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baccarat-simulator · 2 years

Text

Baccarat Simulator

This is the baccarat simulator program input mode. There are two viewer methods. There is a method that sees 2-6 sheets and a method that only sees 6 sheets. In the input mode, the print function is also supported. You can use the prediction function in input mode using the pattern you created in the simulation options. Paying attention to the arrow signals that the pattern you have specified has been found and bets.

How do I make a pattern and how do I set it up?

Inside the program are instructions on how to make all the educational material pictures and patterns. You can make the pattern you want, test it with simulation, and if it is a profitable pattern, you can test it in practice. Create different patterns, prove them through simulation and make it your own. That's the role of the simulator. And use it as a predictor by using the input mode with the created pattern.

Test your baccarat patterns and betting system. ( 100 shoes)

If the combination of the pattern and system bet you made is not excellent, a downward graph will be made as shown in the picture on the left, and if the combination of pattern and system betting is both excellent, an upward graph will be created as in the picture on the right.

You can also set and test the system betting settings according to your standards. Of course, basic system betting combinations can be set automatically within the program. There is also a manual way to build your own system bet step by step.

Practice at the baccarat game.

This button allows you to skip the steps of the hand. You can study patterns by skipping and place bets.

You can get all probability data of baccarat. You can get infinite data from at least 100 shoes.

You can use the edited patterns and system bets as predictors in the input mode. This is because you made the pattern yourself, so you can bet with confidence. And this was verified safety through simulation. If you enter the online baccarat table in the input mode and input the same as the scoreboard, it will send a signal with a message that a pattern has been found. You can place your bets there. Of course, no prediction is made unless a pattern is found.

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Thanks for reading this long post.

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3 notes · View notes

daemonhxckergrrl · 2 years

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I think we overcompensate with our use of 'anthropomorphising' re: pets.

no, your cat doesn't enjoy watching tennis, but they do enjoy watching something move back and forth. however, does your cat form an emotional attachemnt to you ? yes. do they intentionally knock stuff over because they know it gets a reaction ? yes. do they dream ? quite possibly, at some level. do they learn routines and identify patterns much like we do ? yup yup yup

we have this false dichotomy of 'human+ level intelligence' (self-aware, speech/language, can do math, object permanence, abstract concepts) and 'pure instinct' (kill, eat, sleep, repeat) that completely ignores any creatures that fall in between (not to mention how we just assume there's only one way to reach our level of intelligence and that we're at/near the top of smarts).

hot take, but most mammals and birds have (a form of) ~~ society ~~. but seriously, they have complex social structures and roles and, arguably, some have a sort of basic system of self-governance for their groups (look at wolf packs or lion prides etc. and I mean how wolves actually work, not that Big Alpha Male bs).

another way of looking at it is at how computers work.

println("Hello, world!");

a one-line program in Python that will print "Hello, World!" to the screen. but, what it's actually doing under the hood, is storing ASCII representations of those characters as binary values in memory, then using only logic operations entirely in binary, it converts each character to a glyph and prints it out. at the base level, the processor is reading a binary address (x number of 0s and 1s), accessing binary data, then interpreting that as either an instruction (ADD, SUB, JMP, BNE (jumps if not equal to 0), BEQ (jumps if equal to 0), etc.) or data to be used in an instruction.

my (rather messy) point here, is that the computer is only reading and writing values like 01001101, and yet it is able to perform enough logical operations to where it can write text on a screen. or autogenerate tweets.

this might sound like "so an AI can look sentient, or sapient, without actually being; ditto for pets" and yeah that's one way to read it. however, consider that humans have cognitive abilities that rely on pattern-matching, as well as data storage and retrieval. all our fancy ideas and constructs boil down to our ability to perform simple operations: give out and receive information, recall previous info, match patterns, make links between different information. we're complex in the way your smartphone is complex. layers on top of layers over years (or millenia) of development.

we're not that different from the animals we caution about anthropomorphising. we're built from the se base blocks, just to different extents and in different ways.

#daemon.md #AI #sentience #animals #programming #it's rambly as hell #but there's a loose logic here #an argument waiting to break free #come thunder ! strike lightning - strike

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faizasarwar · 2 months

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What is Programming? A comprehensive guide

Programming : Programming is the process of designing and building instructions that computers can execute to perform specific tasks or solve particular problems. It involves writing, testing, debugging, and maintaining sets of instructions, known as code, using a programming language. These instructions tell the computer what actions to take, how to perform them, and in what order.

Here’s a comprehensive guide to programming:

Understanding Programming Languages:

Programming languages are formal languages with a set of rules and syntax used to write computer programs.

There are numerous programming languages, each with its own syntax, semantics, and use cases.

Examples include Python, Java, JavaScript, C++, Ruby, and many more.

Basic Concepts:

Variables: Symbols that represent data stored in computer memory.

Data Types: Categories that classify data, such as integers, floating-point numbers, strings, and arrays.

Operators: Symbols used to perform operations on data, like addition, subtraction, comparison, etc.

Control Structures: Constructs for controlling the flow of execution in a program, such as loops and conditionals.

Writing Code:

Start with defining the problem you want to solve or the task you want to accomplish.

Break down the problem into smaller, more manageable steps.

Write code to implement each step, using appropriate data structures and algorithms.

Ensure that your code is clear, concise, and well-organized to make it easier to understand and maintain.

Testing and Debugging:

Testing involves running your program with various inputs to verify that it produces the expected outputs.

Debugging is the process of identifying and fixing errors or bugs in your code.

Techniques include using debugging tools, print statements, and code review.

Software Development Life Cycle (SDLC):

SDLC is a framework that outlines the stages involved in developing software, including planning, analysis, design, implementation, testing, deployment, and maintenance.

Each stage has its own set of activities and goals to ensure the successful development and delivery of software products.

Version Control:

Version control systems like Git help track changes to code, collaborate with others, and manage different versions of a project.

They enable developers to work on the same codebase simultaneously, merge changes, and revert to previous versions if needed.

Advanced Topics:

Object-Oriented Programming (OOP): A programming paradigm based on the concept of “objects” that encapsulate data and behavior.

Functional Programming: A programming paradigm focused on the evaluation of mathematical functions and immutable data.

Algorithms and Data Structures: Techniques for organizing and processing data efficiently, crucial for writing efficient code.

Design Patterns: Reusable solutions to common problems encountered in software design.

Web Development: Building web applications using technologies like HTML, CSS, JavaScript, and frameworks like React, Angular, or Vue.js.

Continuous Learning:

Programming is a rapidly evolving field, so continuous learning is essential to stay updated with new languages, tools, and best practices.

Resources for learning include online tutorials, books, courses, coding bootcamps, and participating in coding communities and forums.

Ethical Considerations:

As a programmer, it’s important to consider the ethical implications of the software you develop.

Respect user privacy, security, and accessibility.

Avoid biases in algorithms and ensure fairness and transparency in your code.

Building Projects:

Practice is key to mastering programming. Start with small projects and gradually tackle more complex ones.

Building projects allows you to apply what you’ve learned, experiment with different technologies, and showcase your skills to potential employers or collaborators.

