#include <graphCharac.hh>
Public Methods | |
GraphDynaCluster () | |
virtual | ~GraphDynaCluster () |
void | Initialise (char *path_in_Temporal, char *path_in_Timeless, int dimx_tmp, int dimy_tmp, int nb_bands) |
Initialisation of the graph object : Allocate memory and read the input files. | |
void | Build (float PrecisionFactor) |
Method to infer the graph trajectories. More... | |
void | MIEstimation () |
Method to characterize the graph with mutual information. More... | |
void | Out_Evolutions (int format, bool intensiveCalculus) |
Method to characterize the graph and write the graph output. More... | |
Public Attributes | |
int | nb_TimelessClasses |
Number of MT clusters or Classes. | |
Private Methods | |
void | Allocate_variables () |
Memory alocation method. | |
void | Acquire_path (char *path_in_Temporal, int nb_bands, char *path_in_Timeless) |
Method to read the input output file names. | |
void | Aquire_data () |
Method to read the input data. | |
void | Load_Current (int j1, int j2) |
Method to load a TL and a MT cluster Gaussian attributes for divergence calculation. More... | |
void | LoadpreTLTL (int prekTL, int kTL) |
Method to load 2 consecutive TL cluster Gaussian attributes for divergence calculation. More... | |
void | Load_CurrentPMT (int kMT) |
Method to load MT cluster Gaussian attributes for mutual information calculation. The time sample is given by the private attribute "Index_CurrentImage". | |
void | Load_MonoCurrentPMT (int kMT) |
Method to load a single MT cluster Gaussian attribute at a given time.The time sample is given by the private attribute "Index_CurrentImage". | |
void | Create_TxtGraphOutPutVersion1 (bool intensiveCalculus) |
method to characterize and write the graph file in standard format (format 1) . More... | |
void | Create_TxtGraphOutPutVersion2 (bool intensiveCalculus) |
method to characterize and write the graph file (format 2) output file name "./grapheV2" . More... | |
void | ReadWriteGraphVersion2JAVA (char *path, int edge) |
Method to read a graphe at the format 2 and create a graphe at at format adapted to the JAVA GUI. output file name"./grapheV2JAVA" . More... | |
Private Attributes | |
int | nb_dim |
number of dimension of the feature space. | |
int | nb_images |
number of images time sample. | |
unsigned long int | dim |
number of samples in each image. | |
int | nx |
size of a image (in pixels). | |
int | ny |
size of a image (in pixels). | |
int | Index_CurrentImage |
char *** | PathInTemp_Classfile |
path of the TL classfiles. | |
char *** | PathInTemp_MeanCov |
path of the TL attributes. | |
char *** | PathInTimeless_Classfile |
path of the MT classfile. | |
char *** | PathInTimeless_MeanCov |
path of the MT attribute file. | |
Image< unsigned char > | Temp_classfile |
TL Classfile images. | |
Image< float > | Temp_MeanCov |
TL Attribute variables. | |
Image< unsigned char > | Timeless_classfile |
MT Classfile image. | |
Image< float > | Timeless_MeanCov |
MT Attribute variable. | |
float ** | Current_C_A |
Current covariance matrice considered for divergence (or mutual information) calculation. | |
float ** | Current_C_B |
Current covariance matrice considered for divergence (or mutual information) calculation. | |
float * | Current_M_A |
Current mean vector considered for divergence (or mutual information) calculation. | |
float * | Current_M_B |
Current mean vector considered for divergence (or mutual information) calculation. | |
float ** | Current_C_AB |
Current joint MT covariance matrice considered for mutual information calculation. | |
float * | Current_M_AB |
Current joint MT mean vector considered for mutual information calculation. | |
float | PrecisionFactor |
Resolution of the Gaussian-Approximated divergence Integration. | |
DynaClass * | TmpClass |
DynamicCluster : an array of DynamicCluster objects is used to manage the MT cluster trajectories. | |
SuperposeClass * | supClass |
Object for classfiles intersection calculations. | |
MI | mi |
Object for mutual infroamtion calculation. |
Definition at line 21 of file graphCharac.hh.
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Method to infer the graph trajectories.
This method calculates for each MT cluster, the probabilities of association with the TL clusters at each time sample. Thus, for each time sample, collections of time-localized clusters, sorted according the assocation probabilities, are attached to each MT cluster. Definition at line 167 of file graphCharac.cpp. |
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method to characterize and write the graph file in standard format (format 1)
This format is the right one to be adopted in future : the learning and visualization algorithms using the graph must be adpated. However, for the moment, this format is useless
Definition at line 322 of file graphCharac.cpp. |
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method to characterize and write the graph file (format 2)
Definition at line 398 of file graphCharac.cpp. |
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Method to load a TL and a MT cluster Gaussian attributes for divergence calculation.
Definition at line 139 of file graphCharac.cpp. |
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Method to load 2 consecutive TL cluster Gaussian attributes for divergence calculation.
Definition at line 667 of file graphCharac.cpp. |
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Method to characterize the graph with mutual information.
Calculates the mutual information between two consecutive times for each MT cluster by an analytical calculation based on the covariance determinants Definition at line 693 of file graphCharac.cpp. |
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Method to characterize the graph and write the graph output. The characterization of the other graph's edge attributes (flow and the change of distributions of TL clusters) is done while writing the graph output on the disk.
Definition at line 303 of file graphCharac.cpp. |
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Method to read a graphe at the format 2 and create a graphe at at format adapted to the JAVA GUI.
Definition at line 487 of file graphCharac.cpp. |