* variables to the nonnegative real numbers.
* More formally, denoting a discrete variable with label \f$l\f$ by
* \f$x_l\f$ and its state space by \f$X_l = \{0,1,\dots,S_l-1\}\f$,
- * then a factor depending on the variables \f$\{x_l\}_{l\in L}\f$ is
+ * then a factor depending on the variables \f$\{x_l\}_{l\in L}\f$ is
* a function \f$f_L : \prod_{l\in L} X_l \to [0,\infty)\f$.
- *
+ *
* In libDAI, a factor is represented by a TFactor<\a T> object, which has two
- * components:
- * \arg a VarSet, corresponding with the set of variables \f$\{x_l\}_{l\in L}\f$
+ * components:
+ * \arg a VarSet, corresponding with the set of variables \f$\{x_l\}_{l\in L}\f$
* that the factor depends on;
- * \arg a TProb<\a T>, a vector containing the value of the factor for each possible
+ * \arg a TProb<\a T>, a vector containing the value of the factor for each possible
* joint state of the variables.
*
* The factor values are stored in the entries of the TProb<\a T> in a particular
- * ordering, which is defined by the one-to-one correspondence of a joint state
- * in \f$\prod_{l\in L} X_l\f$ with a linear index in
+ * ordering, which is defined by the one-to-one correspondence of a joint state
+ * in \f$\prod_{l\in L} X_l\f$ with a linear index in
* \f$\{0,1,\dots,\prod_{l\in L} S_l-1\}\f$ according to the mapping \f$\sigma\f$
* induced by VarSet::calcState(const std::map<Var,size_t> &).
*
public:
/// Iterator over factor entries
- typedef typename TProb<T>::iterator iterator;
+ typedef typename TProb<T>::iterator iterator;
/// Const iterator over factor entries
- typedef typename TProb<T>::const_iterator const_iterator;
+ typedef typename TProb<T>::const_iterator const_iterator;
/// Constructs TFactor depending on no variables, with value p
TFactor ( Real p = 1.0 ) : _vs(), _p(1,p) {}
/// Constructs TFactor depending on variables in vars, with uniform distribution
TFactor( const VarSet& vars ) : _vs(vars), _p(_vs.nrStates()) {}
-
+
/// Constructs TFactor depending on variables in vars, with all values set to p
TFactor( const VarSet& vars, Real p ) : _vs(vars), _p(_vs.nrStates(),p) {}
-
+
/// Constructs TFactor depending on variables in vars, copying the values from the range starting at begin
/** \param vars contains the variables that the new TFactor should depend on.
* \tparam Iterator Iterates over instances of type T; should support addition of size_t.
for( size_t li = 0; li < p.size(); ++li )
_p[permindex.convert_linear_index(li)] = p[li];
}
-
+
/// Constructs TFactor depending on the variable v, with uniform distribution
TFactor( const Var &v ) : _vs(v), _p(v.states()) {}
/// Returns a reference to the i'th entry of the value vector
T& operator[] (size_t i) { return _p[i]; }
-
+
/// Returns iterator pointing to first entry
iterator begin() { return _p.begin(); }
/// Returns const iterator pointing to first entry
- const_iterator begin() const { return _p.begin(); }
- /// Returns iterator pointing beyond last entry
- iterator end() { return _p.end(); }
- /// Returns const iterator pointing beyond last entry
- const_iterator end() const { return _p.end(); }
+ const_iterator begin() const { return _p.begin(); }
+ /// Returns iterator pointing beyond last entry
+ iterator end() { return _p.end(); }
+ /// Returns const iterator pointing beyond last entry
+ const_iterator end() const { return _p.end(); }
/// Sets all values to p
TFactor<T> & fill (T p) { _p.fill( p ); return(*this); }
}
/// Adds scalar t to *this
- TFactor<T>& operator+= (T t) {
+ TFactor<T>& operator+= (T t) {
_p += t;
return *this;
}
/// Subtracts scalar t from *this
- TFactor<T>& operator-= (T t) {
+ TFactor<T>& operator-= (T t) {
_p -= t;
return *this;
}
/// Returns sum of *this and scalar t
