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com.github.yazidjanati

NormalInit

Related Doc: package yazidjanati

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object NormalInit extends Initialization

Initialization using a gaussian distribution.

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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. def apply(domain: Domain, num_samples: Int): DenseMatrix[Double]

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    Generated a certain number of samples.

    Generated a certain number of samples.

    domain

    The input space of the objective.

    num_samples

    Number of points to sample.

    returns

    Vector of samples.

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  5. final def asInstanceOf[T0]: T0

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  6. def check_within_bounds(sample: DenseVector[Double], domain: Domain): Boolean

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    Since we can't choose bounds for certain probability distributions, we need to make sure that our samples are within the input space.

    Since we can't choose bounds for certain probability distributions, we need to make sure that our samples are within the input space.

    sample

    Sample to investigate.

    domain

    Input space of the objective.

    returns

    whether the sample is within the bounds.

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  7. def clone(): AnyRef

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  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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  11. final def getClass(): Class[_]

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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  14. final def ne(arg0: AnyRef): Boolean

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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

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  18. def toString(): String

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  19. final def wait(): Unit

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

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  21. final def wait(arg0: Long): Unit

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