#jsonutils

[aproblemforlater]

Week 11

Since we have added a new track to the game, there have been multiple issues with the leaderboard and ghost systems not organising data in a way which makes sense. This is because I had originally designed the systems for one player on one track, which wasn’t very forward thinking of me. However now I have the necessary testing environment for adding a more versatile system, I will make it work again.

The current leaderboard system has a list of data, which makes up each leaderboard entry. Because we only had one track, this worked fine. However now, we need a set of these leaderboards separated by an identifier or key. An obvious solution to this would be a dictionary, but because this data needs to be saved, we cannot use a dictionary as JsonUtility will not serialize that data type.

I tried simply creating a list of lists, but this did not work either, so I had to come up with something different.

After some researching, I found that simply wrapping the data in a new class would solve my problem. This way, instead of having a list of lists, I would instead of a list of data classes, which each contain a list. This also means I can create something which mimics a dictionary by having an identifier value in the class.

This worked really well, and although the implementation had to change as the data had changed, the adaption was not hard to create since class responsibilities had been separated throughout this system, meaning I only had to change the bit which gets saved, and not the entire leaderboard system.

There are a lot of classes for the Leaderboard system, but because each class has a specific role, it makes maintaining the system and redesigning it much easier. Each class only depends on one other class in the system, and this makes it very easy to change out classes for adapted ones.

I am very happy with this redesign, as It gives us the versatility to add new tracks in the future, and have the leaderboard system be able to handle this with no extra input, and the system will also organise data associated by track name, save and retrieve the data, and modify it too. Unless something major is added, I am confident that the Leaderboard system is now in a finished state, and can be used in the finished build with no further modification.

To finish up with this week, I modified the menu UI to accommodate the leaderboard's new features, and then tested and fixed a few formatting bugs.

The game is looking closer to being something which could be released as a small game, and I think me and the team should be happy with what we have created. We still have to fix the issues with split screen, and unfortunately I do not think this will be achieved by the end of week 12. This can be the first goal for our next production cycle, and this is where the full scope of the game mechanics will be implemented. Ai would be a nice addition also, but like split screen, this is not in a good enough state to be included, so it will have to be pushed back to the next production stage

[robotbulls]

Revolutionizing Risk Management with AI and Big Data: The Emergence of Advanced Analytics in Tech and Finance Industries

The rapid and continuous evolution of artificial intelligence (AI) and big data has brought forth significant advancements in various industries, including tech and finance. One area where AI and big data have made a significant impact is risk management. This article aims to discuss some of the uncommon tips and content related to the revolutionary role of advanced analytics in risk management in tech and finance industries.

1. Using Python Libraries for Advanced Risk Analysis

Python is a popular programming language widely used in various industries, thanks to its extensive standard library. However, there are several lesser-known essential Python libraries that can be used for advanced risk analysis in the tech and finance industries. Some of these libraries include:

Boltons: A set of general-purpose utilities that are missing from the standard library. This library can be used for data validation, fuzzy string comparison, and data manipulation.

More-Itertools: A library that provides additional itertools for advanced data processing.

SH: A subprocess module replacement that simplifies the orchestration of other processes in Python.

Validators: A small library that allows for the validation of common patterns, such as emails, IP addresses, or credit cards.

TheFuzz (previously FuzzyWuzzy): A library for fuzzy string comparison that provides improved string similarity scoring.

Using these libraries can help perform advanced risk analysis, particularly in the finance industry, where data validation and string comparison play crucial roles in assessing risk factors.

2. Enhanced Debugging Techniques for Risk Management

To ensure accurate and efficient risk management, it is essential to have advanced debugging techniques. Some helpful debugging libraries and techniques that can be used to troubleshoot issues during the implementation of risk management models include:

Stackprinter: A library that provides more helpful versions of Python's built-in exception messages. It can help developers quickly identify issues in their risk management models.

Icecream: A library that provides an improved print function for easier debugging of code. It can be particularly useful for debugging complex risk management models.

Pyperclip: A library that allows for copying and pasting to and from the clipboard. This can be useful for debugging purposes, particularly for copying variable values or error messages.

3. Leveraging AI and Machine Learning for Intelligent Risk Management

AI and machine learning technologies have made it possible to develop intelligent risk management systems that can process large volumes of data and identify patterns in real-time. Some applications of AI and machine learning in risk management include:

Fraud detection: AI-powered algorithms can analyze transactional data in real-time and identify potential fraud by detecting unusual patterns.

Credit scoring: Machine learning models can analyze a wide range of variables to assess an individual's creditworthiness more accurately than traditional methods.

Market risk analysis: AI algorithms can process vast amounts of market data and identify potential risks or investment opportunities.

These applications of AI and machine learning are revolutionizing risk management in the finance industry, enabling organizations to make more informed decisions and mitigate potential risks more effectively.

4. Harnessing the Power of Big Data for Risk Management

Big data technologies have enabled organizations to gather, store, and analyze massive volumes of structured and unstructured data. This data can be harnessed for various risk management purposes, such as:

Real-time monitoring and analysis of financial transactions and market trends.

Generating detailed risk profiles for individual customers or investment portfolios.

Identifying potential areas of vulnerability and implementing proactive risk mitigation measures.

By leveraging big data technologies, organizations can develop more comprehensive and accurate risk management strategies that help them safeguard their assets and maintain a competitive edge in the market.

Conclusion

The emergence of advanced analytics, powered by AI and big data, has revolutionized risk management in tech and finance industries. By harnessing the power of advanced Python libraries, improved debugging techniques, and AI and machine learning technologies, organizations can develop intelligent risk management systems that enable them to make informed decisions and mitigate potential risks more effectively. As technology continues to advance, we can expect even more sophisticated risk management solutions to emerge, shaping the future of the tech and finance industries.

[carrow-games]

I’ve made the most important breakthrough today, and it was thanks to fellow associates and coworkers who assisted me with understanding unity’s serializables and JSONutility.

With this, I can now accurately look up, locate, pull, and use the data from a player’s profile for the app.

Project MLH has reached the most crucial point. If I couldn’t make this part work, then MLH would have just been a “cool idea”.

It should be revealed at this point, but I’m still hesitant that I’d jinx the project success rate...