コード例 #1
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        /// <summary>
        /// This method required training sets for algorithm training and return PxFormula with setted algorithm values.
        /// </summary>
        /// <param name="trainingSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
        /// <param name="nonAnomaliesSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
        /// <param name="anomaliesSet">This is TrainingSet class object. Must contain training set with ONLY ANOMALY records.</param>
        /// <returns></returns>
        public PxFormula TrainAlgorithm(TrainingSet trainingSet, TrainingSet nonAnomaliesSet, TrainingSet anomaliesSet)
        {
            var formula = CalcFormula(trainingSet);
            
            this.accuracy.CalcAccuracy(nonAnomaliesSet, anomaliesSet, formula);

            return formula;
        }
コード例 #2
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 private static TrainingSet createTrainingSetFromResources(IEnumerable<KeyValuePair<User, double[]>> studentsAndMarks)
 {
     TrainingSet resultTrainingSet = new TrainingSet(studentsAndMarks.First().Value.Length);
     foreach (KeyValuePair<User, double[]> studentAndMark in studentsAndMarks)
     {
         resultTrainingSet.addRecord(studentAndMark.Value);
     }
     return resultTrainingSet;
 }
コード例 #3
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        // For demo

        public static IEnumerable<KeyValuePair<KeyValuePair<User, double[]>, bool>> runAlg(IEnumerable<KeyValuePair<User, double[]>> studentsAndMarks, TrainingSet trainingSet1, TrainingSet trainingSet2Normal, TrainingSet trainingSet2Anomalies)
        {            
            AnomalyDetectionAlgorithm train = new AnomalyDetectionAlgorithm();
            var formula = train.trainAlgorithm(trainingSet1, trainingSet2Normal, trainingSet2Anomalies);
            List<KeyValuePair<KeyValuePair<User, double[]>, bool>> res = new List<KeyValuePair<KeyValuePair<User, double[]>, bool>>();
            foreach (KeyValuePair<User, double[]> studentAndMark in studentsAndMarks)
            {
                bool isAnomaly = formula.isAnomaly(studentAndMark.Value);

                res.Add(new KeyValuePair<KeyValuePair<User, double[]>, bool>(studentAndMark, isAnomaly));
            }

            return res;
        }
コード例 #4
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        /// <summary>
        /// This method creates PxFormula class object and set in it calculated nu vector and sigma matrix values.
        /// </summary>
        /// <param name="trainingSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
        /// <returns>PxFormula class object with setted nu vector and sigma matrix values.</returns>
        private static PxFormula CalcFormula(TrainingSet trainingSet)
        {
            var trainingSetCount = trainingSet.GetCountOfRecords();
            var size = trainingSet.GetAllRecords().First().Length;

            // calculate nu vector value
            var nu = new double[size];

            nu = trainingSet.GetAllRecords().Aggregate(nu, (current, featureVector) => current.Add(featureVector));
            nu = nu.Divide(trainingSetCount);

            // calculate sigme matrix
            var sigma = new double[size, size];

            sigma = trainingSet.GetAllRecords().Select(featureVector => featureVector.Subtract(nu)).Aggregate(sigma, (current, xNu) => current.Add(Matrix.ColumnVector(xNu).Multiply(Matrix.RowVector(xNu))));

            sigma = sigma.Divide(trainingSetCount);

            // return PxFormula class object with setted nu and sigma values
            return new PxFormula(nu, sigma);
        }
コード例 #5
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        /// <summary>
        /// This method creates PxFormula class object and set in it calculated nu vector and sigma matrix values.
        /// </summary>
        /// <param name="trainingSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
        /// <returns>PxFormula class object with setted nu vector and sigma matrix values.</returns>
        private static PxFormula CalcFormula(TrainingSet trainingSet)
        {
            var trainingSetCount = trainingSet.GetCountOfRecords();
            var size             = trainingSet.GetAllRecords().First().Length;

            // calculate nu vector value
            var nu = new double[size];

            nu = trainingSet.GetAllRecords().Aggregate(nu, (current, featureVector) => current.Add(featureVector));
            nu = nu.Divide(trainingSetCount);

