Accord.NET Framework

## LogisticRegression Methods |

The LogisticRegression type exposes the following members.

Methods

Name | Description | |
---|---|---|

ChiSquare(Double, Double) |
The likelihood ratio test of the overall model, also called the model chi-square test.
(Inherited from GeneralizedLinearRegression.) | |

ChiSquare(Double, Double, Double) |
The likelihood ratio test of the overall model, also called the model chi-square test.
(Inherited from GeneralizedLinearRegression.) | |

Clone |
Creates a new GeneralizedLinearRegression that is a copy of the current instance.
(Inherited from GeneralizedLinearRegression.) | |

Compute(Double) | Obsolete.
Computes the model output for the given input vector.
(Inherited from GeneralizedLinearRegression.) | |

Compute(Double) | Obsolete.
Computes the model output for each of the given input vectors.
(Inherited from GeneralizedLinearRegression.) | |

Decide(TInput) |
Computes a class-label decision for a given input.
(Inherited from BinaryScoreClassifierBaseTInput.) | |

Decide(TInput) |
Computes class-label decisions for a given set of input vectors.
(Inherited from ClassifierBaseTInput, TClasses.) | |

Decide(TInput, Boolean) |
Computes class-label decisions for the given input.
(Inherited from BinaryClassifierBaseTInput.) | |

Decide(TInput, Boolean) |
Computes a class-label decision for a given input.
(Inherited from BinaryScoreClassifierBaseTInput.) | |

Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) | |

Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) | |

FromWeights(Double) |
Constructs a new LogisticRegression from
an array of weights (linear coefficients). The first
weight is interpreted as the intercept value.
| |

FromWeights(Double, Double) |
Constructs a new LogisticRegression from
an array of weights (linear coefficients). The first
weight is interpreted as the intercept value.
| |

GetCoefficient |
Gets a coefficient value, where 0 is the intercept term
and the other coefficients are indexed starting at 1.
(Inherited from GeneralizedLinearRegression.) | |

GetConfidenceInterval(Int32) |
Gets the 95% confidence interval for the
Odds Ratio for a given coefficient.
| |

GetConfidenceInterval(Double, Int32, Double, Double) |
Gets the confidence interval for an input point.
(Inherited from GeneralizedLinearRegression.) | |

GetDegreesOfFreedom |
Gets the degrees of freedom when fitting the regression.
(Inherited from GeneralizedLinearRegression.) | |

GetDeviance(Double, Double) |
Gets the Deviance for the model.
(Inherited from GeneralizedLinearRegression.) | |

GetDeviance(Double, Double, Double) |
Gets the Deviance for the model.
(Inherited from GeneralizedLinearRegression.) | |

GetHashCode | Serves as the default hash function. (Inherited from Object.) | |

GetLogLikelihood(Double, Double) |
Gets the Log-Likelihood for the model.
(Inherited from GeneralizedLinearRegression.) | |

GetLogLikelihood(Double, Double, Double) |
Gets the Log-Likelihood for the model.
(Inherited from GeneralizedLinearRegression.) | |

GetLogLikelihoodRatio(Double, Double, GeneralizedLinearRegression) |
Gets the Log-Likelihood Ratio between two models.
(Inherited from GeneralizedLinearRegression.) | |

GetLogLikelihoodRatio(Double, Double, Double, GeneralizedLinearRegression) |
Gets the Log-Likelihood Ratio between two models.
(Inherited from GeneralizedLinearRegression.) | |

GetOddsRatio |
Gets the Odds Ratio for a given coefficient.
| |

GetPredictionInterval |
Gets the prediction interval for an input point.
(Inherited from GeneralizedLinearRegression.) | |

GetPredictionStandardError |
Gets the standard error of the prediction for a particular input vector.
(Inherited from GeneralizedLinearRegression.) | |

GetStandardError |
Gets the standard error of the fit for a particular input vector.
(Inherited from GeneralizedLinearRegression.) | |

GetStandardErrors |
Gets the standard error for each coefficient.
(Inherited from GeneralizedLinearRegression.) | |

