A class implementing persistence heat maps. More...

#include <gudhi/Persistence_heat_maps.h>

Public Member Functions
	Persistence_heat_maps ()

	Persistence_heat_maps (const std::vector< std::pair< double, double > > &interval, std::vector< std::vector< double > > filter=create_Gaussian_filter(5, 1), bool erase_below_diagonal=false, size_t number_of_pixels=1000, double min_=std::numeric_limits< double >::max(), double max_=std::numeric_limits< double >::max())

	Persistence_heat_maps (const char *filename, std::vector< std::vector< double > > filter=create_Gaussian_filter(5, 1), bool erase_below_diagonal=false, size_t number_of_pixels=1000, double min_=std::numeric_limits< double >::max(), double max_=std::numeric_limits< double >::max(), unsigned dimension=std::numeric_limits< unsigned >::max())

	Persistence_heat_maps (const std::vector< std::pair< double, double > > &interval, const std::function< double(std::pair< double, double >, std::pair< double, double >)> &kernel, size_t number_of_x_pixels, size_t number_of_y_pixels, double min_x=0, double max_x=1, double min_y=0, double max_y=1)

	Persistence_heat_maps (const std::vector< std::pair< double, double > > &interval, const std::function< double(std::pair< double, double >, std::pair< double, double >)> &kernel)

void	compute_mean (const std::vector< Persistence_heat_maps * > &maps)

void	compute_median (const std::vector< Persistence_heat_maps * > &maps)

void	compute_percentage_of_active (const std::vector< Persistence_heat_maps * > &maps, size_t cutoff=1)

void	print_to_file (const char *filename) const

void	load_from_file (const char *filename)

bool	check_if_the_same (const Persistence_heat_maps &second) const

double	get_min () const

double	get_max () const

bool	operator== (const Persistence_heat_maps &rhs) const

bool	operator!= (const Persistence_heat_maps &rhs) const

void	plot (const char *filename) const

Persistence_heat_maps	multiply_by_scalar (double scalar) const

Persistence_heat_maps	operator* (double scalar)

Persistence_heat_maps	operator+= (const Persistence_heat_maps &rhs)

Persistence_heat_maps	operator-= (const Persistence_heat_maps &rhs)

Persistence_heat_maps	operator*= (double x)

Persistence_heat_maps	operator/= (double x)

std::vector< double >	vectorize (int number_of_function) const

size_t	number_of_vectorize_functions () const

double	project_to_R (int number_of_function) const

size_t	number_of_projections_to_R () const

double	distance (const Persistence_heat_maps &second_, double power=1) const

void	compute_average (const std::vector< Persistence_heat_maps * > &to_average)

double	compute_scalar_product (const Persistence_heat_maps &second_) const

std::pair< double, double >	get_x_range () const

std::pair< double, double >	get_y_range () const

Friends
Persistence_heat_maps	operator+ (const Persistence_heat_maps &first, const Persistence_heat_maps &second)

Persistence_heat_maps	operator- (const Persistence_heat_maps &first, const Persistence_heat_maps &second)

Persistence_heat_maps	operator* (double scalar, const Persistence_heat_maps &A)

Persistence_heat_maps	operator* (const Persistence_heat_maps &A, double scalar)

Detailed Description

template<typename Scalling_of_kernels = constant_scaling_function>
class Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >

A class implementing persistence heat maps.

Examples: persistence_heat_maps.cpp, persistence_heat_maps/average_persistence_heat_maps.cpp, persistence_heat_maps/compute_distance_of_persistence_heat_maps.cpp, persistence_heat_maps/compute_scalar_product_of_persistence_heat_maps.cpp, persistence_heat_maps/create_p_h_m_weighted_by_arctan_of_their_persistence.cpp, persistence_heat_maps/create_p_h_m_weighted_by_distance_from_diagonal.cpp, persistence_heat_maps/create_p_h_m_weighted_by_squared_diag_distance.cpp, persistence_heat_maps/create_persistence_heat_maps.cpp, and persistence_heat_maps/plot_persistence_heat_map.cpp.

Constructor & Destructor Documentation

◆ Persistence_heat_maps() [1/5]

template<typename Scalling_of_kernels = constant_scaling_function>

Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::Persistence_heat_maps ( )

inline

The default constructor. A scaling function from the diagonal is set up to a constant function. The image is not erased below the diagonal. The Gaussian have diameter 5.

