Enzo
1 year ago
parent
f40b3a5631
commit
11adf4814a
@ -1,39 +0,0 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from sklearn.calibration import LabelEncoder
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.linear_model import LinearRegression
|
||||
|
||||
# Load data from CSV
|
||||
df = pd.read_csv("data\\data_emple.csv")
|
||||
|
||||
category = df.iloc[0:len(df),0].values
|
||||
|
||||
otherData = df.iloc[0:len(df),1:6].values
|
||||
|
||||
print(otherData)
|
||||
|
||||
# Encode the categorical target variable
|
||||
label_encoder = LabelEncoder()
|
||||
category_encoded = label_encoder.fit_transform(category)
|
||||
|
||||
model = LinearRegression()
|
||||
|
||||
model.fit(otherData,category_encoded)
|
||||
|
||||
|
||||
new_data = np.array([224.0,228.49,346.6000000000000,1.1007253886010361,101]).reshape(1, -1)
|
||||
|
||||
# Faites une prédiction avec le modèle entraîné
|
||||
prediction = model.predict(new_data)
|
||||
|
||||
# Affichez la prédiction
|
||||
print("Prédiction:", prediction)
|
||||
|
||||
# Supposons que label_encoder soit l'objet LabelEncoder que vous avez utilisé lors de l'entraînement
|
||||
inverse_prediction = label_encoder.inverse_transform(prediction.astype(int))
|
||||
|
||||
# Affichez la prédiction sous forme de chaîne de caractères
|
||||
print("Prédiction (en chaîne de caractères):", inverse_prediction)
|
||||
|
||||
|
||||
|
@ -1,29 +0,0 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.linear_model import LinearRegression
|
||||
from sklearn.model_selection import train_test_split
|
||||
df = pd.read_csv("data\\data_emple.csv")
|
||||
distance = df.iloc[0:len(df),1].values.reshape(-1, 1)
|
||||
time = df.iloc[0:len(df),2].values.reshape(-1, 1)
|
||||
|
||||
model = LinearRegression()
|
||||
|
||||
# Entrainement
|
||||
model.fit(time,distance)
|
||||
|
||||
# Afficher les coefficients du modèle
|
||||
print("Coefficients :", model.coef_)
|
||||
print("Intercept :", model.intercept_)
|
||||
|
||||
|
||||
# Supposons que vous avez de nouvelles données pour lesquelles vous voulez faire des prédictions
|
||||
new_time_data = np.array([1000]).reshape(1,-1) # Exemple de nouvelles données pour 'time'
|
||||
|
||||
# Faire des prédictions sur les nouvelles données
|
||||
predicted_distance = model.predict(new_time_data)
|
||||
|
||||
# Afficher les prédictions
|
||||
print("Prédictions de distance :", predicted_distance[0])
|
||||
|
||||
|
||||
@ -1,140 +0,0 @@
|
||||
|
||||
from sklearn.linear_model import LinearRegression
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import requests
|
||||
from datetime import datetime, time
|
||||
import time as sleep_time
|
||||
|
||||
# --------------- Fonction ----------------- #
|
||||
def generateJsonModel(model:LinearRegression):
|
||||
listCoef = []
|
||||
listIntercept = []
|
||||
for i in range(0,len(model.coef_)):
|
||||
listCoef.append(model.coef_[i][0])
|
||||
listIntercept.append(model.intercept_[i])
|
||||
json = {"coef":listCoef,"intercept":listIntercept}
|
||||
return json
|
||||
|
||||
def generateModele(dataJson:dict[str,str]):
|
||||
# -- Préparation des données
|
||||
arrayBpm = []
|
