|
private GetStatisticFor ( double x ) : |
||
| x | double | The x coordinate. |
| return | ||
/// <summary>Predicts the given o.</summary>
/// <exception cref="InvalidOperationException">Thrown when the requested operation is invalid.</exception>
/// <param name="y">The Vector to process.</param>
/// <returns>An object.</returns>
public override double Predict(Vector y)
{
this.Preprocess(y);
if (Root == null || Descriptor == null)
{
throw new InvalidOperationException("Invalid Model - Missing information");
}
Vector lp = Vector.Zeros(Root.Probabilities.Length);
for (int i = 0; i < Root.Probabilities.Length; i++)
{
Statistic stat = Root.Probabilities[i];
lp[i] = System.Math.Log(stat.Probability);
for (int j = 0; j < y.Length; j++)
{
Measure conditional = stat.Conditionals[j];
var p = conditional.GetStatisticFor(y[j]);
// check for missing range, assign bad probability
lp[i] += System.Math.Log(p == null ? 10e-10 : p.Probability);
}
}
var idx = lp.MaxIndex();
return(Root.Probabilities[idx].X.Min);
}
/// <inheritdoc />
public override Tuple <double, double> Predict(Vector y, bool t)
{
if (this.Root == null || this.Descriptor == null)
{
throw new InvalidOperationException("Invalid Model - Missing information");
}
Vector lp = Vector.Zeros(this.Root.Probabilities.Length);
Vector test = Vector.Zeros(this.Root.Probabilities.Length);
Vector log = Vector.Zeros(this.Root.Probabilities.Length);
for (int i = 0; i < this.Root.Probabilities.Length; i++)
{
Statistic stat = this.Root.Probabilities[i];
lp[i] = (stat.Probability);
test[i] = (stat.Probability);
log[i] = System.Math.Log(stat.Probability);
for (int j = 0; j < y.Length; j++)
{
Measure conditional = stat.Conditionals[j];
var p = conditional.GetStatisticFor(y[j]);
// check for missing range, assign bad probability
lp[i] *= (p == null ? 10e-10 : p.Probability);
test[i] += (p == null ? 10e-10 : p.Probability);
log[i] += System.Math.Log(p == null ? 10e-10 : p.Probability);
}
}
//for (int i = 0; i < lp.Length; i++)
// Console.WriteLine("Prob " +i+ "*: " + lp[i] + "\t +:" + test[i] + "\t Log +:" + log[i]);
var idx = lp.MaxIndex();
var precision = lp[idx] / lp.Sum();
return(new Tuple <double, double>([email protected][idx].X.Min, precision));
}