本文整理匯總了Python中numpy.linalg.linalg._multi_dot_matrix_chain_order方法的典型用法代碼示例。如果您正苦於以下問題：Python linalg._multi_dot_matrix_chain_order方法的具體用法？Python linalg._multi_dot_matrix_chain_order怎麽用？Python linalg._multi_dot_matrix_chain_order使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類numpy.linalg.linalg的用法示例。
在下文中一共展示了linalg._multi_dot_matrix_chain_order方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_dynamic_programming_logic
# 需要導入模塊: from numpy.linalg import linalg [as 別名]
# 或者: from numpy.linalg.linalg import _multi_dot_matrix_chain_order [as 別名]
def test_dynamic_programming_logic(self):
# Test for the dynamic programming part
# This test is directly taken from Cormen page 376.
arrays = [np.random.random((30, 35)),
np.random.random((35, 15)),
np.random.random((15, 5)),
np.random.random((5, 10)),
np.random.random((10, 20)),
np.random.random((20, 25))]
m_expected = np.array([[0., 15750., 7875., 9375., 11875., 15125.],
[0., 0., 2625., 4375., 7125., 10500.],
[0., 0., 0., 750., 2500., 5375.],
[0., 0., 0., 0., 1000., 3500.],
[0., 0., 0., 0., 0., 5000.],
[0., 0., 0., 0., 0., 0.]])
s_expected = np.array([[0, 1, 1, 3, 3, 3],
[0, 0, 2, 3, 3, 3],
[0, 0, 0, 3, 3, 3],
[0, 0, 0, 0, 4, 5],
[0, 0, 0, 0, 0, 5],
[0, 0, 0, 0, 0, 0]], dtype=int)
s_expected -= 1 # Cormen uses 1-based index, python does not.
s, m = _multi_dot_matrix_chain_order(arrays, return_costs=True)
# Only the upper triangular part (without the diagonal) is interesting.
assert_almost_equal(np.triu(s[:-1, 1:]),
np.triu(s_expected[:-1, 1:]))
assert_almost_equal(np.triu(m), np.triu(m_expected)) 示例2: test_dynamic_programming_logic
# 需要導入模塊: from numpy.linalg import linalg [as 別名]
# 或者: from numpy.linalg.linalg import _multi_dot_matrix_chain_order [as 別名]
def test_dynamic_programming_logic(self):
# Test for the dynamic programming part
# This test is directly taken from Cormen page 376.
arrays = [np.random.random((30, 35)),
np.random.random((35, 15)),
np.random.random((15, 5)),
np.random.random((5, 10)),
np.random.random((10, 20)),
np.random.random((20, 25))]
m_expected = np.array([[0., 15750., 7875., 9375., 11875., 15125.],
[0., 0., 2625., 4375., 7125., 10500.],
[0., 0., 0., 750., 2500., 5375.],
[0., 0., 0., 0., 1000., 3500.],
[0., 0., 0., 0., 0., 5000.],
[0., 0., 0., 0., 0., 0.]])
s_expected = np.array([[0, 1, 1, 3, 3, 3],
[0, 0, 2, 3, 3, 3],
[0, 0, 0, 3, 3, 3],
[0, 0, 0, 0, 4, 5],
[0, 0, 0, 0, 0, 5],
[0, 0, 0, 0, 0, 0]], dtype=np.int)
s_expected -= 1 # Cormen uses 1-based index, python does not.
s, m = _multi_dot_matrix_chain_order(arrays, return_costs=True)
# Only the upper triangular part (without the diagonal) is interesting.
assert_almost_equal(np.triu(s[:-1, 1:]),
np.triu(s_expected[:-1, 1:]))
assert_almost_equal(np.triu(m), np.triu(m_expected))