From 0931607c18d28c0e8879a3a3cd98572c5038b8a8 Mon Sep 17 00:00:00 2001 From: Arend Lammertink Date: Fri, 20 Apr 2018 16:40:49 +0200 Subject: [PATCH] Added emphasis --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 3424782..c984138 100644 --- a/README.md +++ b/README.md @@ -17,13 +17,13 @@ Description: ------------ Maps or lookup tables (LUTs) are used to approximate a multidimensional -function *y = f(X)*, with *y* a single dimensional value and *X* an *n* -dimensional vector *X = (x1, x2, ... xn)*. Usually, this technique is used to +function **y = f(X)**, with **y** a single dimensional value and **X** an **n** +dimensional vector **X = (x1, x2, ... xn)**. Usually, this technique is used to either speed up te computation of computationally expensive functions (such as sin, cos or tan) or to approximate functions with unkown characteristics. For the one dimensional case, two arrays are used to store a number of known -values for *y = f(x)*, as follows: +values for **y = f(x)**, as follows: ys[i] = f( xs[i] ) @@ -32,7 +32,7 @@ values for y = f(X) = f(x1,x2), as follows:: ys[i][j] = f( x1s[i], x2s[j] ) -In order to approximate *f(X)* at any point within the range of stored values, +In order to approximate **f(X)** at any point within the range of stored values, we search the xs array(s) to find the nearest known X-es and lineary interpolate the known result stored in the ys array.