inline result_type operator()(const T &t) const
{
//std::cout<<"unary energy\n";
std::map< std::string,double > dist,hasWindow;
_lot->dist2borders(t,dist,hasWindow);
std::map<Var,double> var_value;
std::map< std::string,double >::iterator it;
for(it=dist.begin(); it!=dist.end(); ++it)
{
if(it->second!=it->second || isinf(it->second))
return _eRej*3;
var_value.insert(std::make_pair(Var("d"+it->first),it->second));
}
for(it=hasWindow.begin();it!=hasWindow.end();++it)
var_value.insert(std::make_pair(Var("hasWindow"+it->first),it->second));
var_value.insert(std::make_pair(Var("h"),t.h()));
result_type eFront=0,eSide=0,eBack=0;
if(_lot->hasRule(RuleType::DistFront))
eFront = (_lot->ruleEnergy(RuleType::DistFront))->energy(var_value);
if(_lot->hasRule(RuleType::DistSide))
eSide = (_lot->ruleEnergy(RuleType::DistSide))->energy(var_value);
if(_lot->hasRule(RuleType::DistBack))
eBack = (_lot->ruleEnergy(RuleType::DistBack))->energy(var_value);
return (eFront+eSide+eBack)*_eRej;
}
inline result_type operator()(double x,double h) const
{
std::map<Var,double> var_value;
std::map< std::string,Border* >::iterator it,bbegin,bend;
bbegin = _lot->name_borders().begin();
bend = _lot->name_borders().end();
for(it=bbegin;it!=bend;++it)
{
var_value.insert(std::make_pair(Var("d"+it->first),x));
var_value.insert(std::make_pair(Var("hasWindow"+it->first),1.));
}
var_value.insert(std::make_pair(Var("h"),_lot->ruleGeom()->hMax()));
double eFront=0,eSide=0,eBack=0;
if(_lot->hasRule(RuleType::DistFront))
eFront = (_lot->ruleEnergy(RuleType::DistFront))->energy(var_value);
if(_lot->hasRule(RuleType::DistSide))
eSide = (_lot->ruleEnergy(RuleType::DistSide))->energy(var_value);
if(_lot->hasRule(RuleType::DistBack))
eBack = (_lot->ruleEnergy(RuleType::DistBack))->energy(var_value);
return _eRej*std::max(std::max(eFront,eSide),eBack);
}
static void
Relative(struct dataset *ds, struct dataset *rs, int confidx)
{
double spool, s, d, e, t;
int i;
i = ds->n + rs->n - 2;
if (i > NSTUDENT)
t = student[0][confidx];
else
t = student[i][confidx];
spool = (ds->n - 1) * Var(ds) + (rs->n - 1) * Var(rs);
spool /= ds->n + rs->n - 2;
spool = sqrt(spool);
s = spool * sqrt(1.0 / ds->n + 1.0 / rs->n);
d = Avg(ds) - Avg(rs);
e = t * s;
if (fabs(d) > e) {
printf("Difference at %.1f%% confidence\n", studentpct[confidx]);
printf(" %g +/- %g\n", d, e);
printf(" %g%% +/- %g%%\n", d * 100 / Avg(rs), e * 100 / Avg(rs));
printf(" (Student's t, pooled s = %g)\n", spool);
} else {
printf("No difference proven at %.1f%% confidence\n",
studentpct[confidx]);
}
}
func CreateContructionNear(id def, int x, int y, int owner)
{
Var(0)=x; Var(1)=y;
if (!FindConstructionSite(def, 0,1)) return 0;
x=Var(0); y=Var(1);
return CreateConstruction(def, x,y, owner, 100, true);
}
Lua::Var Lua::State :: load_str(std::string &str)
{ int n = lua_gettop(vm);
if(luaL_dostring(vm, str.c_str()))
lua_error(vm);
int t = lua_gettop(vm) - n;
if (t > 0) return Var(this, n + 1, t);
else return Var();
}
Lua::Var Lua::State :: load_file(const char* fileName)
{ int n = lua_gettop(vm);
if(luaL_dofile(vm, fileName))
lua_error(vm);
int t = lua_gettop(vm) - n;
if (t > 0) return Var(this, n + 1, t);
else return Var();
}
Lua::Var Lua::State :: load_data(Data* data)
{ int n = lua_gettop(vm);
if(luaL_loadbuffer(vm, (const char*)data -> getPtr(), data ->getLength(),
