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@ -180,7 +180,7 @@ window.Quarto = {
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<h5 class="quarto-listing-category-title">Catégories</h5><div class="quarto-listing-category category-default"><div class="category" data-category="">Tous <span class="quarto-category-count">(6)</span></div><div class="category" data-category="Y29sQmlTQk0=">colBiSBM <span class="quarto-category-count">(6)</span></div><div class="category" data-category="aW5mJUMzJUE5cmVuY2U=">inférence <span class="quarto-category-count">(1)</span></div></div></div>
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<h5 class="quarto-listing-category-title">Catégories</h5><div class="quarto-listing-category category-default"><div class="category" data-category="">Tous <span class="quarto-category-count">(7)</span></div><div class="category" data-category="Y29sQmlTQk0=">colBiSBM <span class="quarto-category-count">(7)</span></div><div class="category" data-category="aW5mJUMzJUE5cmVuY2U=">inférence <span class="quarto-category-count">(2)</span></div></div></div>
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<div class="quarto-title-meta-heading">Date de publication</div>
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<div class="quarto-title-meta-heading">Date de publication</div>
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<p class="date">28 avril 2025</p>
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<p class="date">5 mai 2025</p>
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<div class="thumbnail"><a href="./suivi/2025-19/2025-19.html" class="no-external">
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<p class="card-img-top"><img src="suivi/2025-19/figs/density-subdore.png" class="thumbnail-image card-img"/></p>
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</a></div>
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<div class="body">
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<h3 class="no-anchor listing-title">
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<a href="./suivi/2025-19/2025-19.html" class="no-external">Bilan semaine 19 2025 : 5 mai - 9 mai</a>
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</h3>
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<div class="listing-categories">
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<div class="listing-category" onclick="window.quartoListingCategory('Y29sQmlTQk0='); return false;">colBiSBM</div>
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<div class="listing-category" onclick="window.quartoListingCategory('aW5mJUMzJUE5cmVuY2U='); return false;">inférence</div>
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</div>
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<div class="delink listing-description"><a href="./suivi/2025-19/2025-19.html" class="no-external">
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Papier pour comprendre données
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</a></div>
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<div class="metadata">
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<a href="./suivi/2025-19/2025-19.html" class="no-external">
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<div class="listing-date">
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5 mai 2025
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</div>
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<div class="listing-author">
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Louis Lacoste
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</a>
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<div class="thumbnail"><a href="./suivi/2025-18/2025-18.html" class="no-external">
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<div class="thumbnail"><a href="./suivi/2025-18/2025-18.html" class="no-external">
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<p class="card-img-top"><img src="suivi/2025-18/figs/density-subdore.png" class="thumbnail-image card-img"/></p>
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<p class="card-img-top"><img src="suivi/2025-18/figs/density-subdore.png" class="thumbnail-image card-img"/></p>
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@ -252,7 +284,7 @@ Lire biblio fournie Julie, Inférence de réseaux : co-occurence
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<div class="metadata">
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<a href="./suivi/2025-18/2025-18.html" class="no-external">
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<div class="listing-date">
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<div class="listing-date">
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28 avr. 2025
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2 mai 2025
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</div>
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<div class="listing-author">
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<div class="listing-author">
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Louis Lacoste
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Louis Lacoste
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<p class="card-img-top"><img src="suivi/2025-17/figs/density-subdore.png" class="thumbnail-image card-img"/></p>
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<p class="card-img-top"><img src="suivi/2025-15/figs/alluvial-clusterings.png" class="thumbnail-image card-img"/></p>
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<p class="card-img-top"><img src="suivi/2025-15/figs/alluvial-clusterings.png" class="thumbnail-image card-img"/></p>
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<p class="card-img-top"><img src="suivi/2025-13/figs/baldock_meso_iid.png" class="thumbnail-image card-img"/></p>
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"section": "A continuer",
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"section": "A continuer",
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"text": "A continuer\n\nRésultats simus NA Erreur pour certaines conditions : Pour NA robustness générer nb_rep collections de taille M=2 et prélever \\epsilon_{max}n_r n_c liens à retirer puis pour les \\epsilon < \\epsilon_{max} prélever dans la liste des indices afin d’avoir des perturbations emboitées. Il faut que j’ajoute un mécanisme pour reprendre des conditions qui ont plantés et que je skip dans le future_lapply les conditions déjà traitées (pour avoir la même seed quand je vais exécuter le code). Implémenté les missing steps en attente des résultats MIGALE.\nLire Biological Networks - François Képès\nJ’ai esquissé des bouts d’intro\nRelancer simus d’inférence avec n = 240 pour voir si la qualité augmenter (se rassurer). En fait on est déjà à 240, j’ai relancé avec M = 4 au lieu de M = 2. En attente résultats MIGALE\n\n\nCorrection méthodo\n\nIdée Pierre : Regarder la contribution au BICL de la collection des réseaux et comparer au sep BICL pour essayer de repérer les outliers. En regardant la vbound (pas la pénalité) de chaque réseau dans le joint vs sa vbound dans le sep -> Résultats : pas de différences majeures entre les réseaux avec le rapport vbound_joint/vbound_sep, les outliers ne sont pas marqués.\nRegarder si plutôt que k médioid possible meilleurs résultats avec d’autres distances hclust avec min, max etc… -> L’algo PAM donne des clusters équilibrés sans séparer les outliers Je regarde avec plutôt des hclust avec métrique single pour séparer les outliers.\nVoir si in fine possible de repérer des outliers à partir de ces nouvelles métriques\nRegarder la répartition de densité dans les réseaux sub-doré -> déséquilibrée\n\n\nEn faisant des clusterings par densité on constate qu'avec un modèle iid pour des réseaux dont la densité est entre :\n- 0 et 0.05 : Baldock et Souza tout le monde se retrouvait ensemble avec *Partitioning around medoids*\n\n\nApplications\n\nIdée Sophie: Regarder clustering de données plantes-pollinisateur selon gradient d’urbanisation\n\n\nSophie a fait une appli qui marche bien et va dans le sens de l’analyse faite (à savoir pas d’effet du gradien d’urbanisation). À continuer pour l’intégrer dans l’article !\n\n\n\nLancer clustering auteur par auteur du sub-Doré : 5 collections différentes dans l’idée.\nUne fois fait, Sophie ne trouve pas que ce soit le plus pertinent pour illustrer le clustering. Plus intéressant de garder le clustering de données simulées (M = 30) et se servir des exemples dessous et des parcours exhaustif des possibilités de partitionnement comme comparatif.\n\nBaldock\n\n\n\nAlluvial Baldock\n\n\n\n\nGibson\n\n\n\nAlluvial Gibson\n\n\n\n\nSouza\n\n\n\nAlluvial Souza\n\n\n\n\nTraveset\n\n\n\nAlluvial Traveset\n\n\n\n\nTrojelsgaard\n\n\n\nAlluvial Trojelgaard"