TFactor<T> operator+ (T t) const {
- TFactor<T> result(*this);
- result._p += t;
- return result;
+ TFactor<T> result(*this);
+ result._p += t;
+ return result;
}
/// Returns *this minus scalar t
TFactor<T> operator- (T t) const {
- TFactor<T> result(*this);
- result._p -= t;
- return result;
+ TFactor<T> result(*this);
+ result._p -= t;
+ return result;
}
/// Returns *this raised to the power a
- TFactor<T> operator^ (Real a) const {
- TFactor<T> x;
- x._vs = _vs;
- x._p = _p^a;
- return x;
+ TFactor<T> operator^ (Real a) const {
+ TFactor<T> x;
+ x._vs = _vs;
+ x._p = _p^a;
+ return x;
}
/// Multiplies *this with the TFactor f
- TFactor<T>& operator*= (const TFactor<T>& f) {
+ TFactor<T>& operator*= (const TFactor<T>& f) {
if( f._vs == _vs ) // optimize special case
_p *= f._p;
else
- *this = (*this * f);
+ *this = (*this * f);
return *this;
}
/// Divides *this by the TFactor f
- TFactor<T>& operator/= (const TFactor<T>& f) {
+ TFactor<T>& operator/= (const TFactor<T>& f) {
if( f._vs == _vs ) // optimize special case
_p /= f._p;
else
- *this = (*this / f);
+ *this = (*this / f);
return *this;
}
/// Adds the TFactor f to *this
- TFactor<T>& operator+= (const TFactor<T>& f) {
+ TFactor<T>& operator+= (const TFactor<T>& f) {
if( f._vs == _vs ) // optimize special case
_p += f._p;
else
- *this = (*this + f);
+ *this = (*this + f);
return *this;
}
/// Subtracts the TFactor f from *this
- TFactor<T>& operator-= (const TFactor<T>& f) {
+ TFactor<T>& operator-= (const TFactor<T>& f) {
if( f._vs == _vs ) // optimize special case
_p -= f._p;
else
- *this = (*this - f);
+ *this = (*this - f);
return *this;
}
/// Returns product of *this with the TFactor f
- /** The product of two factors is defined as follows: if
+ /** The product of two factors is defined as follows: if
* \f$f : \prod_{l\in L} X_l \to [0,\infty)\f$ and \f$g : \prod_{m\in M} X_m \to [0,\infty)\f$, then
* \f[fg : \prod_{l\in L\cup M} X_l \to [0,\infty) : x \mapsto f(x_L) g(x_M).\f]
*/
}
/// Returns quotient of *this by the TFactor f
- /** The quotient of two factors is defined as follows: if
+ /** The quotient of two factors is defined as follows: if
* \f$f : \prod_{l\in L} X_l \to [0,\infty)\f$ and \f$g : \prod_{m\in M} X_m \to [0,\infty)\f$, then
* \f[\frac{f}{g} : \prod_{l\in L\cup M} X_l \to [0,\infty) : x \mapsto \frac{f(x_L)}{g(x_M)}.\f]
*/
}
/// Returns sum of *this and the TFactor f
- /** The sum of two factors is defined as follows: if
+ /** The sum of two factors is defined as follows: if
* \f$f : \prod_{l\in L} X_l \to [0,\infty)\f$ and \f$g : \prod_{m\in M} X_m \to [0,\infty)\f$, then
* \f[f+g : \prod_{l\in L\cup M} X_l \to [0,\infty) : x \mapsto f(x_L) + g(x_M).\f]
*/
}
/// Returns *this minus the TFactor f
- /** The difference of two factors is defined as follows: if
+ /** The difference of two factors is defined as follows: if
* \f$f : \prod_{l\in L} X_l \to [0,\infty)\f$ and \f$g : \prod_{m\in M} X_m \to [0,\infty)\f$, then
* \f[f-g : \prod_{l\in L\cup M} X_l \to [0,\infty) : x \mapsto f(x_L) - g(x_M).\f]
*/
_p.makePositive( epsilon );
return *this;
}
-
+
/// Returns pointwise inverse of *this.
/** If zero == true, uses 1 / 0 == 0; otherwise 1 / 0 == Inf.
*/
- TFactor<T> inverse(bool zero=true) const {
- TFactor<T> inv;
- inv._vs = _vs;
+ TFactor<T> inverse(bool zero=true) const {
+ TFactor<T> inv;
+ inv._vs = _vs;
inv._p = _p.inverse(zero);
- return inv;
+ return inv;
}
/// Returns pointwise exp of *this
- TFactor<T> exp() const {
- TFactor<T> e;
- e._vs = _vs;
- e._p = _p.exp();
- return e;
+ TFactor<T> exp() const {
+ TFactor<T> e;
+ e._vs = _vs;
+ e._p = _p.exp();
+ return e;
}
/// Returns pointwise logarithm of *this
/** If zero==true, uses log(0)==0; otherwise, log(0)=-Inf.
