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LSST Data Management Base Package
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GaussianProcess.h
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1// -*- LSST-C++ -*-
2
3/*
4 * LSST Data Management System
5 * Copyright 2008, 2009, 2010 LSST Corporation.
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7 * This product includes software developed by the
8 * LSST Project (http://www.lsst.org/).
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24
25#ifndef LSST_AFW_MATH_GAUSSIAN_PROCESS_H
26#define LSST_AFW_MATH_GAUSSIAN_PROCESS_H
27
28#include <Eigen/Dense>
29#include <stdexcept>
30
31#include "ndarray/eigen.h"
32#include <memory>
33
35#include "lsst/pex/exceptions.h"
36
37namespace lsst {
38namespace afw {
39namespace math {
40
61public:
63
69
74 void reset();
75
79 void start();
80
84 void addToEigen();
85
89 void addToVariance();
90
94 void addToSearch();
95
99 void addToIteration();
100
106 void addToTotal(int i);
107
111 void display();
112
113private:
114 double _before, _beginning;
115 double _eigenTime, _iterationTime, _searchTime, _varianceTime, _totalTime;
116 int _interpolationCount;
117};
118
125template <typename T>
127public:
128 virtual ~Covariogram();
129
130 // No copying
131 Covariogram(const Covariogram &) = delete;
133
134 // No moving
137
141 explicit Covariogram(){};
142
150 virtual T operator()(ndarray::Array<const T, 1, 1> const &p1,
151 ndarray::Array<const T, 1, 1> const &p2) const;
152};
153
163template <typename T>
165public:
167
168 explicit SquaredExpCovariogram();
169
174 void setEllSquared(double ellSquared);
175
176 T operator()(ndarray::Array<const T, 1, 1> const &, ndarray::Array<const T, 1, 1> const &) const override;
177
178private:
179 double _ellSquared;
180};
181
192template <typename T>
194public:
196
197 explicit NeuralNetCovariogram();
198
202 void setSigma0(double sigma0);
203
207 void setSigma1(double sigma1);
208
209 T operator()(ndarray::Array<const T, 1, 1> const &, ndarray::Array<const T, 1, 1> const &) const override;
210
211private:
212 double _sigma0, _sigma1;
213};
214
223template <typename T>
224class KdTree {
225public:
226 // No copying
227 KdTree(const KdTree &) = delete;
228 KdTree &operator=(const KdTree &) = delete;
229
230 // No moving
231 KdTree(KdTree &&) = delete;
232 KdTree &operator=(KdTree &&) = delete;
233
234 // Add default constructor (which is no longer generated)
235 KdTree() = default;
236
245 void Initialize(ndarray::Array<T, 2, 2> const &dt);
246
262 void findNeighbors(ndarray::Array<int, 1, 1> neighdex, ndarray::Array<double, 1, 1> dd,
263 ndarray::Array<const T, 1, 1> const &v, int n_nn) const;
264
272 T getData(int ipt, int idim) const;
273
295 ndarray::Array<T, 1, 1> getData(int ipt) const;
296
304 void addPoint(ndarray::Array<const T, 1, 1> const &v);
305
314 void removePoint(int dex);
315
319 int getNPoints() const;
320
328 void getTreeNode(ndarray::Array<int, 1, 1> const &v, int dex) const;
329
330private:
331 ndarray::Array<int, 2, 2> _tree;
332 ndarray::Array<int, 1, 1> _inn;
333 ndarray::Array<T, 2, 2> _data;
334
335 enum { DIMENSION, LT, GEQ, PARENT };
336
337 //_tree stores the relationships between data points
338 //_tree[i][DIMENSION] is the dimension on which the ith node segregates its daughters
339 //
340 //_tree[i][LT] is the branch of the tree down which the daughters' DIMENSIONth component
341 // is less than the parent's
342 //
343 //_tree[i][GEQ] is the branch of the tree down which the daughters' DIMENSIONth component is
344 // greather than or equal to the parent's
345 //
346 //_tree[i][PARENT] is the parent node of the ith node
347
348 //_data actually stores the data points
349
350 int _npts, _dimensions, _room, _roomStep, _masterParent;
351 mutable int _neighborsFound, _neighborsWanted;
352
353 //_room denotes the capacity of _data and _tree. It will usually be larger
354 // than _npts so that we do not have to reallocate
355 //_tree and _data every time we add a new point to the tree
356
357 mutable ndarray::Array<double, 1, 1> _neighborDistances;
358 mutable ndarray::Array<int, 1, 1> _neighborCandidates;
359
