/*
* Copyright 2016 IBM Corp.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
module.exports = function(kernelP) {
return (function() {
var Utils = require('../../utils.js');
var Model = require('../Model')();
var gKernelP = kernelP;
/**
* @classdesc
* :: Experimental ::
*
* Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points
* are drawn from each Gaussian i with probability weights(i).
*
* @param weights Weight for each Gaussian distribution in the mixture.
* This is a multinomial probability distribution over the k Gaussians,
* where weights(i) is the weight for Gaussian i, and weights sum to 1.
* @param gaussians Array of {@link MultivariateGaussian} where gaussians(i) represents
* the Multivariate Gaussian (Normal) Distribution for Gaussian i
* @class
* @memberof module:eclairjs/ml/clustering
* @extends module:eclairjs/ml.Model
*/
function GaussianMixtureModel() {
Utils.handleConstructor(this, arguments, gKernelP);
};
GaussianMixtureModel.prototype = Object.create(Model.prototype);
GaussianMixtureModel.prototype.constructor = GaussianMixtureModel;
/**
* An immutable unique ID for the object and its derivatives.
* @returns {Promise.<string>}
*/
GaussianMixtureModel.prototype.uid = function () {
var args = {
target: this,
method: 'uid',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/ml/clustering.GaussianMixtureModel}
*/
GaussianMixtureModel.prototype.copy = function(extra) {
var args ={
target: this,
method: 'copy',
args: Utils.wrapArguments(arguments),
returnType: GaussianMixtureModel
};
return Utils.generate(args);
};
/**
* @param {module:eclairjs/sql.Dataset} dataset
* @returns {module:eclairjs/sql.Dataset}
*/
GaussianMixtureModel.prototype.transform = function(dataset) {
var Dataset = require('../../sql/Dataset.js');
var args ={
target: this,
method: 'transform',
args: Utils.wrapArguments(arguments),
returnType: Dataset
};
return Utils.generate(args);
};
/**
* @param {module:eclairjs/sql/types.StructType} schema
* @returns {StructType}
*/
GaussianMixtureModel.prototype.transformSchema = function(schema) {
var StructType = require('../../sql/types/StructType.js');
var args ={
target: this,
method: 'transformSchema',
args: Utils.wrapArguments(arguments),
returnType: StructType
};
return Utils.generate(args);
};
/**
* Retrieve Gaussian distributions as a DataFrame.
* Each row represents a Gaussian Distribution.
* Two columns are defined: mean and cov.
* Schema:
* @example
* root
* |-- mean: vector (nullable = true)
* |-- cov: matrix (nullable = true)
*
* @returns {module:eclairjs/sql.Dataset}
*/
GaussianMixtureModel.prototype.gaussiansDF = function() {
var Dataset = require('../../sql/Dataset.js');
var args ={
target: this,
method: 'gaussiansDF',
returnType: Dataset
};
return Utils.generate(args);
};
/**
* @returns {Promise.<Number[]>}
*/
GaussianMixtureModel.prototype.weights = function() {
//var Vector = require('../linalg/Vector');
var args = {
target: this,
method: 'weights',
args: Utils.wrapArguments(arguments),
returnType: [Number]
};
return Utils.generate(args);
};
/**
* Returns a {@link MLWriter} instance for this ML instance.
*
* For [[GaussianMixtureModel]], this does NOT currently save the training {@link summary}.
* An option to save {@link summary} may be added in the future.
*
* @returns {MLWriter}
*/
GaussianMixtureModel.prototype.write = function() {
throw "not implemented by ElairJS";
// var MLWriter = require('../../ml/util/MLWriter.js');
// var args ={
// target: this,
// method: 'write',
// returnType: MLWriter
//
// };
//
// return Utils.generate(args);
};
/**
* Return true if there exists summary of model.
* @returns {Promise.<boolean>}
*/
GaussianMixtureModel.prototype.hasSummary = function() {
var args = {
target: this,
method: 'hasSummary',
args: Utils.wrapArguments(arguments),
returnType: Boolean
};
return Utils.generate(args);
};
/**
* Gets summary of model on training set. An exception is
* thrown if `trainingSummary == None`.
* @returns {module:eclairjs/ml/clustering.GaussianMixtureSummary}
*/
GaussianMixtureModel.prototype.summary = function() {
var GaussianMixtureSummary = require('./GaussianMixtureSummary')();
var args = {
target: this,
method: 'summary',
args: Utils.wrapArguments(arguments),
returnType: GaussianMixtureSummary
};
return Utils.generate(args);
};
//
// static methods
//
/**
* @returns {MLReader}
*/
GaussianMixtureModel.read = function() {
throw "not implemented by ElairJS";
// var MLReader = require('../../ml/util/MLReader.js');
// var args ={
// target: GaussianMixtureModel,
// method: 'read',
// static: true,
// returnType: MLReader
//
// };
//
// return Utils.generate(args);
};
/**
* @param {string} path
* @returns {GaussianMixtureModel}
*/
GaussianMixtureModel.load = function(path) {
var args ={
target: GaussianMixtureModel,
method: 'load',
kernelP: gKernelP,
args: Utils.wrapArguments(arguments),
static: true,
returnType: GaussianMixtureModel
};
return Utils.generate(args);
};
GaussianMixtureModel.moduleLocation = '/ml/clustering/GaussianMixtureModel';
return GaussianMixtureModel;
})();
};