"") || lua_pcall(vm, 0, LUA_MULTRET, 0))
lua_error(vm);
int t = lua_gettop(vm) - n;
if (t > 0) return Var(this, n + 1, t);
else return Var();
}
void constraints() {
OKLIB_MODELS_CONCEPT_TAG(Var, VariablesAsIndex);
OKLIB_MODELS_CONCEPT_REQUIRES(Var, VariablesWithIndex);
OKLIB_MODELS_CONCEPT_TAG(Var, VariablesWithIndex);
dummy_use_v(Var(i));
dummy_use_v(Var(ic));
}
void CBP::construct() {
// prepare datastructures for compression
for (size_t i=0; i<nrVars(); i++) {
_gndVarToSuperVar[i] = i;
}
for (size_t i=0; i<nrFactors(); i++) {
_gndFacToSuperFac[i] = i;
}
// create edge properties
_edges.clear();
_edges.reserve( nrVars() );
for( size_t i = 0; i < nrVars(); ++i ) {
_edges.push_back( vector<EdgeProp>() );
_edges[i].reserve( nbV(i).size() );
foreach( const Neighbor &I, nbV(i) ) {
EdgeProp newEP;
size_t edgeCount = factor(I).counts()[I.dual].size();
newEP.message = vector<Prob>(edgeCount,Prob( var(i).states() ));
newEP.newMessage = vector<Prob>(edgeCount,Prob( var(i).states() ));
newEP.count = vector<int>(edgeCount);
newEP.index = vector<ind_t>(edgeCount);
newEP.nrPos = edgeCount;
// simulate orginal varSet with possibly more variables, must ensure that the number of variables is equal to number of ground variables
VarSet gndVarSet;
size_t varCount = 0;
foreach(const Neighbor &tmpVar, nbF(I)) {
for(map<size_t, int>::const_iterator iter=factor(I).counts()[tmpVar.iter].begin(); iter!=factor(I).counts()[tmpVar.iter].end();iter++) {
gndVarSet |= Var(varCount, var(tmpVar).states());
varCount++;
}
}
varCount = 0;
foreach(const Neighbor &tmpVar, nbF(I)) {
size_t pos=0;
for(map<size_t, int>::const_iterator iter=factor(I).counts()[tmpVar.iter].begin(); iter!=factor(I).counts()[tmpVar.iter].end();iter++,pos++) {
if (tmpVar == i) {
// assumes that counts are iterated in increases order of positions
size_t sortedPos = factor(I).sigma()[(*iter).first];
newEP.count[pos] = (*iter).second;
newEP.index[pos].reserve( factor(I).states() );
for( IndexFor k( Var(sortedPos, var(i).states()), gndVarSet ); k >= 0; ++k ) {
newEP.index[pos].push_back( k );
}
}
varCount++;
}
}
_edges[i].push_back( newEP );
}
TEST(Util, NormalSampleTest) {
VReal tmp;
for (int i = 0; i < 1000000; i++) {
tmp.push_back(NormalSample() / 100);
}
EXPECT_LT(std::abs(0.0 - Mean(tmp)), 0.00001);
EXPECT_LT(std::abs(0.0001 - Var(tmp)), 0.00001);
VVVReal tmp2;
RandomInit(100, 100, 100, &tmp2);
EXPECT_LT(std::abs(0.0 - Mean(tmp2)), 0.00001);
EXPECT_LT(std::abs(0.0001 - Var(tmp2)), 0.00001);
}
TensorIndex::TensorIndex(std::string name, pe::PathExpression pexpr)
: content(new Content) {
content->name = name;
content->pexpr = pexpr;
content->kind = PExpr;
string prefix = (name == "") ? name : name + ".";
content->coordArray = Var(prefix + "coords",
ArrayType::make(ScalarType::Int));
content->sinkArray = Var(prefix + "sinks",
ArrayType::make(ScalarType::Int));
}
global func ExtraLog(szString, a,b,c,d,e,f,g,e,h)
{
var obj, i;
// Alle vorigen Logs speichern und verschieben
while(obj = FindObject(_LOG, 0-GetX(), 0-GetY(), -1, -1, 0, 0, 0, 0, obj)) Var(i++) = obj;
while(--i>=0) SetPosition(GetX(Var(i)), GetY(Var(i))+18, Var(i));
// Neues Log hinzufügen
var szText = Format(szString, a,b,c,d,e,f,g,e,h);
obj = CreateObject(_LOG, -GetX(), 138-GetY(), -1);
AddEffect("Log", obj, 1, 1, 0, _LOG, szText);
return(1);
}
TEST(Util, VarTest) {
VReal tmp;
tmp.push_back(1);
