imp full and remove copypaste in geners

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

@@ -31,28 +31,6 @@ public open class BufferedTensor<T>(
 
     override fun hashCode(): Int = 0
 
-    // todo rename to vector
-    public inline fun forEachVector(vectorAction : (MutableStructure1D<T>) -> Unit): Unit {
-        check(shape.size >= 1) {"todo"}
-        val vectorOffset = strides.strides[0]
-        val vectorShape = intArrayOf(shape.last())
-        for (offset in 0 until numel step vectorOffset) {
-            val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
-            vectorAction(vector)
-        }
-    }
-
-    public inline fun forEachMatrix(matrixAction : (MutableStructure2D<T>) -> Unit): Unit {
-        check(shape.size >= 2) {"todo"}
-        val matrixOffset = strides.strides[1]
-        val matrixShape = intArrayOf(shape[shape.size - 2], shape.last()) //todo better way?
-        for (offset in 0 until numel step matrixOffset) {
-            val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
-            matrixAction(matrix)
-        }
-    }
-    // todo remove code copy-pasting
-
     public fun vectorSequence(): Sequence<MutableStructure1D<T>> = sequence {
         check(shape.size >= 1) {"todo"}
         val vectorOffset = strides.strides[0]
@@ -73,6 +51,18 @@ public open class BufferedTensor<T>(
         }
     }
 
+    public inline fun forEachVector(vectorAction : (MutableStructure1D<T>) -> Unit): Unit {
+        for (vector in vectorSequence()){
+            vectorAction(vector)
+        }
+    }
+
+    public inline fun forEachMatrix(matrixAction : (MutableStructure2D<T>) -> Unit): Unit {
+        for (matrix in matrixSequence()){
+            matrixAction(matrix)
+        }
+    }
+
 }
 
 

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

@@ -10,23 +10,24 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return DoubleTensor(newShape, this.buffer.array(), newStart)
     }
 
-    override fun zeros(shape: IntArray): DoubleTensor {
+    override fun full(shape: IntArray, value: Double): DoubleTensor {
-        TODO("Not yet implemented")
+        val buffer = DoubleArray(TensorStrides(shape).linearSize) { value }
-    }
-
-    override fun DoubleTensor.zeroesLike(): DoubleTensor {
-        val shape = this.shape
-        val buffer = DoubleArray(this.strides.linearSize) { 0.0 }
         return DoubleTensor(shape, buffer)
     }
 
-    override fun ones(shape: IntArray): DoubleTensor {
+    override fun zeros(shape: IntArray): DoubleTensor = full(shape, 0.0)
-        TODO("Not yet implemented")
+
+    override fun ones(shape: IntArray): DoubleTensor = full(shape, 1.0)
+
+    override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
+        val shape = this.shape
+        val buffer = DoubleArray(this.strides.linearSize) { value }
+        return DoubleTensor(shape, buffer)
     }
 
-    override fun DoubleTensor.onesLike(): DoubleTensor {
+    override fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
-        TODO("Not yet implemented")
+
-    }
+    override fun DoubleTensor.onesLike(): DoubleTensor = this.fullLike(1.0)
 
     override fun eye(n: Int): DoubleTensor {
         val shape = intArrayOf(n, n)
@@ -192,15 +193,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
-    override fun full(shape: IntArray, value: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-
     override fun DoubleTensor.sum(dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }

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

@@ -4,18 +4,18 @@ package space.kscience.kmath.tensors
 public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
 
-    public fun zeros(shape: IntArray): TensorType
-    public fun TensorType.zeroesLike(): TensorType // mb it shouldn't be tensor but algebra method (like in numpy/torch) ?
-    public fun ones(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
 
+    public fun ones(shape: IntArray): TensorType
+    public fun zeros(shape: IntArray): TensorType
+
     //https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
     public fun TensorType.fullLike(value: T): TensorType
 
+    public fun TensorType.zeroesLike(): TensorType
+    public fun TensorType.onesLike(): TensorType
+
     //https://pytorch.org/docs/stable/generated/torch.eye.html
     public fun eye(n: Int): TensorType