public double fetchDistance(Sequence seq1, Sequence seq2, string[] arr_input_file, double[,] Dmatrix)
{
double d = -1;
int coord1 = findCoord(seq1, arr_input_file);
int coord2 = findCoord(seq2, arr_input_file);
d = Convert.ToDouble(Dmatrix[coord1, coord2]);
return d;
}
public string label;//this the label value
public int findCoord(Sequence seq, string[] arr_input_file)
{
int coord = -1;
for (int i = 0; i < arr_input_file.Length; i++)
{
string str = arr_input_file[i].Trim();
if ((seq.sequenceValue).Trim() == str)
{
coord = i;
break;
}
}
return coord;
}
static void Main(string[] args)
{
/*Step1--------------BEGIN Import Distance Matrix and Dataset-------------------------------------*/
//args[0]: Path to input dataset
//args[1]: Path to similarity Matrix
//--------------PRUNNING purity threshold -------------------
double thetha = 85;
//----------------------------------------------------------
string input_file_path;
string pruned_tree_path;
string newick_path;
string ouput_file_path;
string distance_matix_path;
int iteration;
System.Console.WriteLine(DateTime.Now.ToString("hh:mm:ss tt"));
string distance_type = "AVERAGELINK";
string graphType = "GabrielGraph"; //or set as "OneNN"
string str_dendogram = "iterationID,merdeCluster iterationID,clusterID,purity,child1ID,child2ID,d(child1;Parent),d(child2;Parent),intraD(mergedcluster)";
int cluster_count = 0;
if (args.Length > 0)
input_file_path = args[0];
else
{
//input_file_path = "C:\\Users\\paul\\Desktop\\STAXAC\\inputData\\TEST\\test_file1.csv";
// input_file_path = "C:\\Users\\paul\\Desktop\\NJ\\inputData\\BFE\\m_BFE_file.csv";
input_file_path = "C:\\Users\\chong\\Desktop\\PhD\\inputData\\BFH\\m_BFH_file.csv";
}
if (args.Length > 1)
distance_matix_path = args[1];
else
{
//distance_matix_path = "C:\\Users\\paul\\Desktop\\STAXAC\\inputData\\TEST\\m_test1.csv";
//distance_matix_path = "C:\\Users\\paul\\Desktop\\NJ\\inputData\\BFE\\m_BFE.csv";
distance_matix_path = "C:\\Users\\chong\\Desktop\\PhD\\inputData\\BFH\\m_BFH.csv";
}
if (args.Length > 2)
ouput_file_path = args[2];
else
{
//ouput_file_path = "C:\\Users\\paul\\Desktop\\STAXAC\\inputData\\TEST\\m_test1_output3.csv";
ouput_file_path = "C:\\Users\\chong\\Desktop\\PhD\\inputData\\BFH\\m_BFH_output.csv";
}
// pruned_tree_path = "C:\\Users\\paul\\Desktop\\STAXAC\\inputData\\TEST\\str_editedTree_" + thetha + "3.csv";
pruned_tree_path = "C:\\Users\\chong\\Desktop\\PhD\\inputData\\BFH\\str_editedTree_" + thetha + "3.csv";
newick_path = "C:\\Users\\chong\\Desktop\\PhD\\inputData\\BFH\\newick_" + thetha + "3.csv";
string depth_purity_path = "C:\\Users\\chong\\Desktop\\PhD\\inputData\\BFH\\depth_purity_" + thetha + "3.csv";
/* Import dataset */
string[] arr_input_file = System.IO.File.ReadAllLines(input_file_path);
string[] arr_distance_matrix = System.IO.File.ReadAllLines(distance_matix_path);
int root_Data_size = arr_input_file.Length;
int distance_matrix_size = arr_distance_matrix.Length;
double distanceThreshold = 1.0; //default set distance threshold equal to one
Sequence[] matrixSequence = new Sequence[root_Data_size]; //used as temporary variable for convenience
