diff --git a/README.md b/README.md
index afb973f..a53d546 100644
--- a/README.md
+++ b/README.md
@@ -60,6 +60,16 @@
     </p>
 </section>
 
+<section>
+    <h2>
+        Pour lancer les demos: 
+    </h2>
+    <p>
+        export PYTHONPATH="./code"</br>
+        python demoX.py
+    </p>
+</section>
+
 <style>
     .titre {
         border-bottom: 1px solid;
diff --git a/function.py b/code/function.py
similarity index 100%
rename from function.py
rename to code/function.py
diff --git a/knn.py b/code/knn.py
similarity index 100%
rename from knn.py
rename to code/knn.py
diff --git a/nnnar.py b/code/nnnar.py
similarity index 86%
rename from nnnar.py
rename to code/nnnar.py
index e6200f5..45394b4 100644
--- a/nnnar.py
+++ b/code/nnnar.py
@@ -22,6 +22,19 @@ class Nnnar:
             raise AttributeError("Error: wrong number of dimensions")
         self.space[*self.getSpaceIdxFromCoord(coord)].append(Point(coord, value))
 
+    def getLabelOfPoint(self, coord,nbNearest):
+        points, dists = self.getNNearest(coord, nbNearest)
+        label = {}
+        for idx in range(len(points)):
+            tvalue = points[idx].value[0]
+            if tvalue in label:
+                label[tvalue] += 1/(dists[idx]+1)
+            else:
+                label[tvalue] = 1/(dists[idx]+1)
+        value = max(label, key=label.get)
+        return value
+
+
     def getValueOfPoint(self, coord,nbNearest):
         points, dists = self.getNNearest(coord, nbNearest)
         ttPart = 1/(dists[0]+1)
diff --git a/point.py b/code/point.py
similarity index 100%
rename from point.py
rename to code/point.py
diff --git a/spaceAround.py b/code/spaceAround.py
similarity index 100%
rename from spaceAround.py
rename to code/spaceAround.py
diff --git a/data/Iris.csv b/data/Iris.csv
new file mode 100644
index 0000000..1bf42f2
--- /dev/null
+++ b/data/Iris.csv
@@ -0,0 +1,151 @@
+Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
+1,5.1,3.5,1.4,0.2,Iris-setosa
+2,4.9,3.0,1.4,0.2,Iris-setosa
+3,4.7,3.2,1.3,0.2,Iris-setosa
+4,4.6,3.1,1.5,0.2,Iris-setosa
+5,5.0,3.6,1.4,0.2,Iris-setosa
+6,5.4,3.9,1.7,0.4,Iris-setosa
+7,4.6,3.4,1.4,0.3,Iris-setosa
+8,5.0,3.4,1.5,0.2,Iris-setosa
+9,4.4,2.9,1.4,0.2,Iris-setosa
+10,4.9,3.1,1.5,0.1,Iris-setosa
+11,5.4,3.7,1.5,0.2,Iris-setosa
+12,4.8,3.4,1.6,0.2,Iris-setosa
+13,4.8,3.0,1.4,0.1,Iris-setosa
+14,4.3,3.0,1.1,0.1,Iris-setosa
+15,5.8,4.0,1.2,0.2,Iris-setosa
+16,5.7,4.4,1.5,0.4,Iris-setosa
+17,5.4,3.9,1.3,0.4,Iris-setosa
+18,5.1,3.5,1.4,0.3,Iris-setosa
+19,5.7,3.8,1.7,0.3,Iris-setosa
+20,5.1,3.8,1.5,0.3,Iris-setosa
+21,5.4,3.4,1.7,0.2,Iris-setosa
+22,5.1,3.7,1.5,0.4,Iris-setosa
+23,4.6,3.6,1.0,0.2,Iris-setosa
+24,5.1,3.3,1.7,0.5,Iris-setosa
+25,4.8,3.4,1.9,0.2,Iris-setosa
+26,5.0,3.0,1.6,0.2,Iris-setosa
+27,5.0,3.4,1.6,0.4,Iris-setosa
+28,5.2,3.5,1.5,0.2,Iris-setosa
+29,5.2,3.4,1.4,0.2,Iris-setosa
+30,4.7,3.2,1.6,0.2,Iris-setosa
+31,4.8,3.1,1.6,0.2,Iris-setosa
+32,5.4,3.4,1.5,0.4,Iris-setosa
+33,5.2,4.1,1.5,0.1,Iris-setosa
+34,5.5,4.2,1.4,0.2,Iris-setosa
+35,4.9,3.1,1.5,0.1,Iris-setosa
