public static string MakeSnaps(ref _song_profile sp, int _snapsize, int _snapcount, string _path, bool doConsecutive)
{
var SR = new SoundReader();
SR.OpenFile(_path);
int samplesDesired = _snapsize; //Math.Max(_snapsize, 65536);
sp.snap_size = _snapsize;
sp.snap_count = _snapcount;
sp.total_samples = SR.FR.Length;
sp.length = SR.FR.TotalTime.Seconds;
sp.path = _path;
long blockscount = (long)(_snapcount * ((float)sp.total_samples / 50000000));
if (!SR.CheckFile())
{
SR.CloseFile();
return "Wrong format";
}
SR.CloseFile();
using (SR.FR = new Mp3FileReader(_path))
{
for (int i = 0; i < blockscount; i++)
{
var data = SR.GetNextChunk(samplesDesired, SR.FR);
double[] leftd = data[0];
double[] rightd = data[1];
//for (int j = 0; j < 1; j++) ///////////
int seek_counter = 0;
//seek: pcm.Seek((i + 1) * (i + 1) * (i + 1) * blockscount * samplesDesired % (total_samples - samplesDesired), SeekOrigin.Begin);
seek:
if (!doConsecutive)
SR.SetPosition((long)(0.20 * sp.total_samples) + (long)(0.70 * sp.total_samples * i / blockscount));
else
SR.SetPosition((long)(0.20 * sp.total_samples) + i * _snapsize);
seek_counter++;
if ((leftd.Max(t => Math.Abs(t)) == 0) && (rightd.Max(t => Math.Abs(t)) == 0))
if (seek_counter > 4)
return "Too much silence";
else
{
blockscount++;
i++;
goto seek;
}
var pow_re_im = SoundProcessing.AmplitudeFromLR(_snapsize, leftd, rightd, 4.0, MWPreCalc);
sp.fft_snaps.Add(pow_re_im);
}
SR.CloseFile();
}
return "OK";
}
/*public List<double> MakeHistogram(_song_profile sp1, _song_profile sp2, double step)
* {
* var ans = new List<double>();
* var interm = new List<double>();
*
* List<double> avg = new List<double>(), avg_smoothed = new List<double>();
* for (int i = 0; i < sp1.fft_smoothed_snaps.Count; i++)
* {
* avg_smoothed.Add(sp1.fft_smoothed_snaps[i].Average());
* }
*
* foreach (var i in sp1.fft_smoothed_snaps)
* foreach (var j in sp2.fft_smoothed_snaps)
* if (i.Count != j.Count)
* return ans;
* else
* interm.Add(Correl(i, j));
*
* for (double k = 0; k <= 1; k += step)
* ans.Add((double)interm.Count(t => (t > k-step)&&(t <= k)) / (double)interm.Count);
*
* return ans;
* }*/
public double HistogramConv(_song_profile sp1, _song_profile sp2, double step)
{
var ans = new List <double>();
var interm = new List <double>();
List <double> avg1_smoothed = new List <double>(), avg2_smoothed = new List <double>();
for (int i = 0; i < sp1.fft_smoothed_snaps.Count; i++)
{
avg1_smoothed.Add(sp1.fft_smoothed_snaps[i].Average());
avg2_smoothed.Add(sp2.fft_smoothed_snaps[i].Average());
}
for (int i = 0; i < sp1.fft_smoothed_snaps.Count; i++) //in sp1.fft_smoothed_snaps)
{
for (int j = 0; j < sp2.fft_smoothed_snaps.Count; j++) //foreach (var j in sp2.fft_smoothed_snaps)
{
interm.Add(Correl(sp1.fft_smoothed_snaps[i], sp2.fft_smoothed_snaps[j], avg1_smoothed[i], avg2_smoothed[j]));
}
}
for (double k = -1; k <= 1; k += step)
{
ans.Add((double)interm.Count(t => (t > k - step) && (t <= k)) / (double)interm.Count);
}
double s = 0;
for (int i = 0; i < ans.Count; i++)
{
s += ans[i] * ((double)(i) - 1 / step);
}
return(s * step);
}
public Dictionary<string, double> CalcAllStats(_song_profile itm)
