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

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+/*
+ * 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
+import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
+
+const val seed = 100500L
+
+// simple PCA
+
+fun main(){
+    DoubleAnalyticTensorAlgebra {
+
+        // assume x is range from 0 until 10
+        val x = fromArray(
+            intArrayOf(10),
+            (0 until 10).toList().map { it.toDouble() }.toDoubleArray()
+        )
+
+        // take y dependent on x with noise
+        val y = 2.0 * x + (3.0 + x.randomNormalLike(seed) * 1.5)
+
+        println("x:\n$x")
+        println("y:\n$y")
+
+        // stack them into single dataset
+        val dataset = stack(listOf(x, y)).transpose()
+
+        // normalize both x and y
+        val xMean = x.mean()
+        val yMean = y.mean()
+
+        val xStd = x.std()
+        val yStd = y.std()
+
+        val xScaled = (x - xMean) / xStd
+        val yScaled = (y - yMean) / yStd
+
+        // save means ans standard deviations for further recovery
+        val mean = fromArray(
+            intArrayOf(2),
+            doubleArrayOf(xMean, yMean)
+        )
+        println("Means:\n$mean")
+
+        val std = fromArray(
+            intArrayOf(2),
+            doubleArrayOf(xStd, yStd)
+        )
+        println("Standard deviations:\n$std")
+
+        // calculate the covariance matrix of scaled x and y
+        val covMatrix = cov(listOf(xScaled, yScaled))
+        println("Covariance matrix:\n$covMatrix")
+
+        // and find out eigenvector of it
+        val (_, evecs) = DoubleLinearOpsTensorAlgebra {covMatrix.symEig()}
+        val v = evecs[0]
+        println("Eigenvector:\n$v")
+
+        // reduce dimension of dataset
+        val datasetReduced  = v dot stack(listOf(xScaled, yScaled))
+        println("Reduced data:\n$datasetReduced")
+
+        // we can restore original data from reduced data.
+        // for example, find 7th element of dataset
+        val n = 7
+        val restored = BroadcastDoubleTensorAlgebra{(datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean}
+        println("Original value:\n${dataset[n]}")
+        println("Restored value:\n$restored")
+    }
+}