def from_file(self, file):
sys.stderr.write('loading model file')
l = file.readline()
if l != '%arts version: 1.0\n':
sys.stderr.write("\nfile not an arts definition file\n")
return None
bias = None
alphas = None
num_kernels = None
while l:
# skip comment or empty line
if not (l.startswith('%') or l.startswith('\n')):
if bias is None: bias = parse_float(l, 'b')
if alphas is None: alphas = parse_vector(l, file, 'alphas')
if num_kernels is None:
num_kernels = parse_int(l, 'num_kernels')
if num_kernels and bias and alphas is not None:
for i in xrange(num_kernels):
s = Sensor()
(k, f) = s.from_file(file, i + 1)
k.io.enable_progress()
self.window = (min(self.window[0], s.window[0]),
max(self.window[1], s.window[2]))
self.sensors.append(s)
self.kernel.append_kernel(k)
self.svs.append_feature_obj(f)
self.kernel.init(self.svs, self.svs)
self.svm = SVM(
self.kernel, alphas,
numpy.arange(len(alphas), dtype=numpy.int32), bias)
self.svm.io.set_target_to_stderr()
self.svm.io.enable_progress()
self.svm.parallel.set_num_threads(
self.svm.parallel.get_num_cpus())
sys.stderr.write('done\n')
return
l = file.readline()
sys.stderr.write('error loading model file\n')
class svm_splice_model(object):
def __init__(self, order, traindat, alphas, b, (window_left, offset,
window_right), consensus):
f = StringCharFeatures(traindat, DNA)
wd_kernel = WeightedDegreeStringKernel(f, f, int(order))
wd_kernel.io.set_target_to_stdout()
self.svm = SVM(wd_kernel, alphas,
numpy.arange(len(alphas), dtype=numpy.int32), b)
self.svm.io.set_target_to_stdout()
self.svm.parallel.set_num_threads(self.svm.parallel.get_num_cpus())
self.svm.set_linadd_enabled(True)
self.svm.set_batch_computation_enabled(True)
self.window_left = int(window_left)
self.window_right = int(window_right)
self.consensus = consensus
self.wd_kernel = wd_kernel
self.traindat = f
self.offset = offset
