本文整理汇总了Python中tools.load.LoadMatrix.load_dna方法的典型用法代码示例。如果您正苦于以下问题：Python LoadMatrix.load_dna方法的具体用法？Python LoadMatrix.load_dna怎么用？Python LoadMatrix.load_dna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类tools.load.LoadMatrix的用法示例。
在下文中一共展示了LoadMatrix.load_dna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: xrange
# 需要导入模块: from tools.load import LoadMatrix [as 别名]
# 或者: from tools.load.LoadMatrix import load_dna [as 别名]
reverse=False
charfeat=StringCharFeatures(DNA)
charfeat.set_features(fm_dna)
feats=StringWordFeatures(charfeat.get_alphabet())
feats.obtain_from_char(charfeat, order-1, order, gap, reverse)
histo=Histogram(feats)
histo.train()
histo.get_histogram()
num_examples=feats.get_num_vectors()
num_param=histo.get_num_model_parameters()
#for i in xrange(num_examples):
# for j in xrange(num_param):
# histo.get_log_derivative(j, i)
histo.get_log_likelihood()
histo.get_log_likelihood_sample()
###########################################################################
# call functions
###########################################################################
if __name__=='__main__':
from tools.load import LoadMatrix
lm=LoadMatrix()
fm_dna=lm.load_dna('../data/fm_train_dna.dat')
histogram()
示例2: matches
# 需要导入模块: from tools.load import LoadMatrix [as 别名]
# 或者: from tools.load.LoadMatrix import load_dna [as 别名]
# This is an example for the initialization of the CommWordString-kernel (aka
# Spectrum or n-gram kernel; its name is derived from the unix command comm). This kernel
# sums over k-mere matches (k='order'). For efficient computing a preprocessor is used
# that extracts and sorts all k-mers. If 'use_sign' is set to one each k-mere is counted
# only once.
from tools.load import LoadMatrix
from sg import sg
lm=LoadMatrix()
traindna=lm.load_dna('../data/fm_train_dna.dat')
testdna=lm.load_dna('../data/fm_test_dna.dat')
parameter_list=[[traindna,testdna,10,3,0,'n',False,'FULL'],
[traindna,testdna,11,4,0,'n',False,'FULL']]
def kernel_commwordstring (fm_train_dna=traindna,fm_test_dna=testdna,
size_cache=10,
order=3,gap=0,reverse='n',
use_sign=False,normalization='FULL'):
sg('add_preproc', 'SORTWORDSTRING')
sg('set_features', 'TRAIN', fm_train_dna, 'DNA')
sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse)
sg('attach_preproc', 'TRAIN')
sg('set_features', 'TEST', fm_test_dna, 'DNA')
sg('convert', 'TEST', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse)
sg('attach_preproc', 'TEST')
sg('set_kernel', 'COMMSTRING', 'WORD', size_cache, use_sign, normalization)
km=sg('get_kernel_matrix', 'TRAIN')示例3: LoadMatrix
# 需要导入模块: from tools.load import LoadMatrix [as 别名]
# 或者: from tools.load.LoadMatrix import load_dna [as 别名]
#!/usr/bin/env python
from tools.load import LoadMatrix
lm = LoadMatrix()
train_dna = lm.load_dna("../data/fm_train_dna.dat")
test_dna = lm.load_dna("../data/fm_test_dna.dat")
label = lm.load_labels("../data/label_train_dna.dat")
parameter_list = [[train_dna, test_dna, label, 20, 0.9, 1e-3, 1], [train_dna, test_dna, label, 20, 2.3, 1e-5, 4]]
def classifier_svmlight_batch_linadd_modular(
fm_train_dna, fm_test_dna, label_train_dna, degree, C, epsilon, num_threads
):
from modshogun import StringCharFeatures, BinaryLabels, DNA
from modshogun import WeightedDegreeStringKernel, MSG_DEBUG
try:
from modshogun import SVMLight
except ImportError:
print("No support for SVMLight available.")
return
feats_train = StringCharFeatures(DNA)
# feats_train.io.set_loglevel(MSG_DEBUG)
feats_train.set_features(fm_train_dna)
feats_test = StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
degree = 20