In this 2019 XRDC talk, Survios’ Alex Silkin discuses how the company makes VR innovation a key component of game development in games like Battlewake.

In addition to this presentation, the GDC Vault and its accompanying YouTube channel offers numerous other free videos, audio recordings, and slides from many of the recent Game Developers Conference events, and the service offers even more members-only content for GDC Vault subscribers.

Those who purchased All Access passes to recent events like GDC already have full access to GDC Vault, and interested parties can apply for the individual subscription via a GDC Vault subscription page. Group subscriptions are also available: game-related schools and development studios who sign up for GDC Vault Studio Subscriptions can receive access for their entire office or company by contacting staff via the GDC Vault group subscription page. Finally, current subscribers with access issues can contact GDC Vault technical support.

The .polyfit() function, accepts three different input values: x, y and the polynomial degree. Arguments x and y correspond to the values of the data points that we want to fit, on the x and y axes, respectively. The third parameter specifies the degree of our polynomial function. For example, to obtain a linear fit, use degree 1.

What is Curve Fitting?

Curve fitting consists in building a mathematical function that is able to fit some specific data points. Most of the times, the fitting equation is subjected to constraints; moreover, it is also possible to make initial guess for providing useful starting points for the estimation of the fitting parameters, this latter procedure has the advantage of lowering the computational work. In this article we will explore the NumPy function .polyfit(), which enables to create polynomial fit functions in a very simple and immediate way.

Linear fit

The simplest type of fit is the linear fit (a first-degree polynomial function), in which the data points are fitted using a straight line. The general equation of a straight line is:

y = mx + q

Where “m” is called angular coefficient and “q” intercept. When we apply a linear fit, we are basically searching the values for the parameters “m” and “q” that yield the best fit for our data points. In Numpy, the function np.polyfit() is a very intuitive and powerful tool for fitting datapoints; let’s see how to fit a random series of data points with a straight line.

In the following example, we want to apply a linear fit to some data points, described by the arrays x and y. The .polyfit() function, accepts three different input values: x, y and the polynomial degree. While x and y correspond to the values of the data points that we want to fit, on the x and y axes, respectively; the third parameter specifies the degree of our polynomial function. Since we want a linear fit, we will specify a degree equal to 1. The outputs of the polyfit() function will be a list containing the fitting parameters; the first is the one that in the function is multiplied by the highest degree term; the others then follow this order. 

import numpy as np

from numpy import random #it will be useful for generating some random noise (on purpose) in the data points that we want to fit

import matplotlib.pyplot as plt #for plotting the data #---LINEAR FIT---- #generate the x array

x = np.linspace(0,60,60) # generate an array of 60 equally space points #generate the y array exploiting the random.randint() function to introduce some random noise

y = np.array([random.randint(i-2, i+2) for i in x]) #each element is a random number with value between +-2 the respective x axis value #Applying a linear fit with .polyfit()

fit = np.polyfit(x,y,1)

ang_coeff = fit[0]

intercept = fit[1]

fit_eq = ang_coeff*x + intercept #obtaining the y axis values for the fitting function #Plotting the data

fig = plt.figure()

ax = fig.subplots()

ax.plot(x, fit_eq,color = 'r', alpha = 0.5, label = 'Linear fit')

ax.scatter(x,y,s = 5, color = 'b', label = 'Data points') #Original data points

ax.set_title('Linear fit example')

ax.legend()

plt.show()

As mentioned before, the variable fit will contain the fitting parameters. The first one is the angular coefficient, the last one the intercept. At this point, in order to plot our fit, we have to build the y-axis values from the obtained parameters, using the original x-axis values. In the example, this step is described by the definition of the fit_eq variable. The last remaining thing is to plot the data and the fitting equation. The result is:

Polynomial fit of second degree

In this second example, we will create a second-degree polynomial fit. The polynomial functions of this type describe a parabolic curve in the xy plane; their general equation is:

y = ax² + bx + c

where a, b and c are the equation parameters that we estimate when generating a fitting function. The data points that we will fit in this example, represent the trajectory of an object that has been thrown from an unknown height. Exploiting the .polyfit() function, we will fit the trajectory of the falling object and we will also obtain an estimate for its initial speed in the x-direction, v₀.

#-----POLYNOMIAL FIT----

x = np.array([1.2,2.5,3.4,4.0,5.4,6.1,7.2,8.1,9.0,10.1,11.2,12.3,13.4,14.1,15.0]) # x coordinates

y = np.array([24.8,24.5,24.0,23.3,22.4,21.3,20.0,18.5,16.8,14.9,12.8,10.5,8.0,5.3,2.4]) # y coordinates

fit = np.polyfit(x, y, 2)

a = fit[0]

b = fit[1]

c = fit[2]

fit_equation = a * np.square(x) + b * x + c

#Plotting

fig1 = plt.figure()

ax1 = fig1.subplots()

ax1.plot(x, fit_equation,color = 'r',alpha = 0.5, label = 'Polynomial fit')

ax1.scatter(x, y, s = 5, color = 'b', label = 'Data points')

ax1.set_title('Polynomial fit example')

ax1.legend()

plt.show()