{
var ans = new Dictionary<string, double>();
//ans.Add("a_mean_ASC", itm.snap_correls.Average());
//ans.Add("a_stdev_ASC", StDev(itm.snap_correls));
//ans.Add("a_skewn_ASC", Skewness(itm.snap_correls));
//ans.Add("a_kurto_ASC", Kurtosis(itm.snap_correls));
//ans.Add("a_median_ASC", Median(itm.snap_correls));
//ans.Add("mean_ASSC", itm.snap_smoothed_correls.Average());
//ans.Add("stdev_ASSC", StDev(itm.snap_smoothed_correls));
//ans.Add("skewn_ASSC", Skewness(itm.snap_smoothed_correls));
//ans.Add("kurto_ASSC", Kurtosis(itm.snap_smoothed_correls));
//ans.Add("median_ASSC", Median(itm.snap_smoothed_correls));
//ans.Add("a_mean_ED", itm.snap_log10_energy.Average());
//ans.Add("a_stdev_ED", StDev(itm.snap_log10_energy));
//ans.Add("a_skewn_ED", Skewness(itm.snap_log10_energy));
//ans.Add("a_kurto_ED", Kurtosis(itm.snap_log10_energy));
//ans.Add("a_median_ED", Median(itm.snap_log10_energy));
if (itm.fft_downscaled_snaps[0].Count > 5)
{
for (int i = 1; i < itm.fft_downscaled_snaps[0].Count / 2; i++)
{
string a = Appdx(i, itm.fft_downscaled_snaps[0].Count);
var aa = new List<double>();
for (int j = 0; j < itm.fft_downscaled_snaps.Count; j++)
{
aa.Add(itm.fft_downscaled_snaps[j][i]);
}
ans.Add("b_mean_FFT1_" + a, aa.Average());
ans.Add("b_stdev_FFT1_" + a, StDev(aa));
ans.Add("b_skewn_FFT1_" + a, Skewness(aa));
ans.Add("b_kurto_FFT1_" + a, Kurtosis(aa));
ans.Add("b_median_FFT1_" + a, Median(aa));
}
/*
for (int i = 0; i < itm.fft2_downscaled_snaps[0].Count / 2; i++)
{
double v = 0;
string a = Appdx(i, itm.fft2_downscaled_snaps[0].Count);
for (int j = 0; j < itm.fft2_downscaled_snaps.Count; j++)
v += itm.fft2_downscaled_snaps[j][i];
ans.Add("mean_FFT2_" + a, v / itm.fft2_downscaled_snaps.Count);
}
*/
}
return ans;
}
public Dictionary <string, double> CalcAllStats(_song_profile itm)
{
var ans = new Dictionary <string, double>();
//ans.Add("a_mean_ASC", itm.snap_correls.Average());
//ans.Add("a_stdev_ASC", StDev(itm.snap_correls));
//ans.Add("a_skewn_ASC", Skewness(itm.snap_correls));
//ans.Add("a_kurto_ASC", Kurtosis(itm.snap_correls));
//ans.Add("a_median_ASC", Median(itm.snap_correls));
//ans.Add("mean_ASSC", itm.snap_smoothed_correls.Average());
//ans.Add("stdev_ASSC", StDev(itm.snap_smoothed_correls));
//ans.Add("skewn_ASSC", Skewness(itm.snap_smoothed_correls));
//ans.Add("kurto_ASSC", Kurtosis(itm.snap_smoothed_correls));
//ans.Add("median_ASSC", Median(itm.snap_smoothed_correls));
//ans.Add("a_mean_ED", itm.snap_log10_energy.Average());
//ans.Add("a_stdev_ED", StDev(itm.snap_log10_energy));
//ans.Add("a_skewn_ED", Skewness(itm.snap_log10_energy));
//ans.Add("a_kurto_ED", Kurtosis(itm.snap_log10_energy));
//ans.Add("a_median_ED", Median(itm.snap_log10_energy));
if (itm.fft_downscaled_snaps[0].Count > 5)
{
for (int i = 1; i < itm.fft_downscaled_snaps[0].Count / 2; i++)
{
string a = Appdx(i, itm.fft_downscaled_snaps[0].Count);
var aa = new List <double>();
for (int j = 0; j < itm.fft_downscaled_snaps.Count; j++)
{
aa.Add(itm.fft_downscaled_snaps[j][i]);
}
ans.Add("b_mean_FFT1_" + a, aa.Average());
ans.Add("b_stdev_FFT1_" + a, StDev(aa));
