Algorithms | Collection of Java Algorithms | Learning library

 by   lewin Java Version: Current License: No License

kandi X-RAY | Algorithms Summary

kandi X-RAY | Algorithms Summary

Algorithms is a Java library typically used in Tutorial, Learning, Example Codes applications. Algorithms has no vulnerabilities and it has low support. However Algorithms has 13 bugs and it build file is not available. You can download it from GitHub.

Collection of Java Algorithms

            kandi-support Support

              Algorithms has a low active ecosystem.
              It has 27 star(s) with 7 fork(s). There are 7 watchers for this library.
              It had no major release in the last 6 months.
              Algorithms has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Algorithms is current.

            kandi-Quality Quality

              Algorithms has 13 bugs (0 blocker, 2 critical, 11 major, 0 minor) and 725 code smells.

            kandi-Security Security

              Algorithms has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Algorithms code analysis shows 0 unresolved vulnerabilities.
              There are 1 security hotspots that need review.

            kandi-License License

              Algorithms does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              Algorithms releases are not available. You will need to build from source code and install.
              Algorithms has no build file. You will be need to create the build yourself to build the component from source.
              Algorithms saves you 1492 person hours of effort in developing the same functionality from scratch.
              It has 3328 lines of code, 254 functions and 60 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Algorithms and discovered the below as its top functions. This is intended to give you an instant insight into Algorithms implemented functionality, and help decide if they suit your requirements.
            • Initialize the graph
            • Depth - first search
            • Depth - first search of a node
            • Solves the QR decomposition
            • Simple phase test
            • Pij
            • The number generator
            • Calculate mod of a long
            • Returns the log logical log of g
            • Returns the inverse of the quaternion
            • Calculates the maximum match
            • Tracks the Tarjan
            • Builds a suffix tree
            • Simple simple matrices
            • Returns the closest pair of points
            • Calculates the KMP for a word
            • Add a key in the tree
            • Calculates the flow between source and sink
            • Returns the inverse of the matrix
            • Solve 2 solver
            • Returns the determinant
            • Returns the number of elements from source and sink
            • Returns the suffix array sorted
            • Initialize depth
            • Calculate the minimum spanning tree cost
            • Returns the convex hull
            Get all kandi verified functions for this library.

            Algorithms Key Features

            No Key Features are available at this moment for Algorithms.

            Algorithms Examples and Code Snippets

            No Code Snippets are available at this moment for Algorithms.

            Community Discussions


            Apache Beam SIGKILL
            Asked 2021-Jun-15 at 13:51

            The Question

            How do I best execute memory-intensive pipelines in Apache Beam?


            I've written a pipeline that takes the Naemura Bird dataset and converts the images and annotations to TF Records with TF Examples of the required format for the TF object detection API.

            I tested the pipeline using DirectRunner with a small subset of images (4 or 5) and it worked fine.

            The Problem

            When running the pipeline with a bigger data set (day 1 of 3, ~21GB) it crashes after a while with a non-descriptive SIGKILL. I do see a memory peak before the crash and assume that the process is killed because of a too high memory load.

            I ran the pipeline through strace. These are the last lines in the trace:



            Answered 2021-Jun-15 at 13:51

            Multiple things could cause this behaviour, because the pipeline runs fine with less Data, analysing what has changed could lead us to a resolution.

            Option 1 : clean your input data

            The third line of the logs you provide might indicate that you're processing unclean data in your bigger pipeline mmap(NULL, could mean that | "Get Content" >> beam.Map(lambda x: x.read_utf8()) is trying to read a null value.

            Is there an empty file somewhere ? Are your files utf8 encoded ?

            Option 2 : use smaller files as input

            I'm guessing using the fileio.ReadMatches() will try to load into memory the whole file, if your file is bigger than your memory, this could lead to errors. Can you split your data into smaller files ?

