def svm_read_problem(data_file_name, return_scipy=False):
"""
svm_read_problem(data_file_name, return_scipy=False) -> [y, x], y: list, x: list of dictionary
svm_read_problem(data_file_name, return_scipy=True) -> [y, x], y: ndarray, x: csr_matrix
Read LIBSVM-format data from data_file_name and return labels y
and data instances x.
"""
prob_y = []
prob_x = []
row_ptr = [0]
col_idx = []
for i, line in enumerate(open(data_file_name)):
line = line.split(None, 1)
# In case an instance with all zero features
if len(line) == 1:
line += ['']
label, features = line
prob_y += [float(label)]
if scipy is not None and return_scipy:
nz = 0
for e in features.split():
ind, val = e.split(":")
val = float(val)
if val != 0:
col_idx += [int(ind) - 1]
prob_x += [val]
nz += 1
row_ptr += [row_ptr[-1] + nz]
else:
xi = {}
for e in features.split():
ind, val = e.split(":")
if val != 0:
xi[int(ind)] = float(val)
prob_x += [xi]
if scipy is not None and return_scipy:
prob_y = scipy.array(prob_y)
prob_x = scipy.array(prob_x)
col_idx = scipy.array(col_idx)
row_ptr = scipy.array(row_ptr)
prob_x = sparse.csr_matrix((prob_x, col_idx, row_ptr))
return (prob_y, prob_x)
def svm_read_problem(data_file_name, return_scipy=False):
"""
svm_read_problem(data_file_name, return_scipy=False) -> [y, x], y: list, x: list of dictionary
svm_read_problem(data_file_name, return_scipy=True) -> [y, x], y: ndarray, x: csr_matrix
Read LIBSVM-format data from data_file_name and return labels y
and data instances x.
"""
prob_y = []
prob_x = []
row_ptr = [0]
col_idx = []
for i, line in enumerate(open(data_file_name)):
line = line.split(None, 1)
# In case an instance with all zero features
if len(line) == 1: line += ['']
label, features = line
prob_y += [float(label)]
if scipy != None and return_scipy:
nz = 0
for e in features.split():
ind, val = e.split(":")
val = float(val)
if val != 0:
col_idx += [int(ind)-1]
prob_x += [val]
nz += 1
row_ptr += [row_ptr[-1]+nz]
else:
xi = {}
for e in features.split():
ind, val = e.split(":")
if val != 0:
xi[int(ind)] = float(val)
prob_x += [xi]
if scipy != None and return_scipy:
prob_y = scipy.array(prob_y)
prob_x = scipy.array(prob_x)
col_idx = scipy.array(col_idx)
row_ptr = scipy.array(row_ptr)
prob_x = sparse.csr_matrix((prob_x, col_idx, row_ptr))
return (prob_y, prob_x)