static void test_knn(string fn)
{
//string fn = @"C:\projects\local\GradeChecker\GradeChecker\bin\Debug\test.json";
try
{
var jss = new System.Web.Script.Serialization.JavaScriptSerializer();
List <Dictionary <string, object> > datas = jss.Deserialize <List <Dictionary <string, object> > >(System.IO.File.ReadAllText(fn));
string[] keys = testMQ.Properties.Resources.keys.Split(new string[] { Environment.NewLine }, StringSplitOptions.RemoveEmptyEntries);
Matrix <float> data;
Matrix <int> response;
load_data(datas.ToArray(), keys, out data, out response);
using (KNearest model = new KNearest())
{
model.Load("knn.xml");
for (int i = 0; i < data.Rows; i++)
{
float r = model.Predict(data.GetRow(i));
Dictionary <string, object> d = datas[i];
System.Console.WriteLine($"imei={d["imei"]}, VZW={d["VZW"]}, FD={grade_level[(int)r]}");
}
}
}
catch (Exception) { }
}
static void test_knn_1()
{
//Regex r = new Regex(@"^Color Temp: (\d+) K - Lux: (\d+) - R: (\d+) G: (\d+) B: (\d+) Rr: (\d+) Gr: (\d+) Br: (\d+) C: (\d+)\s*$");
//string[] lines = System.IO.File.ReadAllLines(@"data\test.txt");
//foreach(string s in lines)
//{
// Match m = r.Match(s);
// if (m.Success)
// {
// if (m.Groups.Count > 9)
// {
// }
// }
//}
string s = "knn.xml";
if (System.IO.File.Exists(s))
{
using (KNearest knn = new KNearest())
{
knn.Load(s);
//bool ok = knn.Train(data, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, response);
Matrix <float> sample;
test_data(out sample);
float r = knn.Predict(sample);
}
}
else
{
Matrix <float> data;
Matrix <float> response;
ReadMushroomData(out data, out response);
//
using (KNearest knn = new KNearest())
{
knn.DefaultK = 3;
knn.IsClassifier = true;
bool ok = knn.Train(data, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, response);
if (ok)
{
knn.Save("knn.xml");
//int cols = data.Cols;
//Matrix<float> sample = new Matrix<float>(1, cols);
Matrix <float> sample;
test_data(out sample);
float r = knn.Predict(sample);
}
}
}
}
public void SaveLoadTest()
{
float[] trainFeaturesData =
{
2, 2, 2, 2,
3, 3, 3, 3,
4, 4, 4, 4,
5, 5, 5, 5,
6, 6, 6, 6,
7, 7, 7, 7
};
var trainFeatures = new Mat(6, 4, MatType.CV_32F, trainFeaturesData);
int[] trainLabelsData = { 2, 3, 4, 5, 6, 7 };
var trainLabels = new Mat(1, 6, MatType.CV_32S, trainLabelsData);
const string fileName = "knearest.yml";
if (File.Exists(fileName))
{
File.Delete(fileName);
}
using (var model = KNearest.Create())
{
model.Train(trainFeatures, SampleTypes.RowSample, trainLabels);
model.Save(fileName);
}
Assert.True(File.Exists(fileName));
string content = File.ReadAllText(fileName);
//Console.WriteLine(content);
// Assert.DoesNotThrow
using (var model2 = KNearest.Load(fileName))
{
GC.KeepAlive(model2);
}
using (var model2 = KNearest.LoadFromString(content))
{
GC.KeepAlive(model2);
}
}
