mirror of
https://github.com/sjwhitworth/golearn.git
synced 2025-04-25 13:48:49 +08:00
25 lines
734 B
Python
25 lines
734 B
Python
import numpy as np
|
|
import itertools
|
|
|
|
from sklearn import mixture
|
|
from sklearn import preprocessing
|
|
|
|
# Number of samples per component
|
|
n_samples = 500
|
|
|
|
# Generate random sample, two components
|
|
np.random.seed(0)
|
|
C = np.array([[0., -0.1], [1.7, .4]])
|
|
X = np.r_[np.dot(np.random.randn(n_samples, 2), C),
|
|
.7 * np.random.randn(n_samples, 2) + np.array([-6, 3])]
|
|
|
|
gmm = mixture.GaussianMixture(n_components=2, init_params='random')
|
|
gmm.fit(X)
|
|
labels = gmm.predict(X)
|
|
|
|
with open('gaussian_mixture.csv', 'w') as fout:
|
|
with open('gaussian_mixture_labels.csv', 'w') as flabout:
|
|
for i in range(1000):
|
|
fout.write(",".join([str(x) for x in X[i,:]]) + "\n")
|
|
flabout.write(str(labels[i]) + "\n")
|