kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/AnalyticTensorAlgebra.kt

@@ -0,0 +1,112 @@
+package space.kscience.kmath.tensors
+
+
+public interface AnalyticTensorAlgebra<T, TensorType : TensorStructure<T>> :
+    TensorPartialDivisionAlgebra<T, TensorType> {
+
+    //https://pytorch.org/docs/stable/generated/torch.exp.html
+    public fun TensorType.exp(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.log.html
+    public fun TensorType.log(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.sqrt.html
+    public fun TensorType.sqrt(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.square.html
+    public fun TensorType.square(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.acos.html#torch.cos
+    public fun TensorType.cos(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.acos.html#torch.acos
+    public fun TensorType.acos(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.cosh
+    public fun TensorType.cosh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.acosh
+    public fun TensorType.acosh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sin
+    public fun TensorType.sin(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asin
+    public fun TensorType.asin(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sinh
+    public fun TensorType.sinh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asinh
+    public fun TensorType.asinh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.atan.html#torch.tan
+    public fun TensorType.tan(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.atan.html#torch.atan
+    public fun TensorType.atan(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.tanh
+    public fun TensorType.tanh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.atanh
+    public fun TensorType.atanh(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.ceil.html#torch.ceil
+    public fun TensorType.ceil(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.floor.html#torch.floor
+    public fun TensorType.floor(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.clamp.html#torch.clamp
+    public fun TensorType.clamp(min: T, max: T): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.erf.html#torch.erf
+    public fun TensorType.erf(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.erfinv.html#torch.erfinv
+    public fun TensorType.erfinv(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.erfc.html#torch.erfc
+    public fun TensorType.erfc(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.lerp.html#torch.lerp
+    public fun TensorType.lerp(end: TensorType, weight: TensorType): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.lgamma.html#torch.lgamma
+    public fun TensorType.lgamma(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.logit.html#torch.logit
+    public fun TensorType.logit(eps: T): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.igamma.html#torch.igamma
+    public fun TensorType.igamma(other: TensorType): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.igammac.html#torch.igammac
+    public fun TensorType.igammac(other: TensorType): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.mvlgamma.html#torch.mvlgamma
+    public fun TensorType.mvlgamma(dimensions: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.polygamma.html#torch.polygamma
+    public fun TensorType.polygamma(order: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.pow.html#torch.pow
+    public fun TensorType.pow(exponent: T): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.round.html#torch.round
+    public fun TensorType.round(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.sigmoid.html#torch.sigmoid
+    public fun TensorType.sigmoid(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.sinc.html#torch.sinc
+    public fun TensorType.sinc(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.heaviside.html#torch.heaviside
+    public fun TensorType.heaviside(values: TensorType): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.trapz.html#torch.trapz
+    public fun TensorType.trapz(xValues: TensorType, dim: Int): TensorType
+
+}

kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/LinearOpsTensorAlgebra.kt

@@ -0,0 +1,43 @@
+package space.kscience.kmath.tensors
+
+
+public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>> :
+    TensorPartialDivisionAlgebra<T, TensorType> {
+
+    //https://pytorch.org/docs/stable/generated/torch.eye.html
+    public fun eye(n: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.matmul.html
+    public infix fun TensorType.dot(other: TensorType): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
+    public fun diagonalEmbedding(
+        diagonalEntries: TensorType,
+        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
+    ): TensorType
+
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
+    public fun TensorType.det(): TensorType
+
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
+    public fun TensorType.inv(): TensorType
+
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.cholesky
+    public fun TensorType.cholesky(): TensorType
+
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
+    public fun TensorType.qr(): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.lu.html
+    public fun TensorType.lu(): Pair<TensorType, IntTensor>
+
+    //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
+    public fun luPivot(aLU: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>
+
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
+    public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
+
+    //https://pytorch.org/docs/stable/generated/torch.symeig.html
+    public fun TensorType.symEig(eigenvectors: Boolean = true): Pair<TensorType, TensorType>
+
+}

kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealAnalyticTensorAlgebra.kt

