apparatus | A collection of low-level machine learning | Natural Language Processing library
kandi X-RAY | apparatus Summary
kandi X-RAY | apparatus Summary
A collection of low-level machine learning algorithms for node.js. This project is quite new and documentation will be on the way shortly. In the meantime you can check out the spec folder for examples of how to use the algorithms. Note that within "apparatus" the interface to the algorithms in primarily arrays of numbers and vectors. If you’re looking for feature extraction from text or natural language check out the "natural" [node package. "natural" uses many of these algorithms but adds a layer of natural language/text feature extraction.
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Trending Discussions on apparatus
QUESTION
I am posting this question again because the project has changed and the previous answers don't return the desired results. Ambulances and fire trucks have the dispatch time when an emergency occurred and an end time for when the emergency was declared over.
Event 1 starts on May 1, 2021 10:17:33 and ends at may 1, 2021 10:33:41.
Event 2 starts on May 1, 2021 11:50:52 and ends at May 1, 2021 13:18:21.
I would like to parse the amount of time from the start to the end and place it into the hour parts when it occurs. For example; Event 1 starts at 10:17 and ends at 10:33. It would place 16 minutes minutes in the 10:00 hour part for that day. Event 2 would place 10 minutes in the 11:00 hour part, 60 minutes in the 12:00 hour part and 18 minutes in the 13:00 hour part. Place the minutes in the hours during which the event occured.
The results should look the following. Although I am flexible. For example, if the name of the truck cannot be returned in the results that would be ok because if the EventID is there, I could relate back to the original table.
EventID Ambulance EventDayOfYear EventHour MinutesAllocated 1 Medic10 121 10 16 1 Medic10 121 11 10 2 Ladder73 121 11 10 2 Ladder73 121 12 60 2 Ladder73 121 13 18 3 Engine41 121 13 33 3 Engine41 121 14 21 4 Medic83 121 15 32 4 Medic83 121 16 5 5 Rescue32 121 16 33 6 Medic09 121 23 16 6 Medic09 122 0 39 7 Engine18 121 23 28 7 Engine18 122 0 60 7 Engine18 122 1 34 8 Rescue63 122 0 35The following SQL code comes close to working to deliver the right result. But it does not overlap days. There are many emergency events that start at 2300 hours and last until 0300 hours the following day.
...ANSWER
Answered 2021-May-17 at 19:56QUESTION
I have a dataframe and its has same common value in the column "Status". I need to split it by two different columns and its urls next to it.
I have tried
pd.DataFrame(df.groupby(['Labels','Pattern','Status])['Count']) its not working as expected.
I have attached the df query and picture for clear understanding.
DF
...ANSWER
Answered 2021-May-05 at 05:44Use DataFrame.set_index with DataFrame.unstack and DataFrame.sort_index, last flatten MultiIndex:
QUESTION
I cannot figure out how to tie in the @State var to a picker so that @FetchRequest will update.
This code compiles, but changing the picker selection does nothing to fetchRequest, because it's not calling the init. All kinds of other variants have failed mostly.
How do I accomplish this?
...ANSWER
Answered 2021-Apr-04 at 14:58There are a few ways to go about this here is mine. Not sure of your intended use of Skill but I think you can figure out how to make it work for you.
I would make apparatus an enum
QUESTION
I want to convert a dataframe to a tensorflow dataset with a TFRecordf format. This is what I have written:
...ANSWER
Answered 2021-Feb-19 at 16:23You have an indentation error. Use the following.
QUESTION
Creating an endpoint which respond with array of classifications based several ML models based on NaturalJS. I have two questions:
- how to resolve this err,
- how to force it to be sync.
