double CheckAccuracy(ANeuralNetwork ann)
{
double error = 0;
for (int i = 0; i < inputs.Count; i++)
{
for (int j = 0; j < outputs[i].Count; j++)
{
var tmp = ann.Predict(inputs[i])[j];
error += (outputs[i][j] - ann.Predict(inputs[i])[j]) * (outputs[i][j] - ann.Predict(inputs[i])[j]);
}
}
return(Math.Sqrt(error / inputs.Count));
}
public void CheckOne()
{
ANeuralNetwork network = new ANeuralNetwork();
network.Load("..\\..\\..\\..\\savedData.txt");
var sampleDirs = Directory.GetDirectories("..\\..\\..\\..\\Data\\TestData\\").ToList();
int fCount = Directory.GetFiles("..\\..\\..\\..\\Data\\GroundData\\", "*.png", SearchOption.AllDirectories).Length;
var poliManager = new PolynomialManager();
poliManager.InitBasis(15, 100);
double count = 0;
foreach (var dir in sampleDirs)
{
var files = Directory.GetFiles(dir, "*.png", SearchOption.TopDirectoryOnly).ToList();
var index = new Random().Next(files.Count);
var file = files[index];
var value = Convert.ToInt32(new StreamReader(dir + "\\" + "value.txt").ReadToEnd());
++count;
ComplexMoments tmpMoments;
ProcessOneImage(file, poliManager, 100, out tmpMoments);
CvInvoke.Imshow(dir, tmpMoments.Real);
var tmpInput = tmpMoments.ToListOfDouble();
var output = network.Predict(tmpInput);
var predictedValue = Convert.ToInt32(output.IndexOf(output.Max()));
Console.WriteLine(predictedValue);
CvInvoke.WaitKey();
}
}
string Recognize(ComplexMoments moments)
{
ANeuralNetwork networt = new ANeuralNetwork();
var output = networt.Predict(moments.ToListOfDouble());
return(output.IndexOf(output.Max()).ToString());
}
static void Main(string[] args)
{
ANeuralNetwork tmp = new ANeuralNetwork();
tmp.Load("..\\..\\..\\..\\PerceptronSavedData.txt");
var line = Console.ReadLine();
while (line!="q")
{
var tmpInput = line.Split().Select(double.Parse).ToList();
var tmpOutput = tmp.Predict(tmpInput);
foreach (var element in tmpOutput)
{
Console.WriteLine(element);
}
line = Console.ReadLine();
}
}
public void Check()
{
ANeuralNetwork network = new ANeuralNetwork();
network.Load("..\\..\\..\\..\\savedData.txt");
var sampleDirs = Directory.GetDirectories("..\\..\\..\\..\\Data\\TestData\\").ToList();
int fCount = Directory.GetFiles("..\\..\\..\\..\\Data\\GroundData\\", "*.png", SearchOption.AllDirectories).Length;
double precision = 0;
var poliManager = new PolynomialManager();
poliManager.InitBasis(15, 100);
double count = 0;
double trueCount = 0;
foreach (var dir in sampleDirs)
{
var files = Directory.GetFiles(dir, "*.png", SearchOption.TopDirectoryOnly).ToList();
var value = Convert.ToInt32(new StreamReader(dir + "\\" + "value.txt").ReadToEnd());
foreach (var file in files)
{
++count;
ComplexMoments tmpMoments;
ProcessOneImage(file, poliManager, 100, out tmpMoments);
var tmpInput = tmpMoments.ToListOfDouble();
