public static void printSplitByComponentHistogram(List <GeoWave>[] rfTreeArr, string analysisFolderName)
{
recordConfig rc = rfTreeArr.First().First().rc;
//relevant only for local pca split
if (rc.split_type != 5)
{
return;
}
StreamWriter sw = new StreamWriter(analysisFolderName + "\\localPCAsplitHistogram.txt", false);
//get config from first tree and first node
double[] splitHist = new double[rc.dim];
foreach (GeoWave node in (from tree in rfTreeArr from node in tree where node.dimIndex != -1 select node))
{
splitHist[node.dimIndex] += node.norm;
}
double allNormSum = splitHist.Sum();
foreach (double splitDimCounter in splitHist)
{
//percent of norm explained
sw.Write(Math.Round(100 * splitDimCounter / allNormSum, 3) + "\n");
}
sw.Close();
}
public DecicionTree(recordConfig rc, DB db)
{
_trainingDt = db.PCAtraining_dt;
_trainingLabel = db.training_label;
_trainingGridIndexDt = db.PCAtraining_GridIndex_dt;
_rc = rc;
}
public static void printDataInRFormat(double[][] trainingData,
double[][] testingData,
double[] trainingLabel,
double[] testingLabel,
recordConfig rc,
string analysisFolderName)
{
string testOutPath = analysisFolderName + "\\testDataR.txt";
string trainOutPath = analysisFolderName + "\\trainDataR.txt";
StreamWriter testWriter = new StreamWriter(testOutPath, false);
StreamWriter trainWriter = new StreamWriter(trainOutPath, false);
//write training heading
int[] rTrainHeading = Enumerable.Range(1, rc.dim).ToArray();
trainWriter.WriteLine(string.Join(",", rTrainHeading) + ",responce");
testWriter.WriteLine(string.Join(",", rTrainHeading) + ",responce");
for (int i = 0; i < trainingData.Count(); i++)
{
trainWriter.WriteLine(string.Join(",", trainingData[i]) + "," + trainingLabel[i]);
}
for (int i = 0; i < testingData.Count(); i++)
{
testWriter.WriteLine(string.Join(",", testingData[i]) + "," + testingLabel[i]);
}
testWriter.Close();
trainWriter.Close();
}
public DecicionTree(recordConfig rc, double[][] trainingDt, double[][] trainingLabel, long[][] trainingGridIndexDt, bool[] dime2Take)
{
_trainingDt = trainingDt;
_trainingLabel = trainingLabel;
_rc = rc;
_trainingGridIndexDt = trainingGridIndexDt;
_dime2Take = dime2Take;
}
public DecicionTree(recordConfig rc, DB db, bool[] dime2Take)
{
_trainingDt = db.PCAtraining_dt;
_trainingLabel = db.training_label;
_trainingGridIndexDt = db.PCAtraining_GridIndex_dt;
_rc = rc;
_dime2Take = dime2Take;
}
public GiniEngine(recordConfig rc, DB db, NormType mode = NormType.Gini)
{
_mode = mode;
_labels = db.training_label;
_training = db.training_dt;
_trainingGridIndex = db.PCAtraining_GridIndex_dt;
_dataDim = rc.dim;
_minWaveSize = rc.minWaveSize;
_labelsDim = _labels[0].Count();
}
public GeoWave(int[][] BOX, int labelDim, recordConfig rc)
{
upperSplitValue = -1;
this.rc = rc;
Init(rc.dim, labelDim);
for (int i = 0; i < 2; i++)
{
for (int j = 0; j < rc.dim; j++)
{
boubdingBox[i][j] = BOX[i][j];
}
}
//Array.Copy(BOX, boubdingBox, BOX[0].Count() * 2);
//boubdingBox = BOX.Select(s => s.ToArray()).ToArray();
//BOX.CopyTo(boubdingBox, 0);
}
// ReSharper disable once InconsistentNaming