Programming is a valuable skill with diverse applications across various industries, from software dev

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aibyrdidini · 3 months

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A DEEP LEARNING MODEL TO FIDELIZATION

Developing a deep learning model for customer loyalty, or fidelization, can be a powerful tool for businesses to retain customers and increase revenue. In this article, we will discuss how to develop a deep learning model for fidelization using Python.

First, we need to gather data on customer behavior and preferences. This can include data on customer purchases, interactions with the business, and feedback. Once we have this data, we can use it to train a deep learning model to predict which customers are most likely to be loyal to the business.

To do this, we can use a variety of deep learning algorithms, such as neural networks or recurrent neural networks. These algorithms can learn to identify patterns in the data that are indicative of customer loyalty.

Once we have trained our model, we can use it to predict which customers are most likely to be loyal to the business. This can help us to identify which customers to target with loyalty programs and other incentives.

Here is a snippet of code in Python that demonstrates how to train a deep learning model for fidelization:

import pandas as pd

import numpy as np

from sklearn.model_selection import train_test_split

from keras.models import Sequential

from keras.layers import Dense

# Load data

data = pd.read_csv('customer_data.csv')

# Split data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(data.drop('loyalty', axis=1), data['loyalty'], test_size=0.2)

# Define model

model = Sequential()

model.add(Dense(128, activation='relu', input_shape=(X_train.shape[1],)))

model.add(Dense(64, activation='relu'))

model.add(Dense(1, activation='sigmoid'))

# Compile model

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train model

model.fit(X_train, y_train, epochs=10, batch_size=32)

# Evaluate model

score = model.evaluate(X_test, y_test, verbose=0)

print('Test loss:', score[0])

Continue

Once we have trained our deep learning model for fidelization, we can use it to predict which customers are most likely to be loyal to the business. This can help us to identify which customers to target with loyalty programs and other incentives.

For example, we can use the model to predict the loyalty of new customers, and then use this information to tailor our marketing efforts to these customers. We can also use the model to identify which customers are at risk of churning and then take steps to retain them.

Overall, developing a deep learning model for fidelization can be a powerful tool for businesses to retain customers and increase revenue. By using deep learning algorithms to identify patterns in customer behavior and preferences, we can predict which customers are most likely to be loyal to the business, and then use this information to tailor our marketing efforts to these customers.

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skillslash · 6 months

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The Fibonacci series in Python : A Perfect Match for Sequences and Series

When it comes to math and programming, there’s no better match than Python and the Fibonacci Series.

Why so? This is simply because - Fibonacci is a beautiful sequence of numbers that’s made up of the sum of all the previous numbers. It’s so easy to understand because it’s so common in nature. Python is a language that’s easy to read and versatile in nature, so it becomes the perfect tool to help you figure out how these numbers work together.

Fibonacci, is a mathematical series of numbers that start with the numbers 0 and 1. It’s an endless sequence that’s woven into everything, even into the branching of trees. The fibonacci series is like a dance of numbers,, with each number growing out of the sum of the previous ones. It’s an amazing pattern that goes far beyond numbers. And Python is the perfect language for programmers to understand and manipulate the Fibonacci Sequences.

Let’s look at how Python and Fibonacci work together; Here, we look at algorithms, optimization, and practical applications that show the perfect combination of math and programming: -

Understanding the Fibonacci Series

The Fibonacci Series is a mathematical concept of a series of numbers in which each term is a sum of the previous ones. The beauty of the Fibonacci series lies in its complexity. The Fibonacci series is one of the most famous sequences in mathematics, and has its implications across many fields.

It is believed that the Fibonacci series dates back to the 13th century, and it is believed to have originated from the work of the Italian mathematician Leonardo (Leonardo of Pisa) who is also known by the name of Fibonacci.

Mathematically, the Fibonacci Series is defined as “ F(n) = F(n - 1) + F(n - 2)” where, F(0) = 0 and F(1) = 1 . At its core, the Fibonacci Series begins with 0 and 1, and each subsequent number in the sequence is the sum of the two preceding ones: 0, 1, 1, 2, 3, 5, 8, 13, and so on.

Python and the Fibonacci Series : The Connection

Python, a widely used programming language known for its readability provides for an ideal platform for exploring and implementing the Fibonacci series. The simplicity of the Python language allows for the expression of complex mathematical concepts with ease thereby making it a perfect match for sequences and series.

Implementing the Fibonacci series in Python can be done through multiple common approaches, these approaches include:

1. Recursive Algorithm :

Fibonacci series can be generated in Python using a recursive algorithm that uses the mathematical definition, making it a pretty straightforward way.

The function, “fibonacci_recursive(n)”, calculates the ‘nth’ Fibonacci number by recursively summing the two preceding numbers until it reaches the base case that is, (n <= 1), at which point it returns the current number.

Example:

def fibonacci_recursive(n):

if n <= 1:

return n

else:

return fibonacci_recursive(n-1) + fibonacci_recursive(n-2)

# Example usage

result = fibonacci_recursive(5)

print(result) # Output: 5

While seemingly a simple algorithm approach, this approach does not prove to be very efficient for bigger numbers of ‘n’. This is because - this approach is a tedious and long process with a lot of work involved and comes with a high computational cost. Therefore, developers tend to often look into the other approaches to solve Fibonacci numbers, like the iterative approach or the dynamic programming approach.

2. Iterative Approach:

If you’re looking to make a Fibonacci sequence in Python, the iterative algorithm approach is a great way to do it. It is a simple and efficient way to build the Fibonacci sequence.

Starting with the first two numbers (0 and 1), the algorithm calculates the following Fibonacci numbers by adding the last two numbers in the sequence. A loop runs through the number of iterations you want to run thereby expanding the series.

Here’s an example of the iterative Fibonacci algorithm in Python:

def fibonacci_iterative(n):

fib_sequence = [0, 1]

for i in range(2, n+1):

fib_sequence.append(fib_sequence[i-1] + fib_sequence[i-2])

return fib_sequence

Calling ‘fibonacci_iterative(8) ‘ in Python would produce the sequence ‘[0, 1, 1, 2, 3, 5, 8, 13]’ , thereby demonstrating the iterative construction of the Fibonacci series up to the 8th term.

3. Dynamic Programming Approach:

Applying the Dynamic Programming approach to the Fibonacci sequence in Python leads to the use of an array in order to store the sequence in an iterative way. This way, each value is only calculated once and then reused, so that you do not have to perform any extra calculations. Therefore, making this approach a much more efficient one for bigger numbers of n .

Here’s an example of the same:

def fibonacci_dynamic(n):

fib_sequence = [0, 1]

for i in range(2, n+1):

fib_sequence.append(fib_sequence[i-1] + fib_sequence[i-2])

return fib_sequence[n]

In this code example, fib_sequence is a dynamically-generated array that stores intermediate fibonacci values by reference to previously calculated values.

This particular algorithm avoids the need for extra calculations and offers a better solution compared to a simple iterative or recursive approach, making it suitable for dealing with large fibonacci series calculations.

4. Memoization:

If you want to use or are using Python to calculate Fibonacci numbers, you’ll want to know about the memoization algorithm. As it is a great way to save time and make the algorithm more efficient.