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"text": "A continuer\n\nRésultats simus NA Erreur pour certaines conditions : Pour NA robustness générer nb_rep collections de taille M=2 et prélever \\epsilon_{max}n_r n_c liens à retirer puis pour les \\epsilon < \\epsilon_{max} prélever dans la liste des indices afin d’avoir des perturbations emboitées. Il faut que j’ajoute un mécanisme pour reprendre des conditions qui ont plantés et que je skip dans le future_lapply les conditions déjà traitées (pour avoir la même seed quand je vais exécuter le code). Implémenté les missing steps en attente des résultats MIGALE.\nLire Biological Networks - François Képès\nJ’ai esquissé des bouts d’intro\nRelancer simus d’inférence avec n = 240 pour voir si la qualité augmenter (se rassurer). En fait on est déjà à 240, j’ai relancé avec M = 4 au lieu de M = 2. En attente résultats MIGALE\n\n\nCorrection méthodo\n\nIdée Pierre : Regarder la contribution au BICL de la collection des réseaux et comparer au sep BICL pour essayer de repérer les outliers. En regardant la vbound (pas la pénalité) de chaque réseau dans le joint vs sa vbound dans le sep -> Résultats : pas de différences majeures entre les réseaux avec le rapport vbound_joint/vbound_sep, les outliers ne sont pas marqués.\nRegarder si plutôt que k médioid possible meilleurs résultats avec d’autres distances hclust avec min, max etc… -> L’algo PAM donne des clusters équilibrés sans séparer les outliers Je regarde avec plutôt des hclust avec métrique single pour séparer les outliers.\nVoir si in fine possible de repérer des outliers à partir de ces nouvelles métriques\nRegarder la répartition de densité dans les réseaux sub-doré -> déséquilibrée\n\n\nEn faisant des clusterings par densité on constate qu'avec un modèle iid pour des réseaux dont la densité est entre :\n- 0 et 0.05 : Baldock et Souza tout le monde se retrouvait ensemble avec *Partitioning around medoids*\n\n\nApplications\n\nIdée Sophie: Regarder clustering de données plantes-pollinisateur selon gradient d’urbanisation\n\n\nSophie a fait une appli qui marche bien et va dans le sens de l’analyse faite (à savoir pas d’effet du gradien d’urbanisation). À continuer pour l’intégrer dans l’article !\n\n\n\nLancer clustering auteur par auteur du sub-Doré : 5 collections différentes dans l’idée.\nUne fois fait, Sophie ne trouve pas que ce soit le plus pertinent pour illustrer le clustering. Plus intéressant de garder le clustering de données simulées (M = 30) et se servir des exemples dessous et des parcours exhaustif des possibilités de partitionnement comme comparatif.\n\nBaldock\n\n\n\nAlluvial Baldock\n\n\n\n\nGibson\n\n\n\nAlluvial Gibson\n\n\n\n\nSouza\n\n\n\nAlluvial Souza\n\n\n\n\nTraveset\n\n\n\nAlluvial Traveset\n\n\n\n\nTrojelsgaard\n\n\n\nAlluvial Trojelgaard"
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"text": "Détails d’inscriptions\n\n\n\n\n\nPapier pour comprendre données\npbs : variance, bcp de zero, covariables, offset et taxonomie (Reseaux arretes differents niveaux : Genre, OTU …)\n\n\nCombine networks at different taxonomic levels\n\n\nInférence + GREMLINS\n\n\n\n\n\nRelire intro St Clair\nS’inspirer structure pour mon intro\nTrouver biblio intro\nRédiger l’intro\nRegarder les applications pour les collections de réseaux recommender system\nLire les papiers de Baldock Traveset Souza Cordeniz Trojelsgaard et Gibson\nDire résultats nettement meilleurs et variabilités inférieures.\nIntégrer les retours de Sophie\n\n\n\n\n\nComparer sur clustering unipartite avec versions symétriser des par blocs des matrices d’adjacences.\nCorriger structure de simus :\n\nPour noisy \\alpha :\n\nLogit pour envoyer la gaussienne vers (0,1)\nBeta contrainte dans (0,1)\n\nPour noisy links : Générer nb_clustering collections de taille M puis prélever \\epsilon_{max}n_r n_c liens à inverser puis pour les \\epsilon < \\epsilon_{max} prélever dans la liste des indices afin d’avoir des perturbations emboitées.\n\n\n\n\n\n\nKmeans sur la densité des réseaux subdoré pour pré-partitionner et clusteriser. Car densités déséquilibrées.\n\n\n\n\n\n\nCréer des vignettes illustrant par exemple des cas de simulations. Possible de mettre l’exemple d’application de Sophie sur les réseaux avec gradient d’urbanisation."
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"text": "Détails d’inscriptions\n\n\n\n\n\nPapier pour comprendre données\npbs : variance, bcp de zero, covariables, offset et taxonomie (Reseaux arretes differents niveaux : Genre, OTU …)\n\n\nCombine networks at different taxonomic levels\n\n\nInférence + GREMLINS\n\n\n\n\n\nRelire intro St Clair\nS’inspirer structure pour mon intro\nTrouver biblio intro\nRédiger l’intro\nRegarder les applications pour les collections de réseaux recommender system\nLire les papiers de Baldock Traveset Souza Cordeniz Trojelsgaard et Gibson\nDire résultats nettement meilleurs et variabilités inférieures.\nIntégrer les retours de Sophie\n\n\n\n\n\nComparer sur clustering unipartite avec versions symétriser des par blocs des matrices d’adjacences.\nCorriger structure de simus :\n\nPour noisy \\alpha :\n\nLogit pour envoyer la gaussienne vers (0,1)\nBeta contrainte dans (0,1)\n\nPour noisy links : Générer nb_clustering collections de taille M puis prélever \\epsilon_{max}n_r n_c liens à inverser puis pour les \\epsilon < \\epsilon_{max} prélever dans la liste des indices afin d’avoir des perturbations emboitées.\n\n\n\n\n\n\nKmeans sur la densité des réseaux subdoré pour pré-partitionner et clusteriser. Car densités déséquilibrées.\n\n\n\n\n\n\nCréer des vignettes illustrant par exemple des cas de simulations. Possible de mettre l’exemple d’application de Sophie sur les réseaux avec gradient d’urbanisation."
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"section": "J’ai fait",
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"text": "A continuer\n\nRésultats simus NA Erreur pour certaines conditions : Pour NA robustness générer nb_rep collections de taille M=2 et prélever \\epsilon_{max}n_r n_c liens à retirer puis pour les \\epsilon < \\epsilon_{max} prélever dans la liste des indices afin d’avoir des perturbations emboitées. Il faut que j’ajoute un mécanisme pour reprendre des conditions qui ont plantés et que je skip dans le future_lapply les conditions déjà traitées (pour avoir la même seed quand je vais exécuter le code). Implémenté les missing steps en attente des résultats MIGALE.\nLire Biological Networks - François Képès\nRelancer simus d’inférence avec n = 240 pour voir si la qualité augmenter (se rassurer). En fait on est déjà à 240, j’ai relancé avec M = 4 au lieu de M = 2. En attente résultats MIGALE\n\n\nApplications\n\nIdée Sophie: Regarder clustering de données plantes-pollinisateur selon gradient d’urbanisation\n\n\nSophie a fait une appli qui marche bien et va dans le sens de l’analyse faite (à savoir pas d’effet du gradien d’urbanisation). À continuer pour l’intégrer dans l’article !\n\n\n\nSimulations article\n\nRelancer simulations de clustering avec M = 30 où M_i = 10, \\forall i. En attente retour MIGALE Relancer simus clustering avec VEM steps = 10 000 et plus nombreux init pour spectral. Ajouter simu clustering métriques nb sous-collections obtenues. Vérifier les résultats obtenus si ARI = 0. Et augmenter la taille M = 30 avec M_1 = M_2 = M_3 = 10.\n\n\n\nAxe inférence\n\nLire biblio fournie Julie, Inférence de réseaux : co-occurence\n\n\nJ’ai commencé à lire Faust et al."
