#include <graphCharac+.hh>
Public Methods | |
GraphManipulator () | |
virtual | ~GraphManipulator () |
void | ReadGraphVersion3 (char *path, char *pathTime) |
method for reading a graph in format 2 and for reading a file containing the time delay between the different time samples specific to the graph. More... | |
void | CreateMatchCostObject () |
This method creates a MatchCost Object used for matching. | |
void | InitGraphforMatch (int nb_sample, int firstSample1, float seuilDiv, int kMT1a, int kMT1b) |
This method initializes the matching procedure relative to the positive semantic definition, with a given reference graph. | |
void | InitGraphforMatchNeg (int nb_samples, float seuilDiv, int b) |
This method initializes the matching procedure relative to the negative semantic definition, with a given reference graph. | |
void | FixWeights (float weightMTinTLDiff, float TL1TL2Div, float flowDiff, float divDiff, float MIDiff, float divMTaMTbCost, float timeDiff, bool meanCalcul) |
This method introduce in the similarity model a given parameter vector. This method fixes also another parameter specific to the matching calculus. More... | |
void | FixLearnedWeights (int posOrNeg, bool meanCalcul, float factorReducList) |
This method updates the parameter vector used in the parametric similarity model, with the estimated parameters which were learned interactively. More... | |
float | Match (int posOrNeg, int minSample, int maxSample, int nbMiniBranches) |
This method performs the matching of a collection of sub-graphes with a reference sub-graph according to the currently defined similarity model parameter vector. More... | |
void | SaveSortedList (int a, int b) |
Method used to save the a posteriori collection of sub-graphs after a learning step and before beginning a matching procedure which selects candidates of this list. This method is also used to save a sub-graph collection after a matching procedure with a positive example. More... | |
float | Learn (int posOrNeg, int interestkMTa, int interestkMTb, int interestfirstSample) |
Method to 1) calculate, after that the user has fed the system with a positive or negative example, the current estimate of the similarity model parameter vector by calling the ParaSearch method 'MMSEestim(...)'; and 2) calculate the posterior probabilities assigned to each sub-graph candidate and to update the posterior sorted collection of sub-graphs. More... | |
void | LearnRefGraph (int posOrNeg) |
Method calling the 'Learn(..)' method to learn with the as an example the first (positive example) or the last (negative example) element of the current sorted collection of sub-graphs. More... | |
void | PrintAPostList () |
method to print on the standard output the sorted a posteriori collection of sub-graphs. More... | |
void | WriteAPostList () |
Method to write a text file destinated to the GUI, which contains the the relevance feedback measurements and the sorted a posteriori collection of sub-graphs. | |
void | InitParaLearn (int a, int b) |
Memory allocation and initialization of ParaSearch objects related to the positive or/and negative semantic. More... | |
void | ScaleParaLearnForSG (int a, int b, bool scale) |
Initialization of ParaSearch objects related to the positive or/and negative semantic. More... | |
void | EraseMemLearn (int posNeg) |
Method to replace by a flat distribution the current multinomial estimated distribution of the similairty model parameters. As the distributions are stored in the ParaSearch object, its method 'ErasePdf(..)' is called. More... | |
void | StrengthProbLink () |
Method to evaluate the divergences measurements between the multinomial distribution related to the positive and negativ semantic. A smoothing of the multinomial distributions by a Gaussian kernel is applied before the symetric Kyullbach-Kleiber divergence calculation. | |
void | ConvergenceQuality () |
Method to calculate the move of the norm of the parameter vector estimates during the last learning step. | |
void | VariancePosterior () |
Method to calculate the mean and the variance of the posterior probabilities attached to the elements of the sub-graph a posteriori collection. | |
void | PrintProbaLink () |
Method to print on the standard output the relevance feedback measurements. | |
Public Attributes | |
int | nb_TimelessClasses |
number of MT classes. | |
int | minSample |
Minimum/Maximum time sample considered in the graph. | |
int | maxSample |
Minimum/Maximum time sample considered in the graph. | |
float * | TimeDelay |
Array of time delays between the different time samples specific to the graph. | |
int | nbSamples |
number of samples of the time-window. | |
ParaSearch | paraSearchObject |
Object used for the estimation of the parameter vector of the similarity model related to a positive semantic. | |
