markov-typescript
Version:
A Markov Chain library written in Typescript.
109 lines • 4.23 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
var chain_state_1 = require("./chain-state");
var fixed_queue_1 = require("./fixed-queue");
var markov_chain_items_1 = require("./markov-chain-items");
var markov_terminal_items_1 = require("./markov-terminal-items");
var utils = require("./utils");
var weighted_dictionary_1 = require("./weighted-dictionary");
var MarkovChain = (function () {
/**
* Initializes a new instance of MarkovChain<T>.
* @param order The number of state transitions to track within the model.
* @param toStrFunction Optional function used to convert keys to strings.
*/
function MarkovChain(order, toStrFunction) {
if (order === void 0) { order = 2; }
this.items = new markov_chain_items_1.MarkovChainItems();
this.terminals = new markov_terminal_items_1.MarkovTerminalItems();
if (order < 0) {
throw new RangeError("Order must not be less than 0.");
}
this.order = order;
this.toStrFunction = toStrFunction;
}
/**
* Learns multiple sequences and their transitions.
* @param items The list of sequences to learn.
*/
MarkovChain.prototype.learnAll = function (items) {
var _this = this;
items.forEach(function (item) { return _this.learn(item); });
};
/**
* Learns a single sequence of elements.
* @param items Lear
*/
MarkovChain.prototype.learn = function (items) {
var _this = this;
if (!items || items.length === 0) {
return;
}
var previous = new fixed_queue_1.FixedQueue(this.order);
items.forEach(function (item) {
var key = new chain_state_1.ChainState(previous);
_this.learnWithPrevious(key, item);
previous.enqueue(item);
});
var terminalKey = new chain_state_1.ChainState(previous);
this.terminals.incrementValue(terminalKey, 1);
};
MarkovChain.prototype.learnWithPrevious = function (previous, next) {
var _this = this;
var weights = this.items.getOrCreateValue(previous, function () { return new weighted_dictionary_1.WeightedDictionary(_this.toStrFunction); });
weights.incrementValue(next, 1);
};
/**
* Walks the model from the beginning.
*/
MarkovChain.prototype.walk = function () {
return this.walkWithPrevious([]);
};
/**
* Walks the model, starting with the preceeding state.
* @param previous The previous state with which to begin.
*/
MarkovChain.prototype.walkWithPrevious = function (previous) {
var retVal = new Array();
var state = new fixed_queue_1.FixedQueue(this.order);
previous.forEach(function (x) { return state.add(x); });
var _loop_1 = function () {
var key = new chain_state_1.ChainState(state);
if (!this_1.items.containsKey(key)) {
return "break";
}
var weights = this_1.items.getValue(key);
var terminalWeight = 0;
if (this_1.terminals.containsKey(key)) {
terminalWeight = this_1.terminals.getValue(key);
}
var value = utils.randomNumberBetween(1, weights.totalWeight + terminalWeight);
// Represents a terminal.
if (value > weights.totalWeight) {
return "break";
}
// Get the value that corresponds to.
var currentWeight = 0;
weights.forEach(function (k, v) {
currentWeight += v;
if (currentWeight >= value) {
retVal.push(k);
state.enqueue(k);
return false;
}
return true;
});
};
var this_1 = this;
while (true) {
var state_1 = _loop_1();
if (state_1 === "break")
break;
}
return retVal;
};
return MarkovChain;
}());
exports.MarkovChain = MarkovChain;
;
//# sourceMappingURL=markov-chain.js.map