Memoization works by storing intermediate values in a dictionary that is generally called a “memo”. When you need to calculate a Fibonacci number, the memoization function checks if it is already present in the dictionary (memo); And if it is, it’ll get the cached result, otherwise it computes the value and stores it in the memo for future reference.

This process of the approach reduces the time complexity of the algorithm, thereby making it more efficient and speedy especially for larger values of ‘n’.

Example:

def fibonacci_memoization(n, memo={}):

if n <= 1:

return n

elif n not in memo:

memo[n] = fibonacci_memoization(n-1, memo) + fibonacci_memoization(n-2, memo)

return memo[n]

result = fibonacci_memoization(10)

print(result) # Output: 55

In the following example, the “memo” dictionary stores the previously computed Fibonacci values, thereby preventing unnecessary calculations and significantly improving the performance of the algorithm.

Practical Applications of the Fibonacci series in Python

From understanding and generating number patterns in artistic designs to optimizing algorithms in computer science, the Fibonacci series has practical applications that work perfectly with the programming language of Python, some of such practical applications are :

Algorithm Optimization:

By leveraging the Fibonacci series, developers can enhance the efficiency of various algorithms that involve repetitive computations. For example, If you're trying to figure out Fibonacci numbers in a certain way, using a memoization algorithm approach can help you avoid doing too many calculations and make the algorithm run faster.

Art and Design:

Fibonacci numbers became popular in art and design because of their ability to generate beautiful patterns and proportions. Python makes it easier to use Fibonacci sequences in your art and design projects. Whether you create intricate patterns in your digital art or designing artistic layouts in your graphics, Python can help you turn mathematics sequences into art and design.

User Interface (UI) Design:

When it comes to creating user interface design, it's important to make sure the layouts and proportions are right for the user experience. You can use the Fibonacci series to create a balanced and visually appealing interface. Python's graphics libraries, like Tkinter and PyQt, make it easy for designers to follow these principles.

Game Development:

The Fibonacci sequence can be used in the context of game development to create sequences of numbers which affect game functions such as level creation, character creation, or resource distribution. Python’s flexibility and ease of use make it an ideal language for including such mathematical elements in game algorithms.

So, there you have it! The Fibonacci sequence is really useful in a lot of different ways, and Python is a great way to use it. You can use it for multiple functions and applications, from optimizing algorithms to making art and designing.

Conclusion

Python and Fibonacci are a great combination. Python's simplicity and adaptability make it easy for developers to work with different algorithms quickly and easily. Whether you're looking to explore math, optimize algorithms, or create something new, Python is a great tool for working with Fibonacci, showing how well math and programming work together in this amazing series of numbers.

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this-week-in-rust · 7 months

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This Week in Rust 520

Hello and welcome to another issue of This Week in Rust! Rust is a programming language empowering everyone to build reliable and efficient software. This is a weekly summary of its progress and community. Want something mentioned? Tag us at @ThisWeekInRust on Twitter or @ThisWeekinRust on mastodon.social, or send us a pull request. Want to get involved? We love contributions.

This Week in Rust is openly developed on GitHub and archives can be viewed at this-week-in-rust.org. If you find any errors in this week's issue, please submit a PR.

Updates from Rust Community

Newsletters

This Month in Rust OSDev: October 2023

Project/Tooling Updates

rust-libp2p v0.53 has been released

Zellij 0.39.0 released

Officially Qualfied - Ferrocene

Rocket's 4th v0.5 Release Candidate

Observations/Thoughts

Writing Rust Bindings for My Python App

A four year plan for async Rust

Cursed Rust: Printing Things The Wrong Way

Rust for JavaScript Developers: An Overview of Testing

Why Golang instead of Rust to develop the Krater desktop app

Google Rewriting Android's Binder In Rust With Promising Results

Dump Rust Struct or Enum Memory Representation as Bytes

How Open Source Projects are Using Kani to Write Better Software in Rust

Creating High Performance Asynchronous Backends With Burn-Compute

Goodbye Python, Hello Rust: Building a RAG CLI Application with Orca

Variadic generics, again

Using Rust, Chrome and NixOS to Take Headless Screenshots for Social Sharing

sudo-rs' first security audit

Destructing trees safely and cheaply

Edge IoT with Rust on ESP: NTP

Rust Walkthroughs

Using Modern Linux Sockets

Miscellaneous

Migrating SecureDrop’s PGP backend from GnuPG to Sequoia

[video] 10x faster - taking charge of the compiler backend

[video] RISC-V Vector Extension in Rust

Embedding simple CSV file in Rust application

Crate of the Week

This week's crate is floem, a native Rust UI library with fine-grained reactivity.

Despite receiving no suggestions, llogiq is reasonably pleased with his choice.

Please submit your suggestions and votes for next week!

Call for Participation

Always wanted to contribute to open-source projects but did not know where to start? Every week we highlight some tasks from the Rust community for you to pick and get started!

Some of these tasks may also have mentors available, visit the task page for more information.

Ockam - Make ockam identity delete (no args) interactive by asking the user to choose from a list of identity names to delete (tuify)

Ockam - Make ockam tcp-outlet delete (no args) interactive by asking the user to choose from a list of tcp-outlet aliases to delete (tuify)

Ockam - Make ockam project show (no args) interactive by asking the user to choose from a list of projects to show (tuify)

If you are a Rust project owner and are looking for contributors, please submit tasks here.

Updates from the Rust Project

366 pull requests were merged in the last week

dropck_outlives check whether generator witness needs_drop

account for ref and mut in the wrong place for pattern ident renaming

add a stable MIR visitor

add all RPITITs when augmenting param-env with GAT bounds in check_type_bounds

add diagnostic items for a few of core's builtin macros

add support for pre-unix-epoch file dates on Apple platforms

add the Span of the move keyword to the HIR

also consider TAIT to be uncomputable if the MIR body is tainted

avoid the path trimming ICE lint in error reporting

avoid unnecessary comparison in partition_equal

check binders with bound vars for global bounds that don't hold

consts: remove dead code around i1 constant values

coverage: replace impossible coverage::Error with assertions

derive TyEncodable/TyDecodable in rustc_type_ir

detect misparsed binop caused by missing semi

detect object safety errors when assoc type is missing

do not ICE on constant evaluation failure in GVN

do not assert in op_to_const

don't check for alias bounds in liveness when aliases have escaping bound vars

don't emit delayed good-path bugs on panic

don't pass -stdlib=libc++ when building C files on macOS

enable cross-crate-inlining when MIR inlining is enabled

enable parallel rustc front end in nightly builds

fallback for construct_generic_bound_failure

fix excessive initialization and reads beyond EOF in io::copy(_, Vec<u8>) specialization

fix incorrect trait bound restriction suggestion

fix order of implementations in the "implementations on foreign types" section

guarantee representation of None in NPO

guarantee that char has the same size and alignment as u32

hint optimizer about try-reserved capacity

inline and remove create_session

make sure that predicates with unmentioned bound vars are still considered global in the old solver

