algorithms | Minimal examples of data structures and algorithms in Python | Learning library

 by   keon Python Version: 0.1.4 License: MIT

kandi X-RAY | algorithms Summary

kandi X-RAY | algorithms Summary

algorithms is a Python library typically used in Tutorial, Learning, Example Codes applications. algorithms has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install algorithms' or download it from GitHub, PyPI.

[Coverage Status] Pythonic Data Structures and Algorithms.

            kandi-support Support

              algorithms has a medium active ecosystem.
              It has 22653 star(s) with 4486 fork(s). There are 640 watchers for this library.
              It had no major release in the last 12 months.
              There are 56 open issues and 100 have been closed. On average issues are closed in 250 days. There are 141 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of algorithms is 0.1.4

            kandi-Quality Quality

              algorithms has 0 bugs and 0 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 0 security hotspots that need review.

            kandi-License License

              algorithms 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

              algorithms releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              algorithms saves you 5709 person hours of effort in developing the same functionality from scratch.
              It has 11941 lines of code, 1262 functions and 382 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.
            • Return the number of islands in a list
            • Determines the depth - first search
            • Adds a neighbor to the graph
            • Return a shallow copy of the graph
            • Strip url params from a URL
            • Splits a path into parts
            • Test the test
            • Return the last kth point in the head of the head
            • Generate a random key
            • Test if n is a prime number
            • Deletes a node
            • Solve a SAT formula
            • Generates a vector traversal
            • Test if two nodes intersect
            • Stops a stoogor
            • Insert a node
            • Multiply a matrix
            • Test for delete_node
            • Length of longest path
            • Recursively find all connected cliques
            • Solve Chinese Remaining
            • Invert a matrix
            • Calculate the kruskal distance
            • Compare two numbers
            • Return a polynomial for a cycle index
            • Generate a greedy set cover
            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

            copy iconCopy
            from mlagents_envs.environment import UnityEnvironment
            from mlagents_envs.envs import UnityToGymWrapper
            from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
            from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
            from baselines.b  
            copy iconCopy
            pip install git+git://
            import gym
            from baselines import deepq
            from baselines import logger
            from mlagents_envs.environment import UnityEnvironment
            from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper
            def main(  
            Unity ML-Agents Gym Wrapper-Run Google Dopamine Algorithms-Hyperparameters
            C#dot img3Lines of Code : 32dot img3License : Non-SPDX (NOASSERTION)
            copy iconCopy
            import dopamine.agents.rainbow.rainbow_agent
            import dopamine.unity.run_experiment
            import dopamine.replay_memory.prioritized_replay_buffer
            RainbowAgent.num_atoms = 51
            RainbowAgent.stack_size = 1
            Finds optimal binary search tree .
            pythondot img4Lines of Code : 67dot img4License : Permissive (MIT License)
            copy iconCopy
            def find_optimal_binary_search_tree(nodes):
                This function calculates and prints the optimal binary search tree.
                The dynamic programming algorithm below runs in O(n^2) time.
                Implemented from CLRS (Introduction to Algorithms) book.
            Validate input .
            pythondot img5Lines of Code : 51dot img5License : Permissive (MIT License)
            copy iconCopy
            def _validate_input(points: list[Point] | list[list[float]]) -> list[Point]:
                validates an input instance before a convex-hull algorithms uses it
                points: array-like, the 2d points to validate before usi  
            Summarize the contents of an OpenLibrary object .
            pythondot img6Lines of Code : 24dot img6License : Permissive (MIT License)
            copy iconCopy
            def summarize_book(ol_book_data: dict) -> dict:
                 Given Open Library book data, return a summary as a Python dict.
                >>> pass  # Placate
                desired_keys = {

            Community Discussions


            polynomial (in n) time algorithm that decides whether N is a power
            Asked 2022-Apr-02 at 22:23

            I am a computer science student; I am studying the Algorithms course independently.

            During the course, I saw this question:

            Given an n-bit integer N, find a polynomial (in n) time algorithm that decides whether N is a power (that is, there are integers a and k > 1 so that a^k = N).

            I thought of a first option that is exponential in n: For all k , 1

            For example, if N = 27, I will start with k = 2 , because 2 doesn't divide 27, I will go to next k =3. I will divide 27 / 3 to get 9, and divide it again until I will get 1. This is not a good solution because it is exponential in n.

            My second option is using Modular arithmetic, using ak ≡ 1 mod (k+1) if gcd(a, k+1 ) = 1 (Euler's theorem). I don't know if a and k are relatively prime.

            I am trying to write an algorithm, but I am struggling to do it:



            Answered 2022-Mar-15 at 10:07

            Ignoring the cases when N is 0 or 1, you want to know if N is representable as a^b for a>1, b>1.

            If you knew b, you could find a in O(log(N)) arithmetic operations (using binary search). Each arithmetic operation including exponentiation runs in polynomial time in log(N), so that would be polynomial too.

            It's possible to bound b: it can be at most log_2(N)+1, otherwise a will be less than 2.

            So simply try each b from 2 to floor(log_2(N)+1). Each try is polynomial in n (n ~= log_2(N)), and there's O(n) trials, so the resulting time is polynomial in n.



