public void ToStringTest()
{
// Issue 51:
SimpleLinearRegression regression = new SimpleLinearRegression();
var x = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
var y = new double[] { 1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321 };
regression.Regress(x, y);
{
string expected = "y(x) = 32x + -44";
expected = expected.Replace(".", System.Globalization.CultureInfo.CurrentCulture.NumberFormat.NumberDecimalSeparator);
string actual = regression.ToString();
Assert.AreEqual(expected, actual);
}
{
string expected = "y(x) = 32x + -44";
string actual = regression.ToString(null, System.Globalization.CultureInfo.GetCultureInfo("en-US"));
Assert.AreEqual(expected, actual);
}
{
string expected = "y(x) = 32.0x + -44.0";
string actual = regression.ToString("N1", System.Globalization.CultureInfo.GetCultureInfo("en-US"));
Assert.AreEqual(expected, actual);
}
{
string expected = "y(x) = 32,00x + -44,00";
string actual = regression.ToString("N2", System.Globalization.CultureInfo.GetCultureInfo("pt-BR"));
Assert.AreEqual(expected, actual);
}
}
public void LogarithmRegressionRegressTest()
{
// This is the same data from the example available at
// http://mathbits.com/MathBits/TISection/Statistics2/logarithmic.htm
// Declare your inputs and output data
double[] inputs = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 };
double[] outputs = { 6, 9.5, 13, 15, 16.5, 17.5, 18.5, 19, 19.5, 19.7, 19.8 };
// Transform inputs to logarithms
double[] logx = Matrix.Log(inputs);
// Compute a simple linear regression
var lr = new SimpleLinearRegression();
// Compute with the log-transformed data
double error = lr.Regress(logx, outputs);
// Get an expression representing the learned regression model
// We just have to remember that 'x' will actually mean 'log(x)'
string result = lr.ToString("N4", CultureInfo.InvariantCulture);
// Result will be "y(x) = 6.1082x + 6.0993"
Assert.AreEqual(2.8760006026675797, error);
Assert.AreEqual(6.1081800414945704, lr.Slope);
Assert.AreEqual(6.0993411396126653, lr.Intercept);
Assert.AreEqual("y(x) = 6.1082x + 6.0993", result);
}
public RegressionResult PerformRegression(double[] trainX, double[] trainY, double[] testX, double[] testY)
{
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
SimpleLinearRegression regression = ols.Learn(trainX, trainY);
return(new RegressionResult
{
FormulaUsed = regression.ToString(),
PredictionOnTestSet = testX.Select(regression.Transform).ToArray(),
PredictionOnTrainingSet = trainX.Select(regression.Transform).ToArray(),
Regression = regression
});
}
private void btnOK_Click(object sender, EventArgs e)
{
//将Input放在X轴，OutPut放在Y轴
var GraphPane = zedGraph.GraphPane;
GraphPane.CurveList.Clear();
GraphPane.XAxis.Title.Text = cmbInputField.Text;
GraphPane.YAxis.Title.Text = cmbOutputField.Text;
//获得Input，Output列表
double[] inliersX = new double[mongoCol.Count()];
double[] inliersY = new double[mongoCol.Count()];
int Cnt = 0;
foreach (var item in mongoCol.FindAllAs <BsonDocument>())
{
inliersX[Cnt] = item[cmbInputField.Text].AsInt32;
inliersY[Cnt] = item[cmbOutputField.Text].AsInt32;
Cnt++;
}
var myCurve = GraphPane.AddCurve("Point", new PointPairList(inliersX, inliersY), Color.Blue, SymbolType.Default);
myCurve.Line.IsVisible = false;
myCurve.Symbol.Fill = new Fill(Color.Blue);
//线性回归
// Create a new simple linear regression
SimpleLinearRegression regression = new SimpleLinearRegression();
// Compute the linear regression
regression.Regress(inliersX, inliersY);
double[] InputX = new double[2];
double[] OutputY = new double[2];
InputX[0] = 0;
InputX[1] = inliersX.Max();
OutputY[0] = regression.Compute(0);
OutputY[1] = regression.Compute(inliersX.Max());
myCurve = GraphPane.AddCurve("Regression:" + regression.ToString(), new PointPairList(InputX, OutputY), Color.Blue, SymbolType.Default);
myCurve.Line.IsVisible = true;
myCurve.Line.Color = Color.Red;
//更新坐标轴和图表
zedGraph.AxisChange();
zedGraph.Invalidate();
}
public void logarithm_learn()
{
#region doc_learn
// This is the same data from the example available at
// http://mathbits.com/MathBits/TISection/Statistics2/logarithmic.htm
// Declare your inputs and output data
double[] inputs = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 };
double[] outputs = { 6, 9.5, 13, 15, 16.5, 17.5, 18.5, 19, 19.5, 19.7, 19.8 };
// Transform inputs to logarithms
double[] logx = Matrix.Log(inputs);
// Use Ordinary Least Squares to learn the regression
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
// Use OLS to learn the simple linear regression
SimpleLinearRegression lr = ols.Learn(logx, outputs);
// Compute predicted values for inputs
double[] predicted = lr.Transform(logx);
// Get an expression representing the learned regression model
// We just have to remember that 'x' will actually mean 'log(x)'
string result = lr.ToString("N4", CultureInfo.InvariantCulture);
// Result will be "y(x) = 6.1082x + 6.0993"
// The mean squared error between the expected and the predicted is
double error = new SquareLoss(outputs).Loss(predicted); // 0.261454
#endregion
Assert.AreEqual(0.26145460024250794, error, 1e-8);
Assert.AreEqual(6.1081800414945704, lr.Slope, 1e-8);
Assert.AreEqual(6.0993411396126653, lr.Intercept, 1e-8);
Assert.AreEqual("y(x) = 6.1082x + 6.0993", result);
}
