Distance between points in a graph

I have plotted a graph which has 5 points. Is there any in built feature in Matlab which can help me calculate the distance between each point? Any help is appreciated, Thank you :)

Answer 1

[Image Analyst]

I your points are in a matrix called xy, with each point being a row, and you want to find the distance of every point to every other points, you can use pdist2() in the Statistics and Machine Learning Toolbox:

xy = rand(5, 2); % Sample points.
% Compute distances between all points:
distances = pdist2(xy, xy)

distances = 5×5

0 0.5189 0.3179 0.6963 0.7453 0.5189 0 0.3392 0.7892 0.5037 0.3179 0.3392 0 0.8942 0.7670 0.6963 0.7892 0.8942 0 0.4612 0.7453 0.5037 0.7670 0.4612 0

The row is one point, and in each column are the distances to the other points, so for example, distances(4, 2) is the distance from the point in row 4 to the point in row 2. Of course this will be the same distance as distances(2, 4) and the diagonal will be zero since the distance of any point to itself is zero.

[Varun Shrishail Soraganvi]

Thanks a lot, man! :)

Answer 2

[Walter Roberson]

pdist() can be configured with a number of different distance metrics.

However, I have to wonder if you are assuming that the 5 points are on a curve, and if you want the distance along the curve. If so then we would need to know what model you are using for the curve joining the points.

[Varun Shrishail Soraganvi]

The coordinates that I am getting are in a 5*2 matrix. I am trying to find distance from the matrix. At first I thought I will try and do it using the graph, but matrix seems easier. Trying to figure out a way. Tried pdist(), but could you please tell me what do you mean by "configured with a number of different distance metrics"?

[Walter Roberson]

You asked for "distance", but you did not specify how distance was to be calculated

xy = rand(5, 2); % Sample points.
distances_cheb = squareform(pdist(xy, 'chebychev'))

distances_cheb = 5×5

0 0.7191 0.6385 0.1655 0.7087 0.7191 0 0.8544 0.5536 0.1438 0.6385 0.8544 0 0.7660 0.8440 0.1655 0.5536 0.7660 0 0.5432 0.7087 0.1438 0.8440 0.5432 0

distances_city = squareform(pdist(xy, 'cityblock'))

distances_city = 5×5

0 0.7639 0.7737 0.2930 0.8973 0.7639 0 1.5376 0.6363 0.1543 0.7737 1.5376 0 1.0668 1.6710 0.2930 0.6363 1.0668 0 0.6043 0.8973 0.1543 1.6710 0.6043 0

distances_euc = squareform(pdist(xy, 'euclidean'))

distances_euc = 5×5

0 0.7205 0.6527 0.2089 0.7334 0.7205 0 1.0940 0.5597 0.1442 0.6527 1.0940 0 0.8229 1.1817 0.2089 0.5597 0.8229 0 0.5466 0.7334 0.1442 1.1817 0.5466 0

distances_ma = squareform(pdist(xy, 'mahalanobis'))

distances_ma = 5×5

0 2.2872 2.2800 0.4380 1.9505 2.2872 0 2.2957 2.0834 0.5991 2.2800 2.2957 0 2.5133 2.5543 0.4380 2.0834 2.5133 0 1.6623 1.9505 0.5991 2.5543 1.6623 0

distances_mi = squareform(pdist(xy, 'minkowski', 3))

distances_mi = 5×5

0 0.7192 0.6405 0.1876 0.7131 0.7192 0 0.9805 0.5542 0.1439 0.6405 0.9805 0 0.7811 1.0528 0.1876 0.5542 0.7811 0 0.5434 0.7131 0.1439 1.0528 0.5434 0

... and others