public override PointF MatchLocation(Fingerprint value, Match_Strategy strategy, double[] args)
{
switch(strategy)
{
case Match_Strategy.Single:
{
double min_dist = double.MaxValue;
PointF best_loc = new PointF();
foreach (KeyValuePair<PointF, Fingerprint> kvp in this.loc_fingerprint_dict)
{
double dist = (kvp.Value as Fingerprint_S).CalcSimilarity(value as Fingerprint_S);
if (dist < min_dist)
{
min_dist = dist;
best_loc = kvp.Key;
}
}
return best_loc;
}
case Match_Strategy.KNN:
{
if (args == null || args.Length < 1)
throw new Exception("KNN匹配方法需要额外一个参数");
Dictionary<PointF, double> dists = new Dictionary<PointF, double>();
foreach (KeyValuePair<PointF, Fingerprint> kvp in this.loc_fingerprint_dict)
{
double dist = (kvp.Value as Fingerprint_S).CalcSimilarity(value as Fingerprint_S);
dists[kvp.Key] = dist;
}
IOrderedEnumerable<KeyValuePair<PointF, double>> sorted = dists.OrderBy(x => x.Value);
int k = (int)args[0];
int i = 0;
KeyValuePair<PointF, double>[] NNs = new KeyValuePair<PointF, double>[k];
double sumr = 0;
foreach(KeyValuePair<PointF, double> kvp in sorted)
{
if (i >= k)
break;
NNs[i] = kvp;
sumr += 1 / kvp.Value;
i++;
}
double mean_x = 0;
double mean_y = 0;
foreach(KeyValuePair<PointF, double> kvp in NNs)
{
mean_x += kvp.Key.X * ((1 / kvp.Value) / sumr);
mean_y += kvp.Key.Y * ((1 / kvp.Value) / sumr);
}
return new PointF((float)mean_x, (float)mean_y);
}
}
return new PointF();
}
//public override double CalcSimilarity(Fingerprint that)
//{
// Fingerprint_S th = that as Fingerprint_S;
// if (this.fingerprint.Length != th.fingerprint.Length)
// return double.MaxValue;
// List<double> tmp_this = new List<double>();
// List<double> tmp_that = new List<double>();
// for (int i = 0; i < this.fingerprint.Length; i++)
// {
// tmp_this.Add(this.fingerprint[i].Strength);
// tmp_that.Add(th.fingerprint[i].Strength);
// }
// double[] this_s = tmp_this.ToArray();
// double[] that_s = tmp_that.ToArray();
// double r_s = new MathUtils.MathUtility().corrcoef(this_s, that_s);
// return 1 / (r_s + 1);
//}
public override double CalcSimilarity(Fingerprint that)
{
Fingerprint_S th = that as Fingerprint_S;
if (this.fingerprint.Length != th.fingerprint.Length)
return double.MaxValue;
double s_rmse = 0;
for (int i = 0; i < this.fingerprint.Length; i++)
{
s_rmse += Math.Pow(this.fingerprint[i].Strength - th.fingerprint[i].Strength, 2);
}
s_rmse = Math.Sqrt(s_rmse / this.fingerprint.Length);
return s_rmse * 1e6;
}
public override Fingerprint Mean(Fingerprint[] prints)
{
int fp_len = prints.Length;
int rec_len = (prints[0] as Fingerprint_S).fingerprint.Length;
List<S> lst = new List<S>();
for (int i = 0; i < rec_len; i++)
{
double sum_s = 0;
foreach (Fingerprint_S print in prints)
{
sum_s += print.fingerprint[i].Strength;
}
lst.Add(new S(sum_s / fp_len));
}
return new Fingerprint_S(lst.ToArray());
}
private void GenerateFingerprintDataBase(object sender, DoWorkEventArgs e)
{
int avg_num = 5;
object[] paras = (object[])e.Argument;
int ray_num = (int)paras[0];
Transmitter.EMIT_OPTION emit_option = (Transmitter.EMIT_OPTION)paras[1];
