8000 Add random_state parameter to AffinityPropagation by rcwoolston · Pull Request #14337 · scikit-learn/scikit-learn · GitHub
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Add random_state parameter to AffinityPropagation #14337

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Add random_state parameter to AffinityPropagation #14337

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5168c91
Added value checks and random state parameter to method
Jul 13, 2019
ff35479
Changed default parameter to None instead of 0
Jul 13, 2019
3760ea3
Added numpy RandomState to the check
Jul 13, 2019
c3d42af
Replaced inline validation with check_random_state from utils and poi…
Jul 14, 2019
a06bb8a
Needed a different default parameter to pass the default way this has…
Jul 14, 2019
12c8a38
Updated to conform with flake8 stds
Jul 14, 2019
4e7fded
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn…
Jul 14, 2019
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10 changes: 7 additions & 3 deletions sklearn/cluster/affinity_propagation_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@

from ..exceptions import ConvergenceWarning
from ..base import BaseEstimator, ClusterMixin
from ..utils import as_float_array, check_array
from ..utils import as_float_array, check_array, check_random_state
from ..utils.validation import check_is_fitted
from ..metrics import euclidean_distances
from ..metrics import pairwise_distances_argmin
Expand All @@ -32,7 +32,7 @@ def all_equal_similarities():

def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
damping=0.5, copy=True, verbose=False,
return_n_iter=False):
return_n_iter=False, random_state=0):
"""Perform Affinity Propagation Clustering of data

Read more in the :ref:`User Guide <affinity_propagation>`.
Expand Down Expand Up @@ -72,6 +72,10 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
return_n_iter : bool, default False
Whether or not to return the number of iterations.

random_state : int, np.random.RandomStateInstance or None, default: None
Pseudo-random number generator to control the starting state.
See :term:`random_state`.

Returns
-------

Expand Down Expand Up @@ -133,7 +137,7 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
if return_n_iter
else (np.array([0]), np.array([0] * n_samples)))

random_state = np.random.RandomState(0)
random_state = check_random_state(random_state)

# Place preference on the diagonal of S
S.flat[::(n_samples + 1)] = preference
Expand Down
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