public KNN(int wantedK, VectorRepository rep)
{
//k is usually 10
_K = wantedK;
_knn = new KNearest();
_repository = rep;
}
public DataLearning(string name, LearningAlgorithm.Algorithm algorithm, Dictionary<ImageVector.ImageParameters, bool> parameterList, int K)
{
// Set name
_name = name;
// Create repository and try to load vectors (if exists)
_repository = new VectorRepository(_name);
// _repository.loadList();
// Create new algorithm instance
/* if (algorithm == LearningAlgorithm.Algorithm.DecisionTree)
_algorithm = new DecisionTree();
_algorithm = new DecisionTreeNumerical();
if (algorithm == LearningAlgorithm.Algorithm.DecisionTreeNumerical)
if (algorithm == LearningAlgorithm.Algorithm.KNN)
_algorithm = new KNN(K,_repository);
*/
// Set parameter list dictionary
_parameterList = new Dictionary<ImageVector.ImageParameters, bool>(parameterList);
}
private void button2_Click_1(object sender, EventArgs e)
{
const string USERNAME = "******";
VectorRepository repository = new VectorRepository(USERNAME);
// Load vectors to repository
repository.loadList();
//use vectors already in repository
// Scan pictures
//Testing.scanPicturesIntoReporistory(txtFolderAllTesting3.Text, txtFolderTrueTesting3.Text, learning.Repository, ParamDictionary3);
// int paramsCount = 0;
foreach (ImageVector.ImageParameters parameter in ParamDictionary.Keys)
{
if (_paramDictionary[parameter])
{
//paramsCount++;
}
}
//array of lists. each list is for 1 parameter, inside are the values of all vectors for this parameter
List<char>[] trueDist = new List<char>[ImageVector.NUMBER_OF_PARAMETERS];
List<char>[] falseDist = new List<char>[ImageVector.NUMBER_OF_PARAMETERS];
//scan lists and check value of each parameter in dictionary
foreach (ImageVector.ImageParameters parameter in ParamDictionary.Keys)
{
if (_paramDictionary[parameter])
{
trueDist[(int)parameter] = new List<char>();
foreach (ImageVector vec in repository.VectorListTrue)
{
char val = Classifier.getParameterClassification(parameter, vec.getParameter(parameter));
trueDist[(int)parameter].Add(val);
}
falseDist[(int)parameter] = new List<char>();
foreach (ImageVector vec in repository.VectorListFalse)
{
char val = Classifier.getParameterClassification(parameter, vec.getParameter(parameter));
falseDist[(int)parameter].Add(val);
}
}
}
List<char>[] allDist = new List<char>[ImageVector.NUMBER_OF_PARAMETERS];
//make order in arrays and count values
for (int i = 0; i < trueDist.Length; i++)
{
if (trueDist[i] != null)
{
allDist[i] = new List<char>(trueDist[i]);
allDist[i].AddRange(falseDist[i]);
allDist[i].Sort();
trueDist[i].Sort();
falseDist[i].Sort();
}
}
countValsAndWriteXML(allDist, "all");
countValsAndWriteXML(trueDist, "true");
countValsAndWriteXML(falseDist, "false");
}
/// <summary>
/// predicts for all images in folder if true or false (For testing, using given repository to retrive already scanned images)
/// </summary>
/// <param name="folder">path to folder with images to predict</param>
/// <param name="res">keeps for each image if true or falde</param>
/// <returns>true if decision was successful. else false.</returns>
public bool DecideForTesting(List<string> allFiles, VectorRepository rep, out double[] res)
{
res = null;
// Get list of files in folders
String[] files = allFiles.ToArray();
DecisionListcount = 0;
String[] cVectors = new string[files.Length];
double[] results;
ImageVector vector;
List<ImageVector> vectorList = new List<ImageVector>();
for (int i = 0; i < files.Length; i++)
{
//get imageVectors
vector = rep.getVectorByPath(files[i]);
if (vector == null)
vector = new ImageVector(files[i], ParameterList);
vectorList.Add(vector);
DecisionListcount++;
}
//load algorithm data and predict
//if (!_algorithm.LoadData())
//{
// return false;
//}
if (!Algorithm.Predict(vectorList, out results))
return false;
res = results;
return true;
}
//private static string[] GetfilesFromDirectory(string folder)
//{
// List<string> files = Directory.GetFiles(folder).ToList();