public static void printMainWithLocalPCAoriginalFeatureHist(List <GeoWave>[] rfTreeArr, string analysisFolderName,
ref int idOfWorse, double predResult, recordConfig _rc)
{
recordConfig rc = rfTreeArr.First().First().rc;
StreamWriter sw = new StreamWriter(analysisFolderName + "\\originalFeatureHist.txt", false);
StreamWriter excludedIndexWriter = new StreamWriter(analysisFolderName + "\\ExcudedIndex.txt", true);
double[] splitHist = new double[rc.dim];
foreach (List <GeoWave> tree in rfTreeArr)
{
List <GeoWave> sortedById = tree.OrderBy(o => o.ID).ToList();
foreach (GeoWave node in sortedById)
{
//skip root node (because not resulted from split)
if (node.parentID == -1)
{
continue;
}
if (sortedById[node.parentID].typeTransformed == DecicionTree.SplitType.MainAxes)
{
splitHist[node.dimIndex] += node.norm;
continue;
}
//node was created as split at pcaInd dimention of parent node
int pcaInd = node.dimIndex;
double[] splitEigenvector = sortedById[node.parentID].localPca.Components[pcaInd].Eigenvector;
//debug ortho test: double vecNorm = splitEigenvector.Select(d => Math.Pow(d,2)).Sum();
//go over original featchers
for (int i = 0; i < _rc.dim; i++)
{
splitHist[i] += (Math.Abs(splitEigenvector[i]) * node.norm);
}
}
}
splitHist = splitHist.Select(d => d / rc.rfNum).ToArray();
foreach (double orgFeatureImportance in splitHist)
{
//percent of norm explained
sw.Write(orgFeatureImportance + "\r\n");
}
sw.Close();
idOfWorse = Array.IndexOf(splitHist, splitHist.Min());
excludedIndexWriter.WriteLine(idOfWorse + "\t" + predResult);
excludedIndexWriter.Close();
}
public static void printWaveletsProperties(List <GeoWave> decisionGeoWaveArr, string filename)
{
StreamWriter sw;
if (Form1.UseS3)
{
string dir_name = Path.GetDirectoryName(filename);
string file_name = Path.GetFileName(filename);
S3DirectoryInfo s3dir = new S3DirectoryInfo(Form1.S3client, Form1.bucketName, dir_name);
S3FileInfo artFile = s3dir.GetFile(file_name);
sw = new StreamWriter(artFile.OpenWrite());
}
else
{
sw = new StreamWriter(filename, false);
}
recordConfig rc = decisionGeoWaveArr[0].rc;
//different log for local pca split
if (rc.split_type == 5)
{
printPcaWaveletsProperties(decisionGeoWaveArr, sw);
return;
}
sw.WriteLine("norm, level, Npoints, volume, dimSplit, MainGridIndexSplit");
foreach (GeoWave t in decisionGeoWaveArr)
{
double volume = 1;
//if (pnt[i] < Form1.MainGrid[i][BoxOfIndeces[0][i]] || pnt[i] > Form1.MainGrid[i][BoxOfIndeces[1][i]])
for (int j = 0; j < t.boubdingBox[0].Count(); j++)
{
volume *= (Form1.MainGrid[j][t.boubdingBox[1][j]] - Form1.MainGrid[j][t.boubdingBox[0][j]]);
}
sw.WriteLine(t.norm + ", " + t.level + ", "
+ t.pointsIdArray.Count() + ", " + volume
+ ", " + t.dimIndex + ", " + t.Maingridindex
+ ", " + t.MaingridValue);
}
sw.Close();
}
public static void printWaveletsProperties(List <GeoWave> decisionGeoWaveArr, string filename)
{
StreamWriter sw;
/* if (Form1.UseS3)
* {*/
/* string dir_name = Path.GetDirectoryName(filename);
* string file_name = Path.GetFileName(filename);
*
* S3DirectoryInfo s3dir = new S3DirectoryInfo(Form1.S3client, Form1.bucketName, dir_name);
