algorithm-tutorial | 算法和数据结构教程 - | Learning library

by dunwu Java Version: Current License: CC-BY-SA-4.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | algorithm-tutorial Summary

algorithm-tutorial is a Java library typically used in Tutorial, Learning, Example Codes, LeetCode applications. algorithm-tutorial has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

algorithm-tutorial

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Quality

Security

License

Reuse

Support

algorithm-tutorial has a low active ecosystem.

It has 158 star(s) with 52 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

algorithm-tutorial has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of algorithm-tutorial is current.

Quality

algorithm-tutorial has 0 bugs and 0 code smells.

Security

algorithm-tutorial has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

algorithm-tutorial code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

algorithm-tutorial is licensed under the CC-BY-SA-4.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

algorithm-tutorial releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

algorithm-tutorial saves you 5225 person hours of effort in developing the same functionality from scratch.

It has 10974 lines of code, 876 functions and 219 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed algorithm-tutorial and discovered the below as its top functions. This is intended to give you an instant insight into algorithm-tutorial implemented functionality, and help decide if they suit your requirements.

The main method
Resize the data
Removes the element at specified index
Add element at index
The main entry point
Inserts an element at the front of the deque
Inserts an element at the end of the deque
Test to see if the two lists are identical
Removes the element with the specified value
Build a BTree tree
Test
Returns the spiral order
Convenience method
Check the permutation
Convert a number to an integer
Order
Find the first occurrence of haystack in haystack
Convert a string to an integer value
Main method for testing
Go back to the current page
Compress string
Test function
Adds the specified key to the tree
Simple test program
Parses two strings
Main method for testing

Get all kandi verified functions for this library.

algorithm-tutorial Key Features

No Key Features are available at this moment for algorithm-tutorial.

algorithm-tutorial Examples and Code Snippets

No Code Snippets are available at this moment for algorithm-tutorial.

Community Discussions

Trending Discussions on algorithm-tutorial

Creating a genetic algorithm

Optimize Input Vector to achieve optimal results

QUESTION

Creating a genetic algorithm

Asked 2021-Feb-19 at 05:50

Im trying to recreate this code: https://github.com/Code-Bullet/Smart-Dots-Genetic-Algorithm-Tutorial/tree/master/BestTutorialEver , but in python, and it doesn't work, it keeps mutating the best dot and every generation starts with less dots. Here is the code (i use pygame for graphics):

Brain class:

...

ANSWER

Answered 2021-Feb-19 at 05:39

I did not try the project you mentioned. You may try PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms. It is open-source where you can find the code at GitHub.

It is simple to use which allows you to control the crossover, mutation, and parent selection operators in an easy way. You can also control many parameters of the genetic algorithm using PyGAD.

PyGAD also works with a user-defined fitness function so you can adapt it to a wide-range of problems.

After installing PyGAD (pip install pygad), here is a simple example to get started that tries to find the best values for W1, W2, and W3 that satisfies the following equation:

44 = 4xW_1 - 2xW_2 + 1.2xW_3

Source https://stackoverflow.com/questions/65863290

QUESTION

Optimize Input Vector to achieve optimal results

Asked 2018-Sep-27 at 22:35

I couldn't figure out the following problem: if I may, I'd like to give you right away an example
Imagine, you work with marketing data and you came up with a good regression model, predicting the "reach" of a certain campaign. All fine and dandy. Data Scientist Job done.
But wait. We can do more.
My question to you is:
Assuming that we have a good model, how can we optimize the input vector ( = marketing campaign) to get the best possible "reach" ( = predictor / goal to optimize)?
I was googling like crazy, but couldn't find any good approach (I am not talking about any hypterparameter optimization). The best approach I found so far is a genetic algorithm... example here and here
Or - a brute force approach - calculate an enormous grid with tons of possible input vectors and then check, which one is the best (straight forward) - but that would be computational expensive.

I would love to hear your opinion on this. Any advice on which topics I should check out?

...

ANSWER

Answered 2018-Sep-27 at 22:35

A very long comment:

Genetic algorithms can be nested. Put your genetic solution finder into a fitness function. Give it to a parent genetic algorithm. Have them search results by "optimizing input vector" from outer GA and "optimizing goal" from inner GA.

You can even add a third layer of GA, to test the construction parameters of middle layer GA because we may not know what kind of search space we need. If we knew it, then we wouldn't need to optimize that vector.

You can even decrease dimensions of problem per GA this way.

Source https://stackoverflow.com/questions/52545116

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

Vulnerabilities

No vulnerabilities reported

Install algorithm-tutorial

You can download it from GitHub.
You can use algorithm-tutorial 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 algorithm-tutorial 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 maven.apache.org. For Gradle installation, please refer gradle.org .

Support

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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