Cluster[] arr_mergedCluster = new Cluster[arr_input_file.Length - 1];//contains the merged cluster
Cluster[] tree = new Cluster[2 * arr_input_file.Length - 1];//contains ALL the clusters bottom up; initial clusters + merged clusters; this means contain the tree
//double checking if input is correct
string error_msg;
error_msg = "Run successfully";
if (root_Data_size != distance_matrix_size)
{
error_msg = "ERROR 001: Input file and distance matrix do not match in size";
System.Console.WriteLine(error_msg);
Console.Read();
System.Environment.Exit(0);
}
/*---------------END Import Distance Matrix and Dataset----------------------------------------------*/
/*STEP2:-----------Fill matrices---------------------------------------------------------------------*/
//label vector for each line
string[] arr_line_label = new string[arr_input_file.Length];
string[] arr_species = new string[arr_input_file.Length];
string str = "";
for (int i = 0; i < arr_input_file.Length; i++)
{
string[] arr_temp = arr_input_file[i].Split(new[] { ',' });
if (str == "")
{
str = arr_temp[arr_temp.Length - 1];
}
else
{
Boolean add_to_list = true;
foreach (string label in arr_line_label)
{
if (label == arr_temp[arr_temp.Length - 1])
add_to_list = false;
}
if (add_to_list)
str = str + ";" + arr_temp[arr_temp.Length - 1];
}
arr_species[i] = arr_temp[0] + " / " + arr_temp[arr_temp.Length - 1];
arr_line_label[i] = arr_temp[arr_temp.Length - 1];
}
//get label vector. Distinct label values
string[] arr_label = str.Split(new[] { ';' });
double[,] Dmatrix = new double[distance_matrix_size, distance_matrix_size];
for (int i = 0; i < distance_matrix_size; i++)
{
string[] arr_temp = arr_distance_matrix[i].Split(new[] { ',' });
for (int j = 0; j < arr_temp.Length; j++)
{
Dmatrix[i, j] = Convert.ToDouble(arr_temp[j]);
}
}
/* STEP3:-----CREATE OBJECTS---------------------------------------------*/
//- create sequences and initial sequence matrix
for (int i = 0; i < arr_input_file.Length; i++)
{
//arr_input_file[i]
Sequence s = new Sequence();
s.sequencePos = i;
s.sequenceValue = arr_input_file[i];//including the class label. need to separate them later
s.label = arr_line_label[i];
if (s != null)
{
matrixSequence[i] = s;
}
}
iteration = -1;
//- Create initial Clusters-Start with a clustering X of one-object clusters
ClusterOperation O = new ClusterOperation();
List<Cluster> ListOfClusters = new List<Cluster>();
for (int i = 0; i < matrixSequence.Length; i++)
{
Cluster c = new Cluster();
List<Sequence> ListOfSeq = new List<Sequence>();
ListOfSeq.Add(matrixSequence[i]);//list of one element initially
c.ListOfSeq.Add(matrixSequence[i]);
cluster_count = cluster_count + 1;
c.clusterID = cluster_count;
c.intraClusterDistance = 0.0;
c.listOfMedoid.Add(matrixSequence[i]);
c.arr_class_proportion = c.computeClassProportion(ListOfSeq, arr_label);
c.purity = c.computePurity(c.arr_class_proportion);
c.child1ID = -1;
c.child2ID = -1;
c.label = arr_line_label[i];
c.majority_class = arr_line_label[i];
ListOfClusters.Add(c);
