code: function (weights, intercept, numFeatures, numClasses) {
          this.logger = Logger.getLogger("LogisticRegressionModel_js");
          var weights_uw = Utils.unwrapObject(weights)
          var jvmObject;
          if (arguments[0] instanceof org.apache.spark.mllib.classification.LogisticRegressionModel) {
              jvmObject = arguments[0];
          } else if (arguments.length === 4) {
              jvmObject = new org.apache.spark.mllib.classification.LogisticRegressionModel(weights_uw, intercept, numFeatures, numClasses);
          } else if (arguments.length === 2) {
              jvmObject = new org.apache.spark.mllib.classification.LogisticRegressionModel(weights_uw, intercept);
          } else {
              throw "LogisticRegressionModel constructor invalid arguments"
          }
  
  
          GeneralizedLinearModel.call(this, jvmObject);
  
      }
docs: var Vector = require(EclairJS_Globals.NAMESPACE + '/mllib/linalg/Vector'); 
  Classification model trained using Multinomial/Binary Logistic Regression.
  
  @memberof module:eclairjs/mllib/classification
  @classdesc
  
  @param {module:eclairjs/mllib/linalg.Vector} weights Weights computed for every feature.
  @param {float} intercept Intercept computed for this model. (Only used in Binary Logistic Regression.
  In Multinomial Logistic Regression, the intercepts will not be a single value,
  so the intercepts will be part of the weights.)
  @param {int} [numFeatures] the dimension of the features.
  @param {int} [numClasses] the number of possible outcomes for k classes classification problem in
  Multinomial Logistic Regression. By default, it is binary logistic regression
  so numClasses will be set to 2.
  @class
  @extends module:eclairjs/mllib/regression.GeneralizedLinearModel