private void btn_algorithm2_Click(object sender, EventArgs e)
{
//get values frome user:
int N_LENGTH, W_LENGTH, K_CENTERS;
string file_name;
//starting time
getValuesFromUsers(out N_LENGTH, out W_LENGTH, out K_CENTERS, out file_name);
var watch = System.Diagnostics.Stopwatch.StartNew();///calc execution time
//call algorithm2
Console.WriteLine("Calling Algorithm 2...");
RawDataType data;
Tuple <double, int> result = BitClusterDiscord.bitClusterDiscord(out data, N_LENGTH, W_LENGTH, K_CENTERS, file_name);
watch.Stop();//stop timer
var elapsedMs = watch.ElapsedMilliseconds;
this.txt_time.Text = elapsedMs.ToString();
this.best_so_far_dist_txt.Text = result.Item1.ToString();
this.best_so_far_loc_txt.Text = result.Item2.ToString();
Console.WriteLine("Writing to file...");
IOFunctions.writeFile(data, N_LENGTH, result.Item2);
//Done
Console.WriteLine(this.Text + " : Done Algorithm 2. Best_so_far_loc = " + result.Item2 + ". Time: " + elapsedMs);
}
private void btn_Squeezer_Click(object sender, EventArgs e)
{
//get values frome user:
int N_LENGTH, W_LENGTH;
double threshold;
string file_name;
//starting time
getValuesFromUsers_Enhancement(out N_LENGTH, out W_LENGTH, out threshold, out file_name);
var watch = System.Diagnostics.Stopwatch.StartNew();///calc execution time
//call the function
Console.WriteLine("Calling BitClustering + Squeezer ...");
RawDataType data;
List <int> cluster_belong;
Tuple <double, int> result = Discord_Time_Series.squeezer(out cluster_belong, out data, N_LENGTH, W_LENGTH, threshold, file_name, Discord_Time_Series.BIT_SERIES);
watch.Stop();//stop timer
var elapsedMs = watch.ElapsedMilliseconds;
this.txt_time.Text = elapsedMs.ToString();
this.txt_best_so_far_dist.Text = result.Item1.ToString();
this.txt_best_so_far_loc.Text = result.Item2.ToString();
Console.WriteLine("Writing to file...");
IOFunctions.writeFile_Cluster(data, N_LENGTH, result.Item2, cluster_belong);
//Done
Console.WriteLine(this.Text + " : \n\tDone \"BitClustering + Squeezer\" algorithm. Best_so_far_loc = " + result.Item2 + ". Time: " + elapsedMs);
Console.WriteLine("====================================================================");
}
private void btn_HotSax_Click(object sender, EventArgs e)
{
//get values frome user:
int N_LENGTH, W_LENGTH;
string file_name;
//starting time
getValuesFromUsers_HOTSAX(out N_LENGTH, out W_LENGTH, out file_name);
//fixed: hard code
W_LENGTH = 3;
var watch = System.Diagnostics.Stopwatch.StartNew();///calc execution time
//call the function
Console.WriteLine("Calling HOTSAX ...");
RawDataType data;
Tuple <double, int> result = Heuristic.Heuristic_Function(N_LENGTH, W_LENGTH, file_name, out data);
watch.Stop();//stop timer
var elapsedMs = watch.ElapsedMilliseconds;
this.txt_time.Text = elapsedMs.ToString();
this.txt_best_so_far_dist.Text = result.Item1.ToString();
this.txt_best_so_far_loc.Text = result.Item2.ToString();
Console.WriteLine("Writing to file...");
IOFunctions.writeFile(data, N_LENGTH, result.Item2);
//Done
Console.WriteLine(this.Text + " : \n\tDone HOTSAX. Best_so_far_loc = " + result.Item2 + ". Time: " + elapsedMs);
Console.WriteLine("====================================================================");
}
}//end bitClusterDiscord()
static public Tuple <double, int> squeezer(out List <int> cluster_belong, out RawDataType data, int N_LENGTH, int W_LENGTH, double threshold, string file_name, int type_convert_raw_data)
{
//read data
data = IOFunctions.readFile(file_name); //raw timeseries data
