C# (CSharp) Elmore.NeuralNetwork.Test TabSeparatedListReader示例

示例#1

        public void CanReadTabSeparatedList()
        {
            var reader = new TabSeparatedListReader();

            List <KeyValuePair <double, double[]> > set = reader.Read(@"..\..\trainingsets\dataset2\biased.txt");

            Assert.AreEqual(208, set.Count);
        }

示例#2

        public void FTClassifiesBiasedData()
        {
            // data has 2 double inputs and an expected binary out
            var network = new PerceptronFactory().BuildFTPerceptron(2);

            var reader = new TabSeparatedListReader();

            List <KeyValuePair <double, double[]> > trainingSet = reader.Read(@"..\..\trainingsets\dataset2\biased.txt");

            network.Train(trainingSet);

            trainingSet.ForEach(kvp => Assert.AreEqual(kvp.Key, network.Classify(kvp.Value)));
        }

示例#3

文件： PerceptronTests.cs 项目： elmore/Elmore.NeuralNetwork

        public void CanReadTabSeparatedList()
        {
            var reader = new TabSeparatedListReader();

            List<KeyValuePair<double, double[]>> set = reader.Read(@"..\..\trainingsets\dataset2\biased.txt");

            Assert.AreEqual(208, set.Count);
        }

示例#4

文件： PerceptronTests.cs 项目： elmore/Elmore.NeuralNetwork

        public void FTClassifiesBiasedData()
        {
            // data has 2 double inputs and an expected binary out
            var network = new PerceptronFactory().BuildFTPerceptron(2);

            var reader = new TabSeparatedListReader();

            List<KeyValuePair<double, double[]>> trainingSet = reader.Read(@"..\..\trainingsets\dataset2\biased.txt");

            network.Train(trainingSet);

            trainingSet.ForEach(kvp => Assert.AreEqual(kvp.Key, network.Classify(kvp.Value)));
        }