The err and console.log:
...ANSWER
Answered 2021-Jan-08 at 14:22This issue occurred because the second file contains internal format issue (not validated JSON)
QUESTION
If an observable is running synchronously, then the callback that is given to subscribe is executed before subscribe returns. The result is that the following code gives an error. (sub is not initialized)
ANSWER
Answered 2020-Nov-27 at 14:10Okay, so here's me answering my own question. After working on this for far too long, I stumbled across the fact that it turns out RxJS comes with a pretty good built-in solution. It's only pretty good because it uses publish/connect which seems to be implemented with subjects internally (Though the memory footprint is still better? Not sure about why).
This is not really the intended use of publish/connect, as I'm not multicasting. The key is that ConnectableObservables do not start with subscribe, but rather with connect.
You can use this to get at the desired behavior without relying on the event loop at all.
Solution Using PublishMini-example:
QUESTION
I am trying to write a VBA script to extract information from a text document and tabulate it into corresponding columns. The code is based on https://stackoverflow.com/questions/51635537/extract-data-from-text-file-into-excel/51636080. I am having an issue doing multiple extractions.
Sample text
...ANSWER
Answered 2020-Sep-30 at 22:15Your "not working" code is actually writting out all the data. But your nextrow logic is flawed, so some data is being overwritten.
Rather than try to fix that code, I would suggest an alternative method
QUESTION
The libraries I'm using are:
...ANSWER
Answered 2020-Aug-13 at 17:53Here is a script to clean the column. Note you may want to add more words to the stopword set to meet your requirements.
QUESTION
I'm using Spark 2.3.1 and I'm performing NLP in spark when I print the type of RDD it shows and when executing
rdd.collect()
command on PipelineRDD it's output is
['embodiment present invention include pairing two wireless device placing least one two device pairing mode performing least one pairing motion event least one wireless device satisfy least one pairing condition detecting satisfaction least one pairing condition pairing two wireless device response detecting satisfaction least one pairing condition numerous aspect provided', 'present invention relates wireless communication system specifically present invention relates method transmitting control information pucch wireless communication system apparatus comprising step of obtaining plurality second modulation symbol stream corresponding plurality scfdma single carrier frequency division multiplexing symbol diffusing plurality first modulation symbol stream form first modulation symbol stream corresponding scfdma symbol within first slot obtaining plurality complex symbol stream performing dft discrete fourier transform precoding process plurality second modulation symbol stream transmitting plurality complex symbol stream pucch wherein plurality second modulation symbol stream scrambled scfdma symbol level dog church aardwolf abacus']
I want to create a data frame like this to add every word into rows of the data frame.
...ANSWER
Answered 2020-Aug-07 at 09:12Something like this, but adapt accordingly:
QUESTION
I am trying to train Gensim Doc2Vec model on tagged documents. I have around 4000000 documents. Following is my code:
...ANSWER
Answered 2020-Jun-14 at 15:57The Doc2Vec mode you've chosen, dm=0 (aka plain "PV-DBOW"), does not train word-vectors at all. Word vectors will still be randomly-initialized, due to shared code-paths of the different models, but never trained and thus meaingless.
So the results of your most_similar(), using a word as the query, will be essentially random. (Using most_similar() on the model itself, rather than its .wv word-vectors or .docvecs doc-vectors, should also be generating a deprecation warning.)
If you need your Doc2Vec model to train word-vectors in addition to the doc-vectors, use either the dm=1 mode ("PV-DM") or dm=0, dbow_words=1 (adding optional interleaved skip-gram word training to plain DBOW training). In both cases, words will be trained very similarly to a Word2Vec model (of the 'CBOW' or 'skip-gram' modes, respectively) – so your word-based most_similar() results should then be very comparable.
Separately:
- if you have enough data to train 300-dimensional vectors, & discard all words with fewer than 100 occurrences, then 50 training epochs may be more than needed.
- those
most_similar()results don't particularly look like they're result of any lemmatization, as seems intended by yourtext_process()method, but maybe that's not an issue, or some other issue entirely. Note, though, that with sufficient data, lemmatization may be a superfluous step - all variants of the same word tend to wind up usefully near each other, when there are plenty of varied examples of al the word variants in real contexts.
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