From f6573cb0d6abc00793f86051eebf28eec3ca6986 Mon Sep 17 00:00:00 2001
From: Jaap van Ekris <82339657+JaapvanEkris@users.noreply.github.com>
Date: Sun, 3 Dec 2023 20:30:27 +0100
Subject: [PATCH] Improvement of the FullTS performance

Improvement of the Full Theil-Sen performance by using a Binary Search Tree for the Median Calculation, which is much more efficient for larger values of maxSeriesLength
---
 app/engine/utils/FullTSLinearSeries.js | 54 ++++++++------------------
 1 file changed, 17 insertions(+), 37 deletions(-)

diff --git a/app/engine/utils/FullTSLinearSeries.js b/app/engine/utils/FullTSLinearSeries.js
index 1eab396..e4464f5 100644
--- a/app/engine/utils/FullTSLinearSeries.js
+++ b/app/engine/utils/FullTSLinearSeries.js
@@ -2,9 +2,9 @@
 /*
   Open Rowing Monitor, https://github.com/laberning/openrowingmonitor
 
-  The TSLinearSeries is a datatype that represents a Linear Series. It allows
+  The FullTSLinearSeries is a datatype that represents a Linear Series. It allows
   values to be retrieved (like a FiFo buffer, or Queue) but it also includes
-  a Theil–Sen estimator Linear Regressor to determine the slope of this timeseries.
+  a Theil-Sen estimator Linear Regressor to determine the slope of this timeseries.
 
   At creation its length is determined. After it is filled, the oldest will be pushed
   out of the queue) automatically.
@@ -19,6 +19,7 @@
 */
 
 import { createSeries } from './Series.js'
+import { createLabelledBinarySearchTree } from './BinarySearchTree.js'
 
 import loglevel from 'loglevel'
 const log = loglevel.getLogger('RowingEngine')
@@ -26,43 +27,37 @@ const log = loglevel.getLogger('RowingEngine')
 function createTSLinearSeries (maxSeriesLength = 0) {
   const X = createSeries(maxSeriesLength)
   const Y = createSeries(maxSeriesLength)
-  const slopes = []
+  const A = createLabelledBinarySearchTree()
 
   let _A = 0
   let _B = 0
   let _goodnessOfFit = 0
 
   function push (x, y) {
+    // Invariant: A contains all a's (as in the general formula y = a * x^2 + b * x + c)
+    // Where the a's are labeled in the Binary Search Tree with their xi when they BEGIN in the point (xi, yi)
+    if (maxSeriesLength > 0 && X.length() >= maxSeriesLength) {
+      // The maximum of the array has been reached, so when pushing the x,y the array gets shifted,
+      // thus we have to remove the a's belonging to the current position X0 as well before this value is trashed
+      A.remove(X.get(0))
+    }
+
     X.push(x)
     Y.push(y)
 
-    if (maxSeriesLength > 0 && slopes.length >= maxSeriesLength) {
-      // The maximum of the array has been reached, we have to create room
-      // in the 2D array by removing the first row from the table
-      removeFirstRow()
-    }
-
-    // Invariant: the indices of the X and Y array now match up with the
-    // row numbers of the slopes array. So, the slope of (X[0],Y[0]) and (X[1],Y[1]
-    // will be stored in slopes[0][.].
-
-    // Calculate the slopes of this new point
+    // Calculate all the slopes of the newly added point
     if (X.length() > 1) {
       // There are at least two points in the X and Y arrays, so let's add the new datapoint
       let i = 0
-      let result = 0
-      while (i < slopes.length) {
-        result = calculateSlope(i, slopes.length)
-        slopes[i].push(result)
+      while (i < X.length() - 1) {
+        A.push(X.get(i), calculateSlope(i, X.length() - 1))
         i++
       }
     }
-    // Add an empty array at the end to store futurs results for the most recent points
-    slopes.push([])
 
     // Calculate the median of the slopes
     if (X.length() > 1) {
-      _A = median()
+      _A = A.median()
     } else {
       _A = 0
     }
@@ -164,10 +159,6 @@ function createTSLinearSeries (maxSeriesLength = 0) {
     return Y.series()
   }
 
-  function removeFirstRow () {
-    slopes.shift()
-  }
-
   function calculateSlope (pointOne, pointTwo) {
     if (pointOne !== pointTwo && X.get(pointOne) !== X.get(pointTwo)) {
       return ((Y.get(pointTwo) - Y.get(pointOne)) / (X.get(pointTwo) - X.get(pointOne)))
@@ -177,21 +168,10 @@ function createTSLinearSeries (maxSeriesLength = 0) {
     }
   }
 
-  function median () {
-    if (slopes.length > 1) {
-      const sortedArray = [...slopes.flat()].sort((a, b) => a - b)
-      const mid = Math.floor(sortedArray.length / 2)
-      return (sortedArray.length % 2 !== 0 ? sortedArray[mid] : ((sortedArray[mid - 1] + sortedArray[mid]) / 2))
-    } else {
-      log.error('TS Linear Regressor, Median calculation on empty dataset attempted!')
-      return 0
-    }
-  }
-
   function reset () {
     X.reset()
     Y.reset()
-    slopes.splice(0, slopes.length)
+    A.reset()
     _A = 0
     _B = 0
     _goodnessOfFit = 0