VTK
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A class for principal component analysis. More...
#include <vtkPCAStatistics.h>
Public Types | |
typedef vtkMultiCorrelativeStatistics | Superclass |
enum | NormalizationType { NONE, TRIANGLE_SPECIFIED, DIAGONAL_SPECIFIED, DIAGONAL_VARIANCE, NUM_NORMALIZATION_SCHEMES } |
enum | ProjectionType { FULL_BASIS, FIXED_BASIS_SIZE, FIXED_BASIS_ENERGY, NUM_BASIS_SCHEMES } |
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typedef vtkStatisticsAlgorithm | Superclass |
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typedef vtkTableAlgorithm | Superclass |
enum | InputPorts { INPUT_DATA = 0, LEARN_PARAMETERS = 1, INPUT_MODEL = 2 } |
enum | OutputIndices { OUTPUT_DATA = 0, OUTPUT_MODEL = 1, ASSESSMENT = 2, OUTPUT_TEST = 2 } |
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typedef vtkAlgorithm | Superclass |
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typedef vtkObject | Superclass |
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typedef vtkObjectBase | Superclass |
Public Member Functions | |
virtual const char * | GetClassName () |
virtual int | IsA (const char *type) |
virtual void | PrintSelf (ostream &os, vtkIndent indent) |
virtual void | SetNormalizationScheme (int) |
virtual int | GetNormalizationScheme () |
virtual void | SetNormalizationSchemeByName (const char *sname) |
virtual const char * | GetNormalizationSchemeName (int scheme) |
virtual vtkTable * | GetSpecifiedNormalization () |
virtual void | SetSpecifiedNormalization (vtkTable *) |
void | GetEigenvalues (int request, vtkDoubleArray *) |
void | GetEigenvalues (vtkDoubleArray *) |
double | GetEigenvalue (int request, int i) |
double | GetEigenvalue (int i) |
void | GetEigenvectors (int request, vtkDoubleArray *eigenvectors) |
void | GetEigenvectors (vtkDoubleArray *eigenvectors) |
void | GetEigenvector (int i, vtkDoubleArray *eigenvector) |
void | GetEigenvector (int request, int i, vtkDoubleArray *eigenvector) |
virtual void | SetBasisScheme (int) |
virtual int | GetBasisScheme () |
virtual const char * | GetBasisSchemeName (int schemeIndex) |
virtual void | SetBasisSchemeByName (const char *schemeName) |
virtual void | SetFixedBasisSize (int) |
virtual int | GetFixedBasisSize () |
virtual void | SetFixedBasisEnergy (double) |
virtual double | GetFixedBasisEnergy () |
virtual bool | SetParameter (const char *parameter, int index, vtkVariant value) |
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virtual void | Aggregate (vtkDataObjectCollection *, vtkMultiBlockDataSet *) |
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void | PrintSelf (ostream &os, vtkIndent indent) |
void | SetAssessOptionParameter (vtkIdType id, vtkStdString name) |
vtkStdString | GetAssessParameter (vtkIdType id) |
virtual void | SetColumnStatus (const char *namCol, int status) |
virtual void | ResetAllColumnStates () |
virtual int | RequestSelectedColumns () |
virtual void | ResetRequests () |
virtual vtkIdType | GetNumberOfRequests () |
virtual vtkIdType | GetNumberOfColumnsForRequest (vtkIdType request) |
virtual void | SetLearnOptionParameterConnection (vtkAlgorithmOutput *params) |
virtual void | SetLearnOptionParameters (vtkDataObject *params) |
virtual void | SetInputModelConnection (vtkAlgorithmOutput *model) |
virtual void | SetInputModel (vtkDataObject *model) |
virtual void | SetLearnOption (bool) |
virtual bool | GetLearnOption () |
virtual void | SetDeriveOption (bool) |
virtual bool | GetDeriveOption () |
virtual void | SetAssessOption (bool) |
virtual bool | GetAssessOption () |
virtual void | SetTestOption (bool) |
virtual bool | GetTestOption () |