@@ -0,0 +1,148 @@
+package space.kscience.kmath.tensors
+
+public class RealAnalyticTensorAlgebra:
+    AnalyticTensorAlgebra<Double, RealTensor>,
+    RealTensorAlgebra()
+{
+    override fun RealTensor.exp(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.log(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sqrt(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.square(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cos(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.acos(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cosh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.acosh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sin(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.asin(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sinh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.asinh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.tan(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.atan(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.tanh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.atanh(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.ceil(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.floor(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.clamp(min: Double, max: Double): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.erf(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.erfinv(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.erfc(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.lerp(end: RealTensor, weight: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.lgamma(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.logit(eps: Double): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.igamma(other: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.igammac(other: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.mvlgamma(dimensions: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.polygamma(order: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.pow(exponent: Double): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.round(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sigmoid(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sinc(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.heaviside(values: RealTensor): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.trapz(xValues: RealTensor, dim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+
+}

kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealLinearOpsTensorAlgebra.kt

@@ -0,0 +1,133 @@
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.structures.array
+
+public class RealLinearOpsTensorAlgebra :
+    LinearOpsTensorAlgebra<Double, RealTensor>,
+    RealTensorAlgebra()
+{
+    override fun eye(n: Int): RealTensor {
+        val shape = intArrayOf(n, n)
+        val buffer = DoubleArray(n * n) { 0.0 }
+        val res = RealTensor(shape, buffer)
+        for (i in 0 until n) {
+            res[intArrayOf(i, i)] = 1.0
+        }
+        return res
+    }
+
+
+    override fun RealTensor.dot(other: RealTensor): RealTensor {
+        TODO("Alya")
+    }
+
+    override fun diagonalEmbedding(diagonalEntries: RealTensor, offset: Int, dim1: Int, dim2: Int): RealTensor {
+        TODO("Alya")
+    }
+
+
+    override fun RealTensor.lu(): Pair<RealTensor, IntTensor> {
+        // todo checks
+        val lu = this.copy()
+        val m = this.shape[0]
+        val pivot = IntArray(m)
+
+
+        // Initialize permutation array and parity
+        for (row in 0 until m) pivot[row] = row
+        var even = true
+
+        for (i in 0 until m) {
+            var maxA = -1.0
+            var iMax = i
+
+            for (k in i until m) {
+                val absA = kotlin.math.abs(lu[k, i])
+                if (absA > maxA) {
+                    maxA = absA
+                    iMax = k
+                }
+            }
+
+            //todo check singularity
+
+            if (iMax != i) {
+
+                val j = pivot[i]
+                pivot[i] = pivot[iMax]
+                pivot[iMax] = j
+                even != even
+
+                for (k in 0 until m) {
+                    val tmp = lu[i, k]
+                    lu[i, k] = lu[iMax, k]
+                    lu[iMax, k] = tmp
+                }
+
+            }
+
+            for (j in i + 1 until m) {
+                lu[j, i] /= lu[i, i]
+                for (k in i + 1 until m) {
+                    lu[j, k] -= lu[j, i] * lu[i, k]
+                }
+            }
+        }
+        return Pair(lu, IntTensor(intArrayOf(m), pivot))
+    }
+
+    override fun luPivot(lu: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
+        // todo checks
+        val n = lu.shape[0]
+        val p = lu.zeroesLike()
+        pivots.buffer.array.forEachIndexed { i, pivot ->
+            p[i, pivot] = 1.0
+        }
+        val l = lu.zeroesLike()
+        val u = lu.zeroesLike()
+
+        for (i in 0 until n) {
+            for (j in 0 until n) {
+                if (i == j) {
+                    l[i, j] = 1.0
+                }
+                if (j < i) {
+                    l[i, j] = lu[i, j]
+                }
+                if (j >= i) {
+                    u[i, j] = lu[i, j]
+                }
+            }
+        }
+        return Triple(p, l, u)
+    }
+
+    override fun RealTensor.det(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.inv(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cholesky(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.qr(): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+
+    override fun RealTensor.svd(): Triple<RealTensor, RealTensor, RealTensor> {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.symEig(eigenvectors: Boolean): Pair<RealTensor, RealTensor> {
+        TODO("Not yet implemented")
+    }
+
+}
+
+public inline fun <R> RealLinearOpsTensorAlgebra(block: RealTensorAlgebra.() -> R): R =
+    RealTensorAlgebra().block()

kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/RealTensorAlgebra.kt

@@ -1,12 +1,9 @@
 package space.kscience.kmath.tensors
 
-import space.kscience.kmath.structures.RealBuffer
 import space.kscience.kmath.structures.array
-import kotlin.math.abs
-import kotlin.math.max
 
 
-public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
+public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
 
     override fun RealTensor.value(): Double {
         check(this.shape contentEquals intArrayOf(1)) {
@@ -15,24 +12,13 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         return this.buffer.array[0]
     }
 
-    override fun eye(n: Int): RealTensor {
-        val shape = intArrayOf(n, n)
-        val buffer = DoubleArray(n * n) { 0.0 }
-        val res = RealTensor(shape, buffer)
-        for (i in 0 until n) {
-            res[intArrayOf(i, i)] = 1.0
-        }
-        return res
-    }
-
     override fun zeros(shape: IntArray): RealTensor {
         TODO("Not yet implemented")
     }
 
-    override fun zeroesLike(other: RealTensor): RealTensor {
+    override fun RealTensor.zeroesLike(): RealTensor {
-        // TODO refactor very bad!!!
+        val shape = this.shape
-        val shape = other.shape
+        val buffer = DoubleArray(this.buffer.size) { 0.0 }
-        val buffer = DoubleArray(other.buffer.size) { 0.0 }
         return RealTensor(shape, buffer)
     }
 
@@ -40,7 +26,7 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
-    override fun onesLike(shape: IntArray): RealTensor {
+    override fun RealTensor.onesLike(): RealTensor {
         TODO("Not yet implemented")
     }
 
@@ -156,14 +142,6 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         return RealTensor(this.shape, resBuffer)
     }
 
-    override fun RealTensor.dot(other: RealTensor): RealTensor {
-        TODO("Alya")
-    }
-
-    override fun diagonalEmbedding(diagonalEntries: RealTensor, offset: Int, dim1: Int, dim2: Int): RealTensor {
-        TODO("Alya")
-    }
-
     override fun RealTensor.transpose(i: Int, j: Int): RealTensor {
         checkTranspose(this.dimension, i, j)
         val n = this.buffer.size
@@ -198,9 +176,64 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun full(shape: IntArray, value: Double): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.fullLike(value: Double): RealTensor {
+        TODO("Not yet implemented")
+    }
 
 
-    override fun RealTensor.sum(): RealTensor {
+    override fun RealTensor.sum(dim: Int, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cumsum(dim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.prod(dim: Int, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cumprod(dim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.max(dim: Int, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cummax(dim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.min(dim: Int, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.cummin(dim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.median(dim: Int, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun maximum(lhs: RealTensor, rhs: RealTensor) {
+        TODO("Not yet implemented")
+    }
+
+    override fun minimum(lhs: RealTensor, rhs: RealTensor) {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.sort(dim: Int, keepDim: Boolean, descending: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun cat(tensors: List<RealTensor>, dim: Int): RealTensor {
         TODO("Not yet implemented")
     }
 
@@ -213,6 +246,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
+    override fun RealTensor.flatten(startDim: Int, endDim: Int): RealTensor {
+        TODO("Not yet implemented")
+    }
+
     override fun RealTensor.divAssign(value: Double) {
         TODO("Not yet implemented")
     }
@@ -221,99 +258,23 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.exp(): RealTensor {
+    override fun RealTensor.mean(dim: Int, keepDim: Boolean): RealTensor {
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.log(): RealTensor {
+    override fun RealTensor.quantile(q: Double, dim: Int, keepDim: Boolean): RealTensor {
         TODO("Not yet implemented")
     }
 
