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.  def argmax(scores: Array[Float]): Int

   Gets the index with the highest score.

   Gets the index with the highest score.

   scores

   Array of Scores to max

   returns

   Index of the highest score

5.  final def asInstanceOf[T0]: T0

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6.  def avgPooling(flattenEmbeddings: Array[Float], attentionMask: Array[Array[Long]], shape: Array[Long]): DenseMatrix[Float]

   Performs average pooling on embeddings using an attention mask.

   Performs average pooling on embeddings using an attention mask.

   This method takes flattened embeddings, an attention mask, and the shape of the embeddings, and computes the average pooling. The pooling is done by grouping the embeddings based on the attention mask and computing the weighted sum of these groups. The result is normalized by the total weight of the attention mask.

   flattenEmbeddings

   Array of flattened embeddings

   attentionMask

   2D Array representing the attention mask

   shape

   Array representing the shape of the embeddings (dimensions)

   returns

   A DenseMatrix of floats representing the average pooled embeddings

7.  def clone(): AnyRef

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8.  def denseMatrixToArray(matrix: DenseMatrix[Float]): Array[Array[Float]]

   Converts a DenseMatrix to a 2D array of floats.

   Converts a DenseMatrix to a 2D array of floats.

   This method is used to transform a DenseMatrix[Float] into a two-dimensional array. It iterates over the rows and columns of the DenseMatrix, copying each element into the corresponding position in the newly created 2D array.

   matrix

   DenseMatrix[Float] that needs to be converted to a 2D array

   returns

   An 2D array representing the same data as the input DenseMatrix

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

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10.  def equals(arg0: Any): Boolean

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11.  def finalize(): Unit

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12.  final def getClass(): Class[_]

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13.  def hashCode(): Int

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14.  final def isInstanceOf[T0]: Boolean

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15.  def l2Normalize(embeddings: DenseMatrix[Float]): DenseMatrix[Float]

    Normalizes each row of a DenseMatrix using the L2 norm.

    Normalizes each row of a DenseMatrix using the L2 norm.

    This method applies L2 normalization to the embeddings. It first computes the L2 norm for each row (embedding) in the input matrix. Then, it creates a matrix where each row is the computed norms vector, ensuring the dimensions match the input embeddings. Finally, it normalizes each row in the embeddings by dividing by the corresponding L2 norm.

    The result is a DenseMatrix where each row (embedding) is L2 normalized, ensuring that embeddings have a consistent scale for further processing.

    embeddings

    DenseMatrix[Float] representing the embeddings to be normalized

    returns

    DenseMatrix[Float] where each row is an L2 normalized version of the corresponding row in the input matrix

16.  def lpNormalizeArray(array: Array[Float], p: Int = 2): Array[Float]
17.  final def ne(arg0: AnyRef): Boolean

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18.  final def notify(): Unit

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19.  final def notifyAll(): Unit

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20.  def softmax(logitValues: Array[Float]): Array[Float]

    Calculates softmax probabilities for an array of logits.

    Calculates softmax probabilities for an array of logits.

    logitValues

    Predicted raw logits

    returns

    Probabilities for each class

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

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22.  def toString(): String

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23.  final def wait(): Unit

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

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

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26.  object implicits