            Java PriorityQueue: how to heapify a Collection with a custom Comparator?
            Asked 2022-Mar-10 at 03:24

            For example, given a List of Integer List list = Arrays.asList(5,4,5,2,2), how can I get a maxHeap from this List in O(n) time complexity?

            The naive method:



            Answered 2021-Aug-28 at 15:09
            If you don't mind some hack

            According to the java doc of PriorityQueue(PriorityQueue)

            Creates a PriorityQueue containing the elements in the specified priority queue. This priority queue will be ordered according to the same ordering as the given priority queue.

            So we can extend PriorityQueue as CustomComparatorPriorityQueue to hold the desired comparator and the Collection we need to heapify. Then call new PriorityQueue(PriorityQueue) with an instance of CustomComparatorPriorityQueue.

            Below is tested to work in Java 15.



            What is a "closure" in Julia?
            Asked 2022-Feb-03 at 18:34

            I am learning how to write a Maximum Likelihood implementation in Julia and currently, I am following this material (highly recommended btw!). So the thing is I do not fully understand what a closure is in Julia nor when should I actually use it. Even after reading the official documentation the concept still remain a bit obscure to me.

            For instance, in the tutorial, I mentioned the author defines the log-likelihood function as:



            Answered 2022-Feb-03 at 18:34

            In the context you ask about you can think that closure is a function that references to some variables that are defined in its outer scope (for other cases see the answer by @phipsgabler). Here is a minimal example:



            Paramiko authentication fails with "Agreed upon 'rsa-sha2-512' pubkey algorithm" (and "unsupported public key algorithm: rsa-sha2-512" in sshd log)
            Asked 2022-Jan-13 at 14:49

            I have a Python 3 application running on CentOS Linux 7.7 executing SSH commands against remote hosts. It works properly but today I encountered an odd error executing a command against a "new" remote server (server based on RHEL 6.10):

            encountered RSA key, expected OPENSSH key

            Executing the same command from the system shell (using the same private key of course) works perfectly fine.

            On the remote server I discovered in /var/log/secure that when SSH connection and commands are issued from the source server with Python (using Paramiko) sshd complains about unsupported public key algorithm:

            userauth_pubkey: unsupported public key algorithm: rsa-sha2-512

            Note that target servers with higher RHEL/CentOS like 7.x don't encounter the issue.

            It seems like Paramiko picks/offers the wrong algorithm when negotiating with the remote server when on the contrary SSH shell performs the negotiation properly in the context of this "old" target server. How to get the Python program to work as expected?

            Python code



            Answered 2022-Jan-13 at 14:49

            Imo, it's a bug in Paramiko. It does not handle correctly absence of server-sig-algs extension on the server side.

            Try disabling rsa-sha2-* on Paramiko side altogether:



            Circular objects rotate angle detection
            Asked 2022-Jan-04 at 05:52

            I'm trying to detect angle difference between two circular objects, which be shown as 2 image below.

            I'm thinking about rotate one of image with some small angle. Every time one image rotated, SSIM between rotated image and the another image will be calculated. The angle with maximum SSIM will be the angle difference.

            But, finding the extremes is never an easy problem. So my question is: Are there another algorithms (opencv) can be used is this case?

            IMAGE #1

            IMAGE #2


            Thanks @Micka, I just do the same way he suggest and remove black region like @Yves Daoust said to improve processing time. Here is my final result:




            Answered 2021-Dec-15 at 09:19

            Here's a way to do it:

            1. detect circles (for the example I assume circle is in the image center and radius is 50% of the image width)
            2. unroll circle images by polar coordinates
            3. make sure that the second image is fully visible in the first image, without a "circle end overflow"
            4. simple template matching

            Result for the following code:



            Android: Iterative queue-based flood fill algorithm 'expandToNeighborsWithMap()' function is unusually slow
            Asked 2021-Dec-30 at 04:27

            (Solution has been found, please avoid reading on.)

            I am creating a pixel art editor for Android, and as for all pixel art editors, a paint bucket (fill tool) is a must need.

            To do this, I did some research on flood fill algorithms online.

            I stumbled across the following video which explained how to implement an iterative flood fill algorithm in your code. The code used in the video was JavaScript, but I was easily able to convert the code from the video to Kotlin:


            Here is an excerpt of the JavaScript code from the video:

            Converted code:



            Answered 2021-Dec-29 at 08:28

            I think the performance issue is because of expandToNeighbors method generates 4 points all the time. It becomes crucial on the border, where you'd better generate 3 (or even 2 on corner) points, so extra point is current position again. So first border point doubles following points count, second one doubles it again (now it's x4) and so on.

            If I'm right, you saw not the slow method work, but it was called too often.



            Sorting an array of integers in nlog(n) time without using comparison operators
            Asked 2021-Nov-04 at 14:49

            Imagine there's have an array of integers but you aren't allowed to access any of the values (so no Arr[i] > Arr[i+1] or whatever). The only way to discern the integers from one another is by using a query() function: this function takes a subset of elements as inputs and returns the number of unique integers in this subset. The goal is to partition the integers into groups based on their values — integers in the same group should have the same value, while integers in different groups have different values. The catch - the code has to be O(nlog(n)), or in other words the query() function can only be called O(nlog(n)) times.

            I've spent hours optimizing different algorithms in Python, but all of them have been O(n^2). For reference, here's the code I start out with:



            Answered 2021-Nov-04 at 14:49

            Let's say you have an element x and an array of distinct elements, A = [x0, x1, ..., x_{k-1}] and want to know if the x is equivalent to some element in the array and if yes, to which element.

            What you can do is a simple recursion (let's call it check-eq):

            • Check if query([x, A]) == k + 1. If yes, then you know that x is distinct from every element in A.
            • Otherwise, you know that x is equivalent to some element of A. Let A1 = A[:k/2], A2 = A[k/2+1:]. If query([x, A1]) == len(A1), then you know that x is equivalent to some element in A1, so recurse in A1. Otherwise recurse in A2.

            This recursion takes at most O(logk) steps. Now, let our initial array be T = [x0, x1, ..., x_{n-1}]. A will be an array of "representative" of the groups of elements. What you do is first take A = [x0] and x = x1. Now use check-eq to see if x1 is in the same group as x0. If no, then let A = [x0, x1]. Otherwise do nothing. Proceed with x = x2. You can see how it goes.

            Complexity is of course O(nlogn), because check-eq is called exactly n-1 times and each call take O(logn) time.



            Minimum moves to transform a list so that each element equals its own frequency
            Asked 2021-Oct-03 at 10:59

            Given a sorted array like [1,2,4,4,4]. Each element in the array should occur exactly same number of times as element. For Example 1 should occur 1 time, 2 should occur 2 times, n should occur n times, After minimum number of moves, the array will be rendered as [1,4,4,4,4]



            Answered 2021-Sep-24 at 19:58

            The error is in this comparison:



            Iterator concepts are weaker than the corresponding named requirements, which ones apply to the non-range standard algorithms?
            Asked 2021-Sep-24 at 18:17

            In C++20 we got concepts for iterator categories, e.g. std::forward_iterator corresponds to named requirement ForwardIterator.

            They are not equivalent. In some (but not all) regards, the concepts are weaker:

            • (1) "Unlike the [ForwardIterator or stricter] requirements, the [corresponding] concept does not require dereference to return an lvalue."

            • (2) "Unlike the [InputIterator] requirements, the input_iterator concept does not require equality_comparable, since input iterators are typically compared with sentinels."

            • ...

            The new std::ranges algorithms seem to use the concepts to check iterator requirements.

            But what about the other standard algorithms? (that existed pre-C++20)

            Do they still use the same iterator requirements in C++20 as they were pre-C++20, or did the requirements for them get relaxed to match the concepts?



            Answered 2021-Sep-24 at 18:17

            Do they still use the same iterator requirements in C++20 as they were pre-C++20, or did the requirements for them get relaxed to match the concepts?

            The existing std:: algorithms still use the named requirements. For instance, [alg.find] has both:



            Computing the volume of the union of axis-aligned cubes
            Asked 2021-Sep-11 at 13:25

            Given is a set S of n axis-aligned cubes. The task is to find the volume of the union of all cubes in S. This means that every volume-overlap of 2 or more cubes is only counted once. The set specifically contains all the coordinates of all the cubes.

            I have found several papers regarding the subject, presenting algorithms to complete the task. This paper for example generalizes the problem to any dimension d rather than the trivial d=3, and to boxes rather than cubes. This paper as well as a few others solve the problem in time O(n^1.5) or slightly better. Another paper which looks promising and is specific to 3d-cubes is this one which solves the task in O(n^4/3 log n). But the papers seem rather complex, at least for me, and I cannot follow them clearly.

            Is there any relatively simple pseudocode or procedure that I can follow to implement this idea? I am looking for a set of instructions, what exactly to do with the cubes. Any implementation will be also excellent. And O(n^2) or even O(n^3) are fine.

            Currently, my approach was to compute the total volume, i.e the sum of all volumes of all the cubes, and then compute the overlap of every 2 cubes, and reduce it from the total volume. The problem is that every such overlap may (or may not) be of a different pair of cubes, meaning an overlap can be for example shared by 5 cubes. In this approach the overlap will be counted 10 times rather just once. So I was thinking maybe an inclusion-exclusion principle may prove itself useful, but I don't know exactly how it may be implemented specifically. Computing every overlap (naively) is already O(n^2), but doable. Is there any better way to compute all such overlaps? Anyways, I don't assume this is a useful approach, to continue along these lines.



            Answered 2021-Sep-10 at 21:41

            Here's some Python (sorry, didn't notice the Java tag) implementing user3386109's suggestion. This algorithm is O(n³ log n). We could get down to O(n³) by sorting the events for all cubes once and extracting the sorted sub-sequence that we need each time, but perhaps this is good enough.


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


            No vulnerabilities reported

            Install algorithms

            You can install using 'pip install algorithms' or download it from GitHub, PyPI.
            You can use algorithms like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            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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