anpcpp Namespace Reference

Analytic Network Process computational library. More...

Classes
class	AnpCluster
	A group of nodes (and cluster-level pairwise comparisons). More...
class	AnpNetwork
	Root ANP model: structure, judgments, supermatrices, and priorities. More...
class	AnpNode
	A decision element within a cluster (may own a subnetwork). More...
struct	ConsistencyOptions
	Options for consistency. More...
struct	ConsistencyResult
	Full consistency analysis result. More...
class	ConvergenceError
	Thrown when power iteration fails to converge within max_iterations. More...
class	DimensionError
	Thrown when vector/matrix dimensions do not match an operation. More...
struct	DivideByConstantInterpreter
	Divide each raw value by a fixed positive constant. More...
struct	DivideByMaxInterpreter
	Divide each raw value by the max among present values. More...
struct	EigenOptions
	Options controlling principal eigen computation. More...
struct	EigenResult
	Result of principal_eigen. More...
struct	IdentityInterpreter
	Pass-through interpreter (values already in [0, 1]). More...
class	JsonIoError
	Thrown on JSON parse, validation, or I/O errors. More...
struct	LimitMatrixOptions
	Options for limit-matrix calculation. More...
class	Matrix
	Dense row-major matrix stored in a flat buffer. More...
struct	MinMaxNormalizeInterpreter
	Map the column's [min, max] onto [0, 1]. More...
struct	NodePrioritizerSlot
	Stores either pairwise or ratings judgments for one dest cluster. More...
class	PairwiseJudgments
	Square pairwise comparison table over a named list of alternatives. More...
struct	PiecewiseLinearInterpreter
	Piecewise-linear map from raw value to [0, 1] via sorted knots. More...
struct	RatingCategory
	A named rating category with an associated score in [0, 1]. More...
class	RatingsPrioritizer
	Ratings table over named alternatives for one (wrt node, dest cluster). More...
class	SynthesisError
	Thrown when synthesis or expression evaluation fails. More...
struct	SynthesisOptions
	Synthesis configuration for networks with subnetworks. More...
class	Vector
	One-dimensional array of doubles with element-wise arithmetic. More...

Typedefs
using	ScoreInterpreter = std::variant< IdentityInterpreter, DivideByMaxInterpreter, DivideByConstantInterpreter, MinMaxNormalizeInterpreter, PiecewiseLinearInterpreter >
	Declarative raw-value → [0, 1] interpreter (JSON-serializable).

Enumerations
enum class	NodePrioritizerKind
	Active prioritizer kind for a node → destination-cluster link.
enum class	SynthesisKind { Additive , Multiplicative , Custom }
	How control-node subnetwork scores are combined. More...

