A quine is a software which outputs its own source code. Quineforge is a very experimental data format for the lossless compression of quines and for translating all possible non-quines (such as pictures of cats, videos, games, GPU matrix multiply algorithms, or nearly anything) into quine form. It uses the (wikipedia page) Chain_rule_for_Kolmogorov_complexity and a 5-way gametree (like a chess or go gametree) to navigate the space of all possible lambda functions. Its security level is planned to be, eventually after the bugs are worked out, enough for the operation of industrial machines and low lag enough to satisfy hardcore gamers. TODO I should copy some of the "fntape" (5 way tree) theory from occamsfuncer readme and various parts of kolmogorov theory. Basically, for example, if we are using sha3_256 (with some pre and post processing of a merkle forest node (or its faster lazy merkle form), then at some few points in a sequence of bits will occur those 256 bits, and the 256 bit ids of other functions, sparsely, and between those are 1 bit at a time opcodes (or 3 or 4 bit opcodes, or something like that), with some opcodes being to say that what follows is a variable size number of 1 bits followed by a 0 bit, then a powOf2 number of bits is a complete binary tree of bits (cbt) which is a lambda function of Lx.Ly.Lz.zxy aka the church encoding of the pair function whose "leafs" are Lx.Ly.x (true) and Lx.L.y.y (false), which goes into the "register" of "fntape" which is basically a lambda datastruct of 2 linkedlists with 5 possible actions from each possible state: move turingtape left (whose cell contents are each a function), move it right, copy register to center of tape, copy center of tape to register, or (heres the only turing complete part) call register on whats at center of turing tape and replace register with what that returns (and using various statistical methods if there is an infinite loop or other nonhalting lambda call it will be given up on quickly before that happens, within some approx specified low lag limits, but compressed forms are expected not to have nonhalters or overly expensive operations etc else they are not shared in the network as often as cheaper faster more useful data structures). So basically theres a bunch of functions, in the space of all possible lambda functions sparsely explored among many computers and people (some of which may be cat memes, minigames, compressed random bitstrings, or whatever) and fntape kind of opcodes aka small bitstrings from one id256 to another id256 such as to say its left child (a few bits of fntape) or its right child etc, or various combinator on eachother, leads to what else. Its a space where, as the name quineforge implies, the distance of bitstring from any function, or from any small set of functions, to any other function, has bigO that is certainly within the distance predicted by kolmogorov complexity theory and which in practice may be able to compete with zip files, 7z files, wavelet sound compression, neuralnet video compression, AIXI compression, andOr any other imaginable kind of compression, and trading compression efficiency for low lag and scalability etc, you might build low lag high voxel count massively multiplayer games with it, or various experiments somewhere within that.

🗺 Um script Python simples que busca páginas aleatórias da Wikipédia e permite que o usuário selecione uma para abrir em seu navegador padrão

# Random Forest Project For this project we will be exploring publicly available data from [LendingClub.com](www.lendingclub.com). Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this. Lending club had a [very interesting year in 2016](https://en.wikipedia.org/wiki/Lending_Club#2016), so let's check out some of their data and keep the context in mind. This data is from before they even went public. We will use lending data from 2007-2010 and be trying to classify and predict whether or not the borrower paid back their loan in full. You can download the data from [here](https://www.lendingclub.com/info/download-data.action) or just use the csv already provided. It's recommended you use the csv provided as it has been cleaned of NA values. Here are what the columns represent: * credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise. * purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other"). * int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates. * installment: The monthly installments owed by the borrower if the loan is funded. * log.annual.inc: The natural log of the self-reported annual income of the borrower. * dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income). * fico: The FICO credit score of the borrower. * days.with.cr.line: The number of days the borrower has had a credit line. * revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle). * revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available). * inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months. * delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years. * pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Random Wikipedia articles. Exploring MVVM, RxSwift and Moya.

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his Python program fetches a random Wikipedia article for you to discover. Decide whether to read or explore another article.

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A simple Wikipedia Viewer built with HTML, CSS and JavaScript. Search Wikipedia articles in real time using the MediaWiki API, or explore random entries with a single click. Inspired by the FreeCodeCamp challenge.

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An infinite scrolling interface for exploring random Wikipedia articles in multiple languages designed in a beautiful UI. Try it out on Mobile and Desktop!