var output = network.Predict(tmpInput);
var predictedValue = Convert.ToInt32(output.IndexOf(output.Max()));
if (predictedValue == value)
{
//++trueCount;
precision += Convert.ToDouble(100 / (double)fCount);
}
}
Console.WriteLine("Точность " + precision + "%");
//Console.WriteLine("Точность " + trueCount/count + "%");
}
}
public void IrisClasificationTest()
{
#region Training Data
//Encoded
// Iris setosa = 1 0 0
// Iris versicolor = 0 1 0
// Iris virginica = 0 0 1
string[] rawData = new[]
{
"setosa",
"versicolor",
"virginica"
};
double[][] encodedData = new double[150][];
encodedData[0] = new double[] { 5.1, 3.5, 1.4, 0.2, 0, 0, 1 };
encodedData[1] = new double[] { 4.9, 3.0, 1.4, 0.2, 0, 0, 1 };
encodedData[2] = new double[] { 4.7, 3.2, 1.3, 0.2, 0, 0, 1 };
encodedData[3] = new double[] { 4.6, 3.1, 1.5, 0.2, 0, 0, 1 };
encodedData[4] = new double[] { 5.0, 3.6, 1.4, 0.2, 0, 0, 1 };
encodedData[5] = new double[] { 5.4, 3.9, 1.7, 0.4, 0, 0, 1 };
encodedData[6] = new double[] { 4.6, 3.4, 1.4, 0.3, 0, 0, 1 };
encodedData[7] = new double[] { 5.0, 3.4, 1.5, 0.2, 0, 0, 1 };
encodedData[8] = new double[] { 4.4, 2.9, 1.4, 0.2, 0, 0, 1 };
encodedData[9] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
encodedData[10] = new double[] { 5.4, 3.7, 1.5, 0.2, 0, 0, 1 };
encodedData[11] = new double[] { 4.8, 3.4, 1.6, 0.2, 0, 0, 1 };
encodedData[12] = new double[] { 4.8, 3.0, 1.4, 0.1, 0, 0, 1 };
encodedData[13] = new double[] { 4.3, 3.0, 1.1, 0.1, 0, 0, 1 };
encodedData[14] = new double[] { 5.8, 4.0, 1.2, 0.2, 0, 0, 1 };
encodedData[15] = new double[] { 5.7, 4.4, 1.5, 0.4, 0, 0, 1 };
encodedData[16] = new double[] { 5.4, 3.9, 1.3, 0.4, 0, 0, 1 };
encodedData[17] = new double[] { 5.1, 3.5, 1.4, 0.3, 0, 0, 1 };
encodedData[18] = new double[] { 5.7, 3.8, 1.7, 0.3, 0, 0, 1 };
encodedData[19] = new double[] { 5.1, 3.8, 1.5, 0.3, 0, 0, 1 };
encodedData[20] = new double[] { 5.4, 3.4, 1.7, 0.2, 0, 0, 1 };
encodedData[21] = new double[] { 5.1, 3.7, 1.5, 0.4, 0, 0, 1 };
encodedData[22] = new double[] { 4.6, 3.6, 1.0, 0.2, 0, 0, 1 };
encodedData[23] = new double[] { 5.1, 3.3, 1.7, 0.5, 0, 0, 1 };
encodedData[24] = new double[] { 4.8, 3.4, 1.9, 0.2, 0, 0, 1 };
encodedData[25] = new double[] { 5.0, 3.0, 1.6, 0.2, 0, 0, 1 };
encodedData[26] = new double[] { 5.0, 3.4, 1.6, 0.4, 0, 0, 1 };
encodedData[27] = new double[] { 5.2, 3.5, 1.5, 0.2, 0, 0, 1 };
encodedData[28] = new double[] { 5.2, 3.4, 1.4, 0.2, 0, 0, 1 };
encodedData[29] = new double[] { 4.7, 3.2, 1.6, 0.2, 0, 0, 1 };
encodedData[30] = new double[] { 4.8, 3.1, 1.6, 0.2, 0, 0, 1 };
encodedData[31] = new double[] { 5.4, 3.4, 1.5, 0.4, 0, 0, 1 };
encodedData[32] = new double[] { 5.2, 4.1, 1.5, 0.1, 0, 0, 1 };
encodedData[33] = new double[] { 5.5, 4.2, 1.4, 0.2, 0, 0, 1 };
encodedData[34] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
