static void Main(string[] args)
{
// Read the Excel worksheet into a DataTable
DataTable table = new ExcelReader("examples.xls").GetWorksheet("Sheet1");
// Convert the DataTable to input and output vectors
double[][] inputs = table.ToArray<double>("X", "Y");
int[] outputs = table.Columns["G"].ToArray<int>();
// Plot the data
ScatterplotBox.Show("Yin-Yang", inputs, outputs).Hold();
naiveBayes(inputs, outputs);
decisionTree(inputs, outputs);
linearSvm(inputs, outputs);
kernelSvm(inputs, outputs);
logistic(inputs, outputs);
network(inputs, outputs);
multilabelsvm();
sequenceclassification();
resilientgradienthiddenlearning();
}
static void Main(string[] args)
{
DataTable tableAttHp= new ExcelReader("HsAttHp.xlsx").GetWorksheet("Sheet1");
double[][] tableAttHpMatrix = tableAttHp.ToArray<double>();
DataTable tableCost = new ExcelReader("HsCost.xlsx").GetWorksheet("Sheet1");
double[] tableCostMatrix = tableCost.Columns[0].ToArray<double>();
//double[,] scores = Accord.Statistics.Tools.ZScores(tableAttHpMatrix);
//double[,] centered = Accord.Statistics.Tools.Center(tableAttHpMatrix);
//double[,] standard = Accord.Statistics.Tools.Standardize(tableAttHpMatrix);
//foreach (double i in scores ) { Console.WriteLine(i); }
//Console.ReadKey();
//foreach (double i in centered) { Console.WriteLine(i); }
//Console.ReadKey();
//foreach (double i in standard) { Console.WriteLine(i); }
// Plot the data
//ScatterplotBox.Show("Hs", tableAttHpMatrix, tableCostMatrix).Hold();
var target = new MultipleLinearRegression(2, true);
double error = target.Regress(tableAttHpMatrix, tableCostMatrix);
double a = target.Coefficients[0]; // a = 0
double b = target.Coefficients[1]; // b = 0
double c = target.Coefficients[2]; // c = 1
Console.WriteLine(a + " " + b + " " + c);
Console.ReadKey();
double[] inputs = { 2005, 2006, 2007, 2008, 2009,2010,2011 };
double[] outputs = { 12,19,29,37,45,23,33 };
// Create a new simple linear regression
SimpleLinearRegression regression = new SimpleLinearRegression();
// Compute the linear regression
regression.Regress(inputs, outputs);
// Compute the output for a given input. The
double y = regression.Compute(85); // The answer will be 28.088
// We can also extract the slope and the intercept term
// for the line. Those will be -0.26 and 50.5, respectively.
double s = regression.Slope;
double cut = regression.Intercept;
Console.WriteLine(s+"x+"+ cut);
Console.ReadKey();
}