Beispiel #1
0
        public void DecisionTreeLanguageTest()
        {
            var           netMLString   = "create classification decisiontree";
            NetMLParser   netMLParser   = new NetMLParser();
            var           result        = netMLParser.Parse(netMLString);
            NetMLCreator  netMLCreator  = new NetMLCreator(result);
            DataSetLoader dataSetLoader = new DataSetLoader();
            var           data          = dataSetLoader.SelectAnimals();

            netMLCreator.Create(data);
            netMLCreator.Train();
            var testData    = dataSetLoader.SelectAnimals();
            var trueCounter = 0;
            var counter     = 0;

            foreach (var item in testData)
            {
                var outputValue = netMLCreator.Classify(item.Item1);
                if (outputValue == item.Item2)
                {
                    trueCounter++;
                }
                Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
                                              item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
                counter++;
            }
            Debug.WriteLine(string.Format("Data {0} - True {1} Verhältnis: {2}",
                                          counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
        }
Beispiel #2
0
 public void RadialbasisfunctionLanguageTest()
 {
     var netMLString =
         "create classification radialbasisfunction inputneurons = 5 outputneurons = 15 firsthiddenlayerneurons = 2 evolutions = 100 learningrate = 0.1 ";
     NetMLParser netMLParser = new NetMLParser();
     var         result      = netMLParser.Parse(netMLString);
 }
Beispiel #3
0
        public void BackprobpagationLanguageTest()
        {
            var netMLString =
                "create classification backpropagation inputneurons = 16 outputneurons = 1 firsthiddenlayerneurons = 16 evolutions = 100 learningrate = 0.1 ";
            NetMLParser   netMLParser   = new NetMLParser();
            var           result        = netMLParser.Parse(netMLString);
            NetMLCreator  netMLCreator  = new NetMLCreator(result);
            DataSetLoader dataSetLoader = new DataSetLoader();
            var           data          = dataSetLoader.SelectAnimals();

            netMLCreator.Create(data);
            netMLCreator.Train();
            var testData    = dataSetLoader.SelectAnimals();
            var trueCounter = 0;
            var counter     = 0;

            foreach (var item in testData)
            {
                var outputValue = netMLCreator.Classify(item.Item1);
                if (outputValue == item.Item2)
                {
                    trueCounter++;
                }
                Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
                                              item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
                counter++;
            }
            Debug.WriteLine(string.Format("Data {0} - True {1} Verhältnis: {2}",
                                          counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
        }
Beispiel #4
0
        public void ClusteringLanguageTest()
        {
            var netMLString =
                "create clustering kmetroids euclidmetric ";
            NetMLParser   netMLParser   = new NetMLParser();
            var           result        = netMLParser.Parse(netMLString);
            NetMLCreator  netMLCreator  = new NetMLCreator(result);
            DataSetLoader dataSetLoader = new DataSetLoader();
            var           irises        = dataSetLoader.SelectClusteringIrises();

            netMLCreator.Create(irises);
            var clusters       = netMLCreator.Cluster(3);
            var clusterCounter = 0;
            Dictionary <int, int> clusterDictonary = new Dictionary <int, int>();

            foreach (var cluster in clusters)
            {
                Debug.WriteLine(string.Format("Cluster {0} - Count {1}", clusterCounter, cluster.Count));
                clusterDictonary.Add(clusterCounter, 0);
                clusterCounter++;
            }
            var irisesTest  = dataSetLoader.SelectClusteringIrises();
            var trueCounter = 0;
            var counter     = 0;

            foreach (var item in irisesTest)
            {
                var outputValue = netMLCreator.CalculateClusterAffinity(item);
                Debug.WriteLine(string.Format("Value {0} - Predicted {1}", item, outputValue));
                clusterDictonary[outputValue]++;
                counter++;
                trueCounter++;
            }
            clusterCounter = 0;
            foreach (var cluster in clusters)
            {
                var calculatedCluster = clusterDictonary[clusterCounter];
                Debug.WriteLine(string.Format("Cluster {0} - Original Count {1} - Calculated Count {2}",
                                              clusterCounter, cluster.Count, calculatedCluster));
                clusterCounter++;
            }
        }