def spcolvec(x):
y = c.dtrp(len(x), 1)
for i in range(len(x)):
y.push_back(i, 0, float(x[i]))
z = c.dcsc(y)
z.keep()
return z
def spcolvec(x):
y = c.dtrp(len(x), 1)
for i in range(len(x)):
y.push_back(i, 0, float(x[i]))
z = c.dcsc(y)
z.keep()
return z
def random_col_vector(m, nnz):
irn = numpy.random.random_integers(0, m-1, nnz).astype(numpy.uint64)
irn = numpy.random.permutation(numpy.unique(irn));
nzv = numpy.random.rand(nnz)
triplet_matrix = c.dtrp(m, 1, nnz)
for ir, nz in zip(irn, nzv):
triplet_matrix.push_back(int(ir), 0, nz)
return c.dcsc(triplet_matrix)
def random_col_vector(m, nnz):
irn = numpy.random.random_integers(0, m - 1, nnz).astype(numpy.uint64)
irn = numpy.random.permutation(numpy.unique(irn))
nzv = numpy.random.rand(nnz)
triplet_matrix = c.dtrp(m, 1, nnz)
for ir, nz in zip(irn, nzv):
triplet_matrix.push_back(int(ir), 0, nz)
return c.dcsc(triplet_matrix)
def build_matrix():
N = 12;
t = c.dtrp(N, N ,4*N)
for row in range(N):
t.push_back(int(row), int(row), 5.0);
for row, col in trips:
t.push_back(int(row), int(col), 2.0);
t.push_back(int(col), int(row), 2.0);
return c.dcsc(t)
def build_matrix():
N = 12
t = c.dtrp(N, N, 4 * N)
for row in range(N):
t.push_back(int(row), int(row), 5.0)
for row, col in trips:
t.push_back(int(row), int(col), 2.0)
t.push_back(int(col), int(row), 2.0)
return c.dcsc(t)
def sprand(m, n, s):
nnz = int(m * n * s)
row_idx = vect(np.random.random_integers(0, m - 1, nnz))
col_idx = vect(np.random.random_integers(0, n - 1, nnz))
vals = vect(np.random.rand(nnz))
for d in range(0, min(m, n)):
row_idx.push_back(int(d))
col_idx.push_back(int(d))
vals.push_back(float(1.0))
tr = c.dtrp(m, n)
for i, j, v in zip(row_idx, col_idx, vals):
tr.push_back(i, j, v)
return c.dcsc(tr)
def sprand(m, n, s):
nnz = int(m * n * s)
row_idx = vect(np.random.random_integers(0, m - 1, nnz))
col_idx = vect(np.random.random_integers(0, n - 1, nnz))
vals = vect(np.random.rand(nnz))
for d in range(0, min(m, n)):
row_idx.push_back(int(d))
col_idx.push_back(int(d))
vals.push_back(float(1.0))
tr = c.dtrp(m, n)
for i, j, v in zip(row_idx, col_idx, vals):
tr.push_back(i, j, v)
return c.dcsc(tr)
import cppsparse as c
x = c.dtrp("fem.dat")
y = c.dcsc(x)
y.sum_duplicates()
m = y.n_rows()
n = y.n_cols()
print m, n
cs = c.dvec(n, 2.0)
rs = c.dvec(m, 3.0)
print (y.find())
y.scale(rs, cs)
print (y.find())
def upper_tri_full(m, n):
u = c.dtrp(m, n)
for col in range(n):
for row in range(col, m):
u.push_back(col, row, float(row + col * m))
return c.dcsc(u)
def lower_tri_full(m, n):
l = c.dtrp(m, n)
for col in range(n):
for row in range(col, m):
l.push_back(row, col, float(row + col * m))
return c.dcsc(l)
import cppsparse as c
def random_triplet (m, n, nnz):
import numpy as np
row_idx = np.random.random_integers(0, m-1, nnz)
col_idx = np.random.random_integers(0, n-1, nnz)
nz__val = np.random.rand(10)
trip = c.dtrp(m, n, nnz)
for i,j,v in zip(row_idx, col_idx, nz__val):
print i, j, v
trip.push_back(int(i), int(j), float(v))
return trip
x = c.dcsc(random_triplet(8, 8, 20))
##print(x)
#print(x.find())
x.sort()
##print(x.find())
def upper_tri_full(m, n):
u = c.dtrp(m, n)
for col in range(n):
for row in range(col, m):
u.push_back(col, row, float(row + col * m))
return c.dcsc(u)
def lower_tri_full(m, n):
