Improved docs
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@ -188,8 +188,9 @@ class GTSAM_EXPORT GaussianMixture
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*
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* error(x;y,m) = K - log(probability(x;y,m))
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*
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* For all x,y,m. But note that K, for the GaussianMixture, cannot depend on
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* any arguments. Hence, we delegate to the underlying Gaussian
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* For all x,y,m. But note that K, the (log) normalization constant defined
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* in Conditional.h, should not depend on x, y, or m, only on the parameters
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* of the density. Hence, we delegate to the underlying Gaussian
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* conditionals, indexed by m, which do satisfy:
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*
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* log(probability_m(x;y)) = K_m - error_m(x;y)
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@ -38,7 +38,9 @@ namespace gtsam {
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* probability(x) = k exp(-error(x))
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* where k is a normalization constant making \int probability(x) == 1.0, and
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* logProbability(x) = K - error(x)
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* i.e., K = log(K).
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* i.e., K = log(K). The normalization constant K is assumed to *not* depend
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* on any argument, only (possibly) on the conditional parameters.
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* This class provides a default logNormalizationConstant() == 0.0.
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*
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* There are four broad classes of conditionals that derive from Conditional:
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*
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@ -181,9 +183,20 @@ namespace gtsam {
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/** Mutable iterator pointing past the last parent key. */
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typename FACTOR::iterator endParents() { return asFactor().end(); }
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/**
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* Check invariants of this conditional, given the values `x`.
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* It tests:
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* - evaluate >= 0.0
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* - evaluate(x) == conditional(x)
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* - exp(logProbability(x)) == evaluate(x)
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* - logProbability(x) == logNormalizationConstant() - error(x)
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*
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* @param conditional The conditional to test, as a reference to the derived type.
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* @tparam VALUES HybridValues, or a more narrow type like DiscreteValues.
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*/
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template <class VALUES>
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static bool CheckInvariants(const DERIVEDCONDITIONAL& conditional,
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const VALUES& values);
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const VALUES& x);
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/// @}
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