t; x[0]]).cache()#.collect()
>
> corpus = grouped.zipWithIndex().map(lambda (term_counts, doc_id): [doc_id,
> term_counts]).cache()
>
> #corpus.cache()
>
> model = LDA.train(corpus, k=10, maxIterations=10, optimizer="online")
>
> #ldaModel = LDA.train(corpus, k=3)
&
34 PM, Mishra, Abhishek wrote:
> Hello All,
>
>
>
> If someone has any leads on this please help me.
>
>
>
> Sincerely,
>
> Abhishek
>
>
>
> *From:* Mishra, Abhishek
> *Sent:* Wednesday, February 24, 2016 5:11 PM
> *To:* user@spark.apache.org
> *Su
Hello All,
If someone has any leads on this please help me.
Sincerely,
Abhishek
From: Mishra, Abhishek
Sent: Wednesday, February 24, 2016 5:11 PM
To: user@spark.apache.org
Subject: LDA topic Modeling spark + python
Hello All,
I am doing a LDA model, please guide me with something.
I
Hello All,
I am doing a LDA model, please guide me with something.
I have a csv file which has two column "user_id" and "status". I have to
generate a word-topic distribution after aggregating the user_id. Meaning to
say I need to model it for users on their grouped status. The topic leng