ans.Add("b_skewn_FFT1_" + a, Skewness(aa));
ans.Add("b_kurto_FFT1_" + a, Kurtosis(aa));
ans.Add("b_median_FFT1_" + a, Median(aa));
}
/*
* for (int i = 0; i < itm.fft2_downscaled_snaps[0].Count / 2; i++)
* {
* double v = 0;
* string a = Appdx(i, itm.fft2_downscaled_snaps[0].Count);
* for (int j = 0; j < itm.fft2_downscaled_snaps.Count; j++)
* v += itm.fft2_downscaled_snaps[j][i];
* ans.Add("mean_FFT2_" + a, v / itm.fft2_downscaled_snaps.Count);
* }
*/
}
return(ans);
}
private void button1_Click(object sender, EventArgs e)
{
listBox1.Items.Clear();
var itm = new _song_profile("", "");
itm.MakeSnaps((int)nUD2.Value, (int)nUD.Value, @"mp3\\diskord.mp3", (int)nUD3.Value);
itm.CalcInternalCorrels();
var itm2 = new _song_profile("", "");
itm2.MakeSnaps((int)nUD2.Value, (int)nUD.Value, @"mp3\\diskord2.mp3", (int)nUD3.Value);
itm2.CalcInternalCorrels();
itm.snap_log10_energy = Normalize(itm.snap_log10_energy);
itm2.snap_log10_energy = Normalize(itm2.snap_log10_energy);
listBox1.Items.Add("convoluted histogram value = " + HistogramConv(itm, itm2, 0.001));
}
private void button3_Click(object sender, EventArgs e)
{
if (MessageBox.Show("Are you sure want to (re)create the database?", "Question", MessageBoxButtons.YesNo) == System.Windows.Forms.DialogResult.No)
return;
listBox1.Items.Clear();
var fll = MakeFilesList(@textBox1.Text);
var fl = fll.OrderBy(t => Path.GetFileName(t).Length.ToString()).ToList();
listBox1.Items.Add("Found files: " + fl.Count);
var db = new SQLiteConnection();
var cmd = db.CreateCommand();
try
{
db.ConnectionString = "Data Source=test.db;";
db.Open();
//cmd.CommandText = "DROP TABLE IF EXISTS MAIN;"; cmd.ExecuteNonQuery();
//cmd.CommandText = "DROP TABLE IF EXISTS STATS;"; cmd.ExecuteNonQuery();
cmd.CommandText = "CREATE TABLE MAIN (id int, name string, path string);"; cmd.ExecuteNonQuery();
cmd.CommandText = "CREATE TABLE STATS (id int, statname string, statparam1 int, statparam2 int, statvalue float);"; cmd.ExecuteNonQuery();
}
catch
{
MessageBox.Show("Something has gone wrong with the database");
}
SQLiteParameter myparam1 = new SQLiteParameter();
SQLiteParameter myparam2 = new SQLiteParameter();
SQLiteParameter myparam3 = new SQLiteParameter();
SQLiteParameter myparam2_1 = new SQLiteParameter();
SQLiteParameter myparam2_2 = new SQLiteParameter();
SQLiteParameter myparam2_3 = new SQLiteParameter();
SQLiteParameter myparam2_4 = new SQLiteParameter();
SQLiteParameter myparam2_5 = new SQLiteParameter();
List<_song_profile> splist = new List<_song_profile>();
int s_count = 0;
PB1.Maximum = fl.Count;
PB1.Minimum = 0;
foreach (var f in fl)
{
//var transaction = db.BeginTransaction();
try
{
if (s_count % 50 == 0)
{
listBox1.Items.Clear();
listBox1.Items.Add("Items done: " + s_count + " of " + fl.Count);
}
var itm = new _song_profile(Path.GetFileName(f), f);
if (!itm.MakeSnaps((int)nUD2.Value, (int)nUD.Value, f, (int)nUD3.Value))
{
listBox1.Items.Add("![" + s_count + "]! Cannot make snapshots for " + itm.path);
s_count++;
continue;
}
//itm.CalcInternalCorrels();
itm.MakeDownscaledSnaps((int)nUD4.Value);
//itm.MakeDownscaledSnaps2((int)nUD4.Value);
var d = CalcAllStats(itm);