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"text": "Bilan semaine 18 2025 : 28 avril - 2 mai\n\n\n\ncolBiSBM\n\ninférence\n\n\n\n\n\n\n\n\n\n28 avr. 2025\n\n\nLouis Lacoste\n\n\n\n\n\n\n\n\n\n\n\n\nBilan semaine 17 2025 : 24 avril - 25 avril\n\n\n\ncolBiSBM\n\n\n\n\n\n\n\n\n\n25 avr. 2025\n\n\nLouis Lacoste\n\n\n\n\n\n\n\n\n\n\n\n\nBilan semaine 16 2025\n\n\n\ncolBiSBM\n\n\n\n\n\n\n\n\n\n18 avr. 2025\n\n\nLouis Lacoste\n\n\n\n\n\n\n\n\n\n\n\n\nBilan semaine 15 2025 : 31 mars-4 avril\n\n\n\ncolBiSBM\n\n\n\n\n\n\n\n\n\n4 avr. 2025\n\n\nLouis Lacoste\n\n\n\n\n\n\n\n\n\n\n\n\nBilan semaine 14 2025 : 24-28 mars\n\n\n\ncolBiSBM\n\n\n\n\n\n\n\n\n\n28 mars 2025\n\n\nLouis Lacoste\n\n\n\n\n\n\n\n\n\n\n\n\nBilan semaine 13 2025 : 17-21 mars\n\n\n\ncolBiSBM\n\n\n\n\n\n\n\n\n\n17 mars 2025\n\n\nLouis Lacoste\n\n\n\n\n\nAucun article correspondant"
|
"text": "Bilan semaine 19 2025 : 5 mai - 9 mai\n\n\n\ncolBiSBM\n\ninférence\n\n\n\n\n\n\n\n\n\n5 mai 2025\n\n\nLouis Lacoste\n\n\n\n\n\n\n\n\n\n\n\n\nBilan semaine 18 2025 : 28 avril - 2 mai\n\n\n\ncolBiSBM\n\ninférence\n\n\n\n\n\n\n\n\n\n2 mai 2025\n\n\nLouis Lacoste\n\n\n\n\n\n\n\n\n\n\n\n\nBilan semaine 17 2025 : 24 avril - 25 avril\n\n\n\ncolBiSBM\n\n\n\n\n\n\n\n\n\n25 avr. 2025\n\n\nLouis Lacoste\n\n\n\n\n\n\n\n\n\n\n\n\nBilan semaine 16 2025\n\n\n\ncolBiSBM\n\n\n\n\n\n\n\n\n\n18 avr. 2025\n\n\nLouis Lacoste\n\n\n\n\n\n\n\n\n\n\n\n\nBilan semaine 15 2025 : 31 mars-4 avril\n\n\n\ncolBiSBM\n\n\n\n\n\n\n\n\n\n4 avr. 2025\n\n\nLouis Lacoste\n\n\n\n\n\n\n\n\n\n\n\n\nBilan semaine 14 2025 : 24-28 mars\n\n\n\ncolBiSBM\n\n\n\n\n\n\n\n\n\n28 mars 2025\n\n\nLouis Lacoste\n\n\n\n\n\n\n\n\n\n\n\n\nBilan semaine 13 2025 : 17-21 mars\n\n\n\ncolBiSBM\n\n\n\n\n\n\n\n\n\n17 mars 2025\n\n\nLouis Lacoste\n\n\n\n\n\nAucun article correspondant"
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"text": "colDEM CSE\n\n\n\n\n\nLire biblio fournie Julie, Inférence de réseaux : co-occurence\nPapier pour comprendre données\npbs : variance, bcp de zero, covariables, offset et taxonomie (Reseaux arretes differents niveaux : Genre, OTU …)\n\n\nCombine networks at different taxonomic levels\n\n\nInférence + GREMLINS\n\n\n\n\n\nRelire intro St Clair\nS’inspirer structure pour mon intro\nTrouver biblio intro\nRédiger l’intro\nRegarder les applications pour les collections de réseaux recommender system\nLire les papiers de Baldock Traveset Souza Cordeniz Trojelsgaard et Gibson\nDire résultats nettement meilleurs et variabilités inférieures.\nIntégrer les retours de Sophie\n\n\n\n\n\nRelancer simus clustering avec VEM steps = 10 000 et plus nombreux init pour spectral\nAjouter simu clustering métriques nb sous-collections obtenues. Vérifier les résultats obtenus si ARI = 0. Et augmenter la taille M = 30 avec M_1 = M_2 = M_3 = 10.\nComparer sur clustering unipartite avec versions symétriser des par blocs des matrices d’adjacences.\nCorriger structure de simus :\n\nPour noisy \\alpha :\n\nLogit pour envoyer la gaussienne vers (0,1)\nBeta contrainte dans (0,1)\n\nPour noisy links : Générer nb_clustering collections de taille M puis prélever \\epsilon_{max}n_r n_c liens à inverser puis pour les \\epsilon < \\epsilon_{max} prélever dans la liste des indices afin d’avoir des perturbations emboitées.\n\n\n\n\n\n\nKmeans sur la densité des réseaux subdoré pour pré-partitionner et clusteriser. Car densités déséquilibrées.\n\n\n\n\n\n\nCréer des vignettes illustrant par exemple des cas de simulations. Possible de mettre l’exemple d’application de Sophie sur les réseaux avec gradient d’urbanisation."
|
"text": "Lire biblio fournie Julie, Inférence de réseaux : co-occurence\nPapier pour comprendre données\npbs : variance, bcp de zero, covariables, offset et taxonomie (Reseaux arretes differents niveaux : Genre, OTU …)\n\n\nCombine networks at different taxonomic levels\n\n\nInférence + GREMLINS\n\n\n\n\n\nRelire intro St Clair\nS’inspirer structure pour mon intro\nTrouver biblio intro\nRédiger l’intro\nRegarder les applications pour les collections de réseaux recommender system\nLire les papiers de Baldock Traveset Souza Cordeniz Trojelsgaard et Gibson\nDire résultats nettement meilleurs et variabilités inférieures.\nIntégrer les retours de Sophie\n\n\n\n\n\nComparer sur clustering unipartite avec versions symétriser des par blocs des matrices d’adjacences.\nCorriger structure de simus :\n\nPour noisy \\alpha :\n\nLogit pour envoyer la gaussienne vers (0,1)\nBeta contrainte dans (0,1)\n\nPour noisy links : Générer nb_clustering collections de taille M puis prélever \\epsilon_{max}n_r n_c liens à inverser puis pour les \\epsilon < \\epsilon_{max} prélever dans la liste des indices afin d’avoir des perturbations emboitées.\n\n\n\n\n\n\nKmeans sur la densité des réseaux subdoré pour pré-partitionner et clusteriser. Car densités déséquilibrées.\n\n\n\n\n\n\nCréer des vignettes illustrant par exemple des cas de simulations. Possible de mettre l’exemple d’application de Sophie sur les réseaux avec gradient d’urbanisation."
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"text": "colDEM CSE\n\n\n\n\n\nLire biblio fournie Julie, Inférence de réseaux : co-occurence\nPapier pour comprendre données\npbs : variance, bcp de zero, covariables, offset et taxonomie (Reseaux arretes differents niveaux : Genre, OTU …)\n\n\nCombine networks at different taxonomic levels\n\n\nInférence + GREMLINS\n\n\n\n\n\nRelire intro St Clair\nS’inspirer structure pour mon intro\nTrouver biblio intro\nRédiger l’intro\nRegarder les applications pour les collections de réseaux recommender system\nLire les papiers de Baldock Traveset Souza Cordeniz Trojelsgaard et Gibson\nDire résultats nettement meilleurs et variabilités inférieures.\nIntégrer les retours de Sophie\n\n\n\n\n\nRelancer simus clustering avec VEM steps = 10 000 et plus nombreux init pour spectral\nAjouter simu clustering métriques nb sous-collections obtenues. Vérifier les résultats obtenus si ARI = 0. Et augmenter la taille M = 30 avec M_1 = M_2 = M_3 = 10.\nComparer sur clustering unipartite avec versions symétriser des par blocs des matrices d’adjacences.\nCorriger structure de simus :\n\nPour noisy \\alpha :\n\nLogit pour envoyer la gaussienne vers (0,1)\nBeta contrainte dans (0,1)\n\nPour noisy links : Générer nb_clustering collections de taille M puis prélever \\epsilon_{max}n_r n_c liens à inverser puis pour les \\epsilon < \\epsilon_{max} prélever dans la liste des indices afin d’avoir des perturbations emboitées.\n\n\n\n\n\n\nKmeans sur la densité des réseaux subdoré pour pré-partitionner et clusteriser. Car densités déséquilibrées.\n\n\n\n\n\n\nCréer des vignettes illustrant par exemple des cas de simulations. Possible de mettre l’exemple d’application de Sophie sur les réseaux avec gradient d’urbanisation."
|
"text": "Lire biblio fournie Julie, Inférence de réseaux : co-occurence\nPapier pour comprendre données\npbs : variance, bcp de zero, covariables, offset et taxonomie (Reseaux arretes differents niveaux : Genre, OTU …)\n\n\nCombine networks at different taxonomic levels\n\n\nInférence + GREMLINS\n\n\n\n\n\nRelire intro St Clair\nS’inspirer structure pour mon intro\nTrouver biblio intro\nRédiger l’intro\nRegarder les applications pour les collections de réseaux recommender system\nLire les papiers de Baldock Traveset Souza Cordeniz Trojelsgaard et Gibson\nDire résultats nettement meilleurs et variabilités inférieures.\nIntégrer les retours de Sophie\n\n\n\n\n\nComparer sur clustering unipartite avec versions symétriser des par blocs des matrices d’adjacences.\nCorriger structure de simus :\n\nPour noisy \\alpha :\n\nLogit pour envoyer la gaussienne vers (0,1)\nBeta contrainte dans (0,1)\n\nPour noisy links : Générer nb_clustering collections de taille M puis prélever \\epsilon_{max}n_r n_c liens à inverser puis pour les \\epsilon < \\epsilon_{max} prélever dans la liste des indices afin d’avoir des perturbations emboitées.\n\n\n\n\n\n\nKmeans sur la densité des réseaux subdoré pour pré-partitionner et clusteriser. Car densités déséquilibrées.\n\n\n\n\n\n\nCréer des vignettes illustrant par exemple des cas de simulations. Possible de mettre l’exemple d’application de Sophie sur les réseaux avec gradient d’urbanisation."