372 void _organize(ndarray::Array<int, 1, 1> const &use, int ct, int parent, int dir);
373
379 int _findNode(ndarray::Array<const T, 1, 1> const &v) const;
380
397 void _lookForNeighbors(ndarray::Array<const T, 1, 1> const &v, int consider, int from) const;
398
402 int _testTree() const;
403
417 int _walkUpTree(int target, int dir, int root) const;
418
427 void _count(int where, int *ct) const;
428
434 void _descend(int root);
435
441 void _reassign(int target);
442
446 double _distance(ndarray::Array<const T, 1, 1> const &p1, ndarray::Array<const T, 1, 1> const &p2) const;
447};
448
470template <typename T>
472public:
473 // No copying
476
477 // No moving
480
493 GaussianProcess(ndarray::Array<T, 2, 2> const &dataIn, ndarray::Array<T, 1, 1> const &ff,
494 std::shared_ptr<Covariogram<T> > const &covarIn);
495
515 GaussianProcess(ndarray::Array<T, 2, 2> const &dataIn, ndarray::Array<T, 1, 1> const &mn,
516 ndarray::Array<T, 1, 1> const &mx, ndarray::Array<T, 1, 1> const &ff,
517 std::shared_ptr<Covariogram<T> > const &covarIn);
530 GaussianProcess(ndarray::Array<T, 2, 2> const &dataIn, ndarray::Array<T, 2, 2> const &ff,
531 std::shared_ptr<Covariogram<T> > const &covarIn);
548 GaussianProcess(ndarray::Array<T, 2, 2> const &dataIn, ndarray::Array<T, 1, 1> const &mn,
549 ndarray::Array<T, 1, 1> const &mx, ndarray::Array<T, 2, 2> const &ff,
550 std::shared_ptr<Covariogram<T> > const &covarIn);
551
555 int getNPoints() const;
556
561 int getDim() const;
562
572 void getData(ndarray::Array<T, 2, 2> pts, ndarray::Array<T, 1, 1> fn,
573 ndarray::Array<int, 1, 1> indices) const;
574
584 void getData(ndarray::Array<T, 2, 2> pts, ndarray::Array<T, 2, 2> fn,
585 ndarray::Array<int, 1, 1> indices) const;
586
604 T interpolate(ndarray::Array<T, 1, 1> variance, ndarray::Array<T, 1, 1> const &vin,
605 int numberOfNeighbors) const;
621 void interpolate(ndarray::Array<T, 1, 1> mu, ndarray::Array<T, 1, 1> variance,
622 ndarray::Array<T, 1, 1> const &vin, int numberOfNeighbors) const;
623
643 T selfInterpolate(ndarray::Array<T, 1, 1> variance, int dex, int numberOfNeighbors) const;
644
659 void selfInterpolate(ndarray::Array<T, 1, 1> mu, ndarray::Array<T, 1, 1> variance, int dex,
660 int numberOfNeighbors) const;
661
683 void batchInterpolate(ndarray::Array<T, 1, 1> mu, ndarray::Array<T, 1, 1> variance,
684 ndarray::Array<T, 2, 2> const &queries) const;
685
703 void batchInterpolate(ndarray::Array<T, 1, 1> mu, ndarray::Array<T, 2, 2> const &queries) const;
704
709 void batchInterpolate(ndarray::Array<T, 2, 2> mu, ndarray::Array<T, 2, 2> variance,
710 ndarray::Array<T, 2, 2> const &queries) const;
711
716 void batchInterpolate(ndarray::Array<T, 2, 2> mu, ndarray::Array<T, 2, 2> const &queries) const;
717
734 void addPoint(ndarray::Array<T, 1, 1> const &vin, T f);
735
745 void addPoint(ndarray::Array<T, 1, 1> const &vin, ndarray::Array<T, 1, 1> const &f);
746
758 void removePoint(int dex);
759
766 void setKrigingParameter(T kk);
767
774 void setCovariogram(std::shared_ptr<Covariogram<T> > const &covar);
775
787 void setLambda(T lambda);
788
804
805private:
806 int _npts, _useMaxMin, _dimensions, _room, _roomStep, _nFunctions;
807
808 T _krigingParameter, _lambda;
809
810 ndarray::Array<T, 1, 1> _max, _min;
811 ndarray::Array<T, 2, 2> _function;
812
813 KdTree<T> _kdTree;
814
815 std::shared_ptr<Covariogram<T> > _covariogram;
816 mutable GaussianProcessTimer _timer;
817};
818} // namespace math
819} // namespace afw
820} // namespace lsst
821
822#endif //#ifndef LSST_AFW_MATH_GAUSSIAN_PROCESS_H
Key< Flag > const & target
Interface for DateTime class.
table::Key< double > sigma1
afw::table::Key< afw::table::Array< VariancePixelT > > variance
The parent class of covariogram functions for use in Gaussian Process interpolation.
Covariogram()
construct a Covariogram assigning default values to the hyper parameters
Covariogram & operator=(Covariogram &&)=delete
Covariogram(const Covariogram &)=delete
Covariogram & operator=(const Covariogram &)=delete
virtual T operator()(ndarray::Array< const T, 1, 1 > const &p1, ndarray::Array< const T, 1, 1 > const &p2) const
Actually evaluate the covariogram function relating two points you want to interpolate from.
