ProportionalHazards Class

[SerializableAttribute]
public sealed class ProportionalHazards : BinaryLikelihoodClassifierBase<Tuple<double[], double>>

<SerializableAttribute>
Public NotInheritable Class ProportionalHazards
	Inherits BinaryLikelihoodClassifierBase(Of Tuple(Of Double(), Double))

// Let's say we have the following survival problem. Each row in the
// table below represents a patient under care in a hospital. The first
// colum represents their age (a single feature, but there could have
// been many like age, height, weight, etc), the time until an event
// has happened (like, for example, unfortunatey death) and the event
// outcome (i.e. what has exactly happened after this amount of time,
// has the patient died or did he simply leave the hospital and we 
// couldn't get more data about him?)

object[,] data =
{
    //    input         time until           outcome 
    // (features)     event happened     (what happened?)
    {       50,              1,         SurvivalOutcome.Censored  },
    {       70,              2,         SurvivalOutcome.Failed    },
    {       45,              3,         SurvivalOutcome.Censored  },
    {       35,              5,         SurvivalOutcome.Censored  },
    {       62,              7,         SurvivalOutcome.Failed    },
    {       50,             11,         SurvivalOutcome.Censored  },
    {       45,              4,         SurvivalOutcome.Censored  },
    {       57,              6,         SurvivalOutcome.Censored  },
    {       32,              8,         SurvivalOutcome.Censored  },
    {       57,              9,         SurvivalOutcome.Failed    },
    {       60,             10,         SurvivalOutcome.Failed    },
}; // Note: Censored means that we stopped recording data for that person,
   // so we do not know what actually happened to them, except that things
   // were going fine until the point in time appointed by "time to event"

// Parse the data above
double[][] inputs = data.GetColumn(0).ToDouble().ToJagged();
double[] time = data.GetColumn(1).ToDouble();
SurvivalOutcome[] output = data.GetColumn(2).To<SurvivalOutcome[]>();

// Create a new PH Newton-Raphson learning algorithm
var teacher = new ProportionalHazardsNewtonRaphson()
{
    ComputeBaselineFunction = true,
    ComputeStandardErrors = true,
    MaxIterations = 100
};

// Use the learning algorithm to infer a Proportional Hazards model
ProportionalHazards regression = teacher.Learn(inputs, time, output);

// Use the regression to make predictions (problematic)
SurvivalOutcome[] prediction = regression.Decide(inputs);

// Use the regression to make score estimates 
double[] score = regression.Score(inputs);

// Use the regression to make probability estimates 
double[] probability = regression.Probability(inputs);