Once initialized the x and y arrays defining the object trajectory, we apply the function .polyfit(), this time inserting “2” as degree of the polynomial fit function. This is because the trajectory of a falling object can be described by a second-degree polynomial; in our case the relation between the x and y coordinates is given by:

y = y₀ – ½ (g/ v₀²)x²

where y₀ is the initial position (the height from which the object has been thrown), g the acceleration of gravity (  ̴9.81 m/s²) and v₀ the initial speed (m/s) in the x-direction (visit: https://en.wikipedia.org/wiki/Equations_for_a_falling_body for more details). We then assign at the variables a, b and c the value of the 3 fitting parameters and we define fit_equation, the polynomial equation that will be plotted; the result is:

If we now print the three fitting parameters, a,b and c, we obtain the following values: a = -0.100 , b = 0.038, c = 24.92. In the equation describing the trajectory of a falling body there is no b term; since the fit is always an approximation of the real result, we will always get a value for all the parameters; however we shall notice that the value of our b term is much smaller than the others and can be somehow neglected, when comparing our fit with the equation describing the physics of the problem. The c term represents the initial height (y₀) while the a term describes the quantity – ½ (g/ v₀²). Hence, the initial velocity v₀ is given by:

v0=2-g2a

Yielding the final value of v₀ = 6.979 m/s.

The post np.ployfit() — Curve Fitting with NumPy Polyfit first appeared on Finxter.

Unity are running their annual Super Sale just in time for Black Friday, with 700+ assets 50% off as well as daily specials that are 70%+ off. Additionally if you purchase Unity pro or Unity Enterprise, you currently get an additional license, such as Unity Build, MARS, ArtEngine, Reflect or Pixyz, a heck of a deal if you were going to get a pro subscription anyways!

The daily deals are as follows:

The above items are only for sale on the date listed, so if you click a link on any other day you will see regular pricing. All of the above links contain an affiliate code that pays GFS a small commission if used (and thanks if you do!). You can learn more about the sale in the video below.

After thorough analysis and lab testing, DisplayMate has declared that Apple’s new iPhone 12 Pro Max flagship has the best display that it’s ever tested.

Nintendo has announced that a free Winter update is set to arrive in Animal Crossing: New Horizons this Thursday.

A new trailer for the update has been shared, giving us our first proper look at the new content we’ll be able to enjoy over the coming months. It looks just as lovely as ever, and as well as the usual batch of new events and the like, there’s also confirmation that the long-awaited save data transfer feature will also be going live (more details on that here).

New seasonal events, Reactions, hairstyles, save data transfer, and more! Get ready to celebrate the heartwarming holiday festivities with the free Winter update, coming to Animal Crossing: New Horizons on November 19!

Here’s a look at what we can expect:

• Turkey Day – On Turkey Day, which will take place this year on 26^th November, top class chef Franklin will arrive and host a gathering in the plaza. Players can help Franklin collect ingredients to go in the dishes that will be on display in the plaza. Players helping Franklin cook will receive a gift from him. Please note: This event requires the construction of the Resident Services building beforehand.
• Toy Day – It’s the time of the year when island residents rejoice … The month of Toy Day is nearly at hand. Players might notice their island and plaza gradually becoming more festive with holiday-themed decorations over the coming weeks. Keep an eye out for decorated trees, too. Shaking these may drop ornaments which will allow players to craft holiday furniture recipes. From 1^st December to 25^th December, it will be possible to purchase toys from Nook’s Cranny and find holiday-themed clothing at the Able Sisters shop. When it’s officially Toy Day on 24^th December, Jingle will pay a special visit to each island to spread holiday cheer. By helping him deliver presents, players will receive a gift too.
• New Reactions and hairstyles – Nine new Reactions and six new hairstyle options can be acquired by redeeming Nook Miles at the Nook Stop terminal located in Resident Services.
• Home storage limit upgrade – Players who have previously expanded their home to the largest size and have paid off their debt will be able to apply for a further home storage expansion by speaking to Tom Nook at the Resident Services. This will expand the home storage to 2400 slots.
• Visiting random islands in dreams – Everyone enjoying dozing off and visiting other islands within dreams will now be able to choose to visit random islands while dreaming, opening up new vistas of inspiration for island customisations.
• Animal Crossing: Pocket Camp collaboration – To commemorate Animal Crossing: Pocket Camp’s third anniversary, a Pocket Camp smartphone model will become available in Animal Crossing: New Horizons.
• … and more: New seasonal items will be available starting 1^st December at Nook Stop. Between 26^th and 31^st December, there will also be several festive items to celebrate New Year’s Eve. In addition to the content of the update, players can look forward to a countdown event on December 31^st, starting at 7 p.m. local time, to ring in the new year with their residents as well as friends and family in the game.

Make sure to update your game on Thursday to check all of this out for yourself, and let us know if you’re excited for a seasonal change in the comments below.