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Python StringCharFeatures.obtain_by_position_list方法代码示例

本文整理汇总了Python中shogun.Features.StringCharFeatures.obtain_by_position_list方法的典型用法代码示例。如果您正苦于以下问题:Python StringCharFeatures.obtain_by_position_list方法的具体用法?Python StringCharFeatures.obtain_by_position_list怎么用?Python StringCharFeatures.obtain_by_position_list使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在shogun.Features.StringCharFeatures的用法示例。


在下文中一共展示了StringCharFeatures.obtain_by_position_list方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: get_predictions_from_seqdict

# 需要导入模块: from shogun.Features import StringCharFeatures [as 别名]
# 或者: from shogun.Features.StringCharFeatures import obtain_by_position_list [as 别名]
	def get_predictions_from_seqdict(self, seqdic, site):
		""" we need to generate a huge test features object
			containing all locations found in each seqdict-sequence
			and each location (this is necessary to efficiently
			(==fast,low memory) compute the splice outputs
		"""

		seqlen=self.window_right+self.window_left+2

		for s in seqdic:
			position_list=DynamicIntArray()

			sequence=s.seq
			positions=s.preds[site].positions
			for j in xrange(len(positions)):
				i=positions[j] - self.offset -self.window_left
				position_list.append_element(i)

			t=StringCharFeatures([sequence], DNA)
			t.obtain_by_position_list(seqlen, position_list)
			self.wd_kernel.init(self.traindat, t)

			self.wd_kernel.io.enable_progress()
			l=self.svm.apply().get_values()
			self.wd_kernel.cleanup()
			sys.stdout.write("\n...done...\n")

			num=len(s.preds[site].positions)
			scores= num * [0]
			for j in xrange(num):
				scores[j]=l[j]
			s.preds[site].set_scores(scores)
开发者ID:42MachineLearning,项目名称:shogun,代码行数:34,代码来源:signal_detectors.py

示例2: get_predictions

# 需要导入模块: from shogun.Features import StringCharFeatures [as 别名]
# 或者: from shogun.Features.StringCharFeatures import obtain_by_position_list [as 别名]
	def get_predictions(self, sequence, positions):

		seqlen=self.window_right+self.window_left+2
		num=len(positions)

		position_list=DynamicIntArray()

		for j in xrange(num):
			i=positions[j] - self.offset - self.window_left
			position_list.append_element(i)

		t=StringCharFeatures([sequence], DNA)
		t.obtain_by_position_list(seqlen, position_list)
		self.wd_kernel.init(self.traindat, t)
		del t

		self.wd_kernel.io.enable_progress()
		l=self.svm.apply().get_values()
		self.wd_kernel.cleanup()
		sys.stdout.write("\n...done...\n")
		return l
开发者ID:42MachineLearning,项目名称:shogun,代码行数:23,代码来源:signal_detectors.py

示例3: features_string_sliding_window_modular

# 需要导入模块: from shogun.Features import StringCharFeatures [as 别名]
# 或者: from shogun.Features.StringCharFeatures import obtain_by_position_list [as 别名]
def features_string_sliding_window_modular(strings):
    from shogun.Features import StringCharFeatures, DNA
    from shogun.Library import DynamicIntArray

    f = StringCharFeatures([strings], DNA)

    # slide a window of length 5 over features
    # (memory efficient, does not copy strings)
    f.obtain_by_sliding_window(5, 1)
    # print(f.get_num_vectors())
    # print(f.get_vector_length(0))
    # print(f.get_vector_length(1))
    # print(f.get_features())

    # slide a window of length 4 over features
    # (memory efficient, does not copy strings)
    f.obtain_by_sliding_window(4, 1)
    # print(f.get_num_vectors())
    # print(f.get_vector_length(0))
    # print(f.get_vector_length(1))
    # print(f.get_features())

    # extract string-windows at position 0,6,16,25 of window size 4
    # (memory efficient, does not copy strings)
    f.set_features([s])
    positions = DynamicIntArray()
    positions.append_element(0)
    positions.append_element(6)
    positions.append_element(16)
    positions.append_element(25)

    f.obtain_by_position_list(4, positions)
    # print(f.get_features())

    # now extract windows of size 8 from same positon list
    f.obtain_by_position_list(8, positions)
    # print(f.get_features())
    return f
开发者ID:behollis,项目名称:muViewBranch,代码行数:40,代码来源:features_string_sliding_window_modular.py


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