lout | API documentation generator | REST library

For debugging, try using a debug build: use gn args to set is_debug = true, then recompile.

If you insist on debugging release-mode binaries, you can enable jlh and friends with the v8_enable_object_print = true GN arg, but your experience will likely be weird in other ways (e.g. stepping and breakpoints won't be reliable, many values will be , etc.).

• you have not provided sample data. Have generated 10 polygons to demonstrate
• the screen shot of your data shows a pandas dataframe with a column st_astext which is well know text https://shapely.readthedocs.io/en/stable/manual.html#well-known-formats
• an approach to using this with plotly
  1. decode WKT to shapely using shapely.wkt.loads()
  2. create a geopandas GeoSeries from this pandas Series. This then has the interface __geo_interface__ to generate GEOJSON
• "with the color proportional to cnt for polygons" I'm assuming means number of points in a polygon

As I understand it you are trying to identify all possible matches for the jumbled string in your list. You could sort the letters in the jumbled word and match the resulting list against sorted lists of the words in your data file.

Your map is being centered somewhere in the Gulf of Guinea because the value of your initialCenter came from your state which has a value of "". This means that the value of your latLng is "","" which the map considered as 0,0 .

Please note that initialCenter parameter takes an object containing latitude and longitude coordinates. Sets the maps center upon loading.

To change the center of the map when changing the value of the state you need to use the center parameter which takes an object containing latitude and longitude coordinates. This is use if you want to re-render the map after the initial render.

action in

Replace the or || with and &&

if (weight >= 1 && weight < 10) {// der code}

so that we can make sure most of them are 0

if there is no strict requirements to the number of 0s (as you might have suggested in the single-column example) you are looking for Lasso regression (so called L1 regularization) which, to simply put, penalizes the magnitude of each weight. The weight will only be big if it is absolutely crucial for the inference.

In tensorflow 2.x this can be done via kernel regularizer. Now, this enforces weights to be small, but it does not guarantee it will be 0. Furthermore, it strongly affects performance if used abusively.

As a side note, the problem you are probably trying to solve is related to machine learning interpretability/explainability, and while your approach is interesting, it might be worth looking at methods/models constructed solely for this purpose (there are models that are able to produce feature significance etc)

You can't invalidate a user authenticating with stateless authentication like JWT which has the signed authentication embedded in the Token which is valid until the JWT expiry.

i.e. you can't revoke a JWT Token after it's already been issued.

There is a JwtAuthProvider.ValidateToken filter you can use to execute custom logic to prevent a user from authenticating which you may be able to use however that would require that you manage a collection of Token info you want to prevent from authenticating before its Token expiry.

Ah, the joys of microbenchmarking :-)

V8 has two compilers for Wasm: a non-optimizing baseline compiler that produces code really fast, and an optimizing compiler that takes quite a bit longer to produce code, but that code is typically about twice as fast. When a module is loaded, current versions first compile all functions with the baseline compiler. Once that's done, execution can start, and optimized compilation jobs are scheduled to run in the background. When an optimized compilation job is complete, the respective function's code is swapped, and the next invocation of the function will use it. (The details here will very likely change in the future, but the general principle will remain.) That way, typical applications get both good startup latency, and good peak performance.

But, as with any heuristic or strategy, you can craft a case where it gets it wrong...

In your benchmark, each function is called only once. In the fast cases, optimizing kernel finishes before init returns. In the slow cases, kernel is called before its optimized compilation job is done, so its baseline version runs. Apparently when embedding V8 directly, you reliably get the latter scenario, whereas when running via WasmFiddle in Chrome, you get the former most of the time, but not always.

I can't explain why your custom embedding runs are even slower than the slow case in Chrome; I'm not seeing that on my machine (OTOH, in Chrome, I'm seeing an even bigger delta: about 1100ms for a fast run and 4400ms for a slow run); however I used the d8 shell instead of compiling my own embedding. One thing that's different is that when measuring with time on the command line, you include process startup and initialization, which the Date.now() calls around main() don't include. But that should only account for 10-50 milliseconds or so, not for a 3.6s → 5.0s difference.

While this situation might look quite unfortunate for your microbenchmark, it is generally working as intended, i.e. not a bug, and hence unlikely to change on V8's side. There are several things you can do to make the benchmark more reflective of real-world behavior (assuming this one doesn't exactly represent some real application you have):

• execute functions multiple times; you'll see that the first run will be slower (or, depending on function size and module size and number of available CPU cores and scheduling luck, the first few runs)

• wait a bit before calling the hottest functions, e.g. by doing