            // calculate sigme matrix
            var sigma = new double[size, size];

            sigma = trainingSet.GetAllRecords().Select(featureVector => featureVector.Subtract(nu)).Aggregate(sigma, (current, xNu) => current.Add(Matrix.ColumnVector(xNu).Multiply(Matrix.RowVector(xNu))));

            sigma = sigma.Divide(trainingSetCount);

            // return PxFormula class object with setted nu and sigma values
            return(new PxFormula(nu, sigma));
        }
コード例 #6
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ファイル: TrainingSetTest.cs プロジェクト: supermuk/iudico
        public void TrainingSetAdd()
        {
            var set = new TrainingSet(3);

            set.AddRecord(new[] { 3.0, 4.0, 5.0 });
            set.AddRecord(new[] { 3.0, 4.0, 5.0 });
            set.AddRecord(new[] { 3.0, 4.0, 5.0 });

            Assert.AreEqual(set.GetCountOfRecords(), 3);

            try
            {
                set.AddRecord(new[] { 3.0, 4.0, 5.0, 6.0 });
            }
            catch (Exception)
            {
                Assert.Pass();
                return;
            }

            Assert.Fail();
        }
コード例 #7
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        /// <summary>
        /// This method creates PxFormula class object and set in it calculated nu vector and sigma matrix values.
        /// </summary>
        /// <param name="trainingSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
        /// <returns>PxFormula class object with setted nu vector and sigma matrix values.</returns>
        private PxFormula calcFormula (TrainingSet trainingSet)
        {
            int training_set_count = trainingSet.getCountOfRecords();
            int size = trainingSet.getAllRecords().First().Length;
            // calculate nu vector value
            double[] nu = new double[size];
            foreach (double[] feature_vector in trainingSet.getAllRecords())
            {
                nu = Matrix.Add(nu, feature_vector);
            }
            nu = Matrix.Divide(nu,training_set_count);

            // calculate sigme matrix
            double[,] sigma = new double[size, size];
            foreach (double[] feature_vector in trainingSet.getAllRecords())
            {
                double[] x_nu = Matrix.Subtract(feature_vector, nu);
                sigma = Matrix.Add(sigma, Matrix.Multiply(Matrix.ColumnVector(x_nu), Matrix.RowVector(x_nu)));
            }
            sigma = Matrix.Divide(sigma, training_set_count);

            // return PxFormula class object with setted nu and sigma values
            return new PxFormula(nu, sigma);
        }
コード例 #8
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ファイル: TrainingSetTest.cs プロジェクト: supermuk/iudico
        public void TrainingSetGetDimmensionCount()
        {
            var set = new TrainingSet(3);

            Assert.AreEqual(set.GetDimensionsCount(), 3);
        }
コード例 #9
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        /// <summary>
        /// Train and run anomaly detection algorithm for passes students.
        /// </summary>
        /// <param name="studentsAndMarks">all students, that we want to check</param>
        /// <param name="trainingSet1">Training set</param>
        /// <param name="trainingSet2Normal">Cross validation set with only normal records</param>
        /// <param name="trainingSet2Anomalies">Cross validation set with inly anomalies records</param>
        /// <returns></returns>
        public static IEnumerable<KeyValuePair<KeyValuePair<User, double[]>, bool>> RunAlg(IEnumerable<KeyValuePair<User, double[]>> studentsAndMarks, TrainingSet trainingSet1, TrainingSet trainingSet2Normal, TrainingSet trainingSet2Anomalies)
        {            
            var train = new AnomalyDetectionAlgorithm();
            var formula = train.TrainAlgorithm(trainingSet1, trainingSet2Normal, trainingSet2Anomalies);

