/*************************** Secondary Methods *******************************/
private Tuple<Double[][], int[]> convertToTrainIntputTable(DataSet trainSet)
{
// Raw Data
int egoCount = trainSet.egoNetworkList.Count;
EgoNetwork[] egoList = new EgoNetwork[egoCount];
trainSet.egoNetworkList.CopyTo(egoList);
double[][] inputArray = new double[egoCount][];
int[] outputVector = new int[egoCount];
for (int i = 0; i < inputArray.Length; i++)
{
inputArray[i] = egoList[i].attributes;
outputVector[i] = egoList[i].optimalLabel;
}
// Normalization: Data Preprocess
int height = inputArray.Length, width = inputArray[0].Length;
mean = new double[width];
leftMost = new double[width];
rightMost = new double[width];
for (var j = 0; j < width; j++)
{
double average = 0.0;
double min = double.PositiveInfinity;
double max = double.NegativeInfinity;
for (var i = 0; i < height; i++)
{
// Left Most Value
if (inputArray[i][j] < min)
min = inputArray[i][j];
// Right Most Value
if (inputArray[i][j] > max)
max = inputArray[i][j];
// Average
average += inputArray[i][j];
}
this.leftMost[j] = min;
this.rightMost[j] = max;
this.mean[j] = average / height;
}
// Normalization: Scaling --> [-1, +1]
for (var j = 0; j < width; j++)
{
for (var i = 0; i < height; i++)
{
double previous = inputArray[i][j];
if (previous <= this.mean[j])
inputArray[i][j] = (previous - this.mean[j]) / (this.mean[j] - this.leftMost[j]);
else
inputArray[i][j] = (previous - this.mean[j]) / (this.rightMost[j] - this.mean[j]);
}
}
return new Tuple<Double[][], int[]>(inputArray, outputVector);
}
/***************************** Override *********************************/
public override bool Equals(Object obj)
{
if (obj is EgoNetwork)
{
EgoNetwork otherTweet = (EgoNetwork)obj;
return(this.egoID == otherTweet.egoID);
}
else
{
throw new ArgumentException("Object is not EgoNetwork");
}
}
/*************************** Primary Methods *******************************/
public void addEgoNetwork(EgoNetwork egoNetwork)
{
this.egoNetworkList.Add(egoNetwork);
}
/*************************** Secondary Methods *******************************/
private Tuple <Double[][], int[]> convertToTrainIntputTable(DataSet trainSet)
{
// Raw Data
int egoCount = trainSet.egoNetworkList.Count;
EgoNetwork[] egoList = new EgoNetwork[egoCount];
trainSet.egoNetworkList.CopyTo(egoList);
double[][] inputArray = new double[egoCount][];
int[] outputVector = new int[egoCount];
for (int i = 0; i < inputArray.Length; i++)
{
inputArray[i] = egoList[i].attributes;
outputVector[i] = egoList[i].optimalLabel;
}
// Normalization: Data Preprocess
int height = inputArray.Length, width = inputArray[0].Length;
mean = new double[width];
leftMost = new double[width];
rightMost = new double[width];
for (var j = 0; j < width; j++)
{
double average = 0.0;
double min = double.PositiveInfinity;
double max = double.NegativeInfinity;
for (var i = 0; i < height; i++)
{
// Left Most Value
if (inputArray[i][j] < min)
{
min = inputArray[i][j];
}
// Right Most Value
if (inputArray[i][j] > max)
{
max = inputArray[i][j];
}
// Average
average += inputArray[i][j];
}
this.leftMost[j] = min;
this.rightMost[j] = max;
this.mean[j] = average / height;
}
// Normalization: Scaling --> [-1, +1]
for (var j = 0; j < width; j++)
{
for (var i = 0; i < height; i++)
{
double previous = inputArray[i][j];
if (previous <= this.mean[j])
{
inputArray[i][j] = (previous - this.mean[j]) / (this.mean[j] - this.leftMost[j]);
}
else
{
inputArray[i][j] = (previous - this.mean[j]) / (this.rightMost[j] - this.mean[j]);
}
}
}
return(new Tuple <Double[][], int[]>(inputArray, outputVector));
}
/*************************** Primary Methods *******************************/
public void dataSetConfiguration(SortedList columnList, string rwrFilePath, string networkFilePath)
{
ArrayList egoIDList = new ArrayList();
SortedDictionary <long, int> egoOptimalLabelDictionary = new SortedDictionary <long, int>();
SortedDictionary <long, double[]> egoRwrResultsDictionary = new SortedDictionary <long, double[]>();
SortedDictionary <long, double[]> egoAttributesDictionary = new SortedDictionary <long, double[]>();
using (StreamReader rwrReader = new StreamReader(rwrFilePath))
{
string line = null;
while ((line = rwrReader.ReadLine()) != null)
{
long egoID;
int egoOptimalLabel;
string[] rwrResult = line.Split('\t');
double[] MAP = new double[rwrResult.Length - 2];
egoID = long.Parse(rwrResult[0]);
egoOptimalLabel = int.Parse(rwrResult[1]);
for (int i = 2; i < rwrResult.Length; i++)
{
MAP[i - 2] = double.Parse(rwrResult[i]);
}
egoOptimalLabelDictionary.Add(egoID, egoOptimalLabel);
egoRwrResultsDictionary.Add(egoID, MAP);
egoIDList.Add(egoID);
}
}
using (StreamReader networkReader = new StreamReader(networkFilePath))
{
string line = null;
while ((line = networkReader.ReadLine()) != null)
{
long egoID;
string[] networkResults = line.Split('\t');
double[] attributes = new double[columnList.Count];
egoID = long.Parse(networkResults[0]);
int i = 0;
for (int j = 1; j < networkResults.Length; j++)
{
if (columnList.ContainsKey(j - 1))
{
attributes[i++] = double.Parse(networkResults[j]);
}
}
egoAttributesDictionary.Add(egoID, attributes);
}
}
// K-Fold Split DataSets
SortedList <long, EgoNetwork> egoNetworkList = new SortedList <long, EgoNetwork>();
egoIDList.Sort(); // Ascending Order
foreach (long egoID in egoIDList)
{
egoNetworkList.Add(egoID, new EgoNetwork(egoID, egoOptimalLabelDictionary[egoID],
egoRwrResultsDictionary[egoID], egoAttributesDictionary[egoID]));
}
int boundary = egoNetworkList.Count / nFold;
for (int i = 0; i < nFold; i++)
{
this.dataSets[i] = new DataSet();
if (i != (nFold - 1))
{
for (int j = i * boundary; j < (i + 1) * boundary; j++) // Each sub-dataset boundary
{
EgoNetwork egoNetWork = egoNetworkList[(long)egoIDList[j]];
dataSets[i].addEgoNetwork(egoNetWork);
}
}
else // Incase: Last sub-dataset
{
for (int j = i * boundary; j < egoNetworkList.Count; j++)
{
EgoNetwork egoNetWork = egoNetworkList[(long)egoIDList[j]];
dataSets[i].addEgoNetwork(egoNetWork);
}
}
}
}
/*************************** Primary Methods *******************************/
public void addEgoNetwork(EgoNetwork egoNetwork)
{
this.egoNetworkList.Add(egoNetwork);
}