tmp.push_back(2);
tmp.push_back(3);
EXPECT_DOUBLE_EQ(2.0 / 3, Var(tmp));
VVReal tmp2;
tmp2.push_back(tmp);
tmp2.push_back(tmp);
EXPECT_DOUBLE_EQ(2.0 / 3, Var(tmp2));
VVVReal tmp3;
tmp3.push_back(tmp2);
tmp3.push_back(tmp2);
EXPECT_DOUBLE_EQ(2.0 / 3, Var(tmp3));
}
Var Interpreter::make_Callable( Vec<Var> lst, Var self ) {
// remove doubles
for( int i = 0; i < lst.size(); ++i )
for( int j = i + 1; j < lst.size(); ++j )
if ( lst[ i ].expr() == lst[ j ].expr() )
lst.remove_unordered( j-- );
//
Expr surdef_list_data = cst( SI32( lst.size() ) );
for( int i = 0; i < lst.size(); ++i )
surdef_list_data = concat( surdef_list_data, pointer_on( lst[ i ].expr() ) );
Var surdef_list( &type_SurdefList, surdef_list_data );
//
Var self_type = type_of( self ); // returns void if self is not defined
// -> Callable[ surdef_list, self_type, parm_type ]
Var *parms[ 3 ];
parms[ 0 ] = &surdef_list;
parms[ 1 ] = &self_type;
parms[ 2 ] = &void_var;
Var *callable_type = type_for( class_info( class_Callable ), parms );
return Var( callable_type, self ? pointer_on( self.expr() ) : cst() ).add_ref( 0, self );
}
Var Interpreter::ext_method( const Var &var ) {
if ( isa_Def( var ) )
if ( DefInfo *d = def_info( pointer_on( var.expr() ) ) )
if ( d->self_as_arg() )
return var;
return Var();
}
__inline__ bool rebelege_Verz(LIT x)
/* Macht x -> 0 wieder rueckgaengig fuer eine Verzweigungsvariable. */
/* rebelege_Verz = true falls verzweigt werden muss. */
{
LITV y, z;
LIT kx;
VAR v;
for (y = erstesVork(x); echtesVork(y, x); y = naechstesVork(y))
{
bindeLK(y);
LaengeP1(KlnVk(y));
}
for (y = erstesVork(kx = Komp(x)); echtesVork(y, kx); y = naechstesVork(y))
for (z = naechstesVorkK(y); z != y; z = naechstesVorkK(z))
bindeLv(z);
bindeV(v = Var(x));
setzenbelegt(v, false);
return enthalten(v);
}
void * reduce_phase2(const void * _exp)
{
if(isA(_exp, Integer()))
return domainCast(_exp, Real());
else if(isA(_exp, Real()) || isA(_exp, Var()))
return copy(_exp);
else if(isA(_exp, Sum()) || isA(_exp, Product()))
{
void * s = copy(_exp);
size_t i;
for(i = 0; i < size(s); ++i)
{
delete(argv(s, i));
setArgv(s, i, reduce_phase2(argv(_exp, i)));
}
return s;
}
else if(isA(_exp, Pow()))
{
void * p = copy(_exp);
delete(base(p));
delete(power(p));
setBase(p, reduce_phase2(base(_exp)));
setPower(p, reduce_phase2(power(_exp)));
return p;
}
else if(isOf(_exp, Apply_1()))
{
void * f = copy(_exp);
delete(arg(f));
setArg(f, reduce_phase2(arg(_exp)));
return f;
}
assert(0);
}
inline
const std::vector<T>
ICR::EnsembleLearning::DeterministicNode<Model,T>::GetVariance()
{
std::vector<T> Var(1,0.0);
return Var;
}
CFactorGraph CompressInterface::createCFactorGraph() {
createVarClustering();
createFacClustering();
// create lifted fg here
vector<CFactor> superFacs;
// clusterIdx => facIdx
for (map<size_t,size_t>::iterator facIter = _facRepr.begin(); facIter != _facRepr.end(); facIter++) {
VarSet superVarSet;
foreach (const dai::BipartiteGraph::Neighbor &tmpVar, _cfg.nbF(facIter->second)) {
Var varCluster = _varRepr[_varColorVec[tmpVar]];
if (!superVarSet.contains(varCluster)) {
superVarSet |= Var(varCluster);
}
}
CFactor superFac = CFactor(superVarSet, _cfg.factor(facIter->second).p());
superFac.sigma() = _cfg.factor(facIter->second).sigma();
superFac.position() = _cfg.factor(facIter->second).position();
superFac.counts() = createCounts(facIter->second, superVarSet);
