private void combox_filename_SelectedIndexChanged(object sender, EventArgs e)
{
var data_name = System.IO.Path.GetFileNameWithoutExtension(combox_filename.Text);
var extension = System.IO.Path.GetExtension(combox_filename.Text);
txt_data_to_calc_W.Text = data_name + "_W" + extension;
txt_stream_data.Text = data_name + "_Stream" + extension;
//set default value for period_max:
List <double> data_to_calc_w = IOFuncs.readStreamFile(txt_data_to_calc_W.Text);
txt_period_max.Text = (data_to_calc_w.Count() / 10).ToString();
switch (this.combox_filename.SelectedItem.ToString())
{
case "ECG.txt":
setValues("40", "5", "371", "0.001", "0.01", "0.95", "2", "25", "12", "10");
break;
case "TEK16.txt":
setValues("128", "5", "1007", "0.001", "0.01", "0.85", "2", "25", "12", "2");
break;
case "power_data.txt":
setValues("200", "4", "672", "0.05", "0.15", "0.85", "2", "25", "12", "5");
break;
case "nprs43.txt":
setValues("160", "3", "40", "0.05", "0.15", "0.85", "2", "25", "12", "75");
break;
case "ERP.csv":
setValues("64", "4", "1280", "0.001", "0.01", "0.95", "2", "25", "12", "2");
break;
}
}
private void btn_statistic_Click(object sender, EventArgs e)
{
string file_name = combox_filename.Text;
string algorithm = combox_algorithm.Text;
List <double> dist = null, loc = null;
double time = 0;
IOFuncs.readStatisticalFile(file_name, algorithm, ref dist, ref loc, ref time);
Statistical_Form new_form = new Statistical_Form(algorithm, file_name, loc, dist, time);
}
private void acf_function()
{
List <double> data_to_calc_w = IOFuncs.readStreamFile(txt_data_to_calc_W.Text);
int data_len = data_to_calc_w.Count;
int period_max = Convert.ToInt16(txt_period_max.Text);
//normalize data:
List <double> norm_data = MathFuncs.zScoreNorm(data_to_calc_w, data_len);
ACF_Form form3 = new ACF_Form(this, norm_data, 1, period_max);
//txt_period.Text = period.ToString();
}
private void btn_GetW_Click(object sender, EventArgs e)
{
int N_LENGTH = Convert.ToInt16(txt_NLength.Text);
//todo /*Duc*/
//try
//{
//read file
List <double> stream_data;
stream_data = IOFuncs.readStreamFile(txt_data_to_calc_W.Text);
stream_data = MathFuncs.zScoreNorm(stream_data, stream_data.Count);
//get threshold:
double threshold = Convert.ToDouble(txt_threshold.Text);
/*todo*/
int result = stream_data.Count;
MinHeap <SegmentNode> segment_heap = null;
BottomUp.Bottom_Up(stream_data, threshold, ref result, ref segment_heap);
result = (int)(N_LENGTH / result);
Segment_Form form2 = new Segment_Form(stream_data, segment_heap);
//form2.Show();
/*show the result:*/
txt_WLength.Text = result.ToString();
Console.WriteLine("W_LENGTH = " + result);
//}
//catch
//{
// MessageBox.Show("re-check the parameters !", "Error", MessageBoxButtons.OK);
//}
}
private void form_SAX_Load(object sender, EventArgs e)
{
string link = System.IO.Path.Combine(System.Environment.CurrentDirectory, @"Data\");
string[] fileArray = System.IO.Directory.GetFiles(@link);
/*combox_filename*/
foreach (string file in fileArray)
{
this.combox_filename.Items.Add(System.IO.Path.GetFileName(file));//get all data filenames in the directory
}
string default_file = "ECG.txt";
var match = fileArray.FirstOrDefault(stringToCheck => stringToCheck.Contains(default_file));
if (match != null)
{
combox_filename.Text = default_file;// set default value for the combox
}
/*combox_algorithm*/
string[] algorithms = { "BFHS", "HOTSAX_Stream", "HOTSAX_Squeezer_Stream", "Bounding_Box", "Sanchez_Method" };
foreach (string algorithm in algorithms)
{
this.combox_algorithm.Items.Add(algorithm);
}
string default_algorithm = "HOTSAX_Squeezer_Stream";
