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https://github.com/Doctorado-ML/STree.git
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Adapt some notebooks
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@@ -152,11 +152,6 @@ class Stree(BaseEstimator, ClassifierMixin):
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# doesn't work with multiclass as each sample has to do inner product with its own coeficients
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# computes positition of every sample is w.r.t. the hyperplane
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res = self._linear_function(data, node)
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# data_up, data_down = self._split_array(data, down)
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# indices_up, indices_down = self._split_array(indices, down)
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# res_up, res_down = self._split_array(res, down)
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# weight_up, weight_down = self._split_array(weights, down)
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#return [data_up, indices_up, data_down, indices_down, weight_up, weight_down, res_up, res_down]
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return res
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def _split_criteria(self, data: np.array) -> np.array:
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@@ -176,7 +171,6 @@ class Stree(BaseEstimator, ClassifierMixin):
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sample_weight = _check_sample_weight(sample_weight, X)
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check_classification_targets(y)
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# Initialize computed parameters
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#self.random_state = check_random_state(self.random_state)
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self.classes_ = np.unique(y)
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self.n_iter_ = self.max_iter
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self.depth_ = 0
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@@ -316,8 +310,7 @@ class Stree(BaseEstimator, ClassifierMixin):
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# sklearn check
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check_is_fitted(self)
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yp = self.predict(X).reshape(y.shape)
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right = (yp == y).astype(int)
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return np.sum(right) / len(y)
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return np.mean(yp == y)
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def __iter__(self) -> Siterator:
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try:
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