Signal_Type signal_type = (Signal_Type)paras[2];
Noise_Type noise_type = (Noise_Type)paras[3];
double[] noise_params = (double[])paras[4];
int sample_cnt = (int)paras[5];
string file_name = (string)paras[6];
int total_pt_num = this.viz_rps.Map.GridPoints.Length;
Transmitter tr = null;
foreach (Transmitter trans in this.viz_rps.Map.Transmitters)
tr = trans;
if(tr == null)
{
e.Result = false;
MessageBox.Show("地图上需要设置一个信号源以作为数据库所用信号源的参数参考。");
return;
}
Dictionary<PointF, Dictionary<Fingerprint_Name, Fingerprint>> dict = new Dictionary<PointF, Dictionary<Fingerprint_Name, Fingerprint>>();
for(int i = 0; i < total_pt_num; i++)
{
int cnt = 0;
this.viz_rps.Map.ClearTransmitters();
Point loc = this.viz_rps.Map.GridPoints[i];
tr.Location = loc;
this.viz_rps.Map.AddTransmitter(tr);
Dictionary<Fingerprint_Name, Fingerprint[]> fps = new Dictionary<Fingerprint_Name,Fingerprint[]>();
for (int j = 0; j < Enum.GetNames(new Fingerprint_Name().GetType()).Length; j++)
fps[(Fingerprint_Name)j] = new Fingerprint[avg_num];
while (cnt < avg_num)
{
Dictionary<Fingerprint_Name, Fingerprint> tmp = this.viz_rps.Map.RunSingleSimulation(ray_num, emit_option, signal_type,
noise_type, noise_params, sample_cnt);
foreach (KeyValuePair<Fingerprint_Name, Fingerprint> kvp in tmp)
fps[kvp.Key][cnt] = kvp.Value;
cnt++;
this.bgwGenFDB.ReportProgress((int)((avg_num * i + cnt) / (double)(avg_num * total_pt_num) * 100));
System.Threading.Thread.Sleep(200);
}
Dictionary<Fingerprint_Name, Fingerprint> tmp_mean = new Dictionary<Fingerprint_Name, Fingerprint>();
foreach (KeyValuePair<Fingerprint_Name, Fingerprint[]> kvp in fps)
{
switch (kvp.Key)
{
case Fingerprint_Name.SA:
tmp_mean.Add(kvp.Key, new Fingerprint_SA().Mean(kvp.Value));
break;
case Fingerprint_Name.S:
tmp_mean.Add(kvp.Key, new Fingerprint_S().Mean(kvp.Value));;
break;
default:
tmp_mean.Add(kvp.Key, new Fingerprint().Mean(kvp.Value));;
break;
}
}
dict.Add(loc, tmp_mean);
}
FingerprintDataBase fdb = new FingerprintDataBase(dict, this.viz_rps.Map);
System.Runtime.Serialization.IFormatter formatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter();
FileStream fs = new FileStream(file_name, FileMode.Create, FileAccess.Write, FileShare.None);
formatter.Serialize(fs, fdb);
fs.Close();
e.Result = true;
}
/// <summary>
/// 计算多个指纹的平均值
/// </summary>
/// <param name="prints"></param>
/// <returns></returns>
public virtual Fingerprint Mean(Fingerprint[] prints) { return null; }
/// <summary>
/// 计算与另一个指纹的相似度
/// </summary>
/// <param name="that"></param>
/// <returns></returns>
public virtual double CalcSimilarity(Fingerprint that) { return double.MaxValue; }
/// <summary>
/// 返回最匹配的位置坐标
/// </summary>
/// <param name="value"></param>
public PointF MatchLocation(Fingerprint value, Match_Strategy strategy, double[] args)
{
switch (strategy)
{
case Match_Strategy.Single:
{
double min_dist = double.PositiveInfinity;
PointF best_loc = new PointF();
foreach (KeyValuePair<PointF, Dictionary<Fingerprint_Name, Fingerprint>> kvp in this.loc_fingerprint_dict)
{
double dist = double.PositiveInfinity;
switch(value.Name)
{