*/
TFactor<T> log(bool zero=false) const {
- TFactor<T> l;
- l._vs = _vs;
- l._p = _p.log(zero);
- return l;
+ TFactor<T> l;
+ l._vs = _vs;
+ l._p = _p.log(zero);
+ return l;
}
/// Returns pointwise absolute value of *this
- TFactor<T> abs() const {
- TFactor<T> e;
- e._vs = _vs;
- e._p = _p.abs();
- return e;
+ TFactor<T> abs() const {
+ TFactor<T> e;
+ e._vs = _vs;
+ e._p = _p.abs();
+ return e;
}
/// Normalizes *this TFactor according to the specified norm
T normalize( typename Prob::NormType norm=Prob::NORMPROB ) { return _p.normalize( norm ); }
/// Returns a normalized copy of *this, according to the specified norm
- TFactor<T> normalized( typename Prob::NormType norm=Prob::NORMPROB ) const {
+ TFactor<T> normalized( typename Prob::NormType norm=Prob::NORMPROB ) const {
TFactor<T> result;
result._vs = _vs;
result._p = _p.normalized( norm );
* \pre \a nsState < ns.states()
*
* The result is a TFactor that depends on the variables in this->vars() except those in \a ns,
- * obtained by setting the variables in \a ns to the joint state specified by the linear index
+ * obtained by setting the variables in \a ns to the joint state specified by the linear index
* \a nsState. Formally, if *this corresponds with the factor \f$f : \prod_{l\in L} X_l \to [0,\infty)\f$,
* \f$M \subset L\f$ corresponds with \a ns and \a nsState corresponds with a mapping \f$s\f$ that
- * maps a variable \f$x_m\f$ with \f$m\in M\f$ to its state \f$s(x_m) \in X_m\f$, then the slice
+ * maps a variable \f$x_m\f$ with \f$m\in M\f$ to its state \f$s(x_m) \in X_m\f$, then the slice
* returned corresponds with the factor \f$g : \prod_{l \in L \setminus M} X_l \to [0,\infty)\f$
* defined by \f$g(\{x_l\}_{l\in L \setminus M}) = f(\{x_l\}_{l\in L \setminus M}, \{s(x_m)\}_{m\in M})\f$.
*/
assert( ns << _vs );
VarSet nsrem = _vs / ns;
TFactor<T> result( nsrem, T(0) );
-
+
// OPTIMIZE ME
IndexFor i_ns (ns, _vs);
IndexFor i_nsrem (nsrem, _vs);
* If *this corresponds with \f$f : \prod_{l\in L} X_l \to [0,\infty)\f$ and \f$L \subset M\f$, then
* the embedded factor corresponds with \f$g : \prod_{m\in M} X_m \to [0,\infty) : x \mapsto f(x_L)\f$.
*/
- TFactor<T> embed(const VarSet & ns) const {
+ TFactor<T> embed(const VarSet & ns) const {
assert( ns >> _vs );
if( _vs == ns )
return *this;
/// Apply binary operator pointwise on two factors
template<typename T, typename binaryOp> TFactor<T> pointwiseOp( const TFactor<T> &f, const TFactor<T> &g, binaryOp op ) {
if( f.vars() == g.vars() ) { // optimizate special case
- TFactor<T> result(f);
+ TFactor<T> result(f);
for( size_t i = 0; i < result.states(); i++ )
result[i] = op( result[i], g[i] );
- return result;
+ return result;
} else {
TFactor<T> result( f.vars() | g.vars(), 0.0 );
for( size_t alpha1 = 0; alpha1 < i.states(); alpha1++ )
for( size_t alpha2 = 0; alpha2 < i.states(); alpha2++ )
if( alpha2 != alpha1 )
- for( size_t beta1 = 0; beta1 < j.states(); beta1++ )
+ for( size_t beta1 = 0; beta1 < j.states(); beta1++ )
for( size_t beta2 = 0; beta2 < j.states(); beta2++ )
if( beta2 != beta1 ) {
size_t as = 1, bs = 1;
if( f > max )
max = f;
}
-
+
return std::tanh( 0.25 * std::log( max ) );
}
/// Calculates the mutual information between the two variables that f depends on, under the distribution given by f
/** \relates TFactor
* \pre f.vars().size() == 2
- */
+ */
template<typename T> Real MutualInfo(const TFactor<T> &f) {
assert( f.vars().size() == 2 );
VarSet::const_iterator it = f.vars().begin();
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