public void ClassifyTest()
{
String text = "雁塔王家村民房凌晨突发火灾 4人死亡13人受伤";
int result = BayesClassifier.Classify(text);
Console.WriteLine(result);
}
static void Main(string[] args)
{
DataTable table = new DataTable();
table.Columns.Add("Sex");
table.Columns.Add("Height", typeof(double));
table.Columns.Add("Weight", typeof(double));
table.Columns.Add("FootSize", typeof(double));
//training data.
table.Rows.Add("male", 6, 180, 12);
table.Rows.Add("male", 5.92, 190, 11);
table.Rows.Add("male", 5.58, 170, 12);
table.Rows.Add("male", 5.92, 165, 10);
table.Rows.Add("female", 5, 100, 6);
table.Rows.Add("female", 5.5, 150, 8);
table.Rows.Add("female", 5.42, 130, 7);
table.Rows.Add("female", 5.75, 150, 9);
BayesClassifier classifier = new BayesClassifier();
classifier.TrainClassifier(table);
Console.WriteLine(classifier.Classify(new double[] { 4, 150, 12 }));
Console.Read();
}
protected Int16 Judgement(String Title)
{
//IsAccident isAccident = new IsAccident(Title);
//if (isAccident.isAccResu)
// return 0;
//每判断一个类别，插入一个类别
// BayesClassifier classifier = new BayesClassifier();// 构造Bayes分类器
Int16 result = BayesClassifier.Classify(Title);// 进行分类
return(result);
}
private void button3_Click(object sender, EventArgs e)
{
Glass newGlass = new Glass(-1, (double)riNum.Value, (double)naNum.Value, (double)mgNum.Value, (double)alNum.Value, (double)siNum.Value, (double)kNum.Value, (double)caNum.Value, (double)baNum.Value, (double)feNum.Value, -1);
var neuralOutput = network.GetOutputs(newGlass);
output(("[BACK PROPOGATION] Siūloma tipas: "******"[K-Nearest-Neighbors] Siūloma tipas: "******"[Bayes] Siūloma tipas: " + bayes.test(newGlass)));
}
public static void Train()
{
classifier = new BayesClassifier("1", "2", "3", "4", "5");
DataHandler.ImportReviewData(3);
for (int i = 1; i <= 5; i++)
{
var data = DataHandler.Reviews.Where(r => r.overall == i);
foreach (ReviewData item in data)
{
var text = item.reviewText;
classifier.Train(i.ToString(), text);
}
}
}
private async Task OnClassifyBayes()
{
await Task.Run(() =>
{
try
{
var bayes = new BayesClassifier(FirstClass, SecondClass);
var stopwatch = new Stopwatch();
stopwatch.Start();
for (var i = 0; i < 5000; i++)
{
var randomPointX = random.NextDouble() * (PlotModel.Axes[0].Maximum - PlotModel.Axes[0].Minimum) + PlotModel.Axes[0].Minimum;
var randomPointY = random.NextDouble() * (PlotModel.Axes[1].Maximum - PlotModel.Axes[1].Minimum) + PlotModel.Axes[1].Minimum;
var result = bayes.ClassifyByCorrelation(randomPointX, randomPointY, new Matrix(CorrelationMatrix));
if (result == FirstClass)
{
FirstClassScatterSeries.Points.Add(new ScatterPoint(randomPointX, randomPointY, 4, double.NaN, FirstClass.Id));
}
else
{
SecondClassScatterSeries.Points.Add(new ScatterPoint(randomPointX, randomPointY, 4, double.NaN, FirstClass.Id));
}
if (stopwatch.Elapsed > TimeSpan.FromMilliseconds(100))
{
PlotModel.InvalidatePlot(true);
stopwatch.Restart();
}
}
PlotModel.InvalidatePlot(true);
}
catch (Exception e)
{
MessageBox.Show(e.Message, "Error", MessageBoxButton.OK, MessageBoxImage.Error);
}
});
}
private void bajes_start_Click(object sender, EventArgs e)
{
double averageAccurasy = 0;
List <Glass> glasses = new List <Glass>();
if (radioButton1.Checked)
{
glasses = dataList;
}
else
{
glasses = clearDistantValuesFromList(dataList);
}
int testingFileCount = glasses.Count / NUMBER_OF_SEGMENTS;
for (int segment = 0; segment < NUMBER_OF_SEGMENTS; segment++)
{
richTextBox1.AppendText(String.Format("Crosscheck segment {0} \n ", segment + 1));
int correctGuesses = 0;
int guessCounter = 0;
int falseGuesses = 0;
int testingDataFromIndex = segment * testingFileCount;
int testingDataToIndex = (segment + 1) * testingFileCount;
var trainingData = new List <Glass>();
var testingData = new List <Glass>();
for (int i = 0; i < glasses.Count; i++)
{
if (i >= testingDataFromIndex && i <= testingDataToIndex)
{
testingData.Add(glasses[i]);
}
else
{
trainingData.Add(glasses[i]);
}
}
BayesClassifier bayes = new BayesClassifier(trainingData.Count);
bayes.trainClassifier(trainingData);
foreach (Glass glass in testingData)
{
int guess = bayes.test(glass);
if (guess == glass.group_type)
{
correctGuesses++;
}
else
{
falseGuesses++;
}
guessCounter++;
}
var totalGlasses = glasses.Count;
var totalTrain = trainingData.Count;
var totalTest = testingData.Count;
double trainProc = Math.Round(((double)totalTrain / totalGlasses) * 100, 2);
double testProc = Math.Round(((double)totalTest / totalGlasses) * 100, 2);
double accuracy = Math.Round(((double)correctGuesses / guessCounter) * 100, 2);
averageAccurasy += accuracy;
richTextBox1.AppendText(string.Format("Total: {0, 3}; Training: {1, 3}({2, 4}%); Testing: {3, 3}({4, 4}%)\n", totalGlasses, totalTrain, trainProc, totalTest, testProc));
richTextBox1.AppendText(string.Format("Correct guesses: {0, 3} ; Total guesses: {1, 3} ; Accuracy: {2, 4}%\n", correctGuesses, guessCounter, accuracy));
}
richTextBox1.AppendText(string.Format("Average accurasy of all segments - {0}%\n", averageAccurasy / NUMBER_OF_SEGMENTS));
}
