/*
* 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.
*/
(function () {
var JavaWrapper = require(EclairJS_Globals.NAMESPACE + '/JavaWrapper');
var Logger = require(EclairJS_Globals.NAMESPACE + '/Logger');
var Utils = require(EclairJS_Globals.NAMESPACE + '/Utils');
var SVMModel = require(EclairJS_Globals.NAMESPACE + '/mllib/classification/SVMModel');
/**
* Train a Support Vector Machine (SVM) using Stochastic Gradient Descent. By default L2
* regularization is used, which can be changed via {@link optimizer}.
* NOTE: Labels used in SVM should be {0, 1}.
* @classdesc
*/
/**
* Construct a SVM object with default parameters: {stepSize: 1.0, numIterations: 100,
* regParm: 0.01, miniBatchFraction: 1.0}.
* @class
* @memberof module:eclairjs/mllib/classification
*/
var SVMWithSGD = function (jvmObject) {
this.logger = Logger.getLogger("SVMWithSGD_js");
JavaWrapper.call(this, jvmObject);
};
SVMWithSGD.prototype = Object.create(JavaWrapper.prototype);
SVMWithSGD.prototype.constructor = SVMWithSGD;
//
// static methods
//
/**
* Train a SVM model given an RDD of (label, features) pairs. We run a fixed number
* of iterations of gradient descent using the specified step size. Each iteration uses
* `miniBatchFraction` fraction of the data to calculate the gradient. The weights used in
* gradient descent are initialized using the initial weights provided.
*
* NOTE: Labels used in SVM should be {0, 1}.
*
* @param {module:eclairjs.RDD} input RDD of (label, array of features) pairs.
* @param {number} numIterations Number of iterations of gradient descent to run.
* @param {number} [stepSize] Step size to be used for each iteration of gradient descent.
* @param {number} [regParam] Regularization parameter.
* @param {number} [miniBatchFraction] Fraction of data to be used per iteration.
* @param {module:eclairjs/mllib/linalg.Vector} [initialWeights] Initial set of weights to be used. Array should be equal in size to
* the number of features in the data.
* @returns {module:eclairjs/mllib/classification.SVMModel}
*/
SVMWithSGD.train = function (input, numIterations, stepSize, regParam, miniBatchFraction, initialWeights) {
var javaObject;
var input_uw = Utils.unwrapObject(arguments[0]).rdd();
if (arguments.length == 2) {
javaObject = org.apache.spark.mllib.classification.SVMWithSGD.train(input_uw,arguments[1]);
} else if (arguments.length == 3) {
javaObject = org.apache.spark.mllib.classification.SVMWithSGD.train(input_uw,arguments[1],arguments[2]);
} else if (arguments.length == 4) {
javaObject = org.apache.spark.mllib.classification.SVMWithSGD.train(input_uw,arguments[1],arguments[2],arguments[3]);
} else if (arguments.length == 5) {
javaObject = org.apache.spark.mllib.classification.SVMWithSGD.train(input_uw,arguments[1],arguments[2],arguments[3],arguments[4]);
} else if (arguments.length == 6) {
var initialWeights_uw = Utils.unwrapObject(arguments[5]);
javaObject = org.apache.spark.mllib.classification.SVMWithSGD.train(input_uw,arguments[1],arguments[2],arguments[3],arguments[4],initialWeights_uw);
} else {
throw "SVMWithSGD.train wrong number of arguments."
}
return new SVMModel(javaObject);
};
module.exports = SVMWithSGD;
})();