import pyminicore

from pyminicore import *

from .util import constants

from .util.constants import CSR as csr_tuple, KMCRSV

if hasattr(pyminicore, "SparseMatrixWrapper"):

from pyminicore import SparseMatrixWrapper as smw

import numpy as np

from .util.compute_variance import variance

from .util import hvg

cluster_from_centers = pyminicore.hcluster

def ctrs2sp(centertups, nc):

import scipy.sparse as sp

return sp.vstack([sp.csr_matrix((x[0],[0] * len(x[0]), [0, len(x[0])]), shape=[1, nc]) for x in centertups])

geometric_median = pyminicore.geomed

# This function provides a single starting point for clustering start-to-finish

def cluster(data, *, msr, k, prior=0., seed=0, nmkc=0,

ntimes=1, lspp=0, use_exponential_skips=False,

n_local_trials=1, weights=None, mbsize=-1, clustereps=1e-4,

temp=-1., cs=False, with_rep=True, outpref="mc.cluster.output",

maxiter=50):

soft = temp > 0. # Enable soft clustering by setting temperature

if isinstance(data, csr_tuple):

mcdata = CSparseMatrix(data)

else:

mcdata = data

ids, asn, costs = pyminicore.kmeanspp(data, msr=msr, k=k,

prior=prior, seed=seed,

ntimes=ntimes, lspp=lspp, expskips=use_exponential_skips,

n_local_trials=n_local_trials, weights=weights)

if soft:

try:

return scluster(mcdata, centers=ids, msr=msr, prior=prior, weights=weights, temp=temp, maxiter=maxiter,

mbn=mbn, savepref=outpref)

except:

raise NotImplementedError("Soft clustering not supported")

else:

return hcluster(data, centers=ids, prior=prior, msr=msr, weights=weights, eps=clustereps, maxiter=maxiter,

with_rep=with_rep, cs=cs)

'''

__all__ = ["cluster", "scluster", "hcluster", "hvg", "variance",

"cstr2sp", "cluster_from_centers", "geomed", "geometric_median",

"CSparseMatrix", "SparseMatrixWrapper", "smw", "csr_tuple", "KMCRSV",

"constants", "pyminicore", "greedy_select", "d2_select", "SumOpts", "meas2str", "meas2dict",

"usable_measures", "valid_measures", "display_measures", "mdict", "Threading", "set_num_threads", "get_num_threads"]

'''