iteration = iteration + 1;
tree[iteration] = c;
// add to tree
//for flat file only
str_dendogram = str_dendogram + "\r\n" + cluster_count + "," + "0" + "," + c.clusterID + "," + c.purity + "," + c.child1ID + "," + c.child2ID + ",-1" + ",-1";
}
/*-----------------------------------------------------------------*/
//at this point we have all leaf-cluster. Each cluster has only one element
/*Step 2: next we need to find the Gabriel Neighbors of each 1-exmple cluster
*/
for (int i = 0; i < ListOfClusters.Count; i++)
{
// System.Console.WriteLine("xxx before: " + DateTime.Now.ToString("hh:mm:ss tt") + " " + i);
if (graphType == "OneNN")
{
ListOfClusters[i].OneNN = O.OneNN(ListOfClusters[i], ListOfClusters, arr_input_file, Dmatrix, distance_type);//contains the 1NN of cluster
}
else if (graphType == "GabrielGraph")
{
ListOfClusters[i].GN = O.GabrielNeighbor(ListOfClusters[i], ListOfClusters, arr_input_file, Dmatrix, distance_type);//contains the gabriel graph cluster
}
}
foreach (Cluster cluster in ListOfClusters)
{
System.Console.WriteLine("\r\n \r\n clusterID: " + cluster.clusterID);
System.Console.WriteLine("===One Nearest Neighbors List====>");
for (int j = 0; j < cluster.GN.Count; j++)
System.Console.Write("cluster.GN[j]:" + cluster.GN[j].clusterID + ":");
}
System.Console.WriteLine("");
Console.Read();
/*
* Merge-Candidate(C’,C)that is (1NNX(C’)=C or 1NNX(C )=C’)
*/
int mergedC_iteration = -1;
//System.Console.WriteLine("ListOfClusters.Count -->" + ListOfClusters.Count);
int NUM_OF_STEPS = ListOfClusters.Count;
while (ListOfClusters.Count > 1)
{
Cluster[] best_GN_cluster = new Cluster[ListOfClusters.Count];
double[] best_GN_mergedPurity = new double[ListOfClusters.Count];
double[] best_GN_distance = new double[ListOfClusters.Count];
/* merge a cluster and its closest G-neighbor and compute impurity growth.
* Retain the merge that yields lowest impurity growth */
for (int i = 0; i < ListOfClusters.Count; i++)
{
double temp_max_purity = -1.0;
foreach (Cluster g_neighor in ListOfClusters[i].GN)
{
Cluster C = O.Merge(ListOfClusters[i], g_neighor, arr_label, distanceThreshold, arr_input_file, Dmatrix, distance_type);//this operation creates a new cluster
if (C != null && (temp_max_purity < C.purity))
{
best_GN_mergedPurity[i] = C.purity;
best_GN_cluster[i] = g_neighor;//the best 1NN is here
temp_max_purity = C.purity;
//best_1NN_distance[i] = O.InterClusterDistance(ListOfClusters[i], oneNNClist, arr_input_file, Dmatrix, distance_type);
}
//System.Console.WriteLine(ListOfClusters[i].clusterID + ":" + oneNNClist.clusterID + " : Merge purity: " + C.purity);
}
}
//now we need to select the best cluster to merge
double max_purity = -1.0;
int best_i = -1;
for (int i = 0; i < ListOfClusters.Count; i++)
{
if (max_purity < best_GN_mergedPurity[i])
{
max_purity = best_GN_mergedPurity[i];
best_i = i;
}
}
//we need to construct a new ListOfClusters excluding ListOfClusters[best_i], and best_GN_cluster[best_i]
List<Cluster> ListOfClustersClone = new List<Cluster>();
for (int i = 0; i < ListOfClusters.Count; i++)
{
ListOfClustersClone.Add(ListOfClusters[i]);
