Class Distribution
Hierarchy
- Distribution
Index
Constructors
constructor
Parameters
__namedParameters: DistributionConstructor
Returns Distribution
Properties
Private _engine
Private _normal
Private Optional _source
Accessors
normal
Returns WeightedDistribution
seed
Returns number | number[]
source
Returns undefined | WeightedDistribution
uses
Returns number
Methods
add
- -
Adds a key / value pair to a Distribution. -Will add to the source distribution by default, unless the distribution -only has normalized values.
-Parameters
key: string
-Key to be added.
-value: number
-Value of the key to add.
-
Returns Distribution
add
- -
Adds an object of values to a Distribution. -Will add to the source distribution by default, unless the distribution -only has normalized values.
-Parameters
additions: WeightedDistribution
-An object containing additions.
-
Returns Distribution
clone
- -
Creates a clone of a Distribution instance.
-Parameters
stripSource: boolean = false
-If true this will strip out the source.
-
Returns Distribution
pick
- -
Picks one more values from a Distribution without exclusion. -If you just need to pick one value, you should use pickOne instead.
-Parameters
count: number = 1
-The number of picks to make (default 1).
-Optional mask: string[]
-A mask containing keys in the distribution that should be ignored.
-exclusive: boolean = false
-If true picks are considered exclusive and are removed.
-
Returns string[]
pick
- -
Picks a single value from a Distribution. -If you are picking multiple values, use pick instead.
-Parameters
Optional mask: string[]
-A mask containing keys in the distribution that should be ignored.
-
Returns undefined | string
remove
- -
Removes a key or array of keys from a Distribution and renormalizes.
-Parameters
keys: string | string[]
-Key or Keys to be removed.
-
Returns Distribution
serialize
- -
Serializes a Distribution instance into a data transfer object.
-Parameters
stripSource: boolean = false
-If true this will strip out the source.
-
Returns DistributionDTO
Private update
- -
Applies a DTO's data to the source and normal distributions.
-Parameters
model: DistributionDTO
-A Distribution data transfer object.
-
Returns Distribution
Static add
- -
Adds a key / value pair to a Distribution. -Will add to the source distribution by default, unless the distribution -only has normalized values.
-Parameters
model: DistributionDTO
-A Distribution data transfer object.
-key: string
-Key to be added.
-value: number
-Value of the key to add.
-
Returns DistributionDTO
Static add
- -
Adds a key / value pair to a normal distribution and renormalizes it. -If a source distribution exists, it will be recalculated based off of the -new normal distribution.
-Parameters
model: DistributionDTO
-A Distribution data transfer object.
-key: string
-Key to be added.
-value: number
-Value of the key to add.
-
Returns DistributionDTO
Static add
- -
Adds an object of values to a normal Distribution and renormalizes it. -If a source distribution exists, it will be recalculated by scaling the -new normal distribution to fit its sum.
-Parameters
model: DistributionDTO
-A Distribution data transfer object.
-additions: WeightedDistribution
-An object containing additions.
-
Returns DistributionDTO
Static add
- -
Adds a key / value pair to a distribution's source and renormalizes it. -If a source distribution exists, it will be recalculated based off of the -new normal distribution.
-Parameters
model: DistributionSourceDTO
-A Distribution data transfer object.
-key: string
-Key to be added.
-value: number
-Value of the key to add.
-
Returns DistributionSourceDTO
Static add
- -
Adds an object of values to a Distribution's source and renormalizes it.
-Parameters
model: DistributionSourceDTO
-A Distribution data transfer object.
-additions: WeightedDistribution
-An object containing additions.
-
Returns DistributionSourceDTO
Static add
- -
Adds an object of values to a Distribution. -Will add to the source distribution by default, unless the distribution -only has normalized values.
-Parameters
model: DistributionDTO
-A Distribution data transfer object.
-additions: WeightedDistribution
-An object containing additions.
-
Returns DistributionDTO
Static clone
- -
Creates a clone of a Distribution DTO.
-Parameters
model: DistributionDTO
-A Distribution data transfer object.
-stripSource: boolean = false
-If true this will strip out the source.
-
Returns DistributionDTO
Static new
- -
Initializes a new DistributionSourceDTO.
-Parameters
Optional source: WeightedDistribution
-An optional source of values to generate the distribution from.
-
Returns DistributionSourceDTO
Static pick
- -
Picks one more values from a Distribution without exclusion. -If you just need to pick one value, you should use pickOne instead.
