Is there a way to do a reshape on numpy arrays but inplace. My problem is that my array is very big so any unnecessary copies strain the memory.
My current approach is like this:
train_x = train_x.reshape(n,32*32*3)
this doesn't exactly solve the problem since it creates a new array and then attributes the label train_x to the new array.
In a normal case this would be ok, since the garbage collector would very soon collect the original array.
The problem is that I have something like this:
train_x, train_y = train_set
train_x = train_x.reshape(n,32*32*3)
So in this case even though the train_x no longer points to the original array, there is still a pointer to the original array inside of train_set.
I want a way that changes all pointers of the previous array to this new array. Is there a way?
Or maybe there is some other way of dealing with this?
For Python keep in mind that several variables or names can point to the same object, such as a numpy array. Arrays can also have views, which are new array objects, but with shared data buffers. A copy has its own data buffer.
In [438]: x = np.arange(12)
In [439]: y = x # same object
In [440]: y.shape = (2,6) # inplace shape change
In [441]: y
Out[441]:
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
In [442]: x
Out[442]:
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
In [443]: y = y.reshape(3,4) # y is a new view
In [444]: y
Out[444]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
In [445]: x
Out[445]:
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
y has a different shape, but shares the data buffer:
In [446]: y += 1
In [447]: y
Out[447]:
array([[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12]])
In [448]: x
Out[448]:
array([[ 1, 2, 3, 4, 5, 6],
[ 7, 8, 9, 10, 11, 12]])