listBox1.Items.Add("[" + s_count + "] " + Path.GetFileName(itm.path));
cmd.CommandText = "BEGIN TRANSACTION;";
cmd.ExecuteNonQuery();
foreach (var di in d)
{
cmd.CommandText = "INSERT INTO STATS VALUES (@id, @statname, @statparam1, @statparam2, @statvalue);";
myparam2_1.ParameterName = "@id";
myparam2_2.ParameterName = "@statname";
myparam2_3.ParameterName = "@statparam1";
myparam2_4.ParameterName = "@statparam2";
myparam2_5.ParameterName = "@statvalue";
myparam2_1.Value = s_count;
myparam2_2.Value = di.Key;
myparam2_3.Value = 0;
myparam2_4.Value = 0;
myparam2_5.Value = di.Value;
cmd.Parameters.Add(myparam2_1); cmd.Parameters.Add(myparam2_2); cmd.Parameters.Add(myparam2_3);
cmd.Parameters.Add(myparam2_4); cmd.Parameters.Add(myparam2_5);
cmd.ExecuteNonQuery();
}
//splist.Add(itm);
cmd.CommandText = "INSERT INTO MAIN VALUES (@id, @name, @path);";
myparam1.ParameterName = "@id";
myparam2.ParameterName = "@name";
myparam3.ParameterName = "@path";
myparam1.Value = s_count;
myparam2.Value = itm.name;
myparam3.Value = itm.path;
cmd.Parameters.Add(myparam1);
cmd.Parameters.Add(myparam2);
cmd.Parameters.Add(myparam3);
cmd.ExecuteNonQuery();
PB1.Value = s_count;
Application.DoEvents();
//transaction.Commit();
cmd.CommandText = "COMMIT;";
cmd.ExecuteNonQuery();
s_count++;
}
catch
{
continue;
}
}
db.Close();
DBPostProcess("test.db");
/*
var dct = new Dictionary<string, double>();
for (int i = 0; i < splist.Count; i++)
{
dct.Add(splist[i].path, VecDiff(splist[0].scalar_stats.Values.ToList(), splist[i].scalar_stats.Values.ToList()));
}
dct.OrderBy(t => t.Value);
dct.ToList().OrderBy(t => t.Value).ToList().ForEach(t => listBox1.Items.Add(t.Key + " ---> [" + t.Value + "]"));
*/
/*************************************************************************
k-means++ clusterization
INPUT PARAMETERS:
XY - dataset, array [0..NPoints-1,0..NVars-1].
NPoints - dataset size, NPoints>=K
NVars - number of variables, NVars>=1
K - desired number of clusters, K>=1
Restarts - number of restarts, Restarts>=1
OUTPUT PARAMETERS:
Info - return code:
* -3, if task is degenerate (number of distinct points is
less than K)
* -1, if incorrect NPoints/NFeatures/K/Restarts was passed
* 1, if subroutine finished successfully
C - array[0..NVars-1,0..K-1].matrix whose columns store
cluster's centers
XYC - array[NPoints], which contains cluster indexes
*************************************************************************/
/*
int NPoints = splist.Count;
int NVars = splist[0].scalar_stats.Count;
int K = (int)Math.Sqrt(NPoints);
int Restarts = 5;
int Info = 0;
var C = new double[NVars, K];
var XYC = new int[NPoints];
var XY = new double[NPoints, NVars];
var _XY = splist[0].scalar_stats.Values.ToArray();
for (int i = 0; i < NPoints; i++)
for (int j = 0; j < NVars; j++)
XY[i, j] = splist[i].scalar_stats.Values.ToArray()[j];
alglib.kmeansgenerate(
XY,
NPoints,
NVars,
K,
Restarts,
out Info,
out C,
out XYC);
Info++;
Application.DoEvents();
for (int i = 0; i < splist.Count; i++)
{
listBox1.Items.Add(Path.GetFileName(splist[i].path) + " -> cluster # " + XYC[i]);
}
*/
/*foreach (var itm in splist)
{
var s1 = new List<double>();