-Parameters
model: DistributionNormalDTO
-A Distribution data transfer object.
-count: number = 1
-The number of picks to make (default 1).
-Optional mask: string[]
-A mask containing keys in the distribution that should be ignored.
-exclusive: boolean = false
-If true picks are considered exclusive and are removed.
-Optional engine: Random
-A Random engine. This is created if not provided.
-
Returns string[]
Static pick
- -
Picks a single value from a Distribution. -If you are picking multiple values, use pick instead.
-Parameters
model: DistributionNormalDTO
-A Distribution data transfer object.
-Optional mask: string[]
-A mask containing keys in the distribution that should be ignored.
-Optional engine: Random
-A Random engine. This is created if not provided.
-
Returns undefined | string
Static remove
- -
Removes a key or array of keys from a Distribution and renormalizes.
-Parameters
model: DistributionDTO
-A Distribution data transfer object.
-keys: string | string[]
-Key or Keys to be removed.
-
Returns DistributionNormalDTO
Generated using TypeDoc
Class MarkovChain
-
-
-
Class
- -Hierarchy
- MarkovChain
Index
Constructors
Properties
Accessors
Methods
Constructors
constructor
Parameters
__namedParameters: MarkovChainConstructor
Returns MarkovChain
Properties
Private _engine
Private _model
Accessors
delimiter
Returns string
dto
Returns MarkovChainDTO
end
Returns string
grams
Returns GramDictionary
max
Returns number
model
Returns MarkovChainDTO
seed
Returns number | number[]
sequences
Returns undefined | string[][]
start
Returns string
uses
Returns number
Methods
add
- -
Adds an edge from a gram to the items before and after it in the sequence.
-Parameters
gram: string | string[]
-The id of a gram, or the gram sequence.
-lastId: undefined | string
-The id of the previous gram in the sequence.
-nextId: undefined | string
-The id of the next gram in the sequence.
-order: number
-The order of the Gram we're adding.
-
Returns MarkovChain
add
- -
Adds or inserts a Sequence into a Markov Chain DTO.
-Parameters
sequence: string[]
-The sequence to be added.
-insert: MCInsertOption = false
-Determines how sequences should be inserted. If false, delimiters will be - prepended and appended to the sequences. - "start" or setting true will only prepend the start delimiter, while - "end" will append the end delimiter. "middle" will not add any delimiters.
-
Returns MarkovChain
add
- -
Adds or inserts a list of Sequences into a Markov Chain DTO.
-Parameters
sequences: string[][]
-The sequences to be added.
-insert: MCInsertOption = false
-Determines how sequences should be inserted. If false, delimiters will be - prepended and appended to the sequences. - "start" or setting true will only prepend the start delimiter, while - "end" will append the end delimiter. "middle" will not add any delimiters.
-
Returns MarkovChain
analyze
- -
Analyze's a sequences sources and sinks. Generates a number of samples from a given gram sequence, and gives the resulting -distribution of where the generated sequences terminated both backwards (sources) and forwards (sinks).
-Parameters
__namedParameters: MCAnalyzeOptions
Returns MCAnalysis
clone
- -
Ceates a clone of the Markov Chain.
-Parameters
stripSequences: boolean = false
-If true this will strip out the sequences, removing the chain's source data.
-
Returns MarkovChain
find
- -
Finds the valid gram of the highest valid order in a sequence.
-Parameters
gramSequence: string[]
-An array containing the Gram sequence.
-Optional order: number
-The highest order to look for.
-direction: string = 'next'
-The direction we are looking for sequences in. - "next" will look for grams at the end of the sequence. - "last" will look for grams at the beginning of the sequence.
-
Returns Gram
generate
- -
Generates a sequence from a Markov Chain.
-Parameters
__namedParameters: MCGeneratorOptions
Returns string[]
get
- -
Returns the corresponding Gram from a sequence.
-Parameters
gramSequence: string[]
-An array containing the Gram sequence.
-
Returns Gram
get
- -
Returns the id of a Gram from its sequence.
-Parameters
gramSequence: string[]
-An array containing the Gram sequence.
-
Returns string
last
- -
Picks the last random value from a Markov Chain given a sequence.
-Parameters
Optional gramSequence: string[]
-The starting Gram sequence. If this isn't supplied this defaults to the start.
