Hooray! Funko has revealed that for the very first time, Pokémon Funko Pops will be officially launching in Europe.

Ten-inch variants of both Pikachu and Bulbasaur are set to launch this October, more than a year after the characters first appeared in the US. They’ll release first at Smyths Toys in the UK and Ireland, and at Fnac, El Corte Inglés, Müller, and Toys Center across mainland Europe.

The Pikachu model will then appear across other stores in the UK and Europe at a later date, but it seems like Bulbasaur may be a little more limited.

Here’s hoping more Pokémon figures make it across the Atlantic in the future!

Announced during September’s Super Mario 35th Anniversary Direct broadcast, the scheduled arrival of Super Mario 3D World + Bowser’s Fury on Switch next February will also bring a couple of furry critters to Nintendo’s amiibo line-up.

We say ‘furry’, but Cat Mario and Cat Peach look a little less fluffy in plastic form. Still, they’re super cute and it’ll be tough to resist adding them to our amiibo collection shelf when the game and the NFC figurines release on 12th February 2021.

Nintendo has a patchy history when it comes to amiibo availability. Traditionally, there’s a shortage at launch which online auction scalpers use to their advantage, although more stock tends to appear at a later date. That can be a very much later date, though — sometimes a year or more — so if you want to guarantee getting your hands on these lovelies on launch day, we’d recommend putting down a pre-order with one of the retailers below.

The pair are available separately or in a double pack (currently we can only find individual packs in the US, but we’re keeping our eyes open). There’s no word yet on their exact in-game functionality, although they may well have something to do with the expanded Bowser’s Fury content.

We’ll keep an eye out for more information and pre-order deals, so check back for your favoured retailer(s) later.

Please note that some external links on this page are affiliate links, which means if you click them and make a purchase we may receive a small percentage of the sale. Please read our FTC Disclosure for more information.

Buy Cat Mario And Cat Peach amiibo In The UK

Buy Cat Mario And Cat Peach amiibo In The US

Let us know below if you’ll be picking up one or both of these feline figurines…

Nintendo has published its Corporate Management Policy Briefing for this financial year, and as well as touching on plans for its future hardware, much of the document is dedicated to exploring how the company is expanding its IP to reach a wider audience.

Most interestingly, Nintendo president Shuntaro Furukawa states that there are already multiple ‘visual content expansion projects’ in the works — presumably referring to movie and/or TV projects — in addition to the Super Mario movie that’s currently in production at Illumination, the company behind the Minions movies.

The animated Mario movie is scheduled for release in 2022, and there’s no word yet on exactly what these other productions are or when they’re coming, but it seems that Nintendo has big expansion plans in the nebulous field of ‘visual content’.

The briefing also specifies that “Going forward, visual content expansion initiatives may not be limited to film”, suggesting a broad spectrum of projects for screens big and small. Check out the full quote below:

We have also already embarked on multiple other visual content expansion projects, and are pursuing further opportunities. As we shared previously, the Super Mario movie is being financed by Nintendo as well as Universal Pictures, and we are actively involved in planning, development and production. Going forward, visual content expansion initiatives may not be limited to film. The scale of our investments will vary based o the type of project, but we will continue to invest in these entertainment expansion initiatives to increase the number of people who have access to our IP.

With the success the animated Castlevania series on Netflix leading to multiple further video game-related projects heading to the streaming service, you’d imagine Nintendo has had its eye on this particular ball for quite some time. Of course, ‘visual content’ is deliberately vague and could mean many things: web series, animated shorts, live-action shows, or something else entirely.

Sounds tantalising, no? We’re certainly intrigued to see what Nintendo has up its sleeves over the coming years.

What would you like to see? A Metroid movie, perhaps? Let us know below which Nintendo IPs you’d like to see expanded into forms of ‘visual content’.

Problem: How to calculate the entropy with the SciPy library?

Solution: Import the entropy() function from the scipy.stats module and pass the probability and the base of the logarithm into it.

from scipy.stats import entropy p = [0.5, 0.25, 0.125, 0.125]

e = entropy(p, base=2) print(e)

# 1.75

Try It Yourself: Run this code in the interactive code shell!

Exercise: Change the probabilities. How does the entropy change?

Let’s start slowly! You’ll going to learn the most relevant background about entropy next.

Entropy Introduction

In thermodynamics, entropy is explained as a state of uncertainty or randomness.

In statistics, we borrow this concept as it easily applies to calculating probabilities.

When we calculate statistical entropy, we are quantifying the amount of information in an event, variable, or distribution. Understanding this measurement is useful in machine learning in many cases, such as building decision trees or choosing the best classifier model.

We will discuss the applications of entropy later in this article, but first we will dig into the theory of entropy and how to calculate it with the use of SciPy.

Calculating the Entropy

Calculating the information of a variable was developed by Claude Shannon, whose approach answers the question, how many “yes” or “no” questions would you expect to ask to get the correct answer?