var s2 = new List<double>();
var s3 = new List<double>();
foreach (var itm2 in splist)
{
/*listBox1.Items.Add(Path.GetFileName(itm.path) + " vs " + Path.GetFileName(itm2.path) +
"cross_energy_correl = " + Correl(itm.snap_log10_energy, itm2.snap_log10_energy, itm.snap_log10_energy.Average(), itm2.snap_log10_energy.Average()) + " " +
"cross_energy_diff = " + VecDiff(itm.snap_log10_energy, itm2.snap_log10_energy) + " " +
"convoluted histogram value = " + HistogramConv(itm, itm2, 0.001)
);*/
//s1.Add(Correl(itm.snap_log10_energy, itm2.snap_log10_energy, itm.snap_log10_energy.Average(), itm2.snap_log10_energy.Average()));
//s2.Add(1 / Math.Log10(VecDiff(itm.snap_log10_energy, itm2.snap_log10_energy)));
//s3.Add(HistogramConv(itm, itm2, 0.001));
/*s1.Add(0);
s2.Add(0);
s3.Add(0);
}
sp_CED.Add(s1);
sp_CEC.Add(s2);
sp_CHV.Add(s3);
}*/
/*List<double> likelihood = new List<double>();
for (int i = 0; i < splist.Count; i++)
{
double lh = 0;
//listBox1.Items.Add(Path.GetFileName(splist[i].path) + ": " + (int)(100*sp_ASC[i]) + " " + (int)(100*sp_ASSC[i]) + " " + (int)(10*sp_ED[i]));
for (int j = 0; j < splist.Count; j++)
{
lh =
sp_ASC[i] * sp_ASC[j] +
sp_ASSC[i] * sp_ASSC[j] +
sp_ED[i] * sp_ED[j] +
sp_CED[i][j] + sp_CEC[i][j] + sp_CHV[i][j]
;
likelihood.Add(lh);
listBox1.Items.Add((int)(100*lh) + " : " + Path.GetFileName(splist[i].path) + " " + Path.GetFileName(splist[j].path));
}
//listBox1.Items.Add("----->" + (int)(100*sp_CED[i][j]) + " " + (int)(100*sp_CEC[i][j]) + " " + (int)(100*sp_CHV[i][j]) + "; " + Path.GetFileName(splist[j].path));
}*/
}
private void button1_Click(object sender, EventArgs e)
{
listBox1.Items.Clear();
var itm = new _song_profile("", "");
itm.MakeSnaps((int)nUD2.Value, (int)nUD.Value, @"mp3\\diskord.mp3", (int)nUD3.Value);
itm.CalcInternalCorrels();
var itm2 = new _song_profile("", "");
itm2.MakeSnaps((int)nUD2.Value, (int)nUD.Value, @"mp3\\diskord2.mp3", (int)nUD3.Value);
itm2.CalcInternalCorrels();
itm.snap_log10_energy = Normalize(itm.snap_log10_energy);
itm2.snap_log10_energy = Normalize(itm2.snap_log10_energy);
listBox1.Items.Add("convoluted histogram value = " + HistogramConv(itm, itm2, 0.001));
}
/*public List<double> MakeHistogram(_song_profile sp1, _song_profile sp2, double step)
{
var ans = new List<double>();
var interm = new List<double>();
List<double> avg = new List<double>(), avg_smoothed = new List<double>();
for (int i = 0; i < sp1.fft_smoothed_snaps.Count; i++)
{
avg_smoothed.Add(sp1.fft_smoothed_snaps[i].Average());
}
foreach (var i in sp1.fft_smoothed_snaps)
foreach (var j in sp2.fft_smoothed_snaps)
if (i.Count != j.Count)
return ans;
else
interm.Add(Correl(i, j));
for (double k = 0; k <= 1; k += step)
ans.Add((double)interm.Count(t => (t > k-step)&&(t <= k)) / (double)interm.Count);
return ans;
}*/
public double HistogramConv(_song_profile sp1, _song_profile sp2, double step)
{
var ans = new List<double>();
var interm = new List<double>();
List<double> avg1_smoothed = new List<double>(), avg2_smoothed = new List<double>();
for (int i = 0; i < sp1.fft_smoothed_snaps.Count; i++)
{
avg1_smoothed.Add(sp1.fft_smoothed_snaps[i].Average());