* S3FileInfo artFile = s3dir.GetFile(file_name);
* sw = new StreamWriter(artFile.OpenWrite());*/
/* }
* else*/
sw = new StreamWriter(filename, false);
recordConfig rc = decisionGeoWaveArr[0].rc;
sw.WriteLine("norm\t\tllevel\t\ttypeTransformed\t\tlNpoints\t\tldimSplit\t\tMeanValue");
foreach (GeoWave t in decisionGeoWaveArr)
{
/*double volume = 1;
*
* for (int j = 0; j < t.boubdingBox[0].Count(); j++)
* volume *= (Form1.MainGrid[j][t.boubdingBox[1][j]] - Form1.MainGrid[j][t.boubdingBox[0][j]]);
*
* sw.WriteLine(t.norm + ", " + t.level + ", " + t.typeTransformed.ToString()
+ t.pointsIdArray.Count() + ", " + volume
+ ", " + t.dimIndex + ", " + t.Maingridindex
+ ", " + t.MaingridValue);*/
double nodeError = Math.Round(t.MeanValue[0], 4);
sw.WriteLine(Math.Round(t.norm, 4) + "\t\t" + t.level + "\t\t" + t.typeTransformed + "\t\t"
+ t.pointsIdArray.Count() + "\t\t"
+ "\t\t" + t.dimIndex + "\t\t" + nodeError.ToString(CultureInfo.InvariantCulture));
}
sw.Close();
}
private bool[] getDim2Take(recordConfig rc, int seed)
{
bool[] Dim2Take = new bool[rc.dim];
var ran = new Random(seed);
//List<int> dimArr = Enumerable.Range(0, rc.dim).OrderBy(x => ran.Next()).ToList().GetRange(0, rc.dim);
//List<int> dimArr = Enumerable.Range(0, rc.dim).OrderBy(x => ran.Next()).ToList().GetRange(0, rc.dim);
for (int i = 0; i < rc.NDimsinRF; i++)
{
//Dim2Take[dimArr[i]] = true;
int index = ran.Next(0, rc.dim);
if (Dim2Take[index])
{
i--;
}
else
{
Dim2Take[index] = true;
}
}
return(Dim2Take);
}
public static void printSplitByComponentHistogram(List <GeoWave>[] rfTreeArr, string analysisFolderName)
{
recordConfig rc = rfTreeArr.First().First().rc;
//relevant only for local pca split
if (rc.split_type != 5)
{
return;
}
StreamWriter sw = new StreamWriter(analysisFolderName + "\\localPCAsplitHistogram.txt", false);
//get config from first tree and first node
int[] splitHist = new int[rc.dim];
foreach (GeoWave node in (from tree in rfTreeArr from node in tree where node.dimIndex != -1 select node))
{
splitHist[node.dimIndex]++;
}
foreach (int splitDimCounter in splitHist)
{
sw.Write(splitDimCounter + " ");
}
sw.Close();
}
public static double[][] copyAndRemoveCategoricalColumns(double[][] originalData, recordConfig rc)
{
if (!rc.hasCategorical)
{
return(originalData); //don't have categorical variables
}
double[][] withRemovedColumns = new double[originalData.Length][];
for (int i = 0; i < originalData.Length; i++)
{
double[] row = originalData[i];
row = rc.indOfCategorical.Aggregate(row, (current, indOfColumnToRemove) => current.Where((val, ind) => ind != indOfColumnToRemove).ToArray());
withRemovedColumns[i] = (double[])row.Clone();
}
return(withRemovedColumns);
}
public GeoWave(int dataDim, int labelDim, recordConfig rc)
{
this.rc = rc;
Init(dataDim, labelDim);
}
public DecicionTree(recordConfig rc, double[][] trainingDt, double[][] trainingLabel)
{
_trainingDt = trainingDt;
_trainingLabel = trainingLabel;
_rc = rc;
}
private void btnScript_Click(object sender, EventArgs e)
{
set2Config();
Refresh();