List <Cluster_Squeezer> b_cluster;
var watch = System.Diagnostics.Stopwatch.StartNew();///calc execution time
//call the function
BitCluster.squeezerCluster(data, type_convert_raw_data, out b_cluster, out cluster_belong, threshold, N_LENGTH, W_LENGTH, file_name);
watch.Stop();//stop timer
var elapsedMs = watch.ElapsedMilliseconds;
Console.WriteLine("________squeezerCluster is Done in " + elapsedMs + ".\nKeep going...Please wait");
// get Outer loop:
List <int> outer_loop = getOuterLoop(b_cluster, b_cluster.Count); //get outer
List <int> inner_loop;
bool continue_to_outer_loop = false;
double nearest_neighbor_dist = 0;
double dist = 0;
double best_so_far_dist = 0;
int best_so_far_loc = 0;
RawDataType p_center, q_center;
bool[] is_skip_at_p = new bool[outer_loop.Count];
for (int i = 0; i < outer_loop.Count; i++)
{
is_skip_at_p[i] = false;
}
int cluster_of_cur_outer; //thang p dang nam o cluster nao
int cluster_of_cur_inner; // tracking for q's cluster at inner loop
//test
int count_skip_at_p = 0;
int count_break_sub_2_dis = 0;
int count_dis_smaller_best_so_far = 0;
int count_center_p_diff_center_q = 0;
foreach (int p in outer_loop)
{
if (is_skip_at_p[p])
{
count_skip_at_p++;
//p was visited at inner loop before
continue;
}
else
{
nearest_neighbor_dist = INFINITE;
cluster_of_cur_outer = cluster_belong[p];
inner_loop = getInnerLoop(b_cluster, b_cluster.Count, cluster_of_cur_outer);
foreach (int q in inner_loop)// inner loop
{
if (Math.Abs(p - q) < N_LENGTH)
{
continue;// self-match => skip to the next one
}
else
{
/*cluster_of_cur_inner = cluster_belong[q];
*
* if (cluster_of_cur_inner != cluster_of_cur_outer)
* {
* count_center_p_diff_center_q++;
* p_center = data.GetRange(b_cluster[cluster_of_cur_outer].getCluster().getCenterIndex(), N_LENGTH);
* q_center = data.GetRange(b_cluster[cluster_of_cur_inner].getCluster().getCenterIndex(), N_LENGTH);
*
* if (distanceBetween2Clusters(p_center, q_center, b_cluster[cluster_of_cur_outer].getCluster().getRadius(), b_cluster[cluster_of_cur_inner].getCluster().getRadius()) >= best_so_far_dist)
* {
* continue_to_outer_loop = true;
* count_break_sub_2_dis++;
* break;
* }
* }
*/
//calculate the Distance between p and q
dist = HelperFunctions.gaussDistance(data.GetRange(p, N_LENGTH), data.GetRange(q, N_LENGTH));
if (dist < best_so_far_dist)
{
//skip the element q at oute_loop, 'cuz if (p,q) is not a solution, so does (q,p).
is_skip_at_p[q] = true;
count_dis_smaller_best_so_far++;
continue_to_outer_loop = true; //break, to the next loop at outer_loop
break; // break at inner_loop first
}
if (dist < nearest_neighbor_dist)
{
nearest_neighbor_dist = dist;
}
} //end else
} //end inner
if (continue_to_outer_loop)
{
continue_to_outer_loop = false; //reset
continue; //go to the next p in outer loop
}
if (nearest_neighbor_dist > best_so_far_dist)
{
best_so_far_dist = nearest_neighbor_dist;
best_so_far_loc = p;
}
} //end else
} //end outter
Console.WriteLine("count_skip_at_p =" + count_skip_at_p);
Console.WriteLine("=======count_break_sub_2_dis =" + count_break_sub_2_dis);
Console.WriteLine("count_dis_smaller_best_so_far =" + count_dis_smaller_best_so_far);
Console.WriteLine("count_center_p_diff_center_q=" + count_center_p_diff_center_q);
return(new Tuple <double, int>(best_so_far_dist, best_so_far_loc));
}//end bitClusterDiscord_Enhancement()
}//end innerArrangement
// Heuristic function
public static Tuple <double, int> Heuristic_Function(int N_LENGTH, int W_DIMENSION, string file_name, out List <double> data)
{
