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I'm using the example on https://www.elastic.co/guide/en/elasticsearch/client/net-api/current/custom-serialization.html#_serializing_type_information to get $type information for the documents in elasticsearch.

However as mentioned on the page this only returns type information for the outer document:

the type information is serialized for the outer MyDocument instance, but not for each MySubDocument instance in the SubDocuments collection.

So my question now is if anyone knows how to also get type information for sub documents?

I've tried using the same JsonSerializerSettings as in their example separate from Elasticsearch (using LinqPad) and there I get type information also for sub documents:

Use the movieDetails collection, calculate the highest, lowest, and average imdb.rating of all films in the that won at least 1 award and had more than 2 nominations.

Example of data:

I'm looking to create a table of data (see "Output Table") from the below JSON. I can't seem to get to the "final mile". Can someone show me the way to properly handle the parsing of the arrays into the desired output table?

Thanks! Russ

Desired Output Table

Output I am getting

I'm running a hurdle lognormal model using the GLMMadaptive package in R. Both the continuous part as well as the zero-part have categorical variables defined in the fixed effects. I would like to run an ANOVA on these categorical variables to detect if there is a main effect.

I've seen that using the glmmTMB package you are able to separately run an ANOVA on the conditional model and the zero-part model separately, as is demonstrated here.

Is there a similar strategy available for the GLMMadaptive package? (The glmmTMB does not support hurdle lognormal models as far as I understood). Perhaps using the joint_tests function from the emmeans package? If so, how do you define that you want to test the zero-part model? As emmeans::joint_tests(hurdlemodel) only gives the F-tests for the conditional part of the model.

Or as an alternative method, could you compare the fit of the models where you exclude the variable of interest against a the full model, as is demonstrated for the relevance of random effects in this vignette?

Many thanks!

The suggestion by Russ Lenth in the comments are implemented below, using the data and model in the GLMMadaptive two-part model vignette:

I want to use the Select and Checkbox components with MUI with the multiple props , so the users can pick multiples options. However, I want , that they are limited to 3 options maximum . I can't find this props to set the min and maximum choice . Is it possible ?! I would be really happy if so .

Here is my code :

I want to run some neural net experiments with PyTorch, but a minimal test case is giving wrong answers. The test case sets up a simple neural network with two input variables and an output variable that is just the sum of the inputs, and tries learning it as a regression problem; I expect it to converge on zero mean squared error, but it actually converges on 0.165. It's probably because of the issue alluded to in the warning message; how can I fix it?

Code:

I am trying to cast my Flask app into a App Factory Pattern, but I got stuck on the following error:

After the Data Analysis & getting the Required Result I'm appending that result to a List
Now I Need to Retrieve Or Separate the Result (Search For Pattern & Obtain It)

Code:

According to Whole-body Motion Planning with Centroidal Dynamics and Full Kinematics (Hongkai Dai, Andrés Valenzuela and Russ Tedrake)

Following the reference, one eventually arrives Resolved Momentum Control: Humanoid Motion Planning based on the Linear and Angular Momentum (Shuuji KAJITA, Fumio KANEHIRO, Kenji KANEKO, Kiyoshi FUJIWARA, Kensuke HARADA, Kazuhito YOKOI and Hirohisa HIRUKAWA)

Does drake currently provide any method for computing this centroidal momentum matrix A(q) or the H matrix (of which I believe A(q) = [0, I_tilde, H])?