-
+    override fun RealTensor.std(dim: Int, unbiased: Boolean, keepDim: Boolean): RealTensor {
-    override fun RealTensor.lu(): Pair<RealTensor, IntTensor> {
-        // todo checks
-        val lu = this.copy()
-        val m = this.shape[0]
-        val pivot = IntArray(m)
-
-
-        // Initialize permutation array and parity
-        for (row in 0 until m) pivot[row] = row
-        var even = true
-
-        for (i in 0 until m) {
-            var maxA = -1.0
-            var iMax = i
-
-            for (k in i until m) {
-                val absA = abs(lu[k, i])
-                if (absA > maxA) {
-                    maxA = absA
-                    iMax = k
-                }
-            }
-
-            //todo check singularity
-
-            if (iMax != i) {
-
-                val j = pivot[i]
-                pivot[i] = pivot[iMax]
-                pivot[iMax] = j
-                even != even
-
-                for (k in 0 until m) {
-                    val tmp = lu[i, k]
-                    lu[i, k] = lu[iMax, k]
-                    lu[iMax, k] = tmp
-                }
-
-            }
-
-            for (j in i + 1 until m) {
-                lu[j, i] /= lu[i, i]
-                for (k in i + 1 until m) {
-                    lu[j, k] -= lu[j, i] * lu[i, k]
-                }
-            }
-        }
-        return Pair(lu, IntTensor(intArrayOf(m), pivot))
-    }
-
-    override fun luUnpack(lu: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
-        // todo checks
-        val n = lu.shape[0]
-        val p = zeroesLike(lu)
-        pivots.buffer.array.forEachIndexed { i, pivot ->
-            p[i, pivot] = 1.0
-        }
-        val l = zeroesLike(lu)
-        val u = zeroesLike(lu)
-
-        for (i in 0 until n){
-            for (j in 0 until n){
-                if (i == j) {
-                    l[i, j] = 1.0
-                }
-                if (j < i) {
-                    l[i, j] = lu[i, j]
-                }
-                if (j >= i) {
-                    u[i, j] = lu[i, j]
-                }
-            }
-        }
-        return Triple(p, l, u)
-    }
-
-    override fun RealTensor.svd(): Triple<RealTensor, RealTensor, RealTensor> {
-        /**
-         * Main first task for @AlyaNovikova
-         */
         TODO("Not yet implemented")
     }
 
-    override fun RealTensor.symEig(eigenvectors: Boolean): Pair<RealTensor, RealTensor> {
+    override fun RealTensor.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): RealTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun RealTensor.histc(bins: Int, min: Double, max: Double): RealTensor {
         TODO("Not yet implemented")
     }
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt

@@ -5,11 +5,17 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
     public fun TensorType.value(): T
 
-    public fun eye(n: Int): TensorType
     public fun zeros(shape: IntArray): TensorType
-    public fun zeroesLike(other: TensorType): TensorType
+    public fun TensorType.zeroesLike(): TensorType
     public fun ones(shape: IntArray): TensorType
-    public fun onesLike(shape: IntArray): TensorType
+    public fun TensorType.onesLike(): TensorType
+
+
+    //https://pytorch.org/docs/stable/generated/torch.full.html
+    public fun full(shape: IntArray, value: T): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
+    public fun TensorType.fullLike(value: T): TensorType
 
     public fun TensorType.copy(): TensorType
 
@@ -33,15 +39,6 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public operator fun TensorType.unaryMinus(): TensorType
 
 
-    //https://pytorch.org/docs/stable/generated/torch.matmul.html
-    public infix fun TensorType.dot(other: TensorType): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
-    public fun diagonalEmbedding(
-        diagonalEntries: TensorType,
-        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
-    ): TensorType
-
     //https://pytorch.org/docs/stable/generated/torch.transpose.html
     public fun TensorType.transpose(i: Int, j: Int): TensorType
 
