public void TestNeuralNetworkMockData()
{
double[,] stringdata = new Double[6, 2] {
{ 1, 1 }, { 1, 3 }, { 2, 1 }, { 3, 4 }, { 4, 5 }, { 5, 5 }
};
int[] prediction = new int[6] {
1, 1, 1, 0, 0, 0
};
int[] predicted;
double[,] predict = new double[2, 2] {
{ 0, 0 }, { 5, 5 }
};
NeuralNetworkClassifier NN = new NeuralNetworkClassifier();
NN.Fit(stringdata, prediction);
Console.WriteLine("prediction made");
predicted = NN.Predict(predict);
int[] ShoudBe = new int[predicted.Length];
ShoudBe[0] = 1;
ShoudBe[1] = 0;
for (int i = 0; i < predicted.GetLength(0); i++)
{
AreEqual(predicted[i], ShoudBe[i]);
}
}
public void DatasetBenchmarkNeuralNetwork()
{
int featuresCount = 8;
const string path = BasePath +
"\\heart.csv";
var lineCount = File.ReadLines(path).Count();
var reader = new StreamReader(File.OpenRead(path));
var properties = new double[lineCount, featuresCount + 1];
for (var i2 = 0; i2 < lineCount; i2++)
{
var line = reader.ReadLine();
for (var i = 0; i < featuresCount + 1; i++)
{
if (line == null)
{
continue;
}
var values = line.Split(',');
properties[i2, i] = Convert.ToDouble(values[i]);
}
}
var xTrain = new double[lineCount, featuresCount];
var yTrain = new int[lineCount];
for (var i = 0; i < lineCount; i++)
{
for (var j = 0; j < featuresCount; j++)
{
xTrain[i, j] = properties[i, j];
}
yTrain[i] = (int)properties[i, featuresCount];
}
var model = new NeuralNetworkClassifier();
model.Fit(xTrain, yTrain);
WriteLine("Training data:");
var predicted = model.Predict(xTrain);
WriteLine("Accuracy:");
var accuracy = Accuracy(predicted, yTrain);
WriteLine(accuracy);
Greater(accuracy, 0.60);
}