l = c.dtrp(m, n)
for col in range(n):
for row in range(col, m):
l.push_back(row, col, float(row + col * m))
return c.dcsc(l)
import cppsparse as cp
x = cp.dcsc(cp.dtrp("fem.dat"))
x.sum_duplicates()
print(x)
import cppsparse as c
def random_triplet(m, n, nnz):
import numpy as np
row_idx = np.random.random_integers(0, m - 1, nnz)
col_idx = np.random.random_integers(0, n - 1, nnz)
nz__val = np.random.rand(10)
trip = c.dtrp(m, n, nnz)
for i, j, v in zip(row_idx, col_idx, nz__val):
print i, j, v
trip.push_back(int(i), int(j), float(v))
return trip
x = c.dcsc(random_triplet(8, 8, 20))
##print(x)
#print(x.find())
x.sort()
##print(x.find())
import cppsparse as c
##import numpyinterface as n
m = 15
conn = [(1, 3), (3, 5), (3, 8), (5, 8), (8, 10), (6, 9), (6, 10), (2, 4),
(2, 7), (2, 9), (4, 9), (6, 9), (6, 10), (7, 10), (7, 11), (8, 10),
(9, 12), (9, 13), (10, 11), (10, 13), (10, 14), (11, 12), (11, 13),
(12, 13), (13, 14)]
trip = c.dtrp(m, m, 2 * m)
for i in range(m):
trip.push_back(i, i, 1.0)
for i, j in conn:
trip.push_back(i, j, 1.0)
G = c.dcsc(trip).transpose()
print(G)
#G.to_graph("tim.dot", True)
rhs = c.dtrp(15, 1, 4)
rhs.push_back(4, 0, 1.0)
rhs.push_back(6, 0, 1.0)
B = c.dcsc(rhs)
top = G.reach(B, 0, c.ivec(m), c.ivec(m))
(8, 10),
(9, 12),
(9, 13),
(10, 11),
(10, 13),
(10, 14),
(11, 12),
(11, 13),
(12, 13),
(13, 14),
]
trip = c.dtrp(m, m, 2 * m)
for i in range(m):
trip.push_back(i, i, 1.0)
for i, j in conn:
trip.push_back(i, j, 1.0)
G = c.dcsc(trip).transpose()
print(G)
# G.to_graph("tim.dot", True)
rhs = c.dtrp(15, 1, 4)
rhs.push_back(4, 0, 1.0)
rhs.push_back(6, 0, 1.0)
B = c.dcsc(rhs)
top = G.reach(B, 0, c.ivec(m), c.ivec(m))
for i in range(len(x)):
y.push_back(i, 0, float(x[i]))
z = c.dcsc(y)
z.keep()
return z
lower_tri = c.dtrp(5, 5)
full_sym = c.dtrp(5, 5)
upper_tri = c.dtrp(5, 5)
y = c.dtrp(5, 1)
for col in range(5):
for row in range(col, 5):
lower_tri.push_back(row, col, float(row + col * 5.0))
full_sym.push_back(row, col, float(row + col * 5.0))
upper_tri.push_back(col, row, float(row + col * 5.0))
if row != col:
full_sym.push_back(col, row, float(row + col * 5.0))
lower_tri = c.dcsc(lower_tri)
upper_tri = c.dcsc(upper_tri)
full_sym = c.dcsc(full_sym)
b = spcolvec(n.array([3.0, 4.0, 0, 0, 2.0]))
z = lower_tri.multiply2(upper_tri)
print(z)
B = nfull(b)
print(n.dot(nfull(lower_tri), nfull(upper_tri)))
print(nfull(z))
y.push_back(i, 0, float(x[i]))
z = c.dcsc(y)
z.keep()
return z
lower_tri = c.dtrp(5, 5)
full_sym = c.dtrp(5, 5)
upper_tri = c.dtrp(5, 5)
y = c.dtrp(5, 1)
for col in range(5):
for row in range(col, 5):
lower_tri.push_back(row, col, float(row + col * 5.0))
full_sym.push_back (row, col, float(row + col * 5.0))
upper_tri.push_back (col, row, float(row + col * 5.0))
if row != col:
full_sym.push_back (col, row, float(row + col * 5.0))
lower_tri = c.dcsc(lower_tri)
upper_tri = c.dcsc(upper_tri)
full_sym = c.dcsc(full_sym)
b = spcolvec(n.array([3.0,4.0,0,0,2.0]))
z = lower_tri.multiply2(upper_tri)
print(z)
B = nfull(b)
print(n.dot(nfull(lower_tri), nfull(upper_tri)))
print(nfull(z))
import cppsparse as c
x = c.dtrp("data.txt")
print(x)
y = c.dcsc(x)
print(y)
print(y == y)
t = y.transpose()
print(t)
u = t.transpose()
print(u)
v = u.transpose()
print(v.find())