bearded | Bearded WordPress Theme | Content Management System library

one ez for loop should suffice :

tqdm doesn't, by default, show every single update if the updates happen fast; by default it only updates 10 times per second. You can set the miniters parameter to 1 if you must have the output update on every iteration.

The default is miniters=None, which means it'll dynamically adjust the iteration count based on mininterval, which is set to 0.1 seconds.

You are also using print(), which replaces the bar output. Don't do that, updates will be overwritten and you get very messy output.

The tqdm class has a dedicated tqdm.write() method, use that instead:

The first clue is the warning

Model convergence problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')

This means the model hasn't converged, or doesn't think it has, to a solution where the log-likelihood surface is downward-curved (i.e., a true maximum). That's why the standard errors can't be calculated (if you did the usual calculation they'd come out negative or complex). The log-likelihood could be calculated, but the model fit is suspect so glmmTMB returns NA instead.

Next question: why? Sometimes this is mysterious and hard to diagnose, but in this case we have a good clue: when you see extreme parameter values (e.g. |beta|>10) in a (non-identity link) GLM, it almost always means that some form of complete separation is occurring. That is, there are some combinations of covariates (e.g. Keyword_1==Lesser Mouse-deer) where you always have zero counts. On the log scale, this means the density is infinitely lower than covariate combinations where you have a positive mean. The parameter is about -16, which corresponds to an expected multiplicative density difference of exp(-16) = 1e-07. This isn't infinitesimal, but it's small enough that glmmTMB gets small enough differences in the log-likelihood that the optimizer stops. However, since the likelihood surface is almost flat, it can't compute curvature etc..

You could lump together or drop categories or do some form of regularization (e.g. see here or here ...); it might also make some sense to treat your Keyword_1 variable as a random effect, which would also have the effect of regularizing the estimates.

You have to change the selector of your TextNodeComponent to include the span:

In this line (book INNER JOIN author ON author.authorid=book.authorid), there is an incorrect syntax that does not exists in SQL. You should use the correct syntax like bellow:

You need to upgrade your postgres_ext gem.

You currently have Arel 4.0.2 instead and PostgresExt 2.0.0 installed.

I see that PostgresExt 2.1.3 says “Fixes Arel 4.0.1 issues”. https://github.com/DavyJonesLocker/postgres_ext/blob/master/CHANGELOG.md#213

Your version of PostgresExt is incompatible with the version of Rails you have installed. I came to this epiphany because of the stacktrace you recently added.

You want FlatBufferBuilder::CreateStruct. This is indeed a bit weird compared to how you normally serialize structs (inlined), which is caused by unions wanting all union members to be the same size, so they are referenced over an offset.

Yes we could have implemented it this way for product categories too. We didn't as we wanted to show multiple types of references in the demo.

As for your question about filtering, yes, it's possible if you use the same input as for segments selection which allow multiple ones.

React-admin is a framework for building administration applications with React. It's not an e-commerce framework nor a UI kit. You can do whatever you want :)

In your activity class:

In onCreate() Method, write this line before setContentView();