virtual void | SetNumberOfPrimaryTables (vtkIdType) |
virtual vtkIdType | GetNumberOfPrimaryTables () |
virtual void | SetAssessParameters (vtkStringArray *) |
virtual vtkStringArray * | GetAssessParameters () |
virtual void | SetAssessNames (vtkStringArray *) |
virtual vtkStringArray * | GetAssessNames () |
virtual const char * | GetColumnForRequest (vtkIdType r, vtkIdType c) |
virtual int | GetColumnForRequest (vtkIdType r, vtkIdType c, vtkStdString &columnName) |
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virtual int | ProcessRequest (vtkInformation *, vtkInformationVector **, vtkInformationVector *) |
vtkTable * | GetOutput () |
vtkTable * | GetOutput (int index) |
void | SetInput (vtkDataObject *obj) |
void | SetInput (int index, vtkDataObject *obj) |
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int | HasExecutive () |
vtkExecutive * | GetExecutive () |
virtual void | SetExecutive (vtkExecutive *executive) |
virtual int | ModifyRequest (vtkInformation *request, int when) |
vtkInformation * | GetInputPortInformation (int port) |
vtkInformation * | GetOutputPortInformation (int port) |
int | GetNumberOfInputPorts () |
int | GetNumberOfOutputPorts () |
void | UpdateProgress (double amount) |
vtkInformation * | GetInputArrayInformation (int idx) |
void | RemoveAllInputs () |
vtkDataObject * | GetOutputDataObject (int port) |
virtual void | RemoveInputConnection (int port, vtkAlgorithmOutput *input) |
int | GetNumberOfInputConnections (int port) |
int | GetTotalNumberOfInputConnections () |
vtkAlgorithmOutput * | GetInputConnection (int port, int index) |
virtual void | Update () |
virtual void | UpdateInformation () |
virtual void | UpdateWholeExtent () |
void | ConvertTotalInputToPortConnection (int ind, int &port, int &conn) |
virtual double | ComputePriority () |
int | ProcessRequest (vtkInformation *request, vtkCollection *inInfo, vtkInformationVector *outInfo) |
virtual int | ComputePipelineMTime (vtkInformation *request, vtkInformationVector **inInfoVec, vtkInformationVector *outInfoVec, int requestFromOutputPort, unsigned long *mtime) |
virtual vtkInformation * | GetInformation () |
virtual void | SetInformation (vtkInformation *) |
virtual void | Register (vtkObjectBase *o) |
virtual void | UnRegister (vtkObjectBase *o) |
virtual void | SetAbortExecute (int) |
virtual int | GetAbortExecute () |
virtual void | AbortExecuteOn () |
virtual void | AbortExecuteOff () |
virtual void | SetProgress (double) |
virtual double | GetProgress () |
void | SetProgressText (const char *ptext) |
virtual char * | GetProgressText () |
virtual unsigned long | GetErrorCode () |
virtual void | SetInputArrayToProcess (int idx, int port, int connection, int fieldAssociation, const char *name) |
virtual void | SetInputArrayToProcess (int idx, int port, int connection, int fieldAssociation, int fieldAttributeType) |
virtual void | SetInputArrayToProcess (int idx, vtkInformation *info) |
virtual void | SetInputArrayToProcess (int idx, int port, int connection, const char *fieldAssociation, const char *attributeTypeorName) |
vtkDataObject * | GetInputDataObject (int port, int connection) |
virtual void | SetInputConnection (int port, vtkAlgorithmOutput *input) |
virtual void | SetInputConnection (vtkAlgorithmOutput *input) |
virtual void | AddInputConnection (int port, vtkAlgorithmOutput *input) |
virtual void | AddInputConnection (vtkAlgorithmOutput *input) |
vtkAlgorithmOutput * | GetOutputPort (int index) |
vtkAlgorithmOutput * | GetOutputPort () |
virtual void | SetReleaseDataFlag (int) |
virtual int | GetReleaseDataFlag () |
void | ReleaseDataFlagOn () |
void | ReleaseDataFlagOff () |
