are read, write for the owner
char resultsDirectoryName[256] = "";
makeResultsDirectory(resultsDirectoryName,
sizeof(resultsDirectoryName),
¶ms);
// start logging
char logFilePath[256] = "";
{
const int outputLength = snprintf(logFilePath,
sizeof(logFilePath),
"%s/run.log",
resultsDirectoryName);
if (outputLength > (int) sizeof(logFilePath)) {
fprintf(stderr, "%s: logFilePath too small", me);
exit(1);
}
}
Log_T log = Log_new(logFilePath, stderr);
// log the command line parameters
LOG(log,"started log file %s\n", logFilePath);
LOG(log,"params: algo=%s\n", params.algo);
LOG(log," : obs=%s\n", params.obs);
LOG(log," : radius=%d\n", params.radius);
LOG(log," : which=%s\n", params.which);
// check the command line parameters
assert(strcmp(params.algo, "knn") == 0);
assert(strcmp(params.obs, "1A") == 0);
// read the input files
const unsigned nObservations = 217376; // adjust of OBS != 1A
const unsigned nFeatures = 55;
double *apns = readCsvNoHeader(nObservations, "aps.csv");
double *dates = readCsvNoHeader(nObservations, "date.csv");
char *featuresHeaderP;
double *features = readFeatures(nObservations,
nFeatures,
&featuresHeaderP);
double *prices = readCsvNoHeader(nObservations, "SALE-AMOUNT-log.csv");
// convert dates to days past the epoch
unsigned dayStdColumn = 5; // the 6th column contains the standardized day value
assert(columnHeaderEqual(featuresHeaderP, dayStdColumn, "day-std"));
double *days = convertDatesToDays(nObservations, dates);
free(dates);
double mean;
double stdv;
determineMeanStdv(nObservations, days, &mean, &stdv);
double *daysStd = standardize(nObservations, days, mean, stdv);
replaceDay(nObservations, nFeatures, features, daysStd, dayStdColumn);
free(days);
free(daysStd);
// generate one set of estimates
FILE *resultFile;
{
char resultFilePath[256];
const int outputLength =
snprintf(resultFilePath,
sizeof(resultFilePath),
"%s/estimates-laufer.csv",
resultsDirectoryName);
if (outputLength > (int) sizeof(resultFilePath)) {
fprintf(stderr, "%s: resultFilePath too small", me);
exit(1);
}
LOG(log, " result file path: %s\n", resultFilePath);
resultFile = fopen(resultFilePath, "w");
}
assert(resultFile);
if (strcmp(params.which, "laufer"))
createLaufer(nObservations, nFeatures,
apns, dates, features, prices,
log,
resultFile);
else
assert(NULL != "logic error");
// OLD CODE BELOW THIS LINE
#if 0
double **pricesHatP = NULL;
if (params.useCache)
pricesHatP = readCache(nObservations, params.obs, log, kMax);
// determine estimated prices for any missing entries in the cache
// this operation could be fast or very slow
// MAYBE: write out cache periodically
const unsigned cacheMutated = completeCache(nObservations,
pricesHatP,
params.obs,
log,
kMax,
pricesP,
debug);
if (params.useCache && cacheMutated)
writeCache(nObservations, pricesHatP, params.obs, log, kMax);
// select which set of estimates to create
if (paramsP->whichIsLaufer)
createEstimatesLaufer(nObservations, nFeatures,
features, dates, prices);
else
assert(false); // should never get here
// pricesHatP[i][k] is
// the estimate priced of transaction indexed i for k nearest neighbors
// for each value of k, determine RMSE overall all the test transactions
// determine kArgMin, the k providing the lowest RMSE
// write CSV containing <k, rmse> values
char resultFilePath[256];
{
const int outputLength =
snprintf(resultFilePath,
sizeof(resultFilePath),
"%s/k-rmse.csv",
directoryName);
if (outputLength > (int) sizeof(resultFilePath)) {
fprintf(stderr, "%s: resultFilePath too small", me);
exit(1);
}
LOG(log, " result file path: %s\n", resultFilePath);
}
FILE *resultFile = fopen(resultFilePath, "w");
assert(resultFile);
// log best k for random sample of test observations
bestK(0.01, nObservations, pricesHatP, pricesP, log, kMax);
// write CSV header
fprintf(resultFile, "k,rmse\n");
unsigned kArgMin = 0;
double lowestRMSE = DBL_MAX;
for (unsigned hpK = 0; hpK < kMax; hpK++) {
// determine rmse for this k
const double rmse = determineRmse(nObservations, pricesHatP, pricesP, hpK);
// check if we have a new best k
