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I am trying to calculate the euclidean distance between two images. For this I am first getting the d array of the image and then using cv2. Below is the code:. Below is how vec and embedding looks like:. I am not very experienced in cv2. Can anyone please help and suggest some good solutions to calculate the euclidean distance.

Please help. When you make the call to the function, your two inputs have different shapes. To overcome the problem, you need to reshape one to the same shape as the second. Learn more. Asked 4 days ago. Active 4 days ago. Viewed 27 times. Below is how vec and embedding looks like: vec: [[ 1. S Andrew S Andrew 2, 4 4 gold badges 27 27 silver badges 77 77 bronze badges. Norm should do what you need. Looks like the arrays might just have different element data types? What is the output of embedding. If that's the case it's an easy fix, just convert them. How can I convert them.? Try embedding. IbrahimYousuf Did you mean embedding. Active Oldest Votes. Ibrahim Yousuf Ibrahim Yousuf 3 3 silver badges 10 10 bronze badges.

Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. What is the most efficient or really efficient in terms of speed way to compute distances between the cluster centroids here?

So far I always did Principal Coordinate analysis in this situation. After that, it is easy to compute distances between the centroids the usual way - as you would do it with grouped points x variables data. In addition, the task is not a dimensionality reduction one and we don't actually need those orthogonal principal axes. So I have a feeling that these decompositions might be an overkill. The centroids are. The polarization identity re-expresses this in terms of squared distances between all points:.

Efficient way to compute distances between centroids from distance matrix Ask Question. Asked 4 years, 11 months ago. Active 1 year, 9 months ago. Viewed 5k times. So, do you have knowledge or ideas about a potentially faster way?

Active Oldest Votes. R code to illustrate and test these calculations follows. I must confess that dispite I knew the parallelogram identities I could not myself see clearly the link to my task and to deduce the formula. So many thanks to you. I've already programmed the function in SPSS based on your formula for any number of centroids and it is indeed faster with large matrix D than the indirect way via PCoA. Sign up or log in Sign up using Google.When working with GPSit is sometimes helpful to calculate distances between points.

So we have to take a look at geodesic distances. There are various ways to handle this calculation problem. For example there is the Great-circle distancewhich is the shortest distance between two points on the surface of a sphere.

Another similar way to measure distances is by using the Haversine formulawhich takes the equation. We can take this formula now and translate it into Python. Important to note is that we have to take the radians of the longitude and latitude values. We can take this function now and apply distances to different cities. Lets say we want to calculate the distances from London to some other cities.

You can also use geopy to measure distances. As you can see, there is a difference between the values, especially since we work with very large distances, which enhances the distortion of our spheroid-shaped Earth. It is a great package to work with map projectionsbut in there you have also the Geod class which offers various geodesic computations.

To calculate the distance between two points we use the inv function, which calculates an inverse transformation and returns forward and back azimuths and distance. On a geographic sidenote, the forward azimuth is the direction which is defined as a horizontal angle measured clockwise from a north base line and a back azimuth is the opposite direction of the forward azimuth. You could use this information for example to sail the ocean if this is what you intend.

Parametric Thoughts.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I'm writing a simple program to compute the euclidean distances between multiple lists using python. This is the code I have so fat.

The output should be [[ I'm guessing it has something to do with the loop.

What should I do to fix it? By the way, I don't want to use numpy or scipy for studying purposes.

### Calculate Distance Between GPS Points in Python

The question has partly been answered by Evgeny. The answer the OP posted to his own question is an example how to not write Python code. Here is a shorter, faster and more readable solution, given test1 and test2 are lists like in the question:.

Not sure what you are trying to achieve for 3 vectors, but for two the code has to be much, much simplier:. With numpy is even a shorter statement. I got it, the trick is to create the first euclidean list inside the first for loop, and then deleting the list after appending it to the complete euclidean list. Learn more. Computing euclidean distance with multiple list in python Ask Question. Asked 1 year, 10 months ago. Active 1 year, 10 months ago.

Viewed 5k times. By the way, I don't want to use numpy or scipy for studying purposes If it's unclear, I want to calculate the distance between lists on test2 to each lists on test1. Iqbal Pratama. Iqbal Pratama Iqbal Pratama 1 1 silver badge 9 9 bronze badges.

StatQuest: K-means clustering

The easiest way to remove the redundant computations is to loop over only half the items. What MateenUlhaq says is correct. You can find these things by stepping through the code with a debugger, if you have one. Or by tracing all the steps by hand.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Use numpy. You can find the theory behind this in Introduction to Data Mining. This works because Euclidean distance is l2 norm and the default value of ord parameter in numpy. There's a function for that in SciPy. It's called Euclidean. For anyone interested in computing multiple distances at once, I've done a little comparison using perfplot a small project of mine. The first advice is to organize your data such that the arrays have dimension 3, n and are C-contiguous obviously.