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Cluster Wikipedia documents using k-means Explore the role of random initialization on the quality of the clustering Explore how results differ after changing the number of clusters Evaluate clustering, both quantitatively and qualitatively

Wikipedia mobile app using Kotlin. Users can explore random Wiki pages, search, view their history and toggle some Wiki pages as favorites so as to see them offline

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Discover NBA players from various eras with a single click. This app randomly presents player names and images. Click on a name to explore their Wikipedia page for more details about their career.

Wiki: Develop an online encyclopedia similar to Wikipedia using Django. Users can view, search, create, edit, and explore entries. Utilizes Markdown for human-friendly entry creation. Implements features like entry pages, an index with clickable entries, search functionality, and the ability to create, edit, and explore random entries.

A lightweight web app that lets you explore Wikipedia in a unique, interactive way. Enter any topic (or use a random one), and the app displays its summary with every word clickable — allowing you to jump deeper into related concepts instantly.

By wikipedia: A Rapidly exploring random tree (RRT) is a data structure and algorithm designed for efficiently searching nonconvex, high-dimensional search spaces. The tree is constructed in such a way that any sample in the space is added by connecting it to the closest sample already in the tree.

Overview In this project, we will make use of Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. We will write code to import the data and answer interesting questions about it by computing descriptive statistics. We will also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics. What Software Do I Need? To complete this project, the following software requirements apply: We should have Python 3, NumPy, and pandas installed Divvy is a bicycle sharing system in the City of Chicago and two adjacent suburbs (image: Wikipedia) Bike Share Data Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day. Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used. In this project, We will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. We will compare the system usage between three large cities: Chicago, New York City, and Washington, DC. The Datasets Randomly selected data for the first six months of 2017 are provided for all three cities. All three of the data files contain the same core six (6) columns: Start Time (e.g., 2017-01-01 00:07:57) End Time (e.g., 2017-01-01 00:20:53) Trip Duration (in seconds - e.g., 776) Start Station (e.g., Broadway & Barry Ave) End Station (e.g., Sedgwick St & North Ave) User Type (Subscriber or Customer) The Chicago and New York City files also have the following two columns: Gender Birth Year Data for the first 10 rides in the new_york_city.csv file The original files are much larger and messier, and we don't need to download them, but they can be accessed here if We would like to see them (Chicago, New York City, Washington). These files had more columns and they differed in format in many cases. Some data wrangling has been performed to condense these files to the above core six columns to make our analysis and the evaluation of our Python skills more straightforward. In the Data Wrangling course that comes later in the Data Analyst Nanodegree program, students learn how to wrangle the dirtiest, messiest datasets, so don't worry, we won't miss out on learning this important skill! Statistics Computed We will learn about bike share use in Chicago, New York City, and Washington by computing a variety of descriptive statistics. In this project,we will write code to provide the following information: #1 Popular times of travel (i.e., occurs most often in the start time) most common month most common day of week most common hour of day #2 Popular stations and trip most common start station most common end station most common trip from start to end (i.e., most frequent combination of start station and end station) #3 Trip duration total travel time average travel time #4 User info counts of each user type counts of each gender (only available for NYC and Chicago) earliest, most recent, most common year of birth (only available for NYC and Chicago) The Files To answer these questions using Python, we will need to write a Python script. To help guide our work in this project, a template with helper code and comments is provided in a bikeshare.py file, and we will do our scripting in there also. We will need the three city dataset files too: chicago.csv new_york_city.csv washington.csv All four of these files are zipped up in the Bikeshare file in the resource tab in the sidebar on the left side of this page. We may download and open up that zip file to do our project work on our local machine. Some versions of this project also include a Project Workspace page in the classroom where the bikeshare.py file and the city dataset files are all included, and we can do all our work with them there. We may use the template provided in bikeshare.py to complete this project. We should feel free to change the template however we would like, as long as our code provides the statistics shown in the template, and allows a user to give input on which data they would like to see. An Interactive Experience The bikeshare.py file is set up as a script that takes in raw input to create an interactive experience in the terminal that answers questions about the dataset. The experience is interactive because depending on a user's input, the answers to the questions on the previous page will change! There are four questions that will change the answers: Would we like to see data for Chicago, New York, or Washington? Would we like to filter the data by month, day, or not at all? (If they chose month) Which month - January, February, March, April, May, or June? (If they chose day) Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? he answers to the questions above will determine the city and timeframe on which we would do data analysis. After filtering the dataset, users will see the statistical result of the data, and