//public static string GetRoom(KNearestNeighbors knn, double[] coordinates)
//{
// // After the algorithm has been created, we can classify a new instance:
// Console.WriteLine("Room: " + getRoomname(knn.Decide(coordinates)));
// return getRoomname(knn.Decide(coordinates));
//}
public static Dictionary <string, double> getOptions(double[] coordinates, KNearestNeighbors Knn, Dictionary <int, string> labelMap)
{
Dictionary <string, int> optionDict = new Dictionary <string, int>();
int[] list = labelMap.Keys.ToArray();
int length = Knn.GetNearestNeighbors(coordinates, out list).Length;
foreach (double[] g in Knn.GetNearestNeighbors(coordinates, out list))
{
string roomname = getRoomname(Knn.Decide(g), labelMap);
if (optionDict.Keys.Contains(roomname))
{
optionDict[roomname] += 1;
}
else
{
optionDict.Add(roomname, 1);
}
}
Dictionary <string, double> options = new Dictionary <string, double>();
foreach (KeyValuePair <string, int> option in optionDict)
{
double factor = (double)1 / (double)length;
double value = option.Value * factor;
options.Add(option.Key, value);
}
return(options);
}
private void computebtn_Click(object sender, EventArgs e)
{
int[] output;
knnTxt.Text = "";
if (comboBox2.SelectedItem.ToString() == "Levenshtein")
{
var answer = knnStr.GetNearestNeighbors(tesTxt.Text.ToUpper(), out output);
for (int i = 0; i < answer.Length; ++i)
{
knnTxt.Text += answer[i] + "," + inverseClassList[output[i]] + Environment.NewLine;
}
int classInt = knnStr.Compute(tesTxt.Text.ToUpper());
label5.Text = inverseClassList[classInt];
}
else
{
var answer = knn.GetNearestNeighbors(wv.transform(tesTxt.Text.ToUpper(), comboBox4.SelectedItem.ToString()), out output);
for (int i = 0; i < answer.Length; ++i)
{
knnTxt.Text += wv.transformInverse(answer[i], comboBox4.SelectedItem.ToString()) + "," + inverseClassList[output[i]] + Environment.NewLine;
}
int classInt = knn.Compute(wv.transform(tesTxt.Text.ToUpper(), comboBox4.SelectedItem.ToString()));
label5.Text = inverseClassList[classInt];
//answer = knn.Compute(wv.transform(GetString(fields, " "), comboBox4.SelectedItem.ToString()));
}
}
public double Predict(double[] query)
{
int[] nearest;
_predictor.GetNearestNeighbors(query, out nearest);
return(InverseDistanceWeightedAverage(nearest));
}
public int[] Predict(double[] input)
{
_loadedKnn.K = _k > 0 ? _k : 1;
int[] result = new int[_k];
var knnMatrix = _loadedKnn.GetNearestNeighbors(input, out result);
return(result);
}
public static List <string> getOptions(double[] coordinates, KNearestNeighbors knn)
{
List <string> options = new List <string>();
Dictionary <int, string> labelMap = Fingerprinting.ReadLabelMap();
int[] list = labelMap.Keys.ToArray();
foreach (double[] g in knn.GetNearestNeighbors(coordinates, out list))
{
options.Add(getRoomname(knn.Decide(g)));
}
return(options);
}
public int[] getMatchingClusters(Descriptor descriptor)
{
// get the nearest 3 classes
int[] labels;
double[][] point = knn.GetNearestNeighbors(descriptor.getArray(), out labels);
double[] distance = new double[GlobalConstant.numberOfKNearestNeighbour];
for (int i = 0; i < GlobalConstant.numberOfKNearestNeighbour; i++)
{
distance[i] = Accord.Math.Distance.Euclidean(point[i], descriptor.getArray());
//Console.WriteLine(distance[i]);
}
// sort 2 arrays related to each other
Array.Sort(distance, labels);
return(labels);
}
private List<String> GetSimilaresDatabaseKNN(List<Double> descriptoresEntrada)
{
ModeloSimilitudEntities db=new ModeloSimilitudEntities();
