hamming | Computes the Hamming distance between two sequences | Math library
kandi X-RAY | hamming Summary
kandi X-RAY | hamming Summary
Hamming Distance === [NPM version][npm-image]][npm-url] [Build Status][travis-image]][travis-url] [Coverage Status][coveralls-image]][coveralls-url] [Dependencies][dependencies-image]][dependencies-url]. In [information theory] the Hamming distance is number of differences between two sequences of the same length. These sequences may be represented as character strings, binary strings, or arrays.
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QUESTION
I've been messing with streams in python and been trying to generate the Hamming numbers (all the numbers with prime factors of 2, 3, or 5 only). The standard way for doing so, described by Dijkstra, is to observe that:
- The sequence of Hamming numbers begins with 1.
- The remaining values in the sequence are of the form 2h, 3h, and 5h, where h is any Hamming number.
- h is be generated by outputting the value 1, and then merging together 2h, 3h, and 5h
My implementation is this:
...ANSWER
Answered 2022-Mar-27 at 05:41The further you get out into your stream, the more duplicates you're going to have to be merging. The number 2**4 * 3 **4 = 1296 is going to appear 70 times in your multiple stream (8 choose 4), and your program is going to be spending more time merging duplicates than it is outputting new items.
The further you go out, the more duplication you'r going to be dealing with. There is no reason to expect your program to be linear.
QUESTION
I am comparing images and I have used BFMatcher to perform feature matching
My actual code is:
...ANSWER
Answered 2022-Mar-15 at 23:03I have finally done this, which seems to work well:
QUESTION
I have 2 dataframes as following:
...ANSWER
Answered 2022-Mar-15 at 00:52A vectorized (read "much faster") solution:
QUESTION
I'm trying to setup a pipeline allowing me to detect musical notes from audio samples, but the input layer where I identify the frequency content of the samples does not land on the expected values. In the example below I...
- build what I expect to be a 440Hz (A4) sine wave in the FFTW input buffer
- apply the Hamming window function
- lookup the first half the output bins to find the 4 top values and their frequency
ANSWER
Answered 2022-Mar-14 at 09:45I think you've misunderstood the M_2_PI constant in your GenerateSinWave function. M_2_PI is defined as 2.0 / PI.
You should be using 2 * M_PI instead.
This mistake will mean that your generated signal has a frequency of only around 45 Hz. This should be close to the output frequencies you are seeing.
The same constant needs correcting in your HammingWindow function too.
QUESTION
I'm attempting to design an algorithm that, given n, m, and vertices (where n = the dimension of a hypercube, m = the dimension of the faces we're trying to generate, and vertices is an ordered list of vertices in an n-dimensional hypercube), returns an array of arrays of vertices representing m-faces in an n-dimensional hypercube.
That may have been a confusing way to word it, so here are a few examples:
Say we wanted to get an array of edges (1-faces) in a cube (3-dimensional hypercube). If we assume vertices is an ordered array of binary representations of vertices in a cube (i.e. [[0, 0, 0], [0, 0, 1], [0, 1, 0], ..., [1, 1, 1]]), we would have:
ANSWER
Answered 2022-Feb-24 at 18:53Every m-sized subset of the n dimensions is an "orientation" with those m dimensions "free". For each orientation, you can generate a face by generating all 2m combinations of the m coordinates in the m free dimensions, while holding the coordinates for the other dimensions fixed. Each of the 2n-m combinations of coordinates for the other dimensions is the "position" of a different face.
The number of orientations is C(n,m) = n!/m!(n-m)!, so you should end up with C(n,m) * 2n-m faces overall.
The number of edges in a cube, for example = C(3,1) * 22 = 3 * 4 = 12.
The number of faces on a cube is C(3,2) * 2 = 3 * 2 = 6.
It's not too difficult to generate all faces:
- For each orientation, determine the free and fixed dimensions
- Count in binary using the fixed dimensions to find all the face positions
- For each face, count in binary over the free dimensions to generate its vertices.
- Count in binary using the fixed dimensions to find all the face positions
QUESTION
I have an R function that calculates the Hamming distance of two vectors:
...ANSWER
Answered 2022-Feb-17 at 23:04If dim(M1) and dim(M2) are identical, then you can simply do:
QUESTION
I am doing an AI with an interface, using Tensorflow and Flask. I have the following code hosted on PythonAnywhere:
...ANSWER
Answered 2022-Jan-28 at 09:43I think if you reshape your image to be 4D, you should be good to go. So, after image = np.expand_dims(image, axis=0), just run image = image.reshape(image.shape + (1,)):
QUESTION
I am trying to downsize images with no form of resampling. I want it to fully maintain hard edges, and not add any blur / antialiasing whatsoever. Essentially, exactly like the photoshop "Hard edges" resampling mode. However, each and every one of Pillow's built in resampling methods gives me some kind of blur or aliasing effects.
The code im using currently is as follows
...ANSWER
Answered 2022-Jan-15 at 01:27Make sure to save your images as .png files. .jpg files are lossy, and don't compress hard lines very well, since they are designed to store regular photos. If you look at the raw photo data, you will see a sharp transition between zones when you interpolated with NEAREST, but since the image is saved as .jpg, that all goes out the window.
Here's what the raw data is when you downsize with NEAREST:
Here's what the .jpg data looks like:
You can see the transition if filled with noise.
QUESTION
I have a following dataframe:
...ANSWER
Answered 2021-Dec-13 at 03:35The function pairwise_distances can take in a matrix, so it might be easier to just provide the features in a year as a matrix, get back a pairwise matrix of distances and just subset on the comparisons we need. For example, a dataset like yours:
QUESTION
I have made a MARIE code that takes the input of 8 information bits and converts it to 12-bit hamming code. How to shorten this code? I am using the same XOR function for each parity bit with different value of each bit. I have first taken input for 8 bits in a loop and stored it at different addresses like an array. Then, I found the XOR of different parity bits (knowing the positions beforehand) and substituted the parity bits in the main information word (using the addresses of each position). Finally gave an output of the hamming code.
I have posted the code below:
'''
...ANSWER
Answered 2021-Nov-30 at 18:24Some small points:
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