__init__ |
module mlstatpy Module mlstatpy. Functions and examples associated to the content of the :epkg:`mlstatpy`. source on GitHub |
__init__ |
module mlstatpy.data shortcut to data source on GitHub |
__init__ |
module mlstatpy.garden shortcut to garden source on GitHub |
__init__ |
module mlstatpy.graph shortcut to graph source on GitHub |
__init__ |
module mlstatpy.image shortcut to image source on GitHub |
__init__ |
module mlstatpy.image.detection_segment shortcut to image source on GitHub |
__init__ |
module mlstatpy.ml shortcut to ml source on GitHub |
__init__ |
module mlstatpy.nlp shortcut to nlp source on GitHub |
__init__ |
module mlstatpy.optim Shortcuts to optim. source on GitHub |
_neural_tree_api |
module mlstatpy.ml._neural_tree_api Conversion from tree to neural network. source on GitHub |
_neural_tree_node |
module mlstatpy.ml._neural_tree_node Conversion from tree to neural network. source on GitHub |
completion |
module mlstatpy.nlp.completion About completion source on GitHub |
completion_simple |
module mlstatpy.nlp.completion_simple About completion, simple algorithm source on GitHub |
data_exceptions |
module mlstatpy.data.data_exceptions Exceptions while retrieving data. source on GitHub |
detection_nfa |
module mlstatpy.image.detection_segment.detection_nfa Ce module determine si un segment est significatif, c’est à dire si le nombre de fausses alarmes n’est pas trop élevé. source on GitHub |
detection_segment |
module mlstatpy.image.detection_segment.detection_segment Détecte les segments dans une image. source on GitHub |
detection_segment_bord |
module mlstatpy.image.detection_segment.detection_segment_bord Ce module définit un segment qui va parcourir l’image, en plus d’être un segment, cette classe inclut la dimension de l’image, et une fonction repérant sur ce segment les gradients presque orthogonaux à l’image. source on GitHub |
detection_segment_segangle |
module mlstatpy.image.detection_segment.detection_segment_segangle Ce module inclut une classe qui permet de parcourir tous les segments de l’image. source on GitHub |
geometrie |
module mlstatpy.image.detection_segment.geometrie Définition de petits éléments géométriques tels que les points et les segments, implemente également des opérations standard telles le produit scalaire entre deux vecteurs, … source on GitHub |
graph_distance |
module mlstatpy.graph.graph_distance First approach for a edit distance between two graphs. See Distance between two graphs. source on GitHub |
graphviz_helper |
module mlstatpy.graph.graphviz_helper graphviz helper source on GitHub |
kppv |
module mlstatpy.ml.kppv Implements classic k-nn. source on GitHub |
kppv_laesa |
module mlstatpy.ml.kppv_laesa Implements optimized k-nn. source on GitHub |
logreg |
module mlstatpy.ml.logreg Helpers on logistic regression. source on GitHub |
matrices |
module mlstatpy.ml.matrices Algorithms about matrices. source on GitHub |
ml_grid_benchmark |
module mlstatpy.ml.ml_grid_benchmark About Machine Learning Benchmark source on GitHub |
neural_tree |
module mlstatpy.ml.neural_tree Conversion from tree to neural network. source on GitHub |
normalize |
module mlstatpy.nlp.normalize Text normalization source on GitHub |
poulet |
module mlstatpy.garden.poulet Functions for Optimisation avec données aléatoires. source on GitHub |
queue_binom |
module mlstatpy.image.detection_segment.queue_binom Ce module construit les probabilités d’une loi binomiale . source on GitHub |
random_image |
module mlstatpy.image.detection_segment.random_image Génère des images aléatoires. source on GitHub |
roc |
module mlstatpy.ml.roc About ROC. source on GitHub |
sgd |
module mlstatpy.optim.sgd Implements simple stochastic gradient optimisation. It is inspired from _stochastic_optimizers.py. source on GitHub |
voronoi |
module mlstatpy.ml.voronoi About Voronoi Diagram source on GitHub |
wikipedia |
module mlstatpy.data.wikipedia Functions to retrieve data from Wikipedia source on GitHub |