-Optional mask: string[]
-A mask containing keys in the chain that should be ignored.
-
Returns undefined | string
next
- -
Picks the next random value from a Markov Chain given a sequence.
-Parameters
Optional gramSequence: string[]
-The starting Gram sequence. If this isn't supplied this defaults to the start.
-Optional mask: string[]
-A mask containing keys in the chain that should be ignored.
-
Returns undefined | string
pick
- -
Picks the next or last random value from a Markov Chain.
-Parameters
Optional gramSequence: string[]
-The starting Gram sequence. If this isn't supplied this defaults to the start.
-next: boolean = true
-If true states that come after the gram will be picked. - If false states that came before the gram will be picked.
-Optional mask: string[]
-A mask containing keys in the chain that should be ignored.
-
Returns undefined | string
serialize
- -
Serializes a Markov Chain instance into a DTO.
-Parameters
stripSequences: boolean = false
-If true this will strip out the sequences, removing the chain's source data.
-
Returns MarkovChainDTO
Private update
- -
Updates a Markov Chain's members from a DTO.
-Parameters
dto: MarkovChainDTO
-
Returns MarkovChain
Static add
- -
Adds an edge from a gram to the items before and after it in the sequence.
-Parameters
model: MarkovChainDTO
-A Markov Chain data transfer object.
-gram: string | string[]
-The id of a gram, or the gram sequence.
-lastId: undefined | string
-The id of the previous gram in the sequence.
-nextId: undefined | string
-The id of the next gram in the sequence.
-order: number
-The order of the Gram we're adding.
-weight: number = 1
Returns MarkovChainDTO
Static add
- -
Adds or inserts a Sequence into a Markov Chain DTO.
-Parameters
model: MarkovChainDTO
-A Markov Chain data transfer object.
-sequence: string[]
-The sequence to be added.
-insert: MCInsertOption = false
-Determines how sequences should be inserted. If false, delimiters will be - prepended and appended to the sequences. - "start" or setting true will only prepend the start delimiter, while - "end" will append the end delimiter. "middle" will not add any delimiters.
-
Returns MarkovChainDTO
Static add
- -
Adds or inserts a list of Sequences into a Markov Chain DTO.
-Parameters
model: MarkovChainDTO
-A Markov Chain data transfer object.
-sequences: string[][]
-The sequences to be added.
-insert: MCInsertOption = false
-Determines how sequences should be inserted. If false, delimiters will be - prepended and appended to the sequences. - "start" or setting true will only prepend the start delimiter, while - "end" will append the end delimiter. "middle" will not add any delimiters.
-
Returns MarkovChainDTO
Static analyze
- -
Analyze's a sequences sources and sinks. Generates a number of samples from a given gram sequence, and gives the resulting -distribution of where the generated sequences terminated both backwards (sources) and forwards (sinks).
-Parameters
__namedParameters: MCAnalyzeStaticOptions
Returns MCAnalysis
Static clone
- -
Create a deep copy of a Markov Chain DTO.
-Parameters
model: MarkovChainDTO
-Markov DTO to clone.
-stripSequences: boolean = false
-If true this will strip out the sequences, removing the chain's source data.
-
Returns MarkovChainDTO
Static find
- -
Finds the valid gram of the highest valid order in a sequence.
-Parameters
model: MarkovChainDTO
-A Markov Chain data transfer object.
-gramSequence: string[]
-An array containing the Gram sequence.
-Optional order: number
-The highest order to look for.
-direction: string = 'next'
-The direction we are looking for sequences in. - "next" will look for grams at the end of the sequence. - "last" will look for grams at the beginning of the sequence.
-
Returns Gram
Static generate
- -
Generates a sequence from a Markov Chain.
-Parameters
__namedParameters: MCGeneratorStaticOptions
Returns string[]
Static get
- -
Returns the corresponding Gram from a sequence.
-Parameters
model: MarkovChainDTO
-A Markov Chain data transfer object.
-gramSequence: string[]
-An array containing the Gram sequence.
-
Returns Gram
Static get
- -
Returns the id of a Gram from its sequence.
-Parameters
model: MarkovChainDTO
-A Markov Chain data transfer object.
-gramSequence: string[]
-An array containing the Gram sequence.
-
Returns string
Static get
- -
Utility function to find a sequence given an order and a direction. -This returns an array containing the first or last elements of an array equal to the order.
-Parameters
gramSequence: string[]
-An array containing the Gram sequence.