avg2_smoothed.Add(sp2.fft_smoothed_snaps[i].Average());
}
for (int i = 0; i < sp1.fft_smoothed_snaps.Count; i++)//in sp1.fft_smoothed_snaps)
for (int j = 0; j < sp2.fft_smoothed_snaps.Count; j++) //foreach (var j in sp2.fft_smoothed_snaps)
interm.Add(Correl(sp1.fft_smoothed_snaps[i], sp2.fft_smoothed_snaps[j], avg1_smoothed[i], avg2_smoothed[j]));
for (double k = -1; k <= 1; k += step)
ans.Add((double)interm.Count(t => (t > k-step)&&(t <= k)) / (double)interm.Count);
double s = 0;
for (int i = 0; i < ans.Count; i++)
s += ans[i] * ((double)(i) - 1/step);
return s*step;
}
public static string MakeSnaps2(ref _song_profile sp, int _snapsize, int _snapcount, string _path, int overlap = 2, double lowpass = 4.0, bool fullnormalize = false)
{
var SR = new SoundReader();
var result = SR.OpenFile(_path);
if (result != "OK")
return result;
int samplesDesired = _snapsize;
sp.snap_size = _snapsize;
sp.snap_count = _snapcount;
sp.total_samples = SR.FR.Length;
sp.length = SR.FR.TotalTime.Seconds;
sp.path = _path;
int blockscount = (int)(_snapcount * ((float)sp.total_samples / (4 * 5 * 60 * 44100))); // 5 minutes song equivalent
blockscount = Math.Min(blockscount, 5 * 60 * 44100 / _snapsize); // haircut to 5 minutes long (maximum)
blockscount = Math.Min(blockscount, (int)(0.8 * _snapcount * ((float)sp.total_samples / (4 * 5 * 60 * 44100)))); // haircut to no more than 80% of song
if (!SR.CheckFile())
{
SR.CloseFile();
return "Wrong format";
}
SR.CloseFile();
using (SR.FR = new Mp3FileReader(_path))
{
SR.SetPosition((long)(0.20 * sp.total_samples));
double[][] alldata;
try
{
alldata = SR.GetNextChunk(samplesDesired * blockscount, SR.FR, fullnormalize);
}
catch (Exception E)
{
return E.Message;
}
for (int i = 0; i < (blockscount - 1) * overlap; i++)
{
double[] leftd = alldata[0];
double[] rightd = leftd;
var pow_re_im = SoundProcessing.AmplitudeFromLR2(_snapsize, leftd, Math.Max((int)(i * samplesDesired / overlap)-1, 0), samplesDesired, lowpass, MWPreCalc);
sp.fft_snaps.Add(pow_re_im);
}
SR.CloseFile();
}
return "OK";
}
public static string MakePCASnaps(ref _song_profile sp, ref List<List<double>> vmatrix)
{
sp.pca_snaps = ClusterAnalysis.PCAGetNewSpace(ref sp.fft_snaps, ref vmatrix, vmatrix.Count);
sp.fft_snaps = sp.pca_snaps;
return "OK";
}
private void button3_Click(object sender, EventArgs e)
{
if (MessageBox.Show("Are you sure want to (re)create the database?", "Question", MessageBoxButtons.YesNo) == System.Windows.Forms.DialogResult.No)
{
return;
}
listBox1.Items.Clear();
var fll = MakeFilesList(@textBox1.Text);
var fl = fll.OrderBy(t => Path.GetFileName(t).Length.ToString()).ToList();
listBox1.Items.Add("Found files: " + fl.Count);
var db = new SQLiteConnection();
var cmd = db.CreateCommand();
try
{
db.ConnectionString = "Data Source=test.db;";
db.Open();
//cmd.CommandText = "DROP TABLE IF EXISTS MAIN;"; cmd.ExecuteNonQuery();
//cmd.CommandText = "DROP TABLE IF EXISTS STATS;"; cmd.ExecuteNonQuery();
cmd.CommandText = "CREATE TABLE MAIN (id int, name string, path string);"; cmd.ExecuteNonQuery();