            return (from studentAndMark in studentsAndMarks
                    let isAnomaly = formula.IsAnomaly(studentAndMark.Value)
                    select new KeyValuePair<KeyValuePair<User, double[]>, bool>(studentAndMark, isAnomaly)).ToList();
        }
コード例 #10
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        /// <summary>
        /// Train and run anomaly detection algorithm for passes students.
        /// </summary>
        /// <param name="studentsAndMarks">all students, that we want to check</param>
        /// <param name="trainingSet1">Training set</param>
        /// <param name="trainingSet2Normal">Cross validation set with only normal records</param>
        /// <param name="trainingSet2Anomalies">Cross validation set with inly anomalies records</param>
        /// <returns></returns>
        public static IEnumerable <KeyValuePair <KeyValuePair <User, double[]>, bool> > RunAlg(IEnumerable <KeyValuePair <User, double[]> > studentsAndMarks, TrainingSet trainingSet1, TrainingSet trainingSet2Normal, TrainingSet trainingSet2Anomalies)
        {
            var train   = new AnomalyDetectionAlgorithm();
            var formula = train.TrainAlgorithm(trainingSet1, trainingSet2Normal, trainingSet2Anomalies);

            return((from studentAndMark in studentsAndMarks
                    let isAnomaly = formula.IsAnomaly(studentAndMark.Value)
                                    select new KeyValuePair <KeyValuePair <User, double[]>, bool>(studentAndMark, isAnomaly)).ToList());
        }
コード例 #11
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        /// <summary>
        /// This method required training sets for algorithm training and return PxFormula with setted algorithm values.
        /// </summary>
        /// <param name="trainingSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
        /// <param name="nonAnomaliesSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
        /// <param name="anomaliesSet">This is TrainingSet class object. Must contain training set with ONLY ANOMALY records.</param>
        /// <returns></returns>
        public PxFormula TrainAlgorithm(TrainingSet trainingSet, TrainingSet nonAnomaliesSet, TrainingSet anomaliesSet)
        {
            var formula = CalcFormula(trainingSet);

            this.accuracy.CalcAccuracy(nonAnomaliesSet, anomaliesSet, formula);

            return(formula);
        }
コード例 #12
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        // For demo

        public static IEnumerable <KeyValuePair <KeyValuePair <User, double[]>, bool> > runAlg(IEnumerable <KeyValuePair <User, double[]> > studentsAndMarks, TrainingSet trainingSet1, TrainingSet trainingSet2Normal, TrainingSet trainingSet2Anomalies)
        {
            AnomalyDetectionAlgorithm train = new AnomalyDetectionAlgorithm();
            var formula = train.trainAlgorithm(trainingSet1, trainingSet2Normal, trainingSet2Anomalies);
            List <KeyValuePair <KeyValuePair <User, double[]>, bool> > res = new List <KeyValuePair <KeyValuePair <User, double[]>, bool> >();

            foreach (KeyValuePair <User, double[]> studentAndMark in studentsAndMarks)
            {
                bool isAnomaly = formula.isAnomaly(studentAndMark.Value);

                res.Add(new KeyValuePair <KeyValuePair <User, double[]>, bool>(studentAndMark, isAnomaly));
            }

            return(res);
        }
コード例 #13
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        /// <summary>
        /// This method required training sets for algorithm training and return PxFormula with setted algorithm values.
        /// </summary>
        /// <param name="trainingSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
        /// <param name="nonAnomaliesSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
        /// <param name="anomaliesSet">This is TrainingSet class object. Must contain training set with ONLY ANOMALY records.</param>
        /// <returns></returns>
        public PxFormula trainAlgorithm(TrainingSet trainingSet, TrainingSet nonAnomaliesSet, TrainingSet anomaliesSet)
        {
            PxFormula formula = calcFormula(trainingSet);

            accuracy.calcAccuracy(nonAnomaliesSet, anomaliesSet, formula);
            return(formula);
        }
コード例 #14
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 /// <summary>
 /// This method required training sets for algorithm training and return PxFormula with setted algorithm values.
 /// </summary>
 /// <param name="trainingSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
 /// <param name="nonAnomaliesSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
 /// <param name="anomaliesSet">This is TrainingSet class object. Must contain training set with ONLY ANOMALY records.</param>
 /// <returns></returns>
 public PxFormula trainAlgorithm (TrainingSet trainingSet, TrainingSet nonAnomaliesSet, TrainingSet anomaliesSet)
 {
     PxFormula formula = calcFormula(trainingSet);
     accuracy.calcAccuracy(nonAnomaliesSet, anomaliesSet, formula);
     return formula;
 }