hamming | Hamming distance and bit counting primitives in Go | Learning library

 by   steakknife Go Version: Current License: MIT

kandi X-RAY | hamming Summary

kandi X-RAY | hamming Summary

hamming is a Go library typically used in Tutorial, Learning, Example Codes applications. hamming has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Copyright 2014, 2015, 2016, 2018 Barry Allard.
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              hamming has a low active ecosystem.
              It has 32 star(s) with 8 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              hamming has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of hamming is current.

            kandi-Quality Quality

              hamming has 0 bugs and 0 code smells.

            kandi-Security Security

              hamming has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              hamming code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              hamming is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              hamming releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.
              It has 1797 lines of code, 176 functions and 15 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed hamming and discovered the below as its top functions. This is intended to give you an instant insight into hamming implemented functionality, and help decide if they suit your requirements.
            • table returns a list of 256 bits .
            • CountBitsUint64 count 1 s in x
            • CountBitsIntsPopCnt count 1 s in x
            • CountBitsUintsPopCnt count 1 s in x
            • CountBitsUintPopCnt count 1 s in x
            • CountBitsUint32 count 1 s in x
            • CountBitsUintReference count 1 s in x
            • CountBitsUint count 1 s in x
            • CountBitsIntPopCnt count 1 s in x
            • Int16s combines two int16 values .
            Get all kandi verified functions for this library.

            hamming Key Features

            No Key Features are available at this moment for hamming.

            hamming Examples and Code Snippets

            No Code Snippets are available at this moment for hamming.

            Community Discussions

            QUESTION

            Using generators to make lazy streams in python
            Asked 2022-Mar-27 at 05:41

            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:

            1. The sequence of Hamming numbers begins with 1.
            2. The remaining values in the sequence are of the form 2h, 3h, and 5h, where h is any Hamming number.
            3. 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 05:41

            The 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.

            Source https://stackoverflow.com/questions/71633932

            QUESTION

            Compute similarity measure in feature matching (BFMatcher) in OpenCV
            Asked 2022-Mar-15 at 23:03

            I am comparing images and I have used BFMatcher to perform feature matching

            My actual code is:

            ...

            ANSWER

            Answered 2022-Mar-15 at 23:03

            I have finally done this, which seems to work well:

            Source https://stackoverflow.com/questions/71473619

            QUESTION

            How to merge for loop output dataframes into one with python?
            Asked 2022-Mar-15 at 01:17

            I have 2 dataframes as following:

            ...

            ANSWER

            Answered 2022-Mar-15 at 00:52

            A vectorized (read "much faster") solution:

            Source https://stackoverflow.com/questions/71475722

            QUESTION

            Audio samples to musical note detection issue
            Asked 2022-Mar-14 at 09:45

            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 09:45

            I 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.

            Source https://stackoverflow.com/questions/71460170

            QUESTION

            Algorithm to dynamically generate m-face list for n-dimensional hypercube
            Asked 2022-Feb-25 at 12:38

            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 18:53

            Every 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.

            Source https://stackoverflow.com/questions/71256623

            QUESTION

            R: using mapply for a function of two vectors
            Asked 2022-Feb-17 at 23:04

            I have an R function that calculates the Hamming distance of two vectors:

            ...

            ANSWER

            Answered 2022-Feb-17 at 23:04

            If dim(M1) and dim(M2) are identical, then you can simply do:

            Source https://stackoverflow.com/questions/71166307

            QUESTION

            ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: (None, 28, 28)
            Asked 2022-Jan-28 at 16:50

            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 09:43

            I 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,)):

            Source https://stackoverflow.com/questions/70890894

            QUESTION

            How to resize an image in python using Pillow like photoshops "hard edges" algorithm
            Asked 2022-Jan-15 at 01:27

            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 01:27

            Make 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.

            Source https://stackoverflow.com/questions/70716024

            QUESTION

            Calculating hamming distance in a given year
            Asked 2021-Dec-13 at 03:35

            I have a following dataframe:

            ...

            ANSWER

            Answered 2021-Dec-13 at 03:35

            The 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:

            Source https://stackoverflow.com/questions/70326263

            QUESTION

            How to shorten the code to convert information word to hamming code in MARIE
            Asked 2021-Nov-30 at 18:24

            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 18:24

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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            You can download it from GitHub.

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