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7 def test_sum(self): 8 self.create_node('Sum', {'input[0]': 5.0, 'input[1]': -3.0, 'input[2]': 2.0}, 4.0)

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431 def _prod(array): 432 prod = 1 433 for value in array: 434 prod *= value 435 return prod

284 def prod(array): 285 """Element-wise product of a numpy array across all the slave processes. 286 The result is only available in the main_slave (rank 1).""" 287 _reduce(array, "PROD")

41 def countArrayElements(array): 42 ''' 43 Simple method to count the repetitions of elements in an array 44 45 @param array: An array of elements 46 @return: A tuple (elements,counters), where elements is a list with the distinct elements and counters is the list with the number of times they appear in the array 47 ''' 48 elements = [] 49 counters = [] 50 for element in array: 51 if element in elements: 52 indx = elements.index(element) 53 counters[indx] += 1 54 else: 55 elements.append(element) 56 counters.append(1) 57 return elements,counters

402 def sum(self, axis=None, dtype=None, out=None): 403 """ 404 Returns the sum of the matrix elements, along the given axis. 405 406 Refer to `numpy.sum` for full documentation. 407 408 See Also 409 -------- 410 numpy.sum 411 412 Notes 413 ----- 414 This is the same as `ndarray.sum`, except that where an `ndarray` would 415 be returned, a `matrix` object is returned instead. 416 417 Examples 418 -------- 419 >>> x = np.matrix([[1, 2], [4, 3]]) 420 >>> x.sum() 421 10 422 >>> x.sum(axis=1) 423 matrix([[3], 424 [7]]) 425 >>> x.sum(axis=1, dtype='float') 426 matrix([[ 3.], 427 [ 7.]]) 428 >>> out = np.zeros((1, 2), dtype='float') 429 >>> x.sum(axis=1, dtype='float', out=out) 430 matrix([[ 3.], 431 [ 7.]]) 432 433 """ 434 return N.ndarray.sum(self, axis, dtype, out)._align(axis)

433 def sum_sum(values: TypeValList) -> float: 434 """Calculate the sum of ``values``.""" 435 if len(values) == 0: 436 return np.nan 437 438 return sum(values)

6 @jit(native=True, xsimd=True) 7 def row_sum(arr, columns): 8 return arr.T[columns].sum(0)