commit
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import pandas as pd
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import numpy as np
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from sklearn.calibration import LabelEncoder
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression
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# Load data from CSV
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df = pd.read_csv("data\\data_emple.csv")
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category = df.iloc[0:len(df),0].values
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otherData = df.iloc[0:len(df),1:6].values
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print(otherData)
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# Encode the categorical target variable
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label_encoder = LabelEncoder()
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category_encoded = label_encoder.fit_transform(category)
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model = LinearRegression()
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model.fit(otherData,category_encoded)
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new_data = np.array([224.0,228.49,346.6000000000000,1.1007253886010361,101]).reshape(1, -1)
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# Faites une prédiction avec le modèle entraîné
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prediction = model.predict(new_data)
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# Affichez la prédiction
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print("Prédiction:", prediction)
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# Supposons que label_encoder soit l'objet LabelEncoder que vous avez utilisé lors de l'entraînement
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inverse_prediction = label_encoder.inverse_transform(prediction.astype(int))
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# Affichez la prédiction sous forme de chaîne de caractères
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print("Prédiction (en chaîne de caractères):", inverse_prediction)
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.linear_model import LinearRegression
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from sklearn.model_selection import train_test_split
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df = pd.read_csv("data\\data_emple.csv")
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distance = df.iloc[0:len(df),1].values.reshape(-1, 1)
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time = df.iloc[0:len(df),2].values.reshape(-1, 1)
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model = LinearRegression()
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# Entrainement
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model.fit(time,distance)
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# Afficher les coefficients du modèle
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print("Coefficients :", model.coef_)
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print("Intercept :", model.intercept_)
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# Supposons que vous avez de nouvelles données pour lesquelles vous voulez faire des prédictions
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new_time_data = np.array([1000]).reshape(1,-1) # Exemple de nouvelles données pour 'time'
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# Faire des prédictions sur les nouvelles données
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predicted_distance = model.predict(new_time_data)
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# Afficher les prédictions
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print("Prédictions de distance :", predicted_distance[0])
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@ -0,0 +1,24 @@
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# IA
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## Fonctionnement
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Déterminé une activité pour l'utilisateur
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### Data
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Utiliser les activityInfo (vitesseAvg,BPM,....)
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### Raisonnement
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L'utilisateur pourra noter son activiter "Facile,moyen,dur" demander une note sur 100
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### Calcul
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En fonction de la note
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Il devra savoir et répondre une séance
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-> Vous devrez faire tant de km à x vitesse
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-> Vous serez entre x BPM
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-> Essayer de faire x denivelé
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-> .... etc
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,55 @@
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import 'package:flutter/material.dart';
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import 'package:smartfit_app_mobile/common_widget/container/container_stats_activities.dart';
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import 'package:smartfit_app_mobile/modele/convertisseur.dart';
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class VolumesList extends StatelessWidget {
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final Map<String, dynamic> volume;
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const VolumesList({super.key, required this.volume});
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@override
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Widget build(BuildContext context) {
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var media = MediaQuery.of(context).size;
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// TODO: True message with variables and context aware
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if (volume["nbActivity"] == 0) {
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return const Text("Aucune activité ces x jours/mois/années");
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}
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return SingleChildScrollView(
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scrollDirection: Axis.horizontal,
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child: Row(
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mainAxisAlignment: MainAxisAlignment.center,
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children: [
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ContainerStatsActivities(volume["nbActivity"].toString(),
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"Nombre Activitée(s)", Icons.numbers),
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SizedBox(
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width: media.width * 0.03,
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),
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ContainerStatsActivities(
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"${Convertisseur.secondeIntoMinute(volume["durationActiviy"]).toStringAsFixed(2)} m",
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"Temps Total",
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Icons.timer),
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SizedBox(
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width: media.width * 0.03,
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),
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ContainerStatsActivities(
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volume["bpmAvg"].toString(), "Bpm Moyens", Icons.favorite),
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SizedBox(
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width: media.width * 0.03,
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),
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ContainerStatsActivities(
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" ${Convertisseur.msIntoKmh(volume["speedAvg"]).toStringAsFixed(2)} km/h",
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"Vitesse Moyenne",
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Icons.bolt),
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SizedBox(
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width: media.width * 0.03,
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),
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ContainerStatsActivities(
