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https://issues.apache.org/jira/browse/MADLIB-927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15843401#comment-15843401
]
ASF GitHub Bot commented on MADLIB-927:
---------------------------------------
Github user njayaram2 commented on the issue:
https://github.com/apache/incubator-madlib/pull/81
Some useful validation functions in
https://github.com/apache/incubator-madlib/blob/master/src/ports/postgres/modules/utilities/validate_args.py_in
(look at the comments in the code for those functions, use if it applies to
your scenario):
```
table_exists
get_cols_and_types
columns_exist_in_table
is_col_array
is_var_valid
input_tbl_valid
output_tbl_valid
cols_in_tbl_valid
```
https://github.com/apache/incubator-madlib/blob/master/src/ports/postgres/modules/utilities/utilities.py_in:
```
unique_string
```
You could probably have one function to validate input all args, include
your current validations checks as part of that python function.
> Initial implementation of k-NN
> ------------------------------
>
> Key: MADLIB-927
> URL: https://issues.apache.org/jira/browse/MADLIB-927
> Project: Apache MADlib
> Issue Type: New Feature
> Reporter: Rahul Iyer
> Labels: gsoc2016, starter
>
> k-Nearest Neighbors is a simple algorithm based on finding nearest neighbors
> of data points in a metric feature space according to a specified distance
> function. It is considered one of the canonical algorithms of data science.
> It is a nonparametric method, which makes it applicable to a lot of
> real-world problems where the data doesn’t satisfy particular distribution
> assumptions. It can also be implemented as a lazy algorithm, which means
> there is no training phase where information in the data is condensed into
> coefficients, but there is a costly testing phase where all data (or some
> subset) is used to make predictions.
> This JIRA involves implementing the naïve approach - i.e. compute the k
> nearest neighbors by going through all points.
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