data = IOFunctions.readFile(file_name);
Dictionary <string, int> count_table;
Dictionary <int, string> total_table;
AugmentedTrie tree;
MakeTree_And_Table_Function(data, N_LENGTH, out tree, out count_table, out total_table, W_DIMENSION);
double best_so_far_dist = 0;
int best_so_far_loc = 0;
double nearest_neighbor_dist = 0;
double dist = 0;
List <int> outer_list, inner_list;
outer_list = OuterArrangement(total_table, count_table);
bool break_to_outer_loop = false;
bool[] is_skip_at_p = new bool[outer_list.Count];
for (int i = 0; i < outer_list.Count; i++)
{
is_skip_at_p[i] = false;
}
foreach (int p in outer_list)
{
//if (is_skip_at_p[p] || Math.Abs(p - 10867) < N_LENGTH || Math.Abs(p - 3994) < N_LENGTH
// || Math.Abs(p - 13492) < N_LENGTH)//
if (is_skip_at_p[p])
{
//p was visited at inner loop before
continue;
}
else
{
nearest_neighbor_dist = INFINITE;
string word = total_table[p];
inner_list = InnerArrangement(total_table, count_table, tree, word);
foreach (int q in inner_list)// inner loop
{
if (Math.Abs(p - q) < N_LENGTH)
{
continue;// self-match => skip to the next one
}
else
{
//calculate the Distance between p and q
dist = Distance(data.GetRange(p, N_LENGTH), data.GetRange(q, N_LENGTH));
if (dist < best_so_far_dist)
{
//skip the element q at oute_loop, 'cuz if (p,q) is not a solution, so does (q,p).
is_skip_at_p[q] = true;
break_to_outer_loop = true; //break, to the next loop at outer_loop
break; // break at inner_loop first
}
if (dist < nearest_neighbor_dist)
{
nearest_neighbor_dist = dist;
}
}
}//end inner
if (break_to_outer_loop)
{
break_to_outer_loop = false; //reset
continue; //go to the next p in outer loop
}
if (nearest_neighbor_dist > best_so_far_dist)
{
best_so_far_dist = nearest_neighbor_dist;
best_so_far_loc = p;
}
} //end else
} //end outter
Tuple <double, int> result = new Tuple <double, int>(best_so_far_dist, best_so_far_loc);
//Console.WriteLine("skip = " + number_skip);
return(result);
}// end Heuristic function
static public void bitCluster(out List <Cluster> b_cluster, out List <int> cluster_belong, int K_CENTERS, int N_LENGTH, int W_LENGTH, string file_name)
{
//read data
RawDataType data = IOFunctions.readFile(file_name);
Dictionary <int, BitseriesType> bit_series_data; //store Bit series data
bit_series_data = HelperFunctions.bitSeriesDataset(data, N_LENGTH, W_LENGTH); //get bit series data from original data
//set some variables:
int bit_series_data_length = bit_series_data.Count;
b_cluster = new List <Cluster>(); //store k clusters
List <int> k_centers; //store k centers
cluster_belong = new List <int>(); //store the cluster whose each point belong to
double dist_i, dist_p;
for (int i = 0; i < bit_series_data_length; i++)
{
cluster_belong.Add(-1);
}
//initialize K centers
k_centers = getKRandomCenter(bit_series_data_length, K_CENTERS);
//k_centers = getKCentersContinuously(K_CENTERS);
//(line 1 - 4 of the paper): initialize k cluster, then append them to 'b_cluster'
for (int i = 0; i < K_CENTERS; i++)
{
Cluster one_cluster = new Cluster(k_centers[i], 1);
b_cluster.Add(one_cluster);
}
//(line 5-15 in the paper):
for (int j = 0; j < bit_series_data_length; j++)//go through the BD data
{
//get each element:
if (cluster_belong[j] == (-1)) //if the point hasn't belong any cluster yet
{
int p = 0;
for (int i = 1; i < K_CENTERS; i++)
{
dist_i = HelperFunctions.bitSeriesDistance(bit_series_data[j], bit_series_data[b_cluster[i].getCenterIndex()]);
dist_p = HelperFunctions.bitSeriesDistance(bit_series_data[j], bit_series_data[b_cluster[p].getCenterIndex()]);