int | UpdateExtentIsEmpty (vtkDataObject *output) |
int | UpdateExtentIsEmpty (vtkInformation *pinfo, int extentType) |
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virtual void | DebugOn () |
virtual void | DebugOff () |
unsigned char | GetDebug () |
void | SetDebug (unsigned char debugFlag) |
virtual void | Modified () |
virtual unsigned long | GetMTime () |
unsigned long | AddObserver (unsigned long event, vtkCommand *, float priority=0.0f) |
unsigned long | AddObserver (const char *event, vtkCommand *, float priority=0.0f) |
vtkCommand * | GetCommand (unsigned long tag) |
void | RemoveObserver (vtkCommand *) |
void | RemoveObservers (unsigned long event, vtkCommand *) |
void | RemoveObservers (const char *event, vtkCommand *) |
int | HasObserver (unsigned long event, vtkCommand *) |
int | HasObserver (const char *event, vtkCommand *) |
void | RemoveObserver (unsigned long tag) |
void | RemoveObservers (unsigned long event) |
void | RemoveObservers (const char *event) |
void | RemoveAllObservers () |
int | HasObserver (unsigned long event) |
int | HasObserver (const char *event) |
template<class U , class T > | |
unsigned long | AddObserver (unsigned long event, U observer, void(T::*callback)(), float priority=0.0f) |
template<class U , class T > | |
unsigned long | AddObserver (unsigned long event, U observer, void(T::*callback)(vtkObject *, unsigned long, void *), float priority=0.0f) |
int | InvokeEvent (unsigned long event, void *callData) |
int | InvokeEvent (const char *event, void *callData) |
int | InvokeEvent (unsigned long event) |
int | InvokeEvent (const char *event) |
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const char * | GetClassName () const |
virtual void | Delete () |
virtual void | FastDelete () |
void | Print (ostream &os) |
void | SetReferenceCount (int) |
void | PrintRevisions (ostream &os) |
virtual void | PrintHeader (ostream &os, vtkIndent indent) |
virtual void | PrintTrailer (ostream &os, vtkIndent indent) |
int | GetReferenceCount () |
Protected Attributes | |
int | NormalizationScheme |
int | BasisScheme |
int | FixedBasisSize |
double | FixedBasisEnergy |
Static Protected Attributes | |
static const char * | BasisSchemeEnumNames [NUM_BASIS_SCHEMES+1] |
static const char * | NormalizationSchemeEnumNames [NUM_NORMALIZATION_SCHEMES+1] |
Additional Inherited Members | |
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int | AbortExecute |
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static vtkInformationIntegerKey * | PORT_REQUIREMENTS_FILLED () |
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A class for principal component analysis.
This class derives from the multi-correlative statistics algorithm and uses the covariance matrix and Cholesky decomposition computed by it. However, when it finalizes the statistics in Learn mode, the PCA class computes the SVD of the covariance matrix in order to obtain its eigenvectors.
In the assess mode, the input data are
or some combination thereof. Additionally, the user may specify some threshold energy or eigenvector entry below which the basis is truncated. This allows projection into a lower-dimensional state while minimizing (in a least squares sense) the projection error.
Definition at line 57 of file vtkPCAStatistics.h.
Definition at line 60 of file vtkPCAStatistics.h.
Methods by which the covariance matrix may be normalized.
Definition at line 67 of file vtkPCAStatistics.h.
These are the enumeration values that SetBasisScheme() accepts and GetBasisScheme returns.
Definition at line 80 of file vtkPCAStatistics.h.
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Reimplemented from vtkMultiCorrelativeStatistics.
