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@@ -6,26 +6,21 @@ from sklearn.utils.validation import has_fit_parameter, validate_data
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class FreqaiMultiOutputRegressor(MultiOutputRegressor):
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def fit(self, X, y, sample_weight=None, fit_params=None):
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"""Fit the model to data, separately for each output variable.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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:param X: {array-like, sparse matrix} of shape (n_samples, n_features)
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The input data.
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y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
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:param y: {array-like, sparse matrix} of shape (n_samples, n_outputs)
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Multi-output targets. An indicator matrix turns on multilabel
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estimation.
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sample_weight : array-like of shape (n_samples,), default=None
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:param sample_weight: array-like of shape (n_samples,), default=None
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Sample weights. If `None`, then samples are equally weighted.
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Only supported if the underlying regressor supports sample
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weights.
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fit_params : A list of dicts for the fit_params
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:param fit_params: A list of dicts for the fit_params
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Parameters passed to the ``estimator.fit`` method of each step.
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Each dict may contain same or different values (e.g. different
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eval_sets or init_models)
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.. versionadded:: 0.23
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Returns
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-------
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self : object
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Returns a fitted instance.
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"""
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if not hasattr(self.estimator, "fit"):
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