if (dist_i < dist_p)
{
p = i;
}
}//end for
// line 12-14 in the paper
cluster_belong[j] = p;
b_cluster[p].plusOneToNumberOfMembers();
b_cluster[p].addToListMemberIndice(j);
}
}//end of for
double radius;
List <double> center_value;
for (int i = 0; i < K_CENTERS; i++)
{
center_value = data.GetRange(b_cluster[i].getCenterIndex(), N_LENGTH);
radius = HelperFunctions.calculateRadius(data, b_cluster[i].getListMemberIndice(), center_value, N_LENGTH);
b_cluster[i].setRadius(radius);
}
}//end of function
//algorithm 2:
static public Tuple <double, int> bitClusterDiscord(out RawDataType data, int N_LENGTH, int W_LENGTH, int K_CENTERS, string file_name)
{
//read data
data = IOFunctions.readFile(file_name); //raw timeseries data
//store Bit series data
Dictionary <int, BitseriesType> bit_series_data = HelperFunctions.bitSeriesDataset(data, N_LENGTH, W_LENGTH);//get bit series data from original data
//line 6: run algorithm 1 to have b_cluster
List <Cluster> b_cluster;
List <int> cluster_belong;
BitCluster.bitCluster(out b_cluster, out cluster_belong, K_CENTERS, N_LENGTH, W_LENGTH, file_name);
Console.WriteLine("Algorithm1 is Done !\nKeep going...Please wait");
// get Outer loop:
List <int> outer_loop = getOuterLoop(b_cluster, K_CENTERS); //get outer
List <int> inner_loop;
bool continue_to_outer_loop = false;
double nearest_neighbor_dist = 0;
double dist = 0;
double best_so_far_dist = 0;
int best_so_far_loc = 0;
RawDataType p_center, q_center;
bool[] is_skip_at_p = new bool[outer_loop.Count];
for (int i = 0; i < outer_loop.Count; i++)
{
is_skip_at_p[i] = false;
}
int cluster_of_cur_outer; //thang p dang nam o cluster nao
int cluster_of_cur_inner; // tracing for q's cluster at inner loop
foreach (int p in outer_loop)
{
//if (is_skip_at_p[p] || Math.Abs(p - 10867) < N_LENGTH || Math.Abs(p - 3994) < N_LENGTH
// || Math.Abs(p - 13492) < N_LENGTH)//
if (is_skip_at_p[p])
{
//p was visited at inner loop before
continue;
}
else
{
nearest_neighbor_dist = INFINITE;
cluster_of_cur_outer = cluster_belong[p];
inner_loop = getInnerLoop(b_cluster, K_CENTERS, cluster_of_cur_outer);
foreach (int q in inner_loop)// inner loop
{
if (Math.Abs(p - q) < N_LENGTH)
{
continue;// self-match => skip to the next one
}
else
{
cluster_of_cur_inner = cluster_belong[q];
p_center = data.GetRange(b_cluster[cluster_of_cur_outer].getCenterIndex(), N_LENGTH);
q_center = data.GetRange(b_cluster[cluster_of_cur_inner].getCenterIndex(), N_LENGTH);
if (distanceBetween2Clusters(p_center, q_center, b_cluster[cluster_of_cur_outer].getRadius(), b_cluster[cluster_of_cur_inner].getRadius()) >= best_so_far_dist)
{
continue_to_outer_loop = true;
break;
}
//calculate the Distance between p and q
dist = HelperFunctions.gaussDistance(data.GetRange(p, N_LENGTH), data.GetRange(q, N_LENGTH));
if (dist < best_so_far_dist)
{
//skip the element q at oute_loop, 'cuz if (p,q) is not a solution, so does (q,p).
is_skip_at_p[q] = true;
continue_to_outer_loop = true; //break, to the next loop at outer_loop
break; // break at inner_loop first
}
if (dist < nearest_neighbor_dist)
{
nearest_neighbor_dist = dist;
}
} //end else
} //end inner
if (continue_to_outer_loop)
{
continue_to_outer_loop = false; //reset
continue; //go to the next p in outer loop
}
if (nearest_neighbor_dist > best_so_far_dist)
{
best_so_far_dist = nearest_neighbor_dist;
best_so_far_loc = p;
}
} //end else
} //end outter
return(new Tuple <double, int>(best_so_far_dist, best_so_far_loc));
}