-order: number
-The length of the array to return.
-next: boolean
-If true, will find elements at the end. If false will find elements at the beginning.
-
Returns string[]
Static last
- -
Picks the last random value from a Markov Chain given a sequence.
-Parameters
model: MarkovChainDTO
-A Markov Chain data transfer object.
-Optional gramSequence: string[]
-The starting Gram sequence. If this isn't supplied this defaults to the start.
-Optional mask: string[]
-A mask containing keys in the chain that should be ignored.
-Optional engine: Random
-A Random engine.
-
Returns undefined | string
Static new
- -
Creates a new Markov Chain data transfer object.
-Parameters
Optional sequences: string[][]
-An optional array of sequences to generate the grams from.
-maxOrder: number = defaultOptions.maxOrder
-The maximum gram size of the markov chain.
-insert: MCInsertOption = false
-Determines how sequences should be inserted. If false, delimiters will be - prepended and appended to the sequences. "start" or setting true will only prepend the start delimiter, while - "end" will append the end delimiter. "middle" will not add any delimiters.
-stripSequences: boolean = false
-If true this will strip out the sequences, removing the chain's source data.
-
Returns MarkovChainDTO
Static next
- -
Picks the next random value from a Markov Chain given a sequence.
-Parameters
model: MarkovChainDTO
-A Markov Chain data transfer object.
-Optional gramSequence: string[]
-The starting Gram sequence. If this isn't supplied this defaults to the start.
-Optional mask: string[]
-A mask containing keys in the chain that should be ignored.
-Optional engine: Random
-A Random engine.
-
Returns undefined | string
Static pick
- -
Picks the next or last random value from a Markov Chain.
-Parameters
model: MarkovChainDTO
-A Markov Chain data transfer object.
-Optional gramSequence: string[]
-The starting Gram sequence. If this isn't supplied this defaults to the start.
-next: boolean = true
-If true states that come after the gram will be picked. - If false states that came before the gram will be picked.
-Optional mask: string[]
-A mask containing keys in the chain that should be ignored.
-Optional engine: Random
-A Random engine.
-
Returns undefined | string
Static pick
- -
Makes a random pick from the next or last state of a given Gram.
-Parameters
gram: Gram
-The starting Gram sequence. If this isn't supplied this defaults to the start.
-next: boolean = true
-If true states that come after the gram will be picked. - If false states that came before the gram will be picked.
-Optional mask: string[]
-A mask containing keys in the chain that should be ignored.
-Optional engine: Random
-A Random engine.
-
Returns undefined | string
Generated using TypeDoc
Class Random
-
-
-
Class
- -Hierarchy
- Random
Index
Constructors
constructor
Properties
Private _engine
Private _seed
Accessors
seed
Returns number | number[]
uses
Returns number
Methods
bool
Parameters
percentage: number = 0.5
Returns boolean
clone
Parameters
Optional useCount: number
Returns Random
integer
Parameters
min: number
max: number
Returns number
pick
Type parameters
T
Parameters
source: ArrayLike<T>
Optional begin: number
Optional end: number
Returns T
pick
- -
Picks a weighted key from a normalized object, ignoring masked values.
-Parameters
object: WeightedDistribution
-An object containing normalized number values.
-Optional mask: string[]
-Array of keys to be ignored while evaluating.
-
Returns undefined | string
real
Parameters
min: number
max: number
Optional inclusive: boolean
Returns number
serialize
Returns RandomDTO
Static new
Parameters
Optional seed: number | number[]
Optional uses: number
Returns Random
Generated using TypeDoc
acausal - v2.0.2
-
-
acausal
- -
acausal is a Typescript module that makes it easy to create, edit and generate pseudo random data from Weighted Random Distributions and Markov Chains.
