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VGG19Cifar10CovAbs.py
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Luc Giffon authored
test architecture follows main architecture load npz and save npz supported by Dataset class
Luc Giffon authoredtest architecture follows main architecture load npz and save npz supported by Dataset class
sgd.py 1.33 KiB
from sklearn.linear_model import SGDClassifier
from ..monoview.monoview_utils import BaseMonoviewClassifier
from summit.multiview_platform.utils.hyper_parameter_search import CustomUniform
# Author-Info
__author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
classifier_class_name = "SGD"
class SGD(SGDClassifier, BaseMonoviewClassifier):
"""
This class is an adaptation of scikit-learn's `SGDClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html>`_
"""
def __init__(self, random_state=None, loss='hinge',
penalty='l2', alpha=0.0001, max_iter=5, tol=None, **kwargs):
SGDClassifier.__init__(self,
loss=loss,
penalty=penalty,
alpha=alpha,
max_iter=max_iter,
tol=tol,
random_state=random_state
)
self.param_names = ["loss", "penalty", "alpha", "random_state"]
self.classed_params = []
self.distribs = [['log', 'modified_huber'],
["l1", "l2", "elasticnet"],
CustomUniform(loc=0, state=1), [random_state]]
self.weird_strings = {}