本文整理汇总了Python中numpy.linalg.linalg._multi_dot_matrix_chain_order方法的典型用法代码示例。如果您正苦于以下问题：Python linalg._multi_dot_matrix_chain_order方法的具体用法？Python linalg._multi_dot_matrix_chain_order怎么用？Python linalg._multi_dot_matrix_chain_order使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类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个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_dynamic_programming_logic
# 需要导入模块: from numpy.linalg import linalg [as 别名]
# 或者: from numpy.linalg.linalg import _multi_dot_matrix_chain_order [as 别名]
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
# 需要导入模块: from numpy.linalg import linalg [as 别名]
# 或者: from numpy.linalg.linalg import _multi_dot_matrix_chain_order [as 别名]
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))