本文整理匯總了Python中sklearn.decomposition.pca.PCA.score方法的典型用法代碼示例。如果您正苦於以下問題：Python PCA.score方法的具體用法？Python PCA.score怎麽用？Python PCA.score使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類sklearn.decomposition.pca.PCA的用法示例。
在下文中一共展示了PCA.score方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: dimensional
# 需要導入模塊: from sklearn.decomposition.pca import PCA [as 別名]
# 或者: from sklearn.decomposition.pca.PCA import score [as 別名]
def dimensional(tx, ty, rx, ry, add=None):
print "pca"
for j in range(tx[1].size):
i = j + 1
print "===" + str(i)
compressor = PCA(n_components = i)
t0 = time()
compressor.fit(tx, y=ty)
newtx = compressor.transform(tx)
runtime=time() - t0
V = compressor.components_
print runtime, V.shape, compressor.score(tx)
distances = np.linalg.norm(tx-compressor.inverse_transform(newtx))
print distances
print "pca done"
print "ica"
for j in range(tx[1].size):
i = j + 1
print "===" + str(i)
compressor = ICA(whiten=True)
t0 = time()
compressor.fit(tx, y=ty)
newtx = compressor.transform(tx)
runtime=time() - t0
print newtx.shape, runtime
distances = np.linalg.norm(tx-compressor.inverse_transform(newtx))
print distances
print "ica done"
print "RP"
for j in range(tx[1].size):
i = j + 1
print "===" + str(i)
compressor = RandomProjection(n_components=i)
t0 = time()
compressor.fit(tx, y=ty)
newtx = compressor.transform(tx)
runtime=time() - t0
shape = newtx.shape
print runtime, shape
print "RP done"
print "K-best"
for j in range(tx[1].size):
i = j + 1
print "===" + str(i)
compressor = best(add, k=i)
t0 = time()
compressor.fit(tx, y=ty.ravel())
newtx = compressor.transform(tx)
runtime=time() - t0
shape = newtx.shape
print runtime, shape
print "K-best done"