◆ Persistence_heat_maps() [2/5]

template<typename Scalling_of_kernels >

Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::Persistence_heat_maps	(	const std::vector< std::pair< double, double > > &	interval,
		std::vector< std::vector< double > >	filter = `create_Gaussian_filter(5, 1)`,
		bool	erase_below_diagonal = `false`,
		size_t	number_of_pixels = `1000`,
		double	min_ = `std::numeric_limits<double>::max()`,
		double	max_ = `std::numeric_limits<double>::max()`
	)

Construction that takes at the input the following parameters: (1) A vector of pairs of doubles (representing persistence intervals). All other parameters are optional. They are: (2) a Gaussian filter generated by create_Gaussian_filter filter (the default value of this variable is a Gaussian filter of a radius 5), (3) a boolean value which determines if the area of image below diagonal should, or should not be erased (it will be erased by default). (4) a number of pixels in each direction (set to 1000 by default). (5) a min x and y value of points that are to be taken into account. By default it is set to std::numeric_limits<double>::max(), in which case the program compute the values based on the data, (6) a max x and y value of points that are to be taken into account. By default it is set to std::numeric_limits<double>::max(), in which case the program compute the values based on the data.

◆ Persistence_heat_maps() [3/5]

template<typename Scalling_of_kernels >

Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::Persistence_heat_maps	(	const char *	filename,
		std::vector< std::vector< double > >	filter = `create_Gaussian_filter(5, 1)`,
		bool	erase_below_diagonal = `false`,
		size_t	number_of_pixels = `1000`,
		double	min_ = `std::numeric_limits<double>::max()`,
		double	max_ = `std::numeric_limits<double>::max()`,
		unsigned	dimension = `std::numeric_limits<unsigned>::max()`
	)

Construction that takes at the input a name of a file with persistence intervals, a filter (radius 5 by default), a scaling function (constant by default), a boolean value which determines if the area of image below diagonal should, or should not be erased (should by default). The next parameter is the number of pixels in each direction (set to 1000 by default) and min and max values of images (both set to std::numeric_limits<double>::max() by default. If this is the case, the program will pick the right values based on the data). Construction that takes at the input the following parameters: (1) A name of a file with persistence intervals. The file should be readable by the function read_persistence_intervals_in_one_dimension_from_file. All other parameters are optional. They are: (2) a Gaussian filter generated by create_Gaussian_filter filter (the default value of this variable is a Gaussian filter of a radius 5), (3) a boolean value which determines if the area of image below diagonal should, or should not be erased (it will be erased by default). (4) a number of pixels in each direction (set to 1000 by default). (5) a min x and y value of points that are to be taken into account. By default it is set to std::numeric_limits<double>::max(), in which case the program compute the values based on the data, (6) a max x and y value of points that are to be taken into account. By default it is set to std::numeric_limits<double>::max(), in which case the program compute the values based on the data.

◆ Persistence_heat_maps() [4/5]

template<typename Scalling_of_kernels >

Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::Persistence_heat_maps	(	const std::vector< std::pair< double, double > > &	interval,
		const std::function< double(std::pair< double, double >, std::pair< double, double >)> &	kernel,
		size_t	number_of_x_pixels,
		size_t	number_of_y_pixels,
		double	min_x = `0`,
		double	max_x = `1`,
		double	min_y = `0`,
		double	max_y = `1`
	)

Construction that takes as inputs (1) the diagram, and (2) the grid parameters (min, max and number of samples for x and y axes)

◆ Persistence_heat_maps() [5/5]

template<typename Scalling_of_kernels >

Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::Persistence_heat_maps	(	const std::vector< std::pair< double, double > > &	interval,
		const std::function< double(std::pair< double, double >, std::pair< double, double >)> &	kernel
	)

Construction that takes only the diagram as input (weight and 2D kernel are template parameters)

Member Function Documentation

◆ check_if_the_same()

template<typename Scalling_of_kernels = constant_scaling_function>

bool Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::check_if_the_same ( const Persistence_heat_maps< Scalling_of_kernels > & second ) const

inline

The procedure checks if min_, max_ and this->heat_maps sizes are the same.

◆ compute_average()

template<typename Scalling_of_kernels >

void Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::compute_average ( const std::vector< Persistence_heat_maps< Scalling_of_kernels > * > & to_average )

A function to compute averaged persistence heat map, based on vector of persistence heat maps. This function is required by Topological_data_with_averages concept.

