private static void TestFit()
{
var lrm = new LinearRegressionModel {
Weights = np.array(new double[] { 2, 4, 6, 2, 2 })
};
var x = np.array(new double[, ] {
{ 1, 1, 1, 1 }, { 2, 5, 2, 2 }, { 2, 3, 2, 2 }, { 5, 2, 2, 2 }, { 2, 2, 3, 2 }, { 7, 2, 2, 2 }, { 2, 6, 2, 2 }, { 2, 4, 2, 2 }, { 2, 3, 3, 2 }, { 2, 2, 2, 8 }
});
var list = LinearRegressionModel.MatrixToDataSet(x);
var yList = lrm.Predict(list);
var y = np.array(yList.Cast <double>().ToArray());
Console.WriteLine($"Weights {lrm.Weights.ToString()}");
Console.WriteLine($"X {x.ToString()}");
Console.WriteLine($"Y { y.ToString()}");
lrm.Fit(list, yList);
Console.WriteLine($"Weights after fit {lrm.Weights.ToString()}");
}
private void buttonAnalyze_Click(object sender, EventArgs e)
{
Model newModel = new Model();
string formula = "SalePrice ~ " + string.Join(" + ", typeof(Flat).GetProperties()
.Where(o => o.Name != "Id" && o.Name != "SalePrice").Select(o => o.Name).ToArray());
string[] stats = null;
Extreme.DataAnalysis.Models.RegressionModel regression = null;
switch (comboBoxModels.Text)
{
case "Linear regression":
newModel.Name = "Linear regression";
LinearRegressionModel model1 = new LinearRegressionModel(DataFrame.FromDataTable(flatsRepos.GetDataFrame()), formula);
model1.Fit();
newModel.ResidualStandardError = model1.StandardError;
newModel.RSquared = model1.RSquared;
newModel.AdjustedRSquared = model1.AdjustedRSquared;
newModel.FStatistic = model1.FStatistic;
newModel.CorrespondingPValue = model1.PValue;
stats = new string[]
{
$"Residual standard error: {model1.StandardError:f2}",
$"R-Squared: {model1.RSquared:f2}",
$"Adjusted R-Squared: {model1.AdjustedRSquared:f2}",
$"F-statistic: {model1.FStatistic:f2}",
$"Corresponding p-value: {model1.PValue:f2}"
};
regression = model1;
break;
/* case "Logistic regression":
* LogisticRegressionModel model2 = new LogisticRegressionModel(DataFrame.FromDataTable(flatsRepos.GetDataFrame()), formula);
* model2.Compute();
*
* Extreme.Statistics.Tests.SimpleHypothesisTest lrt = model2.GetLikelihoodRatioTest();
* newModel.LogLikehood = model2.LogLikelihood;
* newModel.ChiSquared = lrt.Statistic;
* newModel.CorrespondingPValue = lrt.PValue;
*
* stats = new string[]
* {
* $"Log-likelihood: {model2.LogLikelihood:f2}",
* $"Chi-Squared: {lrt.Statistic:f2}",
* $"P-Value: {lrt.PValue:f2}"
* };
* //regression = model2;
* break;*/
case "Generalized linear regression":
newModel.Name = "Generalized linear regression: " + comboBoxGeneralizedType.Text;
GeneralizedLinearModel model3 = new GeneralizedLinearModel(DataFrame.FromDataTable(flatsRepos.GetDataFrame()), formula);
switch (comboBoxGeneralizedType.Text)
{
case "Binomial":
model3.ModelFamily = ModelFamily.Binomial;
break;
case "InverseGaussian":
model3.ModelFamily = ModelFamily.InverseGaussian;
break;
case "Normal":
model3.ModelFamily = ModelFamily.Normal;
break;
case "Poisson":
model3.ModelFamily = ModelFamily.Poisson;
break;
default:
break;
}
model3.Fit();
if (comboBoxGeneralizedType.Text == "Normal" || comboBoxGeneralizedType.Text == "Poisson")
{
newModel.LogLikehood = model3.LogLikelihood;
newModel.KernelLogLikehood = model3.GetKernelLogLikelihood();
newModel.Akaike = model3.GetAkaikeInformationCriterion();
newModel.CorrectedAIC = model3.GetCorrectedAkaikeInformationCriterion();
newModel.Bayesian = model3.GetBayesianInformationCriterion();
newModel.ChiSquared = model3.GetChiSquare();
stats = new string[]
{
$"Log likelihood {model3.LogLikelihood:f2}",
$"Kernel log likelihood: {model3.GetKernelLogLikelihood():f2}",
$"Akaike (AIC): {model3.GetAkaikeInformationCriterion():f2}",
$"Corrected AIC: {model3.GetCorrectedAkaikeInformationCriterion():f2}",
$"Bayesian (BIC): { model3.GetBayesianInformationCriterion():f2}",
$"Chi Square: { model3.GetChiSquare():f2}"
};
}
regression = model3;
break;
}
foreach (Parameter parameter in regression.Parameters)
{
typeof(Model).GetProperty(parameter.Name).SetValue(newModel, parameter.Value);
}
dataGridViewParams.DataSource = regression.Parameters.ToArray();
dataGridViewParams.Columns[dataGridViewParams.Columns.Count - 1].Visible = false;
listBoxStats.Items.Clear();
if (stats != null)
{
listBoxStats.Items.AddRange(stats);
}
modelsRepos.Add(newModel);
}