u_config.printConfig(@"C:\Wavelets decomposition\config.txt");
// AmazonS3Client client = Helpers.configAmazonS3ClientS3Client();
UseS3 = UseS3CB.Checked;
rumPrallel = rumPrallelCB.Checked;
runBoosting = runBoostingCB.Checked;
runProoning = runProoningCB.Checked;
runBoostingProoning = runBoostingProoningCB.Checked;
runRFProoning = runRFProoningCB.Checked;
runRf = runRfCB.Checked;
runBoostingLearningRate = runBoostingLearningRateCB.Checked;
bucketName = bucketTB.Text;
string results_path = @ResultsTB.Text;
string db_path = @DBTB.Text + "\\";//@"C:\Users\Administrator\Dropbox\ADA\ada_valid\"; //"D:\\Phd\\Shai\\code\\tests\\helix tests\\noise_5\\noise_5\\"; // "C:\\reasearch\\tests\\lena\\";
//get dir
MainFolderName = results_path;
//Helpers.createMainDirectoryOrResultPath(results_path, bucketName, client);
Helpers.createMainDirectoryOrResultPath(results_path, bucketName);
//READ DATA
DB db = new DB();
db.training_dt = db.getDataTable(db_path + "trainingData.txt");
db.testing_dt = db.getDataTable(db_path + "testingData.txt");
db.validation_dt = db.getDataTable(db_path + "ValidData.txt");
db.training_label = db.getDataTable(db_path + "trainingLabel.txt");
db.testing_label = db.getDataTable(db_path + "testingLabel.txt");
db.validation_label = db.getDataTable(db_path + "ValidLabel.txt");
upper_label = db.training_label.Max();
lower_label = db.training_label.Min();
double trainingPercent = double.Parse(trainingPercentTB.Text); // 0.02;
long rowToRemoveFrom = Convert.ToInt64(db.training_dt.Count() * trainingPercent);
db.training_dt = db.training_dt.Where((el, i) => i < rowToRemoveFrom).ToArray();
db.training_label = db.training_label.Where((el, i) => i < rowToRemoveFrom).ToArray();
db.testing_dt = db.testing_dt.Where((el, i) => i < rowToRemoveFrom).ToArray();
db.testing_label = db.testing_label.Where((el, i) => i < rowToRemoveFrom).ToArray();
db.validation_dt = db.training_dt.Where((el, i) => i < rowToRemoveFrom).ToArray();
db.validation_label = db.validation_label.Where((el, i) => i < rowToRemoveFrom).ToArray();
//REDUCE DIM, GLOBAL PCA
if (usePCA.Checked)
{
DimReduction dimreduction = new DimReduction(db.training_dt);
db.PCAtraining_dt = dimreduction.getGlobalPca(db.training_dt);
db.PCAtesting_dt = dimreduction.getGlobalPca(db.testing_dt);
db.PCAvalidation_dt = dimreduction.getGlobalPca(db.validation_dt);
}
else
{
//de-activate pca for dbg
db.PCAtraining_dt = db.training_dt;
db.PCAtesting_dt = db.testing_dt;
db.PCAvalidation_dt = db.validation_dt;
}
db.PCAtraining_GridIndex_dt = new long[db.PCAtraining_dt.Count()][];
for (int i = 0; i < db.PCAtraining_dt.Count(); i++)
{
db.PCAtraining_GridIndex_dt[i] = new long[db.PCAtraining_dt[i].Count()];
}
//BOUNDING BOX AND MAIN GRID
boundingBox = db.getboundingBox(db.PCAtraining_dt);
MainGrid = db.getMainGrid(db.PCAtraining_dt, boundingBox, ref db.PCAtraining_GridIndex_dt);
//READ CONFIG
methodConfig mc = new methodConfig(true);
int Nloops = int.Parse(NloopsTB.Text) - 1;
int Kfolds = 0;
if (int.TryParse(croosValidTB.Text, out Kfolds))
{
Nloops = Kfolds - 1;
}
for (int k = 0; k < Nloops; k++)
{