Reimplemented in vtkPPCAStatistics.
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Return 1 if this class is the same type of (or a subclass of) the named class. Returns 0 otherwise. This method works in combination with vtkTypeMacro found in vtkSetGet.h.
Reimplemented from vtkMultiCorrelativeStatistics.
Reimplemented in vtkPPCAStatistics.
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Methods invoked by print to print information about the object including superclasses. Typically not called by the user (use Print() instead) but used in the hierarchical print process to combine the output of several classes.
Reimplemented from vtkMultiCorrelativeStatistics.
Reimplemented in vtkPPCAStatistics.
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This determines how (or if) the covariance matrix cov is normalized before PCA. When set to NONE, no normalization is performed. This is the default. When set to TRIANGLE_SPECIFIED, each entry cov(i,j) is divided by V(i,j). The list V of normalization factors must be set using the SetNormalization method before the filter is executed. When set to DIAGONAL_SPECIFIED, each entry cov(i,j) is divided by sqrt(V(i)*V(j)). The list V of normalization factors must be set using the SetNormalization method before the filter is executed. When set to DIAGONAL_VARIANCE, each entry cov(i,j) is divided by sqrt(cov(i,i)*cov(j,j)). Warning: Although this is accepted practice in some fields, some people think you should not turn this option on unless there is a good physically-based reason for doing so. Much better instead to determine how component magnitudes should be compared using physical reasoning and use DIAGONAL_SPECIFIED, TRIANGLE_SPECIFIED, or perform some pre-processing to shift and scale input data columns appropriately than to expect magical results from a shady normalization hack.
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This determines how (or if) the covariance matrix cov is normalized before PCA. When set to NONE, no normalization is performed. This is the default. When set to TRIANGLE_SPECIFIED, each entry cov(i,j) is divided by V(i,j). The list V of normalization factors must be set using the SetNormalization method before the filter is executed. When set to DIAGONAL_SPECIFIED, each entry cov(i,j) is divided by sqrt(V(i)*V(j)). The list V of normalization factors must be set using the SetNormalization method before the filter is executed. When set to DIAGONAL_VARIANCE, each entry cov(i,j) is divided by sqrt(cov(i,i)*cov(j,j)). Warning: Although this is accepted practice in some fields, some people think you should not turn this option on unless there is a good physically-based reason for doing so. Much better instead to determine how component magnitudes should be compared using physical reasoning and use DIAGONAL_SPECIFIED, TRIANGLE_SPECIFIED, or perform some pre-processing to shift and scale input data columns appropriately than to expect magical results from a shady normalization hack.
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This determines how (or if) the covariance matrix cov is normalized before PCA. When set to NONE, no normalization is performed. This is the default. When set to TRIANGLE_SPECIFIED, each entry cov(i,j) is divided by V(i,j). The list V of normalization factors must be set using the SetNormalization method before the filter is executed. When set to DIAGONAL_SPECIFIED, each entry cov(i,j) is divided by sqrt(V(i)*V(j)). The list V of normalization factors must be set using the SetNormalization method before the filter is executed. When set to DIAGONAL_VARIANCE, each entry cov(i,j) is divided by sqrt(cov(i,i)*cov(j,j)). Warning: Although this is accepted practice in some fields, some people think you should not turn this option on unless there is a good physically-based reason for doing so. Much better instead to determine how component magnitudes should be compared using physical reasoning and use DIAGONAL_SPECIFIED, TRIANGLE_SPECIFIED, or perform some pre-processing to shift and scale input data columns appropriately than to expect magical results from a shady normalization hack.
