def test():
numpyX = np.array([1, 2, 3])
numpyY = np.array([4, 5, 6])
x = lg.array(numpyX)
y = lg.array(numpyY)
z = x + y
# # print(z)
assert np.array_equal(z, numpyX + numpyY)
z = x + 2
# print(z)
assert np.array_equal(z, numpyX + 2)
z = 2 + x
# print(z)
assert np.array_equal(z, 2 + numpyX)
z = x - y
# print(z)
assert np.array_equal(z, numpyX - numpyY)
z = x - 2
# print(z)
assert np.array_equal(z, numpyX - 2)
z = 2 - x
# print(z)
assert np.array_equal(z, 2 - numpyX)
z = x / y
# print(z)
assert np.array_equal(z, numpyX / numpyY)
z = x / 2
# print(z)
assert np.array_equal(z, numpyX / 2)
z = 2 / x
# print(z)
assert np.array_equal(z, 2 / numpyX)
z = x * y
# print(z)
assert np.array_equal(z, numpyX * numpyY)
z = x * 2
# print(z)
assert np.array_equal(z, numpyX * 2)
z = 2 * x
# print(z)
assert np.array_equal(z, 2 * numpyX)
z = x**5
# print(z)
assert np.array_equal(z, numpyX**5)
return
def test():
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a_lg = lg.array(a)
b_lg = a_lg.swapaxes(0, 1)
print("small")
assert lg.array_equal(a_lg.sum(axis=0), b_lg.sum(axis=1))
a_tall = np.concatenate((a, ) * 100)
a_tall_lg = lg.array(a_tall)
b_tall_lg = a_tall_lg.swapaxes(0, 1)
print("tall")
assert lg.array_equal(a_tall_lg.sum(axis=0), b_tall_lg.sum(axis=1))
a_wide = np.concatenate((a, ) * 100, axis=1)
a_wide_lg = lg.array(a_wide)
b_wide_lg = a_wide_lg.swapaxes(0, 1)
print("wide")
assert lg.array_equal(a_wide_lg.sum(axis=0), b_wide_lg.sum(axis=1))
a_big = np.concatenate((a_tall, ) * 100, axis=1)
a_big_lg = lg.array(a_big)
b_big_lg = a_big_lg.swapaxes(0, 1)
print("big")
assert lg.array_equal(a_big_lg.sum(axis=0), b_big_lg.sum(axis=1))
def test():
a = lg.array([[1, 2, 3, 4, 5, 6], [4, 5, 6, 7, 8, 9]], dtype=np.float64)
b = lg.array(
[[10, 11], [12, 13], [14, 15], [16, 17], [18, 19], [20, 21]],
dtype=np.float64,
)
c = a.dot(b)
assert lg.array_equal(c, [[350, 371], [620, 659]])
d = lg.array([1, 2, 3, 4, 5, 6], dtype=np.float64)
e = lg.array([1, 2, 3, 4, 5, 6], dtype=np.float64)
f = d.dot(e)
assert f == 91
# This test does not work ATM. It seems that setting random seed to
# be the same is not sufficient to make the inputs the same.
# lg.random.seed(42)
# a = lg.random.randn(1, 3, 15)
# b = lg.random.randn(15, 16)
# c = a[0].dot(b)
# np.random.seed(42)
# an = np.random.randn(1, 3, 15)
# bn = np.random.randn(15, 16)
# cn = an[0].dot(bn)
# assert lg.allclose(c, cn)
return
def test():
np.random.seed(42)
An = np.random.randn(3, 7).astype(np.float16)
Bn = np.random.randn(7).astype(np.float16)
Cn = An.dot(Bn)
A = lg.array(An)
B = lg.array(Bn)
C = A.dot(B)
# print(C)
# print(Cn)
assert np.allclose(C, Cn)
np.random.seed(42)
An = np.random.randn(3, 7).astype(np.float16)
Bn = np.random.randn(3).astype(np.float16)
Cn = An.transpose().dot(Bn)
A = lg.array(An)
B = lg.array(Bn)
C = A.transpose().dot(B)
# print(C)
# print(Cn)
assert np.allclose(C, Cn)
return
def test():
x = np.linspace(2.0, 3.0, num=5)
y = lg.linspace(2.0, 3.0, num=5)
assert np.array_equal(x, y)
x = np.linspace(2.0, 3.0, num=5, endpoint=False)
y = lg.linspace(2.0, 3.0, num=5, endpoint=False)
assert np.array_equal(x, y)
x = np.linspace(2.0, 3.0, num=5, retstep=True)
y = np.linspace(2.0, 3.0, num=5, retstep=True)
assert np.array_equal(x[0], y[0])
assert x[1] == y[1]
x = np.array([[0, 1], [2, 3]])
y = np.array([[4, 5], [6, 7]])
xp = lg.array(x)
yp = lg.array(y)
z = np.linspace(x, y, num=5, axis=0)
w = lg.linspace(xp, yp, num=5, axis=0)
assert np.array_equal(z, w)
z = np.linspace(x, y, num=5, axis=1)
w = lg.linspace(xp, yp, num=5, axis=1)
assert np.array_equal(z, w)
z = np.linspace(x, y, num=5, axis=2)
