Documentation and headers
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				|  | @ -15,17 +15,17 @@ | ||||||
|  * @date    Dec 8, 2010 |  * @date    Dec 8, 2010 | ||||||
|  */ |  */ | ||||||
| 
 | 
 | ||||||
|  | #include <gtsam/linear/GaussianConditional.h> | ||||||
|  | #include <gtsam/linear/JacobianFactor.h> | ||||||
|  | #include <gtsam/linear/HessianFactor.h> | ||||||
|  | #include <gtsam/linear/GaussianFactorGraph.h> | ||||||
|  | #include <gtsam/inference/VariableSlots.h> | ||||||
|  | #include <gtsam/inference/FactorGraph-inl.h> | ||||||
| #include <gtsam/base/debug.h> | #include <gtsam/base/debug.h> | ||||||
| #include <gtsam/base/timing.h> | #include <gtsam/base/timing.h> | ||||||
| #include <gtsam/base/Matrix.h> | #include <gtsam/base/Matrix.h> | ||||||
| #include <gtsam/base/FastMap.h> | #include <gtsam/base/FastMap.h> | ||||||
| #include <gtsam/base/cholesky.h> | #include <gtsam/base/cholesky.h> | ||||||
| #include <gtsam/inference/VariableSlots.h> |  | ||||||
| #include <gtsam/inference/FactorGraph-inl.h> |  | ||||||
| #include <gtsam/linear/GaussianConditional.h> |  | ||||||
| #include <gtsam/linear/JacobianFactor.h> |  | ||||||
| #include <gtsam/linear/HessianFactor.h> |  | ||||||
| #include <gtsam/linear/GaussianFactorGraph.h> |  | ||||||
| 
 | 
 | ||||||
| #include <boost/foreach.hpp> | #include <boost/foreach.hpp> | ||||||
| #include <boost/format.hpp> | #include <boost/format.hpp> | ||||||
|  | @ -33,6 +33,7 @@ | ||||||
| #include <boost/lambda/bind.hpp> | #include <boost/lambda/bind.hpp> | ||||||
| #include <boost/lambda/lambda.hpp> | #include <boost/lambda/lambda.hpp> | ||||||
| 
 | 
 | ||||||
|  | #include <cmath> | ||||||
| #include <sstream> | #include <sstream> | ||||||
| #include <stdexcept> | #include <stdexcept> | ||||||
| 
 | 
 | ||||||
|  | @ -337,8 +338,11 @@ namespace gtsam { | ||||||
|       // find first column index for this key
 |       // find first column index for this key
 | ||||||
|       size_t column_start = columnIndices[*var]; |       size_t column_start = columnIndices[*var]; | ||||||
|       for (size_t i = 0; i < (size_t) whitenedA.rows(); i++) |       for (size_t i = 0; i < (size_t) whitenedA.rows(); i++) | ||||||
|         for (size_t j = 0; j < (size_t) whitenedA.cols(); j++) |         for (size_t j = 0; j < (size_t) whitenedA.cols(); j++) { | ||||||
|           entries.push_back(boost::make_tuple(i, column_start+j, whitenedA(i,j))); |         	double s = whitenedA(i,j); | ||||||
|  |           if (std::abs(s) > 1e-12) entries.push_back( | ||||||
|  | 							boost::make_tuple(i, column_start + j, s)); | ||||||
|  |         } | ||||||
|     } |     } | ||||||
| 
 | 
 | ||||||
|     Vector whitenedb(model_->whiten(getb())); |     Vector whitenedb(model_->whiten(getb())); | ||||||
|  |  | ||||||
|  | @ -223,7 +223,7 @@ namespace gtsam { | ||||||
|      * Return vector of i, j, and s to generate an m-by-n sparse matrix |      * Return vector of i, j, and s to generate an m-by-n sparse matrix | ||||||
|      * such that S(i(k),j(k)) = s(k), which can be given to MATLAB's sparse. |      * such that S(i(k),j(k)) = s(k), which can be given to MATLAB's sparse. | ||||||
|      * As above, the standard deviations are baked into A and b |      * As above, the standard deviations are baked into A and b | ||||||
|      * @param first column index for each variable |      * @param columnIndices First column index for each variable. | ||||||
|      */ |      */ | ||||||
|     std::vector<boost::tuple<size_t, size_t, double> > |     std::vector<boost::tuple<size_t, size_t, double> > | ||||||
|     sparse(const std::vector<size_t>& columnIndices) const; |     sparse(const std::vector<size_t>& columnIndices) const; | ||||||
|  |  | ||||||
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