LOG(log, "hpK %u rmse %f\n", hpK + 1, rmse);
fprintf(resultFile, "%u,%f\n", hpK + 1, rmse);
if (rmse < lowestRMSE) {
lowestRMSE = rmse;
kArgMin = hpK;
}
}
#endif
//
LOG(log, "%s\n", "finished");
exit(0);
}
Metavalue::Metavalue(const std::string& _keyword, const int i,
const std::string& _comment)
: keyword(_keyword), datatype(typeid(i)), comment(_comment) {
value = stringprintf("%d", i);
standardize();
}
void calendar::increment()
{
second += 6;
if (second >= 60)
standardize();
}
int main(int argc, char * argv[])
{
po::options_description desc("Options");
desc.add_options()
("help", "produce help message")
("bfile", po::value<std::string>(), "PLINK root name")
("w", po::value<std::string>(), "SNP weights filename")
("stand", po::value<std::string>(), "standardization method [none | binom | sd | center]")
("out", po::value<std::string>(), "output filename")
("numthreads", po::value<int>(), "set number of OpenMP threads")
;
po::variables_map vm;
po::store(po::parse_command_line(argc, argv, desc), vm);
po::notify(vm);
if(vm.count("help"))
{
std::cerr << desc << std::endl;
return EXIT_FAILURE;
}
int num_threads = 1;
if(vm.count("numthreads"))
num_threads = vm["numthreads"].as<int>();
int stand_method = STANDARDIZE_BINOM;
if(vm.count("stand"))
{
std::string m = vm["stand"].as<std::string>();
if(m == "binom")
stand_method = STANDARDIZE_BINOM;
else if(m == "sd")
stand_method = STANDARDIZE_SD;
else if(m == "center")
stand_method = STANDARDIZE_CENTER;
else if(m == "none")
stand_method = STANDARDIZE_NONE;
else
{
std::cerr << "Error: unknown standardization method (--stand): "
<< m << std::endl;
return EXIT_FAILURE;
}
}
std::string geno_file, fam_file;
if(vm.count("bfile"))
{
geno_file = vm["bfile"].as<std::string>() + std::string(".bed");
fam_file = vm["bfile"].as<std::string>() + std::string(".fam");
std::cout << ">>> genotype file: " << geno_file << std::endl;
}
else
{
std::cerr << "Error: bfile not specified" << std::endl;
return EXIT_FAILURE;
}
std::string w_file;
if(vm.count("w"))
{
w_file = vm["w"].as<std::string>();
}
std::string out_file = "PredX.txt";
if(vm.count("out"))
{
out_file = vm["out"].as<std::string>();
}
#ifdef _OPENMP
#ifdef EIGEN_HAS_OPENMP
omp_set_num_threads(num_threads);
std::cout << timestamp() << " Using " << num_threads
<< " OpenMP threads" << std::endl;
#endif
#endif
Data data(1);
data.verbose = true;
data.read_pheno(fam_file.c_str(), 6);
data.geno_filename = geno_file.c_str();
data.get_size();
data.read_bed(false);
standardize(data.X, stand_method, false);
MatrixXd W = data.read_plink_pheno(w_file.c_str(), 1);
MatrixXd P = data.X * W;
save_text(P, out_file.c_str());
return EXIT_SUCCESS;
}
static int pca_save_components (VMatrix *cmat,
gretl_matrix *E,
gretl_matrix *C,
DATASET *dset,
int nsave,
gretlopt opt)
{
int save_all = (opt & OPT_A);
double x, **sZ = NULL;
int m = 0, v = dset->v;
int k = cmat->dim;
int i, j, t, vi;
int err = 0;
if (save_all) {
m = k;
} else if (nsave > 0) {
m = nsave > k ? k : nsave;
} else {
for (i=0; E->val[i] > 1.0; i++) {
m++;
}
}
err = dataset_add_series(dset, m);
if (!err) {
/* construct standardized versions of all variables */
sZ = doubles_array_new(k, dset->n);
if (sZ == NULL) {
err = E_ALLOC;
} else {
for (i=0; i<k && !err; i++) {
vi = cmat->list[i+1];
err = standardize(sZ[i], dset->Z[vi], dset->n);
}
}
}
if (!err) {
gchar *label;
double load;
for (i=0; i<m; i++) {
vi = v + i;
sprintf(dset->varname[vi], "PC%d", i+1);
make_varname_unique(dset->varname[vi], vi, dset);
label = g_strdup_printf(_("Component with eigenvalue = %.4f"),
E->val[i]);
series_set_label(dset, vi, label);
g_free(label);
for (t=0; t<dset->n; t++) {
if (t < dset->t1 || t > dset->t2) {
dset->Z[vi][t] = NADBL;
continue;
}
dset->Z[vi][t] = 0.0;
for (j=0; j<k; j++) {
x = sZ[j][t];
if (na(x)) {
dset->Z[vi][t] = NADBL;
break;
} else {
load = gretl_matrix_get(C, j, i);
dset->Z[vi][t] += load * x;
}
}
}
}
}
doubles_array_free(sZ, k);
return err;
}
matrix::Matrix loadInputData()
{
auto matrix = loadInputDataMatrix();
return standardize(slice(matrix, {0, 0}, {matrix.size()[0] - 1, matrix.size()[1]}));
}
