server/src/main/resources/eclairjs/mllib/regression/IsotonicRegressionModel.js
function (boundaries, predictions, isotonic) {
        this.logger = Logger.getLogger("IsotonicRegressionModel_js");
        var jvmObject;
        if (boundaries instanceof org.apache.spark.mllib.regression.IsotonicRegressionModel) {
            jvmObject = boundaries;
        } else {
            jvmObject = new org.apache.spark.mllib.regression.IsotonicRegressionModel(boundaries, predictions, isotonic);
        }

        JavaWrapper.call(this, jvmObject);

    }
function (boundaries, predictions, isotonic) {
        this.logger = Logger.getLogger("IsotonicRegressionModel_js");
        var jvmObject;
        if (boundaries instanceof org.apache.spark.mllib.regression.IsotonicRegressionModel) {
            jvmObject = boundaries;
        } else {
            jvmObject = new org.apache.spark.mllib.regression.IsotonicRegressionModel(boundaries, predictions, isotonic);
        }

        JavaWrapper.call(this, jvmObject);

    }
function ( boundaries , predictions , isotonic ) { this . logger = Logger . getLogger ( "IsotonicRegressionModel_js" ) ; var jvmObject ; if ( boundaries instanceof org . apache . spark . mllib . regression . IsotonicRegressionModel ) { jvmObject = boundaries ; } else { jvmObject = new org . apache . spark . mllib . regression . IsotonicRegressionModel ( boundaries , predictions , isotonic ) ; } JavaWrapper . call ( this , jvmObject ) ; }
Regression model for isotonic regression.

@param boundaries Array of boundaries for which predictions are known.
Boundaries must be sorted in increasing order.
@param predictions Array of predictions associated to the boundaries at the same index.
Results of isotonic regression and therefore monotone.
@param isotonic indicates whether this is isotonic or antitonic.

@memberof module:eclairjs/mllib/regression
@classdesc
 
@param {float[]} boundaries
@param {float[]} predictions
@param {boolean} isotonic
@class
Regression model for isotonic regression .