case Fingerprint_Name.SA:
dist = (kvp.Value[value.Name] as Fingerprint_SA).CalcSimilarity(value as Fingerprint_SA);
break;
case Fingerprint_Name.S:
dist = (kvp.Value[value.Name] as Fingerprint_S).CalcSimilarity(value as Fingerprint_S);
break;
}
if (dist < min_dist)
{
min_dist = dist;
best_loc = kvp.Key;
}
}
return best_loc;
}
case Match_Strategy.KNN:
{
if (args == null || args.Length < 1)
throw new Exception("KNN匹配方法需要额外一个参数");
Dictionary<PointF, double> dists = new Dictionary<PointF, double>();
foreach (KeyValuePair<PointF, Dictionary<Fingerprint_Name, Fingerprint>> kvp in this.loc_fingerprint_dict)
{
double dist = double.PositiveInfinity;
switch(value.Name)
{
case Fingerprint_Name.SA:
dist = (kvp.Value[value.Name] as Fingerprint_SA).CalcSimilarity(value as Fingerprint_SA);
break;
case Fingerprint_Name.S:
dist = (kvp.Value[value.Name] as Fingerprint_S).CalcSimilarity(value as Fingerprint_S);
break;
}
dists[kvp.Key] = dist;
}
IOrderedEnumerable<KeyValuePair<PointF, double>> sorted = dists.OrderBy(x => x.Value);
int k = (int)args[0];
int i = 0;
KeyValuePair<PointF, double>[] NNs = new KeyValuePair<PointF, double>[k];
double sumr = 0;
foreach (KeyValuePair<PointF, double> kvp in sorted)
{
if (double.IsNaN(kvp.Value))
continue;
if (i >= k)
break;
NNs[i] = kvp;
sumr += 1 / kvp.Value;
i++;
}
double mean_x = 0;
double mean_y = 0;
foreach (KeyValuePair<PointF, double> kvp in NNs)
{
mean_x += kvp.Key.X * ((1 / kvp.Value) / sumr);
mean_y += kvp.Key.Y * ((1 / kvp.Value) / sumr);
}
return new PointF((float)mean_x, (float)mean_y);
}
case Match_Strategy.KNN_HC:
{
if (args == null || args.Length < 1)
throw new Exception("KNN匹配方法需要额外一个参数");
Dictionary<PointF, double> dists = new Dictionary<PointF, double>();
foreach (KeyValuePair<PointF, Dictionary<Fingerprint_Name, Fingerprint>> kvp in this.loc_fingerprint_dict)
{
double dist = double.PositiveInfinity;
switch (value.Name)
{
case Fingerprint_Name.SA:
dist = (kvp.Value[value.Name] as Fingerprint_SA).CalcSimilarity(value as Fingerprint_SA);
break;
case Fingerprint_Name.S:
dist = (kvp.Value[value.Name] as Fingerprint_S).CalcSimilarity(value as Fingerprint_S);
break;
}
dists[kvp.Key] = dist;
}
IOrderedEnumerable<KeyValuePair<PointF, double>> sorted = dists.OrderBy(x => x.Value);
int k = (int)args[0];
int i = 0;
List<MathUtils.MathUtility.Cluster> clusters = new List<MathUtils.MathUtility.Cluster>();
Dictionary<PointF, double> NNs = new Dictionary<PointF,double>();
foreach (KeyValuePair<PointF, double> kvp in sorted)
{
if (double.IsNaN(kvp.Value))
continue;
if (i >= k)
break;
NNs.Add(kvp.Key, kvp.Value);
clusters.Add(new MathUtils.MathUtility.Cluster(new List<PointF>() { kvp.Key }));
i++;
}
new MathUtils.MathUtility().HierachicalClustering_InPlace(clusters, 2);
MathUtils.MathUtility.Cluster cluster = clusters.OrderByDescending(x => x.PointNum).First();
double sum_r = 0;
double wx_sum = 0;
double wy_sum = 0;
foreach(PointF pt in cluster.point_list)
{
sum_r += 1 / NNs[pt];
wx_sum += pt.X * 1 / NNs[pt];
wy_sum += pt.Y * 1 / NNs[pt];
}
return new PointF((float)(wx_sum / sum_r), (float)(wy_sum / sum_r));