w = lg.linspace(xp, yp, num=5, axis=2)
assert np.array_equal(z, w)
return
def test():
anp = np.random.randn(4, 5)
bnp = np.random.randn(4, 5)
a = lg.array(anp)
b = lg.array(bnp)
assert np.array_equal(lg.divide(a, b), np.divide(anp, bnp))
def test():
a = lg.array([0, 1, 2])
b = lg.tile(a, 4)
assert lg.array_equal(b, [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2])
c = lg.tile(a, (3, 4))
assert lg.array_equal(
c,
[
[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2],
[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2],
[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2],
],
)
d = lg.tile(a, (3, 1, 4))
assert lg.array_equal(
d,
[
[[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]],
[[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]],
[[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]],
],
)
e = lg.array([[1, 2], [3, 4]])
f = lg.tile(e, 2)
assert lg.array_equal(f, [[1, 2, 1, 2], [3, 4, 3, 4]])
g = lg.tile(e, (2, 1))
assert lg.array_equal(g, [[1, 2], [3, 4], [1, 2], [3, 4]])
return
def test():
anp = np.random.randn(4, 5)
bnp = np.random.randn(4, 5)
a = lg.array(anp)
b = lg.array(bnp)
assert np.array_equal(a / b, anp / bnp)
def test():
anp = np.random.randn(4, 5)
bnp = np.random.randn(4, 5)
a = lg.array(anp)
b = lg.array(bnp)
assert np.array_equal(a * b, anp * bnp)
return
def test():
# test data type conversion
x = lg.array([1, 2, 3])
y = lg.array([1.0, 2.0, 3.0])
assert lg.max(x) == lg.max(y)
return
def test():
# test data type conversion
x = lg.array([1])
y = lg.array([1.0])
assert lg.min(x) == lg.min(y)
return
def test():
anp = np.random.randn(4, 5)
bnp = np.random.randn(4, 5)
a = lg.array(anp)
b = lg.array(bnp)
assert np.array_equal(lg.multiply(a, b), np.multiply(anp, bnp))
return
def test():
anp = np.random.randn(4, 5)
bnp = np.random.randn(4, 5)
a = lg.array(anp)
b = lg.array(bnp)
np.true_divide(anp, bnp, out=anp)
lg.true_divide(a, b, out=a)
assert np.array_equal(a, anp)
def test():
x = lg.array([
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
[17, 18, 19, 20],
])
x[0, :] = 0
assert lg.array_equal(x[0, :], [0, 0, 0, 0])
y = lg.array([[20, 30, 40, 50], [70, 80, 90, 100]])
x[1:4:2] = y
assert lg.array_equal(x[1, :], [20, 30, 40, 50])
assert lg.array_equal(x[3, :], [70, 80, 90, 100])
input_size = 10
hidden_size = 4
WLSTM = lg.random.randn(input_size + hidden_size + 1, 4 *
hidden_size) / lg.sqrt(input_size + hidden_size)
WLSTM[0, :] = 0 # initialize biases to zero
assert lg.array_equal(
WLSTM[0, :],
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
)
lg.random.seed(10)
WLSTM = lg.random.randn(15, 16)
dIFOGt = lg.random.randn(3, 16)
dHoutt_in = lg.random.randn(2, 3, 4)
dHoutt = dHoutt_in.copy()
dHint = dIFOGt.dot(WLSTM.transpose())
temp = dHoutt[0, :] + dHint[:, 11:]
dHoutt[0, :] = temp
assert not lg.array_equal(dHoutt[0, :], dHoutt_in[0, :])
return
def test():
np.random.seed(13)
anp = np.random.randn(4, 5)
bnp = np.random.randn(4, 5)
a = lg.array(anp)
b = lg.array(bnp)
anp *= bnp
a *= b
assert np.array_equal(a, anp)
def test():
bases_np = np.random.randn(4, 5)
# avoid fractional exponents
exponents_np = np.random.randint(10, size=(4, 5)).astype(np.float64)
bases = lg.array(bases_np)
exponents = lg.array(exponents_np)
assert lg.allclose(lg.power(bases, exponents),
np.power(bases_np, exponents_np))
def test():
np.random.seed(13)
anp = np.random.randn(4, 5)
bnp = np.random.randn(4, 5)
a = lg.array(anp)
b = lg.array(bnp)
np.divide(anp, bnp, out=anp)
lg.divide(a, b, out=a)
assert np.array_equal(a, anp)
def test():
anp = np.random.randn(4, 5)
bnp = np.random.randn(4, 5)