encodedData[35] = new double[] { 5.0, 3.2, 1.2, 0.2, 0, 0, 1 };
encodedData[36] = new double[] { 5.5, 3.5, 1.3, 0.2, 0, 0, 1 };
encodedData[37] = new double[] { 4.9, 3.1, 1.5, 0.1, 0, 0, 1 };
encodedData[38] = new double[] { 4.4, 3.0, 1.3, 0.2, 0, 0, 1 };
encodedData[39] = new double[] { 5.1, 3.4, 1.5, 0.2, 0, 0, 1 };
encodedData[40] = new double[] { 5.0, 3.5, 1.3, 0.3, 0, 0, 1 };
encodedData[41] = new double[] { 4.5, 2.3, 1.3, 0.3, 0, 0, 1 };
encodedData[42] = new double[] { 4.4, 3.2, 1.3, 0.2, 0, 0, 1 };
encodedData[43] = new double[] { 5.0, 3.5, 1.6, 0.6, 0, 0, 1 };
encodedData[44] = new double[] { 5.1, 3.8, 1.9, 0.4, 0, 0, 1 };
encodedData[45] = new double[] { 4.8, 3.0, 1.4, 0.3, 0, 0, 1 };
encodedData[46] = new double[] { 5.1, 3.8, 1.6, 0.2, 0, 0, 1 };
encodedData[47] = new double[] { 4.6, 3.2, 1.4, 0.2, 0, 0, 1 };
encodedData[48] = new double[] { 5.3, 3.7, 1.5, 0.2, 0, 0, 1 };
encodedData[49] = new double[] { 5.0, 3.3, 1.4, 0.2, 0, 0, 1 };
encodedData[50] = new double[] { 7.0, 3.2, 4.7, 1.4, 0, 1, 0 };
encodedData[51] = new double[] { 6.4, 3.2, 4.5, 1.5, 0, 1, 0 };
encodedData[52] = new double[] { 6.9, 3.1, 4.9, 1.5, 0, 1, 0 };
encodedData[53] = new double[] { 5.5, 2.3, 4.0, 1.3, 0, 1, 0 };
encodedData[54] = new double[] { 6.5, 2.8, 4.6, 1.5, 0, 1, 0 };
encodedData[55] = new double[] { 5.7, 2.8, 4.5, 1.3, 0, 1, 0 };
encodedData[56] = new double[] { 6.3, 3.3, 4.7, 1.6, 0, 1, 0 };
encodedData[57] = new double[] { 4.9, 2.4, 3.3, 1.0, 0, 1, 0 };
encodedData[58] = new double[] { 6.6, 2.9, 4.6, 1.3, 0, 1, 0 };
encodedData[59] = new double[] { 5.2, 2.7, 3.9, 1.4, 0, 1, 0 };
encodedData[60] = new double[] { 5.0, 2.0, 3.5, 1.0, 0, 1, 0 };
encodedData[61] = new double[] { 5.9, 3.0, 4.2, 1.5, 0, 1, 0 };
encodedData[62] = new double[] { 6.0, 2.2, 4.0, 1.0, 0, 1, 0 };
encodedData[63] = new double[] { 6.1, 2.9, 4.7, 1.4, 0, 1, 0 };
encodedData[64] = new double[] { 5.6, 2.9, 3.6, 1.3, 0, 1, 0 };
encodedData[65] = new double[] { 6.7, 3.1, 4.4, 1.4, 0, 1, 0 };
encodedData[66] = new double[] { 5.6, 3.0, 4.5, 1.5, 0, 1, 0 };
encodedData[67] = new double[] { 5.8, 2.7, 4.1, 1.0, 0, 1, 0 };
encodedData[68] = new double[] { 6.2, 2.2, 4.5, 1.5, 0, 1, 0 };
encodedData[69] = new double[] { 5.6, 2.5, 3.9, 1.1, 0, 1, 0 };
encodedData[70] = new double[] { 5.9, 3.2, 4.8, 1.8, 0, 1, 0 };
encodedData[71] = new double[] { 6.1, 2.8, 4.0, 1.3, 0, 1, 0 };
encodedData[72] = new double[] { 6.3, 2.5, 4.9, 1.5, 0, 1, 0 };
encodedData[73] = new double[] { 6.1, 2.8, 4.7, 1.2, 0, 1, 0 };
encodedData[74] = new double[] { 6.4, 2.9, 4.3, 1.3, 0, 1, 0 };
encodedData[75] = new double[] { 6.6, 3.0, 4.4, 1.4, 0, 1, 0 };
encodedData[76] = new double[] { 6.8, 2.8, 4.8, 1.4, 0, 1, 0 };
encodedData[77] = new double[] { 6.7, 3.0, 5.0, 1.7, 0, 1, 0 };
