This work is based on the original work by
Xiaocheng Tang <[email protected]> in madlib#75.

This PR adds two main features:

- A Minibatch solver that takes as input a batch of data
- SVM code that takes advantage of the minibatch

Closes madlib#229

Co-authored-by: Nikhil Kak <[email protected]>
Co-authored-by: Xiaocheng Tang <[email protected]>

JIRA: MADLIB-1201

Fixed the issue of output of lda_train and lda_get_word_topic_count
not matching each other. Added test case in install check.
See jira for more details and example.

Also added a install check that validates that the output of lda_train and
lda_get_word_topic_count are consistent with each other.
See jira for more details and example.

JIRA: MADLIB-1160

This commit adds a helper function, which will map each wordid with
corresponding topicid that get assigned in output table. Duplicate lines
are removed from the final result.

Also adds a workaround for GPDB4.3 svec

In GPDB4.3, we cannot call madlib.svec directly on a text
format.Instead, we have to call madlib.svec_from_string to convert the
text. This commit fix this issue so the new helper function
madlib.lda_get_word_topic_mapping can work on both gpdb5 and gpdb4.

JIRA:MADLIB-1160

This commit fixes the topicid inconsistency in madlib.lda_train
and madlib.lda_get_topic_desc, where the former one uses 0 based index
and the latter uses 1 index. Now they will all start at 0.

JIRA: MADLIB-1160

Previously, madlib.lda_get_topic_desc got top k - 1 words in the result
table. This commit fixed it to be top k.