稀疏矩阵合并 归一化

 np.hstack((X, X2))
array([ <49998x70000 sparse matrix of type '<class 'numpy.float64'>'
        with 1135520 stored elements in Compressed Sparse Row format>,
        <49998x70000 sparse matrix of type '<class 'numpy.int64'>'
        with 1135520 stored elements in Compressed Sparse Row format>], 
       dtype=object)

    <49998x1400000 sparse matrix of type '<class 'numpy.float64'>'
     with 2271040 stored elements in Compressed Sparse Row format>

from scipy.sparse import hstack
hstack((X, X2))

Using the numpy.hstack will create an array with two sparse matrix objects.

scipy.sparse.bmat

from scipy.sparse import coo_matrix, bmat
>>> A = coo_matrix([[1, 2], [3, 4]])
>>> B = coo_matrix([[5], [6]])
>>> C = coo_matrix([[7]])
>>> bmat([[A, B], [None, C]]).toarray()
array([[1, 2, 5],
       [3, 4, 6],
       [0, 0, 7]])
>>>
>>> bmat([[A, None], [None, C]]).toarray()
array([[1, 2, 0],
       [3, 4, 0],
       [0, 0, 7]])

归一化

>>> X = [[ 1., -1.,  2.],
...      [ 2.,  0.,  0.],
...      [ 0.,  1., -1.]]
>>> X_normalized = preprocessing.normalize(X, norm='l2')

>>> X_normalized                                      
array([[ 0.40..., -0.40...,  0.81...],
       [ 1.  ...,  0.  ...,  0.  ...],
       [ 0.  ...,  0.70..., -0.70...]])

norm : ‘l1’, ‘l2’, or ‘max’, optional (‘l2’ by default)

The norm to use to normalize each non zero sample (or each non-zero feature if axis is 0).

参考：
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html#sklearn.preprocessing.normalize
https://www.baidu.com/link?url=epjyVGjvTldY3YcjfaWPamA76GuWxnf3ZOH0FAeWHiKqp60g6_dyzM95CAyj30j-IX2YRD0o9zdgTO-nVzqFjEb3GX_Q2leL_uS1_1qCyINyv8rxh0h0lW8aLrINECNW&wd=&eqid=83485e4f0000bc06000000035822acd4