@@ -53,35 +50,45 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public fun TensorType.abs(): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.sum.html
-    public fun TensorType.sum(): TensorType
+    public fun TensorType.sum(dim: Int, keepDim: Boolean): TensorType
-}
 
-// https://proofwiki.org/wiki/Definition:Division_Algebra
+    //https://pytorch.org/docs/stable/generated/torch.cumsum.html#torch.cumsum
-public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
+    public fun TensorType.cumsum(dim: Int): TensorType
-    TensorAlgebra<T, TensorType> {
 
-    public operator fun TensorType.div(value: T): TensorType
+    //https://pytorch.org/docs/stable/generated/torch.prod.html#torch.prod
-    public operator fun TensorType.div(other: TensorType): TensorType
+    public fun TensorType.prod(dim: Int, keepDim: Boolean): TensorType
-    public operator fun TensorType.divAssign(value: T)
-    public operator fun TensorType.divAssign(other: TensorType)
 
-    //https://pytorch.org/docs/stable/generated/torch.exp.html
+    //https://pytorch.org/docs/stable/generated/torch.cumprod.html#torch.cumprod
-    public fun TensorType.exp(): TensorType
+    public fun TensorType.cumprod(dim: Int): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.log.html
+    //https://pytorch.org/docs/stable/generated/torch.max.html#torch.max
-    public fun TensorType.log(): TensorType
+    public fun TensorType.max(dim: Int, keepDim: Boolean): TensorType
 
-    // todo change type of pivots
+    //https://pytorch.org/docs/stable/generated/torch.cummax.html#torch.cummax
-    //https://pytorch.org/docs/stable/generated/torch.lu.html
+    public fun TensorType.cummax(dim: Int): TensorType
-    public fun TensorType.lu(): Pair<TensorType, IntTensor>
 
-    //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
+    //https://pytorch.org/docs/stable/generated/torch.min.html#torch.min
-    public fun luUnpack(A_LU: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>
+    public fun TensorType.min(dim: Int, keepDim: Boolean): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.svd.html
+    //https://pytorch.org/docs/stable/generated/torch.cummin.html#torch.cummin
-    public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
+    public fun TensorType.cummin(dim: Int): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.symeig.html
+    //https://pytorch.org/docs/stable/generated/torch.median.html#torch.median
-    public fun TensorType.symEig(eigenvectors: Boolean = true): Pair<TensorType, TensorType>
+    public fun TensorType.median(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum
+    public fun maximum(lhs: TensorType, rhs: TensorType)
+
+    //https://pytorch.org/docs/stable/generated/torch.minimum.html#torch.minimum
+    public fun minimum(lhs: TensorType, rhs: TensorType)
+
+    //https://pytorch.org/docs/stable/generated/torch.sort.html#torch.sort
+    public fun TensorType.sort(dim: Int, keepDim: Boolean, descending: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat
+    public fun cat(tensors: List<TensorType>, dim: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.flatten.html#torch.flatten
+    public fun TensorType.flatten(startDim: Int, endDim: Int): TensorType
 
 }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorPartialDivisionAlgebra.kt

@@ -0,0 +1,27 @@
+package space.kscience.kmath.tensors
+
+// https://proofwiki.org/wiki/Definition:Division_Algebra
+public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
+    TensorAlgebra<T, TensorType> {
+
+    public operator fun TensorType.div(value: T): TensorType
+    public operator fun TensorType.div(other: TensorType): TensorType
+    public operator fun TensorType.divAssign(value: T)
+    public operator fun TensorType.divAssign(other: TensorType)
+
+    //https://pytorch.org/docs/stable/generated/torch.mean.html#torch.mean
+    public fun TensorType.mean(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.quantile.html#torch.quantile
+    public fun TensorType.quantile(q: T, dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.std.html#torch.std
+    public fun TensorType.std(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.var.html#torch.var
+    public fun TensorType.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.histc.html#torch.histc
+    public fun TensorType.histc(bins: Int, min: T, max: T): TensorType
+
+}