examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt

@@ -0,0 +1,46 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors
+
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
+
+// Dataset normalization
+
+fun main() {
+
+    // work in context with analytic methods
+    DoubleAnalyticTensorAlgebra {
+        // take dataset of 5-element vectors from normal distribution
+        val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
+        BroadcastDoubleTensorAlgebra {
+            dataset += fromArray(
+                intArrayOf(5),
+                doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
+            )
+        }
+
+        // find out mean and standard deviation of each column
+        val mean = dataset.mean(0, false)
+        val std = dataset.std(0, false)
+
+        println("Mean:\n$mean")
+        println("Standard deviation:\n$std")
+
+        // also we can calculate other statistic as minimum and maximum of rows
+        println("Minimum:\n${dataset.min(0, false)}")
+        println("Maximum:\n${dataset.max(0, false)}")
+
+        // now we can scale dataset with mean normalization
+        val datasetScaled = BroadcastDoubleTensorAlgebra { (dataset - mean) / std }
+
+        // find out mean and std of scaled dataset
+
+        println("Mean of scaled:\n${datasetScaled.mean(0, false)}")
+        println("Mean of scaled:\n${datasetScaled.std(0, false)}")
+    }
+}

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

@@ -51,7 +51,6 @@ public interface AnalyticTensorAlgebra<T> :
      */
     public fun Tensor<T>.max(dim: Int, keepDim: Boolean): Tensor<T>
 
-
     /**
      * @return the mean of all elements in the input tensor.
      */
@@ -110,7 +109,7 @@ public interface AnalyticTensorAlgebra<T> :
     public fun Tensor<T>.exp(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.log.html
-    public fun Tensor<T>.log(): Tensor<T>
+    public fun Tensor<T>.ln(): Tensor<T>
 
     //For information: https://pytorch.org/docs/stable/generated/torch.sqrt.html
     public fun Tensor<T>.sqrt(): Tensor<T>

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

@@ -25,7 +25,6 @@ public object DoubleAnalyticTensorAlgebra :
     override fun Tensor<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
         foldDim({ x -> x.maxOrNull()!! }, dim, keepDim)
 
-
     override fun Tensor<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
 
     override fun Tensor<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
@@ -70,7 +69,7 @@ public object DoubleAnalyticTensorAlgebra :
 
     override fun Tensor<Double>.exp(): DoubleTensor = tensor.map(::exp)
 
-    override fun Tensor<Double>.log(): DoubleTensor = tensor.map(::ln)
+    override fun Tensor<Double>.ln(): DoubleTensor = tensor.map(::ln)
 
     override fun Tensor<Double>.sqrt(): DoubleTensor = tensor.map(::sqrt)
 

kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt

@@ -2,35 +2,159 @@ package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
-import kotlin.math.abs
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra.tan
-import kotlin.math.exp
+import kotlin.math.*
 import kotlin.test.Test
 import kotlin.test.assertTrue
 
 internal class TestDoubleAnalyticTensorAlgebra {
 
     val shape = intArrayOf(2, 1, 3, 2)
-    val buffer = doubleArrayOf(27.1, 20.0, 19.84, 23.123, 0.0, 1.0, 3.23, 133.7, 25.3, 100.3, 11.0, 12.012)
+    val buffer = doubleArrayOf(
+        27.1, 20.0, 19.84,
+        23.123, 3.0, 2.0,
+
+        3.23, 133.7, 25.3,
+        100.3, 11.0, 12.012
+    )
     val tensor = DoubleTensor(shape, buffer)
 
     fun DoubleArray.fmap(transform: (Double) -> Double): DoubleArray {
         return this.map(transform).toDoubleArray()
     }
 
-    fun DoubleArray.epsEqual(other: DoubleArray, eps: Double = 1e-5): Boolean {
+    fun expectedTensor(transform: (Double) -> Double): DoubleTensor {
-        for ((elem1, elem2) in this.asSequence().zip(other.asSequence())) {
+        return DoubleTensor(shape, buffer.fmap(transform))
-            if (abs(elem1 - elem2) > eps) {
-                return false
-            }
-        }
-        return true
     }
 