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"section": "J’ai fait",
|
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||||||
"text": "J’ai fait"
|
"text": "J’ai fait\n\nJdS\n\ncolDEM CSE\n\n\n\nClustering exhaustif Baldock\n\nLe clustering de toutes les 52 partitions s’est fait en 5h30 ! (Mémoïsation)\nPour iid la meilleure partition avec BICL=-9466.911 contre BICL_{algo} = -9466.873 \\pm 0.02205 trouvé avec l’algo \nPour \\pi\\rho la meilleure partition avec BICL = en cours contre BICL_{algo} =-9497.92 \\pm 0.00009"
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|
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|
||||||
"section": "A continuer",
|
"section": "A continuer",
|
||||||
"text": "A continuer\n\nRésultats simus NA Erreur pour certaines conditions : Pour NA robustness générer nb_rep collections de taille M=2 et prélever \\epsilon_{max}n_r n_c liens à retirer puis pour les \\epsilon < \\epsilon_{max} prélever dans la liste des indices afin d’avoir des perturbations emboitées. Il faut que j’ajoute un mécanisme pour reprendre des conditions qui ont plantés et que je skip dans le future_lapply les conditions déjà traitées (pour avoir la même seed quand je vais exécuter le code). Implémenté les missing steps en attente des résultats MIGALE.\nLire Biological Networks - François Képès\nRelancer simus d’inférence avec n = 240 pour voir si la qualité augmenter (se rassurer). En fait on est déjà à 240, j’ai relancé avec M = 4 au lieu de M = 2. En attente résultats MIGALE\n\n\nApplications\n\nIdée Sophie: Regarder clustering de données plantes-pollinisateur selon gradient d’urbanisation\n\n\nSophie a fait une appli qui marche bien et va dans le sens de l’analyse faite (à savoir pas d’effet du gradien d’urbanisation). À continuer pour l’intégrer dans l’article !"
|
"text": "A continuer\n\nRésultats simus NA Erreur pour certaines conditions : Pour NA robustness générer nb_rep collections de taille M=2 et prélever \\epsilon_{max}n_r n_c liens à retirer puis pour les \\epsilon < \\epsilon_{max} prélever dans la liste des indices afin d’avoir des perturbations emboitées. Il faut que j’ajoute un mécanisme pour reprendre des conditions qui ont plantés et que je skip dans le future_lapply les conditions déjà traitées (pour avoir la même seed quand je vais exécuter le code). Implémenté les missing steps en attente des résultats MIGALE.\nLire Biological Networks - François Képès\nRelancer simus d’inférence avec n = 240 pour voir si la qualité augmenter (se rassurer). En fait on est déjà à 240, j’ai relancé avec M = 4 au lieu de M = 2. En attente résultats MIGALE\n\n\nApplications\n\nIdée Sophie: Regarder clustering de données plantes-pollinisateur selon gradient d’urbanisation\n\n\nSophie a fait une appli qui marche bien et va dans le sens de l’analyse faite (à savoir pas d’effet du gradien d’urbanisation). À continuer pour l’intégrer dans l’article !\n\n\n\nSimulations article\n\nRelancer simulations de clustering avec M = 30 où M_i = 10, \\forall i. En attente retour MIGALE Relancer simus clustering avec VEM steps = 10 000 et plus nombreux init pour spectral. Ajouter simu clustering métriques nb sous-collections obtenues. Vérifier les résultats obtenus si ARI = 0. Et augmenter la taille M = 30 avec M_1 = M_2 = M_3 = 10."
|
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<div class="quarto-title-meta-heading">Date de publication</div>
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<p class="date">28 avril 2025</p>
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<p class="date">2 mai 2025</p>
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<li><a href="#a-faire" id="toc-a-faire" class="nav-link active" data-scroll-target="#a-faire">A faire</a>
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<li><a href="#a-faire" id="toc-a-faire" class="nav-link active" data-scroll-target="#a-faire">A faire</a>
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<ul class="collapse">
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<li><a href="#jds" id="toc-jds" class="nav-link" data-scroll-target="#jds">JdS</a></li>
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<li><a href="#stratégie-suite-inférence" id="toc-stratégie-suite-inférence" class="nav-link" data-scroll-target="#stratégie-suite-inférence">Stratégie suite : Inférence</a></li>
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<li><a href="#stratégie-suite-inférence" id="toc-stratégie-suite-inférence" class="nav-link" data-scroll-target="#stratégie-suite-inférence">Stratégie suite : Inférence</a></li>
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<li><a href="#rédaction-article" id="toc-rédaction-article" class="nav-link" data-scroll-target="#rédaction-article">Rédaction article</a></li>
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<li><a href="#rédaction-article" id="toc-rédaction-article" class="nav-link" data-scroll-target="#rédaction-article">Rédaction article</a></li>
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<li><a href="#simulations-article" id="toc-simulations-article" class="nav-link" data-scroll-target="#simulations-article">Simulations article</a></li>
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<li><a href="#simulations-article" id="toc-simulations-article" class="nav-link" data-scroll-target="#simulations-article">Simulations article</a></li>
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<li><a href="#applications" id="toc-applications" class="nav-link" data-scroll-target="#applications">Applications</a></li>
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<li><a href="#applications" id="toc-applications" class="nav-link" data-scroll-target="#applications">Applications</a></li>
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<li><a href="#autour-de-larticle-et-du-package" id="toc-autour-de-larticle-et-du-package" class="nav-link" data-scroll-target="#autour-de-larticle-et-du-package">Autour de l’article et du package</a></li>
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<li><a href="#autour-de-larticle-et-du-package" id="toc-autour-de-larticle-et-du-package" class="nav-link" data-scroll-target="#autour-de-larticle-et-du-package">Autour de l’article et du package</a></li>
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</ul></li>
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<li><a href="#jai-fait" id="toc-jai-fait" class="nav-link" data-scroll-target="#jai-fait">J’ai fait</a></li>
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<li><a href="#jai-fait" id="toc-jai-fait" class="nav-link" data-scroll-target="#jai-fait">J’ai fait</a>
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<li><a href="#jds" id="toc-jds" class="nav-link" data-scroll-target="#jds">JdS</a></li>
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<li><a href="#clustering-exhaustif-baldock" id="toc-clustering-exhaustif-baldock" class="nav-link" data-scroll-target="#clustering-exhaustif-baldock">Clustering exhaustif Baldock</a></li>
|
||||||
|
</ul></li>
|
||||||
<li><a href="#a-continuer" id="toc-a-continuer" class="nav-link" data-scroll-target="#a-continuer">A continuer</a>
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<li><a href="#a-continuer" id="toc-a-continuer" class="nav-link" data-scroll-target="#a-continuer">A continuer</a>
|
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<ul class="collapse">
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<ul class="collapse">
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<li><a href="#applications-1" id="toc-applications-1" class="nav-link" data-scroll-target="#applications-1">Applications</a></li>
|
