//**************************************************************************************
/// <summary>
/// Returns classifier prediction based on provided parameters.
/// </summary>
/// <param name="iParameters"></param>
public Prediction Predict(float[] iParameters)
{
if (iParameters is null)
{
throw new ArgumentNullException(nameof(iParameters));
}
WaitTillModelReady();
// Create instances object
var instances = new weka.core.Instances("Test", Attributes, 1);
instances.setClassIndex(instances.numAttributes() - 1);
// Create single instance
var instance = new weka.core.DenseInstance(instances.numAttributes() - 1);
instance.setDataset(instances);
// Fill instance with data
for (int i = 0; i < iParameters.Length; i++)
{
instance.setValue(i, iParameters[i]);
}
// Get prediction
double prediction = Model.classifyInstance(instance);
// Convert prediction to decision
return((Prediction)Enum.Parse(typeof(Prediction), instances.classAttribute().value((int)prediction)));
}
//returns the classification result as the lane number
private double getAlgorithmResult(float[] attributes)
{
//sets up the current environment as a new state 'Instance'
//labels as the same type as the training data, sets up the attribute values
//leaves the classifier blank
weka.core.Instance currentState = new weka.core.DenseInstance(6);
currentState.setDataset(insts);
currentState.setValue(0, (attributes[3] + 2));
currentState.setValue(1, attributes[0]);
currentState.setValue(2, attributes[1]);
currentState.setValue(3, attributes[2]);
currentState.setValue(4, totalTime);
currentState.setClassMissing();
//Debug.Log (currentState.ToString());
double result = j48Classifier.classifyInstance(currentState);
return(result);
}
private void Train(string str)
{
if (string.IsNullOrEmpty(str))
return;
m_cls = CreateClassifier(str);
if (m_cls == null)
{
MessageBox.Show("Can't Create Classifier!");
return;
}
var trainInstances = CreateCurrentInstances();
m_cls.buildClassifier(trainInstances);
// TEST
var data = CreateEmptyInstances();
StringBuilder sb = new StringBuilder();
if (m_cls is MLEA.IBatchClassifier)
{
MLEA.IBatchClassifier batchClassifier = m_cls as MLEA.IBatchClassifier;
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
var vals = new double[data.numAttributes()];
vals[0] = (double)i / XLEN;
vals[1] = (double)j / YLEN;
var instance = new weka.core.DenseInstance(1.0, vals);
data.add(instance);
instance.setDataset(data);
}
}
double[] ds = batchClassifier.classifyInstances(data);
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
double d = ds[i * XLEN + j];
if (m_enableLog)
{
string s = string.Format("{0}, {1}: {2}", data.instance(i * XLEN + j).value(0).ToString("N2"), data.instance(i * XLEN + j).value(1).ToString("N2"), d.ToString("N0"));
sb.AppendLine(s);
}
for (int ii = 0; ii < WXLEN / XLEN; ++ii)
for (int jj = 0; jj < WYLEN / YLEN; ++jj)
m_pictureBoxBitmap.SetPixel(i * WXLEN / XLEN + ii, j * WYLEN / YLEN + jj, GetValueColor((int)d, false));
}
}
}
else
{
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
var vals = new double[data.numAttributes()];
vals[0] = (double)i / XLEN;
vals[1] = (double)j / YLEN;
var instance = new weka.core.DenseInstance(1.0, vals);
data.add(instance);
instance.setDataset(data);
double d = m_cls.classifyInstance(instance);
if (m_enableLog)
{
string s = string.Format("{0}, {1}: {2}", vals[0].ToString("N2"), vals[1].ToString("N2"), d.ToString("N0"));
sb.AppendLine(s);
}
for (int ii = 0; ii < WXLEN / XLEN; ++ii)
for (int jj = 0; jj < WYLEN / YLEN; ++jj)
m_pictureBoxBitmap.SetPixel(i * WXLEN / XLEN + ii, j * WYLEN / YLEN + jj, GetValueColor((int)d, false));
}
}
}
draw_all_points();
this.Invoke(new Action(() =>
{
if (m_enableLog)
{
txtLog.AppendText(sb.ToString());
}
}));
if (m_enableEvaluation)
{
Test(trainInstances);
}
}
private void Train(string str)
{
