产生高斯数据集的scikit-learn API

https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html

高斯聚类生成器用法

from sklearn.datasets import make_blobs
from matplotlib import pyplot
%matplotlib inline

data,target=make_blobs(n_samples=100,n_features=2,centers=3)
print("data=",data)
print("target=",target)
# 在2D图中绘制样本，每个样本颜色不同
pyplot.scatter(data[:,0],data[:,1],c=target);
pyplot.show()

#result

data= [[ 2.37778754e-01  5.34540864e+00][ 9.43029034e+00  8.01781401e+00][ 1.09039688e+01  7.18590884e+00][ 1.71043958e+00  6.84998296e+00][ 1.12733230e+01  8.12412322e+00][ 5.30157065e-02  5.48539301e+00][ 1.07639896e+01  9.00512473e+00][ 1.03762851e+01  8.21121642e+00][ 8.01646777e-01  6.08279797e+00][ 3.76261970e-01  1.71349284e+00][ 8.65289556e+00  8.21177534e+00][ 9.28225378e+00  1.00671841e+01][ 1.01404941e+00  5.33888426e+00][ 1.06928640e+01  7.97318301e+00][ 2.53903673e+00  7.12754408e+00][ 1.00647103e+01  8.11526302e+00][-2.48669510e+00 -1.45847836e+00][-6.63113954e-02 -6.38192796e-01][ 9.19505734e+00  8.00221310e+00][ 2.22136420e+00  5.57128882e+00][-4.19364627e-01  6.42245024e+00][-1.02018619e+00 -6.59435275e-01][ 1.48320391e+00  2.46602257e-01][ 9.95355586e+00  8.25950767e+00][-7.84308723e-01  5.32462523e+00][ 8.91115061e+00  7.87000236e+00][-1.73549467e-01  4.27386167e+00][ 8.52190703e-01  6.41413752e+00][ 8.82753945e+00  7.94176793e+00][ 4.46892971e-01 -9.68505563e-01][ 1.04606362e+01  8.39048186e+00][-3.34145729e-01  9.60480901e-01][-4.34291690e-01  6.56151293e+00][ 1.19510216e+01  6.04081596e+00][ 1.16326443e+00  7.11074547e+00][ 8.41762551e+00  7.63451500e+00][-7.17734386e-01  7.75050883e+00][-2.04852317e+00 -1.81743389e+00][-8.17865590e-01 -2.75349419e-01][ 7.16402770e-01  6.86687358e+00][ 1.19246943e-02  8.33435411e+00][ 1.39702449e-01 -1.80224638e+00][ 9.69484430e+00  9.37357095e+00][-4.74924304e-03  5.35619672e+00][ 1.02591184e+01  7.58379139e+00][-8.47873102e-01  5.95889293e+00][ 1.01709232e+01  5.62951252e+00][ 9.87628823e+00  8.16479797e+00][ 7.98526753e-02 -3.46310001e+00][ 6.12151066e-02 -4.51278752e-01][ 1.01291613e+01  9.04605887e+00][ 1.19742015e+00  8.46857159e-01][-5.81416100e-01 -1.03564530e-01][ 6.59044793e-01  7.75215070e+00][ 2.44776077e+00  8.45442572e+00][ 9.32591274e+00  8.51683645e+00][ 9.96730027e+00  7.20233213e+00][-9.90862152e-01  1.16890402e+00][ 5.55795432e-01  7.13378127e+00][ 9.44792266e+00  7.74220922e+00][-4.97665816e-01  4.96654007e-01][ 1.04701753e+01  7.00829225e+00][ 2.01155344e-01  7.52179777e+00][ 1.07657614e+01  5.16882248e+00][ 2.25189003e+00  6.98515582e+00][ 1.60973303e+00  6.34679948e+00][ 2.16425124e+00  6.12305437e+00][ 3.07379285e+00  5.48999608e+00][-2.01448936e+00  5.92072584e-01][ 8.12886295e-01  7.63180927e+00][-1.28743498e+00  2.03617716e-01][ 1.09486550e+01  7.20695687e+00][ 7.85612429e-01  4.58333132e+00][-1.29152168e+00  2.64125709e-01][ 2.42682971e-01  7.50872787e-01][-7.00041339e-01 -9.17176679e-01][ 2.23726982e-01  8.59291240e-01][-1.21700319e-01 -6.85518520e-01][-8.03248482e-01 -5.85764813e-01][-4.51262381e-01  7.52295177e-01][-1.64432005e+00 -1.26268010e-01][ 1.10911877e+01  9.48378607e+00][ 3.00451544e+00  7.74355395e+00][-2.79661482e-01  4.37687083e-01][ 1.11269760e+01  9.52731016e+00][ 1.23213430e+00  8.65009894e+00][ 9.84329476e+00  8.31809617e+00][-2.05926077e+00 -9.70697877e-02][-2.20220487e+00 -5.20657739e-01][ 1.30245997e-01  4.57876448e+00][-1.33510994e+00  1.88213292e+00][-4.20060629e-01 -1.42239086e+00][ 9.54918966e+00  7.29709483e+00][ 1.01813947e+00  6.63700027e+00][ 9.69588505e+00  6.76908564e+00][-4.10605045e-01  1.98129635e-01][ 1.75290554e+00  6.62615978e+00][ 9.88330553e+00  5.88636939e+00][ 8.45149590e-01  7.33380471e+00][-1.10159753e+00 -7.66277916e-01]]
target= [0 1 1 0 1 0 1 1 0 2 1 1 0 1 0 1 2 2 1 0 0 2 2 1 0 1 0 0 1 2 1 2 0 1 0 1 02 2 0 0 2 1 0 1 0 1 1 2 2 1 2 2 0 0 1 1 2 0 1 2 1 0 1 0 0 0 0 2 0 2 1 0 22 2 2 2 2 2 2 1 0 2 1 0 1 2 2 0 2 2 1 0 1 2 0 1 0 2]

https://blog.csdn.net/kevinelstri/article/details/52622960

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