mu | Project Mu Documentation
kandi X-RAY | mu Summary
kandi X-RAY | mu Summary
This repository contains documentation of the Project Mu. You can find the documentation at
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- Processes the root directory
- Process a git repo
- Return the branch of the given path
- Get the commit of the given HEAD
- Builds the directory tree
- Adds the given path to the tree
- Get or make a node
- Builds a tree from the documentation tree
- Clean the directory
- Gather arguments
- Update repo info
- Make the yaml file
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- Writes extra contents
mu Key Features
mu Examples and Code Snippets
Lines of Code : 67License : Non-SPDX (Apache License 2.0)
Copydef batch_normalization(x,
mean,
variance,
offset,
scale,
variance_epsilon,
name=None):
r"""Batch normal def matrix_diag_transform(matrix, transform=None, name=None):
"""Transform diagonal of [batch-]matrix, leave rest of matrix unchanged.
Create a trainable covariance defined by a Cholesky factor:
```python
# Transform network layer into 2 x def __init__(
self,
learning_rate: float,
momentum: float,
use_nesterov: bool = False,
use_gradient_accumulation: bool = True,
clip_weight_min: Optional[float] = None,
clip_weight_max: Optional[float] = None, Community Discussions
Trending Discussions on mu
QUESTION
I am writing a model Series class (kinda like the one in pandas) - and it should be both Positional and Associative.
...ANSWER
Answered 2022-Mar-31 at 13:17First, an MRE with an emphasis on the M1:
QUESTION
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:
ANSWER
Answered 2021-Nov-30 at 05:48It is indeed true that sampling is not a differentiable operation per se. However, there exist two (broad) ways to mitigate this - [1] The REINFORCE way and [2] The reparameterization way. Since your example is related to [1], I will stick my answer to REINFORCE.
What REINFORCE does is it entirely gets rid of sampling operation in the computation graph. However, the sampling operation remains outside the graph. So, your statement
.. how does the gradient passes through a random operation ..
isn't correct. It does not pass through any random operation. Let's see your example
QUESTION
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:
...ANSWER
Answered 2022-Mar-04 at 12:25If I'm understanding correctly, you don't need/want to use the non-specialized role in the final module (that is, you aren't using the Series defined in Dan.rakumod in spike.raku – you're only using the specialized Series defined in Pandas.rakumod). Is that correct?
If so, the solution is simple: just don't export the Series from Dan.rakumod – it's still our scoped (the default for roles) so you can still use it in Pandas.rakumod exactly the way you currently do (Dan::Series). But, since it's not exported, you it won't create a name clash with the non-prefixed Series.
QUESTION
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.
ANSWER
Answered 2022-Feb-28 at 20:11Use uniroot to find the point where the probability equals 0.95. The function f is a way of writing this. The interval's end point are arbitrary, chosen because I wasn't not expecting the solution to be much faraway from the origin.
Note the argument lower.tail set to FALSE.
QUESTION
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.
ANSWER
Answered 2022-Feb-26 at 03:47As per the comments your code attempts to read and write to a map simultaneously and, as per the go 1.6 release notes:
if one goroutine is writing to a map, no other goroutine should be reading or writing the map concurrently
Looking at your code there appears to be no need for this. You can create the channels in advance; after they are created you are only reading from the map so there is no issue:
QUESTION
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
...ANSWER
Answered 2022-Feb-16 at 14:01change max-device-width to max-width
QUESTION
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!
...ANSWER
Answered 2022-Feb-02 at 20:28You need to save x even when it doesn't change. Otherwise the center values are under-counted, and more so as d increases, which increases the variance.
QUESTION
...This is another attempt to do this thing, essentially create a frozen class:
ANSWER
Answered 2022-Jan-16 at 23:56Attribute composition is not the time when attributes are added to the class; rather, it is the time at which:
- We commits to a particular set of attributes for the class, and communicate this to the underlying runtime (usually MoarVM), which calculates a memory layout for the object
- we tell each
Attributeobject to compose itself, which is its trigger to generate accessors,handles-related methods, and so forth
I'm not clear on what you are intend to achieve, in so far as attributes are externally readonly by default anyway, and so it'd make more sense perhaps to produce an error if somebody defines an rw one on something declared frozen. If you do want to modify attributes, you'd probably have more luck overriding add_attribute and tweaking the way the attribute works at that point (for example, something like method add_attribute($obj, $attr) { callwith($obj, $attr.clone(:!rw, :!has_accessor)) } may achieve what you want).
QUESTION
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:
ANSWER
Answered 2022-Jan-16 at 16:02There's a few misunderstandings in this attempt.
- There is one instance of a meta-class per type. Thus if we want to allow a given type to only be instantiated once, the correct scoping is an attribute in the meta-class, not a
my. Amywould mean there's one global object no matter which type we create. - The
composemethod, when subclassingClassHOW, should always call back up to the basecomposemethod (which can be done usingcallsame). Otherwise, the class will not be composed. - The
method_tablemethod returns the table of methods for this exact type. However, most classes won't have anewmethod. Rather, they will inherit the default one. If we wrap that, however, we'd be having a very global effect.
While new is relatively common to override to change the interface to construction, the bless method - which new calls after doing any mapping work - is not something we'd expect language users to be overriding. So one way we could proceed is to just try installing a bless method that does the required logic. (We could also work with new, but really we'd need to check if there was one in this class, wrap it if so, and add a copy of the default one that we then wrap if not, which is a bit more effort.)
Here's a solution that works:
QUESTION
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!
...ANSWER
Answered 2022-Jan-14 at 04:17I got a successful solution by decreasing the final time (max=0.04 for successful solution) and rearranging the equations to avoid a possible divide-by-zero:
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