That actually holds true for just one row as well. I want to expound on the simple answer with various performance notes. Firstly - this function is designed to work over a list and return all of the values, e. Firstly - every time we call it, we have to do a global lookup for "np", a scoped lookup for "linalg" and a scoped lookup for "norm", and the overhead of merely calling the function can equate to dozens of python instructions.

The function call overhead still amounts to some work, though. And you'll want to do benchmarks to determine whether you might be better doing the math yourself:. Your mileage may vary. But if you're comparing distances, doing range checks, etc. Math Great, both functions no-longer do any expensive square roots. That'll be much faster. This can be especially useful if you might chain range checks 'find things that are near X and within Nm of Y', since you don't have to calculate the distance again.

But what about if we're searching a really large list of things and we anticipate a lot of them not being worth consideration? Another instance of this problem solving method :. Starting Python 3. However, if speed is a concern I would recommend experimenting on your machine. You can also experiment with numpy. Here's some concise code for Euclidean distance in Python given two points represented as lists in Python.

Return the Euclidean distance between two points p and q, each given as a sequence or iterable of coordinates.

The two points must have the same dimension. Since Python 3. Find difference of two matrices first. Then, apply element wise multiplication with numpy's multiply command. After then, find summation of the element wise multiplied new matrix. Finally, find square root of the summation.

You first change list to numpy array and do like this: print np. Second method directly from python list as: print np. How are we doing? Please help us improve Stack Overflow.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

Distance computations between datasets have many forms. Among those, euclidean distance is widely used across many domains. Computing it at different computing platforms and levels of computing languages warrants different approaches. At Python level, the most popular one is SciPy's cdist.

In the recent years, we have seen contributions from scikit-learn to the same cause. The motivation with this repository codebase is to bring in noticeable speedups by using an altogether different approach to distance computations and in the process leveraging parallel architectures like GPU.

We will also study the best use cases that are suited to each of those implementations. The proposed method uses matrix-multiplication for better performance.

With most of the GPU implemenations presented in the codebase, we have the option to keep the final output on GPU to handle further compute heavy operations.

Thus, considering both CPU and GPU implementations, there are four possibilities by which euclidean distances could be computed. There are few factors at play depending on the input data and output requirements that lets us propose different configurations for each of those four ways.

Please note that dataset sizes refer to the shapes of the input arrays, which is kept the same for both of the inputs to ease the benchmarking process.

## How can the euclidean distance be calculated with numpy?

The performance boost with proposed methods scales proportionately to the dimensionality number of columns. This makes more sense with lesser dimensions cases. For reference the system configuration used for benchmarking results had the specifications as listed below :. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Latest commit Fetching latest commit…. Introduction Distance computations between datasets have many forms. Quick conclusions could be drawn based upon the speedup figures - The performance boost with proposed methods scales proportionately to the dimensionality number of columns Leveraging MKL always proved to be better than OpenBLAS.However, if speed is a concern I would recommend experimenting on your machine.

You can also experiment with numpy. Find difference of two matrices first. Then, apply element wise multiplication with numpy's multiply command. After then, find summation of the element wise multiplied new matrix. Finally, find square root of the summation. For anyone interested in computing multiple distances at once, I've done a little comparison using perfplot a small project of mine.

It turns out that. This actually holds true for just one row as well! Here's some concise code for Euclidean distance in Python given two points represented as lists in Python. I want to expound on the simple answer with various performance notes. Firstly - this function is designed to work over a list and return all of the values, e.

## Using loops

Firstly - every time we call it, we have to do a global lookup for "np", a scoped lookup for "linalg" and a scoped lookup for "norm", and the overhead of merely calling the function can equate to dozens of python instructions. The function call overhead still amounts to some work, though. And you'll want to do benchmarks to determine whether you might be better doing the math yourself:. Your mileage may vary. But if you're comparing distances, doing range checks, etc.

Math Great, both functions no-longer do any expensive squareroots. That'll be much faster. This can be especially useful if you might chain range checks 'find things that are near X and within Nm of Y', since you don't have to calculate the distance again. But what about if we're searching a really large list of things and we anticipate a lot of them not being worth consideration?

Use numpy. How can the Euclidean distance be calculated with NumPy? I ran my tests using this simple program:! My tests were run with Python 2. Another instance of this problem solving method : def dist x,y : return numpy.

Code to reproduce the plot: import matplotlib import numpy import perfplot from scipy. I find a 'dist' function in matplotlib. I'm posting it here just for reference. Lastly, we wasted two operations on to store the result and reload it for return 