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This determines how (or if) the covariance matrix cov is normalized before PCA. When set to NONE, no normalization is performed. This is the default. When set to TRIANGLE_SPECIFIED, each entry cov(i,j) is divided by V(i,j). The list V of normalization factors must be set using the SetNormalization method before the filter is executed. When set to DIAGONAL_SPECIFIED, each entry cov(i,j) is divided by sqrt(V(i)*V(j)). The list V of normalization factors must be set using the SetNormalization method before the filter is executed. When set to DIAGONAL_VARIANCE, each entry cov(i,j) is divided by sqrt(cov(i,i)*cov(j,j)). Warning: Although this is accepted practice in some fields, some people think you should not turn this option on unless there is a good physically-based reason for doing so. Much better instead to determine how component magnitudes should be compared using physical reasoning and use DIAGONAL_SPECIFIED, TRIANGLE_SPECIFIED, or perform some pre-processing to shift and scale input data columns appropriately than to expect magical results from a shady normalization hack.
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These methods allow you to set/get values used to normalize the covariance matrix before PCA. The normalization values apply to all requests, so you do not specify a single vector but a 3-column table. The first two columns contain the names of columns from input 0 and the third column contains the value to normalize the corresponding entry in the covariance matrix. The table must always have 3 columns even when the NormalizationScheme is DIAGONAL_SPECIFIED. When only diagonal entries are to be used, only table rows where the first two columns are identical to one another will be employed. If there are multiple rows specifying different values for the same pair of columns, the entry nearest the bottom of the table takes precedence. These functions are actually convenience methods that set/get the third input of the filter. Because the table is the third input, you may use other filters to produce a table of normalizations and have the pipeline take care of updates. Any missing entries will be set to 1.0 and a warning issued. An error will occur if the third input to the filter is not set and the NormalizationScheme is DIAGONAL_SPECIFIED or TRIANGLE_SPECIFIED.
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These methods allow you to set/get values used to normalize the covariance matrix before PCA. The normalization values apply to all requests, so you do not specify a single vector but a 3-column table. The first two columns contain the names of columns from input 0 and the third column contains the value to normalize the corresponding entry in the covariance matrix. The table must always have 3 columns even when the NormalizationScheme is DIAGONAL_SPECIFIED. When only diagonal entries are to be used, only table rows where the first two columns are identical to one another will be employed. If there are multiple rows specifying different values for the same pair of columns, the entry nearest the bottom of the table takes precedence. These functions are actually convenience methods that set/get the third input of the filter. Because the table is the third input, you may use other filters to produce a table of normalizations and have the pipeline take care of updates. Any missing entries will be set to 1.0 and a warning issued. An error will occur if the third input to the filter is not set and the NormalizationScheme is DIAGONAL_SPECIFIED or TRIANGLE_SPECIFIED.
void vtkPCAStatistics::GetEigenvalues | ( | int | request, |
vtkDoubleArray * | |||
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Get the eigenvalues. This function: void GetEigenvalues(int request, int i, vtkDoubleArray*); does all of the work. The other functions simply call this function with the appropriate parameters. These functions are not valid unless Update() has been called and the Derive option is turned on.
void vtkPCAStatistics::GetEigenvalues | ( | vtkDoubleArray * | ) |
Get the eigenvalues. This function: void GetEigenvalues(int request, int i, vtkDoubleArray*); does all of the work. The other functions simply call this function with the appropriate parameters. These functions are not valid unless Update() has been called and the Derive option is turned on.
double vtkPCAStatistics::GetEigenvalue | ( | int | request, |
int | i | ||
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Get the eigenvalues. This function: void GetEigenvalues(int request, int i, vtkDoubleArray*); does all of the work. The other functions simply call this function with the appropriate parameters. These functions are not valid unless Update() has been called and the Derive option is turned on.
double vtkPCAStatistics::GetEigenvalue | ( | int | i | ) |
Get the eigenvalues. This function: void GetEigenvalues(int request, int i, vtkDoubleArray*); does all of the work. The other functions simply call this function with the appropriate parameters. These functions are not valid unless Update() has been called and the Derive option is turned on.
