The numpy.load routine is for loading pickled .npy or .npz binary files, which can be created using numpy.save and numpy.savez, respectively. Since you have text data, these are not the routines you want. You can load your comma-separated values with numpy.loadtxt. Full Example Here’s a complete example (using StringIO to simulate the file I/O). Now we have:
If you’d like something a bit more readable, you can do this: Equivalently, you could also do: A = np.asarray(M).reshape(-1), but that’s a bit less easy to read.
The answer lies in your question: Edit: Although my post gives the answer as requested by the OP, the conversion to list and back to NumPy array takes some overhead (noticeable for large arrays). Hence, dstack would be a computationally efficient alternative (ref. @zipa’s answer). I was unaware of dstack at the time of posting … Read more
Not sure if this helps, but: is really the indicator function , as described here. This forms the expression (j == y[i]) in the code. Also, the gradient of the loss with respect to the weights is: where which is the origin of the X[:,i] in the code.
It sounds like you have three arrays itemNameList, array_x, and array_y Assuming they are all the same shape, you can just do: EDIT Your problem is that array_x and array_y are not actual numpy.array objects, but likely numpy.int32 (or some other non-iterable) objects: Perhaps their initialization is not going as expected, or they are being changed from arrays somewhere in your code?
If you still want format
There is a function assert_approx_equal in numpy.testing (source here) which may be a good starting point.
An easier way to get the machine epsilon for a given float type is to use np.finfo():
cv2 uses numpy for manipulating images, so the proper and best way to get the size of an image is using numpy.shape. Assuming you are working with BGR images, here is an example: In case you were working with binary images, img will have two dimensions, and therefore you must change the code to: height, … Read more
For a numpy based solution, you can use numpy.where and then get the row indexes from it and then use it for indexing you matrix. Example – Demo – Another method , as indicated in the comments would be to use boolean masks, Example – Demo –