<li><a href="#applications-1" id="toc-applications-1" class="nav-link" data-scroll-target="#applications-1">Applications</a></li>
|
||||||
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<li><a href="#simulations-article-1" id="toc-simulations-article-1" class="nav-link" data-scroll-target="#simulations-article-1">Simulations article</a></li>
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</ul></li>
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||||||
<h2 class="anchored" data-anchor-id="a-faire">A faire</h2>
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<h2 class="anchored" data-anchor-id="a-faire">A faire</h2>
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<h3 class="anchored" data-anchor-id="jds">JdS</h3>
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||||||
<li>colDEM CSE</li>
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|
||||||
</ul>
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||||||
</section>
|
|
||||||
<section id="stratégie-suite-inférence" class="level3">
|
<section id="stratégie-suite-inférence" class="level3">
|
||||||
<h3 class="anchored" data-anchor-id="stratégie-suite-inférence">Stratégie suite : Inférence</h3>
|
<h3 class="anchored" data-anchor-id="stratégie-suite-inférence">Stratégie suite : Inférence</h3>
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<ul>
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<ul>
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@ -253,8 +251,6 @@ window.Quarto = {
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<section id="simulations-article" class="level3">
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<section id="simulations-article" class="level3">
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||||||
<h3 class="anchored" data-anchor-id="simulations-article">Simulations article</h3>
|
<h3 class="anchored" data-anchor-id="simulations-article">Simulations article</h3>
|
||||||
<ul>
|
<ul>
|
||||||
<li><p>Relancer simus clustering avec VEM steps = 10 000 et plus nombreux init pour spectral</p></li>
|
|
||||||
<li><p>Ajouter simu clustering métriques nb sous-collections obtenues. Vérifier les résultats obtenus si ARI = 0. Et augmenter la taille <span class="math inline">M = 30</span> avec <span class="math inline">M_1 = M_2 = M_3 = 10</span>.</p></li>
|
|
||||||
<li><p>Comparer sur clustering unipartite avec versions symétriser des par blocs des matrices d’adjacences.</p></li>
|
<li><p>Comparer sur clustering unipartite avec versions symétriser des par blocs des matrices d’adjacences.</p></li>
|
||||||
<li><p>Corriger structure de simus :</p>
|
<li><p>Corriger structure de simus :</p>
|
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<ul>
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<ul>
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@ -283,6 +279,20 @@ window.Quarto = {
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</section>
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</section>
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<section id="jai-fait" class="level2">
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||||||
<h2 class="anchored" data-anchor-id="jai-fait">J’ai fait</h2>
|
<h2 class="anchored" data-anchor-id="jai-fait">J’ai fait</h2>
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||||||
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<section id="jds" class="level3">
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||||||
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<h3 class="anchored" data-anchor-id="jds">JdS</h3>
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||||||
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<ul>
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||||||
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<li>colDEM CSE</li>
|
||||||
|
</ul>
|
||||||
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</section>
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||||||
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<section id="clustering-exhaustif-baldock" class="level3">
|
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<h3 class="anchored" data-anchor-id="clustering-exhaustif-baldock">Clustering exhaustif Baldock</h3>
|
||||||
|
<ul>
|
||||||
|
<li><p>Le clustering de toutes les 52 partitions s’est fait en 5h30 ! (Mémoïsation)</p></li>
|
||||||
|
<li><p>Pour iid la meilleure partition avec <span class="math inline">BICL=-9466.911</span> contre <span class="math inline">BICL_{algo} = -9466.873 \pm 0.02205</span> trouvé avec l’algo <img src="figs/partition-iid.svg" class="img-fluid" alt="best_iid"></p></li>
|
||||||
|
<li><p>Pour <span class="math inline">\pi\rho</span> la meilleure partition avec <span class="math inline">BICL = en cours</span> contre <span class="math inline">BICL_{algo} =-9497.92 \pm 0.00009</span></p></li>
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</ul>
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</section>
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<h2 class="anchored" data-anchor-id="a-continuer">A continuer</h2>
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<h2 class="anchored" data-anchor-id="a-continuer">A continuer</h2>
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@ -299,6 +309,12 @@ window.Quarto = {
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<blockquote class="blockquote">
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<blockquote class="blockquote">
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<p>Sophie a fait une appli qui marche bien et va dans le sens de l’analyse faite (à savoir pas d’effet du gradien d’urbanisation). À continuer pour l’intégrer dans l’article !</p>
|
<p>Sophie a fait une appli qui marche bien et va dans le sens de l’analyse faite (à savoir pas d’effet du gradien d’urbanisation). À continuer pour l’intégrer dans l’article !</p>
|
||||||
</blockquote>
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|
||||||
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<section id="simulations-article-1" class="level3">
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||||||
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<h3 class="anchored" data-anchor-id="simulations-article-1">Simulations article</h3>
|
||||||
|
<ul>
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||||||
|
<li>Relancer simulations de clustering avec <span class="math inline">M = 30</span> où <span class="math inline">M_i = 10, \forall i</span>. En attente retour MIGALE Relancer simus clustering avec VEM steps = 10 000 et plus nombreux init pour spectral. Ajouter simu clustering métriques nb sous-collections obtenues. Vérifier les résultats obtenus si ARI = 0. Et augmenter la taille <span class="math inline">M = 30</span> avec <span class="math inline">M_1 = M_2 = M_3 = 10</span>.</li>
|
||||||
|
</ul>
|
||||||
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||||||
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||||||
</section>
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403
suivi/2025-18/figs/partition-iid.svg
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403
suivi/2025-18/figs/partition-iid.svg
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726
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@ -0,0 +1,726 @@
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<h1 class="title">Bilan semaine 19 2025 : 5 mai - 9 mai</h1>
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<div class="quarto-category">colBiSBM</div>
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<div class="quarto-category">inférence</div>
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<div class="quarto-title-meta-heading">Auteur·rice</div>
