sp = gp + sp*beta
sx = gx + sx*beta
sr = gr + sr*beta
gnp = gn
alpha = sr.dot(sr)+eps*(sp.dot(sp)-sx.dot(sx))
alpha = -gn/alpha
p = p + sp*alpha
x = x + sx*alpha
r = r + sr*alpha
return x
if __name__ == "__main__":
# test matrix and data
matrix = rsf.File([[1,1,1,0],
[1,2,0,0],
[1,3,1,0],
[1,4,0,1],
[1,5,1,1]])
y = rsf.File([3,3,5,7,9])
x0 = rsf.File([0,0,0,0])
# matrix multiplication operator
matmult = rsf.matmult(mat=matrix)
copy = rsf.cp(x=1)
x = conjgrad(matmult,copy,1,y,x0,6)
y2 = matmult[x]
print x[:]
print y2[:]
for iter in range(niter):
g = oper(adj=1)[R]
G = oper(adj=0)[g]
RG = R.dot(G)
print "iter %d: %g" % (iter+1,RG)
alpha = - RG/G.dot(G)
x = x+g*alpha
R = R+G*alpha
return x
if __name__ == "__main__":
# test matrix and data
matrix = rsf.File([[1,1,1,0],
[1,2,0,0],
[1,3,1,0],
[1,4,0,1],
[1,5,1,1]])
y = rsf.File([3,3,5,7,9])
x0 = rsf.File([0,0,0,0])
# matrix multiplication operator
matmult = rsf.matmult(mat=matrix)
# Using function above
x = steepd(matmult,y,x0,100)
y2 = matmult[x]
print x[:]
print y2[:]
cs = cg
else:
beta = gn/gnp
s = g+ s*beta
cs = cg+cs*beta
gnp = gn
alpha = -gn/cs.dot(s)
x = x+ s*alpha
g = g+cs*alpha
return x
if __name__ == "__main__":
# test matrix and data
matrix = rsf.File([[1,1,1,0],
[1,2,0,0],
[1,3,1,0],
[1,4,0,1],
[1,5,1,1]])
y = rsf.File([3,3,5,7,9])
x0 = rsf.File([0,0,0,0])
# matrix multiplication operator
d = rsf.matmult(mat=matrix,adj=1)[y]
matmult = rsf.matmult(mat=matrix,adj=0).matmult(mat=matrix,adj=1)
x = conjgrad(matmult,d,x0,6)
y2 = matmult[x]
print x[:]
print y2[:]
print d[:]
print "iter %d: %g" % (iter + 1, gn)
if 0 == iter:
s = g
cs = cg
else:
beta = gn / gnp
s = g + s * beta
cs = cg + cs * beta
gnp = gn
alpha = -gn / cs.dot(s)
x = x + s * alpha
g = g + cs * alpha
return x
if __name__ == "__main__":
# test matrix and data
matrix = rsf.File([[1, 1, 1, 0], [1, 2, 0, 0], [1, 3, 1, 0], [1, 4, 0, 1],
[1, 5, 1, 1]])
y = rsf.File([3, 3, 5, 7, 9])
x0 = rsf.File([0, 0, 0, 0])
# matrix multiplication operator
d = rsf.matmult(mat=matrix, adj=1)[y]
matmult = rsf.matmult(mat=matrix, adj=0).matmult(mat=matrix, adj=1)
x = conjgrad(matmult, d, x0, 6)
y2 = matmult[x]
print x[:]
print y2[:]
print d[:]
##
## You should have received a copy of the GNU General Public License
## along with this program; if not, write to the Free Software
## Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
import rsf.api as rsf
random = rsf.noise(rep=1,type=0)
def dottest(oper,mod,dat):
'Dot product test'
mod = random[mod]
dat = random[dat]
print " L[m]*d=%g" % oper(adj=0)[mod].dot(dat)
print "L'[d]*m=%g" % oper(adj=1)[dat].dot(mod)
if __name__ == "__main__":
# Create random matrix
matrix = rsf.spike(n1=10,n2=5,d1=1,d2=1).noise(rep=1,type=0)[0]
# Matrix multiplication operator
oper = rsf.matmult(mat=matrix)
# Model space and data space vectors
model = rsf.window(n2=1)[matrix]
data = rsf.window(n1=1)[matrix]
# Using sfdottest
rsf.dottest(0,oper,mod=model,dat=data)
# Using function above
dottest(oper,model,data)
##
## You should have received a copy of the GNU General Public License
## along with this program; if not, write to the Free Software
## Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
import rsf.api as rsf
random = rsf.noise(rep=1, type=0)
def dottest(oper, mod, dat):
'Dot product test'
mod = random[mod]
dat = random[dat]
print " L[m]*d=%g" % oper(adj=0)[mod].dot(dat)
print "L'[d]*m=%g" % oper(adj=1)[dat].dot(mod)
if __name__ == "__main__":
# Create random matrix
matrix = rsf.spike(n1=10, n2=5, d1=1, d2=1).noise(rep=1, type=0)[0]
# Matrix multiplication operator
oper = rsf.matmult(mat=matrix)
# Model space and data space vectors
model = rsf.window(n2=1)[matrix]
data = rsf.window(n1=1)[matrix]
# Using sfdottest
rsf.dottest(0, oper, mod=model, dat=data)
# Using function above
dottest(oper, model, data)
