def embed(filename='words.dat'): print 'loading' words = [] with open(filename) as f: words.extend([str.rstrip() for str in f.readlines()]) print 'loaded', words converter = KernelLocallyLinearEmbedding() converter.set_k(20) converter.set_target_dim(2) converter.parallel.set_num_threads(1) embedding = converter.embed_kernel(word_kernel(words)).get_feature_matrix() return embedding,words
def embed(filename='words.dat'):
print 'loading'
words = []
with open(filename) as f:
words.extend([str.rstrip() for str in f.readlines()])
print 'loaded', words
converter = KernelLocallyLinearEmbedding()
converter.set_k(20)
converter.set_target_dim(2)
converter.parallel.set_num_threads(1)
embedding = converter.embed_kernel(word_kernel(words)).get_feature_matrix()
return embedding, words
def converter_kernellocallylinearembedding_modular(data_fname, k):
try:
from modshogun import RealFeatures, KernelLocallyLinearEmbedding, LinearKernel, CSVFile
features = RealFeatures(CSVFile(data_fname))
kernel = LinearKernel()
converter = KernelLocallyLinearEmbedding(kernel)
converter.set_target_dim(1)
converter.set_k(k)
converter.apply(features)
return features
except ImportError:
print('No Eigen3 available')
def converter_kernellocallylinearembedding_modular (data_fname,k):
try:
from modshogun import RealFeatures, KernelLocallyLinearEmbedding, LinearKernel, CSVFile
features = RealFeatures(CSVFile(data_fname))
kernel = LinearKernel()
converter = KernelLocallyLinearEmbedding(kernel)
converter.set_target_dim(1)
converter.set_k(k)
converter.apply(features)
return features
except ImportError:
print('No Eigen3 available')