MLPredictor- Global (deprecated)

function executeRule(current, previous /*null when async*/) {
    var predictor = new MLPredictor();
    predictor.recordFinalValuesInPredictionResults(current, "On close");

}(current, previous);

(function executeRule(current, previous /*null when async*/) {
    var mlPredictor = new MLPredictor();
    //Get the list of active solutions for the glide record table
    var solutions = mlPredictor.findActiveSolutionsForRecord(current);
    //Run prediction and apply predicted value to the record
    mlPredictor.applyPrediction(current, solution);

})(current, previous);

(function executeRule(current, previous /*null when async*/) {
    var solutionNames = ["solution1", "solution2", ...];

    /* For domain separation (MSP) use case */
//  var solutionNames;
//  if (current.sys_domain == "A")
//      solutionNames = ["solution_A1", "solution_A2", ...];
//  else if (current.sys_domain == "B")
//      solutionNames = ["solution_B1", "solution_B2", ...];
//  else
//      ...

    var predictor = new MLPredictor();
    var info = "";
    solutionNames.forEach(function(solutionName) {
        var solution = predictor.findActiveSolution(solutionName);
        if (!solution)
            return;

        /* The next line of code does the prediction and updates the current record. */
        predictor.applyPredictionForSolution(current, solution);

        /* If no prediction is done, do not build the prediction info message. */
        if (!predictor.applyPredictionForSolution(current, solution))
            return;

        /* If user doesn't have 'itil' role, we don't build prediction info message. */
        if (!gs.hasRole('itil'))
            return;

        /* Building prediction info message */
        var fieldName = solution.getPredictedField();
        var fieldLabel = current.getElement(fieldName).getED().getLabel();
        var predictedDisplayValue = current.getDisplayValue(fieldName);
        var msg = gs.getMessage("Predicted {0} for {1}.", [predictedDisplayValue, fieldLabel]);
        if (info.length > 0)
            info += " ";
        info += msg;
    });
    /* Print out prediction info message on screen. */
    if (info.length > 0) {
        var incidentUrl = "<a href='"+current.getLink()+"'>"+current.number+":</a>";
        gs.addInfoMessage(incidentUrl + " " + info);
    }
})(current, previous);

var solutionName = 'ml_incident_assignment';
  var shortDescriptionValue = "Unable to connect!"
  var input = {
  short_description : shortDescriptionValue
  };
  var MLP = new MLPredictor();
  var solution = MLP.findActiveSolution(solutionName);
  var predictedOutcome = solution.predictText(input);
  var outcome = predictedOutcome.predictedValue();
  var confidence = predictedOutcome.confidence();
  gs.info("Predicted value: " + outcome)
  gs.info("Confidence: " + confidence)

DxC_ML:
        ******* Finished Prediction********* *** Script: Predicted value: Software *** Script:
        Confidence: 80.78079080096245

/* This is only to get a hard-coded GR */
     var current = new GlideRecord('incident');
     current.get('965c9e5347c12200e0ef563dbb9a7156');
     var predictor = new MLPredictor();
     var solutions = predictor.findActiveSolutionsForRecord(current);
     solutions.forEach(function(solution) {
         var outcome = solution.predict(current);
         /* Use this to set the field to the predicted value in the GlideRecord */
         var fieldName = solution.getPredictedField();
         current[fieldName] = outcome.predictedValue();
         current.update();

         gs.info("Predicted value: " + outcome.predictedValue())
         gs.info("Confidence: " + outcome.confidence())
});

DxC_ML: ******* Finished Prediction*********
        *** Script: Predicted value: Software *** Script: Confidence:
        99.74530683715315

