Packages

c

provingground.learning

GraphEmbeddingLogisitic

class GraphEmbeddingLogisitic extends AnyRef

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Inherited
  1. GraphEmbeddingLogisitic
  2. AnyRef
  3. Any
Implicitly
  1. by any2stringadd
  2. by StringFormat
  3. by Ensuring
  4. by ArrowAssoc
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new GraphEmbeddingLogisitic(numPoints: Int, batchSize: Int, graph: Graph, maxCoord: Float = 2000f, epsilon: Float = 0.01f)

Value Members

  1. val allXs: Variable[TFloat32]
  2. val allYs: Variable[TFloat32]
  3. var dataSnap: (Vector[(Float, Float)], Vector[((Float, Float), (Float, Float))])
  4. def fit(inc: (Int) => (Int) => Float, scalePeriodOpt: Option[Int] = None, steps: Int = 2000000): Try[Vector[(Float, Float)]]
  5. var fitDone: Boolean
  6. val incidence: Placeholder[TFloat32]
  7. val loss: Neg[TFloat32]
  8. val maxCoordScale: Div[TFloat32]
  9. val maxX: Max[TFloat32]
  10. val maxY: Max[TFloat32]
  11. val minimize: Op
  12. val oneEpsMatrix: Constant[TFloat32]
  13. val oneMatrix: Constant[TFloat32]
  14. val ones: Constant[TFloat32]
  15. val optimizer: Adam
  16. val probs: Div[TFloat32]
  17. val projection: PlaceholderWithDefault[TFloat32]
  18. def rankOne(v: Operand[TFloat32], w: Operand[TFloat32]): MatMul[TFloat32]
  19. val rescaleX: Assign[TFloat32]
  20. val rescaleY: Assign[TFloat32]
  21. val sampleXs: MatMul[TFloat32]
  22. val sampleYs: MatMul[TFloat32]
  23. var stepsRun: Int
  24. val tf: Ops
  25. val totDiff: Add[TFloat32]
  26. val xDiff: SquaredDifference[TFloat32]
  27. val yDiff: SquaredDifference[TFloat32]