// List<String> removeList = new List<string>();
// for (int i = 0; i < files.Count; i++)
// {
// if(files[i].EndsWith("ini"))
// removeList.Add(files[i]);
// }
// foreach (string path in removeList)
// {
// files.Remove(path);
// }
// return files.ToArray();
//}
private static int HandleIdenticalVectorWithDifferentChoise(ref List<string> aFiles, ref List<string> tFiles, ref List<string> fFiles, VectorRepository rep, Testing.HandleIdenticalMethod identical)
{
if (identical == Testing.HandleIdenticalMethod.Ignore)
return -1;
ImageVector tVector;
ImageVector fVector;
string tVectorC;
string fVectorC;
bool same;
int count = 0;
for (int t = 0; t < tFiles.Count; t++)
for (int f = 0; f < fFiles.Count; f++)
{
if (tFiles[t] == "" || fFiles[f] == "")
continue;
tVector = null;
fVector = null;
same = true;
// Load Images as vectors
tVector = rep.getVectorByPath(tFiles[t]);
fVector = rep.getVectorByPath(fFiles[f]);
if (tVector == null || fVector == null)
throw (new Exception("Unexpected Error: a picture was not found in repository"));
// Classify
tVectorC = Classifier.ClassifyVector(tVector);
fVectorC = Classifier.ClassifyVector(fVector);
// Check similarity
for (int c=0; c < ImageVector.NUMBER_OF_PARAMETERS; c++)
if (tVectorC[c] != fVectorC[c])
{
same = false;
break;
}
// Handle identical vectors (Mark as empty for removel)
if (same)
{
count++;
switch (identical)
{
case Testing.HandleIdenticalMethod.Remove:
aFiles.Remove(tFiles[t]);
aFiles.Remove(fFiles[f]);
tFiles[t] = "";
fFiles[f] = "";
break;
}
}
}
// Remove marked
while (tFiles.Remove(""));
while (fFiles.Remove(""));
return count;
}
// Scan image to reporistory
public static bool scanPicturesIntoReporistory(string folderAll, string folderTrue, VectorRepository rep, Dictionary<ImageVector.ImageParameters,bool> parameterList)
{
// Get list of files in folders
ProgressString = "Starting picture scan..";
List<string> allFiles, falseFiles, trueFiles;
try
{
allFiles = LoadImages(folderAll).ToList();
trueFiles = LoadImages(folderTrue).ToList();
falseFiles = SubstractListsByFileName(allFiles, trueFiles);
trueFiles = SubstractListsByFileName(allFiles, falseFiles); // In order for the path to be via 'allFiles' folder
}
catch (Exception exc)
{
MessageBox.Show(exc.Message);
return false;
}
int numOfFiles = allFiles.Count();
int completed = 0;
_progress = 0;
// Scan files
ImageVector existing;
List<ImageVector> newTrue = new List<ImageVector>();
List<ImageVector> newFalse = new List<ImageVector>();
foreach (string path in trueFiles)
{
ProgressString = "Checking if " + path + "is already scanned..";
existing = rep.getVectorByPath(path);
if (existing == null && File.Exists(path))
{
ProgressString = "Scanning " + path;
ImageVector vec = new ImageVector(path, parameterList);
newTrue.Add(vec);
}
completed++;
_progress = (int)((completed * 100) / numOfFiles);
}
foreach (string path in falseFiles)
{
ProgressString = "Checking if " + path + "is already scanned..";
existing = rep.getVectorByPath(path);
if (existing == null && File.Exists(path))
{
ProgressString = "Scanning " + path;
ImageVector vec = new ImageVector(path, parameterList);
newFalse.Add(vec);
}
completed++;
_progress = (int)((completed * 100) / numOfFiles);
}
ProgressString = "Scan done. Saving scanned pictures.";
// Add to repository
rep.AddToList(newTrue, newFalse);
ProgressString = "Done scanning";
return true;
}
/// <summary>
/// trains algorithm
/// </summary>
/// <param name="cVectorsTrue"></param>
/// <param name="cVectorsFalse"></param>
/// <returns>true always</returns>
public override bool Train(List<ImageVector> vectorsTrue, List<ImageVector> vectorsFalse)
{
//delete the test repository if it exists
if(Directory.Exists("knnTest'sVectorData"))
Directory.Delete("knnTest'sVectorData", true);
//create repository only for knn learning
_testingRepository = new VectorRepository("knnTest");
_testingRepository.delete();
_testingRepository.AddToList(vectorsTrue, vectorsFalse);
return true;
}
