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提交 18731a6a authored 作者: Tanjay94's avatar Tanjay94
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Modified numpy test to reduced its length.

上级 11a6b95f
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正在显示 包含 17 行增加 和 151 行删除
theano/sandbox/linalg/tests/test_linalg.py
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...@@ -660,165 +660,31 @@ class T_NormTests(unittest.TestCase): ...@@ -660,165 +660,31 @@ class T_NormTests(unittest.TestCase):
self.assertRaises(ValueError, norm, [[2, 1], [3, 4]], None, 3) self.assertRaises(ValueError, norm, [[2, 1], [3, 4]], None, 3)
def test_numpy_compare(self): def test_numpy_compare(self):
f = []
t_n = []
n_n = []
try: try:
rng = numpy.random.RandomState(utt.fetch_seed()) rng = numpy.random.RandomState(utt.fetch_seed())
M = tensor.matrix("A", dtype=theano.config.floatX) M = tensor.matrix("A", dtype=theano.config.floatX)
N = tensor.vector("N", dtype=theano.config.floatX) V = tensor.vector("V", dtype=theano.config.floatX)
B = tensor.tensor3("B", dtype=theano.config.floatX) N = tensor.tensor3("N", dtype=theano.config.floatX)
Q = norm(M, None, None)
W = norm(M, 'fro', None)
E = norm(M, 'inf', None)
R = norm(M, '-inf', None)
T = norm(M, 1, None)
Y = norm(M, -1, None)
U = norm(N, None, None)
I = norm(N, 'inf', None)
O = norm(N, '-inf', None)
P = norm(N, 0, None)
L = norm(N, 1, None)
K = norm(N, -1, None)
J = norm(N, 2, None)
H = norm(N, -2, None)
QQ = norm(B, None, [0,1])
WW = norm(B, 'fro', [0,1])
EE = norm(B, 'inf', [0,1])
RR = norm(B, '-inf', [0,1])
TT = norm(B, 1, [0,1])
YY = norm(B, -1, [0,1])
UU = norm(B, None, 0)
II = norm(B, 'inf', 0)
OO = norm(B, '-inf', 0)
PP = norm(B, 0, 0)
LL = norm(B, 1, 0)
KK = norm(B, -1, 0)
JJ = norm(B, 2, 0)
HH = norm(B, -2, 0)
fQ = function([M], [Q])
fW = function([M], [W])
fE = function([M], [E])
fR = function([M], [R])
fT = function([M], [T])
fY = function([M], [Y])
fU = function([N], [U])
fI = function([N], [I])
fO = function([N], [O])
fP = function([N], [P])
fL = function([N], [L])
fK = function([N], [K])
fJ = function([N], [J])
fH = function([N], [H])
fQQ = function([B], [QQ])
fWW = function([B], [WW])
fEE = function([B], [EE])
fRR = function([B], [RR])
fTT = function([B], [TT])
fYY = function([B], [YY])
fUU = function([B], [UU])
fII = function([B], [II])
fOO = function([B], [OO])
fPP = function([B], [PP])
fLL = function([B], [LL])
fKK = function([B], [KK])
fJJ = function([B], [JJ])
fHH = function([B], [HH])
a = rng.rand(4, 4).astype(theano.config.floatX) a = rng.rand(4, 4).astype(theano.config.floatX)
b = rng.rand(4).astype(theano.config.floatX) b = rng.rand(4).astype(theano.config.floatX)
c = rng.rand(4, 4, 4).astype(theano.config.floatX) c = rng.rand(4, 4, 4).astype(theano.config.floatX)
n_nQ = numpy.linalg.norm(a, None, None) A = [None, 'fro', 'inf', '-inf', 1, -1, None, 'inf', '-inf', 0, 1, -1, 2, -2, None, 'fro', 'inf', '-inf', 1, -1, None, 'inf', '-inf', 0, 1, -1, 2, -2]
n_nW = numpy.linalg.norm(a, 'fro', None) B = [M, M, M, M, M, M, V, V, V, V, V, V, V, V, N, N, N, N, N, N, N, N, N, N, N, N, N, N]
