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authorTravis Oliphant <oliphant@enthought.com>2006-06-09 19:50:46 +0000
committerTravis Oliphant <oliphant@enthought.com>2006-06-09 19:50:46 +0000
commit13c9eeff4ee39555967f05be054111a695bd7cfc (patch)
tree79efb812ad58c78da0c40f774bf384cf0a1afa1e /numpy/random
parenta5b115b97ffed88cd7d159d944105bb748d44cf6 (diff)
downloadnumpy-13c9eeff4ee39555967f05be054111a695bd7cfc.tar.gz
Use dot instead of matrixmultiply
Diffstat (limited to 'numpy/random')
-rw-r--r--numpy/random/mtrand/mtrand.pyx10
1 files changed, 5 insertions, 5 deletions
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index b01055d83..600cd9321 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -820,11 +820,11 @@ cdef class RandomState:
else:
shape = size
if len(mean.shape) != 1:
- raise ArgumentError("mean must be 1 dimensional")
+ raise ValueError("mean must be 1 dimensional")
if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]):
- raise ArgumentError("cov must be 2 dimensional and square")
+ raise ValueError("cov must be 2 dimensional and square")
if mean.shape[0] != cov.shape[0]:
- raise ArgumentError("mean and cov must have same length")
+ raise ValueError("mean and cov must have same length")
# Compute shape of output
if isinstance(shape, int):
shape = [shape]
@@ -838,7 +838,7 @@ cdef class RandomState:
mean.shape[0])
# Transform matrix of standard normals into matrix where each row
# contains multivariate normals with the desired covariance.
- # Compute A such that matrixmultiply(transpose(A),A) == cov.
+ # Compute A such that dot(transpose(A),A) == cov.
# Then the matrix products of the rows of x and A has the desired
# covariance. Note that sqrt(s)*v where (u,s,v) is the singular value
# decomposition of cov is such an A.
@@ -846,7 +846,7 @@ cdef class RandomState:
from numpy.dual import svd
# XXX: we really should be doing this by Cholesky decomposition
(u,s,v) = svd(cov)
- x = _sp.matrixmultiply(x*_sp.sqrt(s),v)
+ x = _sp.dot(x*_sp.sqrt(s),v)
# The rows of x now have the correct covariance but mean 0. Add
# mean to each row. Then each row will have mean mean.
_sp.add(mean,x,x)