From 68e5d0e1e217aa6fe88b1367c052bb97a8ba1724 Mon Sep 17 00:00:00 2001
From: Christos Choutouridis <cchoutou@ece.auth.gr>
Date: Sat, 2 Jan 2021 19:05:56 +0200
Subject: [PATCH] FIX: remove .m~ files from repo

---
 .gitignore       |  3 +++
 matlab/distXY.m~ | 18 ------------------
 matlab/kNN.m~    | 29 -----------------------------
 matlab/tail.m~   | 14 --------------
 4 files changed, 3 insertions(+), 61 deletions(-)
 delete mode 100644 matlab/distXY.m~
 delete mode 100644 matlab/kNN.m~
 delete mode 100644 matlab/tail.m~

diff --git a/.gitignore b/.gitignore
index 07a920e..121639f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,9 @@ bin/
 out/
 resources/
 
+# matlab
+*.m~
+
 # hpc related
 
 # eclipse
diff --git a/matlab/distXY.m~ b/matlab/distXY.m~
deleted file mode 100644
index 1295e58..0000000
--- a/matlab/distXY.m~
+++ /dev/null
@@ -1,18 +0,0 @@
-function D = distXY(X, Y)
-%distXY Calculate an m x n Euclidean distance matrix 𝐷 of X and Y
-%
-% Calculate an m x n Euclidean distance matrix 𝐷     between two sets points 𝑋 and 𝑌 of 𝑚 and 𝑛 points respectively
-% X : [m x d]   Corpus data points (d dimensions)
-% Y : [n x d]   Query data poinsts (d dimensions)
-% D : [m x n]   Distance matrix where D(i,j) the distance of X(i) and Y(j)
-
-    [m d1] = size(X);
-    [n d2] = size(Y);
-    if d1 == d2
-        d = d1;
-    else
-        error('Corpus(X) and Query(Y) data points have to have the same dimensions');
-    end
-    %D = (X.*X) * ones(d,1)*ones(1,n) -2 * X*Y.' + ones(m,1)*ones(1,d) * (Y.*Y).';
-    D = sum(X.^2, 2) - 2 * X*Y.' + sum(Y.^2, 2).'
-end
diff --git a/matlab/kNN.m~ b/matlab/kNN.m~
deleted file mode 100644
index 356ac5e..0000000
--- a/matlab/kNN.m~
+++ /dev/null
@@ -1,29 +0,0 @@
-function [I, D] = kNN(X, Y, k)
-%kNN return the k-nearest neighbors Of Y into dataset X
-%
-% Outputs:
-% I : [n x k]   The indexes of X where the nearest neighbors of Y lies
-% D : [n x k]   The distances of each neighbor
-%
-% Inputs:
-% X : [m x d]   Corpus data points (d dimensions)
-% Y : [n x d]   Query data points (d dimensions)
-% k : [scalar]  The number of neighbors
-
-    disMat = distXY(X, Y);
-    [m, n] = size(disMat);
-
-    II = repmat([1:k].', 1, n);     % init the min algorithm
-    DD = disMat(1:k,:);
-    for i = k+1:m
-        for j = 1:n
-            [c, ci] = tail(DD);     % calculate candidate and canditate index
-            if disMat(i,j) < c(j)
-                DD()
-            end
-        end
-    end
-    I = II.';
-    D = DD.';
-end
-
diff --git a/matlab/tail.m~ b/matlab/tail.m~
deleted file mode 100644
index 4f65aa3..0000000
--- a/matlab/tail.m~
+++ /dev/null
@@ -1,14 +0,0 @@
-function [M, I] = maxIdx(Vec)
-%tail   Calculate the max,index pair of each Vec(:)
-%
-    n = length(Vec);
-    I = 0;
-    M = -1;
-    for j = 1:n
-        if M < Vec(j)
-            M(j) = Mat(i,j);
-            I(j) = i;
-        end
-    end
-end
-