Beispiel #1
0
    private void btnStochastic1_Click(object sender, EventArgs e)
    {
      var regression = new LogisticRegression();

      double[,] sourceMatrix;
      double[,] inputs;
      double[] labels;

      GetData(out sourceMatrix, out inputs, out labels);

      _weight = regression.StochasticGradientAscent1(inputs.ToArray(), labels);

      var aaa = sourceMatrix.Submatrix(0, sourceMatrix.GetLength(0) - 1, 1, 3);
      CreateRegressPlot(graphInput, aaa, _weight);
    }
Beispiel #2
0
    private double HorseColicTest()
    {
      double[,] sourceMatrix;
      double[,] inputs;
      double[] labels;

      GetData(dgvTest, out sourceMatrix, out inputs, out labels);
      var regression = new LogisticRegression();
      _weight = regression.StochasticGradientAscent1(inputs.ToArray(), labels);
      var errorCount = 0;
      for (var i = 0; i < inputs.GetLength(0); i++)
      {
        var output = regression.Classify(inputs.GetRow(i), _weight);
        if (output != labels[i]) errorCount++;
      }
      var errorRate = (double)errorCount / inputs.GetLength(0);

      return errorRate;
    }
Beispiel #3
0
    private void cmdTest_Click(object sender, EventArgs e)
    {
      var x1 = Convert.ToDouble(txtX1.Text);
      var x2 = Convert.ToDouble(txtX2.Text);

      var input = new[]{1, x1,x2};
      var regression = new LogisticRegression();
      var cls = regression.Classify(input, _weight);
      var s = cls == 0.0 ? "Blue" : "Green";
      MessageBox.Show("Class = " + s);
    }