} // GetTrainTest
static double[][] UploadFiles(string dataPath)
{
List <double[]> dataset = new List <double[]>();
var directories = Directory.GetDirectories(dataPath);
if (directories.Count() != num_of_classes)
{
throw new Exception($"ERROR: missing any folders with training data. Check directories in {dataPath}");
}
for (int i = 0; i < num_of_classes; i++)
{
var files = Directory.GetFiles(directories[i]);
if (files.Count() == 0)
{
throw new Exception($"ERROR: missing any files in folder. Check directory {directories[i]}");
}
foreach (var file in files)
{
var source = Helpers.GetBitmap(file);
if (source.Count() != 25)
{
throw new Exception($"ERROR: the file does not match the format. Check file {file}");
}
Array.Resize(ref source, 29);
switch (i)
{
case 0: source[25] = 1; break;
case 1: source[26] = 1; break;
case 2: source[27] = 1; break;
case 3: source[28] = 1; break;
}
dataset.Add(source);
}
}
return(dataset.ToArray());
}
static void Main(string[] args)
{
double[][] allData = new double[20][];
allData[0] = new double[] { 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0 };
allData[1] = new double[] { 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0 };
allData[2] = new double[] { 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0 };
allData[3] = new double[] { 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0 };
allData[4] = new double[] { 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0 };
allData[5] = new double[] { 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0 };
allData[6] = new double[] { 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0 };
allData[7] = new double[] { 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0 };
allData[8] = new double[] { 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0 };
allData[9] = new double[] { 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0 };
allData[10] = new double[] { 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0 };
allData[11] = new double[] { 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0 };
allData[12] = new double[] { 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0 };
allData[13] = new double[] { 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0 };
allData[14] = new double[] { 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0 };
allData[15] = new double[] { 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1 };
allData[16] = new double[] { 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1 };
allData[17] = new double[] { 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1 };
allData[18] = new double[] { 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1 };
allData[19] = new double[] { 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1 };
bool fl = true;
Console.WriteLine("\nSplitting data into 80%-20% train and test sets");
double[][] trainData = null;
double[][] testData = null;
int seed = 5;
GetTrainTest(allData, seed, out trainData, out testData);
Console.WriteLine("\nTraining data: \n");
Helpers.ShowMatrix(trainData, trainData.Length, 3, true, false);
Console.WriteLine("\nTest data:\n");
Helpers.ShowMatrix(testData, testData.Length, 3, true, false);
Console.WriteLine("\nCreating a 25-5-4 radial basis function network");
int numInput = 25;
int numHidden = 5;
int numOutput = 4;
RadialNetwork RBFN = new RadialNetwork(numInput, numHidden, numOutput);
Console.WriteLine("\nBeginning RBF training\n");
int maxIterations = 100;
double[] bestWeights = RBFN.Train(trainData, maxIterations);
Console.WriteLine("\nEvaluating result RBF classification accuracy on the test data");
RBFN.SetWeights(bestWeights);
double acc = RBFN.Accuracy(testData);
Console.WriteLine("Classification accuracy = " + acc.ToString("F4"));
while (fl)
{
Console.WriteLine("Write path to your image:\n");
string data = Console.ReadLine();
var items = Helpers.GetBitmap(data);
//var bufer = data.Split(' ');
//var items = bufer.Select(x => double.Parse(x)).ToList();
var res = RBFN.Predict(items.ToArray());
switch (res)
{
case 0: Console.WriteLine("Top Arrow");
break;
case 1: Console.WriteLine("Left Arrow");
break;
case 2: Console.WriteLine("Rigth Arrow");
break;
case 3: Console.WriteLine("Bot Arrow");
break;
}
}
}
static void Main(string[] args)
{
try
{
if (args.Length == 0)
{
throw new Exception("ERROR: missing xml file. Please choose xml confoguration file");
}
Console.WriteLine("Start reading xml confoguration file");
string trainFile = args[0];
XElement xElement = XElement.Load(trainFile);
var elementsInXElement = xElement.Elements();
if (elementsInXElement.Count() != 2)
{
throw new Exception("ERROR: file does not match the pattern. Please choose valid xml confoguration file or check this file");
}
string dataPath = elementsInXElement.First().FirstAttribute.Value.ToString();
string predictPath = elementsInXElement.Last().FirstAttribute.Value.ToString();
Console.WriteLine("Start generating dataset");
var allData = UploadFiles(dataPath);
Console.WriteLine("Finish generating dataset");
bool fl = true;
Console.WriteLine("\nSplitting data into 80%-20% train and test sets");
double[][] trainData = null;
double[][] testData = null;
int seed = 5;
GetTrainTest(allData, seed, out trainData, out testData);
Console.WriteLine("\nTraining data: \n");
Helpers.ShowMatrix(trainData, trainData.Length, 3, true, false);
Console.WriteLine("\nTest data:\n");
Helpers.ShowMatrix(testData, testData.Length, 3, true, false);
Console.WriteLine("\nCreating a 25-5-4 radial basis function network");
int numInput = 25;
int numHidden = 5;
int numOutput = 4;
RadialNetwork RBFN = new RadialNetwork(numInput, numHidden, numOutput);
Console.WriteLine("\nBeginning RBF training\n");
int maxIterations = 100;
double[] bestWeights = RBFN.Train(trainData, maxIterations);
Console.WriteLine("\nEvaluating result RBF classification accuracy on the test data");
RBFN.SetWeights(bestWeights);
double acc = RBFN.Accuracy(testData);
Console.WriteLine("Classification accuracy = " + acc.ToString("F4"));
while (fl)
{
Console.WriteLine("Write path to your image:\n");
string data = Console.ReadLine();
var item = Helpers.GetBitmap(data);
if (item != null)
{
var res = RBFN.Predict(item.ToArray());
switch (res)
{
case 0:
Console.WriteLine("Top Arrow");
break;
case 1:
Console.WriteLine("Left Arrow");
break;
case 2:
Console.WriteLine("Rigth Arrow");
break;
case 3:
Console.WriteLine("Bot Arrow");
break;
}
}
else
{
Console.WriteLine("Incorrect image, try again with other image");
}
}
}
catch (Exception e)
{
Console.WriteLine(e.Message);
Console.ReadKey();
}
}