#!/usr/bin/python
# -*- coding: UTF-8 -*-
import numpy
from numpy import sqrt
import scipy.spatial.distance
from sklearn.manifold import MDS
from sklearn.metrics import euclidean_distances
from sklearn.decomposition import PCA
def mds(utv):
rdm = scipy.spatial.distance.squareform(utv)
seed = numpy.random.RandomState(seed=3)
mds = MDS(n_components=2, max_iter=3000, eps=1e-9, random_state=seed,
dissimilarity="precomputed", n_jobs=1)
pos = mds.fit_transform(rdm)
# rescale
#pos *= sqrt((X_true ** 2).sum()) / sqrt((pos ** 2).sum())
# Y = mds.fit_transform(RDM)
# if itime == 0:
# Y = mds.fit_transform(RDM)
# else:
# d, Y, _ = procrustes(
# Y, mds.fit_transform(RDM), scaling=False)
# Rotate the data
# clf = PCA(n_components=2)
# pos = clf.fit_transform(pos)
return pos
