def lu(a):
"""
Performe LU decomposition on the matrix a so A = L*U
"""
dw = True
if dw:
def loop_body(l, u):
c = get_iterator()
l[c:, c - 1] = (u[c:, c - 1] / u[c - 1, c - 1:c])
u[c:, c -
1:] = u[c:, c - 1:] - l[c:, c - 1][:, None] * u[c - 1, c - 1:]
u = a.copy()
l = np.zeros_like(a)
np.diagonal(l)[:] = 1.0
loop.do_while(loop_body, u.shape[0] - 2, l, u)
return (l, u)
else:
u = a.copy()
l = np.zeros_like(a)
np.diagonal(l)[:] = 1.0
for c in range(1, u.shape[0]):
l[c:, c - 1] = (u[c:, c - 1] / u[c - 1, c - 1:c])
u[c:, c -
1:] = u[c:, c - 1:] - l[c:, c - 1][:, None] * u[c - 1, c - 1:]
np.flush()
return (l, u)
def gauss(a):
"""
Performe Gausian elimination on matrix a without pivoting
"""
dw = False
if dw:
def loop_body(a):
c = get_iterator(1)
x1 = a[c:, c - 1:]
# x2 = (a[c:, c - 1, None] / a[c - 1, c - 1:c, None])#[:, None]
x2 = a[c:, c - 1] / a[c - 1, c - 1:c]
# x2 = a[c:, c - 1] / a[c - 1, c - 1:c]
# x2 = (a[c:, c - 1] / a[c - 1, c - 1:c])[:, None]
x3 = a[c - 1, c - 1:]
x1 = x1 - x2 * x3
loop.do_while(loop_body, a.shape[0] - 2, a)
a /= np.diagonal(a)[:, None]
return a
else:
for c in range(1, a.shape[0]):
x1 = a[c:, c - 1:]
x2 = (a[c:, c - 1] / a[c - 1, c - 1:c])[:, None]
x3 = a[c - 1, c - 1:]
x1 = x1 - x2 * x3
# a[c:, c - 1:] = a[c:, c - 1:] - (a[c:, c - 1] / a[c - 1, c - 1:c]) * a[c - 1, c - 1:]
np.flush()
a /= np.diagonal(a)[:, None]
return a
def gauss(a):
"""
Performe Gausian elimination on matrix a without pivoting
"""
for c in xrange(1,a.shape[0]):
a[c:,c-1:] = a[c:,c-1:] - (a[c:,c-1]/a[c-1,c-1:c])[:,None] * a[c-1,c-1:]
np.flush(a)
a /= np.diagonal(a)[:,None]
return a
def gauss(a):
"""
Performe Gausian elimination on matrix a without pivoting
"""
for c in xrange(1,a.shape[0]):
a[c:,c-1:] = a[c:,c-1:] - (a[c:,c-1]/a[c-1,c-1:c])[:,None] * a[c-1,c-1:]
np.flush(a)
a /= np.diagonal(a)[:,None]
return a
def lu(a):
"""
Performe LU decomposition on the matrix a so A = L*U
"""
u = a.copy()
l = np.zeros_like(a)
np.diagonal(l)[:] = 1.0
for c in xrange(1,u.shape[0]):
l[c:,c-1] = (u[c:,c-1]/u[c-1,c-1:c])
u[c:,c-1:] = u[c:,c-1:] - l[c:,c-1][:,None] * u[c-1,c-1:]
np.flush(u)
return (l,u)
def lu(a):
"""
Performe LU decomposition on the matrix a so A = L*U
"""
u = a.copy()
l = np.zeros_like(a)
np.diagonal(l)[:] = 1.0
for c in xrange(1,u.shape[0]):
l[c:,c-1] = (u[c:,c-1]/u[c-1,c-1:c])
u[c:,c-1:] = u[c:,c-1:] - l[c:,c-1][:,None] * u[c-1,c-1:]
np.flush(u)
return (l,u)
