本文整理汇总了Python中shogun.Classifier.SVMLight.get_alphas方法的典型用法代码示例。如果您正苦于以下问题：Python SVMLight.get_alphas方法的具体用法？Python SVMLight.get_alphas怎么用？Python SVMLight.get_alphas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shogun.Classifier.SVMLight的用法示例。
在下文中一共展示了SVMLight.get_alphas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: test_data
# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import get_alphas [as 别名]
#.........这里部分代码省略.........
taxonomy = shogun_factory.create_taxonomy(mss.taxonomy.data)
support = numpy.linspace(0, 100, 4)
distances = [[0, 1, 2, 2], [1, 0, 2, 2], [2, 2, 0, 1], [2, 2, 1, 0]]
# create tree normalizer
tree_normalizer = MultitaskKernelPlifNormalizer(support, data.task_vector_names)
task_names = data.get_task_names()
FACTOR = 1.0
# init gamma matrix
gammas = numpy.zeros((data.get_num_tasks(), data.get_num_tasks()))
for t1_name in task_names:
for t2_name in task_names:
similarity = taxonomy.compute_node_similarity(taxonomy.get_id(t1_name), taxonomy.get_id(t2_name))
gammas[data.name_to_id(t1_name), data.name_to_id(t2_name)] = similarity
helper.save("/tmp/gammas", gammas)
gammas = gammas * FACTOR
cost = param.cost * numpy.sqrt(FACTOR)
print gammas
##########
# regular normalizer
normalizer = MultitaskKernelNormalizer(data.task_vector_nums)
for t1_name in task_names:
for t2_name in task_names:
similarity = gammas[data.name_to_id(t1_name), data.name_to_id(t2_name)]
normalizer.set_task_similarity(data.name_to_id(t1_name), data.name_to_id(t2_name), similarity)
##################################################################
# Train SVMs
##################################################################
# create shogun objects
wdk_tree = shogun_factory.create_kernel(data.examples, param)
lab = shogun_factory.create_labels(data.labels)
wdk_tree.set_normalizer(tree_normalizer)
wdk_tree.init_normalizer()
print "--->",wdk_tree.get_normalizer().get_name()
svm_tree = SVMLight(cost, wdk_tree, lab)
svm_tree.set_linadd_enabled(False)
svm_tree.set_batch_computation_enabled(False)
svm_tree.train()
del wdk_tree
del tree_normalizer
print "finished training tree-norm SVM:", svm_tree.get_objective()
wdk = shogun_factory.create_kernel(data.examples, param)
wdk.set_normalizer(normalizer)
wdk.init_normalizer()
print "--->",wdk.get_normalizer().get_name()
svm = SVMLight(cost, wdk, lab)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
svm.train()
print "finished training manually set SVM:", svm.get_objective()
alphas_tree = svm_tree.get_alphas()
alphas = svm.get_alphas()
assert(len(alphas_tree)==len(alphas))
for i in xrange(len(alphas)):
assert(abs(alphas_tree[i] - alphas[i]) < 0.0001)
print "success: all alphas are the same"示例2: SVMLight
# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import get_alphas [as 别名]
del tree_normalizer
print "finished training tree-norm SVM:", svm_tree.get_objective()
wdk = shogun_factory.create_kernel(data.examples, param)
wdk.set_normalizer(normalizer)
wdk.init_normalizer()
print "--->",wdk.get_normalizer().get_name()
svm = SVMLight(cost, wdk, lab)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
svm.train()
print "finished training manually set SVM:", svm.get_objective()
alphas_tree = svm_tree.get_alphas()
alphas = svm.get_alphas()
assert(len(alphas_tree)==len(alphas))
for i in xrange(len(alphas)):
assert(abs(alphas_tree[i] - alphas[i]) < 0.0001)
print "success: all alphas are the same"
示例3: xrange
# 需要导入模块: from shogun.Classifier import SVMLight [as 别名]
# 或者: from shogun.Classifier.SVMLight import get_alphas [as 别名]
for idx in xrange(10):
#f = (-numpy.ones(N)-2)*numpy.random.randn()
print "############################"
print "############################"
print ""
print "f:", f
print "\n"
svm.set_linear_term(numpy.double(f))
svm.train()
sv_idx = svm.get_support_vectors()
alphas = svm.get_alphas()
alphas_full = numpy.zeros(N)
alphas_full[sv_idx] = alphas
alphas_full = alphas_full * y
print "svmlight objective:", svm.get_objective()
print "svmlight alphas:", numpy.array(alphas_full[0:5])
external_objective = 0.0
for j in xrange(N):
external_objective += alphas_full[j] * f[j]