def test_dot_scalar_and_matrix_of_objects():
# Ticket #2469
# 2018-04-29: moved here from core.tests.test_multiarray
arr = np.matrix([1, 2], dtype=object)
desired = np.matrix([[3, 6]], dtype=object)
assert_equal(np.dot(arr, 3), desired)
assert_equal(np.dot(3, arr), desired)
def test_pow(self):
"""Test raising a matrix to an integer power works as expected."""
m = matrix("1. 2.; 3. 4.")
m2 = m.copy()
m2 **= 2
mi = m.copy()
mi **= -1
m4 = m2.copy()
m4 **= 2
assert_array_almost_equal(m2, m**2)
assert_array_almost_equal(m4, np.dot(m2, m2))
assert_array_almost_equal(np.dot(mi, m), np.eye(2))
def test_svd_build(self):
# Ticket 627.
a = array([[0., 1.], [1., 1.], [2., 1.], [3., 1.]])
m, n = a.shape
u, s, vh = linalg.svd(a)
b = dot(transpose(u[:, n:]), a)
assert_array_almost_equal(b, np.zeros((2, 2)))
def test_lagvander3d(self):
# also tests lagval3d for non-square coefficient array
x1, x2, x3 = self.x
c = np.random.random((2, 3, 4))
van = lag.lagvander3d(x1, x2, x3, [1, 2, 3])
tgt = lag.lagval3d(x1, x2, x3, c)
res = np.dot(van, c.flat)
assert_almost_equal(res, tgt)
# check shape
van = lag.lagvander3d([x1], [x2], [x3], [1, 2, 3])
assert_(van.shape == (1, 5, 24))
def test_chebvander2d(self):
# also tests chebval2d for non-square coefficient array
x1, x2, x3 = self.x
c = np.random.random((2, 3))
van = cheb.chebvander2d(x1, x2, [1, 2])
tgt = cheb.chebval2d(x1, x2, c)
res = np.dot(van, c.flat)
assert_almost_equal(res, tgt)
# check shape
van = cheb.chebvander2d([x1], [x2], [1, 2])
assert_(van.shape == (1, 5, 6))
def test_100(self):
x, w = lag.laggauss(100)
# test orthogonality. Note that the results need to be normalized,
# otherwise the huge values that can arise from fast growing
# functions like Laguerre can be very confusing.
v = lag.lagvander(x, 99)
vv = np.dot(v.T * w, v)
vd = 1 / np.sqrt(vv.diagonal())
vv = vd[:, None] * vv * vd
assert_almost_equal(vv, np.eye(100))
# check that the integral of 1 is correct
tgt = 1.0
assert_almost_equal(w.sum(), tgt)
def test_basic(self):
import numpy1.linalg as linalg
A = np.array([[1., 2.], [3., 4.]])
mA = matrix(A)
B = np.identity(2)
for i in range(6):
assert_(np.allclose((mA**i).A, B))
B = np.dot(B, A)
Ainv = linalg.inv(A)
B = np.identity(2)
for i in range(6):
assert_(np.allclose((mA**-i).A, B))
B = np.dot(B, Ainv)
assert_(np.allclose((mA * mA).A, np.dot(A, A)))
assert_(np.allclose((mA + mA).A, (A + A)))
assert_(np.allclose((3 * mA).A, (3 * A)))
mA2 = matrix(A)
mA2 *= 3
assert_(np.allclose(mA2.A, 3 * A))
def test_half_funcs(self):
"""Test the various ArrFuncs"""
# fill
assert_equal(np.arange(10, dtype=float16), np.arange(10,
dtype=float32))
# fillwithscalar
a = np.zeros((5, ), dtype=float16)
a.fill(1)
assert_equal(a, np.ones((5, ), dtype=float16))
# nonzero and copyswap
a = np.array([0, 0, -1, -1 / 1e20, 0, 2.0**-24, 7.629e-6],
dtype=float16)
assert_equal(a.nonzero()[0], [2, 5, 6])
a = a.byteswap().newbyteorder()
assert_equal(a.nonzero()[0], [2, 5, 6])
# dot
a = np.arange(0, 10, 0.5, dtype=float16)
b = np.ones((20, ), dtype=float16)
assert_equal(np.dot(a, b), 95)
# argmax
a = np.array([0, -np.inf, -2, 0.5, 12.55, 7.3, 2.1, 12.4],
dtype=float16)
assert_equal(a.argmax(), 4)
a = np.array([0, -np.inf, -2, np.inf, 12.55, np.nan, 2.1, 12.4],
dtype=float16)
assert_equal(a.argmax(), 5)
# getitem
a = np.arange(10, dtype=float16)
for i in range(10):
assert_equal(a.item(i), i)