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60 lines
1.6 KiB
60 lines
1.6 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 matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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# varriable globale
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trainTestRatio = 0.8
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nnnarSubDiv = 10
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# lecture des données
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nnnar = Nnnar(4, 0, 100, nnnarSubDiv)
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knn = Knn()
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df = pd.read_csv('./data/maison.csv')
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df = df.iloc[:, 0:5]
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# Normalisation des données
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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[:, 4] = df.iloc[:, 4] - df.iloc[:, 4].min()
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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[:, 4] = df.iloc[:, 4] / df.iloc[:, 4].max()
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df.iloc[:, 1:5] = df.iloc[:, 1:5] * 99
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# Création des données d'entrainement et de test
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train = df.sample(frac=trainTestRatio)
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test = df.drop(train.index)
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# Entrainement du modèle
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coord = train.iloc[:, 1:].values
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value = train.iloc[:, 0].values
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for i in range(len(coord)):
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knn.addPoint(np.array(coord[i]), np.array([value[i]]))
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nnnar.addPoint(np.array(coord[i]), np.array([value[i]]))
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# Test du modèles
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coord = test.iloc[:, 1:].values
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value = test.iloc[:, 0].values
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nbErrorNnnar = 0
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for i in range(len(coord)):
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v = nnnar.getValueOfPoint(np.array(coord[i]),5)[0]
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if v != value[i]:
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nbErrorNnnar += 100*abs(v-value[i])/value.max()
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print("accuracy moyenne:",str(100-nbErrorNnnar/len(coord))) |