-Design Philosophy
--
-
- Immutable: all classes are built on top of pure functions which do not mutate state, ensuring that models retain their integrity, and making them easy to use with Redux. -
- Portable: all classes are easily serializable and deserializable into data transfer objects, making them easy to store, transfer, and rebuild regardless of whether it's on the client or the server. -
- Easy to Use: all APIs are written to prioritize developer usability, making it easy to rapidly prototype and implement new models. -
- Minimal Dependencies: acausal only depends on random-js and scalr, (which formerly was part of acausal, but separated out for the 2.0.0 release). -
Basic Examples:
-import { MarkovChain, Distribution, Random } from 'acausal';
// Random Rarity Distribution
const dist = new Distribution({ seed: 1 });
dist.add('Green', 10); // Common
dist.add('Blue', 5); // Uncommon
dist.add('Purple', 1); // Rare
dist.pick(10);
/* Results in:
[
'Green', 'Green', 'Green', 'Blue', 'Green',
'Blue', 'Purple', 'Green', 'Green', 'Green'
]
*/
// Markov Chain Name Generator
const mc = new MarkovChain({ seed: 1 });
mc.addSequence('alice'.split(''));
mc.addSequence('bob'.split(''));
mc.addSequence('erwin'.split(''));
console.log(mc.generate({ order: 1 }));
/* Results in:
[ 'a', 'l', 'i', 'n' ]
*/
// Random Numbers
const rand = new Random({ seed: 1 });
rand.integer(1, 6); // Roll 1d6
// Results in: 6
-Quick Links
- - - - -Installation
- -Run:
-npm install -s acausal
-Gocausal
- -acausal is also implemented in Golang. You can find the module here:
--
-
- Gocausal -
Random Distributions
- -A Random Distribution is a simple model which can simulate picks from a weighted distribution of items.
-Distributions can be used to model random draws from a discrete collection of items, where each item has a different probability of appearing.
-Example Use Cases:
--
-
- Simulating drawing a hand from a standard deck of cards (see below). -
- Simulating the outcome of a game of roulette (or nearly any casino game). -
- Generating eye or hair color for a fictional person. -
- Generating the spectral class of fictional stars. -
- Modeling how many McDonalds meals you'd need to buy to win Monopoly. -
- Modeling any pseudo-random system through observation. -
Distribution Quickstart Example - Card Deck:
-import { Distribution } from 'acausal';
// Create a Deck of Cards
const suits = ['♣️', '♦️', '♥️', '♠️'];
const ranks = ['A', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K'];
// Combine the Suits and Ranks
const cards = suits.reduce((last, suit) => {
return [...last, ...ranks.map(rank => `${rank}${suit}`)];
}, []);
/* Should result in:
[
'A♣️', '2♣️', '3♣️', '4♣️', '5♣️', '6♣️', '7♣️',
'8♣️', '9♣️', '10♣️', 'J♣️', 'Q♣️', 'K♣️', 'A♦️',
'2♦️', '3♦️', '4♦️', '5♦️', '6♦️', '7♦️', '8♦️',
'9♦️', '10♦️', 'J♦️', 'Q♦️', 'K♦️', 'A♥️', '2♥️',
'3♥️', '4♥️', '5♥️', '6♥️', '7♥️', '8♥️', '9♥️',
'10♥️', 'J♥️', 'Q♥️', 'K♥️', 'A♠️', '2♠️', '3♠️',
'4♠️', '5♠️', '6♠️', '7♠️', '8♠️', '9♠️', '10♠️',
'J♠️', 'Q♠️', 'K♠️'
]
*/
// Create weighted source data for the Distribution
const src = cards.reduce((last, card) => ({ ...last, [card]: 1 }), {});
/* Should result in:
{
'A♣️': 1,
'2♣️': 1,
'3♣️': 1,
...
'J♠️': 1,
'Q♠️': 1,
'K♠️': 1,
}
*/
// Create the Distribution from the deck.
const deck = new Distribution({
seed: 23, // Random Seed - if this is empty it will be generated.
source: src, // The weighted source to generate the normalized Distribution from.
});
// Add in 2 Jokers
deck.add('🃏', 2);
// Generate 4 picks from the deck without replacement.
const picks = deck.pick(4, undefined, true);
console.log(picks);
/* Should print:
[ 'J♣️', '10♠️', '3♦️', '9♣️' ]
*/
-You can learn more about how to use Random Distributions with acausal in the Random Distribution Quickstart.
- - -Markov Chains
- -A Markov Chain is a mathematical model of a system in which the future state of the system depends only on its present state.
-Markov Chains are usually generated by building a statistical model off of sample data, such as a list of names, which can then be used to output sequences which resemble the sampled data. A useful property of this process is that sample data can be "mixed" together like paint to achieve a desired result.
-For example, if you wanted to generate names which sounded like a mix of Irish and Japanese, you could generate a Markov Chain from a sample of Irish and Japanese names and the resulting model would be able to output names that mixed the two.
