def cache_kmeans_init(X, K, methods, datasetID):
global kmeans_init
if datasetID == -1:
return
# Get the k-means parameters
methodsloc = [
n if isinstance(n, (list, tuple, np.ndarray)) else [n] for n in methods
]
kmeansFound = False
for ms in range(len(methodsloc)):
if methodsloc[ms][0].lower() in ['k-means', 'kmeans']:
params = methodsloc[ms][1:]
pnames = ['init', 'max_iter', 'n_jobs', 'distance']
dflts = ['KA', 300, -1, 'euclidean']
if isinstance(params, np.ndarray):
paramsloc = params.tolist()
else:
paramsloc = params
(init, max_iter, n_jobs,
distance) = ds.resolveargumentpairs(pnames, dflts, paramsloc)
kmeansFound = True
# Perform initialisation over the largest K value and cache it, if k-means was found and init is some 'KA'
if kmeansFound:
if init == 'KA':
if X.shape[0] <= maxgenesinsetforpdist:
kmeans_init[datasetID] = initclusterKA(X, np.max(K), distance)
else:
kmeans_init[datasetID] = initclusterKA_memorysaver(
X, np.max(K), distance)
def ckmeans(X, K, datasetID=-1, params=()):
global kmeans_init
pnames = ['init', 'max_iter', 'n_jobs', 'distance', 'n_init']
#dflts = ['k-means++', 300, -1, 'euclidean', 10]
dflts = ['KA', 300, -1, 'euclidean', 1]
if isinstance(params, np.ndarray):
paramsloc = params.tolist()
else:
paramsloc = params
(init, max_iter, n_jobs, distance,
n_init) = ds.resolveargumentpairs(pnames, dflts, paramsloc)
if datasetID in kmeans_init:
init = kmeans_init[datasetID][0:K]
elif init == 'KA':
if X.shape[0] <= maxgenesinsetforpdist:
init = initclusterKA(X, K, distance)
else:
init = initclusterKA_memorysaver(X, K, distance)
C = skcl.KMeans(K,
init=init,
max_iter=max_iter,
n_init=n_init,
n_jobs=n_jobs).fit(X).labels_
return clustVec2partMat(C, K)
def csoms(X, D, params=()):
pnames = ['neighbour', 'learning_rate', 'input_length_ratio']
dflts = [0.1, 0.2, -1]
if isinstance(params, np.ndarray):
paramsloc = params.tolist()
else:
paramsloc = params
(neighbour, learning_rate,
input_length_ratio) = ds.resolveargumentpairs(pnames, dflts, paramsloc)
Xloc = np.array(X)
K = D[0] * D[1] # Number of clusters
N = Xloc.shape[0] # Number of genes
Ndim = Xloc.shape[1] # Number of dimensions in X
som = sompy.SOM(D, Xloc)
som.set_parameter(neighbor=neighbour,
learning_rate=learning_rate,
input_length_ratio=input_length_ratio)
centres = som.train(N).reshape(K, Ndim)
dists = [[spdist.euclidean(c, x) for c in centres] for x in Xloc]
C = [np.argmin(d) for d in dists]
return clustVec2partMat(C, K)
def chc(X, K, params=()):
pnames = ['linkage_method', 'distance']
dflts = ['ward', 'euclidean']
if isinstance(params, np.ndarray):
paramsloc = params.tolist()
else:
paramsloc = params
(linkage_method,
distance) = ds.resolveargumentpairs(pnames, dflts, paramsloc)
Z = sphc.linkage(X, method=linkage_method, metric=distance)
C = sphc.fcluster(Z, K, criterion='maxclust')
return clustVec2partMat(C, K)