本文整理匯總了Python中modshogun.RealFeatures.load_serializable方法的典型用法代碼示例。如果您正苦於以下問題：Python RealFeatures.load_serializable方法的具體用法？Python RealFeatures.load_serializable怎麽用？Python RealFeatures.load_serializable使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類modshogun.RealFeatures的用法示例。
在下文中一共展示了RealFeatures.load_serializable方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: predict_new_data
# 需要導入模塊: from modshogun import RealFeatures [as 別名]
# 或者: from modshogun.RealFeatures import load_serializable [as 別名]
def predict_new_data(graph_file, cons_file, tri_file, other_feature_file):
print "reading extracted features"
graph_feature = read_feature_data(graph_file)
graph_feature = get_normalized_given_max_min(graph_feature, "models/grtaph_max_size")
cons_feature = read_feature_data(cons_file)
cons_feature = get_normalized_given_max_min(cons_feature, "models/cons_max_size")
CC_feature = read_feature_data(tri_file)
CC_feature = get_normalized_given_max_min(CC_feature, "models/tri_max_size")
ATOS_feature = read_feature_data(other_feature_file)
ATOS_feature = get_normalized_given_max_min(ATOS_feature, "models/alu_max_size")
width, C, epsilon, num_threads, mkl_epsilon, mkl_norm = 0.5, 1.2, 1e-5, 1, 0.001, 3.5
kernel = CombinedKernel()
feats_train = CombinedFeatures()
feats_test = CombinedFeatures()
# pdb.set_trace()
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(graph_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/graph.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(cons_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/cons.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(CC_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/tri.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(ATOS_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/alu.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
model_file = "models/mkl.dat"
if not os.path.exists(model_file):
print "downloading model file"
url_add = "http://rth.dk/resources/mirnasponge/data/mkl.dat"
urllib.urlretrieve(url_add, model_file)
print "loading trained model"
fstream = SerializableAsciiFile("models/mkl.dat", "r")
new_mkl = MKLClassification()
status = new_mkl.load_serializable(fstream)
print "model predicting"
kernel.init(feats_train, feats_test)
new_mkl.set_kernel(kernel)
y_out = new_mkl.apply().get_labels()
return y_out