vector - Python: Cosine Similarity m * n matrices -


I have two M x N matrix which I created after removing the data from the images. Both are the first line of the vectors and after the third line they all become only the first pillar. For example, the raw vector looks like this 

  1,23,2,5,6,2,2,6,2, 12,4,5,5, 1,2,4 , 1, 2, 2:   
 Both vectors have similar patterns where the first three rows are long lines and then the progress is as thin as I do the cosine parallelism I add zero Was thinking to use a padding technique and these two vectors had made it to N XN. I looked at the Python option of cosine parity but some examples were numpy using package calls. I could not understand how actually samples can do this type of padding and a cosine can take equality, any guidance will be greatly appreciated.  
 
 If both arrays have the same dimension, then I will level them using NumPy. NumPy (and SciPy) is a powerful scientific computational tool that makes matrix manipulation easier.  
 Here is an example of how I will do it with NumPy and SciPy:  
  Import from NP to SPP as a copy. SpaceIm imports distance A = NP. Array ([1,23,2,5,6,2,2,6,2], [12,4,5,5], [1,2, 4], [1], [2], [2 ]], Dtype = object b = np. Array ([[1,23,2,5,6,2,2,6,2], [12], 4,5,5], [1,2,4], [1], [2], [2]], dtype = object) Aflat = np.hstack (A) Bflat = np.hstack (B) dist = Distance.cosine (Aflat, Bflat)   
 results here  Dist = 1.10e-16  (i.e. 0).  
 Note that I have used  dtype = object  here because it is the only way I know to be able to store different sizes in an array in NumPy . This is the reason that later I used the  hstack ()  to adjust the array (instead of using the more common  flatten ()  function).

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