encodedData[78] = new double[] { 6.0, 2.9, 4.5, 1.5, 0, 1, 0 };
encodedData[79] = new double[] { 5.7, 2.6, 3.5, 1.0, 0, 1, 0 };
encodedData[80] = new double[] { 5.5, 2.4, 3.8, 1.1, 0, 1, 0 };
encodedData[81] = new double[] { 5.5, 2.4, 3.7, 1.0, 0, 1, 0 };
encodedData[82] = new double[] { 5.8, 2.7, 3.9, 1.2, 0, 1, 0 };
encodedData[83] = new double[] { 6.0, 2.7, 5.1, 1.6, 0, 1, 0 };
encodedData[84] = new double[] { 5.4, 3.0, 4.5, 1.5, 0, 1, 0 };
encodedData[85] = new double[] { 6.0, 3.4, 4.5, 1.6, 0, 1, 0 };
encodedData[86] = new double[] { 6.7, 3.1, 4.7, 1.5, 0, 1, 0 };
encodedData[87] = new double[] { 6.3, 2.3, 4.4, 1.3, 0, 1, 0 };
encodedData[88] = new double[] { 5.6, 3.0, 4.1, 1.3, 0, 1, 0 };
encodedData[89] = new double[] { 5.5, 2.5, 4.0, 1.3, 0, 1, 0 };
encodedData[90] = new double[] { 5.5, 2.6, 4.4, 1.2, 0, 1, 0 };
encodedData[91] = new double[] { 6.1, 3.0, 4.6, 1.4, 0, 1, 0 };
encodedData[92] = new double[] { 5.8, 2.6, 4.0, 1.2, 0, 1, 0 };
encodedData[93] = new double[] { 5.0, 2.3, 3.3, 1.0, 0, 1, 0 };
encodedData[94] = new double[] { 5.6, 2.7, 4.2, 1.3, 0, 1, 0 };
encodedData[95] = new double[] { 5.7, 3.0, 4.2, 1.2, 0, 1, 0 };
encodedData[96] = new double[] { 5.7, 2.9, 4.2, 1.3, 0, 1, 0 };
encodedData[97] = new double[] { 6.2, 2.9, 4.3, 1.3, 0, 1, 0 };
encodedData[98] = new double[] { 5.1, 2.5, 3.0, 1.1, 0, 1, 0 };
encodedData[99] = new double[] { 5.7, 2.8, 4.1, 1.3, 0, 1, 0 };
encodedData[100] = new double[] { 6.3, 3.3, 6.0, 2.5, 1, 0, 0 };
encodedData[101] = new double[] { 5.8, 2.7, 5.1, 1.9, 1, 0, 0 };
encodedData[102] = new double[] { 7.1, 3.0, 5.9, 2.1, 1, 0, 0 };
encodedData[103] = new double[] { 6.3, 2.9, 5.6, 1.8, 1, 0, 0 };
encodedData[104] = new double[] { 6.5, 3.0, 5.8, 2.2, 1, 0, 0 };
encodedData[105] = new double[] { 7.6, 3.0, 6.6, 2.1, 1, 0, 0 };
encodedData[106] = new double[] { 4.9, 2.5, 4.5, 1.7, 1, 0, 0 };
encodedData[107] = new double[] { 7.3, 2.9, 6.3, 1.8, 1, 0, 0 };
encodedData[108] = new double[] { 6.7, 2.5, 5.8, 1.8, 1, 0, 0 };
encodedData[109] = new double[] { 7.2, 3.6, 6.1, 2.5, 1, 0, 0 };
encodedData[110] = new double[] { 6.5, 3.2, 5.1, 2.0, 1, 0, 0 };
encodedData[111] = new double[] { 6.4, 2.7, 5.3, 1.9, 1, 0, 0 };
encodedData[112] = new double[] { 6.8, 3.0, 5.5, 2.1, 1, 0, 0 };
encodedData[113] = new double[] { 5.7, 2.5, 5.0, 2.0, 1, 0, 0 };
encodedData[114] = new double[] { 5.8, 2.8, 5.1, 2.4, 1, 0, 0 };
encodedData[115] = new double[] { 6.4, 3.2, 5.3, 2.3, 1, 0, 0 };
encodedData[116] = new double[] { 6.5, 3.0, 5.5, 1.8, 1, 0, 0 };
encodedData[117] = new double[] { 7.7, 3.8, 6.7, 2.2, 1, 0, 0 };
encodedData[118] = new double[] { 7.7, 2.6, 6.9, 2.3, 1, 0, 0 };
encodedData[119] = new double[] { 6.0, 2.2, 5.0, 1.5, 1, 0, 0 };
encodedData[120] = new double[] { 6.9, 3.2, 5.7, 2.3, 1, 0, 0 };
