Modules#

Summary#

module

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__init__

module mlstatpy Module mlstatpy. Functions and examples associated to the content of the :epkg:`mlstatpy`. source on GitHub

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module mlstatpy.data shortcut to data source on GitHub

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module mlstatpy.garden shortcut to garden source on GitHub

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module mlstatpy.graph shortcut to graph source on GitHub

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module mlstatpy.image shortcut to image source on GitHub

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module mlstatpy.image.detection_segment shortcut to image source on GitHub

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module mlstatpy.ml shortcut to ml source on GitHub

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module mlstatpy.nlp shortcut to nlp source on GitHub

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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 B(n,p). 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