            Option 3 : use a bigger infrastructure

            If files are too big for your current machine with a DirectRunner you could try to use an on-demand infrastructure using another runner on the Cloud such as DataflowRunner



            Python: Converting a list of strings into pandas data-frame with two columns a and b, corresponding to odd and even strings respectively
            Asked 2021-Jun-15 at 12:32

            I have this kind of input as below. It is a list of strings, every odd string is a number starting with MR and every even string is some mixed text. I need to convert this list of strings to a pandas data-frame which strictly has two columns, but because some of the MR numbers are present several times paired with different mixed text counter parts I am getting extra columns everywhere where an MR is repeated, as I am demonstrating below:



            Answered 2021-Jun-15 at 11:48


            Algorithm calculations on perfromance and size
            Asked 2021-Jun-15 at 07:53

            I'm kinda new at algorithms and I'm afraid that my solutions are not correct, help me fix them.



            Answered 2021-Jun-15 at 07:53
            • the first one is correct
            • so as you said valuation instructions are how many time we are assigning values with the operator then I can see almost 10 assign variables(considering that initializing of i and j as well as i++ (i=i+1) and j++)
            • for the third question as you are using an array of size n your space complexity is O(n)
            • for the 4th question, as you are using two nestedfor loops and you are iterating like n+(n-1)+(n-2)+... = n*(n+1)/2 = (n^2+n)/2 = O(n^2) (Time complexity)



            How can I limit and sort on document ID in firestore?
            Asked 2021-Jun-15 at 04:04

            I have a collection where the documents are uniquely identified by a date, and I want to get the n most recent documents. My first thought was to use the date as a document ID, and then my query would sort by ID in descending order. Something like .orderBy(FieldPath.documentId, descending: true).limit(n). This does not work, because it requires an index, which can't be created because __name__ only indexes are not supported.

            My next attempt was to use .limitToLast(n) with the default sort, which is documented here.

            By default, Cloud Firestore retrieves all documents that satisfy the query in ascending order by document ID

            According to that snippet from the docs, .limitToLast(n) should work. However, because I didn't specify a sort, it says I can't limit the results. To fix this, I tried .orderBy(FieldPath.documentId).limitToLast(n), which should be equivalent. This, for some reason, gives me an error saying I need an index. I can't create it for the same reason I couldn't create the previous one, but I don't think I should need to because they must already have an index like that in order to implement the default ordering.

            Should I just give up and copy the document ID into the document as a field, so I can sort that way? I know it should be easy from an algorithms perspective to do what I'm trying to do, but I haven't been able to figure out how to do it using the API. Am I missing something?

            Edit: I didn't realize this was important, but I'm using the flutterfire firestore library.



            Answered 2021-Jun-15 at 00:56

            From what I've found:

            FieldPath.documentId does not match on the documentId, but on the refPath (which it gets automatically if passed a document reference).

            As such, since the documents are to be sorted by timestamp, it would be more ideal to create a timestamp fieldvalue for createdAt rather than a human-readable string which is prone to string length sorting over the value of the string.

            From there, you can simply sort by date and limit to last. You can keep the document ID's as you intend.



            Encrypt in JS front end and decrypt in python backend using AES GCM
            Asked 2021-Jun-14 at 18:01

            I am trying encrypting in JS front end and decrypt in python backend using AES GCM cryptographic algorithm. I am using Web cryptography api for JS front end and python cryptography library for python backend as cryptographic library. I have fixed the IV for now in both side. I have implemented encryption-decryption code in both side, they work on each side. But I think the padding is done differently, can't seem to figure out how the padding is done in web cryptography api. Here is the encryption and decryption for the python backend:



            Answered 2021-Jun-14 at 18:01

            GCM is a stream cipher mode and therefore does not require padding. During encryption, an authentication tag is implicitly generated, which is used for authentication during decryption. Also, an IV/nonce of 12 bytes is recommended for GCM.

            The posted Python code unnecessarily pads and doesn't take the authentication tag into account, unlike the JavaScript code, which may be the main reason for the different ciphertexts. Whether this is the only reason and whether the JavaScript code implements GCM correctly, is difficult to say, since the getMessageEncoding() method was not posted, so testing this was not possible.

            Also, both codes apply a 16 bytes IV/nonce instead of the recommended 12 bytes IV/nonce.

            Cryptography offers two possible implementations for GCM. One implementation uses the architecture of the non-authenticating modes like CBC. The posted Python code applies this design, but does not take authentication into account and therefore implements GCM incompletely. A correct example for this design can be found here.
            Cryptography generally recommends the other approach for GCM (s. the Danger note), namely the AESGCM class, which performs implicit authentication so that this cannot be accidentally forgotten or incorrectly implemented.

