briefs | Microblogging service in node express passport | Runtime Evironment library

At the end of your code, is len(briefs) what you expect it to be? Does looking at items like briefs[0] or briefs[-1] show the individual TaggedDocument items you expect?

You probably don't want two nested for … in loops - one going over all briefs to open the files, and the other, for each brief, again going over all briefs to assign them all the same tokens value.

Try changing your lines:

You're likely going to want to write your own preprocessing/tokenization functions. But don't worry, it's not hard to outdo Gensim's simple_preprocess, even with very crude code.

The only thing Doc2Vec needs as the words of a TaggedDocument is a list of string tokens (typically words).

So first, you might be surprised how well it works to just do a default Python string .split() on your raw strings - which just breaks text on whitespace.

Sure, a bunch of the resulting tokens will then be mixes of words & adjoining punctuation, which may be nearly nonsense.

For example, the word 'lawsuit' at the end of the sentence might appear as 'lawsuit.', which then won't be recognized as the same token as 'lawsuit', and might not appear enough min_count times to even be considered, or otherwise barely rise above serving as noise.

But especially for both longer documents, and larger datasets, no one token, or even 1% of all tokens, has that much influence. This isn't exact-keyword-search, where failing to return a document with 'lawsuit.' for a query on 'lawsuit' would be a fatal failure. A bunch of words 'lost' to such cruft may have hadly any effect on the overall document, or model, performance.

As your datasets seem manageable enough to run lots of experiments, I'd suggest trying this dumbest-possible tokenization – only .split() – just as a baseline to become confident that the algorithm still mostly works as well as some more intrusive operation (like simple_preprocess()).

Then, as you notice, or suspect, or ideally measure with some repeatable evaluation, that some things you'd want to be meaningful tokens aren't treated right, gradually add extra steps of stripping/splitting/canonicalizing characters or tokens. But as much as possible: checking that the extra complexity of code, and runtime, is actually delivering benefits.

For example, further refinements could be some mix of:

• For each token created by the simple split(), strip off any non-alphanumeric leading/trailing chars. (Advantages: eliminates that punctuation-fouling-words cruft. Disadvantages: might lose useful symbols, like the leading $ of monetary amounts.)
• Before splitting, replace certain single-character punctuation-marks (like say ['.', '"', ',', '(', ')', '!', '?', ';', ':']) with the same character with spaces on both sides - so that they're never connected with nearby words, and instead survive a simple .split() as standalone tokens. (Advantages: also prevents words-plus-punctuation cruft. Disadvantages: breaks up numbers like 2,345.77 or some useful abbreviations.)
• At some appropriate stage in tokenization, canonicalize many varied tokens into a smaller set of tokens that may be more meaningful than each of them as rare standalone tokens. For example, $0.01 through $0.99 might all be turned into $0_XX - which then has a better chance of influencting the model, & being associated with 'tiny amount' concepts, than the original standalone tokens. Or replacing all digits with #, so that numbers of similar magnitudes share influence, without diluting the model with a token for every single number.

The exact mix of heuristics, and order of operations, will depend on your goals. But with a corpus only in the thousands of docs (rather than hundreds-of-thousands or millions), even if you do these replacements in a fairly inefficient way (lots of individual string- or regex- replacements in serial), it'll likely be a manageable preprocessing cost.

But you can start simple & only add complexity that your domain-specific knowledge, and evaluations, justifies.

Here's your bug:

Suppose you have a structured reference table called SKU as below:

Please note, you must sort the Code column in alphabetic order (A-Z).

Suppose you want to return the product name in Column B based on the SKU code in Column C, in Cell B2, enter the following formula and drag it down:

To place footer at bottom, you have to set footer position to fixed. I have removed all unwanted css from footer.

There are a few problems with the script, I've changed the way it loops over the data and now it loops over the $arraysource using a foreach loop. Then for each loop it outputs that set of data to the CSV file. I've also changed the file name to include the corresponding value from $keycompare so the file name is more relevant. You can change the values in $headers to be the actual names of the fields, these are just to show how to do it...

Your code expects each object in the array to have both men and women properties. But these properties are in separate array elements. So you need to check whether the property exists before trying to loop over its elements. Otherwise you'll try to get the length of an undefined property.

Similarly, each object in the men or women array has just a single property, not all the properties in the category array.

Looping will be much easier if you assign variables to the current element of the array, so you don't need long expressions with lots of indexes like categories[i][gender[g]][j][category[c]][k]. And using forEach() will simplify this.

id should be unique in the same HTML doc, or they should be specified in a class if you are reusing the same 'name'.

for the above, the easiest fix is to give your briefs different ids and then pass in the id in the toggle function for it to be toggled.

i.e. you have two sections of id proj-details-1 and proj-details-2 in your HTML.

Then for your toggle, you could pass in the id that needs to be toggled. ... onclick="toggle(this, 'proj-details-1'); ...". The other button will have a different id.

In your toggle function:

It's too general information, you need debug and figure out the detailed error information.

My test demo: