该.transform方法适用于您已经计算过PCA的情况，即如果您已经调用了它的.fit方法。

In [12]: pc2 = RandomizedPCA(n_components=3)

In [13]: pc2.transform(X) # can't transform because it does not know how to do it.
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-13-e3b6b8ea2aff> in <module>()
----> 1 pc2.transform(X)

/usr/local/lib/python3.4/dist-packages/sklearn/decomposition/pca.py in transform(self, X, y)
    714         # XXX remove scipy.sparse support here in 0.16
    715         X = atleast2d_or_csr(X)
--> 716         if self.mean_ is not None:
    717             X = X - self.mean_
    718

AttributeError: 'RandomizedPCA' object has no attribute 'mean_'

In [14]: pc2.ftransform(X) 
pc2.fit            pc2.fit_transform

In [14]: pc2.fit_transform(X)
Out[14]: 
array([[-1.38340578, -0.2935787 ],
       [-2.22189802,  0.25133484],
       [-3.6053038 , -0.04224385],
       [ 1.38340578,  0.2935787 ],
       [ 2.22189802, -0.25133484],
       [ 3.6053038 ,  0.04224385]])

所以你想fit RandomizedPCA然后transform作为：

In [20]: pca = RandomizedPCA(n_components=3)

In [21]: pca.fit(X)
Out[21]: 
RandomizedPCA(copy=True, iterated_power=3, n_components=3, random_state=None,
       whiten=False)

In [22]: pca.transform(z)
Out[22]: 
array([[ 2.76681156,  0.58715739],
       [ 1.92831932,  1.13207093],
       [ 0.54491354,  0.83849224],
       [ 5.53362311,  1.17431479],
       [ 6.37211535,  0.62940125],
       [ 7.75552113,  0.92297994]])

In [23]:

特别是 PCA.transform将通过矩阵的 PCA 分解获得的基变化应用于X矩阵Z。