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"${volume["denivelePositif"].toStringAsFixed(2)} m",
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"Dénivelé Positif",
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Icons.hiking),
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],
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),
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);
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}
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}
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@ -0,0 +1,104 @@
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import 'package:flutter/material.dart';
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import 'package:provider/provider.dart';
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import 'package:smartfit_app_mobile/common_widget/container/list/volumes_list.dart';
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import 'package:smartfit_app_mobile/common_widget/other/entete_home_view.dart';
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import 'package:smartfit_app_mobile/common/colo_extension.dart';
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import 'package:smartfit_app_mobile/modele/user.dart';
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import 'package:smartfit_app_mobile/modele/utile/home_view/data_home_view.dart';
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class VolumesView extends StatefulWidget {
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const VolumesView({super.key});
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@override
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State<VolumesView> createState() => _VolumesViews();
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}
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class _VolumesViews extends State<VolumesView> {
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late DataHomeView data;
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TextEditingController bpmController = TextEditingController();
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@override
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Widget build(BuildContext context) {
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var media = MediaQuery.of(context).size;
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User user = context.watch<User>();
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DateTime date = DateTime.now();
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Map<String, dynamic> volume7Days =
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user.getVolumeWhithDuration(const Duration(days: 7));
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Map<String, dynamic> volume1Months =
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user.getVolumeWhithDuration(const Duration(days: 30));
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Map<String, dynamic> volume1Year =
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user.getVolumeWhithDuration(const Duration(days: 366));
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Map<String, dynamic> volumeAllTime = user.getVolumeAllTime();
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return Scaffold(
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backgroundColor: TColor.white,
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body: SingleChildScrollView(
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child: SafeArea(
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child: Padding(
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padding: const EdgeInsets.symmetric(horizontal: 15),
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child: Column(
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crossAxisAlignment: CrossAxisAlignment.start,
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children: [
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SizedBox(
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height: media.width * 0.03,
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),
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const EnteteHomeView(),
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SizedBox(
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height: media.width * 0.05,
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),
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Text(
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"Derniere semaine : ${date.day}/${date.month}/${date.year} - ${date.subtract(const Duration(days: 7)).day}/${date.subtract(const Duration(days: 7)).month}/${date.subtract(const Duration(days: 7)).year}",
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style: TextStyle(
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color: TColor.black,
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fontSize: 16,
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fontWeight: FontWeight.w700),
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),
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SizedBox(
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height: media.width * 0.03,
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),
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VolumesList(volume: volume7Days),
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SizedBox(
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height: media.width * 0.03,
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),
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Text(
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"Dernier Mois : ${date.day}/${date.month}/${date.year} - ${date.subtract(const Duration(days: 30)).day}/${date.subtract(const Duration(days: 30)).month}/${date.subtract(const Duration(days: 30)).year}",
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style: TextStyle(
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color: TColor.black,
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fontSize: 16,
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fontWeight: FontWeight.w700),
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),
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VolumesList(volume: volume1Months),
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SizedBox(
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height: media.width * 0.03,
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),
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Text(
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"Dernière année : ${date.day}/${date.month}/${date.year} - ${date.subtract(const Duration(days: 366)).day}/${date.subtract(const Duration(days: 366)).month}/${date.subtract(const Duration(days: 366)).year}",
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style: TextStyle(
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color: TColor.black,
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fontSize: 16,
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fontWeight: FontWeight.w700),
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),
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VolumesList(volume: volume1Year),
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SizedBox(
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height: media.width * 0.03,
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),
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Text(
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"Total",
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style: TextStyle(
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color: TColor.black,
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fontSize: 16,
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fontWeight: FontWeight.w700),
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),
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VolumesList(volume: volumeAllTime),
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SizedBox(
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height: media.width * 0.03,
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),
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],
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),
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),
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),
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),
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);
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}
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}
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Loading…
Reference in new issue