abranhe · lucasres · Oct 2, 2019 · Oct 3, 2019
diff --git a/.vscode/settings.json b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "/usr/bin/python3"
+}
diff --git a/allalgorithms/classification/__init__.py b/allalgorithms/classification/__init__.py
diff --git a/allalgorithms/classification/nearest_neighbor.py b/allalgorithms/classification/nearest_neighbor.py
@@ -0,0 +1,43 @@
+# -*- 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 NN():
+    """
+    Nearest neighbour classifier for two-dimensional dataset
+    """
+    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
+        return self.labels[best_index]
diff --git a/tests/test_classification.py 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()