indices

Assume you have an array of values that will need to be summed together d = [1,1,1,1,1] and a second array specifying which elements need to be summed together i = [0,0,1,2,2] The result will be stored in a new array of size max(i)+1 . So for example i=[0,0,0,0,0] would be equivalent to summing all the elements of d and storing the result at position 0 of a new array of size 1 . I tried to implement this using c = zeros(max(i)+1) c[i] += d However, the += operation adds each element only once, thus giving the unexpected result of [1,1,1] instead of [2,1,2] How would one correctly implement

I want to do something like this. Let's say we have a tensor A. A = [[1,0],[0,4]] And I want to get nonzero values and their indices from it. Nonzero values: [1,4] Nonzero indices: [[0,0],[1,1]] There are similar operations in Numpy. np.flatnonzero(A) return indices that are non-zero in the flattened A. x.ravel()[np.flatnonzero(x)] extract elements according to non-zero indices. Here's a link for these operations. How can I do somthing like above Numpy operations in Tensorflow with python? (Whether a matrix is flattened or not doesn't really matter.) You can achieve same result in Tensorflow

I wonder what could be the easiest way to create a bi-dimensional array, that has for each row the indices to another multi-dimensional array. For example, let's say I have a cube 4x4, the "indices matrix" would be the following: np.concatenate([ np.expand_dims(curr.ravel(),axis=0).T for curr in np.meshgrid( np.arange(4), np.arange(4), np.arange(4) ) ],axis=1) with the following result: array([[0, 0, 0], [0, 0, 1], [0, 0, 2], [0, 0, 3], [1, 0, 0], [1, 0, 1], ... [2, 3, 2], [2, 3, 3], [3, 3, 0], [3, 3, 1], [3, 3, 2], [3, 3, 3]]) Besides the fact that it seems that the second column should be in

seq1_values = [5, 40, 180, 13, 30, 20, 25, 24, 29, 31, 54, 46, 42, 50, 67, 17, 76, 33, 84, 35, 100, 37, 110, 32, 112, 15, 123, 3, 130, 42] def get_num_values(): global seq1_values return len( seq1_values ) def get_values(): global seq1_values for value in seq1_values: yield value yield None def next_value(): return next( next_value.values ) next_value.values = get_values() def main(): numbers = [] value = next_value() while value is not None: numbers.append(value) input_size = len(numbers)-1 for i in range(input_size): print("|", str(input_size).rjust(4), end="") for j in range(input_size):

I'd like to generate a np.ndarray NumPy array for a given shape of another NumPy array. The former array should contain the corresponding indices for each cell of the latter array. Example 1 Let's say we have a = np.ones((3,)) which has a shape of (3,) . I'd expect [[0] [1] [2]] since there is a[0] , a[1] and a[2] in a which can be accessed by their indices 0 , 1 and 2 . Example 2 For a shape of (3, 2) like b = np.ones((3, 2)) there is already very much to write. I'd expect [[[0 0] [0 1]] [[1 0] [1 1]] [[2 0] [2 1]]] since there are 6 cells in b which can be accessed by the corresponding