            The following implementation uses the AESGCM class (and also takes into account the optional additional authenticated data):



            Nodejs Restify - Valide Jwt id_token with jwks
            Asked 2021-Jun-14 at 09:19

            We develop an application with VuejS in front and an api Nodejs(Restify) in back. We use a third party for give us authentification (Identity provider with OpenId Connect protocole).

            So with VueJs we can authenticate, get an access_token and id_token and we pass it in each nodejs request header with bearer.

            Now we need to verify,in back, if this token is valid and if the user can access this routes.

            Our Identity provider give us an endpoint (jwks_uri) with a keys like:



            Answered 2021-Jun-04 at 17:54

            I believe the optimal way for small to medium sized application is just to make jwt verification work as a middleware. Something like:



            How to write an efficient algorithm that generates a list of unique random numbers?
            Asked 2021-Jun-13 at 22:15

            I'm pretty new to algorithms and am trying to solve a problem that involves generating a list of 5,000 numbers in random order each time it is run. Each number in the list must be unique and be between 1 and 5,000 (inclusive).



            Answered 2021-Jun-13 at 18:23


            Web cryptography implement HKDF for the output of ECDH
            Asked 2021-Jun-13 at 11:02

            I want implement a elliptic curve diffie hellman using HKDF as key derivation function. I am using a python backend and (vanilla) javascript in frontend. I am using python cryptography library in backend and Web Crypto api in frontend as cryptographic library. I created ECDH key pair in both side and exchanged the pbulic keys. Now I am trying to create the AES shared key with the exchanged public key and private key along with HKDF algorithm. I am able to do it in the python backend (I followed this example for the python code):



            Answered 2021-Jun-13 at 11:02

            The referenced Python code uses P-384 (aka secp384r1) as elliptic curve. This is compatible with the WebCrypto API, which supports three curves P-256 (aka secp256r1), P-384 and P-521 (aka secp521r1), see EcKeyImportParams.

            The following WebCrypto code generates a shared secret using ECDH and derives an AES key from the shared secret using HKDF. In detail the following happens:

            • To allow comparison of the derived key with that of the referenced Python code, predefined EC keys are applied. The private key is imported as PKCS#8, the public key as X.509/SPKI. Note that due to a Firefox bug concerning the import of EC keys, the script below cannot be run in the Firefox browser.
            • After the import the shared secret is created with ECDH using deriveBits() (and not deriveKey()).
            • The shared secret is imported with importKey() and then the AES key is derived using HKDF, again with deriveBits().



            " samples: %r" % [int(l) for l in lengths]) ValueError: Found input variables with inconsistent numbers of samples: [219870, 0, 0]
            Asked 2021-Jun-12 at 20:22

            I'm trying to train some ML algorithms on some data that I collected, but I received an error for input variables with inconsistent numbers of samples. I'm not really sure what variables needs to be changed or not. I've posted my code below to give you a better understanding of what I'm trying to accomplish:



            Answered 2021-Jun-12 at 12:14

            The file has to be opened in binary mode.

            open(DATA_FILE, 'rb')



            Issues running OSMnx on conda
            Asked 2021-Jun-12 at 02:13

            I'm trying to get the Python package OSMnx running on my Windows10 machine. I'm still new to python so struggling with the basics. I've followed the instructions here and have successfully created a new conda environment for it to run in. The installation seems to have gone ok. However, as soon as I try and import it, I get the following error



            Answered 2021-Apr-28 at 10:07

            The module fractions is part of the Python standard library. There used to be a function gcd, which, as the linked documentation says, is:

            Deprecated since version 3.5: Use math.gcd() instead.

            Since the function gcd was removed from the module fractions in Python 3.9, it seems that the question uses Python 3.9, not Python 3.7.6 as the question notes, because that Python version still had fractions.gcd.

            The error is raised by networkx. Upgrading to the latest version of networkx is expected to avoid this issue:


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


            No vulnerabilities reported

            Install Algorithms

            You can download it from GitHub.
            You can use Algorithms like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Algorithms component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer For Gradle installation, please refer .


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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