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69 lines
1.7 KiB
69 lines
1.7 KiB
import os
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import sys
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parent_dir_name = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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sys.path.append(parent_dir_name + "/3nar/code")
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from nnnar import *
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from knn import *
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import numpy as np
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import pandas as pd
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from time import time
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df = pd.read_csv('./data/Iris.csv')
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df = df.iloc[:, 1:]
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# Normalisation des données
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df.iloc[:, 0] = df.iloc[:, 0] - df.iloc[:, 0].min()
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df.iloc[:, 1] = df.iloc[:, 1] - df.iloc[:, 1].min()
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df.iloc[:, 2] = df.iloc[:, 2] - df.iloc[:, 2].min()
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df.iloc[:, 3] = df.iloc[:, 3] - df.iloc[:, 3].min()
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df.iloc[:, 0] = df.iloc[:, 0] / df.iloc[:, 0].max()
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df.iloc[:, 1] = df.iloc[:, 1] / df.iloc[:, 1].max()
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df.iloc[:, 2] = df.iloc[:, 2] / df.iloc[:, 2].max()
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df.iloc[:, 3] = df.iloc[:, 3] / df.iloc[:, 3].max()
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df.iloc[:, 0:4] = df.iloc[:, 0:4] * 100
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def runOneTest(model,df):
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# Création des données d'entrainement et de test
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train = df.sample(frac=0.8)
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test = df.drop(train.index)
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# Entrainement du modèle
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trainCoord = train.iloc[:, :-1].values
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trainValue = train.iloc[:, -1].values
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# Test du modèle
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coord = test.iloc[:, :-1].values
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value = test.iloc[:, -1].values
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for i in range(len(trainCoord)):
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model.addPoint(np.array(trainCoord[i]), np.array([trainValue[i]]))
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nbError = 0
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for i in range(len(coord)):
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if model.getLabelOfPoint(np.array(coord[i]), 5) != value[i]:
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nbError += 1
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return 100 - nbError / len(coord) * 100
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if (sys.argv[1] == "knn"):
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model = Knn()
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else:
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model = Nnnar(4, 0, 100.001, 5)
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t1 = time()
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nbRepetition = 100
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accuracy = 0
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for i in range(nbRepetition):
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model.reset()
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accuracy += runOneTest(model,df)
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t2 = time()
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print("accuracy moyenne:",str(accuracy/nbRepetition))
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print("delta temps:",str(t2-t1)) |