hasting | 使用java nio，oio，aio等原生态io和线程池，实现的分布式跨语言服务化框架 | Web Framework library

Firstly, you want to start your python for loop at i=1 in the range(1, trials) since Python starts at 0.

Secondly, at if np.random.uniform(1) you are just producing 1.0, so that needs to change, e.g. np.random.uniform(min, max, size=1) or np.random.uniform(size=1) if you just want a uniform number between 0 and 1. Have a look at the documentation for np.random.uniform if this isn't clear.

Update
Thirdly, you are unknowingly casting to integers, so this needs to be handled also. When being used to R this is easy to forget (I just did myself). I have refactored your code below, and this solution should provide you with a similar result to what you see in R.

Here I turned sim into a float vector, and I made sure to subtract and divide using floats inside the exp-functions. Hope this works for you.

The problem does not comes from Pandas but ArcGIS().geocode(address) which is insanely slow. Indeed, on my machine, this line takes 400 ms/request. Each request send a slow network query to the online ArcGIS API. Using multiprocessing will not help much as you will quickly reach additional limitations (limited rate of API request, saturation of the website). You need to send batch requests. Unfortunately this does not seems supported by the geopy package. If you are tied to ArcGIS, you need to use their own API. You can find more information about how to do that on the ArcGIS documentation.

The error message is telling you that your function should return a promise that resolves when all the async work is complete. If you don't, then the function will terminate early, and the async work is highly unlikely to complete, as Cloud Functions shuts down right afterward.

Is suggest reading the documentation to better understand how it works.

The solution is pretty straightforward. Return a promise that resolves after both the fetch and Firestore write are fully complete by chaining promises correctly.

The json gives an array

The problem is in what you are passing to the constructor of DataContext. It should be a proper connection string to your local server. The mdf file must be attached to it.

Here is a working version of the code you're looking for.

Example of working query in DB fiddle: https://www.db-fiddle.com/f/rQKazPgbtGS766WEiuiXyR/0

You should use justify with the 'a' tag not with div.

You have a very typical text classification task.

There are many classification algorithms you could use, but the main areas for choice/improvement in your task are likely to be:

• feature-extraction & feature-engineering: how do you turn those short texts into numerical data against which rules/thresholds can be learned?
• overall process issues: for whatever "tough cases" exist that can't be learned from existing data, either initially or over time, how are necessary corrections fed back into an improved system

Initially, you should try 'bag of words' and 'character n-grams' (either alone or together) as ways to turn your short texts into feature vectors. That, alone, with sufficient training data should handle most of the kinds of cases you've shown so far, since it will help any classification algorithm discover certain 'slam-dunk' rules.

For example, that will effectively learn that 'shop' may always imply 'retail', or 'house' always implies 'residential', or 'office' implies commercial. And using character n-grams will also give the model clues as to how to handle other typos or variant forms of the same words.

There will be cases it can't handle well. I'd guess that you'd want `3 bedroom dwelling', alone, to be 'residential' – but in your examples, you've binned 'retail unit with 3 bedroom dwelling above' as 'retail'. With enough examples of desired behavior, a classifier might get that right, because it either sees 'retail' as a category with more precedence, or other words (like 'above') implying mixed-use that usually should be binned one way or another.

When you look at the cases that doesn't handle well, you'll then be able to consider more advanced approaches, like perhaps using word-vectors to represent words that weren't necessarily in your (small) training set, but could be considered near-synonyms to known words. (For example, one possible policy for handling words unknown to your training set, that arrive later, would be to use some external, larger word2vec model to replace any unknown word with the known word that's closest.)

But, you should really start with the most-simple feature approaches, see how far those get you and thus set a baseline for later improvements. Then, consider more advanced & custom techniques.