}
Cluster best_merged_Cluster = new Cluster();
//best_merged_Cluster = ListOfClusters[best_i];
best_merged_Cluster = O.Merge(ListOfClustersClone[best_i], best_GN_cluster[best_i], arr_label, distanceThreshold, arr_input_file, Dmatrix, distance_type);//this operation create a new cluster
cluster_count = cluster_count + 1;
best_merged_Cluster.clusterID = cluster_count;
best_merged_Cluster.child1ID = ListOfClustersClone[best_i].clusterID;
best_merged_Cluster.child2ID = best_GN_cluster[best_i].clusterID;
best_merged_Cluster.arr_class_proportion = best_merged_Cluster.computeClassProportion(best_merged_Cluster.ListOfSeq, arr_label);
best_merged_Cluster.purity = best_merged_Cluster.computePurity(best_merged_Cluster.arr_class_proportion);
ListOfClusters.Remove(ListOfClustersClone[best_i]);
ListOfClusters.Remove(best_GN_cluster[best_i]);
if (best_merged_Cluster.GN.Count == 0)
{
if (graphType == "OneNN")
{
best_merged_Cluster.OneNN = O.OneNN(best_merged_Cluster, ListOfClusters, arr_input_file, Dmatrix, distance_type);
}
else if (graphType == "GabrielGraph")
{
best_merged_Cluster.GN = O.GabrielGraph(best_merged_Cluster, ListOfClusters, arr_input_file, Dmatrix, distance_type);
}
}
//update 1NN list
for (int i = 0; i < ListOfClusters.Count; i++)
{
for (int j = 0; j < ListOfClusters[i].GN.Count; j++)
{
if (ListOfClusters[i].GN[j].clusterID == ListOfClustersClone[best_i].clusterID || ListOfClusters[i].GN[j].clusterID == best_GN_cluster[best_i].clusterID)
ListOfClusters[i].GN[j] = best_merged_Cluster;
}
}
ListOfClusters.Add(best_merged_Cluster);
// add this to tree
iteration = iteration + 1;
tree[iteration] = best_merged_Cluster;
mergedC_iteration = mergedC_iteration + 1;
arr_mergedCluster[mergedC_iteration] = best_merged_Cluster; // this does not contain the singleton clusters. It only contain the merged clusters
}
System.Console.WriteLine(DateTime.Now.ToString("hh:mm:ss tt"));
System.Console.WriteLine("");
System.Console.WriteLine("--------------END-----------------");
Console.Read();
string[] arr_Newick = new string[arr_line_label.Length + arr_mergedCluster.Length];
for (int i = 0; i < arr_line_label.Length; i++)
{
arr_Newick[i] = arr_species[i];
}
int int_count = arr_line_label.Length - 1;
for (int iter = 0; iter < arr_mergedCluster.Length; iter++)
{
//temp_count = temp_count + 1;
string child1 = "";
string purity1 = "";
double d1 = 0.0;
double intra_d = 0.0;//average intra cluster distance
int child_clustr_indice = 0;
if (arr_mergedCluster[iter].child1ID < arr_line_label.Length)
{
// child1=(arr_line_label[arr_mergedCluster[iter].child1ID -1]).ToString();
child1 = (arr_species[arr_mergedCluster[iter].child1ID - 1]).ToString();
purity1 = "1.0";
}
else
{
child1 = arr_Newick[arr_mergedCluster[iter].child1ID - 1];
child_clustr_indice = arr_mergedCluster[iter].child1ID - 1;
purity1 = (Math.Round(tree[child_clustr_indice].purity, 3)).ToString();
}
//ClusterOperation O = new ClusterOperation();
//intra cluster distance
// intra_d = O.IntraClusterDistance(arr_mergedCluster[iter], arr_input_file, Dmatrix, distance_type);
