Oct 2, 2019 · Oct 3, 2019
diff --git a/.vscode/settings.json b/.vscode/settings.json
 {
    "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
 # -*- 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
 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()
Original file line number	Diff line number	Diff line change
		@@ -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]
Original file line number	Diff line number	Diff line change
		@@ -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()