+36,5.0,3.2,1.2,0.2,Iris-setosa
+37,5.5,3.5,1.3,0.2,Iris-setosa
+38,4.9,3.1,1.5,0.1,Iris-setosa
+39,4.4,3.0,1.3,0.2,Iris-setosa
+40,5.1,3.4,1.5,0.2,Iris-setosa
+41,5.0,3.5,1.3,0.3,Iris-setosa
+42,4.5,2.3,1.3,0.3,Iris-setosa
+43,4.4,3.2,1.3,0.2,Iris-setosa
+44,5.0,3.5,1.6,0.6,Iris-setosa
+45,5.1,3.8,1.9,0.4,Iris-setosa
+46,4.8,3.0,1.4,0.3,Iris-setosa
+47,5.1,3.8,1.6,0.2,Iris-setosa
+48,4.6,3.2,1.4,0.2,Iris-setosa
+49,5.3,3.7,1.5,0.2,Iris-setosa
+50,5.0,3.3,1.4,0.2,Iris-setosa
+51,7.0,3.2,4.7,1.4,Iris-versicolor
+52,6.4,3.2,4.5,1.5,Iris-versicolor
+53,6.9,3.1,4.9,1.5,Iris-versicolor
+54,5.5,2.3,4.0,1.3,Iris-versicolor
+55,6.5,2.8,4.6,1.5,Iris-versicolor
+56,5.7,2.8,4.5,1.3,Iris-versicolor
+57,6.3,3.3,4.7,1.6,Iris-versicolor
+58,4.9,2.4,3.3,1.0,Iris-versicolor
+59,6.6,2.9,4.6,1.3,Iris-versicolor
+60,5.2,2.7,3.9,1.4,Iris-versicolor
+61,5.0,2.0,3.5,1.0,Iris-versicolor
+62,5.9,3.0,4.2,1.5,Iris-versicolor
+63,6.0,2.2,4.0,1.0,Iris-versicolor
+64,6.1,2.9,4.7,1.4,Iris-versicolor
+65,5.6,2.9,3.6,1.3,Iris-versicolor
+66,6.7,3.1,4.4,1.4,Iris-versicolor
+67,5.6,3.0,4.5,1.5,Iris-versicolor
+68,5.8,2.7,4.1,1.0,Iris-versicolor
+69,6.2,2.2,4.5,1.5,Iris-versicolor
+70,5.6,2.5,3.9,1.1,Iris-versicolor
+71,5.9,3.2,4.8,1.8,Iris-versicolor
+72,6.1,2.8,4.0,1.3,Iris-versicolor
+73,6.3,2.5,4.9,1.5,Iris-versicolor
+74,6.1,2.8,4.7,1.2,Iris-versicolor
+75,6.4,2.9,4.3,1.3,Iris-versicolor
+76,6.6,3.0,4.4,1.4,Iris-versicolor
+77,6.8,2.8,4.8,1.4,Iris-versicolor
+78,6.7,3.0,5.0,1.7,Iris-versicolor
+79,6.0,2.9,4.5,1.5,Iris-versicolor
+80,5.7,2.6,3.5,1.0,Iris-versicolor
+81,5.5,2.4,3.8,1.1,Iris-versicolor
+82,5.5,2.4,3.7,1.0,Iris-versicolor
+83,5.8,2.7,3.9,1.2,Iris-versicolor
+84,6.0,2.7,5.1,1.6,Iris-versicolor
+85,5.4,3.0,4.5,1.5,Iris-versicolor
+86,6.0,3.4,4.5,1.6,Iris-versicolor
+87,6.7,3.1,4.7,1.5,Iris-versicolor
+88,6.3,2.3,4.4,1.3,Iris-versicolor
+89,5.6,3.0,4.1,1.3,Iris-versicolor
+90,5.5,2.5,4.0,1.3,Iris-versicolor
+91,5.5,2.6,4.4,1.2,Iris-versicolor
+92,6.1,3.0,4.6,1.4,Iris-versicolor
+93,5.8,2.6,4.0,1.2,Iris-versicolor
+94,5.0,2.3,3.3,1.0,Iris-versicolor
+95,5.6,2.7,4.2,1.3,Iris-versicolor
+96,5.7,3.0,4.2,1.2,Iris-versicolor
+97,5.7,2.9,4.2,1.3,Iris-versicolor
+98,6.2,2.9,4.3,1.3,Iris-versicolor
+99,5.1,2.5,3.0,1.1,Iris-versicolor
+100,5.7,2.8,4.1,1.3,Iris-versicolor
+101,6.3,3.3,6.0,2.5,Iris-virginica
+102,5.8,2.7,5.1,1.9,Iris-virginica
+103,7.1,3.0,5.9,2.1,Iris-virginica
+104,6.3,2.9,5.6,1.8,Iris-virginica
+105,6.5,3.0,5.8,2.2,Iris-virginica
+106,7.6,3.0,6.6,2.1,Iris-virginica
+107,4.9,2.5,4.5,1.7,Iris-virginica
+108,7.3,2.9,6.3,1.8,Iris-virginica
+109,6.7,2.5,5.8,1.8,Iris-virginica
+110,7.2,3.6,6.1,2.5,Iris-virginica
+111,6.5,3.2,5.1,2.0,Iris-virginica
+112,6.4,2.7,5.3,1.9,Iris-virginica
+113,6.8,3.0,5.5,2.1,Iris-virginica
+114,5.7,2.5,5.0,2.0,Iris-virginica
+115,5.8,2.8,5.1,2.4,Iris-virginica
+116,6.4,3.2,5.3,2.3,Iris-virginica
+117,6.5,3.0,5.5,1.8,Iris-virginica
+118,7.7,3.8,6.7,2.2,Iris-virginica
+119,7.7,2.6,6.9,2.3,Iris-virginica
+120,6.0,2.2,5.0,1.5,Iris-virginica
+121,6.9,3.2,5.7,2.3,Iris-virginica
