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Accord.NET (logo) LogisticRegression Methods

The LogisticRegression type exposes the following members.

Methods
  NameDescription
Public methodChiSquare(Double, Double)
The likelihood ratio test of the overall model, also called the model chi-square test.
(Inherited from GeneralizedLinearRegression.)
Public methodChiSquare(Double, Double, Double)
The likelihood ratio test of the overall model, also called the model chi-square test.
(Inherited from GeneralizedLinearRegression.)
Public methodClone
Creates a new GeneralizedLinearRegression that is a copy of the current instance.
(Inherited from GeneralizedLinearRegression.)
Public methodCompute(Double) Obsolete.
Computes the model output for the given input vector.
(Inherited from GeneralizedLinearRegression.)
Public methodCompute(Double) Obsolete.
Computes the model output for each of the given input vectors.
(Inherited from GeneralizedLinearRegression.)
Public methodDecide(TInput)
Computes class-label decisions for a given set of input vectors.
(Inherited from ClassifierBaseTInput, TClasses.)
Public methodDecide(Double)
Computes a class-label decision for a given input.
(Inherited from GeneralizedLinearRegression.)
Public methodDecide(TInput, Boolean)
Computes class-label decisions for the given input.
(Inherited from BinaryClassifierBaseTInput.)
Public methodDecide(TInput, TClasses)
Computes a class-label decision for a given input.
(Inherited from ClassifierBaseTInput, TClasses.)
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodStatic memberFromWeights(Double) Obsolete.
Constructs a new LogisticRegression from an array of weights (linear coefficients). The first weight is interpreted as the intercept value.
Public methodStatic memberFromWeights(Double, Double) Obsolete.
Constructs a new LogisticRegression from an array of weights (linear coefficients). The first weight is interpreted as the intercept value.
Public methodGetCoefficient
Gets a coefficient value, where 0 is the intercept term and the other coefficients are indexed starting at 1.
(Inherited from GeneralizedLinearRegression.)
Public methodGetConfidenceInterval(Int32)
Gets the 95% confidence interval for the Odds Ratio for a given coefficient.
Public methodGetConfidenceInterval(Double, Int32, Double, Double)
Gets the confidence interval for an input point.
(Inherited from GeneralizedLinearRegression.)
Public methodGetDegreesOfFreedom
Gets the degrees of freedom when fitting the regression.
(Inherited from GeneralizedLinearRegression.)
Public methodGetDeviance(Double, Double)
Gets the Deviance for the model.
(Inherited from GeneralizedLinearRegression.)
Public methodGetDeviance(Double, Double, Double)
Gets the Deviance for the model.
(Inherited from GeneralizedLinearRegression.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetLogLikelihood(Double, Double)
Gets the Log-Likelihood for the model.
(Inherited from GeneralizedLinearRegression.)
Public methodGetLogLikelihood(Double, Double, Double)
Gets the Log-Likelihood for the model.
(Inherited from GeneralizedLinearRegression.)
Public methodGetLogLikelihoodRatio(Double, Double, GeneralizedLinearRegression)
Gets the Log-Likelihood Ratio between two models.
(Inherited from GeneralizedLinearRegression.)
Public methodGetLogLikelihoodRatio(Double, Double, Double, GeneralizedLinearRegression)
Gets the Log-Likelihood Ratio between two models.
(Inherited from GeneralizedLinearRegression.)
Public methodGetOddsRatio
Gets the Odds Ratio for a given coefficient.
Public methodGetPredictionInterval
Gets the prediction interval for an input point.
(Inherited from GeneralizedLinearRegression.)
Public methodGetPredictionStandardError
Gets the standard error of the prediction for a particular input vector.
(Inherited from GeneralizedLinearRegression.)
Public methodGetStandardError
Gets the standard error of the fit for a particular input vector.
(Inherited from GeneralizedLinearRegression.)
Public methodGetStandardErrors
Gets the standard error for each coefficient.
