Decision Tree: Enhance error information on empty dataset

Jira: MADLIB-641
Decision Tree: Avoid 'divided by zero' in scoring

Jira: MADLIB-632

We use left join to ensure all the rows of classification/scoring table were breakup.  And
the class labels which don't appear in the training dataset will be treat as 0 (encoded
value).  Therefore, during scoring, those samples with 0 class label will be regarded
misclassified.  Found in madelon dataset where the class column is malformed.
Decision Tree: Insert information of the columns in which all the values are null.

Jira: MADLIB-635

During encoding process, if all the values of a column is null, then we will
remove the column from breakup table in 'ignore' mode.  Therefore, besides
insert the column information in the breakup table to the metatable, we need
to insert those columns in which all the values are null to the meta table.

Also use the quote_literal and quote_ident for the column names.
Association Rules: Add check for support and confidence

Jira: MADLIB-442 MADLIB-443
Association Rules: Make the result stable

Jira: MADLIB-461 MADLIB-449

We use the MADLIB_SCHEMA.svec_to_string(svec) as a unique identifier for a svec, so
that the results are stable now.  According to the definition of support and confidence,
the support and confidence of the generated association rules should be >= give values
rather than >.  We also fix that. Furthurmore, we remove the dead code of the
implementation.
Association Rules: Avoid hash aggregate on svec

Jira: MADLIB-416

GPDB apparently chooses hash aggregate although svec doesn't support it.
Use text type to make the plan correct.
Quantile: Beautify code format

Replace all tabs with 4 spaces. Using 4 spaces as indent character
other than tabs.
Quantile: Suppress integer overflow

Jira: MADLIB-601

quantile_big used INT/FLOAT to store the number of rows in the table.
If the number of rows in the table exceeds 2^31, the integer will
overflow.  At the same time, cast count(*) to FLOAT may cause issues.

To fix it, we use INT8 to store the number of rows in the table.
Doxygen: linregr
k-means: Replace row_number() for scalability

Jira: MADLIB-643

Because we don't need continous values for the id, we replace row_number()
with serial data type to enhance scalability.
Association Rules: Rewrite Apriori for performance improvement

Jira: MADLIB-462 MADLIB-475 MADLIB-638

After studying the current implementation of Apriori, we found the following
issues:

1) The result was unstable, since it used the hash key to unique identify a
   svec.  As we know, two svecs may have the same hash key.  Therefore, we
   will not use the hash key.
2) There were a lot cross joins.  Actually, we can use inner join to do the
   same thing as the cross join did.  The cross join was making the process
   anti-scalable.
3) It was changing the sparse vector in many place, which caused the memory
   issues.  Now, we don't change the svec.  We just construct the svec and
   then do some operations(such as minus, multiply, etc) on that.
4) It used PLPGSQL to implement the algorithm, including aggregate function.

Based on the above analysis, we rewrite it. We use PLPYTHON and C++ AL to
address these.

Although we add code in UDF class for SRF, it is still pretty premature.
It has a lot of spaces to redesign for general purpose.
k-means: Check uniqueness of user-provided IDs

Jira: MADLIB-649
Madpack: Add pg_catalog prefix to version() call

Jira: MADLIB-473

Users may have search_path with madlib, public and madpack gets confused
when calling version() function.  Add pg_catalog prefix to make sure it's
the one in the platform.
Merge remote-tracking branch 'upstream/master' into madlib_643
Address Hitoshi's comments. Tune the style of the code