Double[] vectorEntrada=descriptoresEntrada.ToArray();
vectorEntrada = Normalizar(vectorEntrada);
Double[][] matriz = csvtoMatrix("descriptoresNormalizados");
int[] pertenencia=new int[matriz.Length];
for(int i=0;i<pertenencia.Length;i++){
pertenencia[i]=1;
}
pertenencia[23] = 2;
KNearestNeighbors knn = new KNearestNeighbors(k: 10, inputs: matriz, outputs: pertenencia);
int answer = knn.Compute(vectorEntrada);
int[] a = new int[1]; a[0] = 1;
Double[][] cercanos = knn.GetNearestNeighbors(vectorEntrada,out a);
List<String> listaSimilares = new List<String>();
List<canciones> dbcanciones = db.canciones.ToList();
for (int i = 0; i < matriz.Length; i++)
{
if (cercanos.Contains(matriz[i]))
{
listaSimilares.Add(dbcanciones[i].id_spotify.Substring(14));
}
}
//string select="select * from canciones where energy={0} and liveness={1} and tempo={2} and speechiness={3} and acousticness={4} and loudness={5} and valence={6} and danceability={7} and instrumentalness={8} and key={9}";
//string select2 = "select * from canciones";
//for(int j=0;j<cercanos.Length;j++){
// object[] parameters = new object[10];
// for (int i = 0; i < 10; i++)
// {
// SqlParameter param = new SqlParameter("i", cercanos[j][i]);
// parameters[i] = cercanos[j][i];
// }
// var stores = db.Database.SqlQuery<canciones>(select, parameters).ToList();
// listaSimilares.Add(stores[0].id_spotify);
//}
return listaSimilares;
}
private int OptimizeK()
{
Tuple <KNearestNeighbors, double> bestModel = null;
for (int k = 50; k <= 200; k++)
{
var crossvalidation = new CrossValidation <KNearestNeighbors>(_input.Length, 5);
crossvalidation.Fitting = delegate(int fold, int[] indicesTrain, int[] indicesValidation)
{
var trainingInputs = _input.Submatrix(indicesTrain);
var trainingOutputs = _output.Submatrix(indicesTrain);
var validationInputs = _input.Submatrix(indicesValidation);
var validationOutputs = _output.Submatrix(indicesValidation);
var predictor = new KNearestNeighbors(k, _classes, trainingInputs, trainingOutputs);
// Create a training algorithm and learn the training data
var trainingError = 0.0;
for (int i = 0; i < trainingInputs.Length; i++)
{
int[] nearest;
predictor.GetNearestNeighbors(trainingInputs[i], out nearest);
var prediction = InverseDistanceWeightedAverage(nearest);
if (prediction > 0 && trainingOutputs[i] > 0 ||
prediction < 0 && trainingOutputs[i] < 0 ||
prediction.Equals(trainingOutputs[i]))
{
continue;
}
trainingError++;
}
double validationError = 0.0;
for (int i = 0; i < validationInputs.Length; i++)
{
int[] nearest;
predictor.GetNearestNeighbors(validationInputs[i], out nearest);
var prediction = InverseDistanceWeightedAverage(nearest);
if (prediction > 0 && validationOutputs[i] > 0 ||
prediction < 0 && validationOutputs[i] < 0 ||
prediction.Equals(validationOutputs[i]))
{
continue;
}
validationError++;
}
trainingError /= trainingInputs.Length;
validationError /= validationInputs.Length;
return(new CrossValidationValues <KNearestNeighbors>(predictor, trainingError, validationError));
};
var result = crossvalidation.Compute();
//var minError = result.Models.Select(y => y.ValidationValue).Min();
var minError = result.Models.Select(y => Math.Sqrt(Math.Pow(y.TrainingValue + y.ValidationValue, 2.0))).Min();
if (bestModel == null || minError < bestModel.Item2)
{
var bestFit = result.Models.FirstOrDefault(x => minError.Equals(x.ValidationValue))?.Model;
bestModel = bestFit == null ? bestModel : new Tuple <KNearestNeighbors, double>(bestFit, minError);
}
}
return(bestModel?.Item1.K ?? 80);
}