(Inherited from GeneralizedLinearRegression.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodGetWaldTest
Gets the Wald Test for a given coefficient.
(Inherited from GeneralizedLinearRegression.)
Public methodLogLikelihood(TInput)
Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodLogLikelihood(Double)
Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class.
(Inherited from GeneralizedLinearRegression.)
Public methodLogLikelihood(TInput, Boolean)
Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodLogLikelihood(TInput, Double)
Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodLogLikelihood(Double, Boolean)
Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class.
(Inherited from GeneralizedLinearRegression.)
Public methodLogLikelihood(TInput, Boolean, Double)
Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodLogLikelihoods(TInput, Boolean)
Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodLogLikelihoods(TInput, Boolean)
Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodLogLikelihoods(TInput, Boolean, Double)
Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodLogLikelihoods(TInput, Boolean, Double)
Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodProbabilities(TInput, Boolean)
Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodProbabilities(TInput, Boolean)
Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodProbabilities(TInput, Boolean, Double)
Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodProbabilities(TInput, Boolean, Double)
Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodProbability(TInput)
Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodProbability(TInput)
Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodProbability(TInput, Boolean)
Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodProbability(TInput, Boolean)
Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodProbability(TInput, Double)
Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodProbability(TInput, Boolean, Double)
Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodScore(TInput)
Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier).
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScore(Double)
Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier).
(Inherited from GeneralizedLinearRegression.)
Public methodScore(TInput, Boolean)
Predicts a class label for the input vector, returning a numerical score measuring the strength of association of the input vector to its most strongly related class.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScore(TInput, Boolean)
Predicts a class label for each input vector, returning a numerical score measuring the strength of association of the input vector to the most strongly related class.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScore(TInput, Double)
Computes a numerical score measuring the association between the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScore(TInput, Boolean, Double)
Predicts a class label for each input vector, returning a numerical score measuring the strength of association of the input vector to the most strongly related class.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScores(TInput)
Computes a numerical score measuring the association between the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScores(TInput)
Computes a numerical score measuring the association between the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScores(TInput, Boolean)
Predicts a class label vector for the given input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScores(TInput, Double)
Computes a numerical score measuring the association between the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScores(TInput, Boolean)
Predicts a class label vector for each input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScores(TInput, Double)
Computes a numerical score measuring the association between the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScores(TInput, Boolean, Double)
Predicts a class label vector for the given input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodScores(TInput, Boolean, Double)
Predicts a class label vector for each input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.)
Public methodSetCoefficient
Sets a coefficient value, where 0 is the intercept term and the other coefficients are indexed starting at 1.
(Inherited from GeneralizedLinearRegression.)
Public methodToMulticlass
Views this instance as a multi-class generative classifier, giving access to more advanced methods, such as the prediction of integer labels.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodToMultilabel
Views this instance as a multi-label generative classifier, giving access to more advanced methods, such as the prediction of one-hot vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
Public methodTransform(TInput)
Applies the transformation to an input, producing an associated output.
(Inherited from ClassifierBaseTInput, TClasses.)
Public methodTransform(TInput)
Applies the transformation to a set of input vectors, producing an associated set of output vectors.
(Inherited from TransformBaseTInput, TOutput.)
Public methodTransform(TInput, Boolean)
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.)
Public methodTransform(TInput, Int32)
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.)
Public methodTransform(TInput, Boolean)
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.)
Public methodTransform(TInput, Int32)
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.)
Public methodTransform(TInput, Int32)
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.)
Public methodTransform(TInput, Double)
Applies the transformation to a set of input vectors, producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodTransform(TInput, Double)
Applies the transformation to a set of input vectors, producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodTransform(TInput, Double)
Applies the transformation to a set of input vectors, producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.)
Public methodTransform(TInput, TClasses)
Applies the transformation to an input, producing an associated output.
(Inherited from ClassifierBaseTInput, TClasses.)
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodToTOverloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
Public Extension MethodToTOverloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by Matrix.)
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See Also