cmd.CommandText = "CREATE TABLE STATS (id int, statname string, statparam1 int, statparam2 int, statvalue float);"; cmd.ExecuteNonQuery();
}
catch
{
MessageBox.Show("Something has gone wrong with the database");
}
SQLiteParameter myparam1 = new SQLiteParameter();
SQLiteParameter myparam2 = new SQLiteParameter();
SQLiteParameter myparam3 = new SQLiteParameter();
SQLiteParameter myparam2_1 = new SQLiteParameter();
SQLiteParameter myparam2_2 = new SQLiteParameter();
SQLiteParameter myparam2_3 = new SQLiteParameter();
SQLiteParameter myparam2_4 = new SQLiteParameter();
SQLiteParameter myparam2_5 = new SQLiteParameter();
List <_song_profile> splist = new List <_song_profile>();
int s_count = 0;
PB1.Maximum = fl.Count;
PB1.Minimum = 0;
foreach (var f in fl)
{
//var transaction = db.BeginTransaction();
try
{
if (s_count % 50 == 0)
{
listBox1.Items.Clear();
listBox1.Items.Add("Items done: " + s_count + " of " + fl.Count);
}
var itm = new _song_profile(Path.GetFileName(f), f);
if (!itm.MakeSnaps((int)nUD2.Value, (int)nUD.Value, f, (int)nUD3.Value))
{
listBox1.Items.Add("![" + s_count + "]! Cannot make snapshots for " + itm.path);
s_count++;
continue;
}
//itm.CalcInternalCorrels();
itm.MakeDownscaledSnaps((int)nUD4.Value);
//itm.MakeDownscaledSnaps2((int)nUD4.Value);
var d = CalcAllStats(itm);
listBox1.Items.Add("[" + s_count + "] " + Path.GetFileName(itm.path));
cmd.CommandText = "BEGIN TRANSACTION;";
cmd.ExecuteNonQuery();
foreach (var di in d)
{
cmd.CommandText = "INSERT INTO STATS VALUES (@id, @statname, @statparam1, @statparam2, @statvalue);";
myparam2_1.ParameterName = "@id";
myparam2_2.ParameterName = "@statname";
myparam2_3.ParameterName = "@statparam1";
myparam2_4.ParameterName = "@statparam2";
myparam2_5.ParameterName = "@statvalue";
myparam2_1.Value = s_count;
myparam2_2.Value = di.Key;
myparam2_3.Value = 0;
myparam2_4.Value = 0;
myparam2_5.Value = di.Value;
cmd.Parameters.Add(myparam2_1); cmd.Parameters.Add(myparam2_2); cmd.Parameters.Add(myparam2_3);
cmd.Parameters.Add(myparam2_4); cmd.Parameters.Add(myparam2_5);
cmd.ExecuteNonQuery();
}
//splist.Add(itm);
cmd.CommandText = "INSERT INTO MAIN VALUES (@id, @name, @path);";
myparam1.ParameterName = "@id";
myparam2.ParameterName = "@name";
myparam3.ParameterName = "@path";
myparam1.Value = s_count;
myparam2.Value = itm.name;
myparam3.Value = itm.path;
cmd.Parameters.Add(myparam1);
cmd.Parameters.Add(myparam2);
cmd.Parameters.Add(myparam3);
cmd.ExecuteNonQuery();
PB1.Value = s_count;
Application.DoEvents();
//transaction.Commit();
cmd.CommandText = "COMMIT;";
cmd.ExecuteNonQuery();
s_count++;
}
catch
{
continue;
}
}
db.Close();
DBPostProcess("test.db");
/*
* var dct = new Dictionary<string, double>();
*
* for (int i = 0; i < splist.Count; i++)
* {
* dct.Add(splist[i].path, VecDiff(splist[0].scalar_stats.Values.ToList(), splist[i].scalar_stats.Values.ToList()));
* }
*
* dct.OrderBy(t => t.Value);
*
* dct.ToList().OrderBy(t => t.Value).ToList().ForEach(t => listBox1.Items.Add(t.Key + " ---> [" + t.Value + "]"));
*/
/*************************************************************************
* k-means++ clusterization
* INPUT PARAMETERS:
* XY - dataset, array [0..NPoints-1,0..NVars-1].