n_nE = numpy.linalg.norm(a, inf, None) C = [None, None, None, None, None, None, None, None, None, None, None, None, None, None, [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], 0, 0, 0, 0, 0, 0, 0, 0]
n_nR = numpy.linalg.norm(a, -inf, None) D = [a, a, a, a, a, a, b, b, b, b, b, b, b, b, c, c, c, c, c, c, c, c, c, c, c, c, c, c]
n_nT = numpy.linalg.norm(a, 1, None) E = [None, 'fro', inf, -inf, 1, -1, None, inf, -inf, 0, 1, -1, 2, -2, None, 'fro', inf, -inf, 1, -1, None, inf, -inf, 0, 1, -1, 2, -2]
n_nY = numpy.linalg.norm(a, -1, None)
n_nU = numpy.linalg.norm(b, None, None) for i in range(0, 28):
n_nI = numpy.linalg.norm(b, inf, None) f.append(function([B[i]], [norm(B[i], A[i], C[i])]))
n_nO = numpy.linalg.norm(b, -inf, None) t_n.append(f[i](D[i]))
n_nP = numpy.linalg.norm(b, 0, None) n_n.append(numpy.linalg.norm(D[i], E[i], C[i]))
n_nL = numpy.linalg.norm(b, 1, None) assert _allclose(n_n[i], t_n[i])
n_nK = numpy.linalg.norm(b, -1, None)
n_nJ = numpy.linalg.norm(b, 2, None)
n_nH = numpy.linalg.norm(b, -2, None)
n_nQQ = numpy.linalg.norm(c, None, [0,1])
n_nWW = numpy.linalg.norm(c, 'fro', [0,1])
n_nEE = numpy.linalg.norm(c, inf, [0,1])
n_nRR = numpy.linalg.norm(c, -inf, [0,1])
n_nTT = numpy.linalg.norm(c, 1, [0,1])
n_nYY = numpy.linalg.norm(c, -1, [0,1])
n_nUU = numpy.linalg.norm(c, None, 0)
n_nII = numpy.linalg.norm(c, inf, 0)
n_nOO = numpy.linalg.norm(c, -inf, 0)
n_nPP = numpy.linalg.norm(c, 0, 0)
n_nLL = numpy.linalg.norm(c, 1, 0)
n_nKK = numpy.linalg.norm(c, -1, 0)
n_nJJ = numpy.linalg.norm(c, 2, 0)
n_nHH = numpy.linalg.norm(c, -2, 0)
t_nQ = fQ(a)
t_nW = fW(a)
t_nE = fE(a)
t_nR = fR(a)
t_nT = fT(a)
t_nY = fY(a)
t_nU = fU(b)
t_nI = fI(b)
t_nO = fO(b)
t_nP = fP(b)
t_nL = fL(b)
t_nK = fK(b)
t_nJ = fJ(b)
t_nH = fH(b)
t_nQQ = fQ(c)
t_nWW = fW(c)
t_nEE = fE(c)
t_nRR = fR(c)
t_nTT = fT(c)
t_nYY = fY(c)
t_nUU = fU(c)
t_nII = fI(c)
t_nOO = fO(c)
t_nPP = fP(c)
t_nLL = fL(c)
t_nKK = fK(c)
t_nJJ = fJ(c)
t_nHH = fH(c)
assert _allclose(n_nQ, t_nQ)
assert _allclose(n_nW, t_nW)
assert _allclose(n_nE, t_nE)
assert _allclose(n_nR, t_nR)
assert _allclose(n_nT, t_nT)
assert _allclose(n_nY, t_nY)
assert _allclose(n_nU, t_nU)
assert _allclose(n_nI, t_nI)
assert _allclose(n_nO, t_nO)
assert _allclose(n_nP, t_nP)
assert _allclose(n_nL, t_nL)
assert _allclose(n_nK, t_nK)
assert _allclose(n_nJ, t_nJ)
assert _allclose(n_nH, t_nH)
assert _allclose(n_nQQ, t_nQQ)
assert _allclose(n_nWW, t_nWW)
assert _allclose(n_nEE, t_nEE)
assert _allclose(n_nRR, t_nRR)
assert _allclose(n_nTT, t_nTT)
assert _allclose(n_nYY, t_nYY)
assert _allclose(n_nUU, t_nUU)
assert _allclose(n_nII, t_nII)
assert _allclose(n_nOO, t_nOO)
assert _allclose(n_nPP, t_nPP)
assert _allclose(n_nLL, t_nLL)
assert _allclose(n_nKK, t_nKK)
assert _allclose(n_nJJ, t_nJJ)
assert _allclose(n_nHH, t_nHH)
except TypeError: except TypeError:
raise SkipTest('Your numpy version is outdated.') raise SkipTest('Your numpy version is outdated.')
......
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