Fix conflicts.

.github/CONTRIBUTORS.md

@@ -12,5 +12,5 @@ Thanks goes to these ❤️ wonderful people who made this possible:
 | [<img src="https://avatars3.githubusercontent.com/u/23060327?v=4" width="100px;"/><br /><sub>Navendu Gupta</sub>](https://github.com/navendu29)<br /> | [<img src="https://avatars1.githubusercontent.com/u/22820957?v=4" width="100px;"/><br /><sub>Sirjanpreet Singh Banga</sub>](https://github.com/sirjan13)<br /> | [<img src="https://avatars0.githubusercontent.com/u/11931391?v=4" width="100px;"/><br /><sub>Daniela Ruz</sub>](https://github.com/druzmieres)<br /> | [<img src="https://avatars1.githubusercontent.com/u/32430978?v=4" width="100px;"/><br /><sub>aman-mi</sub>](https://github.com/aman-mi)<br /> | [<img src="https://avatars3.githubusercontent.com/u/6378532?v=4" width="100px;"/><br /><sub>Anurag El Dorado</sub>](https://github.com/aedorado)<br /> | [<img src="https://avatars2.githubusercontent.com/u/23054280?v=4" width="100px;"/><br /><sub>charul97</sub>](https://github.com/charul97)<br /> | 
 [<img src="https://avatars1.githubusercontent.com/u/26293279?v=4&s=460" width="100px;"/><br /><sub>Manan Manwani</sub>](https://github.com/manan904)<br /> | [<img src="https://avatars3.githubusercontent.com/u/22936570?v=4&s=400" width="100px;"/><br /><sub>Bhavesh Anand</sub>](https://github.com/bhaveshAn)<br /> | [<img src="https://avatars2.githubusercontent.com/u/24618078?v=4&s=400" width="100px;"/><br /><sub>Prashant Nigam</sub>](https://github.com/prashant0598)<br /> | [<img src="https://avatars2.githubusercontent.com/u/20587669?v=4&s=460" width="100px;"/><br /><sub>Vítor Gomes Chagas</sub>](https://github.com/Vitorvgc)<br /> | [<img src="https://avatars3.githubusercontent.com/u/12906090?v=4&s=460" width="100px;"/><br /><sub>Amit Singh</sub>](https://github.com/amitsin6h)<br /> | [<img src="https://avatars0.githubusercontent.com/u/6058114?v=4&s=400" width="100px;"/><br /><sub>Alabhya Vaibhav</sub>](https://github.com/AlabhyaVaibhav)<br /> | 
 [<img src="https://avatars1.githubusercontent.com/u/30810402?s=460&v=4" width="100px;"/><br /><sub>Arnav Borborah</sub>](https://github.com/arnavb)<br /> | [<img src="https://avatars2.githubusercontent.com/u/28451366?s=400&v=4" width="100px;"/><br /><sub>Yatharth Shah</sub>](https://github.com/yatharthshahjpr)<br /> | [<img src="https://avatars2.githubusercontent.com/u/16862997?v=4" width="100px;"/> <br /><sub>David Wu</sub>](https://github.com/Pl4gue)<br /> | [<img src="https://avatars3.githubusercontent.com/u/16002727?v=4" width="100px;"/> <br /><sub>Mohit Khare</sub>](https://github.com/mkfeuhrer)<br /> | [<img src="https://avatars0.githubusercontent.com/u/14811383?v=4" width="100px"/><br /><sub>Saif Ahmad</sub>](https://github.com/the-saif-ahmad)<br /> | [<img src="https://avatars0.githubusercontent.com/u/22833885?v=4" width="100px"/><br /><sub>Sanchit Jalan</sub>](https://github.com/Sanchit-20)<br /> | 
-[<img src="https://avatars3.githubusercontent.com/u/4724181?v=4" width="100px;"/><br /><sub>Yogesh Badhe</sub>](https://github.com/warlockz)<br /> |
+[<img src="https://avatars3.githubusercontent.com/u/4724181?v=4" width="100px;"/><br /><sub>Yogesh Badhe</sub>](https://github.com/warlockz)<br /> | [<img src="https://avatars2.githubusercontent.com/u/31393258?s=460&v=4" width="100px;"/><br /><sub>Garvit Kothari</sub>](https://github.com/Garvit-k)<br /> | 
 <!-- ALL-CONTRIBUTORS-LIST:END -->

