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distributed -> remote (#82)

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Robert Nishihara
2016-06-03 18:41:45 -07:00
committed by Philipp Moritz
parent 67086f663e
commit 2b52b91acb
10 changed files with 86 additions and 86 deletions
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lib/python/arrays/single/linalg.py
+20 -20
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@@ -7,82 +7,82 @@ __all__ = ["matrix_power", "solve", "tensorsolve", "tensorinv", "inv",
"svd", "eig", "eigh", "lstsq", "norm", "qr", "cond", "matrix_rank",
"LinAlgError", "multi_dot"]
@halo.distributed([np.ndarray, int], [np.ndarray])
@halo.remote([np.ndarray, int], [np.ndarray])
def matrix_power(M, n):
return np.linalg.matrix_power(M, n)
@halo.distributed([np.ndarray, np.ndarray], [np.ndarray])
@halo.remote([np.ndarray, np.ndarray], [np.ndarray])
def solve(a, b):
return np.linalg.solve(a, b)
@halo.distributed([np.ndarray], [np.ndarray, np.ndarray])
@halo.remote([np.ndarray], [np.ndarray, np.ndarray])
def tensorsolve(a):
raise NotImplementedError
@halo.distributed([np.ndarray], [np.ndarray, np.ndarray])
@halo.remote([np.ndarray], [np.ndarray, np.ndarray])
def tensorinv(a):
raise NotImplementedError
@halo.distributed([np.ndarray], [np.ndarray])
@halo.remote([np.ndarray], [np.ndarray])
def inv(a):
return np.linalg.inv(a)
@halo.distributed([np.ndarray], [np.ndarray])
@halo.remote([np.ndarray], [np.ndarray])
def cholesky(a):
return np.linalg.cholesky(a)
@halo.distributed([np.ndarray], [np.ndarray])
@halo.remote([np.ndarray], [np.ndarray])
def eigvals(a):
return np.linalg.eigvals(a)
@halo.distributed([np.ndarray], [np.ndarray])
@halo.remote([np.ndarray], [np.ndarray])
def eigvalsh(a):
raise NotImplementedError
@halo.distributed([np.ndarray], [np.ndarray])
@halo.remote([np.ndarray], [np.ndarray])
def pinv(a):
return np.linalg.pinv(a)
@halo.distributed([np.ndarray], [int])
@halo.remote([np.ndarray], [int])
def slogdet(a):
raise NotImplementedError
@halo.distributed([np.ndarray], [float])
@halo.remote([np.ndarray], [float])
def det(a):
return np.linalg.det(a)
@halo.distributed([np.ndarray], [np.ndarray, np.ndarray, np.ndarray])
@halo.remote([np.ndarray], [np.ndarray, np.ndarray, np.ndarray])
def svd(a):
return np.linalg.svd(a)
@halo.distributed([np.ndarray], [np.ndarray, np.ndarray])
@halo.remote([np.ndarray], [np.ndarray, np.ndarray])
def eig(a):
return np.linalg.eig(a)
@halo.distributed([np.ndarray], [np.ndarray, np.ndarray])
@halo.remote([np.ndarray], [np.ndarray, np.ndarray])
def eigh(a):
return np.linalg.eigh(a)
@halo.distributed([np.ndarray], [np.ndarray, np.ndarray, int, np.ndarray])
@halo.remote([np.ndarray], [np.ndarray, np.ndarray, int, np.ndarray])
def lstsq(a, b):
return np.linalg.lstsq(a)
@halo.distributed([np.ndarray], [float])
@halo.remote([np.ndarray], [float])
def norm(x):
return np.linalg.norm(x)
@halo.distributed([np.ndarray], [np.ndarray, np.ndarray])
@halo.remote([np.ndarray], [np.ndarray, np.ndarray])
def qr(a):
return np.linalg.qr(a)
@halo.distributed([np.ndarray], [float])
@halo.remote([np.ndarray], [float])
def cond(x):
return np.linalg.cond(x)
@halo.distributed([np.ndarray], [int])
@halo.remote([np.ndarray], [int])
def matrix_rank(M):
return np.linalg.matrix_rank(M)
@halo.distributed([np.ndarray, None], [np.ndarray])
@halo.remote([np.ndarray, None], [np.ndarray])
def multi_dot(a):
raise NotImplementedError