* NPoints - dataset size, NPoints>=K
* NVars - number of variables, NVars>=1
* K - desired number of clusters, K>=1
* Restarts - number of restarts, Restarts>=1
*
* OUTPUT PARAMETERS:
* Info - return code:
* -3, if task is degenerate (number of distinct points is
* less than K)
* -1, if incorrect NPoints/NFeatures/K/Restarts was passed
* 1, if subroutine finished successfully
* C - array[0..NVars-1,0..K-1].matrix whose columns store
* cluster's centers
* XYC - array[NPoints], which contains cluster indexes
*************************************************************************/
/*
* int NPoints = splist.Count;
* int NVars = splist[0].scalar_stats.Count;
* int K = (int)Math.Sqrt(NPoints);
* int Restarts = 5;
* int Info = 0;
*
* var C = new double[NVars, K];
* var XYC = new int[NPoints];
* var XY = new double[NPoints, NVars];
*
* var _XY = splist[0].scalar_stats.Values.ToArray();
* for (int i = 0; i < NPoints; i++)
* for (int j = 0; j < NVars; j++)
* XY[i, j] = splist[i].scalar_stats.Values.ToArray()[j];
*
* alglib.kmeansgenerate(
* XY,
* NPoints,
* NVars,
* K,
* Restarts,
* out Info,
* out C,
* out XYC);
*
* Info++;
*
* Application.DoEvents();
*
* for (int i = 0; i < splist.Count; i++)
* {
* listBox1.Items.Add(Path.GetFileName(splist[i].path) + " -> cluster # " + XYC[i]);
* }
*/
/*foreach (var itm in splist)
* {
* var s1 = new List<double>();
* var s2 = new List<double>();
* var s3 = new List<double>();
*
* foreach (var itm2 in splist)
* {
* /*listBox1.Items.Add(Path.GetFileName(itm.path) + " vs " + Path.GetFileName(itm2.path) +
* "cross_energy_correl = " + Correl(itm.snap_log10_energy, itm2.snap_log10_energy, itm.snap_log10_energy.Average(), itm2.snap_log10_energy.Average()) + " " +
* "cross_energy_diff = " + VecDiff(itm.snap_log10_energy, itm2.snap_log10_energy) + " " +
* "convoluted histogram value = " + HistogramConv(itm, itm2, 0.001)
* );*/
//s1.Add(Correl(itm.snap_log10_energy, itm2.snap_log10_energy, itm.snap_log10_energy.Average(), itm2.snap_log10_energy.Average()));
//s2.Add(1 / Math.Log10(VecDiff(itm.snap_log10_energy, itm2.snap_log10_energy)));
//s3.Add(HistogramConv(itm, itm2, 0.001));
/*s1.Add(0);
* s2.Add(0);
* s3.Add(0);
* }
*
* sp_CED.Add(s1);
* sp_CEC.Add(s2);
* sp_CHV.Add(s3);
* }*/
/*List<double> likelihood = new List<double>();
*
* for (int i = 0; i < splist.Count; i++)
* {
* double lh = 0;
* //listBox1.Items.Add(Path.GetFileName(splist[i].path) + ": " + (int)(100*sp_ASC[i]) + " " + (int)(100*sp_ASSC[i]) + " " + (int)(10*sp_ED[i]));
* for (int j = 0; j < splist.Count; j++)
* {
* lh =
* sp_ASC[i] * sp_ASC[j] +
* sp_ASSC[i] * sp_ASSC[j] +
* sp_ED[i] * sp_ED[j] +
* sp_CED[i][j] + sp_CEC[i][j] + sp_CHV[i][j]
* ;
* likelihood.Add(lh);
* listBox1.Items.Add((int)(100*lh) + " : " + Path.GetFileName(splist[i].path) + " " + Path.GetFileName(splist[j].path));
* }
* //listBox1.Items.Add("----->" + (int)(100*sp_CED[i][j]) + " " + (int)(100*sp_CEC[i][j]) + " " + (int)(100*sp_CHV[i][j]) + "; " + Path.GetFileName(splist[j].path));
*
* }*/
}