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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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6.  def countCardinality[T](input: Seq[T]): Array[Int]
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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10.  def getArrayType(rowsIter: Iterator[Row], matrixType: String, featuresColumn: String): (Iterator[Row], Boolean)

    Get whether to use dense or sparse data, using configuration and/or data sampling.

    Get whether to use dense or sparse data, using configuration and/or data sampling.

    rowsIter

    Iterator of rows.

    matrixType

    Matrix type as configured by user..

    featuresColumn

    The name of the features column.

    returns

    A reconstructed iterator with the same original rows and whether the matrix should be sparse or dense.

11.  final def getClass(): Class[_]

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12.  def getRowAsDoubleArray(row: Row, columnParams: ColumnParams): Array[Double]
13.  def hashCode(): Int

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14.  final def isInstanceOf[T0]: Boolean

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

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16.  final def notify(): Unit

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17.  final def notifyAll(): Unit

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18.  def sampleRowsForArrayType(rowsIter: Iterator[Row], featuresColumn: String): (Iterator[Row], Boolean)

    Sample the first several rows to determine whether to construct sparse or dense matrix in lightgbm native code.

    Sample the first several rows to determine whether to construct sparse or dense matrix in lightgbm native code.

    rowsIter

    Iterator of rows.

    featuresColumn

    The name of the features column.

    returns

    A reconstructed iterator with the same original rows and whether the matrix should be sparse or dense.

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

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20.  def toString(): String

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21.  def validateGroupColumn(col: String, schema: StructType): Unit
22.  final def wait(): Unit

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

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

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