def test_custom_tolerance_broadcasts(self):
q = linalg.qr(random_ops.random_uniform([3, 3], dtype=self.dtype))[0]
e = constant_op.constant([0.1, 0.2, 0.3], dtype=self.dtype)
a = linalg.solve(q, linalg.transpose(a=e * q), adjoint=True)
self.assertAllEqual([3, 2, 1, 0],
self.evaluate(
linalg.matrix_rank(a,
tol=[[0.09], [0.19], [0.29],
[0.31]])))
def test_nonsquare(self):
x_ = np.array(
[
[
[2, 3, -2, 2], # = row2+row3
[-1, 1, -2, 4],
[3, 2, 0, -2]
],
[
[0, 2, 0, 6], # = 2*row2
[0, 1, 0, 3],
[0, 3, 0, 9]
]
], # = 3*row2
self.dtype)
x = array_ops.placeholder_with_default(
x_, shape=x_.shape if self.use_static_shape else None)
self.assertAllEqual([2, 1], self.evaluate(linalg.matrix_rank(x)))
# In[234]: a1 | b1 # In[237]: linalg.inv(e) # In[253]: linalg.pinv(e) # In[238]: linalg.matrix_rank(e) # In[249]: a = array([[1, 5], [2, 2]]) b = array([[3], [5]]) linalg.solve(a, b) # In[257]: U, S, Vh = linalg.svd(a) V = Vh.T print(U, S, Vh, V) # In[272]:
def matrix_rank(mat):
return linalg.matrix_rank(mat)