superFacs.push_back(superFac);
}
return CFactorGraph(superFacs);
}
Contents(const Type &t, int dims, const std::string &name) :
t(t), name(name) {
sizes.resize(dims);
for (int i = 0; i < dims; i++) {
sizes[i] = Var(std::string(".") + name + ".dim." + int_to_str(i)); // Connelly: std::ostringstream broken in Python binding, use string + instead
}
}
void DirectHandler::handleData(const JSONEntity& val)
{
if (isArray())
{
switch(val.type())
{
case JSONEntity::JSON_T_STRING:
_data.push_back(val.toString());
break;
case JSONEntity::JSON_T_INTEGER:
_data.push_back(val.toInteger());
break;
case JSONEntity::JSON_T_FLOAT:
_data.push_back(val.toFloat());
break;
case JSONEntity::JSON_T_TRUE:
_data.push_back(true);
break;
case JSONEntity::JSON_T_FALSE:
_data.push_back(false);
break;
case JSONEntity::JSON_T_NULL:
_data.push_back(Var());
break;
default:
throw Poco::InvalidArgumentException("Unknown type.");
}
}
}
int WalksatAlgorithm::pickrandom(void)
{
int tofix;
tofix = falsified[random()%numfalse];
return Var(tofix, random()%size[tofix]);
}
__inline__ void rebelege(LIT x)
/* Macht x -> 0 wieder rueckgaengig */
/* ("belegt" wird wieder rueckgesetzt). */
/* Ist BAUMRES gesetzt, so wird aktrelV aktualisiert. */
{
LITV y, z;
LIT kx;
VAR v;
for (y = erstesVork(x); echtesVork(y, x); y = naechstesVork(y))
{
bindeLK(y);
LaengeP1(KlnVk(y));
}
for (y = erstesVork(kx = Komp(x)); echtesVork(y, kx); y = naechstesVork(y))
for (z = naechstesVorkK(y); z != y; z = naechstesVorkK(z))
bindeLv(z);
bindeV(v = Var(x));
setzenbelegt(v, false);
#ifdef BAUMRES
if (enthalten(v))
relVhinzufuegen();
#endif
}
Stmt makeLoopNest(Expr tensor) {
const TensorType *ttype = tensor.type().toTensor();
std::vector<Var> indices;
std::vector<Expr> indicesExpr;
std::vector<IndexSet> domains;
for (auto &is : ttype->getOuterDimensions()) {
indices.push_back(Var("index", Int));
indicesExpr.push_back(indices.back());
domains.push_back(is);
}
Stmt stmt;
// Recursively build write statement with any inner loops
if (!isScalar(ttype->getBlockType())) {
stmt = makeLoopNest(TensorRead::make(tensor, indicesExpr));
}
else {
stmt = TensorWrite::make(tensor, indicesExpr, getZeroVal(ttype));
}
// Wrap in current level loops
for (int i = domains.size()-1; i >= 0; --i) {
stmt = For::make(indices[i], domains[i], stmt);
}
return stmt;
}
int WalksatAlgorithm::picktabu(void)
{
int numbreak[MAXLENGTH];
int tofix;
int clausesize;
int i; /* a loop counter */
int best[MAXLENGTH]; /* best possibility so far */
int numbest; /* how many are tied for best */
int bestvalue; /* best value so far */
int noisypick;
tofix = falsified[random()%numfalse];
clausesize = size[tofix];
for(i = 0;i < clausesize;i++)
numbreak[i] = breakcount[ABS(clause[tofix][i])];
numbest = 0;
bestvalue = BIG;
noisypick = (numerator > 0 && random()%denominator < numerator);
for (i=0; i < clausesize; i++) {
if (numbreak[i] == 0) {
if (bestvalue > 0) {
bestvalue = 0;
numbest = 0;
}
best[numbest++] = i;
}
else if (tabu_length < numflip - changed[ABS(clause[tofix][i])]) {
if (noisypick && bestvalue > 0) {
best[numbest++] = i;
}
else {
if (numbreak[i] < bestvalue) {
bestvalue = numbreak[i];
numbest = 0;
}
if (numbreak[i] == bestvalue) {
best[numbest++] = i;
}
}
}
}
if (numbest == 0) return NOVALUE;
if (numbest == 1) return Var(tofix, best[0]);