◆ compute_mean()

template<typename Scalling_of_kernels >

void Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::compute_mean ( const std::vector< Persistence_heat_maps< Scalling_of_kernels > * > & maps )

Compute a mean value of a collection of heat maps and store it in the current object. Note that all the persistence maps send in a vector to this procedure need to have the same parameters. If this is not the case, the program will throw an exception.

◆ compute_median()

template<typename Scalling_of_kernels >

void Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::compute_median ( const std::vector< Persistence_heat_maps< Scalling_of_kernels > * > & maps )

Compute a median value of a collection of heat maps and store it in the current object. Note that all the persistence maps send in a vector to this procedure need to have the same parameters. If this is not the case, the program will throw an exception.

◆ compute_percentage_of_active()

template<typename Scalling_of_kernels >

void Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::compute_percentage_of_active	(	const std::vector< Persistence_heat_maps< Scalling_of_kernels > * > &	maps,
		size_t	cutoff = `1`
	)

Compute a percentage of active (i.e) values above the cutoff of a collection of heat maps.

◆ compute_scalar_product()

template<typename Scalling_of_kernels >

double Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::compute_scalar_product ( const Persistence_heat_maps< Scalling_of_kernels > & second_ ) const

A function to compute scalar product of persistence heat maps. The parameter of this function is a const reference to an object of a class Persistence_heat_maps. This function is required in Topological_data_with_scalar_product concept.

◆ distance()

template<typename Scalling_of_kernels >

double Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::distance	(	const Persistence_heat_maps< Scalling_of_kernels > &	second_,
		double	power = `1`
	)		const

A function to compute distance between persistence heat maps. The parameter of this function is a const reference to an object of a class Persistence_heat_maps. This function is required in Topological_data_with_distances concept. For max norm distance, set power to std::numeric_limits<double>::max()

◆ get_max()

template<typename Scalling_of_kernels = constant_scaling_function>

double Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::get_max ( ) const

inline

Return maximal range value of persistent image.

◆ get_min()

template<typename Scalling_of_kernels = constant_scaling_function>

double Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::get_min ( ) const

inline

Return minimal range value of persistent image.

◆ get_x_range()

template<typename Scalling_of_kernels = constant_scaling_function>

std::pair< double, double > Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::get_x_range ( ) const

inline

The x-range of the persistence heat map.

◆ get_y_range()

template<typename Scalling_of_kernels = constant_scaling_function>

std::pair< double, double > Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::get_y_range ( ) const

inline

The y-range of the persistence heat map.

◆ load_from_file()

template<typename Scalling_of_kernels >

void Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::load_from_file ( const char * filename )

A function that load a heat map from file to the current object (and erase whatever was stored in the current object before).

◆ multiply_by_scalar()

template<typename Scalling_of_kernels = constant_scaling_function>

Persistence_heat_maps Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::multiply_by_scalar ( double scalar ) const

inline

Multiplication of Persistence_heat_maps by scalar (so that all values of the heat map gets multiplied by that scalar).

◆ number_of_projections_to_R()

template<typename Scalling_of_kernels = constant_scaling_function>

size_t Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::number_of_projections_to_R ( ) const

inline

The function gives the number of possible projections to R. This function is required by the Real_valued_topological_data concept.

◆ number_of_vectorize_functions()

template<typename Scalling_of_kernels = constant_scaling_function>

size_t Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::number_of_vectorize_functions ( ) const

inline

This function return the number of functions that allows vectorization of persistence heat map. It is required in a concept Vectorized_topological_data.

◆ operator!=()

template<typename Scalling_of_kernels = constant_scaling_function>

bool Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::operator!= ( const Persistence_heat_maps< Scalling_of_kernels > & rhs ) const

inline

Operator != to check if to persistence heat maps are different.

◆ operator*()

template<typename Scalling_of_kernels = constant_scaling_function>

Persistence_heat_maps Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::operator* ( double scalar )

inline

This function computes a product of an object of a type Persistence_heat_maps with real number.

◆ operator*=()

template<typename Scalling_of_kernels = constant_scaling_function>

Persistence_heat_maps Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::operator*= ( double x )

inline

*= operator for Persistence_heat_maps.

◆ operator+=()

template<typename Scalling_of_kernels = constant_scaling_function>

Persistence_heat_maps Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::operator+= ( const Persistence_heat_maps< Scalling_of_kernels > & rhs )

inline

+= operator for Persistence_heat_maps.