||||
arrayStartTime = []
|
||||
arrayTimeOfActivity = []
|
||||
|
||||
for data in dataJson["Data"]:
|
||||
arrayBpm.append(data["BpmAvg"])
|
||||
arrayTimeOfActivity.append(data["TimeOfActivity"])
|
||||
|
||||
arrayStartTime.append(data["StartTime"])
|
||||
# -- DataFrame
|
||||
data = pd.DataFrame({
|
||||
"Bpm": arrayBpm,
|
||||
"TimeOfActivity": arrayTimeOfActivity
|
||||
})
|
||||
# -- Régression linéaire
|
||||
model = LinearRegression()
|
||||
model.fit(np.array(arrayStartTime).reshape(-1,1),data)
|
||||
return model
|
||||
|
||||
|
||||
def getUserWithData(url:str):
|
||||
response = requests.get(urlGetAllData)
|
||||
if ( response.status_code != 200):
|
||||
print('problème lors de l extraction des données avec l api !! -> "getUserWithData" (status_code != 200)')
|
||||
exit()
|
||||
return response.json
|
||||
|
||||
def sendJsonToApi(url,json):
|
||||
response = requests.post(url,json)
|
||||
if ( response.status_code != 200):
|
||||
print('Problème lors de l envoi des données avec l api !! -> "sendJsonToApi" (status_code != 200)')
|
||||
exit()
|
||||
return
|
||||
|
||||
# ---------------- Main ------------------- #
|
||||
|
||||
#urlGetAllData = "https://codefirst.iut.uca.fr/containers/SmartFit-smartfit_api/xxx"
|
||||
|
||||
#jsonBack = { "Users" : []}
|
||||
|
||||
dataUser = {
|
||||
"Users": [
|
||||
{
|
||||
"Identifiant": "x",
|
||||
"Info": [
|
||||
{"Category": "walking", "Data": [{"StartTime": 1234, "BpmAvg": 100,"TimeOfActivity": 1716.5}, {"StartTime" : 123456789,"BpmAvg":100,"TimeOfActivity": 1716.5}]},
|
||||
{"Category": "Cycling", "Data": [{"StartTime": 1234, "BpmAvg": 100,"TimeOfActivity": 1716.5}, {"StartTime" : 123456087,"BpmAvg":100,"TimeOfActivity": 1716.5}]},
|
||||
]
|
||||
},
|
||||
{ "Identifiant": "x",
|
||||
"Info": [
|
||||
{"Category": "walking", "Data": [{"StartTime": 1234, "BpmAvg": 120,"TimeOfActivity": 1716.5}, {"StartTime" : 123456789,"BpmAvg":120,"TimeOfActivity": 1716.5}]},
|
||||
{"Category": "Cycling", "Data": [{"StartTime": 1234, "BpmAvg": 120,"TimeOfActivity": 1716.5}, {"StartTime" : 123456087,"BpmAvg":120,"TimeOfActivity": 1716.5}]},
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
'''
|
||||
# -- Call Api
|
||||
#dataUser = getUserWithData(url=urlGetAllData)
|
||||
|
||||
for user in dataUser["Users"]:
|
||||
jsonTmp = {}
|
||||
|
||||
jsonTmp["Identifiant"] = user["Identifiant"]
|
||||
jsonTmp["Info"] = []
|
||||
|
||||
for category in user["Info"]:
|
||||
#Mettre la condition longueur ici
|
||||
model = generateModele(category)
|
||||
jsonTmp["Info"].append({"Category": category["Category"],"Model" : generateJsonModel(model)})
|
||||
|
||||
# Add User
|
||||
jsonBack["Users"].append(jsonTmp)
|
||||
print(jsonBack)
|
||||
# -- Send Data to Api
|
||||
#sendJsonToApi(urlGetAllData,jsonBack)
|
||||
'''
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
urlGetAllData = "https://codefirst.iut.uca.fr/containers/SmartFit-smartfit_api/xxx"
|
||||
while(True):
|
||||
print("Boucle")
|
||||
jsonBack = { "Users" : []}
|
||||
heure_actuelle = datetime.now().time()
|
||||
if ( heure_actuelle == time(8, 0)):
|
||||
# --- Call Api
|
||||