     @Test
     fun testExp() = DoubleAnalyticTensorAlgebra {
-        tensor.exp().let {
+        assertTrue { tensor.exp() eq expectedTensor(::exp) }
-            assertTrue { shape contentEquals it.shape }
-            assertTrue { buffer.fmap(::exp).epsEqual(it.mutableBuffer.array())}
-        }
     }
+
+    @Test
+    fun testLog() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.ln() eq expectedTensor(::ln) }
+    }
+
+    @Test
+    fun testSqrt() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.sqrt() eq expectedTensor(::sqrt) }
+    }
+
+    @Test
+    fun testCos() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.cos() eq expectedTensor(::cos) }
+    }
+
+
+    @Test
+    fun testCosh() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.cosh() eq expectedTensor(::cosh) }
+    }
+
+    @Test
+    fun testAcosh() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.acosh() eq expectedTensor(::acosh) }
+    }
+
+    @Test
+    fun testSin() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.sin() eq expectedTensor(::sin) }
+    }
+
+    @Test
+    fun testSinh() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.sinh() eq expectedTensor(::sinh) }
+    }
+
+    @Test
+    fun testAsinh() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.asinh() eq expectedTensor(::asinh) }
+    }
+
+    @Test
+    fun testTan() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.tan() eq expectedTensor(::tan) }
+    }
+
+    @Test
+    fun testAtan() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.atan() eq expectedTensor(::atan) }
+    }
+
+    @Test
+    fun testTanh() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.tanh() eq expectedTensor(::tanh) }
+    }
+
+    @Test
+    fun testCeil() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.ceil() eq expectedTensor(::ceil) }
+    }
+
+    @Test
+    fun testFloor() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor.floor() eq expectedTensor(::floor) }
+    }
+
+    val shape2 = intArrayOf(2, 2)
+    val buffer2 = doubleArrayOf(
+        1.0, 2.0,
+        -3.0, 4.0
+    )
+    val tensor2 = DoubleTensor(shape2, buffer2)
+
+    @Test
+    fun testMin() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor2.min() == -3.0 }
+        assertTrue { tensor2.min(0, true) eq fromArray(
+            intArrayOf(1, 2),
+            doubleArrayOf(-3.0, 2.0)
+        )}
+        assertTrue { tensor2.min(1, false) eq fromArray(
+            intArrayOf(2),
+            doubleArrayOf(1.0, -3.0)
+        )}
+    }
+
+    @Test
+    fun testMax() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor2.max() == 4.0 }
+        assertTrue { tensor2.max(0, true) eq fromArray(
+            intArrayOf(1, 2),
+            doubleArrayOf(1.0, 4.0)
+        )}
+        assertTrue { tensor2.max(1, false) eq fromArray(
+            intArrayOf(2),
+            doubleArrayOf(2.0, 4.0)
+        )}
+    }
+
+    @Test
+    fun testSum() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor2.sum() == 4.0 }
+        assertTrue { tensor2.sum(0, true) eq fromArray(
+            intArrayOf(1, 2),
+            doubleArrayOf(-2.0, 6.0)
+        )}
+        assertTrue { tensor2.sum(1, false) eq fromArray(
+            intArrayOf(2),
+            doubleArrayOf(3.0, 1.0)
+        )}
+    }
+
+    @Test
+    fun testMean() = DoubleAnalyticTensorAlgebra {
+        assertTrue { tensor2.mean() == 1.0 }
+        assertTrue { tensor2.mean(0, true) eq fromArray(
+            intArrayOf(1, 2),
+            doubleArrayOf(-1.0, 3.0)
+        )}
+        assertTrue { tensor2.mean(1, false) eq fromArray(
+            intArrayOf(2),
+            doubleArrayOf(1.5, 0.5)
+        )}
+    }
+
 }