Value Members

1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  final def asInstanceOf[T0]: T0

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5.  def clone(): AnyRef

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   protected[java.lang]

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   @native() @throws( ... )

6.  def deserializeObject(bytes: Array[Byte]): Any

   Deserialize a JVM object from a byte array

   Deserialize a JVM object from a byte array

   bytes

   Bytes to be unserialized

   returns

   The object

7.  final def eq(arg0: AnyRef): Boolean

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8.  def equals(arg0: Any): Boolean

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9.  def finalize(): Unit

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   @throws( classOf[java.lang.Throwable] )

10.  def get1DArray(input: Array[Byte], getDouble: Boolean, bigEndian: Boolean): Seq[Any]

    Receive a 1D array of floats or integers

    Receive a 1D array of floats or integers

    input

    Input byte array

    getDouble

    Whether to read doubles (or integers)

    bigEndian

    Whether the file

11.  final def getClass(): Class[_]

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12.  def getClassifierExpected(predictionResult: PredictionResult[Any]): Array[Byte]

    Get the predicted class from a classification model

    Get the predicted class from a classification model

    Note: The class labels must be integers

    predictionResult

    Prediction result from a classifier

    returns

    Bytes of a integer array of the predicted class labels

13.  def getClassifierProbabilities(predictionResult: PredictionResult[Any], nClasses: Int): Array[Byte]

    Get the classification probabilities as an array

    Get the classification probabilities as an array

    Note: Model must be trained with sequential nonnegative integers starting at 0 as class names.

    predictionResult

    Prediction result from a classifier

    nClasses

    Number of classes in the problem

    returns

    Bytes of a double array of probabilities, stored row-wise (i.e., first entry, then second, ...)

14.  def getFeatureArray(input: Array[Byte], numAttributes: Integer, bigEndian: Boolean): Seq[Vector[Double]]

    Receives the feature array.

    Receives the feature array. Assumes it to be in a C-ordering

    input

    Input array as a byte array

    numAttributes

    Number of features in the array

    bigEndian

    Whether the numbers are is big-endian or not

    returns

    The array as a Scala array

15.  def getRegressionExpected(predictionResult: PredictionResult[Any]): Array[Byte]

    Generate the results of a regression model, which are assumed to be doubles

    Generate the results of a regression model, which are assumed to be doubles

    predictionResult

    Prediction result object

    returns

    Byte array of doubles in native system order

16.  def getRegressionUncertainty(predictionResult: PredictionResult[Any]): Array[Byte]

    Generate the uncertainties of a regression model, which are assumed to be doubles

    Generate the uncertainties of a regression model, which are assumed to be doubles

    predictionResult

    Prediction result object

    returns

    Byte array of doubles in native system order

17.  def hashCode(): Int

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18.  final def isInstanceOf[T0]: Boolean

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19.  final def ne(arg0: AnyRef): Boolean

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20.  final def notify(): Unit

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    @native()

21.  final def notifyAll(): Unit

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    @native()

22.  def serializeObject(obj: Any, compressLevel: Int): Array[Byte]

    Convert a JVM object to a byte array

    Convert a JVM object to a byte array

    Used for saving a model in lolopy inside of a pickle file.

    obj

    Object to be saved

    compressLevel

    Compression level used to condense the serialized representation

    returns

    Object as a serialized byte array

23.  final def synchronized[T0](arg0: ⇒ T0): T0

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24.  def toString(): String

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25.  final def wait(): Unit

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    @throws( ... )

26.  final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )

27.  final def wait(arg0: Long): Unit

    Definition Classes

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28.  def zipTrainingData(X: Seq[Vector[Double]], y: Seq[Any]): Seq[(Vector[Double], Any)]

    Zips the features and labels together

    Zips the features and labels together

    This function was created to provide the ability to easily Zip training and testing data when using Lolo from lolopy. Py4J does not support generic methods well, and this function is a workaround for being able to call zip when needed generate a training set.

    X

    Feature array

    y

    Label array

    returns

    Zipped arrays