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cov + docs
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@ -68,6 +68,16 @@ public interface AnalyticTensorAlgebra<T> :
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*/
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public fun Tensor<T>.variance(dim: Int, keepDim: Boolean): Tensor<T>
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/**
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* Returns the covariance matrix M of given vectors.
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*
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* M[i, j] contains covariance of i-th and j-th given vectors
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*
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* @param tensors the [List] of 1-dimensional tensors with same shape
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* @return the covariance matrix
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*/
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public fun cov(tensors: List<Tensor<T>>): Tensor<T>
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//For information: https://pytorch.org/docs/stable/generated/torch.exp.html
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public fun Tensor<T>.exp(): Tensor<T>
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@ -57,6 +57,28 @@ public object DoubleAnalyticTensorAlgebra :
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keepDim
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)
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private fun cov(x: DoubleTensor, y:DoubleTensor): Double{
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val n = x.shape[0]
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return ((x - x.mean()) * (y - y.mean())).mean() * n / (n - 1)
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}
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override fun cov(tensors: List<Tensor<Double>>): DoubleTensor {
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check(tensors.isNotEmpty()) { "List must have at least 1 element" }
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val n = tensors.size
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val m = tensors[0].shape[0]
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check(tensors.all { it.shape contentEquals intArrayOf(m) }) { "Tensors must have same shapes" }
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val resTensor = DoubleTensor(
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intArrayOf(n, n),
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DoubleArray(n * n) {0.0}
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)
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for (i in 0 until n){
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for (j in 0 until n){
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resTensor[intArrayOf(i, j)] = cov(tensors[i].tensor, tensors[j].tensor)
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}
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}
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return resTensor
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}
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override fun Tensor<Double>.exp(): DoubleTensor = tensor.map(::exp)
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override fun Tensor<Double>.ln(): DoubleTensor = tensor.map(::ln)
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@ -91,4 +113,4 @@ public object DoubleAnalyticTensorAlgebra :
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override fun Tensor<Double>.floor(): DoubleTensor = tensor.map(::floor)
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}
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}
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@ -401,19 +401,31 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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public fun Tensor<Double>.randomNormalLike(seed: Long = 0): DoubleTensor =
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DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
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// stack tensors by axis 0
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public fun stack(tensors: List<DoubleTensor>): DoubleTensor {
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val shape = tensors.firstOrNull()?.shape
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check(shape != null) { "Collection must have at least 1 element" }
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check(tensors.all { it.shape contentEquals shape }) { "Stacking tensors must have same shapes" }
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/**
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* Concatenates a sequence of tensors along a new dimension.
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*
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* @param tensors the [List] of tensors with same shapes to concatenate
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* @param dim the dimension to insert
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* @return tensor with concatenation result
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*/
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public fun stack(tensors: List<Tensor<Double>>, dim: Int = 0): DoubleTensor {
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check(dim == 0) { "Stack by non-zero dimension not implemented yet" }
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check(tensors.isNotEmpty()) { "List must have at least 1 element" }
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val shape = tensors[0].shape
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check(tensors.all { it.shape contentEquals shape }) { "Tensors must have same shapes" }
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val resShape = intArrayOf(tensors.size) + shape
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val resBuffer = tensors.flatMap {
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it.tensor.mutableBuffer.array().drop(it.bufferStart).take(it.numElements)
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it.tensor.mutableBuffer.array().drop(it.tensor.bufferStart).take(it.tensor.numElements)
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}.toDoubleArray()
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return DoubleTensor(resShape, resBuffer, 0)
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}
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// build tensor from this rows by given indices
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/**
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* Build tensor from rows of input tensor
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*
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* @param indices the [IntArray] of 1-dimensional indices
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* @return tensor with rows corresponding to rows by [indices]
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*/
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public fun Tensor<Double>.rowsByIndices(indices: IntArray): DoubleTensor {
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return stack(indices.map { this[it] })
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}
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@ -450,7 +462,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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override fun Tensor<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim({ x -> x.sum() }, dim, keepDim)
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override fun Tensor<Double>.min(): Double = this.fold { it.minOrNull()!! }
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override fun Tensor<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
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