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add documentation to map and randomNormal function

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This commit is contained in:
AlyaNovikova 2021-05-06 14:42:15 +03:00
parent 56cadbd9ef
commit 90149e3965
1 changed files with 36 additions and 0 deletions
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36
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt
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@ -415,6 +415,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
return resTensor.tensor
}
/**
* Applies the [transform] function to each element of the tensor and returns the resulting modified tensor.
*
* @param transform the function to be applied to each element of the tensor.
* @return the resulting tensor after applying the function.
*/
public fun Tensor<Double>.map(transform: (Double) -> Double): DoubleTensor {
return DoubleTensor(
tensor.shape,
@ -423,10 +429,24 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
)
}
/**
* Compares element-wise two tensors with a specified precision.
*
* @param other the tensor to compare with `input` tensor.
* @param epsilon permissible error when comparing two Double values.
* @return true if two tensors have the same shape and elements, false otherwise.
*/
public fun Tensor<Double>.eq(other: Tensor<Double>, epsilon: Double): Boolean {
return tensor.eq(other) { x, y -> abs(x - y) < epsilon }
}
/**
* Compares element-wise two tensors.
* Comparison of two Double values occurs with 1e-5 precision.
*
* @param other the tensor to compare with `input` tensor.
* @return true if two tensors have the same shape and elements, false otherwise.
*/
public infix fun Tensor<Double>.eq(other: Tensor<Double>): Boolean = tensor.eq(other, 1e-5)
private fun Tensor<Double>.eq(
@ -450,9 +470,25 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
return true
}
/**
* Returns a tensor of random numbers drawn from normal distributions with 0.0 mean and 1.0 standard deviation.
*
* @param shape the desired shape for the output tensor.
* @param seed the random seed of the pseudo-random number generator.
* @return tensor of a given shape filled with numbers from the normal distribution
* with 0.0 mean and 1.0 standard deviation.
*/
public fun randomNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
DoubleTensor(shape, getRandomNormals(shape.reduce(Int::times), seed))
/**
* Returns a tensor with the same shape as `input` of random numbers drawn from normal distributions
* with 0.0 mean and 1.0 standard deviation.
*
* @param seed the random seed of the pseudo-random number generator.
* @return tensor with the same shape as `input` filled with numbers from the normal distribution
* with 0.0 mean and 1.0 standard deviation.
*/
public fun Tensor<Double>.randomNormalLike(seed: Long = 0): DoubleTensor =
DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))