DxC_ML:
        ******* Finished Prediction********* *** Script: Predicted value: Software *** Script:
        Confidence: 98.7172805135524

function printOutcomeArr(outcomeArr) {
gs.print('################## Results ##################');
for (var i=0; i<outcomeArr.length; i++) {
    var outcome = outcomeArr[i];
    gs.print((i+1) + ' : ' + outcome.predictedValue() + ', ' + outcome.predictedValueSysId() + ', ' + outcome.confidence());
}
}

var solutionName = 'ml_x_snc_global_prop_flip_test';
var predictor = new MLPredictor();
var solution = predictor.findActiveSolution(solutionName);

var now_GR = new GlideRecord('incident');
now_GR.get('1c741bd70b2322007518478d83673af3');

var options = {};
options.top_n = '10';            // top_n is an integer between 1 and 1000
options.apply_threshold = false; // Value can be set to true or false

printOutcomeArr(predictor.getPredictions(now_GR, solution, options));

var isClassificationSolution = this.isClassificationSolution(solution);

    //classification solution each class has different threshold
    //therefore needs to get all the results from ml engine
    if (applyThreshold && isClassificationSolution) {
        var maxClassificationTopN = 50;
        outcomeArr = solution.predictTopN(now_GR, maxClassificationTopN);
    }
    else  {
    outcomeArr = solution.predictTopN(now_GR, topN);
    }

    if (outcomeArr === null) {
        //instead of returning null returning empty array
        return [];
    }

var MLP = new MLPredictor();
  var solution = MLP.findActiveSolution(solutionName);
  var predictedOutcome = solution.predictText(input);
  var outcome = predictedOutcome.predictedValue();
  var confidence = predictedOutcome.confidence();
  gs.info("Predicted value: " + outcome)
  gs.info("Confidence: " + confidence)

var MLP = new MLPredictor();
  var solution = MLP.findActiveSolution(solutionName);
  var predictedOutcome = solution.predictText(input);
  var outcome = predictedOutcome.predictedValue();
  var confidence = predictedOutcome.confidence();
  gs.info("Predicted value: " + outcome)
  gs.info("Confidence: " + confidence)

function printOutcomeArr(outcomeArr) {
gs.print('################## Results ##################');
for (var i=0; i<outcomeArr.length; i++) {
    var outcome = outcomeArr[i];
    gs.print((i+1) + ' : ' + outcome.predictedValue() + ', ' + outcome.predictedValueSysId() + ', ' + outcome.confidence());
}

(function executeRule(current, previous /*null when async*/) {
    var predictor = new MLPredictor();
    predictor.recordFinalValuesInPredictionResults(current, "On close");

})(current, previous);

/* Get a hard-coded GR */
     var current = new GlideRecord('incident');
     current.get('965c9e5347c12200e0ef563dbb9a7156');
     var predictor = new MLPredictor();
     var solutions = predictor.findActiveSolutionsForRecord(current);
     solutions.forEach(function(solution) {
         var outcome = solution.predict(current);
         /* Use this to set the field to the predicted value in the GlideRecord */

         var fieldName = solution.getPredictedField();
         current[fieldName] = outcome.predictedValue();
         current.update();
         gs.info("Predicted value: " + outcome.predictedValue())
         gs.info("Confidence: " + outcome.confidence())
});

solutions.forEach(function(solution) {
         var outcome = solution.predict(current);
         /* Use this to set the field to the predicted value in the GlideRecord
         var fieldName = solution.getPredictedField();
         current[fieldName] = outcome.predictedValue();
         current.update();
         */
         gs.info("Predicted value: " + outcome.predictedValue())
         gs.info("Confidence: " + outcome.confidence())
});

var isClassificationSolution = this.isClassificationSolution(solution);

    //classification solution each class has different threshold
    //therefore needs to get all the results from ml engine
    if (applyThreshold && isClassificationSolution) {
        var maxClassificationTopN = 50;
        outcomeArr = solution.predictTopN(now_GR, maxClassificationTopN);
    }
    else  {
        outcomeArr = solution.predictTopN(now_GR, topN);
    }

    if (outcomeArr === null) {
        //instead of returning null returning empty array
        return [];
    }