d1 = O.InterClusterDistance(arr_mergedCluster[iter], tree[arr_mergedCluster[iter].child1ID - 1], arr_input_file, Dmatrix, distance_type);
d1 = Math.Round(d1, 3);
string child2 = "";
string purity2 = "";
double d2 = 0.0;
if (arr_mergedCluster[iter].child2ID < arr_line_label.Length)
{
// child2 = (arr_line_label[arr_mergedCluster[iter].child2ID-1]).ToString();
child2 = (arr_species[arr_mergedCluster[iter].child2ID - 1]).ToString();
purity2 = "1.0";
}
else
{
child2 = arr_Newick[arr_mergedCluster[iter].child2ID - 1];
child_clustr_indice = arr_mergedCluster[iter].child2ID - 1;
purity2 = (Math.Round(tree[child_clustr_indice].purity, 3)).ToString();
}
d2 = O.InterClusterDistance(arr_mergedCluster[iter], tree[arr_mergedCluster[iter].child2ID - 1], arr_input_file, Dmatrix, distance_type);
d2 = Math.Round(d2, 3);
int_count = int_count + 1;
string thisClusterPurity = "";
thisClusterPurity = (Math.Round(arr_mergedCluster[iter].purity, 3)).ToString();
arr_Newick[int_count] = "(" + child1 + ":" + d1 + "," + child2 + ":" + d2 + ")P=" + thisClusterPurity;
str_dendogram = str_dendogram + "\r\n" + (iter + arr_line_label.Length + 1) + "," + arr_mergedCluster[iter].clusterID + "," + arr_mergedCluster[iter].clusterID + "," + arr_mergedCluster[iter].purity + "," + arr_mergedCluster[iter].child1ID + "," + arr_mergedCluster[iter].child2ID + "," + d1 + "," + d2 + "," + intra_d;
System.IO.File.WriteAllText(ouput_file_path, str_dendogram);
}
string str_Newick = "";
string str_Newick_temp = "";
bool first_time = true;
for (int iter = arr_line_label.Length; iter < arr_Newick.Length; iter++)
{
if (first_time)
{
str_Newick = str_Newick + arr_Newick[iter];
str_Newick_temp = "\"" + arr_Newick[iter] + ";\"";
first_time = false;
}
else
{
if (iter == (arr_Newick.Length - 1))
str_Newick = "\"" + arr_Newick[iter] + ";\"";
else
str_Newick_temp = "\"" + arr_Newick[iter] + ";\"";
}
}
System.IO.File.WriteAllText(newick_path, str_Newick);
//postprocessing; pruning. This is based on user-defined purity threshold thetha. CLone the tree
Cluster[] tree_temp = new Cluster[2 * arr_input_file.Length - 1];//contains ALL the clusters bottom up; initial clusters + merged clusters; this means contain the tree
for (int iter = 0; iter < tree_temp.Length; iter++)
{
Cluster new_c = new Cluster();
new_c.child1ID = tree[iter].child1ID;
new_c.child2ID = tree[iter].child2ID;
new_c.ListOfSeq = tree[iter].ListOfSeq;
new_c.clusterID = tree[iter].clusterID;
new_c.intraClusterDistance = tree[iter].intraClusterDistance;
new_c.label = tree[iter].label;
new_c.listOfMedoid = tree[iter].listOfMedoid;
new_c.node_type = tree[iter].node_type;
new_c.GN = tree[iter].GN;
new_c.purity = tree[iter].purity;
new_c.arr_class_proportion = tree[iter].arr_class_proportion;
new_c.majority_class = tree[iter].majority_class;
new_c.in_reduced_tree = "N";
tree_temp[iter] = new_c; //make a copy
}
for (int iter = arr_line_label.Length; iter < tree_temp.Length; iter++)
{
// the leaf-nodes have child1ID=-1 and child2ID=-1
if (100 * tree_temp[iter].purity < thetha)//then the children are leaf-nodes.
