MayaFlux::Kinesis::Stochastic::Stochastic Class Reference

Unified generative infrastructure for stochastic and procedural algorithms. More...

#include <Stochastic.hpp>

Collaboration diagram for MayaFlux::Kinesis::Stochastic::Stochastic:

Public Member Functions
	Stochastic (Algorithm algo=Algorithm::UNIFORM)
	Constructs generator with specified algorithm.
void	seed (uint64_t seed)
	Seeds entropy source.
void	set_algorithm (Algorithm algo)
	Changes active algorithm.
Algorithm	get_algorithm () const
	Gets current algorithm.
void	configure (const std::string &key, std::any value)
	Configures algorithm-specific parameters.
std::optional< std::any >	get_config (const std::string &key) const
	Gets configuration parameter.
double	operator() (double min, double max)
	Generates single value in range.
double	at (double x, double y=0.0, double z=0.0)
	Multi-dimensional generation (Perlin, spatial noise)
std::vector< double >	batch (double min, double max, size_t count)
	Batch generation.
void	reset_state ()
	Resets internal state for stateful algorithms.
const GeneratorState &	state () const
	Gets current internal state.
GeneratorState &	state_mutable ()
	Gets mutable internal state.

Private Member Functions
double	generate_memoryless (double min, double max)
double	generate_stateful (double min, double max)
double	generate_perlin_impl (double x, double y, double z)
double	generate_gendy_impl (double min, double max)
double	generate_brownian_impl (double min, double max)
double	generate_colored_noise_impl (double min, double max)
void	validate_range (double min, double max) const
void	rebuild_distributions_if_needed (double min, double max)
double	fast_uniform () noexcept

Private Attributes
std::mt19937	m_engine
uint64_t	m_xorshift_state
Algorithm	m_algorithm
GeneratorState	m_state
std::map< std::string, std::any >	m_config
std::normal_distribution< double >	m_normal_dist { 0.0, 1.0 }
std::exponential_distribution< double >	m_exponential_dist { 1.0 }
double	m_cached_min { 0.0 }
double	m_cached_max { 1.0 }
bool	m_dist_dirty { true }

Detailed Description

Unified generative infrastructure for stochastic and procedural algorithms.

Provides mathematical primitives for controlled randomness and procedural generation across all computational domains. Unlike traditional random number generators focused on independent samples, Stochastic embraces both memoryless distributions and stateful processes that evolve over time.

Architectural Philosophy

Treats stochastic generation as fundamental mathematical infrastructure rather than domain-specific processing. The same primitives that generate sonic textures can drive visual phenomena, parametric modulation, or data synthesis - the numbers themselves are discipline-agnostic.

Algorithm Categories

Memoryless Distributions (each call independent):

• UNIFORM: Flat probability across range
• NORMAL: Gaussian distribution
• EXPONENTIAL: Exponential decay
• POISSON: Discrete event distribution

Stateful Processes (evolution over successive calls):

• PERLIN: Coherent gradient noise with spatial/temporal continuity
• GENDY: Xenakis dynamic stochastic synthesis (pitch/amplitude breakpoints)
• BROWNIAN: Random walk (integrated white noise)
• PINK: 1/f noise (equal energy per octave)
• BLUE: Rising spectral energy

Usage Patterns

Memoryless generation:

Stochastic gen(Algorithm::NORMAL);
double value = gen(0.0, 1.0);

Stateful evolution:

Stochastic walker(Algorithm::BROWNIAN);
walker.configure("step_size", 0.01);
for (int i = 0; i < 1000; ++i) {
    double pos = walker(0.0, 1.0);
}

Multi-dimensional generation:

Stochastic perlin(Algorithm::PERLIN);
double noise_2d = perlin.at(x, y);
double noise_3d = perlin.at(x, y, z);

Note

Thread-unsafe for maximum performance. Use separate instances per thread.

Definition at line 117 of file Stochastic.hpp.

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

• Stochastic.hpp
• Stochastic.cpp