SearchTree **** | searchTreeCollec |
Array of SearchTree objects related to a positive semantic, which is used to save the search tree during a macthing procedure. | |
float **** | costs |
array of distorsion costs related to a positive semantic, assigned to the search tree nodes during the matching procedure. | |
vector< float > | costSorted |
vector of sorted costs or posterior probabilities related to the elements of the collection of sub-graphs. | |
vector< int > | kMT2aSorted |
vector of index of MT class attached to the elements of the collection of sub-graphs. | |
int * | kMT2aSortedAux |
array of the MT class indexes attached to the collection of sub-graphs. | |
int * | firstSample2aSortedAux |
array of first time sample indexes attached to the collection of sub-graphs. | |
float * | kMT2aCostAux |
array of costs attached to the collection of sub-graphs. | |
vector< int > | kMT2bSorted |
vector of indexes of 2nd MT class attached to the elements of the collection of sub-graphs. (only when 2 MT classes are considered simultaneously per sub-graph). | |
int * | kMT2bSortedAux |
array of 2nd MT class indexes attached to the elements of the collection of sub-graphs (only when 2 MT classes are considered simultaneously per sub-graph). | |
int * | firstSample2bSortedAux |
array of first time sample indexes related to the 2nd MT classes and attached to the elements of the collection of sub-graphs (only when 2 MT classes are considered simultaneously per sub-graph). | |
float * | kMT2bCostAux |
array of costs related to the 2nd MT classes and attached to the elements of the collection of sub-graphs (only when 2 MT classes are considered simultaneously per sub-graph). | |
vector< int > | firstSample2Sorted |
vector of first time sample indexes related to the elements of the collection of sub-graphs. | |
ParaSearch | paraSearchObjectNeg |
Object used for the estimation of the parameter vector of the similarity model related to a negative semantic. | |
SearchTree **** | searchTreeCollecNeg |
Array of SearchTree objects related to a negative semantic, which is used to save the search tree during a macthing procedure. | |
float **** | costsNeg |
array of distorsion costs related to a negative semantic, assigned to the search tree nodes during the matching procedure. | |
float * | divergences |
Relevance feedback mesures. Divergence between the positive and negative related parameter distributions. | |
Private Methods | |
void | AddMTtoSubGraph (int subGraph, int kMT, float seuilDiv, int firstSample, int nb_samples) |
Method to load a sub-graph in order to perform after a matching procedure. More... | |
Private Attributes | |
int | nb_dim |
dimensionnality of the feature space. | |
int | nb_images |
number of image time samples. | |
unsigned long int | dim |
number of pixels in each image. | |
int | nx |
spatial dimension of the images. | |
int | ny |
spatial dimension of the images. | |
int | Index_CurrentImage |
index of the curent image time sample. | |
DynaClass * | TmpClass |
DynamicCluster Object for storing dynamic clusters trajectories. | |
SubGraph * | graph1 |
SubGraph Object used to select a 1 st sub-graph for inexact matching. | |
SubGraph * | graph2 |
SubGraph Object used to select a 2 nd sub-graph for inexact matching. | |
MatchCost * | matchCostObject |
MatchCost Object used for performing the sub-graph matching with the paramteric similarity model. | |
float | seuilDivergence |
Divergence treshold to fix the graph complexity. | |
int | sizeList |
Current number of elements in the posterior sub-graph collection. | |
bool | predefinedList |
Boolean flag to precise if a sub-graph collection was previously created or not. | |
float | factorReducList |
Current factor used to reduce the size of the posterior sub-graph collection. | |
bool | noTree |
Boolean flag to precise if a search-tree was previously created by a positive semantic matching. | |
bool | noTreeNeg |
Boolean flag to precise if a search-tree was previously created by a negative semantic matching. | |
float | meanPosterior |
Current statistical mean of the probability attached to the elements of the posterior sub-graph colection. | |
float | varPosterior |
Current statistical variance of the probability attached to the elements of the posterior sub-graph colection. | |
float | convQualityPos |
Move of the norm of the parameter vector estimates during the last learning step, for the positive parameter vector estimation. | |
float | convQualityNeg |
Move of the norm of the parameter vector estimates during the last learning step, for the negative parameter vector estimation. |
This object definition present methods defined in "readholegraph.cpp" and in "fillsubgraph+.cpp".
