regressions in 0.13-git

regressions.txt

======================================================================
FAIL: Check that oob prediction is as acurate as
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Traceback (most recent call last):
  File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
    self.test(*self.arg)
  File "/home/andy/sklearn_tests/sklearn/ensemble/tests/test_forest.py", line 198, in test_oob_score_classification
    assert_almost_equal(training_score, clf.oob_score_)
  File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 468, in assert_almost_equal
    raise AssertionError(msg)
AssertionError: 
Arrays are not almost equal to 7 decimals
 ACTUAL: 1.0
 DESIRED: 0.66666666666666663


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ERROR: Assure that estimators receiving multidimensional y do the right thing
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Traceback (most recent call last):
  File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
    self.test(*self.arg)
  File "/home/andy/sklearn_tests/sklearn/linear_model/tests/test_least_angle.py", line 267, in test_multitarget
    assert_array_almost_equal(alphas[k, :], estimator.alphas_)
TypeError: list indices must be integers, not tuple

======================================================================
FAIL: Test when input is a singular matrix
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Traceback (most recent call last):
  File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
    self.test(*self.arg)
  File "/home/andy/sklearn_tests/sklearn/linear_model/tests/test_least_angle.py", line 162, in test_singular_matrix
    assert_array_almost_equal(coef_path.T, [[0, 0], [1, 0], [1, 0]])
  File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 800, in assert_array_almost_equal
    header=('Arrays are not almost equal to %d decimals' % decimal))
  File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 600, in assert_array_compare
    raise AssertionError(msg)
AssertionError: 
Arrays are not almost equal to 6 decimals

(shapes (2, 2), (3, 2) mismatch)
 x: array([[ 0.,  0.],
       [ 1.,  0.]])
 y: array([[0, 0],
       [1, 0],
       [1, 0]])