mc.boostlamda_0.Add(3.8); // - create variant in number of pixels
}
mc.generateRecordConfigArr();
for (int k = 0; k < mc.recArr.Count(); k++)
{
//manual set indeces of categorical variable
//mc.recArr[k].indOfCategorical.Add(3);
mc.recArr[k].learningType = (recordConfig.LearnigType)comboLearningType.SelectedIndex; // regression, binary class, multy class
if (mc.recArr[k].learningType == recordConfig.LearnigType.BinaryClassification)
{
mc.recArr[k].binaryMinClass = lower_label;
mc.recArr[k].binaryMaxClass = upper_label;
mc.recArr[k].midClassValue = 0.5 * (lower_label + upper_label);
}
mc.recArr[k].dim = NfeaturesTB.Text == @"all" ? db.PCAtraining_dt[0].Count() : int.Parse(evaluateString(NfeaturesTB.Text, k));
mc.recArr[k].approxThresh = double.Parse(evaluateString(approxThreshTB.Text, k)); // 0.1;
mc.recArr[k].partitionErrType = int.Parse(evaluateString(partitionTypeTB.Text, k)); //2;
mc.recArr[k].minWaveSize = int.Parse(evaluateString(minNodeSizeTB.Text, k)); //1;//CHANGE AFTER DBG
mc.recArr[k].hopping_size = int.Parse(evaluateString(waveletsSkipEstimationTB.Text, k)); //25;// 10 + 5 * (k + 1);// +5 * (k % 10);// 1;//25;
mc.recArr[k].test_error_size = double.Parse(evaluateString(waveletsPercentEstimationTB.Text, k)); // +0.05 * (k % 10);// 1;// 0.1;//percent of waves to check
mc.recArr[k].NskipsinKfunc = double.Parse(evaluateString(boostingKfuncPercentTB.Text, k)); // 0.0025;
mc.recArr[k].rfBaggingPercent = double.Parse(evaluateString(bagginPercentTB.Text, k)); // 0.6;
mc.recArr[k].rfNum = int.Parse(evaluateString(NrfTB.Text, k)); // k + 1;//10 + k*10;// 100 / (k + 46) * 2;// int.Parse(Math.Pow(10, k + 1).ToString());
mc.recArr[k].boostNum = int.Parse(evaluateString(NboostTB.Text, k)); // 10;
mc.recArr[k].boostProoning_0 = int.Parse(evaluateString(NfirstPruninginBoostingTB.Text, k)); //13
mc.recArr[k].boostlamda_0 = double.Parse(evaluateString(boostingLamda0TB.Text, k)); // 0.01 - (k + 1) * 0.001; //0.05;// 0.0801 + k * 0.001;// Math.Pow(0.1, k);// 0.22 + k*0.005;
mc.recArr[k].NwaveletsBoosting = int.Parse(evaluateString(NfirstwaveletsBoostingTB.Text, k)); // 4;// k + 1;
//mc.recArr[k].learningRate = 0;// 0.01;
mc.recArr[k].boostNumLearningRate = int.Parse(evaluateString(NboostingLearningRateTB.Text, k)); // 55;// 18;
mc.recArr[k].percent_training_db = trainingPercent;
mc.recArr[k].BoundLevel = int.Parse(evaluateString(boundLevelTB.Text, k)); //1024;
mc.recArr[k].NDimsinRF = NfeaturesrfTB.Text == @"all" ? db.PCAtraining_dt[0].Count() : int.Parse(evaluateString(NfeaturesrfTB.Text, k));
mc.recArr[k].split_type = int.Parse(evaluateString(splitTypeTB.Text, k)); //0
mc.recArr[k].NormLPType = int.Parse(evaluateString(errTypeEstimationTB.Text, k));
mc.recArr[k].RFpruningTestRange[1] = int.Parse(evaluateString(RFpruningEstimationRange1TB.Text, k)); // 12;// k + 9;
mc.recArr[k].boundDepthTree = int.Parse(evaluateString(boundDepthTB.Text, k)); //1024;
mc.recArr[k].CrossValidFold = k;
// 2m0rr0w2 save labels dim in confif
mc.recArr[k].labelDim = db.training_label[0].Count();
//mc.recArr[k].boostNum = t ;// tmp to delete !!!!!!!
