hoo2/PDS_kNN
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

DEV: matlab proof of concept

Browse Source
This commit is contained in:
Christos Choutouridis 2021-01-02 19:04:06 +02:00
parent 6a33ed4976
commit 5d3af03fb6
9 changed files with 164 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
.gitignore vendored
View File

@ -1,6 +1,7 @@
# project
bin/
out/
resources/
# hpc related

10
Makefile
View File

@ -45,7 +45,7 @@ REL_CFLAGS := -Wall -Wextra -O2
# ============== Linker settings ==============
# Linker flags (example: -pthread -lm)
LDFLAGS :=
LDFLAGS := -lm -lopenblas
# Map output file
MAP_FILE := output.map
MAP_FLAG := -Xlinker -Map=$(BUILD_DIR)/$(MAP_FILE)
@ -53,11 +53,11 @@ MAP_FLAG := -Xlinker -Map=$(BUILD_DIR)/$(MAP_FILE)
# ============== Docker settings ==============
# We need:
# - Bind the entire project directory(the dir that icludes all the code) as volume.
# - In docker instance, change to working directory(where the makefile is).
# - In docker instance, change to working directory(where the makefile is).
DOCKER_VOL_DIR := $${PWD}
DOCKER_WRK_DIR :=
DOCKER_RUN := docker run
DOCKER_FLAGS := --rm -v $(DOCKER_VOL_DIR):/usr/src/$(PROJECT) -w /usr/src/$(PROJECT)/$(DOCKER_WRK_DIR)
DOCKER_WRK_DIR :=
DOCKER_RUN := docker run --rm
DOCKER_FLAGS := -v $(DOCKER_VOL_DIR):/usr/src/$(PROJECT) -w /usr/src/$(PROJECT)/$(DOCKER_WRK_DIR)
# docker invoke mechanism (edit with care)
# note:

21
matlab/distXY.m Normal file
View File

@ -0,0 +1,21 @@
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 set
% 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 points (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 must have the same dimensions (d)');
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).'
D = sqrt(D);
end

18
matlab/distXY.m~ Normal file
View File

@ -0,0 +1,18 @@
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

30
matlab/kNN.m Normal file
View File

@ -0,0 +1,30 @@
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 j = 1:n
for i = k+1:m
% calculate candidate and canditate index
[tail, taili] = maxIdx(DD(:, j));
if disMat(i,j) < tail
DD(taili, j) = disMat(i,j);
II(taili, j) = i;
end
end
end
I = II.';
D = DD.';
end

29
matlab/kNN.m~ Normal file
View File

@ -0,0 +1,29 @@
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

14
matlab/maxIdx.m Normal file
View File

@ -0,0 +1,14 @@
function [M, I] = maxIdx(Vec)
%maxIdx Calculate the max,index pair of each element of a vector
%
n = length(Vec);
I = 0;
M = -Inf;
for j = 1:n
if M < Vec(j)
M = Vec(j);
I = j;
end
end
end

14
matlab/tail.m~ Normal file
View File

@ -0,0 +1,14 @@
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

36
src/main.cpp
View File

@ -1,11 +1,39 @@
/*
* main.cpp
/*!
* \file main.cpp
* \brief Main application file
*
* Created on: Jan 2, 2021
* Author: hoo2
* \author
* Christos Choutouridis AEM:8997
* <cchoutou@ece.auth.gr>
*/
#include <iostream>
// Definition of the kNN result struct
typedef struct knnresult{
int * nidx; //!< Indices (0-based) of nearest neighbors [m-by-k]
double * ndist; //!< Distance of nearest neighbors [m-by-k]
int m; //!< Number of query points [scalar]
int k; //!< Number of nearest neighbors [scalar]
} knnresult;
//! Compute k nearest neighbors of each point in X [n-by-d]
/*!
\param X Corpus data points [n-by-d]
\param Y Query data points [m-by-d]
\param n Number of corpus points [scalar]
\param m Number of query points [scalar]
\param d Number of dimensions [scalar]
\param k Number of neighbors [scalar]
\return The kNN result
*/
knnresult kNN(double * X, double * Y, int n, int m, int d, int k) {
}
int main () {
std::cout << "Lets start!\n";