本文整理汇总了Python中sklearn.decomposition.PCA.fit_ttransform方法的典型用法代码示例。如果您正苦于以下问题：Python PCA.fit_ttransform方法的具体用法？Python PCA.fit_ttransform怎么用？Python PCA.fit_ttransform使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.decomposition.PCA的用法示例。
在下文中一共展示了PCA.fit_ttransform方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: plot_kmeans_interactive
# 需要导入模块: from sklearn.decomposition import PCA [as 别名]
# 或者: from sklearn.decomposition.PCA import fit_ttransform [as 别名]
def plot_kmeans_interactive(min_clusters=1, max_clusters=6):
from IPython.html.widgets import interact
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.datasets.samples_generator import make_blobs
with warnings.catch_warnings():
#warnings.filterwarnings('ignore')
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
iris = load_iris()
X, y = iris.data, iris.target
pca = PCA(n_components = 0.95) # keep 95% of variance
X = pca.fit_ttransform(X)
#X = X[:, 1:3]
def _kmeans_step(frame=0, n_clusters=3):
rng = np.random.RandomState(2)
labels = np.zeros(X.shape[0])
centers = rng.randn(n_clusters, 2)
nsteps = frame // 3
for i in range(nsteps + 1):
old_centers = centers
if i < nsteps or frame % 3 > 0:
dist = euclidean_distances(X, centers)
labels = dist.argmin(1)
if i < nsteps or frame % 3 > 1:
centers = np.array([X[labels == j].mean(0)
for j in range(n_clusters)])
nans = np.isnan(centers)
centers[nans] = old_centers[nans]
# plot the data and cluster centers
plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='rainbow',
vmin=0, vmax=n_clusters - 1);
plt.scatter(old_centers[:, 0], old_centers[:, 1], marker='o',
c=np.arange(n_clusters),
s=200, cmap='rainbow')
plt.scatter(old_centers[:, 0], old_centers[:, 1], marker='o',
c='black', s=50)
# plot new centers if third frame
if frame % 3 == 2:
for i in range(n_clusters):
plt.annotate('', centers[i], old_centers[i],
arrowprops=dict(arrowstyle='->', linewidth=1))
plt.scatter(centers[:, 0], centers[:, 1], marker='o',
c=np.arange(n_clusters),
s=200, cmap='rainbow')
plt.scatter(centers[:, 0], centers[:, 1], marker='o',
c='black', s=50)
plt.xlim(-4, 4)
plt.ylim(-2, 10)
if frame % 3 == 1:
plt.text(3.8, 9.5, "1. Reassign points to nearest centroid",
ha='right', va='top', size=14)
elif frame % 3 == 2:
plt.text(3.8, 9.5, "2. Update centroids to cluster means",
ha='right', va='top', size=14)
return interact(_kmeans_step, frame=[0, 50],
n_clusters=[min_clusters, max_clusters])