本文整理匯總了Python中numpy.polynomial.legendre.legvander方法的典型用法代碼示例。如果您正苦於以下問題：Python legendre.legvander方法的具體用法？Python legendre.legvander怎麽用？Python legendre.legvander使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類numpy.polynomial.legendre的用法示例。
在下文中一共展示了legendre.legvander方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_legvander
# 需要導入模塊: from numpy.polynomial import legendre [as 別名]
# 或者: from numpy.polynomial.legendre import legvander [as 別名]
def test_legvander(self):
# check for 1d x
x = np.arange(3)
v = leg.legvander(x, 3)
assert_(v.shape == (3, 4))
for i in range(4):
coef = [0]*i + [1]
assert_almost_equal(v[..., i], leg.legval(x, coef))
# check for 2d x
x = np.array([[1, 2], [3, 4], [5, 6]])
v = leg.legvander(x, 3)
assert_(v.shape == (3, 2, 4))
for i in range(4):
coef = [0]*i + [1]
assert_almost_equal(v[..., i], leg.legval(x, coef)) 示例2: test_legvander
# 需要導入模塊: from numpy.polynomial import legendre [as 別名]
# 或者: from numpy.polynomial.legendre import legvander [as 別名]
def test_legvander(self) :
# check for 1d x
x = np.arange(3)
v = leg.legvander(x, 3)
assert_(v.shape == (3, 4))
for i in range(4) :
coef = [0]*i + [1]
assert_almost_equal(v[..., i], leg.legval(x, coef))
# check for 2d x
x = np.array([[1, 2], [3, 4], [5, 6]])
v = leg.legvander(x, 3)
assert_(v.shape == (3, 2, 4))
for i in range(4) :
coef = [0]*i + [1]
assert_almost_equal(v[..., i], leg.legval(x, coef)) 示例3: test_100
# 需要導入模塊: from numpy.polynomial import legendre [as 別名]
# 或者: from numpy.polynomial.legendre import legvander [as 別名]
def test_100(self):
x, w = leg.leggauss(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 = leg.legvander(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 = 2.0
assert_almost_equal(w.sum(), tgt) 示例4: _setup
# 需要導入模塊: from numpy.polynomial import legendre [as 別名]
# 或者: from numpy.polynomial.legendre import legvander [as 別名]
def _setup(self, config):
torch.manual_seed(config['seed'])
self.model = ButterflyProduct(size=config['size'],
complex=False,
fixed_order=config['fixed_order'],
softmax_fn=config['softmax_fn'])
if (not config['fixed_order']) and config['softmax_fn'] == 'softmax':
self.semantic_loss_weight = config['semantic_loss_weight']
self.optimizer = optim.Adam(self.model.parameters(), lr=config['lr'])
self.n_steps_per_epoch = config['n_steps_per_epoch']
size = config['size']
# Need to transpose as dct acts on rows of matrix np.eye, not columns
n = size
x = np.linspace(-1, 1, n + 2)[1:-1]
E = legendre.legvander(x, n - 1).T
# E = np.zeros((n, n), dtype=np.float32)
# for i, coef in enumerate(np.eye(n)):
# E[i] = legendre.legval(x, coef)
self.target_matrix = torch.tensor(E, dtype=torch.float)
arange_ = np.arange(size)
dct_perm = np.concatenate((arange_[::2], arange_[::-2]))
br_perm = bitreversal_permutation(size)
assert config['perm'] in ['id', 'br', 'dct']
if config['perm'] == 'id':
self.perm = torch.arange(size)
elif config['perm'] == 'br':
self.perm = br_perm
elif config['perm'] == 'dct':
self.perm = torch.arange(size)[dct_perm][br_perm]
else:
assert False, 'Wrong perm in config'