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[MRG + 1] Use immutable default arguments throughtout repo #4409

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Merged
merged 1 commit into from
Mar 20, 2015
Merged

[MRG + 1] Use immutable default arguments throughtout repo #4409

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4 changes: 2 additions & 2 deletions examples/applications/plot_species_distribution_modeling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,8 +87,8 @@ def create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):
return bunch


def plot_species_distribution(species=["bradypus_variegatus_0",
"microryzomys_minutus_0"]):
def plot_species_distribution(species=("bradypus_variegatus_0",
"microryzomys_minutus_0")):
"""
Plot the species distribution.
"""
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5 changes: 4 additions & 1 deletion sklearn/manifold/t_sne.py
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Expand Up @@ -114,7 +114,7 @@ def _kl_divergence(params, P, alpha, n_samples, n_components):
def _gradient_descent(objective, p0, it, n_iter, n_iter_without_progress=30,
momentum=0.5, learning_rate=1000.0, min_gain=0.01,
min_grad_norm=1e-7, min_error_diff=1e-7, verbose=0,
args=[]):
args=None):
"""Batch gradient descent with momentum and individual gains.

Parameters
Expand Down Expand Up @@ -173,6 +173,9 @@ def _gradient_descent(objective, p0, it, n_iter, n_iter_without_progress=30,
i : int
Last iteration.
"""
if args is None:
args = []

p = p0.copy().ravel()
update = np.zeros_like(p)
gains = np.ones_like(p)
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5 changes: 4 additions & 1 deletion sklearn/metrics/pairwise.py
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Expand Up @@ -346,7 +346,7 @@ def pairwise_distances_argmin_min(X, Y, axis=1, metric="euclidean",


def pairwise_distances_argmin(X, Y, axis=1, metric="euclidean",
batch_size=500, metric_kwargs={}):
batch_size=500, metric_kwargs=None):
"""Compute minimum distances between one point and a set of points.

This function computes for each row in X, the index of the row of Y which
Expand Down Expand Up @@ -419,6 +419,9 @@ def pairwise_distances_argmin(X, Y, axis=1, metric="euclidean",
sklearn.metrics.pairwise_distances
sklearn.metrics.pairwise_distances_argmin_min
"""
if metric_kwargs is None:
metric_kwargs = {}

return pairwise_distances_argmin_min(X, Y, axis, metric, batch_size,
metric_kwargs)[0]

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