match = algorithms.FirstOrDefault(stringToCheck => stringToCheck.Contains(default_algorithm));
if (match != null)
{
combox_algorithm.Text = default_algorithm;// set default value for the combox
}
//set default value for period_max:
List <double> data_to_calc_w = IOFuncs.readStreamFile(txt_data_to_calc_W.Text);
txt_period_max.Text = (data_to_calc_w.Count() / 10).ToString();
}
/*phase coherence analysis*/
private void phaseCoherenceFuntion()
{
Console.WriteLine("Running phaseCohenrence...");
System.Diagnostics.Stopwatch watch2;; ///calc execution time
long elapsedMs2 = 0;
watch2 = System.Diagnostics.Stopwatch.StartNew(); ///calc execution time
List <double> std_list = new List <double>(); //store std(s)
List <double> norm_data = new List <double>(); //data after normalizing all its subsets
List <double> raw_data = IOFuncs.readStreamFile(txt_data_to_calc_W.Text);
int data_len = raw_data.Count;
int K_MAX = Convert.ToInt16(txt_period_max.Text);
for (int k = 1; k <= K_MAX; k++)//do calc std for each value of 'k'
{
//reset variables:
norm_data.Clear();
double[] sum_subsets = new double[k]; //store k sums
double[] mean_subsets = new double[k]; //store k means
int len_average_subset = data_len / k;
int remainder_len_subset = data_len % k;
/*calc mean for each subsets:*/
//find the sum(s):
for (int j = 0; j < data_len; j++)
{
sum_subsets[j % k] += raw_data[j];
}
//calc mean(s):
for (int i = 0; i < k; i++)
{
if (i < remainder_len_subset)
{
mean_subsets[i] = sum_subsets[i] / (len_average_subset + 1);
}
else
{
mean_subsets[i] = sum_subsets[i] / len_average_subset;
}
}
for (int i = 0; i < data_len; i++)
{
norm_data.Add(raw_data[i] - mean_subsets[i % k]);
}
//calc std then append it to std_list
std_list.Add(MathFuncs.CalcStdMeanZero(norm_data));
}//end for
double min = std_list.Min();
int period = std_list.FindIndex(n => Math.Abs(n - min) < 0.0000001);
watch2.Stop();
elapsedMs2 = watch2.ElapsedMilliseconds;
//print file to check:
IOFuncs.WriteFile_2(std_list, "std_list.csv");
txt_period.Text = period.ToString();
Console.WriteLine("DONE CALC PERIOD_ REF 7, time = " + elapsedMs2 + "; period = " + period);
}
/*Fig. 11 ('Luo_2011' paper): Estimating period with the median gap between two neighboring local minima*/
private void Luo_2011_Period()
{
//get max value for p, q:
List <double> data_to_calc_w = IOFuncs.readStreamFile(txt_data_to_calc_W.Text);
int data_len = data_to_calc_w.Count;
int N_length = Convert.ToInt16(txt_NLength.Text);
//normalize data:
List <double> norm_data = MathFuncs.zScoreNorm(data_to_calc_w, data_len);
//create a list to restore dists(p,q):
List <double> dist_pq = new List <double>();
//calc collection of gaps delta for local minima of dist(p,q), here:
// p is picked randomly
// q = 0 to (bufer_len - N_length)
//choose a random p:
Random random_obj = new Random();
int p = random_obj.Next(data_len - N_length + 1);
//get segment p:
List <double> p_segment = norm_data.GetRange(p, N_length);
//go through all q(s), compute dist(p,q) for every q:
for (int q = 0; q <= (data_len - N_length); q++)
{
dist_pq.Add(MathFuncs.EuDistance(p_segment, norm_data.GetRange(q, N_length)));
}
/* Find the lower K_LOWER_PERCENT quantile of all distances calculated in the previous step.*/
int dist_pq_len = dist_pq.Count();
// WriteFile.WriteFile_2(dist_pq);
// WriteFile.WriteFile_2(norm_data, "norm_data.csv");
int k_percent_index = (int)(dist_pq_len * K_LOWER_PERCENT);
var sorted_dist_pq = dist_pq.OrderBy(n => n);
//lower K_LOWER_PERCENT:
double cp = sorted_dist_pq.ElementAt(k_percent_index);
//get all local minima q such that dist(p, q) <= cp.