make the randomize feature of rustc_abi additive

match usize/isize exhaustively with half-open ranges

prepopulate opaque ty storage before using it

pretty print Fn traits in rustc_on_unimplemented

recover from missing param list in function definitions

refactor: move suggestion functions from demand to suggestions

remove obsolete support for linking unwinder on Android

remove support for alias -Z symbol-mangling-version

remove support for compiler plugins

replace switch to unreachable by assume statements

set max_atomic_width for riscv32*-esp-espidf to 32

turn const_caller_location from a query to a hook

use FxIndexSet in the symbol interner

use derivative for Clone/PartialOrd/Ord/Hash in rustc_type_ir

use global cache when computing proof trees

use the correct span when emitting the env! result

warn users who set non_exhaustive_omitted_patterns lint level on a match arm

when encountering unclosed delimiters during lexing, check for diff markers

enable src/math for all UEFI targets

intrinsics macro: fix non-weak aeabi generation

this enables math module for riscv32 targets

stabilize const_maybe_uninit_zeroed and const_mem_zeroed

stabilize file_set_times

fix switch_stdout_to on Windows7

add track_caller to transmute_copy

delegate <Box<E> as Error>::provide to <E as Error>::provide

support enum variants in offset_of!

feature gate enums in offset_of

override Waker::clone_from to avoid cloning Wakers unnecessarily

codegen_gcc: fix vector compilation error

cargo: feat(trim-paths): set env CARGO_TRIM_PATHS for build scripts

cargo toml: Pull out the schema

cargo: fix an unhelpful panic message

cargo: implement -Ztrim-paths (RFC #3127)

cargo: merge trim-paths from different profiles

rustdoc: accept less invalid Rust

rustfmt: fixes comma added to comment in where-clause

clippy: unused_enumerate_index: don't ICE on empty tuples

clippy: add unused_enumerate_index lint

clippy: fix dbg_macro semi span calculation

clippy: fix enum_variant_names depending lint depending on order

clippy: fix get_first false negative for VecDeque

clippy: new lint: unnecessary_fallible_conversions

rust-analyzer: add generate_mut_trait_impl assist

rust-analyzer: import trait with alias

rust-analyzer: skip checking token tree count for include! macro call

rust-analyzer: fix docs path for derive macros

rust-analyzer: vSCode metadata. category:formatters

Rust Compiler Performance Triage

A difficult week for triage, due to what appears to be system-level disruption to measurement apparatus, yielding transient noise (and potentially masking actual problems). The main non-noise performance change was huge regression to bitmaps introduced by PR 117131, and that already has a fix in-flight fix (PR #117542). The other thing worth noting is that the parallel rustc front-end has been enabled in the nighlty builds, which has introduced some overhead that was expected by wg-parallel-rustc.

Triage done by @pnkfelix. Revision range: 650991d6..7b97a5ca

10 Regressions, 4 Improvements, 3 Mixed; 3 of them in rollups 68 artifact comparisons made in total

Full report here

Approved RFCs

Changes to Rust follow the Rust RFC (request for comments) process. These are the RFCs that were approved for implementation this week:

Add "crates.io Policy Update" RFC

Merge RFC 3498: "Lifetime Capture Rules 2024"

Final Comment Period

Every week, the team announces the 'final comment period' for RFCs and key PRs which are reaching a decision. Express your opinions now.

RFCs

No RFCs entered Final Comment Period this week.

Tracking Issues & PRs

[disposition: merge] Add T: ?Sized to RwLockReadGuard and RwLockWriteGuard's Debug impls.

[disposition: merge] Tracking Issue for file_create_new

[disposition: merge] feat: implement DoubleEndedSearcher for CharArray[Ref]Searcher

[disposition: merge] TAIT defining scope options

[disposition: merge] Add std::hash::{DefaultHasher, RandomState} exports (needs FCP)

Language Reference

No Language Reference RFCs entered Final Comment Period this week.

Unsafe Code Guidelines

Decide on zero-sized offsets and memory accesses

New and Updated RFCs

Arbitrary self types v2.

Call for Testing

An important step for RFC implementation is for people to experiment with the implementation and give feedback, especially before stabilization. The following RFCs would benefit from user testing before moving forward:

No RFCs issued a call for testing this week.

If you are a feature implementer and would like your RFC to appear on the above list, add the new call-for-testing label to your RFC along with a comment providing testing instructions and/or guidance on which aspect(s) of the feature need testing.

Upcoming Events

Rusty Events between 2023-11-08 - 2023-12-06 🦀

Virtual

2023-11-08 | Virtual(Boulder, CO, US) | Solid State Depot - The Boulder Makerspace

Placeholder: Boulder Rust Meetup

2023-11-09 | Virtual (Linz, AT) | Rust Linz

Rust Meetup Linz - 34rd Edition

2023-11-09 | Virtual (Nuremberg, DE) | Rust Nuremberg

Rust Nürnberg online

2023-11-10 | Virtual (Bangalore, IN) | Learn Everything About Programming

Getting started with rust-lang

2023-11-12 | Virtual (Tel Aviv-Yafo, IL) | Code Mavens

Rust in Israel - Rust Digger

2023-11-14 | Virtual (Dallas, TX, US) | Dallas Rust

Second Tuesday

2023-11-14 | Virtual (Kyiv, UA) | Yalantis Education

Довгий шлях до першого комерційного досвіду або до чого тут Rust?

2023-11-15 | Virtual (Cardiff, UK)| Rust and C++ Cardiff

Building Our Own Locks (Atomics & Locks Chapter 9)

2023-11-15 | Virtual (Richmond, VA, US) | Linux Plumbers Conference

Rust Microconference in LPC 2023 (Nov 13-16)

2023-11-15 | Virtual (Vancouver, BC, CA) | Vancouver Rust

Nightly Night: impl Trait in Type Aliases

2023-11-16 | Virtual (Charlottesville, NC, US) | Charlottesville Rust Meetup

Crafting Interpreters in Rust Collaboratively

2023-11-16 | Virtual (Vilnius, LT) | Vilnius Rust and Go Meetup Group

Enjoy our first Rust event

2023-11-21 | Virtual (Berlin, DE) | OpenTechSchool Berlin

Rust Hack and Learn

2023-11-21 | Virtual (Washington, DC, US) | Rust DC

Mid-month Rustful

2023-11-23 | Virtual (Edmonton, AB, CA) | Edmonton R User Group - Yegrug

Edmonton R User Group Meetup: R and Rust, like a match made in heaven

2023-11-28 | Virtual (Dallas, TX, US) | Dallas Rust

Last Tuesday

2023-11-29 | Virtual (Cardiff, UK)| Rust and C++ Cardiff

Atomics & Locks Book Club Final Chapter! (Chapter 10)

2023-11-30 | Virtual (Charlottesville, NC, US) | Charlottesville Rust Meetup

Crafting Interpreters in Rust Collaboratively

2023-11-30 | Virtual (Dublin, IE) | Rust Dublin

Automating expertise with cargo-semver-checks

2023-12-01 | Virtual (Cardiff, UK)| Rust and C++ Cardiff

Rust & C++ Christmas Game Jam Kick-Off!