void vtkPCAStatistics::GetEigenvectors | ( | int | request, |
vtkDoubleArray * | eigenvectors | ||
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Get the eigenvectors. This function: void GetEigenvectors(int request, vtkDoubleArray* eigenvectors) does all of the work. The other functions are convenience functions that call this function with default arguments. These functions are not valid unless Update() has been called and the Derive option is turned on.
void vtkPCAStatistics::GetEigenvectors | ( | vtkDoubleArray * | eigenvectors | ) |
Get the eigenvectors. This function: void GetEigenvectors(int request, vtkDoubleArray* eigenvectors) does all of the work. The other functions are convenience functions that call this function with default arguments. These functions are not valid unless Update() has been called and the Derive option is turned on.
void vtkPCAStatistics::GetEigenvector | ( | int | i, |
vtkDoubleArray * | eigenvector | ||
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Get the eigenvectors. This function: void GetEigenvectors(int request, vtkDoubleArray* eigenvectors) does all of the work. The other functions are convenience functions that call this function with default arguments. These functions are not valid unless Update() has been called and the Derive option is turned on.
void vtkPCAStatistics::GetEigenvector | ( | int | request, |
int | i, | ||
vtkDoubleArray * | eigenvector | ||
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Get the eigenvectors. This function: void GetEigenvectors(int request, vtkDoubleArray* eigenvectors) does all of the work. The other functions are convenience functions that call this function with default arguments. These functions are not valid unless Update() has been called and the Derive option is turned on.
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This variable controls the dimensionality of output tuples in Assess mode. Consider the case where you have requested a PCA on D columns. When set to vtkPCAStatistics::FULL_BASIS, the entire set of basis vectors is used to derive new coordinates for each tuple being assessed. In this mode, you are guaranteed to have output tuples of the same dimension as the input tuples. (That dimension is D, so there will be D additional columns added to the table for the request.) When set to vtkPCAStatistics::FIXED_BASIS_SIZE, only the first N basis vectors are used to derive new coordinates for each tuple being assessed. In this mode, you are guaranteed to have output tuples of dimension min(N,D). You must set N prior to assessing data using the SetFixedBasisSize() method. When N < D, this turns the PCA into a projection (instead of change of basis). When set to vtkPCAStatistics::FIXED_BASIS_ENERGY, the number of basis vectors used to derive new coordinates for each tuple will be the minimum number of columns N that satisfy
You must set T prior to assessing data using the SetFixedBasisEnergy() method. When T < 1, this turns the PCA into a projection (instead of change of basis). By default BasisScheme is set to vtkPCAStatistics::FULL_BASIS.
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This variable controls the dimensionality of output tuples in Assess mode. Consider the case where you have requested a PCA on D columns. When set to vtkPCAStatistics::FULL_BASIS, the entire set of basis vectors is used to derive new coordinates for each tuple being assessed. In this mode, you are guaranteed to have output tuples of the same dimension as the input tuples. (That dimension is D, so there will be D additional columns added to the table for the request.) When set to vtkPCAStatistics::FIXED_BASIS_SIZE, only the first N basis vectors are used to derive new coordinates for each tuple being assessed. In this mode, you are guaranteed to have output tuples of dimension min(N,D). You must set N prior to assessing data using the SetFixedBasisSize() method. When N < D, this turns the PCA into a projection (instead of change of basis). When set to vtkPCAStatistics::FIXED_BASIS_ENERGY, the number of basis vectors used to derive new coordinates for each tuple will be the minimum number of columns N that satisfy
You must set T prior to assessing data using the SetFixedBasisEnergy() method. When T < 1, this turns the PCA into a projection (instead of change of basis). By default BasisScheme is set to vtkPCAStatistics::FULL_BASIS.