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<div class="quarto-title-meta-heading">Affiliation</div>
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<div class="quarto-title-meta-contents">
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<p class="author">Louis Lacoste <a href="mailto:louis.lacoste@agroparistech.fr" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> <a href="https://orcid.org/0009-0004-0178-9821" class="quarto-title-author-orcid"> <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAA2ZpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADw/eHBhY2tldCBiZWdpbj0i77u/IiBpZD0iVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkIj8+IDx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IkFkb2JlIFhNUCBDb3JlIDUuMC1jMDYwIDYxLjEzNDc3NywgMjAxMC8wMi8xMi0xNzozMjowMCAgICAgICAgIj4gPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4gPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIgeG1sbnM6eG1wTU09Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9tbS8iIHhtbG5zOnN0UmVmPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvc1R5cGUvUmVzb3VyY2VSZWYjIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iIHhtcE1NOk9yaWdpbmFsRG9jdW1lbnRJRD0ieG1wLmRpZDo1N0NEMjA4MDI1MjA2ODExOTk0QzkzNTEzRjZEQTg1NyIgeG1wTU06RG9jdW1lbnRJRD0ieG1wLmRpZDozM0NDOEJGNEZGNTcxMUUxODdBOEVCODg2RjdCQ0QwOSIgeG1wTU06SW5zdGFuY2VJRD0ieG1wLmlpZDozM0NDOEJGM0ZGNTcxMUUxODdBOEVCODg2RjdCQ0QwOSIgeG1wOkNyZWF0b3JUb29sPSJBZG9iZSBQaG90b3Nob3AgQ1M1IE1hY2ludG9zaCI+IDx4bXBNTTpEZXJpdmVkRnJvbSBzdFJlZjppbnN0YW5jZUlEPSJ4bXAuaWlkOkZDN0YxMTc0MDcyMDY4MTE5NUZFRDc5MUM2MUUwNEREIiBzdFJlZjpkb2N1bWVudElEPSJ4bXAuZGlkOjU3Q0QyMDgwMjUyMDY4MTE5OTRDOTM1MTNGNkRBODU3Ii8+IDwvcmRmOkRlc2NyaXB0aW9uPiA8L3JkZjpSREY+IDwveDp4bXBtZXRhPiA8P3hwYWNrZXQgZW5kPSJyIj8+84NovQAAAR1JREFUeNpiZEADy85ZJgCpeCB2QJM6AMQLo4yOL0AWZETSqACk1gOxAQN+cAGIA4EGPQBxmJA0nwdpjjQ8xqArmczw5tMHXAaALDgP1QMxAGqzAAPxQACqh4ER6uf5MBlkm0X4EGayMfMw/Pr7Bd2gRBZogMFBrv01hisv5jLsv9nLAPIOMnjy8RDDyYctyAbFM2EJbRQw+aAWw/LzVgx7b+cwCHKqMhjJFCBLOzAR6+lXX84xnHjYyqAo5IUizkRCwIENQQckGSDGY4TVgAPEaraQr2a4/24bSuoExcJCfAEJihXkWDj3ZAKy9EJGaEo8T0QSxkjSwORsCAuDQCD+QILmD1A9kECEZgxDaEZhICIzGcIyEyOl2RkgwAAhkmC+eAm0TAAAAABJRU5ErkJggg=="></a></p>
|
||||||
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</div>
|
||||||
|
<div class="quarto-title-meta-contents">
|
||||||
|
<p class="affiliation">
|
||||||
|
MIA Paris-Saclay, INRAE, AgroParisTech, Université Paris-Saclay
|
||||||
|
</p>
|
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||||||
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<div class="quarto-title-meta-heading">Date de publication</div>
|
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|
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<p class="date">5 mai 2025</p>
|
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<li><a href="#a-faire" id="toc-a-faire" class="nav-link active" data-scroll-target="#a-faire">A faire</a>
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<li><a href="#stratégie-suite-inférence" id="toc-stratégie-suite-inférence" class="nav-link" data-scroll-target="#stratégie-suite-inférence">Stratégie suite : Inférence</a></li>
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|
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||||||
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|
||||||
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<h2 class="anchored" data-anchor-id="a-faire">A faire</h2>
|
||||||
|
<section id="ml-at-aussois" class="level3">
|
||||||
|
<h3 class="anchored" data-anchor-id="ml-at-aussois">ML at Aussois</h3>
|
||||||
|
<ul>
|
||||||
|
<li>Détails d’inscriptions</li>
|
||||||
|
</ul>
|
||||||
|
</section>
|
||||||
|
<section id="stratégie-suite-inférence" class="level3">
|
||||||
|
<h3 class="anchored" data-anchor-id="stratégie-suite-inférence">Stratégie suite : Inférence</h3>
|
||||||
|
<ul>
|
||||||
|
<li><p>Papier pour comprendre données</p></li>
|
||||||
|
<li><p>pbs : variance, bcp de zero, covariables, offset et taxonomie (Reseaux arretes differents niveaux : Genre, OTU …)</p></li>
|
||||||
|
</ul>
|
||||||
|
<blockquote class="blockquote">
|
||||||
|
<p>Combine networks at different taxonomic levels</p>
|
||||||
|
</blockquote>
|
||||||
|
<ul>
|
||||||
|
<li>Inférence + GREMLINS</li>
|
||||||
|
</ul>
|
||||||
|
</section>
|
||||||
|
<section id="rédaction-article" class="level3">
|
||||||
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<h3 class="anchored" data-anchor-id="rédaction-article">Rédaction article</h3>
|
||||||
|
<ul>
|
||||||
|
<li><p>Relire intro St Clair</p></li>
|
||||||
|
<li><p>S’inspirer structure pour mon intro</p></li>
|
||||||
|
<li><p>Trouver biblio intro</p></li>
|
||||||
|
<li><p>Rédiger l’intro</p></li>
|
||||||
|
<li><p>Regarder les applications pour les collections de réseaux recommender system</p></li>
|
||||||
|
<li><p>Lire les papiers de Baldock Traveset Souza Cordeniz Trojelsgaard et Gibson</p></li>
|
||||||
|
<li><p>Dire résultats nettement meilleurs et variabilités inférieures.</p></li>
|
||||||
|
<li><p>Intégrer les retours de Sophie</p></li>
|
||||||
|
</ul>
|
||||||
|
</section>
|
||||||
|
<section id="simulations-article" class="level3">
|
||||||
|
<h3 class="anchored" data-anchor-id="simulations-article">Simulations article</h3>
|
||||||
|
<ul>
|
||||||
|
<li><p>Comparer sur clustering unipartite avec versions symétriser des par blocs des matrices d’adjacences.</p></li>
|
||||||
|
<li><p>Corriger structure de simus :</p>
|
||||||
|
<ul>
|
||||||
|
<li>Pour noisy <span class="math inline">\alpha</span> :
|
||||||
|
<ul>
|
||||||
|
<li>Logit pour envoyer la gaussienne vers (0,1)</li>
|
||||||
|
<li>Beta contrainte dans (0,1)</li>
|
||||||
|
</ul></li>
|
||||||
|
<li>Pour noisy links : Générer <code>nb_clustering</code> collections de taille M puis prélever <span class="math inline">\epsilon_{max}n_r n_c</span> liens à inverser puis pour les <span class="math inline">\epsilon < \epsilon_{max}</span> prélever dans la liste des indices afin d’avoir des perturbations emboitées.</li>
|
||||||
|
</ul></li>
|
||||||
|
</ul>
|
||||||
|
</section>
|
||||||
|
<section id="applications" class="level3">
|
||||||
|
<h3 class="anchored" data-anchor-id="applications">Applications</h3>
|
||||||
|
<ul>
|
||||||
|
<li>Kmeans sur la densité des réseaux subdoré pour pré-partitionner et <em>clusteriser</em>. Car densités déséquilibrées.</li>
|
||||||
|
</ul>
|
||||||
|
<p><img src="figs/density-subdore.png" class="img-fluid"></p>
|
||||||
|
</section>
|
||||||
|
<section id="autour-de-larticle-et-du-package" class="level3">
|
||||||
|
<h3 class="anchored" data-anchor-id="autour-de-larticle-et-du-package">Autour de l’article et du package</h3>
|
||||||
|
<ul>
|
||||||
|
<li>Créer des vignettes illustrant par exemple des cas de simulations. <strong>Possible de mettre l’exemple d’application de Sophie sur les réseaux avec gradient d’urbanisation</strong>.</li>
|
||||||
|
</ul>
|
||||||
|
</section>
|
||||||
|
</section>
|
||||||
|
<section id="jai-fait" class="level2">
|
||||||
|
<h2 class="anchored" data-anchor-id="jai-fait">J’ai fait</h2>
|
||||||
|
</section>
|
||||||
|
<section id="a-continuer" class="level2">
|
||||||
|
<h2 class="anchored" data-anchor-id="a-continuer">A continuer</h2>
|
||||||
|
<ul>
|
||||||
|
<li><p>Résultats simus NA <strong>Erreur pour certaines conditions</strong> : Pour NA robustness générer <code>nb_rep</code> collections de taille <span class="math inline">M=2</span> et prélever <span class="math inline">\epsilon_{max}n_r n_c</span> liens à retirer puis pour les <span class="math inline">\epsilon < \epsilon_{max}</span> prélever dans la liste des indices afin d’avoir des perturbations emboitées. Il faut que j’ajoute un mécanisme pour reprendre des conditions qui ont plantés et que je skip dans le future_lapply les conditions déjà traitées (pour avoir la même seed quand je vais exécuter le code). Implémenté les missing steps <em>en attente des résultats MIGALE</em>.</p></li>