if (string.IsNullOrEmpty(str))
{
return;
}
m_cls = CreateClassifier(str);
if (m_cls == null)
{
MessageBox.Show("Can't Create Classifier!");
return;
}
var trainInstances = CreateCurrentInstances();
m_cls.buildClassifier(trainInstances);
// TEST
var data = CreateEmptyInstances();
StringBuilder sb = new StringBuilder();
if (m_cls is MLEA.IBatchClassifier)
{
MLEA.IBatchClassifier batchClassifier = m_cls as MLEA.IBatchClassifier;
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
var vals = new double[data.numAttributes()];
vals[0] = (double)i / XLEN;
vals[1] = (double)j / YLEN;
var instance = new weka.core.DenseInstance(1.0, vals);
data.add(instance);
instance.setDataset(data);
}
}
double[] ds = batchClassifier.classifyInstances(data);
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
double d = ds[i * XLEN + j];
if (m_enableLog)
{
string s = string.Format("{0}, {1}: {2}", data.instance(i * XLEN + j).value(0).ToString("N2"), data.instance(i * XLEN + j).value(1).ToString("N2"), d.ToString("N0"));
sb.AppendLine(s);
}
for (int ii = 0; ii < WXLEN / XLEN; ++ii)
{
for (int jj = 0; jj < WYLEN / YLEN; ++jj)
{
m_pictureBoxBitmap.SetPixel(i * WXLEN / XLEN + ii, j * WYLEN / YLEN + jj, GetValueColor((int)d, false));
}
}
}
}
}
else
{
for (int i = 0; i < XLEN; i++)
{
for (int j = 0; j < YLEN; j++)
{
var vals = new double[data.numAttributes()];
vals[0] = (double)i / XLEN;
vals[1] = (double)j / YLEN;
var instance = new weka.core.DenseInstance(1.0, vals);
data.add(instance);
instance.setDataset(data);
double d = m_cls.classifyInstance(instance);
if (m_enableLog)
{
string s = string.Format("{0}, {1}: {2}", vals[0].ToString("N2"), vals[1].ToString("N2"), d.ToString("N0"));
sb.AppendLine(s);
}
for (int ii = 0; ii < WXLEN / XLEN; ++ii)
{
for (int jj = 0; jj < WYLEN / YLEN; ++jj)
{
m_pictureBoxBitmap.SetPixel(i * WXLEN / XLEN + ii, j * WYLEN / YLEN + jj, GetValueColor((int)d, false));
}
}
}
}
}
draw_all_points();
this.Invoke(new Action(() =>
{
if (m_enableLog)
{
txtLog.AppendText(sb.ToString());
}
}));
if (m_enableEvaluation)
{
Test(trainInstances);
}
}
//private void ConvertNorminalToString(string fileName)
//{
// List<string> list = new List<string>();
// using (System.IO.StreamReader sr = new System.IO.StreamReader(fileName))
// {
// while (true)
// {
// if (sr.EndOfStream)
// break;
// string s = sr.ReadLine();
// if (string.IsNullOrEmpty(s))
// continue;
// int idx = s.IndexOf(' ');
// string c = idx == -1 ? s : s.Substring(0, idx);
// if (Convert.ToDouble(c) == 0)
// {
// list.Add("-1.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else if (Convert.ToDouble(c) == 1)
// {
// list.Add("0.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else if (Convert.ToDouble(c) == 2)
// {
// list.Add("+1.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else
// {
// list.Add(s);
// }
// }
// }
// using (System.IO.StreamWriter sw = new System.IO.StreamWriter(fileName))
// {
// foreach (string s in list)
// {
// sw.WriteLine(s);
// }
// }
//}
//private Random m_randomGenerator;
private void AddInstancesAccordWeight(Instances instances)
{
// 0, 2
double[] weights = MincostLiblinearClassifier.GetCount(instances);
if (weights == null)
return;
double c = m_tp / m_sl;
if (c == 1 && weights[0] == weights[1])
return;
int n = 0;
int toCopyClass = 0;
if (c >= 1)
{
int shouldWeight1 = (int)(c * weights[1]);
n = (int)(shouldWeight1 - weights[1]);
toCopyClass = 2;
}
else
{
int shouldShouldWeight0 = (int)(1 / c * weights[0]);
n = (int)(weights[1] - weights[0]);
toCopyClass = 0;
}
//m_randomGenerator = new Random((int)System.DateTime.Now.Ticks);
List<Instance> copyInstances = new List<Instance>();
for (int i = 0; i < instances.numInstances(); ++i)
{
if (instances.instance(i).classValue() == toCopyClass)
{
copyInstances.Add(instances.instance(i));
}
}
int nAll = n / copyInstances.Count;
for (int j = 0; j < nAll; ++j)