GetType | Gets the Type of the current instance. (Inherited from Object.) | |

GetWaldTest |
Gets the Wald Test for a given coefficient.
(Inherited from GeneralizedLinearRegression.) | |

LogLikelihood(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.) | |

LogLikelihood(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.) | |

LogLikelihood(TInput, 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 BinaryLikelihoodClassifierBaseTInput.) | |

LogLikelihood(TInput, Int32) |
Predicts a class label for each input vector, returning the
log-likelihood that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

LogLikelihood(Double, Double) |
Predicts a class label vector for the given input vectors, returning the
log-likelihood that the input vector belongs to its predicted class.
(Inherited from GeneralizedLinearRegression.) | |

LogLikelihood(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.) | |

LogLikelihoods(TInput) |
Computes the log-likelihood that the given input
vector belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

LogLikelihoods(TInput) |
Computes the log-likelihoods that the given input
vectors belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

LogLikelihoods(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.) | |

LogLikelihoods(TInput, Double) |
Computes the log-likelihood that the given input
vector belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

LogLikelihoods(TInput, Double) |
Computes the log-likelihoods that the given input
vectors belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

LogLikelihoods(TInput, Int32) |
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.) | |

LogLikelihoods(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.) | |

LogLikelihoods(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.) | |

MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) | |

Probabilities(TInput) |
Computes the probabilities that the given input
vector belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

Probabilities(TInput) |
Computes the probabilities that the given input
vectors belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

Probabilities(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.) | |

Probabilities(TInput, Double) |
Computes the probabilities that the given input
vector belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

Probabilities(TInput, Double) |
Computes the probabilities that the given input
vectors belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

Probabilities(TInput, Int32) |
Predicts a class label vector for each input vector, returning the
probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

Probabilities(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.) | |

Probabilities(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.) | |

Probability(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.) | |

Probability(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.) | |

Probability(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.) | |

Probability(TInput, Int32) |
Predicts a class label for each input vector, returning the
probability that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

Probability(Double, Double) | ||

Probability(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.) | |

Score(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.) | |

Score(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.) | |

Score(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.) | |

Score(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.) | |

Score(Double, Double) |
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from GeneralizedLinearRegression.) | |

Score(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.) | |

Scores(TInput) |
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.) | |

Scores(TInput) |
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.) | |

Scores(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.) | |

Scores(TInput, Double) |
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.) | |

Scores(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.) | |

Scores(TInput, Double) |
Computes a numerical score measuring the association between
the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.) | |

Scores(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.) | |

Scores(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.) | |

SetCoefficient |
Sets a coefficient value, where 0 is the intercept term
and the other coefficients are indexed starting at 1.
(Inherited from GeneralizedLinearRegression.) | |

ToMulticlass |
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.) | |

ToMultilabel |
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.) | |

ToString | Returns a string that represents the current object. (Inherited from Object.) | |

Transform(TInput) |
Applies the transformation to an input, producing an associated output.
(Inherited from ClassifierBaseTInput, TClasses.) | |

Transform(TInput) |
Applies the transformation to a set of input vectors,
producing an associated set of output vectors.
(Inherited from TransformBaseTInput, TOutput.) | |

Transform(TInput, Boolean) |
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.) | |

Transform(TInput, Int32) |
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.) | |

Transform(TInput, Boolean) |
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.) | |

Transform(TInput, Int32) |
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.) | |

Transform(TInput, Int32) |
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.) | |

Transform(TInput, Double) |
Applies the transformation to a set of input vectors,
producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

Transform(TInput, Double) |
Applies the transformation to a set of input vectors,
producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

Transform(TInput, Double) |
Applies the transformation to a set of input vectors,
producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) | |

Transform(TInput, TClasses) |
Applies the transformation to an input, producing an associated output.
(Inherited from ClassifierBaseTInput, TClasses.) |

Extension Methods

Name | Description | |
---|---|---|

HasMethod |
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.) | |

IsEqual |
Compares two objects for equality, performing an elementwise
comparison if the elements are vectors or matrices.
(Defined by Matrix.) | |

To(Type) | Overloaded.
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.) | |

ToT | Overloaded.
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.) |

See Also