Functions
Matrix	harker_fix (const Matrix &mat)
	Applies Harker's fix for incomplete pairwise matrices.
EigenResult	principal_eigen (const Matrix &mat, const EigenOptions &options={})
	Power iteration with sum-normalization (pyanp pri_eigen compatible).
Vector	principal_eigenvector (const Matrix &mat, const EigenOptions &options={})
	Returns the principal eigenvector only.
double	principal_eigenvalue (const Matrix &mat, const EigenOptions &options={})
	Returns the principal eigenvalue only.
double	random_index (std::size_t n)
	Saaty random index (RI) for matrix order n.
double	consistency_index (double lambda_max, std::size_t n)
	Consistency index CI = (lambda_max - n) / (n - 1).
double	consistency_ratio (double lambda_max, std::size_t n)
	Consistency ratio CR = CI / RI.
ConsistencyResult	consistency (const Matrix &mat, ConsistencyOptions options={})
	Computes Saaty CI and CR for a pairwise comparison matrix.
double	consistency_index (const Matrix &mat, ConsistencyOptions options={})
	Consistency index from a matrix (throws on non-convergence).
double	consistency_ratio (const Matrix &mat, ConsistencyOptions options={})
	Consistency ratio from a matrix (throws on non-convergence).
std::string	network_to_json (const AnpNetwork &network)
	Serializes a network (including subnetworks and layout hints) to JSON.
std::unique_ptr< AnpNetwork >	network_from_json (const std::string &json_text)
	Parses JSON produced by network_to_json.
void	save_network_file (const AnpNetwork &network, const std::string &path)
	Writes a network to a file.
std::unique_ptr< AnpNetwork >	load_network_file (const std::string &path)
	Loads a network from a file.
Matrix	column_normalize (const Matrix &mat)
	Column-normalizes each column by its sum (pyanp normalize).
void	column_normalize_inplace (Matrix &mat)
	In-place column_normalize.
Matrix	hierarchy_formula (const Matrix &mat)
	Hierarchy limit formula: normalize(sum of W^k for k=1..n-1).
Matrix	calculus_limit (const Matrix &mat, const LimitMatrixOptions &options={})
	SuperDecisions / pyanp calculus limit matrix.
Vector	priority_from_limit (const Matrix &limit_matrix)
	Row sums of the limit matrix, L1-normalized (pyanp priority_from_limit).
std::vector< std::optional< double > >	apply_score_interpreter (const ScoreInterpreter &interpreter, const std::vector< std::optional< double > > &raw)
	Apply interpreter to a column of optional raw values.
std::map< std::string, double >	synthesize_additive (const std::map< std::string, double > &subnet_weights, const std::map< std::string, std::map< std::string, double > > &alt_scores)
	Weighted average synthesis (matches AnpNetwork::sum_subnetwork_formula).
std::map< std::string, double >	synthesize_multiplicative (const std::map< std::string, double > &subnet_weights, const std::map< std::string, std::map< std::string, double > > &alt_scores)
	Multiplicative synthesis: product of score^weight per alt, then L1-normalize.
std::map< std::string, double >	synthesize_custom (const std::string &expression, const std::map< std::string, std::map< std::string, double > > &alt_scores, const std::vector< std::string > &alt_order)
	Custom expression synthesis per alternative.
std::map< std::string, double >	synthesize (const SynthesisOptions &options, const std::map< std::string, double > &subnet_weights, const std::map< std::string, std::map< std::string, double > > &alt_scores, const std::vector< std::string > &alt_order)
	Dispatches on SynthesisOptions::kind.
double	eval_expression (const std::string &expression, const std::map< std::string, double > &variables)
	Evaluates a numeric expression with named variables.

Detailed Description

Analytic Network Process computational library.

Enumeration Type Documentation

SynthesisKind

enum class anpcpp::SynthesisKind

strong

How control-node subnetwork scores are combined.

Enumerator
Additive	Weighted average of subnet scores (pyanp default).
Multiplicative	Product of subnet scores across control nodes, then normalize.
Custom	Custom expression over subnet-host node names.

Function Documentation

apply_score_interpreter()

std::vector< std::optional< double > > anpcpp::apply_score_interpreter	(	const ScoreInterpreter &	interpreter,
		const std::vector< std::optional< double > > &	raw
	)

Apply interpreter to a column of optional raw values.

Missing entries stay nullopt. Present entries become scores in [0, 1] (clamped). Interpreters that need column statistics ignore missing cells.

calculus_limit()

Matrix anpcpp::calculus_limit	(	const Matrix &	mat,
		const LimitMatrixOptions &	options = {}
	)

SuperDecisions / pyanp calculus limit matrix.

Parameters

mat	Column-stochastic supermatrix.
options	Iteration and hierarchy options.

column_normalize()

Matrix anpcpp::column_normalize

(

const Matrix &

mat

)

Column-normalizes each column by its sum (pyanp normalize).

Returns

Normalized copy; zero columns remain zero.

column_normalize_inplace()

void anpcpp::column_normalize_inplace

(

Matrix &

mat

)

In-place column_normalize.

Parameters

mat	Matrix to normalize.

consistency()

ConsistencyResult anpcpp::consistency	(	const Matrix &	mat,
		ConsistencyOptions	options = {}
	)

Computes Saaty CI and CR for a pairwise comparison matrix.

Parameters

mat	Square comparison matrix.
options	Harker and eigen options.

consistency_index() [1/2]

double anpcpp::consistency_index	(	const Matrix &	mat,
		ConsistencyOptions	options = {}
	)

Consistency index from a matrix (throws on non-convergence).

Parameters

mat	Square comparison matrix.
options	Harker and eigen options.

consistency_index() [2/2]

double anpcpp::consistency_index	(	double	lambda_max,
		std::size_t	n
	)

Consistency index CI = (lambda_max - n) / (n - 1).

Returns

0 for n < 2.

consistency_ratio() [1/2]

double anpcpp::consistency_ratio	(	const Matrix &	mat,
		ConsistencyOptions	options = {}
	)

Consistency ratio from a matrix (throws on non-convergence).