+122,5.6,2.8,4.9,2.0,Iris-virginica
+123,7.7,2.8,6.7,2.0,Iris-virginica
+124,6.3,2.7,4.9,1.8,Iris-virginica
+125,6.7,3.3,5.7,2.1,Iris-virginica
+126,7.2,3.2,6.0,1.8,Iris-virginica
+127,6.2,2.8,4.8,1.8,Iris-virginica
+128,6.1,3.0,4.9,1.8,Iris-virginica
+129,6.4,2.8,5.6,2.1,Iris-virginica
+130,7.2,3.0,5.8,1.6,Iris-virginica
+131,7.4,2.8,6.1,1.9,Iris-virginica
+132,7.9,3.8,6.4,2.0,Iris-virginica
+133,6.4,2.8,5.6,2.2,Iris-virginica
+134,6.3,2.8,5.1,1.5,Iris-virginica
+135,6.1,2.6,5.6,1.4,Iris-virginica
+136,7.7,3.0,6.1,2.3,Iris-virginica
+137,6.3,3.4,5.6,2.4,Iris-virginica
+138,6.4,3.1,5.5,1.8,Iris-virginica
+139,6.0,3.0,4.8,1.8,Iris-virginica
+140,6.9,3.1,5.4,2.1,Iris-virginica
+141,6.7,3.1,5.6,2.4,Iris-virginica
+142,6.9,3.1,5.1,2.3,Iris-virginica
+143,5.8,2.7,5.1,1.9,Iris-virginica
+144,6.8,3.2,5.9,2.3,Iris-virginica
+145,6.7,3.3,5.7,2.5,Iris-virginica
+146,6.7,3.0,5.2,2.3,Iris-virginica
+147,6.3,2.5,5.0,1.9,Iris-virginica
+148,6.5,3.0,5.2,2.0,Iris-virginica
+149,6.2,3.4,5.4,2.3,Iris-virginica
+150,5.9,3.0,5.1,1.8,Iris-virginica
diff --git a/demo2.py b/demo2.py
index cdc5651..2144e05 100644
--- a/demo2.py
+++ b/demo2.py
@@ -1,5 +1,6 @@
 from nnnar import *
 from knn import *
+
 import matplotlib.pyplot as plt
 from random import *
 
diff --git a/demo4.py b/demo4.py
new file mode 100644
index 0000000..5d3fb8e
--- /dev/null
+++ b/demo4.py
@@ -0,0 +1,53 @@
+from nnnar import *
+from knn import *
+import numpy as np
+import pandas as pd
+
+
+def runOneTest(trainTestRatio):
+    nnnar = Nnnar(4, 0, 100.001, 10)# le 100.001 est pour être sûr d'être au dessus de la valeur max (100.0)
+
+    df = pd.read_csv('./data/Iris.csv')
+    df = df.iloc[:, 1:]
+    # Normalisation des données
+
+    df.iloc[:, 0] = df.iloc[:, 0] - df.iloc[:, 0].min()
+    df.iloc[:, 1] = df.iloc[:, 1] - df.iloc[:, 1].min()
+    df.iloc[:, 2] = df.iloc[:, 2] - df.iloc[:, 2].min()
+    df.iloc[:, 3] = df.iloc[:, 3] - df.iloc[:, 3].min()
+
+    df.iloc[:, 0] = df.iloc[:, 0] / df.iloc[:, 0].max()
+    df.iloc[:, 1] = df.iloc[:, 1] / df.iloc[:, 1].max()
+    df.iloc[:, 2] = df.iloc[:, 2] / df.iloc[:, 2].max()
+    df.iloc[:, 3] = df.iloc[:, 3] / df.iloc[:, 3].max()
+
+    df.iloc[:, 0:4] = df.iloc[:, 0:4] * 100
+
+    # Création des données d'entrainement et de test
+    train = df.sample(frac=trainTestRatio)
+    test = df.drop(train.index)
+
+    # Entrainement du modèle
+    coord = train.iloc[:, :-1].values
+    value = train.iloc[:, -1].values
+
+    for i in range(len(coord)):
+        nnnar.addPoint(np.array(coord[i]), np.array([value[i]]))
+
+    # Test du modèle
+    coord = test.iloc[:, :-1].values
+    value = test.iloc[:, -1].values
+
+    nbError = 0
+    for i in range(len(coord)):
+        if nnnar.getLabelOfPoint(np.array(coord[i]), 5) != value[i]:
+            nbError += 1
+
+    return 100 - nbError / len(coord) * 100
+
+nbRepetition = 100
+accuracy = 0
+for i in range(nbRepetition):
+    accuracy += runOneTest(0.8)
+
+print("accuracy moyenne: ",str(accuracy/nbRepetition))
\ No newline at end of file