def fit(self, X, y=None, **kwargs):
"""If hyper parameters are set to None, then instance's variable is used,
this functionality is used Grid search with `set_params` method.
Also if instance's variable is not set, _default_hyperparam is used.
Usage: model.fit(train_dataset) or model.fit(X, y)
Args:
train: training dataset, assumes chainer's dataset class
test: test dataset for evaluation, assumes chainer's dataset class
batchsize: batchsize for both training and evaluation
iterator_class: iterator class used for this training,
currently assumes SerialIterator or MultiProcessIterator
optimizer: optimizer instance to update parameter
epoch: training epoch
out: directory path to save the result
snapshot_frequency (int): snapshot frequency in epoch.
Negative value indicates not to take snapshot.
dump_graph: Save computational graph info or not, default is False.
log_report: Enable LogReport or not
plot_report: Enable PlotReport or not
print_report: Enable PrintReport or not
progress_report: Enable ProgressReport or not
resume: specify trainer saved path to resume training.
"""
kwargs = self.filter_sk_params(self.fit_core, kwargs)
return self.fit_core(X, y, **kwargs)
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