First note that this object posses similarties with the GraphDynaCluster object used previously for infering the trajectories, with the difference that the GraphManipulator object does not possess any methods and attributes related to the graph inference; It possesses attributes and methods to read a already created graph, to manipulate sub-graphs, to create sorted collections of sub-graphs with supervised learning methods, etc....
Second, note this object comprises a large diversity of attributes and methods. This is probably an error : this object should be split in various objects.
Definition at line 28 of file graphCharac+.hh.
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Method to load a sub-graph in order to perform after a matching procedure.
Definition at line 806 of file fillsubgraph+.cpp. |
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Method to replace by a flat distribution the current multinomial estimated distribution of the similairty model parameters. As the distributions are stored in the ParaSearch object, its method 'ErasePdf(..)' is called. In other words, this methods sets to 1 all the hyperparameters of the dirichlet model related to either the positive or negative semantic learning.
Definition at line 625 of file fillsubgraph+.cpp. |
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This method updates the parameter vector used in the parametric similarity model, with the estimated parameters which were learned interactively. This method updates the parameter relative either to the positive or to the negative semantic. By this method, a couple of paramters are also updated
Definition at line 90 of file fillsubgraph+.cpp. |
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This method introduce in the similarity model a given parameter vector. This method fixes also another parameter specific to the matching calculus.
Definition at line 81 of file fillsubgraph+.cpp. |
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Memory allocation and initialization of ParaSearch objects related to the positive or/and negative semantic. The ParaSearch object is used for the estimation of the parameter vector of the similarity model. This method is called once at the first iteration
Definition at line 397 of file fillsubgraph+.cpp. |
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Method to 1) calculate, after that the user has fed the system with a positive or negative example, the current estimate of the similarity model parameter vector by calling the ParaSearch method 'MMSEestim(...)'; and 2) calculate the posterior probabilities assigned to each sub-graph candidate and to update the posterior sorted collection of sub-graphs.
The method runs sequentially the following steps :
Definition at line 452 of file fillsubgraph+.cpp. |
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Method calling the 'Learn(..)' method to learn with the as an example the first (positive example) or the last (negative example) element of the current sorted collection of sub-graphs. This method is only used once to learn with the first positive example provided by the user.
Definition at line 439 of file fillsubgraph+.cpp. |
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This method performs the matching of a collection of sub-graphes with a reference sub-graph according to the currently defined similarity model parameter vector. The algorithm runs sequentially the following steps :
Definition at line 123 of file fillsubgraph+.cpp. |
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method to print on the standard output the sorted a posteriori collection of sub-graphs. used only duringthe developpement of the code !!! not usefull anymore Definition at line 632 of file fillsubgraph+.cpp. |
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method for reading a graph in format 2 and for reading a file containing the time delay between the different time samples specific to the graph.
Definition at line 27 of file readholegraph.cpp. |
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Method used to save the a posteriori collection of sub-graphs after a learning step and before beginning a matching procedure which selects candidates of this list. This method is also used to save a sub-graph collection after a matching procedure with a positive example.
The case of saving a sub-graph collection after a matching procedure with a positive example, is only usefull for the preselection of likely MT assocaitions when considering simultaneously 2 MT class per sub-graph.
Definition at line 352 of file fillsubgraph+.cpp. |
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Initialization of ParaSearch objects related to the positive or/and negative semantic. The ParaSearch object is used for the estimation of the parameter vector of the similarity model.
Definition at line 420 of file fillsubgraph+.cpp. |