2023-12-02 | Virtual (Kampala, UG) | Rust Circle Kampala

Rust Circle Meetup

2023-12-05 | Virtual (Berlin, DE) | OpenTechSchool Berlin

Rust Hack and Learn | Mirror

2023-12-05 | Virtual (Buffalo, NY, US) | Buffalo Rust Meetup

Buffalo Rust User Group, First Tuesdays

Europe

2023-11-09 | Barcelona, ES | BcnRust

11th BcnRust Meetup

2023-11-09 | Paris, FR | Paris Rustaceans

Rust Meetup in Paris

2023-11-09 | Reading, UK | Reading Rust Workshop

Reading Rust Meetup at Browns

2023-11-21 | Augsburg, DE | Rust - Modern Systems Programming in Leipzig

GPU processing in Rust

2023-11-23 | Biel/Bienne, CH | Rust Bern

Rust Talks Bern @ Biel: Embedded Edition

North America

2023-11-08 | Boulder, CO, US | Boulder Rust Meetup

Let's make a Discord bot!

2023-11-14 | New York, NY, US | Rust NYC

Rust NYC Monthly Mixer: Share, Show, & Tell! 🦀

2023-11-14 | Seattle, WA, US | Cap Hill Rust Coding/Hacking/Learning

Rusty Coding/Hacking/Learning Night

2023-11-15 | Richmond, VA, US + Virtual | Linux Plumbers Conference

Rust Microconference in LPC 2023 (Nov 13-16)

2023-11-16 | Mountain View, CA, US | Mountain View Rust Meetup

Rust Meetup at Hacker Dojo

2023-11-16 | Nashville, TN, US | Music City Rust Developers

Python loves Rust!

2023-11-16 | Seattle, WA, US | Seattle Rust User Group

Seattle Rust User Group Meetup

2023-11-21 | San Francisco, CA, US | San Francisco Rust Study Group

Rust Hacking in Person

2023-11-22 | Austin, TX, US | Rust ATX

Rust Lunch - Fareground

2023-11-28 | Pasadena, CA, US | Pasadena Thursday Go / Rust

Monthly Rust group

Oceania

2023-11-21 | Christchurch, NZ | Christchurch Rust Meetup Group

Christchurch Rust meetup meeting

2023-11-28 | Canberra, ACT, AU | Rust Canberra

November Meetup

If you are running a Rust event please add it to the calendar to get it mentioned here. Please remember to add a link to the event too. Email the Rust Community Team for access.

Jobs

Please see the latest Who's Hiring thread on r/rust

Quote of the Week

For Binder to continue to meet Android's needs, we need better ways to manage (and reduce!) complexity without increasing the risk.

The biggest change is obviously the choice of programming language. We decided to use Rust because it directly addresses a number of the challenges within Binder that we have faced during the last years.

– Alice Ryhl on the Linux Kernel Mailing List

Thanks to Vincent de Phily for the suggestion!

Please submit quotes and vote for next week!

This Week in Rust is edited by: nellshamrell, llogiq, cdmistman, ericseppanen, extrawurst, andrewpollack, U007D, kolharsam, joelmarcey, mariannegoldin, bennyvasquez.

Email list hosting is sponsored by The Rust Foundation

Discuss on r/rust

#rust-lang #rust #rustlang #twir #long post

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attitudetallyacademy · 8 months

Text

Mastering Python: Tips and Tricks for Efficient Coding

Introduction:

Python, celebrated for its versatility and readability, stands as the preferred programming language for computer science enthusiasts. However, achieving true mastery of Python involves not just grasping the language's basics, but also embracing advanced techniques for efficient coding. In this blog, we embark on a journey to explore an array of tips and tricks that will set you on the path to becoming a Python virtuoso. Whether you're a computer science student in Yamuna Vihar and Uttam Nagar, someone looking into Python development classes, or in pursuit of a reputable computer science training institute, this guide is tailored to equip you with the insights needed to excel in Python development.

1. Code Optimization:

Efficiency is the lifeblood of Python coding, particularly when tackling resource-intensive projects like data analysis or machine learning. Unlock the power of list comprehensions and wield built-in functions to supercharge your code's speed and performance.

2. Harnessing Libraries:

Python boasts a rich library ecosystem that can be your greatest ally. Whether you're diving into data manipulation with NumPy and pandas or exploring the realms of machine learning with TensorFlow, integrating these established libraries can streamline your work and save you from reinventing the wheel.

3. The Craft of Debugging:

Efficient coding requires adept debugging skills. Dive deep into Python's debugging tools and techniques to swiftly unearth and resolve errors in your code. Proficiency with the Python debugger (PDB) and judicious use of print statements can revolutionize your coding journey.

4. Virtual Environments:

Creating virtual environments for your Python projects is fundamental for dependency management and system hygiene. Tools such as virtualenv and pipenv serve as your allies, enabling you to isolate project-specific dependencies, averting conflicts, and ensuring reproducibility.

5. Prioritizing Code Readability:

Python's hallmark is its readability. Adhering to the PEP 8 style guide is your compass for maintaining consistent code formatting. Embrace meaningful variable names and liberally sprinkle comments to transform your code into an accessible and maintainable masterpiece.

6. Unleashing Regular Expressions:

Uncover the formidable potential of regular expressions (regex) for data parsing and manipulation. Python's 're' module places the power of regex patterns in your hands, empowering you to efficiently process and extract information from text data.

7. The Art of Memory Management:

When confronting substantial datasets, understanding memory management is non-negotiable. Acquaint yourself with how Python handles memory and employ strategies such as generator functions and memory views to minimize memory consumption.

8. Test-Driven Development (TDD):

Adopting Test-Driven Development (TDD) is a secret weapon for efficient code development. Writing tests before implementing functionality ensures your code performs as intended and simplifies maintenance and debugging.

Conclusion:

In the ever-evolving realm of computer science, Python remains an indispensable asset for constructing diverse applications, from web development to data analysis and machine learning. By incorporating the advanced tips and tricks shared in this blog, you'll elevate your Python coding skills, evolving into a more efficient and effective developer. Whether you're a student attending Python development classes in Uttam nagar and Yamuna vihar or seeking a reputable computer science training institute in the area, the wisdom acquired from this blog will empower you to excel in your Python projects. Remember that mastering Python is an ongoing journey, where continuous learning is the key to unlocking the realm of efficient coding.

#learnwithattitudeacademy #enrollnow #attitudeacademy #bestcourse #python course

0 notes

anantradingpvtltd · 2 years

Text

Price: [price_with_discount] (as of [price_update_date] - Details) [ad_1] Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment. Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics with reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. What You'll Learn Build intelligent systems for enterpriseReview time series analysis, classifications, regression, and clusteringExplore supervised learning, unsupervised learning, reinforcement learning, and transfer learning Use cloud platforms like GCP and AWS in data analyticsUnderstand Covers design patterns in Python Who This Book Is For Data scientists and software developers interested in the field of data analytics. ASIN ‏ : ‎ B0BNF5LF1K Publisher ‏ : ‎ Apress; 2nd edition (25 November 2022) Language ‏ : ‎ English File size ‏ : ‎ 8341 KB Text-to-Speech ‏ : ‎ Enabled Screen Reader ‏ : ‎ Supported Enhanced typesetting ‏ : ‎ Enabled X-Ray ‏ : ‎ Not Enabled Word Wise ‏ : ‎ Not Enabled Print length ‏ : ‎ 269 pages [ad_2]

0 notes

mypythonteacher · 2 years

Text

Classes

Python equips us with many different ways to store data. A float is a different kind of number from an int, and we store different data in a list than we do in a dict. These are known as different types. We can check the type of a Python variable using the type() function.