{
if (tree_temp[iter].in_reduced_tree != "Y")
{
tree_temp[iter].in_reduced_tree = "Y";
System.Console.WriteLine("tree_temp[iter].purity: " + tree_temp[iter].purity);
System.Console.WriteLine("tree_temp[tree_temp[iter].child1ID - 1]: " + tree_temp[tree_temp[iter].child1ID - 1].purity + " nodeid: " + tree_temp[tree_temp[iter].child1ID - 1].clusterID);
System.Console.WriteLine("tree_temp[tree_temp[iter].child2ID - 1]: " + tree_temp[tree_temp[iter].child2ID - 1].purity + " nodeid: " + tree_temp[tree_temp[iter].child2ID - 1].clusterID);
if (100 * tree_temp[tree_temp[iter].child1ID - 1].purity >= thetha || 100 * tree_temp[tree_temp[iter].child2ID - 1].purity >= thetha)
{
tree_temp[tree_temp[iter].child1ID - 1].node_type = "L";
tree_temp[tree_temp[iter].child2ID - 1].node_type = "L";
// tree_temp[tree_temp[iter].child1ID - 1].child1ID = -1;
// tree_temp[tree_temp[iter].child1ID - 1].child2ID = -1;
tree_temp[tree_temp[iter].child1ID - 1].in_reduced_tree = "Y";
tree_temp[tree_temp[iter].child2ID - 1].in_reduced_tree = "Y";
}
}
}
}
int count_reduced_node = 0;
string str_reduced_node = "";
first_time = true;
for (int iter = 0; iter < tree_temp.Length; iter++)
{
if (tree_temp[iter].in_reduced_tree == "Y")
{
count_reduced_node = count_reduced_node + 1;
if (first_time)
{
str_reduced_node = tree_temp[iter].clusterID.ToString();
first_time = false;
}
else
str_reduced_node = str_reduced_node + "\r\n" + tree_temp[iter].clusterID.ToString();
}
}
System.IO.File.WriteAllText(pruned_tree_path, str_reduced_node);
/*------------END----------------*/
/* ------Purity of each depth-----------------------*/
List<Cluster> treeClone11 = new List<Cluster>();
List<Cluster> treeClone12 = new List<Cluster>();
// foreach (Node node in tree)
for (int iter = 0; iter < tree.Length; iter++)
{
Cluster new_c = new Cluster();
new_c.child1ID = tree[iter].child1ID;
new_c.child2ID = tree[iter].child2ID;
new_c.ListOfSeq = tree[iter].ListOfSeq;
new_c.clusterID = tree[iter].clusterID;
new_c.intraClusterDistance = tree[iter].intraClusterDistance;
new_c.label = tree[iter].label;
new_c.listOfMedoid = tree[iter].listOfMedoid;
new_c.node_type = tree[iter].node_type;
new_c.GN = tree[iter].GN;
new_c.purity = tree[iter].purity;
new_c.arr_class_proportion = tree[iter].arr_class_proportion;
new_c.majority_class = tree[iter].majority_class;
for (int i = 0; i < tree_temp.Length; i++)
{
if (tree[iter].clusterID == tree_temp[i].child1ID || tree[iter].clusterID == tree_temp[i].child2ID)
{
new_c.parentID = tree_temp[i].clusterID;
break;
}
}
treeClone11.Add(new_c); //make a copy
}
string str_debug = "nodeID,parentID,Purity" + "\r\n";
for (int iter = 0; iter < treeClone11.Count; iter++)
{
treeClone12.Add(tree[iter]);
System.Console.WriteLine("\r\n treeClone11[i]: " + treeClone11[iter].clusterID + " parent :" + treeClone11[iter].parentID + " cluster purity :" + treeClone11[iter].purity);
str_debug = str_debug + treeClone11[iter].clusterID + "," + treeClone11[iter].parentID + "," + treeClone11[iter].purity + "\r\n";
}
bool bln_continue = true;
List<Cluster> depth_list = new List<Cluster>();
List<Cluster> depth_listClone = new List<Cluster>();
List<double> depth_purity = new List<Double>();
double d_purity = -1;
int test = treeClone11[treeClone11.Count - 1].clusterID;