◆ operator-=()

template<typename Scalling_of_kernels = constant_scaling_function>

Persistence_heat_maps Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::operator-= ( const Persistence_heat_maps< Scalling_of_kernels > & rhs )

inline

-= operator for Persistence_heat_maps.

◆ operator/=()

template<typename Scalling_of_kernels = constant_scaling_function>

Persistence_heat_maps Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::operator/= ( double x )

inline

/= operator for Persistence_heat_maps.

◆ operator==()

template<typename Scalling_of_kernels = constant_scaling_function>

bool Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::operator== ( const Persistence_heat_maps< Scalling_of_kernels > & rhs ) const

inline

Operator == to check if to persistence heat maps are the same.

◆ plot()

template<typename Scalling_of_kernels >

void Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::plot ( const char * filename ) const

A function to generate a gnuplot script to visualize the persistent image.

◆ print_to_file()

template<typename Scalling_of_kernels >

void Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::print_to_file ( const char * filename ) const

The function outputs the persistence image to a text file. The format as follow: In the first line, the values min and max of the image are stored In the next lines, we have the persistence images in a form of a bitmap image.

◆ project_to_R()

template<typename Scalling_of_kernels >

double Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::project_to_R ( int number_of_function ) const

This function is required by the Real_valued_topological_data concept. It returns various projections on the persistence heat map to a real line. At the moment this function is not tested, since it is quite likely to be changed in the future. Given this, when using it, keep in mind that it will be most likely changed in the next versions.

◆ vectorize()

template<typename Scalling_of_kernels >

std::vector< double > Gudhi::Persistence_representations::Persistence_heat_maps< Scalling_of_kernels >::vectorize ( int number_of_function ) const

This function produce a vector of doubles based on a persistence heat map. It is required in a concept Vectorized_topological_data

Friends And Related Function Documentation

◆ operator* [1/2]

template<typename Scalling_of_kernels = constant_scaling_function>

Persistence_heat_maps operator*	(	const Persistence_heat_maps< Scalling_of_kernels > &	A,
		double	scalar
	)

friend

This function computes a product of an object of a type Persistence_heat_maps with real number.

◆ operator* [2/2]

template<typename Scalling_of_kernels = constant_scaling_function>

Persistence_heat_maps operator*	(	double	scalar,
		const Persistence_heat_maps< Scalling_of_kernels > &	A
	)

friend

This function computes a product of an object of a type Persistence_heat_maps with real number.

◆ operator+

template<typename Scalling_of_kernels = constant_scaling_function>

Persistence_heat_maps operator+	(	const Persistence_heat_maps< Scalling_of_kernels > &	first,
		const Persistence_heat_maps< Scalling_of_kernels > &	second
	)

friend

This function computes a sum of two objects of a type Persistence_heat_maps.

◆ operator-

template<typename Scalling_of_kernels = constant_scaling_function>

Persistence_heat_maps operator-	(	const Persistence_heat_maps< Scalling_of_kernels > &	first,
		const Persistence_heat_maps< Scalling_of_kernels > &	second
	)

friend

This function computes a difference of two objects of a type Persistence_heat_maps.

The documentation for this class was generated from the following file:

include/gudhi/Persistence_heat_maps.h

Public Member Functions

Friends

Detailed Description

Constructor & Destructor Documentation

◆ Persistence_heat_maps() [1/5]

◆ Persistence_heat_maps() [2/5]

◆ Persistence_heat_maps() [3/5]

◆ Persistence_heat_maps() [4/5]

◆ Persistence_heat_maps() [5/5]

Member Function Documentation

◆ check_if_the_same()

◆ compute_average()

◆ compute_mean()

◆ compute_median()

◆ compute_percentage_of_active()

◆ compute_scalar_product()

◆ distance()

◆ get_max()

◆ get_min()

◆ get_x_range()

◆ get_y_range()

◆ load_from_file()

◆ multiply_by_scalar()

◆ number_of_projections_to_R()

◆ number_of_vectorize_functions()

◆ operator!=()

◆ operator*()

◆ operator*=()

◆ operator+=()

◆ operator-=()

◆ operator/=()

◆ operator==()

◆ plot()

◆ print_to_file()

◆ project_to_R()

◆ vectorize()

Friends And Related Function Documentation

◆ operator* [1/2]

◆ operator* [2/2]

◆ operator+

◆ operator-