Above, we defined two variables and checked the type of these two variables. A variable’s type determines what you can do with it and how you can use it. You can’t .get() something from an integer, just as you can’t add two dictionaries together using +. This is because those operations are defined at the type level.

A class is a template for a data type. It describes the kinds of information which that class will hold and how a programmer will interact with that data. Define a class using the class keyword. PEP 8 Style Guide for Python Code recommends capitalizing the names of classes to make them easier to identify.

In the above example we created a class and named it CoolClass. We used the pass keyword in Python to indicate that the body of the class was intentionally left blank so we don’t cause an IndentationError. We’ll learn about all the things we can put in the body of a class in the next few exercises.

A class doesn’t accomplish anything simply by being defined. A class must be instantiated. In other words, we must create an instance of the class, in order to breathe life into the schematic.

Instantiating a class looks a lot like calling a function. We would be able to create an instance of our defined CoolClass as follows:

Above, we created an object by adding parentheses to the name of the class. We then assigned that new instance to the variable cool_instance for safe-keeping so we can access our instance of CoolClass at a later time.

A class instance is also called an object. The pattern of defining classes and creating objects to represent the responsibilities of a program is known as Object Oriented Programming or OOP.

Instantiation takes a class and turns it into an object, the type() function does the opposite of that. When called with an object, it returns the class that the object is an instance of.

We then print out the type() of cool_instance and it shows us that this object is of type __main__.CoolClass.

In Python __main__ means “this current file that we’re running” and so one could read the output from type() to mean “the class CoolClass that was defined here, in the script you’re currently running.”

When we want the same data to be available to every instance of a class we use a class variable. A class variable is a variable that’s the same for every instance of the class.

You can define a class variable by including it in the indented part of your class definition, and you can access all of an object’s class variables with object.variable syntax.

Above we defined the class Musician, then instantiated drummer to be an object of type Musician. We then printed out the drummer’s .title attribute, which is a class variable that we defined as the string “Rockstar”.

If we defined another musician, like guitarist = Musician() they would have the same .title attribute.

Methods are functions that are defined as part of a class. The first argument in a method is always the object that is calling the method. Convention recommends that we name this first argument self. Methods always have at least this one argument.

We define methods similarly to functions, except that they are indented to be part of the class.

Above we created a Dog class with a time_explanation method that takes one argument, self, which refers to the object calling the function. We created a Dog named pipi_pitbull and called the .time_explanation() method on our new object for Pipi.

Notice we didn’t pass any arguments when we called .time_explanation(), but were able to refer to self in the function body. When you call a method it automatically passes the object calling the method as the first argument.

Methods can also take more arguments than just self:

Above we defined a DistanceConverter class, instantiated it, and used it to convert 5 miles into kilometers. NOTE that even though how_many_kms takes two arguments in its definition, we only pass miles, because self is implicitly passed (and refers to the object converter).

There are several methods that we can define in a Python class that have special behaviour. These methods are sometimes called “magic,” because they behave differently from regular methods. Another popular term is dunder methods, so-named because they have two underscores (double-underscore abbreviated to “dunder”) on either side of them.

The first dunder method we’re going to use is the __init__() method (note the two underscores before and after the word “init”). This method is used to initialize a newly created object. It is called every time the class is instantiated.

Methods that are used to prepare an object being instantiated are called constructors. The word “constructor” is used to describe similar features in other object-oriented programming languages but programmers who refer to a constructor in Python are usually talking about the __init__() method.

Above we created a class called Shouter and every time we create an instance of Shouter the program prints out a shout. Don’t worry, this doesn’t hurt the computer at all.

Pay careful attention to the instantiation syntax we use. Shouter() looks a lot like a function call, doesn’t it? If it’s a function, can we pass parameters to it? We absolutely can, and those parameters will be received by the __init__() method.

Above we’ve updated our Shouter class to take the additional parameter phrase. When we created each of our objects we passed an argument to the constructor. The constructor takes the argument phrase and, if it’s a string, prints out the all-caps version of phrase.

We’ve learned so far that a class is a schematic for a data type and an object is an instance of a class, but why is there such a strong need to differentiate the two if each object can only have the methods and class variables the class has? This is because each instance of a class can hold different kinds of data.

The data held by an object is referred to as an instance variable. Instance variables aren’t shared by all instances of a class — they are variables that are specific to the object they are attached to.

Let’s say that we have the following class definition:

We can instantiate two different objects from this class, fake_dict1 and fake_dict2, and assign instance variables to these objects using the same attribute notation that was used for accessing class variables.

Instance variables and class variables are both accessed similarly in Python. This is no mistake, they are both considered attributes of an object. If we attempt to access an attribute that is neither a class variable nor an instance variable of the object Python will throw an AttributeError.

What if we aren’t sure if an object has an attribute or not? hasattr() will return True if an object has a given attribute and False otherwise. If we want to get the actual value of the attribute, getattr() is a Python function that will return the value of a given object and attribute. In this function, we can also supply a third argument that will be the default if the object does not have the given attribute.

The syntax and parameters for these functions look like this:

hasattr(object, “attribute”) has two parameters:

object : the object we are testing to see if it has a certain attribute

attribute : name of attribute we want to see if it exists

getattr(object, “attribute”, default) has three parameters (one of which is optional):

object : the object whose attribute we want to evaluate

attribute : name of attribute we want to evaluate

default : the value that is returned if the attribute does not exist (note: this parameter is optional)

Calling those functions looks like this:

Above we checked if the attributeless object has the attribute fake_attribute. Since it does not, hasattr() returned False. After that, we used getattr to attempt to retrieve other_fake_attribute. Since other_fake_attribute isn’t a real attribute on attributeless, our call to getattr() returned the supplied default value 800, instead of throwing an AttributeError.

returns

This is because dictionaries and integers both do not have a count attribute, while strings and lists do. In this exercise, we have iterated through can_we_count_it and used hasattr() to determine which elements have a count attribute. We never actually used the count method, but you can read more about it here.

Since we can already use dictionaries to store key-value pairs, using objects for that purpose is not really useful. Instance variables are more powerful when you can guarantee rigidity to the data the object is holding.

This convenience is most apparent when the constructor creates the instance variables, using the arguments passed in to it. If we were creating a search engine, and we wanted to create classes for each separate entry we could return. We’d do that like this:

Since the self keyword refers to the object and not the class being called, we can define a secure method on the SearchEngineEntry class that returns the secure link to an entry.

Above we define our secure() method to take just the one required argument, self. We access both the class variable self.secure_prefix and the instance variable self.url to return a secure URL.

