Python KMeans.get_cluster_centers示例

示例#1

文件： clustering_kmeans_modular.py 项目： 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()

示例#2

文件： evaluation_clustering.py 项目： 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()

示例#3

文件： evaluation_clustering.py 项目： 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()

示例#4

文件： evaluation_clustering.py 项目： 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()

示例#5

文件： evaluation_clustering.py 项目： 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()

示例#6

文件： bench_kmeans.py 项目： 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

示例#7

文件： clustering_sg.py 项目： 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

示例#8

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

示例#9

文件： clustering_kmeans_modular.py 项目： 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

示例#10

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