public List<Dish> CalculatePredictedRateForUnratedDishes(List<Rating> rates, List<Dish> unratedDishes)
{
List<Dish> recommendeDishes = new List<Dish>();
double[][] inputs = new double[rates.Count][];
double[][] outputs = new double[rates.Count][];
for (int i = 0; i < rates.Count; i++)
{
inputs[i] = rates[i].DishParameters;
outputs[i] = new double[] {rates[i].Rate};
}
MultivariateLinearRegression regression = new MultivariateLinearRegression(6, 1);
double errorRegression = regression.Regress(inputs, outputs);
foreach (var unratedDish in unratedDishes)
{
unratedDish.PredictedRate = regression.Compute(unratedDish.DishParameters)[0];
}
recommendeDishes.AddRange(unratedDishes.OrderByDescending(dish => dish.PredictedRate).ToList().Take(3));
return recommendeDishes;
}
private void btnRegress_Click(object sender, EventArgs e)
{
if (pls == null || dgvRegressionInput.DataSource == null)
{
MessageBox.Show("Please compute the analysis first.");
return;
}
int components = (int)numComponentsRegression.Value;
regression = pls.CreateRegression(components);
DataTable table = dgvRegressionInput.DataSource as DataTable;
DataTable inputTable = table.DefaultView.ToTable(false, inputColumnNames);
DataTable outputTable = table.DefaultView.ToTable(false, outputColumnNames);
double[][] sourceInput = Matrix.ToArray(inputTable as DataTable);
double[][] sourceOutput = Matrix.ToArray(outputTable as DataTable);
double[,] result = Matrix.ToMatrix(regression.Compute(sourceInput));
double[] rSquared = regression.CoefficientOfDetermination(sourceInput, sourceOutput, cbAdjusted.Checked);
dgvRegressionOutput.DataSource = new ArrayDataView(result, outputColumnNames);
dgvRSquared.DataSource = new ArrayDataView(rSquared, outputColumnNames);
}