This is the strength of writing object-oriented programs. We can write our classes to structure the data that we need and write methods that will interact with that data in a meaningful way.

Attributes can be added to user-defined objects after instantiation, so it’s possible for an object to have some attributes that are not explicitly defined in an object’s constructor. We can use the dir() function to investigate an object’s attributes at runtime. dir() is short for directory and offers an organized presentation of object attributes.

That’s certainly a lot more attributes than we defined! Python automatically adds a number of attributes to all objects that get created. These internal attributes are usually indicated by double-underscores. But sure enough, attribute is in that list.

Do you remember being able to use type() on Python’s native data types? This is because they are also objects in Python. Their classes are int, float, str, list, and dict. These Python classes have special syntax for their instantiation, 1, 1.0, "hello", [], and {} specifically. But these instances are still full-blown objects to Python.

Above we define a new list. We check it’s type and see that’s an instantiation of class list. We use dir() to explore its attributes, and it gives us a large number of internal Python dunder attributes, but, afterward, we get the usual list methods.

Functions are objects too!

One of the first things we learn as programmers is how to print out information that we need for debugging. Unfortunately, when we print out an object we get a default representation that seems fairly useless.

This default string representation gives us some information, like where the class is defined and our computer’s memory address where this object is stored but is usually not useful information to have when we are trying to debug our code.

We learned about the dunder method __init__(). Now, we will learn another dunder method called __repr__(). This is a method we can use to tell Python what we want the string representation of the class to be. __repr__() can only have one parameter, self, and must return a string.

In our Employee class above, we have an instance variable called name that should be unique enough to be useful when we’re printing out an instance of the Employee class.

We implemented the __repr__() method and had it return the .name attribute of the object. When we printed the object out it simply printed the .name of the object! Cool!

0 notes

garzamccurdy8 · 2 years

Text

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certainnutpatrol · 2 years

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thesketcherat · 2 years

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techalertr · 2 years

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aibyrdidini · 3 months

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A Comparative Study: Rule-Based AI vs. Machine Learning-Based AI

Introduction:

Artificial Intelligence (AI) systems have revolutionized various industries by enabling machines to perform tasks that typically require human intelligence. There are two main types of AI systems: rule-based AI and AI based on machine learning. In this white paper, we will delve into the details of these two types of AI systems, their definitions, and applications in corporate AI, and provide snippets of Python code to demonstrate their implementation.

1. Rule-Based AI:

Definition: Rule-based AI, also known as expert systems, relies on manually programmed rules to make decisions or solve problems. These rules are created by human experts who possess domain knowledge and expertise. The system follows a set of predefined rules to determine its actions or outputs.

Applications in Corporate AI:

Rule-based AI finds extensive applications in corporate AI systems, such as:

1. Decision Support Systems: Rule-based AI is used to provide recommendations or make decisions based on predefined rules and business logic.

2. Fraud Detection: Rule-based AI systems can be employed to identify patterns of fraudulent activities and trigger alerts or take appropriate actions.

3. Customer Support: Rule-based AI can automate responses to frequently asked questions and provide instant support to customers.

Snippet Code (Rule-Based AI):

```python

# Rule-based AI example

def rule_based_ai(input):

if input == "question":

return "Answer to the question"

elif input == "greeting":

return "Hello, how can I assist you?"

else:

return "I'm sorry, I didn't understand your input."

# Usage example

user_input = input("Enter your input: ")

response = rule_based_ai(user_input)

print(response)

```

Explanation:

- The code defines a function called `rule_based_ai` that takes an input and returns a corresponding response based on predefined rules.

- The function checks the input against different conditions using `if-elif-else` statements.

- If the input matches any of the predefined conditions (e.g., "question" or "greeting"), the function returns the corresponding response.

- If the input does not match any condition, a default response is provided.

2. AI Based on Machine Learning:

Definition: AI based on machine learning involves training a system using examples or data, allowing it to learn patterns and make predictions or decisions. Machine learning algorithms analyze data, identify patterns, and create models that can be used to make predictions or classify new data.

Applications in Corporate AI:

AI based on machine learning is widely used in corporate AI applications, including:

1. Natural Language Processing: Machine learning algorithms can be trained to understand and process human language, enabling applications like sentiment analysis, chatbots, and language translation.

2. Image and Video Analysis: Machine learning models can be trained to recognize and classify objects in images and videos, enabling applications like facial recognition, object detection, and content moderation.

3. Predictive Analytics: Machine learning algorithms can analyze historical data to make predictions and forecasts, aiding in areas such as sales forecasting, demand prediction, and risk assessment.

Snippet Code (AI Based on Machine Learning):

```python

# AI based on machine learning example using scikit-learn

from sklearn import svm

# Training data

X = [[0, 0], [1, 1]]

y = [0, 1]

# Create a support vector machine classifier

clf = svm.SVC()

# Train the classifier

clf.fit(X, y)

# Predict new data

new_data = [[2, 2]]

prediction = clf.predict(new_data)

print(prediction)

```

Explanation:

- The code demonstrates the use of a machine learning algorithm (Support Vector Machines) from the scikit-learn library.

- The training data consists of two samples (`X`) with corresponding labels (`y`).

- The code creates a classifier (`clf`) using the `svm.SVC()` function.

- The classifier is trained using the `fit()` method, which takes the training data and labels as input.

- Once trained, the classifier can predict the label of new data (`new_data`) using the `predict()` method.

- The predicted label is printed as the output.

Conclusion:

This white paper provided an overview of rule-based AI and AI based on machine learning, their definitions, applications in corporate AI, and step-by-step explanations of Python code snippets for both types. These AI systems have distinct characteristics and find diverse applications across industries, enabling automation, decision-making, and pattern recognition. By understanding the fundamentals and implementation techniques, heterogeneous audiences can gain insights into the capabilities and potential of these AI systems.

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a-coda · 3 years

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Advent of Code

As a child, advent calendars always added to the sense of anticipation in the lead up to Christmas. In my day you would be lucky to get a small picture behind each of the doors. These days, children expect chocolates or sweets. My wife has once even had a "Ginvent Calendar", with gin behind each door.

This year I marked Advent by having a go at the "Advent of Code" which has Christmas-themed programming puzzles posted each day. Most days are in two parts, with an easier puzzle followed by a harder one. Traditionally, I've posted a (mostly ignored) programming puzzle to our development team each Christmas. Last year I just recycled one of the Advent of Code puzzles, but this year I suggested we attempt the whole thing. The puzzles are so well thought out, in comparison to my efforts, that it seemed pointless to compete.