//mc.recArr[k].RFwaveletsTestRange[0] = 25;
//mc.recArr[k].RFwaveletsTestRange[1] = 50;
}
// Helpers.createOutputDirectories(mc.recArr, client, u_config, bucketName, results_path);
Helpers.createOutputDirectories(mc.recArr, u_config, bucketName, results_path);
//SET ID ARRAY LIST
List <int> trainingID = Enumerable.Range(0, db.PCAtraining_dt.Count()).ToList();
List <int> testingID = Enumerable.Range(0, db.PCAtesting_dt.Count()).ToList();
//cross validation
List <List <int> > trainingFoldId = new List <List <int> >();
List <List <int> > testingFoldId = new List <List <int> >();
Random ran = new Random(2);
List <int> training_rand = trainingID.OrderBy(x => ran.Next()).ToList().GetRange(0, trainingID.Count);
//THE LARGEST GROUP IS TRAINING
if (int.TryParse(croosValidTB.Text, out Kfolds))
{
createCrossValid(Kfolds, training_rand, trainingFoldId, testingFoldId);
}
//bounding intervals
int[][] BB = new int[2][];
BB[0] = new int[boundingBox[0].Count()];
BB[1] = new int[boundingBox[0].Count()];
for (int i = 0; i < boundingBox[0].Count(); i++)
{
BB[1][i] = MainGrid[i].Count() - 1; //set last index in each dim
}
//loop over folds DEBUG i=2
for (int i = 3; i < mc.recArr.Count; i++)
{
recordConfig rc = mc.recArr[i];
Analizer analizer = new Analizer(MainFolderName + "\\" + rc.getShortName(), MainGrid, db, rc);
//exclude variables one by one
double[] numFeachuresVSerror = new double[rc.dim];
int indFeachureToExclude = -1;
double predResult = 0;
//loop over feachures
for (int j = rc.dim - 1; j >= 0; j--)
{
//DEBUG, DISABLE FEACHURE EXCLUDE
// j = 0;
if (!croosValidCB.Checked)
{
analizer.analize(trainingID, testingID, BB, ref indFeachureToExclude, ref predResult);
}
else
{
analizer.analize(trainingFoldId[i], testingFoldId[i], BB, ref indFeachureToExclude, ref predResult); //cross validation
}
numFeachuresVSerror[j] = predResult; // index j, used j+1 featchures
analizer.excludeFeatureFromDb(indFeachureToExclude);
}
PrintEngine.printBestErrorByNumberOfFeatchures(analizer._analysisFolderName, numFeachuresVSerror);
}
btnScript.BackColor = Color.Green;
}
public void generateRecordConfigArr()
{
for (int i0 = 0; i0 < dim.Count; i0++)
{
for (int i1 = 0; i1 < approxThresh.Count; i1++)
{
for (int i2 = 0; i2 < partitionErrType.Count; i2++)
{
for (int i3 = 0; i3 < approxOrder.Count; i3++)
{
for (int i4 = 0; i4 < boostNum.Count; i4++)
{
for (int i5 = 0; i5 < boostlamda_0.Count; i5++)
{
for (int i6 = 0; i6 < boostNormTarget.Count; i6++)
{
for (int i7 = 0; i7 < boostNormsecond.Count; i7++)
{
for (int i8 = 0; i8 < boostTau.Count; i8++)
{
for (int i9 = 0; i9 < rfNum.Count; i9++)
{
for (int i10 = 0; i10 < rfBaggingPercent.Count; i10++)
{
for (int i11 = 0; i11 < NskipsinKfunc.Count; i11++)
{
for (int i12 = 0; i12 < minWaveSize.Count; i12++)
{
for (int i13 = 0; i13 < hopping_size.Count; i13++)
{
for (int i14 = 0; i14 < test_error_size.Count; i14++)
{
for (int i15 = 0; i15 < boostProoning_0.Count; i15++)
{
