I try to understand how to handle a 1D
array (vector in linear algebra) with NumPy
.
In the following example, I generate two numpy.array
a
and b
:
>>> import numpy as np
>>> a = np.array([1,2,3])
>>> b = np.array([[1],[2],[3]]).reshape(1,3)
>>> a.shape
(3,)
>>> b.shape
(1, 3)
For me, a
and b
have the same shape according linear algebra definition: 1 row, 3 columns, but not for NumPy
.
Now, the NumPy
dot
product:
>>> np.dot(a,a)
14
>>> np.dot(b,a)
array([14])
>>> np.dot(b,b)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: objects are not aligned
I have three different outputs.
What's the difference between dot(a,a)
and dot(b,a)
? Why dot(b,b)
doesn't work?
I also have some differencies with those dot products:
>>> c = np.ones(9).reshape(3,3)
>>> np.dot(a,c)
array([ 6., 6., 6.])
>>> np.dot(b,c)
array([[ 6., 6., 6.]])
In NumPy, we will use a function called shape that returns a tuple, the elements of the tuple give the lengths of the array dimensions. The shape attribute always returns a tuple that represents the length of each dimension. The 1-d array is a row vector and its shape is a single value sequence followed by a comma.
In general numpy arrays can have more than one dimension. One way to create such array is to start with a 1-dimensional array and use the numpy reshape() function that rearranges elements of that array into a new shape.
NumPy arrays have an attribute called shape that returns a tuple with each index having the number of corresponding elements.
Notice you are not only working with 1D arrays:
In [6]: a.ndim
Out[6]: 1
In [7]: b.ndim
Out[7]: 2
So, b
is a 2D array.
You also see this in the output of b.shape
: (1,3) indicates two dimensions as (3,) is one dimension.
The behaviour of np.dot
is different for 1D and 2D arrays (from the docs):
For 2-D arrays it is equivalent to matrix multiplication, and for 1-D arrays to inner product of vectors
That is the reason you get different results, because you are mixing 1D and 2D arrays. Since b
is a 2D array, np.dot(b, b)
tries a matrix multiplication on two 1x3 matrices, which fails.
With 1D arrays, np.dot does a inner product of the vectors:
In [44]: a = np.array([1,2,3])
In [45]: b = np.array([1,2,3])
In [46]: np.dot(a, b)
Out[46]: 14
In [47]: np.inner(a, b)
Out[47]: 14
With 2D arrays, it is a matrix multiplication (so 1x3 x 3x1 = 1x1, or 3x1 x 1x3 = 3x3):
In [49]: a = a.reshape(1,3)
In [50]: b = b.reshape(3,1)
In [51]: a
Out[51]: array([[1, 2, 3]])
In [52]: b
Out[52]:
array([[1],
[2],
[3]])
In [53]: np.dot(a,b)
Out[53]: array([[14]])
In [54]: np.dot(b,a)
Out[54]:
array([[1, 2, 3],
[2, 4, 6],
[3, 6, 9]])
In [55]: np.dot(a,a)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-55-32e36f9db916> in <module>()
----> 1 np.dot(a,a)
ValueError: objects are not aligned
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