public void SanFranciscoCrimeSVMClassificationDataSetTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var crimes = dataSetLoader.SelectCrimes();
Kernel kernel = new GaussianKernel(0.9);
SVMClassifier svmClassifier =
new SVMClassifier(crimes, kernel);
svmClassifier.Train();
var crimeTests = dataSetLoader.SelectCrimes();
var trueCounter = 0;
var counter = 0;
foreach (var item in crimeTests)
{
var outputValue = svmClassifier.Classify(item.Item1);
if (outputValue == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format("Data {0} - True {1} Verhältnis: {2}",
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
public void SanFranciscoCrimeClassificationTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var crimes = dataSetLoader.SelectCrimes();
RandomForestClassifier decisionTreeClassifier =
new RandomForestClassifier(crimes, new ShannonEntropySplitter(), new BaggingAlgorithmus(1));
decisionTreeClassifier.Train();
var crimeTests = dataSetLoader.SelectCrimes();
var trueCounter = 0;
var counter = 0;
foreach (var item in crimeTests)
{
var outputValue = decisionTreeClassifier.Classify(item.Item1);
if (outputValue == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format("Data {0} - True {1} Verhältnis: {2}",
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
public void SanFranciscoCrimeClassificationTestDataSetTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var crimes = dataSetLoader.SelectNeuronalNetworkCrimes();
//DecisionTreeClassifier decisionTreeClassifier =
//new DecisionTreeClassifier(crimes, new ShannonEntropySplitter());
NeuronalNetworkClassifier neuronalNetworkClassifier =
new NeuronalNetworkClassifier(crimes, 2, 38, 2, 5000, 0.1);
//Kernel kernel = new LinearKernel();
//NaiveBayesClassifier naiveBayes =
// new NaiveBayesClassifier(crimes);
neuronalNetworkClassifier.Train();
var crimeTests = dataSetLoader.SelectCrimes();
var trueCounter = 0;
var counter = 0;
foreach (var item in crimeTests)
{
var outputValue = neuronalNetworkClassifier.ClassifiyMultibleResultValue(item.Item1);
var resultString = String.Empty;
double maxValue = 0.0;
int innerCounter = 0;
int maxItem = 0;
foreach (var value in outputValue)
{
if (value > maxValue)
{
maxValue = value;
maxItem = innerCounter;
}
innerCounter++;
}
if (maxItem == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, maxItem, (maxItem == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format("Data {0} - True {1} Verhältnis: {2}",
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}