You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
63 lines
1.6 KiB
63 lines
1.6 KiB
import os
|
|
import sys
|
|
parent_dir_name = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
|
|
sys.path.append(parent_dir_name + "/3nar/code")
|
|
|
|
from nnnar import *
|
|
from knn import *
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
|
|
# varriable globale
|
|
trainTestRatio = 0.8
|
|
nnnarSubDiv = 10
|
|
|
|
# initialisation du modèle
|
|
if (sys.argv[1] == "knn"):
|
|
model = Knn()
|
|
else:
|
|
model = Nnnar(4, 0, 100, nnnarSubDiv)
|
|
|
|
# lecture des données
|
|
df = pd.read_csv('./data/maison.csv')
|
|
df = df.iloc[:, 0:5]
|
|
|
|
# Normalisation des données
|
|
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[:, 4] = df.iloc[:, 4] - df.iloc[:, 4].min()
|
|
|
|
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[:, 4] = df.iloc[:, 4] / df.iloc[:, 4].max()
|
|
|
|
df.iloc[:, 1:5] = df.iloc[:, 1:5] * 99
|
|
|
|
# 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[:, 0].values
|
|
|
|
for i in range(len(coord)):
|
|
model.addPoint(np.array(coord[i]), np.array([value[i]]))
|
|
|
|
# Test du modèles
|
|
coord = test.iloc[:, 1:].values
|
|
value = test.iloc[:, 0].values
|
|
|
|
nbError = 0
|
|
|
|
for i in range(len(coord)):
|
|
v = model.getValueOfPoint(np.array(coord[i]),5)[0]
|
|
if v != value[i]:
|
|
nbError += 100*abs(v-value[i])/value.max()
|
|
|
|
print("accuracy moyenne:",str(100-nbError/len(coord))) |