|
||||||
|
<li><p>Lire Biological Networks - François Képès</p></li>
|
||||||
|
<li><p>Relancer simus d’inférence avec n = 240 pour voir si la qualité augmenter (se rassurer). En fait on est déjà à 240, j’ai relancé avec M = 4 au lieu de M = 2. En attente résultats MIGALE</p></li>
|
||||||
|
</ul>
|
||||||
|
<section id="applications-1" class="level3">
|
||||||
|
<h3 class="anchored" data-anchor-id="applications-1">Applications</h3>
|
||||||
|
<ul>
|
||||||
|
<li>Idée Sophie: Regarder clustering de données plantes-pollinisateur selon gradient d’urbanisation</li>
|
||||||
|
</ul>
|
||||||
|
<blockquote class="blockquote">
|
||||||
|
<p>Sophie a fait une appli qui marche bien et va dans le sens de l’analyse faite (à savoir pas d’effet du gradien d’urbanisation). À continuer pour l’intégrer dans l’article !</p>
|
||||||
|
</blockquote>
|
||||||
|
</section>
|
||||||
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<section id="simulations-article-1" class="level3">
|
||||||
|
<h3 class="anchored" data-anchor-id="simulations-article-1">Simulations article</h3>
|
||||||
|
<ul>
|
||||||
|
<li>Relancer simulations de clustering avec <span class="math inline">M = 30</span> où <span class="math inline">M_i = 10, \forall i</span>. En attente retour MIGALE Relancer simus clustering avec VEM steps = 10 000 et plus nombreux init pour spectral. Ajouter simu clustering métriques nb sous-collections obtenues. Vérifier les résultats obtenus si ARI = 0. Et augmenter la taille <span class="math inline">M = 30</span> avec <span class="math inline">M_1 = M_2 = M_3 = 10</span>.</li>
|
||||||
|
</ul>
|
||||||
|
</section>
|
||||||
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|
||||||
|
<h3 class="anchored" data-anchor-id="axe-inférence">Axe inférence</h3>
|
||||||
|
<ul>
|
||||||
|
<li>Lire biblio fournie Julie, Inférence de réseaux : co-occurence</li>
|
||||||
|
</ul>
|
||||||
|
<blockquote class="blockquote">
|
||||||
|
<p>J’ai commencé à lire Faust et al.</p>
|
||||||
|
</blockquote>
|
||||||
|
|
||||||
|
|
||||||
|
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||||||
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||||||
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||||||
|
const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
|
||||||
|
text: getTextToCopy,
|
||||||
|
container: window.document.getElementById('quarto-embedded-source-code-modal')
|
||||||
|
});
|
||||||
|
clipboardModal.on('success', onCopySuccess);
|
||||||
|
}
|
||||||
|
var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
|
||||||
|
var mailtoRegex = new RegExp(/^mailto:/);
|
||||||
|
var filterRegex = new RegExp('/' + window.location.host + '/');
|
||||||
|
var isInternal = (href) => {
|
||||||
|
return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
|
||||||
|
}
|
||||||
|
// Inspect non-navigation links and adorn them if external
|
||||||
|
var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
|
||||||
|
for (var i=0; i<links.length; i++) {
|
||||||
|
const link = links[i];
|
||||||
|
if (!isInternal(link.href)) {
|
||||||
|
// undo the damage that might have been done by quarto-nav.js in the case of
|
||||||
|
// links that we want to consider external
|
||||||
|
if (link.dataset.originalHref !== undefined) {
|
||||||
|
link.href = link.dataset.originalHref;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
|
||||||
|
const config = {
|
||||||
|
allowHTML: true,
|
||||||
|
maxWidth: 500,
|
||||||
|
delay: 100,
|
||||||
|
arrow: false,
|
||||||
|
appendTo: function(el) {
|
||||||
|
return el.parentElement;
|
||||||
|
},
|
||||||
|
interactive: true,
|
||||||
|
interactiveBorder: 10,
|
||||||
|
theme: 'quarto',
|
||||||
|
placement: 'bottom-start',
|
||||||
|
};
|
||||||
|
if (contentFn) {
|
||||||
|
config.content = contentFn;
|
||||||
|
}
|
||||||
|
if (onTriggerFn) {
|
||||||
|
config.onTrigger = onTriggerFn;
|
||||||
|
}
|
||||||
|
if (onUntriggerFn) {
|
||||||
|
config.onUntrigger = onUntriggerFn;
|
||||||
|
}
|
||||||
|
window.tippy(el, config);
|
||||||
|
}
|
||||||
|
const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
|
||||||
|
for (var i=0; i<noterefs.length; i++) {
|
||||||
|
const ref = noterefs[i];
|
||||||
|
tippyHover(ref, function() {
|
||||||
|
// use id or data attribute instead here
|
||||||
|
let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
|
||||||
|
try { href = new URL(href).hash; } catch {}
|
||||||
|
const id = href.replace(/^#\/?/, "");
|
||||||
|
const note = window.document.getElementById(id);
|
||||||
|
if (note) {
|
||||||
|
return note.innerHTML;
|
||||||
|
} else {
|
||||||
|
return "";
|
||||||
|
}
|
||||||
|
});
|
||||||
|
}
|
||||||
|
const xrefs = window.document.querySelectorAll('a.quarto-xref');
|
||||||
|
const processXRef = (id, note) => {
|
||||||
|
// Strip column container classes
|
||||||
|
const stripColumnClz = (el) => {
|
||||||
|
el.classList.remove("page-full", "page-columns");
|
||||||
|
if (el.children) {
|
||||||
|
for (const child of el.children) {
|
||||||
|
stripColumnClz(child);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
stripColumnClz(note)
|
||||||
|
if (id === null || id.startsWith('sec-')) {
|
||||||
|
// Special case sections, only their first couple elements
|
||||||
|
const container = document.createElement("div");
|
||||||
|
if (note.children && note.children.length > 2) {
|
||||||
|
container.appendChild(note.children[0].cloneNode(true));
|
||||||
|
for (let i = 1; i < note.children.length; i++) {
|
||||||
|
const child = note.children[i];
|
||||||
|
if (child.tagName === "P" && child.innerText === "") {
|
||||||
|
continue;
|
||||||
|
} else {
|
||||||
|
container.appendChild(child.cloneNode(true));
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if (window.Quarto?.typesetMath) {
|
||||||
|
window.Quarto.typesetMath(container);
|
||||||
|
}
|
||||||
|
return container.innerHTML
|
||||||
|
} else {
|
||||||
|
if (window.Quarto?.typesetMath) {
|
||||||
|
window.Quarto.typesetMath(note);
|
||||||
|
}
|
||||||
|
return note.innerHTML;
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
// Remove any anchor links if they are present
|
||||||
|
const anchorLink = note.querySelector('a.anchorjs-link');
|
||||||
|
if (anchorLink) {
|
||||||
|
anchorLink.remove();
|
||||||
|
}
|
||||||
|
if (window.Quarto?.typesetMath) {
|
||||||
|
window.Quarto.typesetMath(note);
|
||||||
|
}
|
||||||
|
if (note.classList.contains("callout")) {
|
||||||
|
return note.outerHTML;
|
||||||
|
} else {
|
||||||
|
return note.innerHTML;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
for (var i=0; i<xrefs.length; i++) {
|
||||||
|
const xref = xrefs[i];
|
||||||
|
tippyHover(xref, undefined, function(instance) {
|
||||||
|
instance.disable();
|
||||||
|
let url = xref.getAttribute('href');
|
||||||
|
let hash = undefined;
|
||||||
|
if (url.startsWith('#')) {
|
||||||
|
hash = url;
|
||||||
|
} else {
|
||||||
|
try { hash = new URL(url).hash; } catch {}
|
||||||
|
}
|
||||||
|
if (hash) {
|
||||||
|
const id = hash.replace(/^#\/?/, "");
|
||||||
|
const note = window.document.getElementById(id);
|
||||||
|
if (note !== null) {
|
||||||
|
try {
|
||||||
|
const html = processXRef(id, note.cloneNode(true));
|
||||||
|
instance.setContent(html);
|
||||||
|
} finally {
|
||||||
|
instance.enable();
|
||||||
|
instance.show();
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
