From a78fa1f856ec74777346356d5795009e148f5d55 Mon Sep 17 00:00:00 2001 From: Pierre Alexandre Tremblay Date: Sat, 25 Jan 2020 12:10:25 +0000 Subject: [PATCH] typoesses --- .../super-simple-normalization-standardization-example.scd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/release-packaging/ignore/Examples/dataset/super-simple-normalization-standardization-example.scd b/release-packaging/ignore/Examples/dataset/super-simple-normalization-standardization-example.scd index 47c5539..62193a4 100644 --- a/release-packaging/ignore/Examples/dataset/super-simple-normalization-standardization-example.scd +++ b/release-packaging/ignore/Examples/dataset/super-simple-normalization-standardization-example.scd @@ -60,8 +60,8 @@ ~standardized_dataset.getPoint("point-0",~query_buf,{~query_buf.getn(0,~nb_of_dim,{|x|x.postln;});}); // 10 numbers that are standardize, which mean that, for each column/dimension/descriptor, the average of all the points will be 0. and the standard deviation 1. -///////////////////////////////////////////////////// -// exploring point querying conceepts via norm and std +//////////////////////////////////////////////////// +// exploring point querying concepts via norm and std // Once a dataset is normalized / standardized, query points have to be scaled accordingly to be used in distance measurement. In our instance, values were originally between 0 and 100, and now they will be between 0 and 1 (norm), or their average will be 0. (std). If we have data that we want to match from a similar ranging input, which is usually the case, we will need to normalize the searching point in each dimension using the same coefficients.