{
for (int i = 0; i < copyInstances.Count; ++i)
{
Instance newInstance = new weka.core.DenseInstance(copyInstances[i]);
instances.add(newInstance);
newInstance.setDataset(instances);
}
}
//for (int j = 0; j < n - nAll * copyInstances.Count; ++j)
//{
// int idx = (int)(m_randomGenerator.NextDouble() * copyInstances.Count);
// idx = Math.Min(idx, copyInstances.Count - 1);
// Instance newInstance = new weka.core.DenseInstance(copyInstances[idx]);
// instances.add(newInstance);
// newInstance.setDataset(instances);
//}
if (n - nAll * copyInstances.Count > 0)
{
Instance avgInstance = new weka.core.DenseInstance(instances.numAttributes());
for (int i = 0; i < avgInstance.numAttributes(); ++i)
{
double sum = 0;
for (int j = 0; j < copyInstances.Count; ++j)
{
sum += copyInstances[j].value(i);
}
avgInstance.setValue(i, sum / copyInstances.Count);
}
for (int j = 0; j < n - nAll * copyInstances.Count; ++j)
{
Instance newInstance = new weka.core.DenseInstance(avgInstance);
instances.add(newInstance);
}
}
}
//private void ConvertNorminalToString(string fileName)
//{
// List<string> list = new List<string>();
// using (System.IO.StreamReader sr = new System.IO.StreamReader(fileName))
// {
// while (true)
// {
// if (sr.EndOfStream)
// break;
// string s = sr.ReadLine();
// if (string.IsNullOrEmpty(s))
// continue;
// int idx = s.IndexOf(' ');
// string c = idx == -1 ? s : s.Substring(0, idx);
// if (Convert.ToDouble(c) == 0)
// {
// list.Add("-1.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else if (Convert.ToDouble(c) == 1)
// {
// list.Add("0.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else if (Convert.ToDouble(c) == 2)
// {
// list.Add("+1.0 " + (idx == -1 ? string.Empty : s.Substring(idx + 1)));
// }
// else
// {
// list.Add(s);
// }
// }
// }
// using (System.IO.StreamWriter sw = new System.IO.StreamWriter(fileName))
// {
// foreach (string s in list)
// {
// sw.WriteLine(s);
// }
// }
//}
//private Random m_randomGenerator;
private void AddInstancesAccordWeight(Instances instances)
{
// 0, 2
double[] weights = MincostLiblinearClassifier.GetCount(instances);
if (weights == null)
{
return;
}
double c = m_tp / m_sl;
if (c == 1 && weights[0] == weights[1])
{
return;
}
int n = 0;
int toCopyClass = 0;
if (c >= 1)
{
int shouldWeight1 = (int)(c * weights[1]);
n = (int)(shouldWeight1 - weights[1]);
toCopyClass = 2;
}
else
{
int shouldShouldWeight0 = (int)(1 / c * weights[0]);
n = (int)(weights[1] - weights[0]);
toCopyClass = 0;
}
//m_randomGenerator = new Random((int)System.DateTime.Now.Ticks);
List <Instance> copyInstances = new List <Instance>();
for (int i = 0; i < instances.numInstances(); ++i)
{
if (instances.instance(i).classValue() == toCopyClass)
{
copyInstances.Add(instances.instance(i));
}
}
int nAll = n / copyInstances.Count;
for (int j = 0; j < nAll; ++j)
{
for (int i = 0; i < copyInstances.Count; ++i)
{
Instance newInstance = new weka.core.DenseInstance(copyInstances[i]);
instances.add(newInstance);
newInstance.setDataset(instances);
}
}
//for (int j = 0; j < n - nAll * copyInstances.Count; ++j)
//{
// int idx = (int)(m_randomGenerator.NextDouble() * copyInstances.Count);
// idx = Math.Min(idx, copyInstances.Count - 1);
// Instance newInstance = new weka.core.DenseInstance(copyInstances[idx]);
// instances.add(newInstance);
// newInstance.setDataset(instances);
//}
if (n - nAll * copyInstances.Count > 0)
{
Instance avgInstance = new weka.core.DenseInstance(instances.numAttributes());
for (int i = 0; i < avgInstance.numAttributes(); ++i)
{
double sum = 0;
for (int j = 0; j < copyInstances.Count; ++j)
{
sum += copyInstances[j].value(i);
}
avgInstance.setValue(i, sum / copyInstances.Count);
}
for (int j = 0; j < n - nAll * copyInstances.Count; ++j)
{
Instance newInstance = new weka.core.DenseInstance(avgInstance);
instances.add(newInstance);
}
}
}
