From 0fca6c3c1a4817803dccbfe8f5a3ee68c334a199 Mon Sep 17 00:00:00 2001
From: Stuart Lynn <stuart.lynn@gmail.com>
Date: Thu, 26 May 2016 12:31:58 -0400
Subject: [PATCH] inital commit of similarity functions

---
 src/pg/sql/80_similarity_rank.sql             | 13 ++++++
 .../crankshaft/similarity/__init__.py         |  1 +
 .../crankshaft/similarity/similarity.py       | 43 +++++++++++++++++++
 3 files changed, 57 insertions(+)
 create mode 100644 src/pg/sql/80_similarity_rank.sql
 create mode 100644 src/py/crankshaft/crankshaft/similarity/__init__.py
 create mode 100644 src/py/crankshaft/crankshaft/similarity/similarity.py

diff --git a/src/pg/sql/80_similarity_rank.sql b/src/pg/sql/80_similarity_rank.sql
new file mode 100644
index 0000000..6c3867f
--- /dev/null
+++ b/src/pg/sql/80_similarity_rank.sql
@@ -0,0 +1,13 @@
+CREATE OR REPLACE FUNCTION cdb_SimilarityRank(cartodb_id numeric, query string)
+returns TABLE (cartodb_id NUMERIC, similarity NUMERIC)
+as $$
+  from crankshaft.similarity import similarity_rank
+  return similarity_rank(cartodb_id, query)
+$$ LANGUAGE plpythonu
+
+CREATE OR REPLACE FUNCTION cdb_MostSimilar(cartodb_id numeric, query string ,matches numeric)
+returns TABLE (cartodb_id NUMERIC, similarity NUMERIC)
+as $$
+  from crankshaft.similarity import most_similar
+  return most_similar(matches, query)
+$$ LANGUAGE plpythonu
diff --git a/src/py/crankshaft/crankshaft/similarity/__init__.py b/src/py/crankshaft/crankshaft/similarity/__init__.py
new file mode 100644
index 0000000..7df975c
--- /dev/null
+++ b/src/py/crankshaft/crankshaft/similarity/__init__.py
@@ -0,0 +1 @@
+from similarity import * 
diff --git a/src/py/crankshaft/crankshaft/similarity/similarity.py b/src/py/crankshaft/crankshaft/similarity/similarity.py
new file mode 100644
index 0000000..ac39459
--- /dev/null
+++ b/src/py/crankshaft/crankshaft/similarity/similarity.py
@@ -0,0 +1,43 @@
+from sklearn.neighbors import BallTree
+import numpy as np
+import plpy
+
+def similarity_rank(target_cartodb_id, query):
+    data = plpy.execute(query)    
+    features, target = extract_features_target(data,target_cartodb_id)
+    tree = train(features)
+    dist, ind  = tree.query(target, k=len(data))
+    cartodb_ids  = [ dist[ind]['cartodb_id'] for index in ind ]
+    return cartodb_ids, dist
+
+def most_similar(matches,query):
+    data = plpy.execute(query)    
+    features, _ = extract_features_target(data)
+    tree = train(features)
+    results = []
+    for i in features:
+        target = features
+        dist,ind = tree.query(target, k=matches)
+        cartodb_ids  = [ dist[ind]['cartodb_id'] for index in ind ]
+        results.append(cartodb_ids)
+    return cartodb_ids, results
+    
+def train(features):
+    normed_features  = normalize_features(featuers)
+    normed_target    = normalize_features([target])
+    tree = BallTree(normed_features, leaf_size=2)
+    return tree
+    
+def normalize_features(features):
+    maxes = features.max(axis=0)
+    mins  = features.min(axis=0)
+    return (features - mins)/(maxes-mins)
+    
+def extract_features_target:(data, target_cartodb_id=None):
+    target = None
+    for row in data:
+        data.keys().difference(['the_geom', 'the_geom_webmercator','cartodb_id'])
+        if data['cartodb_id'] == target_cartodb_id
+            target = data.values()
+    return np.array(data), np.array(target)
+