Parameters

mat	Square comparison matrix.
options	Harker and eigen options.

consistency_ratio() [2/2]

double anpcpp::consistency_ratio	(	double	lambda_max,
		std::size_t	n
	)

Consistency ratio CR = CI / RI.

Returns

0 when RI == 0 (n <= 2).

eval_expression()

double anpcpp::eval_expression	(	const std::string &	expression,
		const std::map< std::string, double > &	variables
	)

Evaluates a numeric expression with named variables.

Parameters

expression	Infix expression (+ - * / parentheses).
variables	Name -> value substitutions.

harker_fix()

Matrix anpcpp::harker_fix

(

const Matrix &

mat

)

Applies Harker's fix for incomplete pairwise matrices.

Each row diagonal becomes 1 + (number of zero off-diagonal entries in that row), enabling power iteration on incomplete comparisons.

Parameters

mat	Square pairwise matrix.

Returns

Adjusted matrix.

hierarchy_formula()

Matrix anpcpp::hierarchy_formula

(

const Matrix &

mat

)

Hierarchy limit formula: normalize(sum of W^k for k=1..n-1).

Returns

Empty matrix if mat is not a hierarchy.

load_network_file()

std::unique_ptr< AnpNetwork > anpcpp::load_network_file

(

const std::string &

path

)

Loads a network from a file.

Parameters

path	Input file path.

Returns

Owned network tree.

Exceptions

JsonIoError

on read or parse failure.

network_from_json()

std::unique_ptr< AnpNetwork > anpcpp::network_from_json

(

const std::string &

json_text

)

Parses JSON produced by network_to_json.

Parameters

json_text

JSON document.

Returns

Owned network tree.

Exceptions

JsonIoError

on invalid or unsupported documents.

network_to_json()

std::string anpcpp::network_to_json

(

const AnpNetwork &

network

)

Serializes a network (including subnetworks and layout hints) to JSON.

Parameters

network

Network to serialize.

Returns

JSON text (anpcpp format v1).

principal_eigen()

EigenResult anpcpp::principal_eigen	(	const Matrix &	mat,
		const EigenOptions &	options = {}
	)

Power iteration with sum-normalization (pyanp pri_eigen compatible).

Non-convergence is reported via converged == false rather than throwing.

Parameters

mat	Square matrix.
options	Iteration and Harker options.

Returns

Eigenvector, eigenvalue estimate, and convergence metadata.

principal_eigenvalue()

double anpcpp::principal_eigenvalue	(	const Matrix &	mat,
		const EigenOptions &	options = {}
	)

Returns the principal eigenvalue only.

Exceptions

ConvergenceError

if iteration does not converge.

principal_eigenvector()

Vector anpcpp::principal_eigenvector	(	const Matrix &	mat,
		const EigenOptions &	options = {}
	)

Returns the principal eigenvector only.

Exceptions

ConvergenceError

if iteration does not converge.

random_index()

double anpcpp::random_index

(

std::size_t

n

)

Saaty random index (RI) for matrix order n.

n = 1,2: 0 (CR defined as 0). n = 3..15: Saaty table. n > 15: Alonso-Lamata approximation RI ≈ 1.98 * (n - 2) / n.

save_network_file()

void anpcpp::save_network_file	(	const AnpNetwork &	network,
		const std::string &	path
	)

Writes a network to a file.

Parameters

network	Network to save.
path	Output file path.

Exceptions

JsonIoError

on write failure.

synthesize_additive()

std::map< std::string, double > anpcpp::synthesize_additive	(	const std::map< std::string, double > &	subnet_weights,
		const std::map< std::string, std::map< std::string, double > > &	alt_scores
	)

Weighted average synthesis (matches AnpNetwork::sum_subnetwork_formula).

Parameters

subnet_weights	Weight per control subnet (host node name -> weight).
alt_scores	Per-subnet alternative scores (subnet -> alt -> score).

synthesize_custom()

std::map< std::string, double > anpcpp::synthesize_custom	(	const std::string &	expression,
		const std::map< std::string, std::map< std::string, double > > &	alt_scores,
		const std::vector< std::string > &	alt_order
	)

Custom expression synthesis per alternative.

Parameters

expression	Arithmetic expression with subnet-host names as variables.
alt_scores	Per-subnet alternative scores.
alt_order	Output ordering of alternatives.