int testP = treeClone11[treeClone11.Count - 1].parentID;
depth_list.Add(treeClone11[treeClone11.Count - 1]);
depth_listClone.Add(treeClone11[treeClone11.Count - 1]);
depth_purity.Add(treeClone11[treeClone11.Count - 2].purity);
int depth_count = 0;
string str_depth_purity = "Depth#,NodeIDs,%Node, Purity" + "\r\n";
bool leaf_node = true;
int subFamilyCount = 0;
while (bln_continue)
{
bln_continue = false;
List<Cluster> this_depth = new List<Cluster>();
foreach (Cluster parent in depth_list)
{
int k = 0;
bool bln_break = false;
leaf_node = true;
for (int i = treeClone11.Count - 1; i >= 0; i--) //nodeID
{
if (treeClone11[i].parentID == parent.clusterID)
{
leaf_node = false;
k = k + 1;
this_depth.Add(treeClone11[i]);
if ((treeClone11[i].majority_class).Trim() != (parent.majority_class).Trim())
{
subFamilyCount = subFamilyCount + 1;
}
if (k == 2)
{
k = 0;
if (depth_listClone.Count > 0)
depth_listClone.Remove(parent);
if (depth_listClone.Count == 0)
{
bln_break = true;
break;
}
}
}
if (bln_break)
break;
}
}
if ((this_depth.Count > 0 && depth_listClone.Count == 0) || (leaf_node = true && depth_list.Count > 0))
{
bln_continue = true;
d_purity = 0;
string str_d = "";
int f = 0;
foreach (Cluster d in depth_list)
{
if (f == 0)
str_d = (d.clusterID).ToString();
else
str_d = str_d + ";" + d.clusterID;
d_purity = d_purity + d.purity;
f = f + 1;
}
d_purity = d_purity / depth_list.Count;
str_d = depth_count.ToString() + "," + str_d + "," + Math.Round((100 * depth_list.Count / Math.Pow(2, depth_count)), 2).ToString() + "," + Math.Round(d_purity, 2).ToString() + ", sub-family: " + subFamilyCount + "\r\n";
str_depth_purity = str_depth_purity + str_d;
//prepare for next depth
depth_list.Clear();
foreach (Cluster d in this_depth)
depth_list.Add(d);
depth_count = depth_count + 1;
}
}
System.IO.File.WriteAllText(depth_purity_path, str_depth_purity);
/*-----------------------------END-------------------------*/
}
public double IntraClusterDistance(Cluster C, string[] arr_input_file, double[,] Dmatrix, string distance_type)
{
//type of distance measure: single link: singleLink, complete link:completLink, Average Link: AverageLink, median link:MedianLink or MedoidLink
distance_type = distance_type.Trim();
//use UPPERCASE
distance_type = distance_type.ToUpper();
if (distance_type != "SINGLELINK" && distance_type != "COMPLETELINK" && distance_type != "AVERAGELINK" && distance_type != "MEDOIDLINK" && distance_type != "MEDIANLINK")
distance_type = "AVERAGELINK";
// List<Sequence> temp_medoid = new List<Sequence>();
Sequence OP = new Sequence();
Hashtable h_interClusterDistance = new Hashtable();//Host the contribution of each sequence to the InterCluster distance; the key is the sequenceID
/* for each object in cluster C1 compute distance to all object in C2 */
double inter_d = 0.0;
switch (distance_type)
{
case "AVERAGELINK":
{
double interClusterDistance = 0.0;
int totalPairwiseDistances = 0;
// System.Console.WriteLine("C1.clusterID:" + C1.clusterID+ "C2.clusterID :"+ C2.clusterID);
foreach (Sequence seq1 in C.ListOfSeq)
{
foreach (Sequence seq2 in C.ListOfSeq)
{
if (seq1.sequenceValue != seq2.sequenceValue)
{
double d = OP.fetchDistance(seq1, seq2, arr_input_file, Dmatrix);
// System.Console.WriteLine("dD:" + d);
//Console.Read();
inter_d = inter_d + d;
totalPairwiseDistances = totalPairwiseDistances + 1;
