Assume I have 2 variables for player Strength and Agility. I want to use these 2 variables to calculate damage dealt to enemies based on a simple formula, which is Damage = Strength * 2 + Agility * 0.1
How would I go about creating this in blueprints?
In blueprint you would have to first do your calculation using the float multiplier node (to do Strength * 2 and Agility * 0.1). Then use the float additional node to add these two together and assign the result of the addition node to your Damage variable
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I am doing a regression analysis of hunting and wind power turbine data.
My response variable is hunting harvest density, and a set of wind turbine variables: height, number of turbines, distance to turbines and establishment phase (pre-construction, construction and operative.
examplemodel <- lme(harvest ~ WP_distance + WP_height + phase + phase:WP_distance + phase:WP_turbines, weight = ~ I(1/Area), data = mydata, random =~ 1 | randomvar, method="ML")
In pre-construction phase, height, turbines and distance is set as zero (not built yet).
When conducting linear mixed effects, i try to include the turbine variables and interactions phase:distance, phase:height, phase:turbines. However, when doing this i get error:
Error in MEEM(object, conLin, control$niterEM) :
Singularity in backsolve at level 0, block 1
I figure it's because the variables phase and the wp-variables get confounded in this way, but how could i possibly deal with it otherwise? I want to display that the height, number of turbines and distance to the wind power parks depend on the construction phase.
Thank you!
I have been working on this all day but I haven't figured it out yet. So I thought I may as well ask on here and see if someone can help.
The problem is as follow:
----------
F(input)(t) --> | | --> F(output)(t)
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Given a sample with a known length, density, and spring constant (or young's modulus), find the 'output' force against time when a known variable force is applied at the 'input'.
My current solution can already discretise the sample into finite elements, however I am struggling to figure out how the force should transmit given that the change in transmission speed in the material changes itself with respect to the force (using equation c = sqrt(force*area/density)).
If someone could point me to a solution or any other helpful resources, it would be highly appreciated.
A method for applying damping to the system would also be helpful but I should be able to figure out that part myself. (losses to the environment via sound or internal heating)
I will remodel the porbem in the following way:
___ ___
F_input(t) --> |___|--/\/\/\/\/\/\/\/\--|___|
At time t=0 the system is in equilibrium, the distance between the two objects is L, the mass of the left one (object 1) is m1 and the mass of the right one (object 2) is m2.
___ ___
F_input(t) --> |<-x->|___|--/\/\/\/\/\/-|<-y->|___|
During the application of the force F_input(t), at time t > 0, denote by x the oriented distance of the position of object 1 from its original position at time t=0. Similarly, at time t > 0, denote by y the oriented distance of the position of object 2 from its original position at time t=0 (see the diagram above). Then the system is subject to the following system of ordinary differential equations:
x'' = -(k/m1) * x + (k/m2) * y + F_input(t)/m2
y'' = (k/m2) * x - (k/m2) * y
When you solve it, you get the change of x and y with time, i.e. you get two functions x = x(t), y = y(t). Then, the output force is
F_output(t) = m2 * y''(t)
The problem isn't well defined at all. For starters for F_out to exist, there must be some constraint it must obey. Otherwise, the system will have more unknowns than equations.
The discretization will lead you to a system like
M*xpp = -K*x + F
with m=ρ*A*Δx and k=E*A/Δx
But to solve this system with n equations, you either need to know F_in and F_out, or prescribe the motion of one of the nodes, like x_n = 0, which would lead to xpp_n = 0
As far as damping, usually, you employ proportional damping, with a damping matrix proportional to the stiffness matrix D = α*K multiplied by the vector of speeds.
I'm trying to use graph-tool to quickly calculate the distance from a source vertex to all vertices within a maximum distance, using a cost property that I have available for each edge.
I suppose I have to use the dijkstra_search function, but how do I specify the stop-criterium ? I have a working example, but I think it traverses the entire graph (taking several seconds as it's the entire road network of Holland).
Second, what's the fastest way of generating a list of: (vertex-id, distance) once the dijkstra_search function finishes ?