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This variable controls the dimensionality of output tuples in Assess mode. Consider the case where you have requested a PCA on D columns. When set to vtkPCAStatistics::FULL_BASIS, the entire set of basis vectors is used to derive new coordinates for each tuple being assessed. In this mode, you are guaranteed to have output tuples of the same dimension as the input tuples. (That dimension is D, so there will be D additional columns added to the table for the request.) When set to vtkPCAStatistics::FIXED_BASIS_SIZE, only the first N basis vectors are used to derive new coordinates for each tuple being assessed. In this mode, you are guaranteed to have output tuples of dimension min(N,D). You must set N prior to assessing data using the SetFixedBasisSize() method. When N < D, this turns the PCA into a projection (instead of change of basis). When set to vtkPCAStatistics::FIXED_BASIS_ENERGY, the number of basis vectors used to derive new coordinates for each tuple will be the minimum number of columns N that satisfy
You must set T prior to assessing data using the SetFixedBasisEnergy() method. When T < 1, this turns the PCA into a projection (instead of change of basis). By default BasisScheme is set to vtkPCAStatistics::FULL_BASIS.
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This variable controls the dimensionality of output tuples in Assess mode. Consider the case where you have requested a PCA on D columns. When set to vtkPCAStatistics::FULL_BASIS, the entire set of basis vectors is used to derive new coordinates for each tuple being assessed. In this mode, you are guaranteed to have output tuples of the same dimension as the input tuples. (That dimension is D, so there will be D additional columns added to the table for the request.) When set to vtkPCAStatistics::FIXED_BASIS_SIZE, only the first N basis vectors are used to derive new coordinates for each tuple being assessed. In this mode, you are guaranteed to have output tuples of dimension min(N,D). You must set N prior to assessing data using the SetFixedBasisSize() method. When N < D, this turns the PCA into a projection (instead of change of basis). When set to vtkPCAStatistics::FIXED_BASIS_ENERGY, the number of basis vectors used to derive new coordinates for each tuple will be the minimum number of columns N that satisfy
You must set T prior to assessing data using the SetFixedBasisEnergy() method. When T < 1, this turns the PCA into a projection (instead of change of basis). By default BasisScheme is set to vtkPCAStatistics::FULL_BASIS.
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The number of basis vectors to use. See SetBasisScheme() for more information. When FixedBasisSize <= 0 (the default), the fixed basis size scheme is equivalent to the full basis scheme.
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The number of basis vectors to use. See SetBasisScheme() for more information. When FixedBasisSize <= 0 (the default), the fixed basis size scheme is equivalent to the full basis scheme.
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The minimum energy the new basis should use, as a fraction. See SetBasisScheme() for more information. When FixedBasisEnergy >= 1 (the default), the fixed basis energy scheme is equivalent to the full basis scheme.
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The minimum energy the new basis should use, as a fraction. See SetBasisScheme() for more information. When FixedBasisEnergy >= 1 (the default), the fixed basis energy scheme is equivalent to the full basis scheme.
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A convenience method (in particular for access from other applications) to set parameter values. Return true if setting of requested parameter name was excuted, false otherwise.
Reimplemented from vtkStatisticsAlgorithm.
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This algorithm accepts a vtkTable containing normalization values for its fourth input (port 3). We override FillInputPortInformation to indicate this.
Reimplemented from vtkStatisticsAlgorithm.
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Execute the calculations required by the Derive option.
Reimplemented from vtkMultiCorrelativeStatistics.
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Execute the calculations required by the Test option.
Reimplemented from vtkMultiCorrelativeStatistics.
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Execute the calculations required by the Assess option.
Reimplemented from vtkMultiCorrelativeStatistics.
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Provide the appropriate assessment functor.
Reimplemented from vtkMultiCorrelativeStatistics.
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Definition at line 252 of file vtkPCAStatistics.h.
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Definition at line 253 of file vtkPCAStatistics.h.
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Definition at line 254 of file vtkPCAStatistics.h.
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Definition at line 255 of file vtkPCAStatistics.h.
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Definition at line 258 of file vtkPCAStatistics.h.
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Definition at line 259 of file vtkPCAStatistics.h.