8000 MAINT Parameters validation for sklearn.metrics.pairwise.rbf_kernel (… · scikit-learn/scikit-learn@d431aa6 · GitHub
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MAINT Parameters validation for sklearn.metrics.pairwise.rbf_kernel (#26071)
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
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sklearn/metrics/pairwise.py

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@@ -1327,6 +1327,17 @@ def sigmoid_kernel(X, Y=None, gamma=None, coef0=1):
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return K
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@validate_params(
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{
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"X": ["array-like", "sparse matrix"],
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"Y": ["array-like", "sparse matrix", None],
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"gamma": [
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Interval(Real, 0, None, closed="left"),
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None,
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Hidden(np.ndarray),
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],
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}
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)
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def rbf_kernel(X, Y=None, gamma=None):
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"""Compute the rbf (gaussian) kernel between X and Y.
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@@ -1338,10 +1349,10 @@ def rbf_kernel(X, Y=None, gamma=None):
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Parameters
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----------
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X : ndarray of shape (n_samples_X, n_features)
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X : {array-like, sparse matrix} of shape (n_samples_X, n_features)
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A feature array.
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Y : ndarray of shape (n_samples_Y, n_features), default=None
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Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None
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An optional second feature array. If `None`, uses `Y=X`.
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gamma : float, default=None

sklearn/tests/test_public_functions.py

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@@ -216,6 +216,7 @@ def _check_function_param_validation(
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"sklearn.metrics.pairwise.paired_euclidean_distances",
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"sklearn.metrics.pairwise.paired_manhattan_distances",
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"sklearn.metrics.pairwise.polynomial_kernel",
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"sklearn.metrics.pairwise.rbf_kernel",
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"sklearn.metrics.precision_recall_curve",
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"sklearn.metrics.precision_recall_fscore_support",
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"sklearn.metrics.precision_score",

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