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Return correct real type in eval_batch. #1403

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Aug 2, 2023
Merged

Return correct real type in eval_batch. #1403

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529afa9
Return correct real type in eval_batch.
samuelburbulla Jul 25, 2023
204e096
Resolve dtype issue in the first place.
samuelburbulla Jul 28, 2023
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12 changes: 6 additions & 6 deletions deepxde/data/function_spaces.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,13 +85,13 @@ def __init__(self, N=100, M=1):
self.M = M

def random(self, size):
return 2 * self.M * np.random.rand(size, self.N) - self.M
return 2 * self.M * np.random.rand(size, self.N).astype(config.real(np)) - self.M

def eval_one(self, feature, x):
return np.dot(feature, x ** np.arange(self.N))

def eval_batch(self, features, xs):
mat = np.ones((self.N, len(xs)))
mat = np.ones((self.N, len(xs)), dtype=config.real(np))
for i in range(1, self.N):
mat[i] = np.ravel(xs**i)
return np.dot(features, mat)
Expand Down Expand Up @@ -120,7 +120,7 @@ def eval_one(self, feature, x):
return np.polynomial.chebyshev.chebval(2 * x - 1, feature)

def eval_batch(self, features, xs):
return np.polynomial.chebyshev.chebval(2 * np.ravel(xs) - 1, features.T)
return np.polynomial.chebyshev.chebval(2 * np.ravel(xs) - 1, features.T).astype(config.real(np))


class GRF(FunctionSpace):
Expand Down Expand Up @@ -218,14 +218,14 @@ def bases(self, sensors):
return np.array([np.ravel(f(sensors)) for f in self.eigfun])

def random(self, size):
return np.random.randn(size, self.num_eig)
return np.random.randn(size, self.num_eig).astype(config.real(np))

def eval_one(self, feature, x):
eigfun = [f(x) for f in self.eigfun]
return np.sum(eigfun * feature)

def eval_batch(self, features, xs):
eigfun = np.array([np.ravel(f(xs)) for f in self.eigfun])
eigfun = np.array([np.ravel(f(xs)) for f in self.eigfun], dtype=config.real(np))
return np.dot(features, eigfun)


Expand Down Expand Up @@ -287,7 +287,7 @@ def eval_batch(self, features, xs):
points = (self.x, self.y)
ys = np.reshape(features, (-1, self.N, self.N))
res = map(lambda y: interpolate.interpn(points, y, xs, method=self.interp), ys)
return np.vstack(list(res))
return np.vstack(list(res)).astype(config.real(np))


def wasserstein2(space1, space2):
Expand Down