-Markov Chain Quickstart Example - Name Generator:
-import { MarkovChain } from 'acausal';
// Sample Data
const jpNames = ['honoka', 'akari', 'himari', 'mei', 'ema'];
const ieNames = ['grace', 'fiadh', 'emily', 'sophie', 'ava'];
const names = [...jpNames, ...ieNames];
// Prepare Data Source - the class expects an array of arrays.
const src = names.map(name => name.split(''));
/* Should result in:
[
[ 'h', 'o', 'n', 'o', 'k', 'a' ],
[ 'a', 'k', 'a', 'r', 'i' ],
[ 'h', 'i', 'm', 'a', 'r', 'i' ],
[ 'm', 'e', 'i' ],
[ 'e', 'm', 'a' ],
[ 'g', 'r', 'a', 'c', 'e' ],
[ 'f', 'i', 'a', 'd', 'h' ],
[ 'e', 'm', 'i', 'l', 'y' ],
[ 's', 'o', 'p', 'h', 'i', 'e' ],
[ 'a', 'v', 'a' ]
]
*/
// Create the Markov Chain from the source data.
const chain = new MarkovChain({
seed: 33, // Random Seed - if this is empty it will be generated.
maxOrder: 2, // Maximum Order - Chain will generate orders up to this value.
sequences: src, // Source data, expects an array of arrays.
});
// Generate 5 picks.
for (let i = 0; i < 3; i += 1) {
const pick = chain.generate({
min: 4, // Min Picks - This will force the model to pick at least 4 times.
max: 10, // Max Picks - Stops generation after 10 picks if no end has been reached.
order: 2, // Order - The largest gram size used to calculate the next pick.
strict: false // Strict Order - Dynamically adjusts order up or down each pick if false.
});
console.log(pick.join(''));
}
/* Should print:
sophimari
emari
hie
*/
-You can learn more about how to use Markov Chains with acausal in the Markov Chain Quickstart
- - -Extended API Documentation
- -For documentation of underlying classes and functions, please see the API documentation.
-Generated using TypeDoc
Interface DistributionConstructor
Generated using TypeDoc
Interface DistributionNormalDTO
Generated using TypeDoc
Interface DistributionSourceDTO
Generated using TypeDoc
Interface Gram
Generated using TypeDoc
Interface MCAnalysis
Generated using TypeDoc
Interface MCAnalyzeOptions
Generated using TypeDoc
Interface MCAnalyzeStaticOptions
Hierarchy
- MCAnalyzeOptions
- MCGeneratorStaticOptions
- MCAnalyzeStaticOptions
Index
Properties
Optional direction
Optional engine
Optional mask
Optional max
Optional min
model
Optional normalize
Optional order
Optional samples
Optional start
Optional strict
Optional trim
Generated using TypeDoc
Interface MCGeneratorOptions
Generated using TypeDoc
Interface MCGeneratorStaticOptions
Generated using TypeDoc
Interface MarkovChainConstructor
Hierarchy
- RandomDTO
- MarkovChainConstructor
Index
Properties
Optional delimiter
Optional end
Optional engine
Optional grams
Optional insert
Optional max
Optional seed
Optional sequences
Optional start
Optional uses
Generated using TypeDoc
Interface MarkovChainGramDTO
Hierarchy
- MarkovChainOptions
- MarkovChainGramDTO
Index
Properties
delimiter
end
grams
max
Optional seed
Optional sequences
start
Optional uses
Generated using TypeDoc
Interface MarkovChainOptions
Hierarchy
- RandomDTO
- MarkovChainOptions
Index
Properties
delimiter
end
max
Optional seed
start
Optional uses
Generated using TypeDoc
Interface MarkovChainSequenceDTO
Hierarchy
- MarkovChainOptions
- MarkovChainSequenceDTO
Index
Properties
delimiter
end
grams
max
Optional seed
sequences
start
Optional uses
Generated using TypeDoc
Interface RandomDTO
Generated using TypeDoc
acausal - v2.0.2
Index
Classes
Interfaces
Type aliases
Type aliases
DistributionDTO
Gram
Type declaration
[key: string]: Gram
MCDelimiters
MCDirection
-
-
-
Types
- -MCInsert
Markov
Require
Type parameters
T
Keys: keyof T = keyof T
Weighted
-
-
-
Declarations
- -Type declaration
[key: string]: number
Generated using TypeDoc
+
Training Data
+ +
+
+
+
Class
- -