def read_mtx(fname, dtype):
"""
Read a MatrixMarket file. Assume coordinate format, and double precision.
Very crude!
"""
fd = open(fname)
lines = map(lambda x: x.strip().split(" "), fd.readlines())
ln = -1
for line in lines:
ln += 1
if line[ln][0] == "%":
continue
else:
break
n = int(lines[ln][0])
m = int(lines[ln][1])
nnz = int(lines[ln][2])
mat = p.CompressedMatrix(n, m, nnz, dtype=dtype)
mat_type = p.np_result_type(mat).type
def assign(l):
try:
i, j, v = int(l[0]), int(l[1]), mat_type(l[2])
mat[i - 1, j - 1] = v
except ValueError:
pass
map(assign, lines[ln + 1:])
return mat
def read_mtx(fname, dtype):
"""
Read a MatrixMarket file. Assume coordinate format, and double precision.
Very crude!
"""
fd = open(fname)
lines = map(lambda x: x.strip().split(" "), fd.readlines())
ln = -1
for line in lines:
ln += 1
if line[ln][0] == "%":
continue
else:
break
n = int(lines[ln][0])
m = int(lines[ln][1])
nnz = int(lines[ln][2])
mat = p.CompressedMatrix(n, m, nnz, dtype=dtype)
mat_type = p.np_result_type(mat).type
def assign(l):
try:
i, j, v = int(l[0]), int(l[1]), mat_type(l[2])
mat[i-1, j-1] = v
except ValueError:
pass
map(assign, lines[ln+1:])
return mat
def read_vector(fname, dtype):
fd = open(fname)
lines = map(lambda x: x.strip().split(" "), fd.readlines())
count = int(lines[0][0])
vector = list(map(lambda x: p.np_result_type(dtype).type(x), lines[1]))
vector = p.Vector(vector, dtype=dtype)
if vector.size != count:
raise Exception("Sizes %d and %d do not match!" % (vector.size, count))
return vector
def read_vector(fname, dtype=np.float32):
fd = open(fname)
lines = list(map(lambda x: x.strip().split(" "), fd.readlines()))
count = int(lines[0][0])
vector = list(map(lambda x: p.np_result_type(dtype).type(x), lines[1]))
vector = p.Vector(vector, dtype=dtype)
if vector.size != count:
raise Exception("Sizes %d and %d do not match!" % (vector.size, count))
return vector
def read_mtx(fname, dtype=p.float32, sparse_type=p.CompressedMatrix):
"""
Read a MatrixMarket file. Assume coordinate format. Very crude!
"""
fd = open(fname)
lines = list(map(lambda x: x.strip().split(" "), fd.readlines()))
ln = -1
for line in lines:
ln += 1
if line[ln][0] == "%":
continue
else:
break
n = int(lines[ln][0])
m = int(lines[ln][1])
try:
nnz = int(lines[ln][2])
except:
nnz = n * m
if m == 1:
vec_type = np.result_type(dtype).type
values = list(map(lambda x: vec_type(" ".join(x)), lines[ln + 1 :]))
values = np.array(values)
vec = p.Vector(values, dtype=dtype)
return vec
else:
mat = sparse_type(n, m, nnz, dtype=dtype)
mat_type = p.np_result_type(mat).type
def assign(l):
try:
i, j, v = int(l[0]), int(l[1]), mat_type(l[2])
mat.insert(i - 1, j - 1, v)
except ValueError:
pass
result = list(map(assign, lines[ln + 1 :]))
return mat
