Packages

  • package root

    Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala.

    Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.

    Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.

    Definition Classes
    root
  • package smile
    Definition Classes
    root
  • package data

    Data manipulation functions.

    Data manipulation functions.

    Definition Classes
    smile
  • package math

    Mathematical and statistical functions.

    Mathematical and statistical functions.

    Definition Classes
    smile
  • package plot
    Definition Classes
    smile
  • package regression

    Regression analysis.

    Regression analysis. Regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables. Therefore, the estimation target is a function of the independent variables called the regression function. Regression analysis is widely used for prediction and forecasting.

    Definition Classes
    smile
  • package util

    Utility functions.

    Utility functions.

    Definition Classes
    smile
  • time
  • package validation

    Model validation.

    Model validation.

    Definition Classes
    smile

package util

Utility functions.

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LazyLogging, AnyRef, Any
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  1. util
  2. LazyLogging
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Value Members

  1. lazy val logger: Logger
    Attributes
    protected
    Definition Classes
    LazyLogging
    Annotations
    @transient()
  2. def pdist(data: Array[Array[Double]], half: Boolean = true): Array[Array[Double]]

    Returns the pairwise Euclidean distance matrix.

    Returns the pairwise Euclidean distance matrix.

    data

    the data set.

    half

    if true, only the lower half of matrix is allocated to save space.

    returns

    the lower half of proximity matrix.

  3. def proximity[T](data: Array[T], dist: Distance[T], half: Boolean = true): Array[Array[Double]]

    Returns the proximity matrix of a dataset for given distance function.

    Returns the proximity matrix of a dataset for given distance function.

    data

    the data set.

    dist

    the distance function.

    half

    if true, only the lower half of matrix is allocated to save space.

    returns

    the lower half of proximity matrix.

  4. implicit def toJavaBiFunction[T, U, R](f: (T, U) => R): BiFunction[T, U, R]

    Wraps Scala lambda as Java's.

  5. implicit def toJavaFunction[T, R](f: (T) => R): Function[T, R]

    Wraps Scala lambda as Java's.

  6. object time

    Measure running time of a function/block

Inherited from LazyLogging

Inherited from AnyRef

Inherited from Any

Ungrouped