recordConfig rc = new recordConfig(dim[i0],
approxThresh[i1],
partitionErrType[i2],
approxOrder[i3],
boostNum[i4],
boostlamda_0[i5],
boostNormTarget[i6],
boostNormsecond[i7],
boostTau[i8],
rfNum[i9],
rfBaggingPercent[i10],
NskipsinKfunc[i11],
minWaveSize[i12],
hopping_size[i13],
test_error_size[i14],
boostProoning_0[i15], 0);
recArr.Add(rc);
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
public static List <GeoWave> getConstWaveletsFromFile(string filename, recordConfig rc)
{
if (!Form1.UseS3 && !File.Exists(filename))//this func was not debugged after modification
{
MessageBox.Show(@"the file " + Path.GetFileName(filename) + @" doesnt exist in " + Path.GetFullPath(filename));
return(null);
}
StreamReader sr;
/* if (Form1.UseS3)
* {
* string dir_name = Path.GetDirectoryName(filename);
* string file_name = Path.GetFileName(filename);
*
* S3DirectoryInfo s3dir = new S3DirectoryInfo(Form1.S3client, Form1.bucketName, dir_name);
* S3FileInfo artFile = s3dir.GetFile(file_name);
* sr = artFile.OpenText();
* }
* else*/
sr = new StreamReader(File.OpenRead(filename));
string[] values = { "" };
string line;
string DimensionReductionMatrix = "";
int numOfWavlets = -1;
int dimension = -1;
int labelDimension = -1;
double approxOrder = -1;
while (!sr.EndOfStream && values[0] != "StartReading")
{
line = sr.ReadLine();
values = line.Split(Form1.seperator, StringSplitOptions.RemoveEmptyEntries);
if (values[0] == "DimensionReductionMatrix")
{
DimensionReductionMatrix = values[1];
}
else if (values[0] == "numOfWavlets")
{
numOfWavlets = int.Parse(values[1]);
}
else if (values[0] == "approxOrder")
{
approxOrder = int.Parse(values[1]);
}
else if (values[0] == "dimension")
{
dimension = int.Parse(values[1]);
}
else if (values[0] == "labelDimension")
{
labelDimension = int.Parse(values[1]);
}
else if (values[0] == "StartReading")
{
;
}
else
{
MessageBox.Show(@"the file " + Path.GetFileName(filename) + @" already exist in " + Path.GetFullPath(filename) + @" might have bad input !");
}
}
//read values
List <GeoWave> gwArr = new List <GeoWave>();
while (!sr.EndOfStream)
{
GeoWave gw = new GeoWave(dimension, labelDimension, rc);
line = sr.ReadLine();
if (line != null)
{
values = line.Split(Form1.seperator, StringSplitOptions.RemoveEmptyEntries);
}
gw.ID = int.Parse(values[0]);
gw.child0 = int.Parse(values[1]);
gw.child1 = int.Parse(values[2]);
int counter = 0;
for (int j = 0; j < dimension; j++)
{
gw.boubdingBox[0][j] = int.Parse(values[3 + 4 * j]);//the next are the actual values and not the indeces int the maingrid - so we skip 4 elementsat a time
gw.boubdingBox[1][j] = int.Parse(values[4 + 4 * j]);
counter = 4 + 2 * 4;
}
gw.level = int.Parse(values[counter + 1]);
counter = counter + 2;
for (int j = 0; j < labelDimension; j++)
{
gw.MeanValue[j] = double.Parse(values[counter + j]);
counter++;
}
gw.norm = double.Parse(values[counter]);
gw.parentID = int.Parse(values[counter + 1]);
gwArr.Add(gw);
}
sr.Close();
return(gwArr);
}