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Posted to commits@madlib.apache.org by ri...@apache.org on 2017/12/05 07:44:59 UTC
madlib git commit: K-NN: Fix minor issues in documentation
Repository: madlib
Updated Branches:
refs/heads/master 775afd05d -> c73cf8507
K-NN: Fix minor issues in documentation
Closes #208
Project: http://git-wip-us.apache.org/repos/asf/madlib/repo
Commit: http://git-wip-us.apache.org/repos/asf/madlib/commit/c73cf850
Tree: http://git-wip-us.apache.org/repos/asf/madlib/tree/c73cf850
Diff: http://git-wip-us.apache.org/repos/asf/madlib/diff/c73cf850
Branch: refs/heads/master
Commit: c73cf850791210c47ffea66c87da546c2049265a
Parents: 775afd0
Author: Frank McQuillan <fm...@pivotal.io>
Authored: Mon Dec 4 12:24:46 2017 -0800
Committer: Rahul Iyer <ri...@apache.org>
Committed: Mon Dec 4 23:44:02 2017 -0800
----------------------------------------------------------------------
src/ports/postgres/modules/knn/knn.sql_in | 43 +++++++++++++-------------
1 file changed, 22 insertions(+), 21 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/madlib/blob/c73cf850/src/ports/postgres/modules/knn/knn.sql_in
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diff --git a/src/ports/postgres/modules/knn/knn.sql_in b/src/ports/postgres/modules/knn/knn.sql_in
index 17d81ad..cdc9704 100644
--- a/src/ports/postgres/modules/knn/knn.sql_in
+++ b/src/ports/postgres/modules/knn/knn.sql_in
@@ -133,21 +133,18 @@ neighbors that were used in the voting/averaging, sorted
from closest to furthest.</dd>
<dt>fn_dist (optional)</dt>
-<dd>TEXT, default: squared_dist_norm2'. The name of the function to use to calculate the distance from a data point to a centroid.
+<dd>TEXT, default: 'squared_dist_norm2'. The name of the function
+used to calculate the distance between data points.
-The following distance functions can be used (computation of barycenter/mean in parentheses):
+The following distance functions can be used:
<ul>
-<li><b>\ref dist_norm1</b>: 1-norm/Manhattan (element-wise median
-[Note that MADlib does not provide a median aggregate function for support and
-performance reasons.])</li>
-<li><b>\ref dist_norm2</b>: 2-norm/Euclidean (element-wise mean)</li>
-<li><b>\ref squared_dist_norm2</b>: squared Euclidean distance (element-wise mean)</li>
-<li><b>\ref dist_angle</b>: angle (element-wise mean of normalized points)</li>
-<li><b>\ref dist_tanimoto</b>: tanimoto (element-wise mean of normalized points <a href="#kmeans-lit-5">[5]</a>)</li>
+<li><b>\ref dist_norm1</b>: 1-norm/Manhattan</li>
+<li><b>\ref dist_norm2</b>: 2-norm/Euclidean</li>
+<li><b>\ref squared_dist_norm2</b>: squared Euclidean distance</li>
+<li><b>\ref dist_angle</b>: angle</li>
+<li><b>\ref dist_tanimoto</b>: tanimoto</li>
<li><b>user defined function</b> with signature <tt>DOUBLE PRECISION[] x, DOUBLE PRECISION[] y -> DOUBLE PRECISION</tt></li></ul></dd>
-
-
</dl>
@@ -168,6 +165,11 @@ The output of the KNN module is a table with the following columns:
<th>prediction</th>
<td>INTEGER. Label in case of classification, average value in case of regression.</td>
</tr>
+ <tr>
+ <th>k_nearest_neighbours</th>
+ <td>INTEGER[]. List of nearest neighbors, sorted closest to furthest
+ from the corresponding test point.</td>
+ </tr>
</table>
@@ -236,14 +238,14 @@ DROP TABLE IF EXISTS knn_result_classification;
SELECT * FROM madlib.knn(
'knn_train_data', -- Table of training data
'data', -- Col name of training data
- 'id', -- Col Name of id in train data
+ 'id', -- Col name of id in train data
'label', -- Training labels
'knn_test_data', -- Table of test data
'data', -- Col name of test data
'id', -- Col name of id in test data
'knn_result_classification', -- Output table
3, -- Number of nearest neighbors
- True -- True if you want to show Nearest-Neighbors by id, False otherwise
+ True, -- True to list nearest-neighbors by id
'madlib.squared_dist_norm2' -- Distance function
);
SELECT * from knn_result_classification ORDER BY id;
@@ -260,7 +262,8 @@ Result:
6 | {50,45} | 0 | {6,7,8}
(6 rows)
</pre>
-Note that the nearest neighbors are sorted from closest to furthest from the corresponding test point.
+Note that the nearest neighbors are sorted from closest
+to furthest from the corresponding test point.
-# Run KNN for regression:
<pre class="example">
@@ -275,8 +278,8 @@ SELECT * FROM madlib.knn(
'id', -- Col name of id in test data
'knn_result_regression', -- Output table
3, -- Number of nearest neighbors
- True -- True if you want to show Nearest-Neighbors, False otherwise
- 'madlib.squared_dist_norm2' -- Distance function
+ True, -- True to list nearest-neighbors by id
+ 'madlib.dist_norm2' -- Distance function
);
SELECT * FROM knn_result_regression ORDER BY id;
</pre>
@@ -293,7 +296,8 @@ Result:
(6 rows)
</pre>
--# List nearest neighbors only, without doing classification or regression:
+-# List nearest neighbors only, without doing classification
+or regression:
<pre class="example">
DROP TABLE IF EXISTS knn_result_list_neighbors;
SELECT * FROM madlib.knn(
@@ -334,9 +338,6 @@ vector (a test point) is classified by assigning the label which is most
frequent among the k training samples nearest to that test point.
In case of regression, average of the values of these k training samples
is assigned to the test point.
-The only distance metric supported in this version is MADlib's squared_dist_norm2.
-Other distance metrics will be added in a future release of this module.
-
@anchor literature
@literature
@@ -535,4 +536,4 @@ BEGIN
RETURN returnstring;
END;
$$ LANGUAGE plpgsql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');