API Reference - Models - LocalOutlierProbability
Stored Model Parameters
Contains a table of matrices.
- ModelParameters: Feature Matrix
Constructors
new()
Create new model object. If any of the arguments are nil, default argument values for that argument will be used.
LocalOutlierProbability.new(kValue: integer, distanceFunction: string, use, useWeightedDistance: boolean): ModelObject
Parameters:
-
kValue: The number of closest data points taken into consideration for majority voting to determine the class of a given data point.
-
distanceFunction: The distance function to be used to measure the similarity between two data points. Available options are:
-
Euclidean
-
Manhattan
-
Cosine
-
Returns:
- ModelObject: The generated model object.
Functions
train()
Train the model.
LocalOutlierProbability:train(featureMatrix: matrix): number[]
Parameters:
- featureMatrix: Matrix containing all data.
score()
Generates the score vector.
LocalOutlierProbability:score(): matrix
Returns:
- scoreVector: A vector containing the scores for each data stored in train() function. The higher the value, the more likely the datapoint is an outlier.