}
}
}
interClusterDistance = Math.Round(inter_d / totalPairwiseDistances, 3);
// System.Console.WriteLine("inside distance interClusterDistance:" + interClusterDistance);
return interClusterDistance;
}
case "SINGLELINK":
{
double interClusterDistance = 0.0;
foreach (Sequence seq1 in C.ListOfSeq)
{
foreach (Sequence seq2 in C.ListOfSeq)
{
if (seq1.sequenceValue != seq2.sequenceValue)
{
double d = OP.fetchDistance(seq1, seq2, arr_input_file, Dmatrix);
if (d < inter_d)
inter_d = d;
}
}
}
interClusterDistance = inter_d;
return interClusterDistance;
}
case "COMPLETELINK":
{
double interClusterDistance = 0.0;
foreach (Sequence seq1 in C.ListOfSeq)
{
foreach (Sequence seq2 in C.ListOfSeq)
{
if (seq1.sequenceValue != seq2.sequenceValue)
{
double d = OP.fetchDistance(seq1, seq2, arr_input_file, Dmatrix);
if (d > inter_d)
inter_d = d;
}
}
}
interClusterDistance = inter_d;
return interClusterDistance;
}
case "MEDOIDLINK":
{
double interClusterDistance = 0.0;
//there could be many medoids for a cluster. We select the two with the largest distance
foreach (Sequence seq1 in C.listOfMedoid)
{
foreach (Sequence seq2 in C.listOfMedoid)
{
if (seq1.sequenceValue != seq2.sequenceValue)
{
double d = OP.fetchDistance(seq1, seq2, arr_input_file, Dmatrix);
if (d > inter_d)
inter_d = d;
}
}
}
interClusterDistance = inter_d;
return interClusterDistance;
}
default:
return inter_d;
}//switch
//return interClusterDistance;
}
public List<Sequence> ComputeMedoid(Cluster C, string[] arr_input_file, double[,] Dmatrix)
{
List<Sequence> temp_medoid = new List<Sequence>();
Sequence OP = new Sequence();
Hashtable h_intraClusterDistance = new Hashtable();//Host the contribution of each sequence to the intraCluster distance; the key is the sequenceID
// Hashtable h_sequence = new Hashtable();//Host the contribution of each sequence to the intraCluster distance; the key is the sequenceID
/* for each object in this cluster select the object in middle by computing the distance of each object to all objects*/
double intraCluster = 0.0;
// int numOfSeq=C.ListOfSeq.Count;
// double[] arr_intraClusterD = new double[numOfSeq];
foreach (Sequence seq1 in C.ListOfSeq)
{
intraCluster = 0;
foreach (Sequence seq2 in C.ListOfSeq)
{
if (seq1.sequenceValue != seq2.sequenceValue)
{
double d = OP.fetchDistance(seq1, seq2, arr_input_file, Dmatrix);
intraCluster = intraCluster + d;
}
}
//The contribution of each sequ is in the hash.
seq1.contributionToIntraClusterDistance = intraCluster;
h_intraClusterDistance.Add(seq1.sequenceValue, seq1);
}
// The squence with the minimum distance is the medoid
double minDistance = -1.0;
bool first_time = true;
foreach (DictionaryEntry entry in h_intraClusterDistance)
{
Sequence temp = (Sequence)(entry.Value);
if (first_time)
minDistance = temp.contributionToIntraClusterDistance;
if (temp.contributionToIntraClusterDistance < minDistance)
minDistance = temp.contributionToIntraClusterDistance;
}
// At this point the minimumn value of intra cluster distance is known. What is needed to find out all the sequences with same value
foreach (DictionaryEntry entry in h_intraClusterDistance)
{
Sequence temp = (Sequence)(entry.Value);
if (temp.contributionToIntraClusterDistance == minDistance)
temp_medoid.Add(temp);
}
return temp_medoid;
}
