# author: Daniel Burkhardt <*****@*****.**>
# (C) 2017 Krishnaswamy Lab GPLv2
from __future__ import print_function, division
from sklearn import manifold
from sklearn.decomposition import PCA
from scipy.spatial.distance import pdist, squareform
import scipy.spatial
from deprecated import deprecated
import tasklogger
import scprep
import s_gd2
_logger = tasklogger.get_tasklogger("graphtools")
# Fast classical MDS using random svd
@deprecated(version="1.0.0", reason="Use phate.mds.classic instead")
def cmdscale_fast(D, ndim):
return classic(D=D, n_components=ndim)
def classic(D, n_components=2, random_state=None):
"""Fast CMDS using random SVD
Parameters
----------
D : array-like, shape=[n_samples, n_samples]
pairwise distances
def test_get_logger():
logger = tasklogger.get_tasklogger()
logger2 = tasklogger.get_tasklogger()
assert logger is logger2
logger2 = tasklogger.get_tasklogger("test")
assert logger is not logger2