def create_empty_promoter_kernel(param):
"""
creates an uninitialized promoter kernel
@param param:
"""
# centered WDK/WDK-shift
if param["shifts"] == 0:
kernel_center = WeightedDegreeStringKernel(param["degree"])
else:
kernel_center = WeightedDegreePositionStringKernel(10, param["degree"])
shifts_vector = numpy.ones(param["center_offset"] * 2,
dtype=numpy.int32) * param["shifts"]
kernel_center.set_shifts(shifts_vector)
kernel_center.set_cache_size(param["kernel_cache"] / 3)
# border spetrum kernels
size = param["kernel_cache"] / 3
use_sign = False
kernel_left = WeightedCommWordStringKernel(size, use_sign)
kernel_right = WeightedCommWordStringKernel(size, use_sign)
# assemble combined kernel
kernel = CombinedKernel()
kernel.append_kernel(kernel_center)
kernel.append_kernel(kernel_left)
kernel.append_kernel(kernel_right)
return kernel
def create_empty_promoter_kernel(param):
"""
creates an uninitialized promoter kernel
@param param:
"""
# centered WDK/WDK-shift
if param["shifts"] == 0:
kernel_center = WeightedDegreeStringKernel(param["degree"])
else:
kernel_center = WeightedDegreePositionStringKernel(10, param["degree"])
shifts_vector = numpy.ones(param["center_offset"]*2, dtype=numpy.int32)*param["shifts"]
kernel_center.set_shifts(shifts_vector)
kernel_center.set_cache_size(param["kernel_cache"]/3)
# border spetrum kernels
size = param["kernel_cache"]/3
use_sign = False
kernel_left = WeightedCommWordStringKernel(size, use_sign)
kernel_right = WeightedCommWordStringKernel(size, use_sign)
# assemble combined kernel
kernel = CombinedKernel()
kernel.append_kernel(kernel_center)
kernel.append_kernel(kernel_left)
kernel.append_kernel(kernel_right)
return kernel
def train(self, data, labels):
"""
model training
"""
# centered WDK/WDK-shift
if self.param["shifts"] == 0:
kernel_center = WeightedDegreeStringKernel(self.param["degree"])
else:
kernel_center = WeightedDegreePositionStringKernel(
10, self.param["degree"])
shifts_vector = numpy.ones(
self.param["center_offset"] * 2,
dtype=numpy.int32) * self.param["shifts"]
kernel_center.set_shifts(shifts_vector)
kernel_center.set_cache_size(self.param["kernel_cache"] / 3)
# border spetrum kernels
size = self.param["kernel_cache"] / 3
use_sign = False
kernel_left = WeightedCommWordStringKernel(size, use_sign)
kernel_right = WeightedCommWordStringKernel(size, use_sign)
# assemble combined kernel
kernel = CombinedKernel()
kernel.append_kernel(kernel_center)
kernel.append_kernel(kernel_left)
kernel.append_kernel(kernel_right)
## building features
feat = create_features(data, self.param["center_offset"],
self.param["center_pos"])
# init combined kernel
kernel.init(feat, feat)
print "len(labels) = %i" % (len(labels))
lab = BinaryLabels(numpy.double(labels))
self.svm = SVMLight(self.param["cost"], kernel, lab)
# show debugging output
self.svm.io.enable_progress()
self.svm.io.set_loglevel(MSG_DEBUG)
# optimization settings
num_threads = 2
self.svm.parallel.set_num_threads(num_threads)
self.svm.set_epsilon(10e-8)
self.svm.train()
return self
def train(self, data, labels):
"""
model training
"""
# centered WDK/WDK-shift
if self.param["shifts"] == 0:
kernel_center = WeightedDegreeStringKernel(self.param["degree"])
else:
kernel_center = WeightedDegreePositionStringKernel(10, self.param["degree"])
shifts_vector = numpy.ones(self.param["center_offset"]*2, dtype=numpy.int32)*self.param["shifts"]
kernel_center.set_shifts(shifts_vector)
kernel_center.set_cache_size(self.param["kernel_cache"]/3)
# border spetrum kernels
size = self.param["kernel_cache"]/3
use_sign = False
kernel_left = WeightedCommWordStringKernel(size, use_sign)
kernel_right = WeightedCommWordStringKernel(size, use_sign)
# assemble combined kernel
kernel = CombinedKernel()
kernel.append_kernel(kernel_center)
kernel.append_kernel(kernel_left)
kernel.append_kernel(kernel_right)
## building features
feat = create_features(data, self.param["center_offset"], self.param["center_pos"])
# init combined kernel
kernel.init(feat, feat)
print "len(labels) = %i" % (len(labels))
lab = BinaryLabels(numpy.double(labels))
self.svm = SVMLight(self.param["cost"], kernel, lab)
# show debugging output
self.svm.io.enable_progress()
self.svm.io.set_loglevel(MSG_DEBUG)
# optimization settings
num_threads = 2
self.svm.parallel.set_num_threads(num_threads)
self.svm.set_epsilon(10e-8)
self.svm.train()
return self