return (Var(tofix, best[random()%numbest]));
}
void AusgabeBelegung(FILE* const fp) {
assert(fp);
extern enum Ausgabeformat Format;
const char* Einrueckung = 0;
if (Format == Dimacs_Format)
fprintf(fp, "v");
else {
assert(Format == XML_Format);
extern bool Dateiausgabe;
Einrueckung = (Dateiausgabe) ? "" : " ";
fprintf(fp, "%s<solution>\n", Einrueckung);
}
if (EinerKlausel)
for (unsigned int i = 0; i < InitEinerRed; ++i) {
assert(Pfad0[i] > INT_MIN);
const unsigned int v = abs(Pfad0[i]);
const VZ e = (Pfad0[i] > 0) ? Pos : Neg;
if (Format == Dimacs_Format) {
if (e == Neg)
fprintf(fp, " %s", Symbol1(v));
else
fprintf(fp, " -%s", Symbol1(v));
}
else {
assert(Einrueckung);
fprintf(fp, "%s <value var = \"%s\"> %d </value>\n", Einrueckung, Symbol1(v), e);
}
}
{
const Pfadinfo* const Z = Tiefe;
for (Tiefe = Pfad; Tiefe < Z; ++Tiefe) {
const LIT l = PfadLit(); const VAR v = Var(l);
const VZ e = (l == Literal(v, Pos)) ? Pos : Neg;
if (Format == Dimacs_Format) {
if (e == Neg)
fprintf(fp, " %s", Symbol(v));
else
fprintf(fp, " -%s", Symbol(v));
}
else {
assert(Einrueckung);
fprintf(fp, "%s <value var = \"%s\"> %d </value>\n", Einrueckung, Symbol(v), e);
}
}
Tiefe = (Pfadinfo*) Z;
}
if (Format == Dimacs_Format)
fprintf(fp, " 0\n");
else {
assert(Einrueckung);
fprintf(fp, "%s</solution>\n", Einrueckung);
extern bool Dateiausgabe;
if (! Dateiausgabe)
fprintf(fp, "</SAT-Solver.output>\n");
}
}
/*!
\brief In monitoring mode, output branching literal x (with the given
depth in the search tree).
Currently output actually only happens if output of a satisfying assignment
is enabled.
*/
__inline__ static void Verzweigungsliteralausgabe(const LIT x, const unsigned int Tiefe) {
const VAR v = Var(x);
const VZ e = (x == Literal(v, Pos)) ? Pos : Neg;
if (Belegung) {
fprintf(fpmo, "# %-6d %7s %d\n", Tiefe, Symbol(v), e);
}
fflush(NULL);
}
bool RefSliceUnk::indirect_set( const Var &src, Scope *set_scope, const Expr &sf, int off, Expr ext_cond ) {
Expr expr = set_scope->simplified_expr( src, sf, off );
ASSERT( expr.size_in_bits() == len, "..." );
Expr res = setval( var.expr(), expr, beg );
set_scope->set( var, Var( var.type, res ), sf, off, ext_cond );
return true;
}
void split_predicate_test() {
Expr x = Var("x"), y = Var("y"), z = Var("z"), w = Var("w");
{
std::vector<Expr> expected;
expected.push_back(z < 10);
check(z < 10, expected);
}
{
std::vector<Expr> expected;
expected.push_back((x < y) || (x == 10));
check((x < y) || (x == 10), expected);
}
{
std::vector<Expr> expected;
expected.push_back(x < y);
expected.push_back(x == 10);
check((x < y) && (x == 10), expected);
}
{
std::vector<Expr> expected;
expected.push_back(x < y);
expected.push_back(x == 10);
expected.push_back(y == z);
check((x < y) && (x == 10) && (y == z), expected);
}
{
std::vector<Expr> expected;
expected.push_back((w == 1) || ((x == 10) && (y == z)));
check((w == 1) || ((x == 10) && (y == z)), expected);
}
{
std::vector<Expr> expected;
expected.push_back(x < y);
expected.push_back((w == 1) || ((x == 10) && (y == z)));
check((x < y) && ((w == 1) || ((x == 10) && (y == z))), expected);
}
std::cout << "Split predicate test passed" << std::endl;
}
//=========================================================
bool Parser::Block () {
PrintRule rule("Block");
while (
Class() ||
Function() ||
(Var() && Expect(TokenType::Semicolon))
);
return rule.Accept();
}