Covariogram(Covariogram &&)=delete
Stores values of a function sampled on an image and allows you to interpolate the function to unsampl...
GaussianProcessTimer & getTimes() const
Give the user acces to _timer, an object keeping track of the time spent on various processes within ...
void setLambda(T lambda)
set the value of the hyperparameter _lambda
int getDim() const
return the dimensionality of data points stored in the GaussianProcess
GaussianProcess & operator=(GaussianProcess &&)=delete
void setKrigingParameter(T kk)
Assign a value to the Kriging paramter.
void addPoint(ndarray::Array< T, 1, 1 > const &vin, T f)
Add a point to the pool of data used by GaussianProcess for interpolation.
void batchInterpolate(ndarray::Array< T, 1, 1 > mu, ndarray::Array< T, 1, 1 > variance, ndarray::Array< T, 2, 2 > const &queries) const
Interpolate a list of query points using all of the input data (rather than nearest neighbors)
GaussianProcess(const GaussianProcess &)=delete
T interpolate(ndarray::Array< T, 1, 1 > variance, ndarray::Array< T, 1, 1 > const &vin, int numberOfNeighbors) const
Interpolate the function value at one point using a specified number of nearest neighbors.
T selfInterpolate(ndarray::Array< T, 1, 1 > variance, int dex, int numberOfNeighbors) const
This method will interpolate the function on a data point for purposes of optimizing hyper parameters...
void removePoint(int dex)
This will remove a point from the data set.
void setCovariogram(std::shared_ptr< Covariogram< T > > const &covar)
Assign a different covariogram to this GaussianProcess.
void getData(ndarray::Array< T, 2, 2 > pts, ndarray::Array< T, 1, 1 > fn, ndarray::Array< int, 1, 1 > indices) const
Return a sub-sample the data underlying the Gaussian Process.
GaussianProcess(GaussianProcess &&)=delete
int getNPoints() const
return the number of data points stored in the GaussianProcess
GaussianProcess & operator=(const GaussianProcess &)=delete
This is a structure for keeping track of how long the interpolation methods spend on different parts ...
GaussianProcessTimer & operator=(GaussianProcessTimer &&)=default
void addToSearch()
Adds time to _searchTime.
void addToVariance()
Adds time to _varianceTime.
void addToIteration()
Adds time to _iterationTime.
void start()
Starts the timer for an individual call to an interpolation routine.
void display()
Displays the current values of all times and _interpolationCount.
GaussianProcessTimer(GaussianProcessTimer const &)=default
void reset()
Resets all of the data members of GaussianProcessTimer to zero.
void addToEigen()
Adds time to _eigenTime.
void addToTotal(int i)
Adds time to _totalTime and adds counts to _interpolationCount.
GaussianProcessTimer & operator=(GaussianProcessTimer const &)=default
GaussianProcessTimer(GaussianProcessTimer &&)=default
The data for GaussianProcess is stored in a KD tree to facilitate nearest-neighbor searches.
KdTree(const KdTree &)=delete
void removePoint(int dex)
Remove a point from the tree.
void findNeighbors(ndarray::Array< int, 1, 1 > neighdex, ndarray::Array< double, 1, 1 > dd, ndarray::Array< const T, 1, 1 > const &v, int n_nn) const
Find the nearest neighbors of a point.
void addPoint(ndarray::Array< const T, 1, 1 > const &v)
Add a point to the tree.
void Initialize(ndarray::Array< T, 2, 2 > const &dt)
Build a KD Tree to store the data for GaussianProcess.
KdTree & operator=(KdTree &&)=delete
KdTree & operator=(const KdTree &)=delete
void getTreeNode(ndarray::Array< int, 1, 1 > const &v, int dex) const
Return the _tree information for a given data point.
int getNPoints() const
return the number of data points stored in the tree
T getData(int ipt, int idim) const
Return one element of one node on the tree.
KdTree(KdTree &&)=delete
a Covariogram that recreates a neural network with one hidden layer and infinite units in that layer
void setSigma0(double sigma0)
set the _sigma0 hyper parameter
T operator()(ndarray::Array< const T, 1, 1 > const &, ndarray::Array< const T, 1, 1 > const &) const override
Actually evaluate the covariogram function relating two points you want to interpolate from.
void setSigma1(double sigma1)
set the _sigma1 hyper parameter
void setEllSquared(double ellSquared)
set the _ellSquared hyper parameter (the square of the characteristic length scale of the covariogram...
T operator()(ndarray::Array< const T, 1, 1 > const &, ndarray::Array< const T, 1, 1 > const &) const override
Actually evaluate the covariogram function relating two points you want to interpolate from.