Turns out it was pretty easy:
import graph_tool.all as gt
dm = G.new_vp("double", np.inf)
gt.shortest_distance(G, source=sourcenode, weights=EdgePropertyMap, dist_map = dm, max_dist=0.1)
I can't figure out how a genetically programmed A.I. can determine when there should be a constant in the final equation. If I take the formula F(m) = ma; F(m) = m9.8, how can the A.I. know what the real number 9.8 actually is? I understand that instead of putting the final number in the binary tree, you can actually put a symbol that describes a constant and then later calculate or guess what is its value in a certain way.
Thank you
Given a predefined set of constants (part of the terminal set) they'll be combined to form new constants (using a tree-representation, any sub-tree with only numeric constants as leaves can itself be thought of as a new numeric constant).
Even with a single constant (c) the system will create:
the 1.0 constant (constant divided by itself: c / c);
the 2.0 constant (1.0 + 1.0 i.e. c / c + c / c);
the 0.5 constant (1.0 / 2.0 i.e. c / c / (c / c + c / c));
many constants will be created this way (if you are lucky... 9.8).
Sometimes special terminals named "ephemeral random constant" (Koza) are used. For each ephemeral in the initial population, a random number in a specified range is generated. Then these random constants are moved around and combined.
Anyway, even with the use of the ephemeral random constant, GP can be hard put to generate the right constants (Koza said "the finding of numeric constants is a skeleton in the GP closet").
So other techniques can be used during/after the evolution, e.g. numeric mutation, hill climbing...
These hybrid systems often have significant improvements in the success ratios (at least for regression problems).
I have a lisp program on roulette wheel selection,I am trying to understand the theory behind it but I cannot understand anything.
How to calculate the fitness of the selected strng?
For example,if I have a string 01101,how did they get the fitness value as 169?
Is it that the binary coding of 01101 evaluates to 13,so i square the value and get the answer as 169?
That sounds lame but somehow I am getting the right answers by doing that.
The fitness function you have is therefore F=X^2.
The roulette wheel calculates the proportion (according to its fitness) of the whole that that individual (string) takes, this is then used to randomly select a set of strings for the next generation.
Suggest you read this a few times.
The "fitness function" for a given problem is chosen (often) arbitrarily keeping in mind that as the "fitness" metric rises, the solution should approach optimality. For example for a problem in which the objective is to minimize a positive value, the natural choice for F(x) would be 1/x.
For the problem at hand, it seems that the fitness function has been given as F(x) = val(x)*val(x) though one cannot be certain from just a single value pair of (x,F(x)).
Roulette-wheel selection is just a commonly employed method of fitness-based pseudo-random selection. This is easy to understand if you've ever played roulette or watched 'Wheel of Fortune'.
Let us consider the simplest case, where F(x) = val(x),
Suppose we have four values, 1,2,3 and 4.
This implies that these "individuals" have fitnesses 1,2,3 and 4 respectively. Now the probability of selection of an individual 'x1' is calculated as F(x1)/(sum of all F(x)). That is to say here, since the sum of the fitnesses would be 10, the probabilities of selection would be, respectively, 0.1,0.2,0.3 and 0.4.
Now if we consider these probabilities from a cumulative perspective the values of x would be mapped to the following ranges of "probability:
1 ---> (0.0, 0.1]
2 ---> (0.1, (0.1 + 0.2)] ---> (0.1, 0.3]
3 ---> (0.3, (0.1 + 0.2 + 0.3)] ---> (0.3, 0.6]
4 ---> (0.6, (0.1 + 0.2 + 0.3 + 0.4)] ---> (0.6, 1.0]
That is, an instance of a uniformly distributed random variable generated, say R lying in the normalised interval, (0, 1], is four times as likely to be in the interval corresponding to 4 as to that corresponding to 1.
To put it another way, suppose you were to spin a roulette-wheel-type structure with each x assigned a sector with the areas of the sectors being in proportion to their respective values of F(x), then the probability that the indicator will stop in any given sector is directly propotional to the value of F(x) for that x.