List <int> Q = new List <int>();//store the result
for (int q = 0; q < dist_pq_len; q++)
{
if (dist_pq[q] <= cp)
{
Q.Add(q);
}
}
//Order Q list from smallest to largest:
Q.Sort();
int Q_len = Q.Count;
//calc lag 1 (delta):
List <int> delta = new List <int>();
for (int k = 0; k < Q_len - 1; k++)
{
if (Q[k + 1] - Q[k] > 2)
{
delta.Add(Q[k + 1] - Q[k]);
}
}
//sort delta:
delta.Sort();
int median_index = delta.Count / 2;
int delta_median = delta[median_index];
txt_period.Text = delta_median.ToString();
}
private void btn_Stream_Click(object sender, EventArgs e)
{
is_stream_clicked = true;
//read file
List <double> data_to_calc_w, stream_data;
int buffer_len;
data_to_calc_w = IOFuncs.readStreamFile(txt_data_to_calc_W.Text);
//WriteFile.rewriteStreamFile(txt_stream_data.Text, 0, 100);
stream_data = IOFuncs.readStreamFile(txt_stream_data.Text);
//get N_LENGTH
int N_LENGTH = Convert.ToInt16(txt_NLength.Text);
int period = Convert.ToInt16(txt_period.Text);
buffer_len = Convert.ToInt16(txt_bufferLength.Text) * period;
// get W_LENGTH
int W_LENGTH = Convert.ToInt16(txt_WLength.Text);
// get Threshold Mean
double threshold_mean = Convert.ToDouble(txt_threshold_mean.Text);
// get Threshold Std
double threshold_std = Convert.ToDouble(txt_threshold_std.Text);
// get algorithm
String algorithm = combox_algorithm.Text;
// get Threshold Similarity
double threshold_sim = Convert.ToDouble(txt_threshold_sim.Text);
// get parameter for RTree
int R = Convert.ToInt16(txtR.Text);
int maxEntry = Convert.ToInt16(txtMaxEntry.Text);
int minEntry = Convert.ToInt16(txtMinEntry.Text);
// get file_name
String file_name = combox_filename.Text;
if (buffer_len > data_to_calc_w.Count)
{
MessageBox.Show("buffer must be smaller than data_to_calc_w !, please modify Buffer_length.");
return;
}
//starting:
Console.WriteLine("Running Stream HOTSAX...\n");
List <double> result = new List <double>();
//result[0]: dist
//result[1]: loc
//get the buffer:
List <double> raw_buffer = data_to_calc_w.GetRange(data_to_calc_w.Count - buffer_len, buffer_len);
//normalize buffer:
List <double> norm_buffer = HOTSAX.HOTSAX.normalizeData(raw_buffer, buffer_len, N_LENGTH);
if (algorithm == "Bounding_Box" || algorithm == "Sanchez_Method")
{
//SCALE:
chart_timeSeries.ChartAreas[0].AxisY.Maximum = raw_buffer.Max() * SCALE;
chart_timeSeries.ChartAreas[0].AxisY.Minimum = raw_buffer.Min() / SCALE;
// turn the labels off:
chart_timeSeries.ChartAreas["ChartArea1"].AxisX.LabelStyle.Enabled = false; //turn off X-axis labels
chart_timeSeries.ChartAreas["ChartArea1"].AxisY.LabelStyle.Enabled = false; //turn off Y-axis labels
}
//updateChart(raw_buffer, (int)result[1], N_LENGTH);
// create a new thread to run the chart:
chart_thread = new Thread(() => this.doStream(file_name, algorithm, result, norm_buffer, data_to_calc_w, stream_data, N_LENGTH, W_LENGTH, raw_buffer, threshold_mean, threshold_std, threshold_sim, R, maxEntry, minEntry, period));
chart_thread.IsBackground = true;
chart_thread.Start();
}