a = lg.array(anp)
b = lg.array(bnp)
np.subtract(anp, bnp, out=anp)
lg.subtract(a, b, out=a)
assert np.array_equal(a, anp)
return
def test():
anp = np.random.randn(4, 5)
bnp = np.random.randn(4, 5)
a = lg.array(anp)
b = lg.array(bnp)
anp += bnp
a += b
assert np.array_equal(a, anp)
return
def test():
x = lg.array([[1, 2, 3], [4, 5, 6]])
y = lg.array([[7, 8, 9], [10, 11, 12]])
xc = x.copy()
yc = y.copy()
x[0, :] = [7, 8, 9]
y = lg.array([[10, 12, 13], [25, 26, 27]])
w = x + y
wc = xc + yc
assert np.array_equal(w, [[17, 20, 22], [29, 31, 33]])
assert np.array_equal(wc, [[8, 10, 12], [14, 16, 18]])
return
def test():
x = lg.array([[1, 2, 3], [4, 5, 6]])
y = lg.transpose(x)
assert lg.array_equal(y, [[1, 4], [2, 5], [3, 6]])
z = lg.transpose(y)
assert lg.array_equal(x, z)
x = lg.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = x.transpose()
assert lg.array_equal(y, [[1, 4, 7], [2, 5, 8], [3, 6, 9]])
z = lg.transpose(y)
assert lg.array_equal(x, z)
return
def test():
sr = pd.Series([1, 2, 3])
array = np.asarray(sr)
x = np.array([1, 2, 3])
assert np.array_equal(array, x)
y = np.array([4, 5, 6])
z = np.add(sr, y)
assert np.array_equal(z, x + y)
df = pd.DataFrame({"x": x, "y": y})
z = np.add(df["x"], df["y"])
assert np.array_equal(z, x + y)
return
def test():
x = lg.ndarray.convert_to_legate_ndarray([])
y = np.array([])
asserts.assert_equal(x, y)
z = lg.array([])
asserts.assert_equal(y, z)
x = lg.ndarray.convert_to_legate_ndarray([[]])
y = np.array([[]])
asserts.assert_equal(x, y)
z = lg.array([[]])
asserts.assert_equal(y, z)
x = lg.ndarray.convert_to_legate_ndarray([[[]]])
y = np.array([[[]]])
asserts.assert_equal(x, y)
z = lg.array([[[]]])
asserts.assert_equal(y, z)
x = lg.ndarray.convert_to_legate_ndarray([[], []])
y = np.array([[], []])
asserts.assert_equal(x, y)
z = lg.array([[], []])
asserts.assert_equal(y, z)
x = lg.ndarray.convert_to_legate_ndarray([1])
y = np.array([1])
asserts.assert_equal(x, y)
z = lg.array([1])
asserts.assert_equal(y, z)
x = lg.ndarray.convert_to_legate_ndarray([[1]])
y = np.array([[1]])
asserts.assert_equal(x, y)
z = lg.array([[1]])
asserts.assert_equal(y, z)
x = lg.ndarray.convert_to_legate_ndarray([[[1]]])
y = np.array([[[1]]])
asserts.assert_equal(x, y)
z = lg.array([[[1]]])
asserts.assert_equal(y, z)
x = lg.ndarray.convert_to_legate_ndarray([[1], [1]])
y = np.array([[1], [1]])
asserts.assert_equal(x, y)
z = lg.array([[1], [1]])
asserts.assert_equal(y, z)
x = lg.ndarray.convert_to_legate_ndarray([1, 2, 3, 4])
y = np.array([1, 2, 3, 4])
asserts.assert_equal(x, y)
z = lg.array([1, 2, 3, 4])
asserts.assert_equal(y, z)
def test():
x = [1.0, 2, 3]
y = [4, 5, 6]
z = x + y
numpyResult = np.sum(z)
# print(numpyResult)
gx = lg.array(x)
gy = lg.array(y)
z = gx + gy
legate_oldResult = lg.sum(z)
# print(legate_oldResult)
assert legate_oldResult == numpyResult
return
def test():
x = lg.array([1, 2, 3, 4, 5])
assert 4 in x
assert 6 not in x
return
def test():
anp = np.random.random_integers(100, size=(4, 5))
a = lg.array(anp)
assert np.array_equal(~a, ~anp)
return
def test():
anp = np.random.randn(4, 5)
a = lg.array(anp)
assert np.array_equal(lg.negative(a), np.negative(anp))
return
def test():
anp = np.random.randn(4, 5)
a = lg.array(anp)
assert np.array_equal(lg.logical_not(a), np.logical_not(anp))
return
def test():
x = lg.array([1.0, 2.0, 3.0, 4.0])
y = lg.exp(x)
# print(y)
# print(np.exp([1, 2, 3, 4]))
assert np.allclose(y, np.exp([1, 2, 3, 4]))
return
def test():
npa = np.array([-np.pi, 0, np.pi / 2, np.pi])
a = lg.array(npa)
lg.tanh(a, out=a)
np.tanh(npa, out=npa)
assert np.array_equal(a, npa)
return
