static void Test_Kfold_Iris() { NeuralNetwork network = new NeuralNetwork(new int[] { 4, 7, 5, 3 }); List <Tuple <double[], double[]> > input = new List <Tuple <double[], double[]> >(); using (var reader = new StreamReader(@".\input.csv")) { reader.ReadLine(); while (!reader.EndOfStream) { var line = reader.ReadLine(); var values = line.Split(','); input.Add(new Tuple <double[], double[]>(new double[] { Double.Parse(values[0], CultureInfo.InvariantCulture), Double.Parse(values[1], CultureInfo.InvariantCulture), Double.Parse(values[2], CultureInfo.InvariantCulture), Double.Parse(values[3], CultureInfo.InvariantCulture) }, MagicSwitch(values[4]))); } } Random rng = new Random(); int n = input.Count; while (n > 1) { n--; int l = rng.Next(n + 1); Tuple <double[], double[]> value = input[l]; input[l] = input[n]; input[n] = value; } int k = 10; double score = 0; double[] results; List <Tuple <double[], double[]> >[] chunks = new List <Tuple <double[], double[]> > [10]; int chunkSize = input.Count() / k; for (int i = 0; i < k; i++) { chunks[i] = input.GetRange(i * chunkSize, chunkSize); } for (int j = 0; j < k; j++) { Console.WriteLine("Chunk " + (j + 1)); for (int i = 0; i < k; i++) { if (i != j) { for (int m = 0; m < 5000; m++) { foreach (Tuple <double[], double[]> t in chunks[i]) { network.FeedForward(t.Item1); network.BackProp(t.Item2); } } } } score = 0.0d; foreach (Tuple <double[], double[]> t in chunks[j]) { results = network.FeedForward(t.Item1); List <double> r1 = results.ToList(); List <double> r2 = t.Item2.ToList(); if (r1.IndexOf(r1.Max()) == r2.IndexOf(r2.Max())) { score += 1.0d; } } score = score / chunkSize; Console.WriteLine("Accuracy: " + score * 100 + "%"); } //double[] result = network.FeedForward(new double[] { 5.8d, 4.0d, 1.2d, 0.2d }); //foreach (double d in result) //{ // Console.Write(d + " "); //} score = 0.0d; foreach (Tuple <double[], double[]> t in input) { results = network.FeedForward(t.Item1); List <double> r1 = results.ToList(); List <double> r2 = t.Item2.ToList(); if (r1.IndexOf(r1.Max()) == r2.IndexOf(r2.Max())) { score += 1.0d; } } score = score / input.Count(); Console.WriteLine("Accuracy: " + score * 100 + "%"); Console.ReadKey(); }
static void Test_Iris() { NeuralNetwork network = new NeuralNetwork(new int[] { 4, 7, 5, 3 }); List <Tuple <double[], double[]> > input = new List <Tuple <double[], double[]> >(); using (var reader = new StreamReader(@".\input.csv")) { reader.ReadLine(); while (!reader.EndOfStream) { var line = reader.ReadLine(); var values = line.Split(','); input.Add(new Tuple <double[], double[]>(new double[] { Double.Parse(values[0], CultureInfo.InvariantCulture), Double.Parse(values[1], CultureInfo.InvariantCulture), Double.Parse(values[2], CultureInfo.InvariantCulture), Double.Parse(values[3], CultureInfo.InvariantCulture) }, MagicSwitch(values[4]))); } } Random rng = new Random(); int n = input.Count; while (n > 1) { n--; int l = rng.Next(n + 1); Tuple <double[], double[]> value = input[l]; input[l] = input[n]; input[n] = value; } for (int i = 1; i < 5000; i++) { foreach (Tuple <double[], double[]> t in input) { network.FeedForward(t.Item1); network.BackProp(t.Item2); } } double[] result = network.FeedForward(new double[] { 5.0, 3.4, 1.6, 0.4 }); Console.Write("Setosa:\t\t"); foreach (double d in result) { Console.Write("{0:F3}\t", d); } Console.WriteLine(); result = network.FeedForward(new double[] { 6.0, 2.2, 4.0, 1.0 }); Console.Write("Versicolor:\t"); foreach (double d in result) { Console.Write("{0:F3}\t", d); } Console.WriteLine(); result = network.FeedForward(new double[] { 6.1, 3.0, 4.9, 1.8 }); Console.Write("Virginica:\t"); foreach (double d in result) { Console.Write("{0:F3}\t", d); } Console.WriteLine("\n"); //Console.ReadKey(); }