In the end, several of the team had a go. Some of the puzzles were harder than others, but I managed to solve them all by Boxing Day. What follows are some personal anecdotes from the various days with some general thoughts at the end. Note that there are some spoilers and the notes won't mean much if you've not done the puzzles. So in this case just skip to the end.

a sum-finder. I implemented the search tree via recursive calls. I drifted into using Python right from the start. It just felt like the easiest way to hack the puzzles quickly. In the past I had thought about using the puzzles to learn a new language. A colleague had done that with Rust in a previous year. Despite these good intentions, expediency took a firm hold. That said, in several puzzles I would have liked immutable collections or at least Lisp-style lists.

a pattern counter. Not that interesting except patterns were emerging in the programs themselves. Regular expressions got used a lot to read in the puzzle data. I learnt about things like match.group(1,2,3) which returns a tuple of the first three match groups, so you don't have to write (m.group(1), m.group(2), m.group(3)).

a grid tracer. The first interesting one because it was unfamiliar. Some other patterns started emerging: problem parameters got promoted to command line arguments, and data structure printers got hacked to help debugging. These two were often added between part 1 and part 2 of each problem.

a data validator. This felt like a bit of a slog. It was mostly about capturing the validation rules as code. Even though I made a point of reminding myself at the start that re.search doesn't match the whole string I still forgot it later. Duh.

an indexing problem. I patted myself on the back for realizing that the index was a binary number (or pair of binary numbers as I did it). At this point the solutions were still neat and I would do a little code golfing after the solution to tidy them up a bit and make them more concise.

another pattern counter. Pre-calculating some things during data reading kept the later code simple.

a recursive calculator. This was one of those puzzles where I had to reread the description several times to try and understand what it was asking for. It entailed a slightly tricky recursive sum and product, which was again made easier by creating more supporting data structures while reading the input data.

an interpreter. Probably my favourite individual puzzle because it was so sweet, especially after a bit of refactoring to make the language more data-driven.

another sum-finder. I found I didn't particularly like these.

an order-finder. This was the first one that made me pause for thought. An overly naive search algorithm from part 1 hit a computational complexity wall in part 2. I beat the problem by realizing that the search only had to be done on small islands of the data, but a colleague pointed out there was a better linear solution. The code was starting to get a bit ragged, with commented out debugging statements.

the game of life. The classic simulation but with some out-of-bounds spaces and some line-of-sight rules. It helped to print the board.

a map navigator. I liked this one even though I forgot to convert degrees to radians and that rotation matrices go anti-clockwise. I even introduced an abstract data type (ADT) to see if it would simplify the code (I'm not sure it ever did - I mostly used lists, tuples, strings, and numbers). The second parts of the puzzles were starting to get their own files now (usually bootstrapped by copying and pasting the first part's file).

a prime number theorem. I actually got stalled on this one for a bit. It eventually turned out I had a bug in the code and was missing a modulus. In effect I wasn't accounting for small primes far to the right. I left the puzzle and went on to complete a couple of others before coming back to this one. I checked what I was doing by Googling for hints, but in the end I had to take a long hard look at the data and find my own bug.

some bit twiddling. Part 1 felt like I found the expected bitwise operations, but part 2 felt like I was bashing square pegs into round holes.

a number sequence problem. Another pat on the back, this time for keeping a dictionary of recent occurrences and not searching back down the list of numbers each time. Another recurring pattern is evident: running a sequence of steps over the data. I liked to code the step as its own function.

a constraint solver. A nice one about labelling fields that satisfy the known constraints. Half the code was parsing the textual rules into data.

another game of life simulation. This time it was in more dimensions. I generalized from 3 dimensions to N instead of just doing 4. This made it more of a drag. I started naming auxiliary functions with placeholder names (social services should have been called). Also, I tacked on extra space along each dimension to make room at each step. This felt very ugly. I should have used a sparser representation like I did for day 24.

an expression evaluator. I used another actual ADT and wrote a simple but horrible tokenizer. The evaluator was okay but I hacked the precedence by inserting parentheses into the token stream. Don't try this at home kids.

another pattern matcher. Probably my biggest hack. My code compiled the pattern rules into a single regular expression. This was cute but meant the recursive rules in part 2 needed special treatment. One rule just compiled into a repeated pattern with +. Unfortunately, the other rule entailed matching balanced sub-patterns, which every schoolchild knows regular languages can't do. Perhaps some recursive pattern extensions might have worked, but I assumed there would be no more than 10 elements of the sub-patterns and compiled the rule into a large alternative of the possible symmetrical matchers. Yuck.

a map assembler. I did this one the most methodically. It had proper comments and unit tests. Overall it took the most code but perhaps it was just dealing with all the edge cases (ba dum tss). But seriously, it seemed to take a lot of code for rotating and flipping the tiles even after knowing how they must be connected. So probably there was a better approach. It was still satisfying the see the answer come out after all that work. Curiously, this one involved little debugging. I wonder if perhaps there is some connection between preparation and outcome?

a constraint solver. I tried a dumb approach first based on searching all the possible bindings. That didn't look like it was terminating any time soon. So I reverted to a previously successful technique of intersecting the associations and then then refining them based on the already unique ones.

a recursive card game. This card game playing puzzle seemed to be going okay, but the real data didn't converge for part 2. Had a quick Google for a hint after battling with it for a while, and the first hit was from someone who said they'd misread the question. Sure enough I had too. My recursive games were on the whole deck instead of the part dictated by the cards played. Duh. The description was clear enough and included a whole worked game. I just hadn't read it properly. It still seemed to need some game state memoization to run tolerably fast.

a circular sequence. Took three attempts. A brute force approach using an array was good enough for part 1, but no way was it going to work on part 2. Even optimizing it to use ranges was still 'non-terminating' for the array-based solution. So I Googled for a little inspiration and found the phrase "linked lists" and slapped my forehead hard. I switched to a dictionary of labels to labels and the solution popped out very easily, without any further optimization. Embarrassing. Was it time to ceremonially hand in my Lisp symbol and fall on a sharpened parenthesis?

another game of life. This one sounded neat because it was about a hex grid, but I didn't know how hex grids are usually indexed. So for the first time I did a little bit of general research at the start. Turns out there are a bunch of ways to index a hex grid. I opted for using 3-axes as that seemed natural despite the redundancy. The map itself was just a dictionary of locations. I should have looked up how to have structured dictionary keys in Python (implement __hash__) but I couldn't be bothered so I (look away now) serialized and deserialized the locations to and from strings. I still had a bug which I couldn't find until I hacked a crude hex board printer and realized I wasn't carrying the unchanged cells over from one iteration to the next.

a cryptographic puzzle. Came out quite short but only after some faffing around. Main trick seemed to be to keep the transformation ticking along instead of recalculating it from scratch each time. There was slight disappointment (tinged with relief) that there was no part 2.

Some general lessons I felt I (re)learned:

Read the questions very carefully, then reread them.

Try and use terms from the questions. Don't invent your own terminology and then have to map back and forth.

Make the trace output exactly like the examples to help comparison.

Next time I'd consider using BDD to turn their examples directly into tests. Next time.

Try the problem for a while by yourself, then think about it offline, and only then Google for hints.

Next time I'd consider using some form of source control from the start, or just a better set of file naming conventions.

Regular expressions go a long way, but can then they can get in the way.

Next time I'll consider doing it using a language I'm learning.

Sometimes when you get stuck you have to start again.

During some low moments it all felt like make-work that I'd inflicted on myself, but in the end it was a nice set of training exercises. I'd encourage others to have a go at their leisure.

"Practice is the best of all instructors." -- Publilius Syrus

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