/// <summary>
        /// Test cross validation
        /// </summary>
        public static void Test_CalcProbability_Xval(int data_set_index)
        {
            List <MLDataPoint> X    = MLDataPointUtil.LoadDataSet(string.Format("X{0}.txt", data_set_index));
            List <MLDataPoint> Xval = MLDataPointUtil.LoadDataSet(string.Format("Xval{0}.txt", data_set_index));

            MultiVariateGaussianDistributionAD <MLDataPoint> algorithm = new MultiVariateGaussianDistributionAD <MLDataPoint>();

            algorithm.ComputeGaussianDistribution(X);

            List <MLDataPoint> correct_pval = MLDataPointUtil.LoadDataSet(string.Format("pval{0}.txt", data_set_index));

            int row_count = Xval.Count;

            double total_error = 0;

            for (int i = 0; i < row_count; ++i)
            {
                double pval  = algorithm.CalcProbability(Xval[i]);
                double error = System.Math.Abs(correct_pval[i][0] - pval);
                total_error += error;
                Console.WriteLine("pval={0} correct_pval={1}", pval, correct_pval[i]);
            }

            Console.WriteLine("Total error: {0}", total_error);
        }
        public static void Test_FindOutliers(int data_set_index)
        {
            double threshold, F1Score;

            SelectThreshold(data_set_index, out F1Score, out threshold);

            List <MLDataPoint> X = MLDataPointUtil.LoadDataSet(string.Format("X{0}.txt", data_set_index));
            List <MLDataPoint> correct_outliers = MLDataPointUtil.LoadDataSet(string.Format("outliers{0}.txt", data_set_index));

            Console.WriteLine("Correct Outliers:");
            for (int i = 0; i < correct_outliers.Count; ++i)
            {
                Console.WriteLine("{0}", correct_outliers[i][0]);
            }

            MultiVariateGaussianDistributionAD <MLDataPoint> algorithm = new MultiVariateGaussianDistributionAD <MLDataPoint>();

            algorithm.ComputeGaussianDistribution(X);
            List <int> outliers = algorithm.FindOutliers(X, threshold);

            Console.WriteLine("Predict Outliers:");
            for (int i = 0; i < outliers.Count; ++i)
            {
                Console.WriteLine("{0}", outliers[i] + 1);
            }

            Console.WriteLine("Point Count: {0}", X.Count);

            Console.WriteLine("Threshold: {0}", threshold);

            Console.WriteLine("Predict Outliers Count; {0} Correct Outliers Count: {1}", outliers.Count, correct_outliers.Count);
        }