#vulnerable code 
                                private void batchGradientDescent(Dataframe trainingData, Map<Object, Double> newThitas, double learningRate) {
        //NOTE! This is not the stochastic gradient descent. It is the batch gradient descent optimized for speed (despite it looks more than the stochastic). 
        //Despite the fact that the loops are inverse, the function still changes the values of Thitas at the end of the function. We use the previous thitas 
        //to estimate the costs and only at the end we update the new thitas.
        ModelParameters modelParameters = kb().getModelParameters();
        
        double multiplier = learningRate/modelParameters.getN();
        Map<Object, Double> thitas = modelParameters.getThitas();
        
        for(Record r : trainingData) { 
            //mind the fact that we use the previous thitas to estimate the new ones! this is because the thitas must be updated simultaniously
            double error = TypeInference.toDouble(r.getY()) - hypothesisFunction(r.getX(), thitas);
            
            double errorMultiplier = multiplier*error;
            
            
            //update the weight of constant
            newThitas.put(Dataframe.COLUMN_NAME_CONSTANT, newThitas.get(Dataframe.COLUMN_NAME_CONSTANT)+errorMultiplier);

            //update the rest of the weights
            for(Map.Entry<Object, Object> entry : r.getX().entrySet()) {
                Object feature = entry.getKey();
                
                Double thitaWeight = newThitas.get(feature);
                if(thitaWeight!=null) {//ensure that the feature is in the supported features
                    Double value = TypeInference.toDouble(entry.getValue());
                    newThitas.put(feature, thitaWeight+errorMultiplier*value);
                }
            }
        }
    } 
                            #location 12 
                            #vulnerability type NULL_DEREFERENCE
#fixed code
                                private void batchGradientDescent(Dataframe trainingData, Map<Object, Double> newThitas, double learningRate) {
        //NOTE! This is not the stochastic gradient descent. It is the batch gradient descent optimized for speed (despite it looks more than the stochastic). 
        //Despite the fact that the loops are inverse, the function still changes the values of Thitas at the end of the function. We use the previous thitas 
        //to estimate the costs and only at the end we update the new thitas.
        ModelParameters modelParameters = kb().getModelParameters();
        
        double multiplier = learningRate/modelParameters.getN();
        Map<Object, Double> thitas = modelParameters.getThitas();
        
        StreamMethods.stream(trainingData.stream(), isParallelized()).forEach(r -> { 
            //mind the fact that we use the previous thitas to estimate the new ones! this is because the thitas must be updated simultaniously
            double error = TypeInference.toDouble(r.getY()) - hypothesisFunction(r.getX(), thitas);
            
            double errorMultiplier = multiplier*error;
            
            synchronized(newThitas) {
                //update the weight of constant
                newThitas.put(Dataframe.COLUMN_NAME_CONSTANT, newThitas.get(Dataframe.COLUMN_NAME_CONSTANT)+errorMultiplier);

                //update the rest of the weights
                for(Map.Entry<Object, Object> entry : r.getX().entrySet()) {
                    Object feature = entry.getKey();

                    Double thitaWeight = newThitas.get(feature);
                    if(thitaWeight!=null) {//ensure that the feature is in the supported features
                        Double value = TypeInference.toDouble(entry.getValue());
                        newThitas.put(feature, thitaWeight+errorMultiplier*value);
                    }
                }
            }
        });
    }