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I'm learning about policy gradients and I'm having hard time understanding how does the gradient passes through a random operation. From here: It is not possible to directly backpropagate through random samples. However, there are two main methods for creating surrogate functions that can be backpropagated through.

They have an example of the score function:

I am seeking to re-use the same role/class names in a child module as in its parent. You know, like a specialization.

The aim is to be able to re-use the same script code for both the parent and child Series variants by simply changing use Dan to use Dan::Pandas at the top.

I am trying to stick to role rather than class compostion where I can so that the behaviours can be applied to other objects with eg. class GasBill does Series;

Here is the MRE:

I try to write an R code to find value of mu s.t. the Normal distribution satisfies the probability P(N(mu, 1)>1.96)=0.95 (i.e., P(Z>1.96)=0.95 where Z~N(mu, 1) and mu is what I want to get). Is there any code for solving the parameter of distribution? It seems that this will be a integral equation about mu such that

int_{1.96}^{\infty} 1/\sqrt{2\pi} \exp(-(x-mu)^2/2)dx=0.95

We can sample Normal distribution from dnorm(x, mean, sd) or rnorm(n, mean, sd). But we need to first take value for mean and sd.

I have a server to handle events, this server has a mutex lock and a events table(map structure). When the server receives a new event, it will acquire lock to prevent data race, store this event in the events table, and start a goroutine to monitor this event has done. If I run the program with -race flag, it will output data race.

I already put < meta name="viewport" content="width=device-width, initial-scale=1.0"> in the head. But the @media only screen and (max-device-width : 480px) will only work for 1 second and then changed to 720px and 1333.41px CSS.

I'm trying to make the bg picture to change for 480px and 720px, the 720px and 1333.41px works well, only 480 not. What to do?

what 480px looks like right now

what the background supposed to look like

Here's my style tag

I am currently having issue with the implementation of the Metropolis-Hastings algorithm.

I am trying to use the algorithm to calculate integrals of the form

In using this algorithm, we can obtain a long chain of configurations ( in this case, each configuration is just a single numbers) such that in the tail-end of the chain the probability of having a particular configuration follows (or rather tends to) a gaussian distribution.

My code seems to be messing up with obtaining the said gaussian distributions. There is a strange dependence on the transition probablity (the probablity of picking a new candidate configuration depending on the previous configuration in the chain). However, if this transition probability is symmetric, there should be no dependence on this function at all (it only affects speed at which phase space [space of potential configurations] is explored and how quickly the chain converges to the desired distribution)!

In my case I am using a normal distribution transition function (which satisfies the need to be symmetric), with width d. For each d I use I do indeed get a gaussian distribution however the standard deviation, sigma, depends on my choice of d. The resulting gaussian should have a sigma of roughly 0.701 but I find that the value I actually get depends on the parameter d, when it shouldn't.

I am not sure where the error in this code is, any help would be greatly appreciated!

This is another attempt to do this thing, essentially create a frozen class:

I know that it's way easier to ensure single instances from the class level, and that there's the excellent Staticish module from Jonathan Stowe that does the same by using roles, but I just want to try and understand a bit better how the class higher order working can be handled, mainly for a FOSDEM talk. I could think of several ways of doing to at the metamodel level, but eventually this is what I came up with:

I am seeking to find optimal control (aoa and bank angle) to maximize cross range for a shuttle type reentry vehicle using Gekko. Below is my code currently and I am getting a "Solution not found" with "EXIT: Maximum Number of Iterations Exceeded". The simulation assumes a point mass with a non-rotating earth frame. The EOMS are 6 coupled, non-linear ODEs. I have tried using different solvers, implementing/removing state and control constraints, increasing maximum number of iterations, etc. I am not confident with my setup and implementation of the problem in Gekko and am hoping for some feedback on what I can try next. I have tried to follow the setup and layouts in APMonitor's Example 11. Optimal Control with Integral Objective, Inverted Pendulum Optimal Control, and Example 13. Optimal Control: Minimize Final Time. Solutions I'm seeking are below.

Any help is very much appreciated!