np.c_ 这个玩意儿不是个方法，而是返回一个类然后直接和后面的ndarry相乘。。。相当于concatenate(【】,dim)

import numpy as np
# from tensorflow import keras

X = np.random.randint(100, size=10*1000).reshape(1000, 10)
Y = np.random.randint(100, size=15*1000).reshape(1000, 15)
X_decoder = np.c_[np.zeros((1000, 1)), Y[:, :-1]]
seq_lengths = np.full([1000], 15)
print(X)

# history = model.fit([X, X_decoder, seq_lengths], Y, epochs=2)
'''(1,3) 和 (1,3) 变 (1,6)'''
qq = np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])]
print(qq)

D:ProgramDataMiniconda3python.exe E:/新脚本主文件夹/训练测试项目/test3/测试乱七八糟的3.py
[[65 97 98 ... 78 15 79]
 [92 18 68 ...  6 41 21]
 [ 9 20 49 ... 95 99 91]
 ...
 [29 17 53 ...  6 68 41]
 [48 72  2 ... 62  6 22]
 [60  3 19 ... 55 74 49]]
[[1 2 3 0 0 4 5 6]]

Process finished with exit code 0

 np.c_ 这个玩意儿不是个方法，而是返回一个类然后直接和后面的ndarry相乘。。。相当于concatenate(【】,dim):

c_ = concatenate([np.zeros((1000, 1)), Y[:, :-1]],1)
print(c_)

r_和c_里面源码是这样的，虽然我看不太懂。。。。

class AxisConcatenator:
    """
    Translates slice objects to concatenation along an axis.

    For detailed documentation on usage, see `r_`.
    """
    # allow ma.mr_ to override this
    concatenate = staticmethod(_nx.concatenate)
    makemat = staticmethod(matrixlib.matrix)

    def __init__(self, axis=0, matrix=False, ndmin=1, trans1d=-1):
        self.axis = axis
        self.matrix = matrix
        self.trans1d = trans1d
        self.ndmin = ndmin

    def __getitem__(self, key):
        # handle matrix builder syntax
        if isinstance(key, str):
            frame = sys._getframe().f_back
            mymat = matrixlib.bmat(key, frame.f_globals, frame.f_locals)
            return mymat

        if not isinstance(key, tuple):
            key = (key,)

        # copy attributes, since they can be overridden in the first argument
        trans1d = self.trans1d
        ndmin = self.ndmin
        matrix = self.matrix
        axis = self.axis

        objs = []
        scalars = []
        arraytypes = []
        scalartypes = []

        for k, item in enumerate(key):
            scalar = False
            if isinstance(item, slice):
                step = item.step
                start = item.start
                stop = item.stop
                if start is None:
                    start = 0
                if step is None:
                    step = 1
                if isinstance(step, complex):
                    size = int(abs(step))
                    newobj = linspace(start, stop, num=size)
                else:
                    newobj = _nx.arange(start, stop, step)
                if ndmin > 1:
                    newobj = array(newobj, copy=False, ndmin=ndmin)
                    if trans1d != -1:
                        newobj = newobj.swapaxes(-1, trans1d)
            elif isinstance(item, str):
                if k != 0:
                    raise ValueError("special directives must be the "
                            "first entry.")
                if item in ('r', 'c'):
                    matrix = True
                    col = (item == 'c')
                    continue
                if ',' in item:
                    vec = item.split(',')
                    try:
                        axis, ndmin = [int(x) for x in vec[:2]]
                        if len(vec) == 3:
                            trans1d = int(vec[2])
                        continue
                    except Exception as e:
                        raise ValueError(
                            "unknown special directive {!r}".format(item)
                        ) from e
                try:
                    axis = int(item)
                    continue
                except (ValueError, TypeError):
                    raise ValueError("unknown special directive")
            elif type(item) in ScalarType:
                newobj = array(item, ndmin=ndmin)
                scalars.append(len(objs))
                scalar = True
                scalartypes.append(newobj.dtype)
            else:
                item_ndim = ndim(item)
                newobj = array(item, copy=False, subok=True, ndmin=ndmin)
                if trans1d != -1 and item_ndim < ndmin:
                    k2 = ndmin - item_ndim
                    k1 = trans1d
                    if k1 < 0:
                        k1 += k2 + 1
                    defaxes = list(range(ndmin))
                    axes = defaxes[:k1] + defaxes[k2:] + defaxes[k1:k2]
                    newobj = newobj.transpose(axes)
            objs.append(newobj)
            if not scalar and isinstance(newobj, _nx.ndarray):
                arraytypes.append(newobj.dtype)

        # Ensure that scalars won't up-cast unless warranted
        final_dtype = find_common_type(arraytypes, scalartypes)
        if final_dtype is not None:
            for k in scalars:
                objs[k] = objs[k].astype(final_dtype)

        res = self.concatenate(tuple(objs), axis=axis)

        if matrix:
            oldndim = res.ndim
            res = self.makemat(res)
            if oldndim == 1 and col:
                res = res.T
        return res

    def __len__(self):
        return 0

# separate classes are used here instead of just making r_ = concatentor(0),
# etc. because otherwise we couldn't get the doc string to come out right
# in help(r_)

注意下倒数第三行源码有错误，就是那个注释，里面写的concatentor应该是concatenate

然后调用的类很简单，如下，在同一个文件里，indextricks.py

class CClass(AxisConcatenator):
    """
    Translates slice objects to concatenation along the second axis.

    This is short-hand for ``np.r_['-1,2,0', index expression]``, which is
    useful because of its common occurrence. In particular, arrays will be
    stacked along their last axis after being upgraded to at least 2-D with
    1's post-pended to the shape (column vectors made out of 1-D arrays).
    
    See Also
    --------
    column_stack : Stack 1-D arrays as columns into a 2-D array.
    r_ : For more detailed documentation.

    Examples
    --------
    >>> np.c_[np.array([1,2,3]), np.array([4,5,6])]
    array([[1, 4],
           [2, 5],
           [3, 6]])
    >>> np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])]
    array([[1, 2, 3, ..., 4, 5, 6]])

    """

    def __init__(self):
        AxisConcatenator.__init__(self, -1, ndmin=2, trans1d=0)


c_ = CClass()

就是返回了一个上面的类，然而很高级的样子，也看不太懂。。。。