static void PredictByKNN(string trainPath, string[] classes, int k, string testPath, int skiplines, string predictPath, KNearestNeighbors.MetricType type, string headline = "")
{
StreamReader train = new StreamReader(trainPath);
for (int i = 0; i < skiplines; i++)
{
train.ReadLine();
}
List <Vector> inputData = new List <Vector>();
List <int> outputData = new List <int>();
Console.WriteLine("Read train data\n");
int rowNumber = 1;
while (!train.EndOfStream)
{
string line = train.ReadLine();
string[] splited = line.Split(',');
Vector input = new Vector(splited.Length - 1);
int index = Array.IndexOf(classes, splited[0]);
if (index == -1)
{
throw new Exception("Can't find class for '" + splited[0] + "'");
}
outputData.Add(index);
for (int i = 0; i < input.Length; i++)
{
input[i] = 0.01 + double.Parse(splited[i + 1]) / 255 * 0.99;
}
inputData.Add(input);
Console.SetCursorPosition(0, Console.CursorTop - 1);
Console.WriteLine("Read train row{0}", rowNumber);
rowNumber++;
}
KNearestNeighbors KNN = new KNearestNeighbors(inputData.ToArray(), outputData.ToArray(), classes.Length, k, type);
StreamReader reader = new StreamReader(testPath);
StreamWriter writer = new StreamWriter(predictPath);
for (int i = 0; i < skiplines; i++)
{
reader.ReadLine();
}
if (headline.Length > 0)
{
writer.WriteLine(headline);
}
rowNumber = 1;
Console.WriteLine("Classifing:\n");
while (!reader.EndOfStream)
{
string line = reader.ReadLine();
string[] splited = line.Split(',');
Vector input = new Vector(splited.Length);
for (int i = 0; i < input.Length; i++)
{
input[i] = 0.01 + double.Parse(splited[i]) / 255 * 0.99; // 0.01 .. 1
}
int index = KNN.GetClassIndex(input);
Console.SetCursorPosition(0, Console.CursorTop - 1);
Console.WriteLine("Classified row{0} ({1})", rowNumber, classes[index]);
writer.WriteLine("{0},{1}", rowNumber, classes[index]);
rowNumber++;
}
reader.Close();
writer.Close();
}