.gitignore

@@ -1 +1,3 @@
-.vscode
+.vscode
+
+.DS_Store

.travis.yml

@@ -63,6 +63,13 @@ matrix:
         - pip install namanager
         - namanager -s third_party/namanager_settings.json --required
 
+    # Autoformat Python code
+    - os: linux
+      language: python
+      python: 3.6
+      script:
+        - pip install black
+        - ./scripts/python_code_style_checker.sh
+
 before_install:
    - eval "${MATRIX_EVAL}"
-

code/artificial_intelligence/src/dbscan_clustering/dbscan.py → code/artificial_intelligence/src/DBSCAN_clustering/dbscan.py

@@ -6,20 +6,24 @@
 UNCLASSIFIED = False
 NOISE = None
 
-def _dist(p,q):
-	return math.sqrt(np.power(p-q,2).sum())
 
-def _eps_neighborhood(p,q,eps):
-	return _dist(p,q) < eps
+def _dist(p, q):
+    return math.sqrt(np.power(p - q, 2).sum())
+
+
+def _eps_neighborhood(p, q, eps):
+    return _dist(p, q) < eps
+
 
 def _region_query(m, point_id, eps):
     n_points = m.shape[1]
     seeds = []
     for i in range(0, n_points):
-        if _eps_neighborhood(m[:,point_id], m[:,i], eps):
+        if _eps_neighborhood(m[:, point_id], m[:, i], eps):
             seeds.append(i)
     return seeds
 
+
 def _expand_cluster(m, classifications, point_id, cluster_id, eps, min_points):
     seeds = _region_query(m, point_id, eps)
     if len(seeds) < min_points:
@@ -36,14 +40,17 @@ def _expand_cluster(m, classifications, point_id, cluster_id, eps, min_points):
             if len(results) >= min_points:
                 for i in range(0, len(results)):
                     result_point = results[i]
-                    if classifications[result_point] == UNCLASSIFIED or \
-                       classifications[result_point] == NOISE:
+                    if (
+                        classifications[result_point] == UNCLASSIFIED
+                        or classifications[result_point] == NOISE
+                    ):
                         if classifications[result_point] == UNCLASSIFIED:
                             seeds.append(result_point)
                         classifications[result_point] = cluster_id
             seeds = seeds[1:]
         return True
 
+
 def dbscan(m, eps, min_points):
     """Implementation of Density Based Spatial Clustering of Applications with Noise
     See https://en.wikipedia.org/wiki/DBSCAN
@@ -65,22 +72,29 @@ def dbscan(m, eps, min_points):
     n_points = m.shape[1]
     classifications = [UNCLASSIFIED] * n_points
     for point_id in range(0, n_points):
-        point = m[:,point_id]
+        point = m[:, point_id]
         if classifications[point_id] == UNCLASSIFIED:
-            if _expand_cluster(m, classifications, point_id, cluster_id, eps, min_points):
+            if _expand_cluster(
+                m, classifications, point_id, cluster_id, eps, min_points
+            ):
                 cluster_id = cluster_id + 1
     return classifications
 
+
 # def test_dbscan():
 #     m = np.matrix('1 1.2 0.8 3.7 3.9 3.6 10; 1.1 0.8 1 4 3.9 4.1 10')
 #     eps = 0.5
 #     min_points = 2
 #     assert dbscan(m, eps, min_points) == [1, 1, 1, 2, 2, 2, None]
 
+
 def main():
-	m = np.matrix('-0.99 -0.98 -0.97 -0.96 -0.95 0.95 0.96 0.97 0.98 0.99; 1.1 1.09 1.08 1.07 1.06 1.06 1.07 1.08 1.09 1.1')
-	eps = 1
-	min_points = 3
-	print(dbscan(m, eps, min_points))
+    m = np.matrix(
+        "-0.99 -0.98 -0.97 -0.96 -0.95 0.95 0.96 0.97 0.98 0.99; 1.1 1.09 1.08 1.07 1.06 1.06 1.07 1.08 1.09 1.1"
+    )
+    eps = 1
+    min_points = 3
+    print(dbscan(m, eps, min_points))
+
 
 main()