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/** * [Bayesian Classifier](http://en.wikipedia.org/wiki/Naive_Bayes_classifier) * * This is a naïve bayesian classifier that takes * singly-nested objects. * * @class * @example * var bayes = new BayesianClassifier(); * bayes.train({ *   species: 'Cat' * }, 'animal'); * var result = bayes.score({ *   species: 'Cat' * }) * // result * // { * //   animal: 1 * // } */class BayesianClassifier {    /*:: totalCount: number */    /*:: data: Object */    constructor() {        // The number of items that are currently        // classified in the model        this.totalCount = 0;        // Every item classified in the model        this.data = {};    }
    /**     * Train the classifier with a new item, which has a single     * dimension of Javascript literal keys and values.     *     * @param {Object} item an object with singly-deep properties     * @param {string} category the category this item belongs to     * @return {undefined} adds the item to the classifier     */    train(item, category) {        // If the data object doesn't have any values        // for this category, create a new object for it.        if (!this.data[category]) {            this.data[category] = {};        }
        // Iterate through each key in the item.        for (const k in item) {            const v = item[k];            // Initialize the nested object `data[category][k][item[k]]`            // with an object of keys that equal 0.            if (this.data[category][k] === undefined) {                this.data[category][k] = {};            }            if (this.data[category][k][v] === undefined) {                this.data[category][k][v] = 0;            }
            // And increment the key for this key/value combination.            this.data[category][k][v]++;        }
        // Increment the number of items classified        this.totalCount++;    }
    /**     * Generate a score of how well this item matches all     * possible categories based on its attributes     *     * @param {Object} item an item in the same format as with train     * @returns {Object} of probabilities that this item belongs to a     * given category.     */    score(item) {        // Initialize an empty array of odds per category.        const odds = {};        let category;        // Iterate through each key in the item,        // then iterate through each category that has been used        // in previous calls to `.train()`        for (const k in item) {            const v = item[k];            for (category in this.data) {                // Create an empty object for storing key - value combinations                // for this category.                odds[category] = {};
                // If this item doesn't even have a property, it counts for nothing,                // but if it does have the property that we're looking for from                // the item to categorize, it counts based on how popular it is                // versus the whole population.                if (this.data[category][k]) {                    odds[category][k + "_" + v] =                        (this.data[category][k][v] || 0) / this.totalCount;                } else {                    odds[category][k + "_" + v] = 0;                }            }        }
        // Set up a new object that will contain sums of these odds by category        const oddsSums = {};
        for (category in odds) {            // Tally all of the odds for each category-combination pair -            // the non-existence of a category does not add anything to the            // score.            oddsSums[category] = 0;            for (const combination in odds[category]) {                oddsSums[category] += odds[category][combination];            }        }
        return oddsSums;    }}
export default BayesianClassifier;