// See if we can fetch this
|
||||||
|
fetch(url.split('#')[0])
|
||||||
|
.then(res => res.text())
|
||||||
|
.then(html => {
|
||||||
|
const parser = new DOMParser();
|
||||||
|
const htmlDoc = parser.parseFromString(html, "text/html");
|
||||||
|
const note = htmlDoc.getElementById(id);
|
||||||
|
if (note !== null) {
|
||||||
|
const html = processXRef(id, note);
|
||||||
|
instance.setContent(html);
|
||||||
|
}
|
||||||
|
}).finally(() => {
|
||||||
|
instance.enable();
|
||||||
|
instance.show();
|
||||||
|
});
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
// See if we can fetch a full url (with no hash to target)
|
||||||
|
// This is a special case and we should probably do some content thinning / targeting
|
||||||
|
fetch(url)
|
||||||
|
.then(res => res.text())
|
||||||
|
.then(html => {
|
||||||
|
const parser = new DOMParser();
|
||||||
|
const htmlDoc = parser.parseFromString(html, "text/html");
|
||||||
|
const note = htmlDoc.querySelector('main.content');
|
||||||
|
if (note !== null) {
|
||||||
|
// This should only happen for chapter cross references
|
||||||
|
// (since there is no id in the URL)
|
||||||
|
// remove the first header
|
||||||
|
if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
|
||||||
|
note.children[0].remove();
|
||||||
|
}
|
||||||
|
const html = processXRef(null, note);
|
||||||
|
instance.setContent(html);
|
||||||
|
}
|
||||||
|
}).finally(() => {
|
||||||
|
instance.enable();
|
||||||
|
instance.show();
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}, function(instance) {
|
||||||
|
});
|
||||||
|
}
|
||||||
|
let selectedAnnoteEl;
|
||||||
|
const selectorForAnnotation = ( cell, annotation) => {
|
||||||
|
let cellAttr = 'data-code-cell="' + cell + '"';
|
||||||
|
let lineAttr = 'data-code-annotation="' + annotation + '"';
|
||||||
|
const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
|
||||||
|
return selector;
|
||||||
|
}
|
||||||
|
const selectCodeLines = (annoteEl) => {
|
||||||
|
const doc = window.document;
|
||||||
|
const targetCell = annoteEl.getAttribute("data-target-cell");
|
||||||
|
const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
|
||||||
|
const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
|
||||||
|
const lines = annoteSpan.getAttribute("data-code-lines").split(",");
|
||||||
|
const lineIds = lines.map((line) => {
|
||||||
|
return targetCell + "-" + line;
|
||||||
|
})
|
||||||
|
let top = null;
|
||||||
|
let height = null;
|
||||||
|
let parent = null;
|
||||||
|
if (lineIds.length > 0) {
|
||||||
|
//compute the position of the single el (top and bottom and make a div)
|
||||||
|
const el = window.document.getElementById(lineIds[0]);
|
||||||
|
top = el.offsetTop;
|
||||||
|
height = el.offsetHeight;
|
||||||
|
parent = el.parentElement.parentElement;
|
||||||
|
if (lineIds.length > 1) {
|
||||||
|
const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
|
||||||
|
const bottom = lastEl.offsetTop + lastEl.offsetHeight;
|
||||||
|
height = bottom - top;
|
||||||
|
}
|
||||||
|
if (top !== null && height !== null && parent !== null) {
|
||||||
|
// cook up a div (if necessary) and position it
|
||||||
|
let div = window.document.getElementById("code-annotation-line-highlight");
|
||||||
|
if (div === null) {
|
||||||
|
div = window.document.createElement("div");
|
||||||
|
div.setAttribute("id", "code-annotation-line-highlight");
|
||||||
|
div.style.position = 'absolute';
|
||||||
|
parent.appendChild(div);
|
||||||
|
}
|
||||||
|
div.style.top = top - 2 + "px";
|
||||||
|
div.style.height = height + 4 + "px";
|
||||||
|
div.style.left = 0;
|
||||||
|
let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
|
||||||
|
if (gutterDiv === null) {
|
||||||
|
gutterDiv = window.document.createElement("div");
|
||||||
|
gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
|
||||||
|
gutterDiv.style.position = 'absolute';
|
||||||
|
const codeCell = window.document.getElementById(targetCell);
|
||||||
|
const gutter = codeCell.querySelector('.code-annotation-gutter');
|
||||||
|
gutter.appendChild(gutterDiv);
|
||||||
|
}
|
||||||
|
gutterDiv.style.top = top - 2 + "px";
|
||||||
|
gutterDiv.style.height = height + 4 + "px";
|
||||||
|
}
|
||||||
|
selectedAnnoteEl = annoteEl;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
const unselectCodeLines = () => {
|
||||||
|
const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
|
||||||
|
elementsIds.forEach((elId) => {
|
||||||
|
const div = window.document.getElementById(elId);
|
||||||
|
if (div) {
|
||||||
|
div.remove();
|
||||||
|
}
|
||||||
|
});
|
||||||
|
selectedAnnoteEl = undefined;
|
||||||
|
};
|
||||||
|
// Handle positioning of the toggle
|
||||||
|
window.addEventListener(
|
||||||
|
"resize",
|
||||||
|
throttle(() => {
|
||||||
|
elRect = undefined;
|
||||||
|
if (selectedAnnoteEl) {
|
||||||
|
selectCodeLines(selectedAnnoteEl);
|
||||||
|
}
|
||||||
|
}, 10)
|
||||||
|
);
|
||||||
|
function throttle(fn, ms) {
|
||||||
|
let throttle = false;
|
||||||
|
let timer;
|
||||||
|
return (...args) => {
|
||||||
|
if(!throttle) { // first call gets through
|
||||||
|
fn.apply(this, args);
|
||||||
|
throttle = true;
|
||||||
|
} else { // all the others get throttled
|
||||||
|
if(timer) clearTimeout(timer); // cancel #2
|
||||||
|
timer = setTimeout(() => {
|
||||||
|
fn.apply(this, args);
|
||||||
|
timer = throttle = false;
|
||||||
|
}, ms);
|
||||||
|
}
|
||||||
|
};
|
||||||
|
}
|
||||||
|
// Attach click handler to the DT
|
||||||
|
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
|
||||||
|
for (const annoteDlNode of annoteDls) {
|
||||||
|
annoteDlNode.addEventListener('click', (event) => {
|
||||||
|
const clickedEl = event.target;
|
||||||
|
if (clickedEl !== selectedAnnoteEl) {
|
||||||
|
unselectCodeLines();
|
||||||
|
const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
|
||||||
|
if (activeEl) {
|
||||||
|
activeEl.classList.remove('code-annotation-active');
|
||||||
|
}
|
||||||
|
selectCodeLines(clickedEl);
|
||||||
|
clickedEl.classList.add('code-annotation-active');
|
||||||
|
} else {
|
||||||
|
// Unselect the line
|
||||||
|
unselectCodeLines();
|
||||||
|
clickedEl.classList.remove('code-annotation-active');
|
||||||
|
}
|
||||||
|
});
|
||||||
|
}
|
||||||
|
const findCites = (el) => {
|
||||||
|
const parentEl = el.parentElement;
|
||||||
|
if (parentEl) {
|
||||||
|
const cites = parentEl.dataset.cites;
|
||||||
|
if (cites) {
|
||||||
|
return {
|
||||||
|
el,
|
||||||
|
cites: cites.split(' ')
|
||||||
|
};
|
||||||
|
} else {
|
||||||
|
return findCites(el.parentElement)
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
return undefined;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
|
||||||
|
for (var i=0; i<bibliorefs.length; i++) {
|
||||||
|
const ref = bibliorefs[i];
|
||||||
|
const citeInfo = findCites(ref);
|
||||||
|
if (citeInfo) {
|
||||||
|
tippyHover(citeInfo.el, function() {
|
||||||
|
var popup = window.document.createElement('div');
|
||||||
|
citeInfo.cites.forEach(function(cite) {
|
||||||
|
var citeDiv = window.document.createElement('div');
|
||||||
|
citeDiv.classList.add('hanging-indent');
|
||||||
|
citeDiv.classList.add('csl-entry');
|
||||||
|
var biblioDiv = window.document.getElementById('ref-' + cite);
|
||||||
|
if (biblioDiv) {
|
||||||
|
citeDiv.innerHTML = biblioDiv.innerHTML;
|
||||||
|
}
|
||||||
|
popup.appendChild(citeDiv);
|
||||||
|
});
|
||||||
|
return popup.innerHTML;
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
});
|
||||||
|
</script>
|
||||||
|
</div> <!-- /content -->
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
</body></html>
|
||||||
Loading…
Add table
Reference in a new issue