From 4143969ff29fbac8fef840e8a7e34c791c84e2d3 Mon Sep 17 00:00:00 2001
From: mandala21 <lucasresone@gmail.com>
Date: Wed, 2 Oct 2019 09:30:31 -0300
Subject: [PATCH 1/2] add Nearest Neighbor

---
 allalgorithms/classification/__init__.py      |  0
 .../classification/nearest_neighbor.py        | 35 +++++++++++++++++++
 2 files changed, 35 insertions(+)
 create mode 100644 allalgorithms/classification/__init__.py
 create mode 100644 allalgorithms/classification/nearest_neighbor.py

diff --git a/allalgorithms/classification/__init__.py b/allalgorithms/classification/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/allalgorithms/classification/nearest_neighbor.py b/allalgorithms/classification/nearest_neighbor.py
new file mode 100644
index 0000000..f3a0baf
--- /dev/null
+++ b/allalgorithms/classification/nearest_neighbor.py
@@ -0,0 +1,35 @@
+from math import sqrt
+
+class KNN():
+    """
+    This is my first classifier :)
+    """
+    def fit(self,data,labels):
+        self.data = data
+        self.labels = labels
+        
+        return self
+    
+    def predict(self,x_test):
+        predicts = []
+        for row in x_test:
+            label = self.closest(row)
+            predicts.append(label)
+        return predicts
+    
+    def euc(self,a,b):
+        return sqrt((a[0]-b[0])**2 + (a[1]-b[1])**2)
+            
+    
+    def closest(self,row):
+        #initial best distance is alwas first
+        best_distance = self.euc(row,self.data[0])
+        best_index = 0
+        #intering in data
+        for i in range(1,len(self.data)):
+            dist = self.euc(row,self.data[i])
+            if dist < best_distance:
+                best_distance = dist
+                best_index = i
+            print(dist,best_distance, best_index)
+        return self.labels[best_index]
\ No newline at end of file

From defe811a4e7802e9f84358b3a4e86a59883b2a12 Mon Sep 17 00:00:00 2001
From: mandala21 <lucasresone@gmail.com>
Date: Thu, 3 Oct 2019 13:55:02 -0300
Subject: [PATCH 2/2] add test case

---
 .vscode/settings.json                         |  3 ++
 .../classification/nearest_neighbor.py        | 14 +++++--
 tests/test_classification.py                  | 40 +++++++++++++++++++
 3 files changed, 54 insertions(+), 3 deletions(-)
 create mode 100644 .vscode/settings.json
 create mode 100644 tests/test_classification.py

diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..615aafb
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "/usr/bin/python3"
+}
\ No newline at end of file
diff --git a/allalgorithms/classification/nearest_neighbor.py b/allalgorithms/classification/nearest_neighbor.py
index f3a0baf..9d018d5 100644
--- a/allalgorithms/classification/nearest_neighbor.py
+++ b/allalgorithms/classification/nearest_neighbor.py
@@ -1,8 +1,17 @@
+# -*- coding: UTF-8 -*-
+#
+# Nearest neighbour classifier one entry
+# The All ▲lgorithms library for python
+#
+# Contributed by: Carlos Abraham Hernandez
+# Github: @abranhe
+#
+
 from math import sqrt
 
-class KNN():
+class NN():
     """
-    This is my first classifier :)
+    Nearest neighbour classifier for two-dimensional dataset
     """
     def fit(self,data,labels):
         self.data = data
@@ -31,5 +40,4 @@ def closest(self,row):
             if dist < best_distance:
                 best_distance = dist
                 best_index = i
-            print(dist,best_distance, best_index)
         return self.labels[best_index]
\ No newline at end of file
diff --git a/tests/test_classification.py b/tests/test_classification.py
new file mode 100644
index 0000000..3828e05
--- /dev/null
+++ b/tests/test_classification.py
@@ -0,0 +1,40 @@
+import unittest
+from allalgorithms.classification import nearest_neighbor
+
+class TesteClassifications(unittest.TestCase):
+
+    def test_nn(self):
+        #datas
+        #the first data is Weight and second is number of wheels
+        features = [
+            [110,2],
+            [125,2],
+            [100,2],
+            [110,2],
+            [300,4],
+            [278,4],
+            [290,4],
+            [260,4],
+        ]
+        #labels, the labels is classification of features line
+        #in this exemple 0 = Motorcicler, 1 = car
+        label = [
+            0,
+            0,
+            0,
+            0,
+            1,
+            1,
+            1,
+            1,
+        ]
+        #instance classifier
+        clf = nearest_neighbor.NN()
+        #treaning
+        clf = clf.fit(features,label)
+        #predict
+        rs = clf.predict([[2,115]])
+        self.assertEqual(rs,0)
+    
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file