This is my code below:
a = np.array([[1,2,3]])
b = np.array([[2,2,2]])
x = np.dot(b,a.T)
print(type(x))
This give me 'numpy.ndarray',I want to convert 'numpy.ndarray' to float64:
x = x.astype(np.float64)
print(type(x))
but it returned 'numpy.ndarray', why doesn't work?
Short answer: you probably want to use float(np.dot(a,b.T)).
Your np.dot generates a 3×3 matrix:
>>> np.dot(a.T,b)
array([[2, 2, 2],
[4, 4, 4],
[6, 6, 6]])
And there is no universal inherent way to convert a matrix to a float (you can of course calculate for instance the determinant, but that is a conversion).
You probably want to use the np.dot the other way around:
>>> np.dot(a,b.T)
array([[12]])
Now it is a 1×1 matrix. Here it is of course reasonable to simply pop the element out of the matrix. We can do this through indexing:
>>> np.dot(a,b.T)[0,0]
12
>>> type(np.dot(a,b.T)[0,0])
<class 'numpy.int64'>
So now we obtained the single in64 element. This is still not a float, but now we can use the float constructor of Python to obtain a float:
>>> float(np.dot(a,b.T)[0,0])
12.0
>>> type(float(np.dot(a,b.T)[0,0]))
<class 'float'>
Now the best part is that in case the matrix contains only a single element, numpy makes it more convenient, and you can simply call float(..) on the matrix. So:
>>> float(np.dot(a,b.T))
12.0
In case you want to use a np.float64, it is more or less the same:
>>> np.float64(np.dot(a,b.T))
12.0
>>> type(np.float64(np.dot(a,b.T)))
<class 'numpy.float64'>
item. Just doing : np.dot(b,a.T).item() would return 12, although if float is really desired, call np.float54 is more suited.
xis a 3x3 matrix. How would you convert that to a float?astypeconverts the elements of the matrix.