encodedData[121] = new double[] { 5.6, 2.8, 4.9, 2.0, 1, 0, 0 };
encodedData[122] = new double[] { 7.7, 2.8, 6.7, 2.0, 1, 0, 0 };
encodedData[123] = new double[] { 6.3, 2.7, 4.9, 1.8, 1, 0, 0 };
encodedData[124] = new double[] { 6.7, 3.3, 5.7, 2.1, 1, 0, 0 };
encodedData[125] = new double[] { 7.2, 3.2, 6.0, 1.8, 1, 0, 0 };
encodedData[126] = new double[] { 6.2, 2.8, 4.8, 1.8, 1, 0, 0 };
encodedData[127] = new double[] { 6.1, 3.0, 4.9, 1.8, 1, 0, 0 };
encodedData[128] = new double[] { 6.4, 2.8, 5.6, 2.1, 1, 0, 0 };
encodedData[129] = new double[] { 7.2, 3.0, 5.8, 1.6, 1, 0, 0 };
encodedData[130] = new double[] { 7.4, 2.8, 6.1, 1.9, 1, 0, 0 };
encodedData[131] = new double[] { 7.9, 3.8, 6.4, 2.0, 1, 0, 0 };
encodedData[132] = new double[] { 6.4, 2.8, 5.6, 2.2, 1, 0, 0 };
encodedData[133] = new double[] { 6.3, 2.8, 5.1, 1.5, 1, 0, 0 };
encodedData[134] = new double[] { 6.1, 2.6, 5.6, 1.4, 1, 0, 0 };
encodedData[135] = new double[] { 7.7, 3.0, 6.1, 2.3, 1, 0, 0 };
encodedData[136] = new double[] { 6.3, 3.4, 5.6, 2.4, 1, 0, 0 };
encodedData[137] = new double[] { 6.4, 3.1, 5.5, 1.8, 1, 0, 0 };
encodedData[138] = new double[] { 6.0, 3.0, 4.8, 1.8, 1, 0, 0 };
encodedData[139] = new double[] { 6.9, 3.1, 5.4, 2.1, 1, 0, 0 };
encodedData[140] = new double[] { 6.7, 3.1, 5.6, 2.4, 1, 0, 0 };
encodedData[141] = new double[] { 6.9, 3.1, 5.1, 2.3, 1, 0, 0 };
encodedData[142] = new double[] { 5.8, 2.7, 5.1, 1.9, 1, 0, 0 };
encodedData[143] = new double[] { 6.8, 3.2, 5.9, 2.3, 1, 0, 0 };
encodedData[144] = new double[] { 6.7, 3.3, 5.7, 2.5, 1, 0, 0 };
encodedData[145] = new double[] { 6.7, 3.0, 5.2, 2.3, 1, 0, 0 };
encodedData[146] = new double[] { 6.3, 2.5, 5.0, 1.9, 1, 0, 0 };
encodedData[147] = new double[] { 6.5, 3.0, 5.2, 2.0, 1, 0, 0 };
encodedData[148] = new double[] { 6.2, 3.4, 5.4, 2.3, 1, 0, 0 };
encodedData[149] = new double[] { 5.9, 3.0, 5.1, 1.8, 1, 0, 0 };
#endregion
var networkData = new NeuralNetworkData(rawData, ' ');
networkData.PrepareTrainingSet(
encodedData: encodedData,
trainDataPercentage: 0.80d,
seed: 72);
ANeuralNetwork nn = new ANeuralNetwork(
inputNodes: 4,
hiddenNodes: 7,
outputNodes: 3,
hiddenNodeActivation: Activation.HyperTan,
outputNodeActivation: Activation.Softmax);
nn.Train(
networkData.TrainingData,
maxEpochs: 1000,
learnRate: 0.05d,
momentum: 0.01d,
meanSquaredErrorThreshold: 0.040d);
Assert.IsTrue(nn.GetAccuracyFor(networkData.TrainingData) > 0.95d);
Assert.IsTrue(nn.GetAccuracyFor(networkData.TestData) > 0.95d);
Assert.AreEqual("virginica", networkData.DecodeOutput(nn.Predict(new double[] { 5.1, 3.5, 1.4, 0.2 })));
Assert.AreEqual("versicolor", networkData.DecodeOutput(nn.Predict(new double[] { 7.0, 3.2, 4.7, 1.4 })));
Assert.AreEqual("setosa", networkData.DecodeOutput(nn.Predict(new double[] { 7.4, 2.8, 6.1, 1.9 })));
}
