Ejemplos de KMeans en Python

Ejemplo n.º 1

Archivo: evaluation_clustering.py Proyecto: harshitsyal/shogun

def run_clustering(data, k):
    from shogun.Clustering import KMeans
    from shogun.Mathematics import Math_init_random

    Math_init_random(42)
    fea = RealFeatures(data)
    distance = EuclidianDistance(fea, fea)
    kmeans=KMeans(k, distance)

    print("Running clustering...")
    kmeans.train()

    return kmeans.get_cluster_centers()

Ejemplo n.º 2

Archivo: evaluation_clustering.py Proyecto: theoryno3/shogun

def run_clustering(data, k):
    from shogun.Clustering import KMeans
    from shogun.Distance import EuclideanDistance
    from shogun.Features import RealFeatures

    fea = RealFeatures(data)
    distance = EuclideanDistance(fea, fea)
    kmeans = KMeans(k, distance)

    #print("Running clustering...")
    kmeans.train()

    return kmeans.get_cluster_centers()

Ejemplo n.º 3

Archivo: evaluation_clustering.py Proyecto: serialhex/shogun

def run_clustering(data, k):
    from shogun.Clustering import KMeans
    from shogun.Mathematics import Math_init_random

    Math_init_random(42)
    fea = RealFeatures(data)
    distance = EuclidianDistance(fea, fea)
    kmeans = KMeans(k, distance)

    print("Running clustering...")
    kmeans.train()

    return kmeans.get_cluster_centers()

Ejemplo n.º 4

Archivo: evaluation_clustering.py Proyecto: Argram/shogun

def run_clustering(data, k):
	from shogun.Clustering import KMeans
	from shogun.Distance import EuclideanDistance
	from shogun.Features import RealFeatures

	fea = RealFeatures(data)
	distance = EuclideanDistance(fea, fea)
	kmeans=KMeans(k, distance)

	#print("Running clustering...")
	kmeans.train()

	return kmeans.get_cluster_centers()

Ejemplo n.º 5

Archivo: bench_kmeans.py Proyecto: fabianp/ml-benchmarks

def bench_shogun(X, y, T, valid):
#
#       .. Shogun ..
#
    from shogun.Distance import EuclidianDistance
    from shogun.Features import RealFeatures
    from shogun.Clustering import KMeans
    start = datetime.now()
    feat = RealFeatures(X.T)
    distance = EuclidianDistance(feat, feat)
    clf = KMeans(n_components, distance)
    clf.train()
    return inertia(X, clf.get_cluster_centers()), datetime.now() - start

Ejemplo n.º 6

def bench_shogun(X, y, T, valid):
#
#       .. Shogun ..
#
    from shogun.Distance import EuclidianDistance
    from shogun.Features import RealFeatures
    from shogun.Clustering import KMeans
    start = datetime.now()
    feat = RealFeatures(X.T)
    distance = EuclidianDistance(feat, feat)
    clf = KMeans(n_components, distance)
    clf.train()
    delta = datetime.now() - start
    return inertia(X, clf.get_cluster_centers().T), delta

Ejemplo n.º 7

Archivo: clustering_kmeans_modular.py Proyecto: memimo/shogun-liblinear

def kmeans ():
	print 'KMeans'

	from shogun.Distance import EuclidianDistance
	from shogun.Features import RealFeatures
	from shogun.Clustering import KMeans

	k=3
	feats_train=RealFeatures(fm_train)
	distance=EuclidianDistance(feats_train, feats_train)

	kmeans=KMeans(k, distance)
	kmeans.train()

	kmeans.get_cluster_centers()
	kmeans.get_radiuses()

Ejemplo n.º 8

Archivo: clustering_sg.py Proyecto: pchronz/GenericWorkloadModeler

def kmeans(train, k=3):

    Math_init_random(17)

    feats_train = RealFeatures(train)
    distance = EuclidianDistance(feats_train, feats_train)

    kmeans = KMeans(k, distance)
    kmeans.train()

    out_centers = kmeans.get_cluster_centers()
    kmeans.get_radiuses()

    return out_centers, kmeans

Ejemplo n.º 9

def clustering_kmeans_modular(fm_train=traindat, k=3):

    from shogun.Distance import EuclidianDistance
    from shogun.Features import RealFeatures
    from shogun.Clustering import KMeans
    from shogun.Mathematics import Math_init_random
    Math_init_random(17)

    feats_train = RealFeatures(fm_train)
    distance = EuclidianDistance(feats_train, feats_train)

    kmeans = KMeans(k, distance)
    kmeans.train()

    out_centers = kmeans.get_cluster_centers()
    kmeans.get_radiuses()

    return out_centers, kmeans

Ejemplo n.º 10

Archivo: clustering_kmeans_modular.py Proyecto: AlexBinder/shogun

def clustering_kmeans_modular (fm_train=traindat,k=3):

	from shogun.Distance import EuclideanDistance
	from shogun.Features import RealFeatures
	from shogun.Clustering import KMeans
	from shogun.Mathematics import Math_init_random
	Math_init_random(17)

	feats_train=RealFeatures(fm_train)
	distance=EuclideanDistance(feats_train, feats_train)

	kmeans=KMeans(k, distance)
	kmeans.train()

	out_centers = kmeans.get_cluster_centers()
	kmeans.get_radiuses()

	return out_centers, kmeans