/// <summary>
/// Computes the model's baseline survival function. This method
/// simply calls the <see cref="UnivariateContinuousDistribution.ComplementaryDistributionFunction"/>
/// of the <see cref="BaselineHazard"/> function.
/// </summary>
///
/// <param name="time">The event time.</param>
///
/// <returns>The baseline survival function at the given time.</returns>
///
public double Survival(double time)
{
if (BaselineHazard == null)
{
throw new InvalidOperationException();
}
return(BaselineHazard.ComplementaryDistributionFunction(time));
}
/// <summary>
/// Computes the model output for the given time.
/// </summary>
///
/// <param name="time">The event time.</param>
///
/// <returns>The probabilities of the event occurring at the given time.</returns>
///
public double Compute(double time)
{
if (BaselineHazard == null)
{
throw new InvalidOperationException();
}
return(BaselineHazard.CumulativeHazardFunction(time));
}
/// <summary>
/// Predicts a class label for the given input vector, returning the
/// log-likelihood that the input vector belongs to its predicted class.
/// </summary>
///
/// <param name="time">The event times.</param>
///
public double LogLikelihood(double time)
{
if (BaselineHazard == null)
{
throw new InvalidOperationException();
}
return(BaselineHazard.LogCumulativeHazardFunction(time));
}
/// <summary>
/// Predicts a class label vector for the given input vector, returning the
/// log-likelihood that the input vector belongs to its predicted class.
/// </summary>
/// <param name="input">The input vector.</param>
public override double LogLikelihood(Tuple <double[], double> input)
{
double sum = Intercept;
for (int i = 0; i < Coefficients.Length; i++)
{
sum += Coefficients[i] * input.Item1[i];
}
double h0 = BaselineHazard.LogCumulativeHazardFunction(input.Item2);
return(h0 + sum);
}
/// <summary>
/// Predicts a class label vector for the given input vectors, returning the
/// log-likelihood that the input vector belongs to its predicted class.
/// </summary>
///
/// <param name="input">The input vector.</param>
/// <param name="result">An array where the log-likelihoods will be stored,
/// avoiding unnecessary memory allocations.</param>
///
public override double[] LogLikelihood(Tuple <double[], double>[] input, double[] result)
{
#pragma warning disable 612, 618
for (int j = 0; j < input.Length; j++)
{
double sum = Intercept;
for (int i = 0; i < Coefficients.Length; i++)
{
sum += Coefficients[i] * (input[j].Item1[i] - Offsets[i]);
}
double h0 = BaselineHazard.LogCumulativeHazardFunction(input[j].Item2);
result[j] = h0 + sum;
}
return(result);
#pragma warning restore 612, 618
}
/// <summary>
/// Computes the model output for the given input vector.
/// </summary>
///
/// <param name="input">The input vector.</param>
/// <param name="time">The event time.</param>
///
/// <returns>The probabilities of the event occurring at
/// the given time for the given observation.</returns>
///
public double Compute(double[] input, double time)
{
if (BaselineHazard == null)
{
throw new InvalidOperationException();
}
double sum = 0;
for (int i = 0; i < Coefficients.Length; i++)
{
sum += Coefficients[i] * (input[i] - Offsets[i]);
}
double exp = Math.Exp(sum);
double h0 = BaselineHazard.CumulativeHazardFunction(time);
return(h0 * exp);
}
