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skglm/estimators.py

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@@ -102,11 +102,6 @@ def _glm_fit(X, y, model, datafit, penalty, solver):
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n_samples, n_features = X_.shape
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# if issparse(X):
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# datafit.initialize_sparse(X_.data, X_.indptr, X_.indices, y)
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# else:
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# datafit.initialize(X_, y)
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# if model.warm_start and hasattr(model, 'coef_') and model.coef_ is not None:
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if solver.warm_start and hasattr(model, 'coef_') and model.coef_ is not None:
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if isinstance(datafit, QuadraticSVC):

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