def get_scaled_vectors(vectors,
user_seed=None,
n_components=12,
normalize=True,
progress=progress):
if user_seed:
seed = np.random.RandomState(seed=user_seed)
else:
seed = np.random.RandomState()
# FIXME: Make this optional:
from sklearn.metrics.pairwise import euclidean_distances as d
vectors = get_normalized_vectors(
np.array(vectors)) if normalize else np.array(vectors)
# compute similarities based on d
progress.update('Computing similarity matrix')
similarities = d(vectors)
progress.update('Scaling using %d components' % n_components)
mds = manifold.MDS(n_components=n_components,
max_iter=300,
eps=1e-10,
random_state=seed,
dissimilarity="precomputed",
n_jobs=1)
progress.update('Fitting')
scaled_vectors = mds.fit(similarities).embedding_
return scaled_vectors
def get_scaled_vectors(vectors, user_seed = None, n_components = 12, normalize=True, progress = progress):
if user_seed:
seed = np.random.RandomState(seed=user_seed)
else:
seed = np.random.RandomState()
# FIXME: Make this optional:
from sklearn.metrics.pairwise import euclidean_distances as d
vectors = get_normalized_vectors(np.array(vectors)) if normalize else np.array(vectors)
# compute similarities based on d
progress.update('Computing similarity matrix')
similarities = d(vectors)
progress.update('Scaling using %d components' % n_components)
mds = manifold.MDS(n_components=n_components, max_iter=300, eps=1e-10, random_state=seed,
dissimilarity="precomputed", n_jobs=1)
progress.update('Fitting')
scaled_vectors = mds.fit(similarities).embedding_
return scaled_vectors