Consider flipping a coin. Assuming the coin is fair, you have 1 in 2 chance of predicting the outcome. You would guess either heads or tails, and whether you are correct or incorrect, you need just one question to determine the outcome.

Now, say we have a bag with four equally sized disks, but each is a different color:

To guess which disk has been drawn from the bag, one of the better strategies is to eliminate half of the colors. For example, start by asking if it is Blue or Red. If the answer is yes, then only one more question is required since the answer must be Blue or Red. If the answer is no, then you can assume it is Green or Gray, so only one more question is needed to correctly predict the outcome, bringing our total to two questions regardless if the answer to our question is Green of Gray.

We can see that when an event is less likely to occur, choosing 1 in 4 compared to 1 in 2, there is more information to learn, i.e., two questions needed versus one.

Shannon wrote his calculation this way:

Information(x) = -log(p(x))

In this formula log() is a base-2 algorithm (because the result is either true or false), and p(x) is the probability of x.

As the higher the information value grows, the less predictable the outcome becomes.

When a probability is certain (e.g., a two-headed coin flip coming up heads), the probability is 1.0, which yields an information calculation of 0.

We can run Shannon’s calculation in python using the math library shown here:

When we change the probability to 0.25, as in the case of choosing the correct color of the disk, we get this result:

While it appears that the increase in information is linear, what happens when we calculate the roll of a single die, or ask someone to guess a number between 1 and 10? Here is a visual of the information calculations for a list of probabilities from less certain (p = 0.1) to certain (p = 1.0):

The graph shows that with greater uncertainty, the information growth is sub-linear, not linear.

Unequal Probabilities

Going back to the colored disks example, what if we now have 8 disks in the bag, and they are not equally distributed? Look at this breakout by color:

If we use the original strategy of eliminating half of the colors by asking if the disk Blue or Green, we become less efficient since there is a combined 0.25 probability of either color being correct in this scenario.

We know that gray has the highest probability. Using a slightly different strategy, we first ask if Gray is correct (1 question), then move on to the next highest probability, Red (2^nd question), and then to check if it is Blue or Green (3^rd question).

In this new scenario, weighting our guesses will lead to less information required. The tables below show the comparison of the two methods. The info column is the product of the Probability and Questions columns.

The Equal guess method takes an average of 2 questions, but the weighted guess method takes an average of 1.75.

We can use the Scipy library to perform the entropy calculation. Scipy’s “stats” sub-library has an entropy calculation that we can use. Here is the code to calculate the entropy for the scenario where the four disks have different probabilities:

The entropy method takes two entries: the list of probabilities and your base. Base=2 is the choice here since we are using a binary log for the calculation.

We get the same result as in the table shown above. With minimal code, the Scipy library allows us to quickly calculate Shannon’s entropy.

Further Uses

Entropy calculation is successfully used in real-world application in Machine Learning. Here are some examples.

Decision Trees

A Decision Tree is based on a set of binary decisions (True or False, Yes or No). It is constructed with a series of nodes where each node is question: Does color == blue? Is the test score > 90? Each node splits into two and decomposes into smaller and smaller subsets as you move through the tree.

 Accuracy with your Decision Tree is maximized by reducing your loss. Using entropy as your loss function is a good choice here. At each step moving through the branches, entropy is calculated before and after each step. If the entropy decreases, the step is validated. Otherwise you must try another branch.

Classification with Logistic Regression

The key to a logistic regression is minimizing the loss or error for the best model fit. Entropy is the standard loss function for logistic regression and neural networks.

Code Sample

While there are several choices for using entropy as your loss function in machine learning, here is a snippet of code to show how the selection is made during model compilation:

Conclusion

The purpose of this article was to shed some light on the use of entropy with Machine Learning and how it can be calculated with Python.

The Gamasutra Job Board is the most diverse, active and established board of its kind for the video game industry!

Here is just one of the many, many positions being advertised right now.

Location: 

Volition is seeking an energetic, driven, and skilled engine programmer to participate in the development of technology for our next generation of titles. You will be working in a collaborative environment of artists, designers, and programmers and will have the opportunity to develop and enhance systems for large scale open world games. We are considering applicants for a senior or principal programming position.

Responsibilities:

• Develop and enhance core systems for AAA Xbox One, PlayStation 4, and PC titles
• Develop and enhance core pipelines for all of our titles

Requirements:

• Extensive C/C++ programming skills with an in depth understanding of software construction
• Strong understanding of software engineering principles as applied to architecting systems for use in large scale code bases and teams
• Significant game development experience in the core/engine area
• Familiarity with open-world engine concepts like streaming, memory management and multi-threading
• Ability to debug efficiently on multiple platforms
• Demonstrated ability of strong communication and collaboration on a team

Pluses:

• Experience developing in Python
• Experience developing AAA titles on Xbox One or PlayStation 4 consoles
• Experience with code optimization

Interested? Apply now.

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