def features_read_svmlight_format_modular(fname):
import os
from modshogun import SparseRealFeatures
from modshogun import LibSVMFile
f = SparseRealFeatures()
lab = f.load_with_labels(LibSVMFile(fname))
f.save_with_labels(LibSVMFile("testwrite.light", "w"), lab)
def features_read_svmlight_format_modular(fname):
import os
from modshogun import SparseRealFeatures
from modshogun import LibSVMFile
f = SparseRealFeatures()
lab = f.load_with_labels(LibSVMFile(fname))
f.save_with_labels(LibSVMFile('testwrite.light', 'w'), lab)
def load_sparse_data(filename, dimension=None):
input_file = LibSVMFile(args.dataset)
sparse_feats = SparseRealFeatures()
label_array = sparse_feats.load_with_labels(input_file)
labels = BinaryLabels(label_array)
if dimension!=None:
sparse_feats.set_num_features(dimension)
return {'data':sparse_feats, 'labels':labels}
def load_sparse_data(filename, dimension=None):
input_file = LibSVMFile(args.dataset)
sparse_feats = SparseRealFeatures()
label_array = sparse_feats.load_with_labels(input_file)
labels = BinaryLabels(label_array)
if dimension != None:
sparse_feats.set_num_features(dimension)
return {'data': sparse_feats, 'labels': labels}
def get_features_and_labels(input_file): feats = SparseRealFeatures() label_array = feats.load_with_labels(input_file) labels = MulticlassLabels(label_array) return feats, labels
def get_features_and_labels(input_file): feats = SparseRealFeatures() label_array = feats.load_with_labels(input_file) labels = MulticlassLabels(label_array) return feats, labels
