From ea5d8e8f77d1dfec37ce404881e11d54c959e7ec Mon Sep 17 00:00:00 2001
From: Jake Vanderplas <vanderplas@astro.washington.edu>
Date: Wed, 21 Dec 2011 00:31:56 -0800
Subject: [PATCH] randomized_power_iteration -> randomized_range_finder
---
doc/developers/utilities.rst | 2 +-
sklearn/utils/extmath.py | 4 ++--
2 files changed, 3 insertions(+), 3 deletions(-)
diff --git a/doc/developers/utilities.rst b/doc/developers/utilities.rst
index 05325badd1..466e821647 100644
--- a/doc/developers/utilities.rst
+++ b/doc/developers/utilities.rst
@@ -72,7 +72,7 @@ For example:
Efficient Linear Algebra & Array Operations
===========================================
-- :func:`extmath.randomized_power_iteration`: construct an orthonormal matrix
+- :func:`extmath.randomized_range_finder`: construct an orthonormal matrix
whose range approximates the range of the input. This is used in
:func:`extmath.fast_svd`, below.
diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py
index b6cf041424..b944791b03 100644
--- a/sklearn/utils/extmath.py
+++ b/sklearn/utils/extmath.py
@@ -79,7 +79,7 @@ def safe_sparse_dot(a, b, dense_output=False):
return np.dot(a, b)
-def randomized_power_iteration(A, size, n_iterations, random_state=None):
+def randomized_range_finder(A, size, n_iterations, random_state=None):
"""Computes an orthonormal matrix whose range approximates the range of A.
Parameters
@@ -187,7 +187,7 @@ def fast_svd(M, k, p=None, n_iterations=0, transpose='auto', random_state=0):
# this implementation is a bit faster with smaller shape[1]
M = M.T
- Q = randomized_power_iteration(M, k+p, n_iterations, random_state)
+ Q = randomized_range_finder(M, k+p, n_iterations, random_state)
# project M to the (k + p) dimensional space using the basis vectors
B = safe_sparse_dot(Q.T, M)
--
GitLab