diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt index d8b59daa7..8c76de0de 100644 --- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt +++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt @@ -415,6 +415,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra { 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.map(transform: (Double) -> Double): DoubleTensor { return DoubleTensor( tensor.shape, @@ -423,10 +429,24 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra { ) } + /** + * 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.eq(other: Tensor, 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.eq(other: Tensor): Boolean = tensor.eq(other, 1e-5) private fun Tensor.eq( @@ -450,9 +470,25 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra { 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.randomNormalLike(seed: Long = 0): DoubleTensor = DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))