}
}
return new PointF();
}
//public override double CalcSimilarity(Fingerprint that)
//{
// Fingerprint_SA th = that as Fingerprint_SA;
// if (this.fingerprint.Length != th.fingerprint.Length)
// return double.MaxValue;
// List<double> tmp_this = new List<double>();
// List<double> tmp_that = new List<double>();
// for (int i = 0; i < this.fingerprint.Length; i++)
// {
// tmp_this.Add(this.fingerprint[i].Strength);
// tmp_that.Add(th.fingerprint[i].Strength);
// }
// double[] this_s = tmp_this.ToArray();
// double[] that_s = tmp_that.ToArray();
// double r_s = new MathUtils.MathUtility().corrcoef(this_s, that_s);
// tmp_this.Clear();
// tmp_that.Clear();
// for (int i = 0; i < this.fingerprint.Length; i++)
// {
// tmp_this.Add(this.fingerprint[i].AOA / 180 * Math.PI);
// tmp_that.Add(th.fingerprint[i].AOA / 180 * Math.PI);
// }
// double[] this_a = tmp_this.ToArray();
// double[] that_a = tmp_that.ToArray();
// double r_a = new MathUtils.MathUtility().corrcoef(this_a, that_a);
// return 1 / ((r_s + r_a) / 2 + 1);
//}
//public override double CalcSimilarity(Fingerprint that)
//{
// Fingerprint_SA th = that as Fingerprint_SA;
// if (this.fingerprint.Length != th.fingerprint.Length)
// return double.MaxValue;
// double s_rmse = 0;
// double aoa_rmse = 0;
// for (int i = 0; i < this.fingerprint.Length; i++)
// {
// s_rmse += Math.Pow(this.fingerprint[i].Strength - th.fingerprint[i].Strength, 2);
// aoa_rmse += Math.Pow(new MathUtils.MathUtility().diff_degree(this.fingerprint[i].AOA, th.fingerprint[i].AOA), 2);
// }
// s_rmse = Math.Sqrt(s_rmse / this.fingerprint.Length);
// aoa_rmse = Math.Sqrt(aoa_rmse / this.fingerprint.Length) / 180 * Math.PI;
// return s_rmse * 1e2 + aoa_rmse;
//}
public override double CalcSimilarity(Fingerprint that)
{
Fingerprint_SA th = that as Fingerprint_SA;
if (this.fingerprint.Length != th.fingerprint.Length)
return double.MaxValue;
double total_s_this = 0;
double total_s_that = 0;
for (int i = 0; i < this.fingerprint.Length; i++)
{
total_s_this += this.fingerprint[i].Strength;
total_s_that += th.fingerprint[i].Strength;
}
double rmse = 0;
for (int i = 0; i < this.fingerprint.Length; i++)
{
double this_c = this.fingerprint[i].Strength * this.fingerprint[i].AOA / total_s_this;
double that_c = th.fingerprint[i].Strength * th.fingerprint[i].AOA / total_s_that;
rmse += Math.Pow(new MathUtils.MathUtility().diff_degree(this_c, that_c), 2);
}
rmse = Math.Sqrt(rmse / this.fingerprint.Length);
return rmse;
}
public override Fingerprint Mean(Fingerprint[] prints)
{
int fp_len = prints.Length;
int rec_len = (prints[0] as Fingerprint_SA).fingerprint.Length;
List<SA> lst = new List<SA>();
for (int i = 0; i < rec_len; i++)
{
double sum_s = 0;
double sum_a = 0;
foreach (Fingerprint_SA print in prints)
{
sum_s += print.fingerprint[i].Strength;
sum_a += double.IsInfinity(print.fingerprint[i].AOA) ? 0 : print.fingerprint[i].AOA;
}
double mean_s = sum_s / fp_len;
double mean_a = sum_a == 0 ? double.NegativeInfinity : sum_a / fp_len;
lst.Add(new SA(mean_s, mean_a));
}
return new Fingerprint_SA(lst.ToArray());
}