protected AbstractModel(
Context[] parameters,
string[] predLabels,
string[] outcomeNames,
int correctionConstant,
double correctionParam) : this(predLabels, outcomeNames) {
evalParameters = new EvalParameters(parameters, correctionParam, correctionConstant, outcomeNames.Length);
}
/// <summary>
/// Initializes a new instance of the <see cref="EvalParameters"/> which can be evaluated.
/// </summary>
/// <param name="parameters">The parameters of the model.</param>
/// <param name="correctionParam">The correction parameter.</param>
/// <param name="correctionConstant">The correction constant.</param>
/// <param name="numOutcomes">The number outcomes.</param>
public EvalParameters(Context[] parameters, double correctionParam, double correctionConstant, int numOutcomes) {
Parameters = parameters;
NumOutcomes = numOutcomes;
CorrectionParam = correctionParam;
CorrectionConstant = correctionConstant;
// check if the double is "equal" to zero
if (Math.Abs(correctionConstant) < 0.000001)
ConstantInverse = 1/correctionConstant;
else
ConstantInverse = 1d;
}
/// <summary>
/// Creates a new model with the specified parameters, outcome names, and predicate/feature labels.
/// </summary>
/// <param name="parameters">The parameters of the model.</param>
/// <param name="predLabels">The names of the predicates used in this model.</param>
/// <param name="outcomeNames">The names of the outcomes this model predicts.</param>
/// <param name="correctionConstant">The maximum number of active features which occur in an event.</param>
/// <param name="correctionParam">The parameter associated with the correction feature.</param>
/// <param name="prior">The prior to be used with this model.</param>
public GISModel(
Context[] parameters,
string[] predLabels,
string[] outcomeNames,
int correctionConstant,
double correctionParam,
IPrior prior)
: base(parameters, predLabels, outcomeNames, correctionConstant, correctionParam) {
this.prior = prior;
this.prior.SetLabels(outcomeNames, predLabels);
ModelType = ModelType.Maxent;
}
/// <summary>
/// Initializes a new instance of the <see cref="EvalParameters"/> which can be evaluated.
/// </summary>
/// <param name="parameters">The parameters of the model.</param>
/// <param name="numOutcomes">The number outcomes.</param>
public EvalParameters(Context[] parameters, int numOutcomes) : this(parameters, 0, 0, numOutcomes) {}
public QNModel(Context[] parameters, string[] predLabels, string[] outcomeNames, int correctionConstant,
double correctionParam) : base(parameters, predLabels, outcomeNames, correctionConstant, correctionParam) {}
public QNModel(Context[] parameters, IndexHashTable<string> map, string[] outcomeNames)
: base(parameters, map, outcomeNames) {}
public QNModel(Context[] parameters, string[] predLabels, string[] outcomeNames)
: base(parameters, predLabels, outcomeNames) {}
/// <summary>
/// Creates a new model with the specified parameters, outcome names, and predicate/feature labels using the <see cref="UniformPrior"/> as prior.
/// </summary>
/// <param name="parameters">The parameters of the model.</param>
/// <param name="predLabels">The names of the predicates used in this model.</param>
/// <param name="outcomeNames">The names of the outcomes this model predicts.</param>
/// <param name="correctionConstant">The maximum number of active features which occur in an event.</param>
/// <param name="correctionParam">The parameter associated with the correction feature.</param>
public GISModel(Context[] parameters, string[] predLabels, string[] outcomeNames, int correctionConstant,
double correctionParam) : this(parameters, predLabels, outcomeNames, correctionConstant, correctionParam, new UniformPrior()) {
ModelType = ModelType.Maxent;
}
/// <summary>
/// Reads the parameters from a file and populates an array of context objects.
/// </summary>
/// <param name="outcomePatterns">The outcomes patterns for the model. The first index refers to which
/// outcome pattern (a set of outcomes that occurs with a context) is being specified. The
/// second index specifies the number of contexts which use this pattern at index 0, and the
/// index of each outcomes which make up this pattern in indices 1-n.</param>
/// <returns>An array of context objects.</returns>
protected Context[] GetParameters(int[][] outcomePatterns) {
var par = new Context[NUM_PREDS];
var pid = 0;
for (var i = 0; i < outcomePatterns.Length; i++) {
//construct outcome pattern
var outcomePattern = new int[outcomePatterns[i].Length - 1];
for (var k = 1; k < outcomePatterns[i].Length; k++) {
outcomePattern[k - 1] = outcomePatterns[i][k];
}
//populate parameters for each context which uses this outcome pattern.
for (var j = 0; j < outcomePatterns[i][0]; j++) {
var contextParameters = new double[outcomePatterns[i].Length - 1];
for (var k = 1; k < outcomePatterns[i].Length; k++) {
contextParameters[k - 1] = ReadDouble();
}
par[pid] = new Context(outcomePattern, contextParameters);
pid++;
}
}
return par;
}
public PerceptronModel(Context[] parameters, string[] predLabels, string[] outcomeNames, int correctionConstant,
double correctionParam) : base(parameters, predLabels, outcomeNames, correctionConstant, correctionParam) {
ModelType = ModelType.Perceptron;
}
public PerceptronModel(Context[] parameters, IndexHashTable<string> map, string[] outcomeNames)
: base(parameters, map, outcomeNames) {
ModelType = ModelType.Perceptron;
}
public PerceptronModel(Context[] parameters, string[] predLabels, string[] outcomeNames)
: base(parameters, predLabels, outcomeNames) {
ModelType = ModelType.Perceptron;
}
protected AbstractModel(Context[] parameters, string[] predLabels, IndexHashTable<string> map, string[] outcomeNames) {
this.map = map;
this.outcomeNames = outcomeNames;
evalParameters = new EvalParameters(parameters, outcomeNames.Length);
}
protected AbstractModel(Context[] parameters, string[] predLabels, string[] outcomeNames)
: this(predLabels, outcomeNames) {
evalParameters = new EvalParameters(parameters, outcomeNames.Length);
}