#dataUser = getUserWithData(url=urlGetAllData)
|
||||
for user in dataUser["Users"]:
|
||||
jsonTmp = {}
|
||||
|
||||
jsonTmp["Identifiant"] = user["Identifiant"]
|
||||
jsonTmp["Info"] = []
|
||||
|
||||
for category in user["Info"]:
|
||||
#Mettre la condition longueur ici
|
||||
model = generateModele(category)
|
||||
jsonTmp["Info"].append({"Category": category["Category"],"Model" : generateJsonModel(model)})
|
||||
|
||||
# Add User
|
||||
jsonBack["Users"].append(jsonTmp)
|
||||
# -- Send Api
|
||||
#sendJsonToApi(urlGetAllData,jsonBack)
|
||||
else :
|
||||
print("Sleep")
|
||||
if (heure_actuelle < time(7,0) and heure_actuelle > time(8,0) ):
|
||||
sleep_time.sleep(3600) # Pause 1 heure
|
||||
elif ( heure_actuelle < time(7,55) ):
|
||||
sleep_time.sleep(300) # Pause de 5 minutes
|
||||
else :
|
||||
sleep_time.sleep(30) # Pause de 30 secondes
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,24 +0,0 @@
|
||||
# IA
|
||||
|
||||
## Fonctionnement
|
||||
|
||||
Déterminé une activité pour l'utilisateur
|
||||
|
||||
### Data
|
||||
|
||||
Utiliser les activityInfo (vitesseAvg,BPM,....)
|
||||
|
||||
### Raisonnement
|
||||
|
||||
L'utilisateur pourra noter son activiter "Facile,moyen,dur" demander une note sur 100
|
||||
|
||||
### Calcul
|
||||
|
||||
En fonction de la note
|
||||
Il devra savoir et répondre une séance
|
||||
-> Vous devrez faire tant de km à x vitesse
|
||||
-> Vous serez entre x BPM
|
||||
-> Essayer de faire x denivelé
|
||||
-> .... etc
|
||||
|
||||
|
||||
@ -1,57 +0,0 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from sklearn.linear_model import LinearRegression
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
import pickle
|
||||
import sklearn_json as skljson
|
||||
|
||||
|
||||
|
||||
|
||||
def generateJson(model:LinearRegression):
|
||||
listCoef = []
|
||||
listIntercept = []
|
||||
for i in range(0,len(model.coef_)):
|
||||
listCoef.append(model.coef_[i][0])
|
||||
listIntercept.append(model.intercept_[i])
|
||||
json = {"coef":listCoef,"intercept":listIntercept}
|
||||
return json
|
||||
|
||||
|
||||
|
||||
|
||||
# Load data from CSV
|
||||
df = pd.read_csv("data\\data_emple.csv")
|
||||
|
||||
startTime = df.iloc[0:len(df),6].values.reshape(-1,1)
|
||||
|
||||
category = df.iloc[0:len(df),0].values
|
||||
#print("Category : ",category)
|
||||
|
||||
data = pd.DataFrame({
|
||||
"Distance": df.iloc[:, 1].values,
|
||||
"Time": df.iloc[:, 2].values,
|
||||
"Denivele": df.iloc[:, 3].values,
|
||||
"Speed": df.iloc[:, 4].values,
|
||||
"Bpm": df.iloc[:, 5].values
|
||||
})
|
||||
|
||||
model = LinearRegression()
|
||||
|
||||
model.fit(startTime,data)
|
||||
|
||||
datePredict = np.array([[1271]])
|
||||
prediction = model.predict(datePredict)
|
||||
|
||||
print("Prédiction -> ")
|
||||
#print(prediction)
|
||||
print("Distance : ",float(prediction[0][0]))
|
||||
print("Time : ",float(prediction[0][1]))
|
||||
print("Denivele : ",float(prediction[0][2]))
|
||||
print("Speed : ",float(prediction[0][3]))
|
||||
print("BPM : ",float(prediction[0][4]))
|
||||
|
||||
|
||||
generateJson(model=model)
|
||||
|
||||
|
||||
Loading…
Reference in new issue