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import-error.json
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import-error.json
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[
"\nnp_arr = list(np.array(array))\ncum_arr = []\nsum = 0\nfor i in range(len(np_arr)):\n sum += np_arr[i]\n cum_arr.append(sum)",
"import numpy as np\nfrom operator import add\ncum_arr = np.fromiter(accumulate(array, add), dtype=int)",
"\ncum_arr = [0] * len(array)\nfor i in range(len(array)):\n cum_arr[i] = reduce(lambda x, y: x + y, array[:i + 1])",
"import itertools\narr_combinations = list(itertools.combinations(array, i))\ncum_arr = [sum(combination) for combination in arr_combinations for i in range(1, len(array) + 1)]",
"import numpy as np\narray_len = len(array)\nindices = list(itertools.chain.from_iterable((itertools.combinations(range(array_len), r) for r in range(1, array_len + 1))))\ncum_arr = np.zeros(len(indices))\nfor (i, index) in enumerate(indices):\n cum_arr[i] = np.sum(array[:index + 1])",
"import numpy as np\narray_len = len(array)\nindices = list(itertools.chain.from_iterable((itertools.combinations(range(array_len), r) for r in range(1, array_len + 1))))\ncum_arr = np.array([np.sum(array[:index + 1]) for index in indices])",
"import numpy as np\narray_len = len(array)\nindices = list(itertools.chain.from_iterable((itertools.combinations(range(array_len), r) for r in range(1, array_len + 1))))\ncum_arr = []\nfor index in indices:\n cum_arr.append(np.sum(array[:index + 1]))",
"\narray_len = len(array)\nindices = list(itertools.chain.from_iterable((itertools.combinations(range(array_len), r) for r in range(1, array_len + 1))))\ncum_arr = []\nfor index in indices:\n temp = sum(array[:index + 1])\n cum_arr.append(temp)",
"import numpy as np\narray_len = len(array)\nindices = list(itertools.chain.from_iterable((itertools.combinations(range(array_len), r) for r in range(1, array_len + 1))))\ncum_arr = np.cumsum([sum(array[:index + 1]) for index in indices])",
"import numpy as np\narray_len = len(array)\nindices = list(itertools.chain.from_iterable((itertools.combinations(range(array_len), r) for r in range(1, array_len + 1))))\nsummed_indices = [sum(array[:index + 1]) for index in indices]\ncum_arr = np.cumsum(summed_indices)",
"\narray_len = len(array)\nindices = list(itertools.chain.from_iterable((itertools.combinations(range(array_len), r) for r in range(1, array_len + 1))))\nsummed_indices = [sum(array[:index + 1]) for index in indices]\ncum_arr = []\nfor value in summed_indices:\n cum_arr.append(value)",
"import itertools\nimport functools\ncum_arr = list(itertools.accumulate(array, functools.partial(operator.add)))",
"import numpy as np\nfunct_arr = np.empty(len(arr))\ncum_arr = np.cumsum(arr, out=funct_arr)",
"import numpy as np\ncum_arr = np.add.reduce(arr)",
"import itertools as it\ntemp_arr = list(it.accumulate(arr))\ncum_arr = np.array(temp_arr)",
"\ncumulative_sum = 0\nfor i in range(len(array)):\n cumulative_sum += array[i]\n cum_arr.append(cumulative_sum)",
"\ncumulative_sum = 0\nfor i in range(len(array)):\n if i == 0:\n cum_arr.append(array[i])\n else:\n cumulative_sum = cum_arr[i - 1] + array[i]\n cum_arr.append(cumulative_sum)",
"import numpy as np\narr_sum = np.cumsum(arr)\ncum_arr = np.insert(arr_sum, 0, 0)",
"\ncum_arr = [0]\ncurr_sum = 0\nfor i in range(len(arr)):\n curr_sum += arr[i]\n cum_arr.append(curr_sum)",
"\nrunning_sum = 0\ncum_arr = [0]\nfor (index, value) in enumerate(arr):\n running_sum += value\n cum_arr.append(running_sum)",
"\ncum_arr = [0]\nfor i in range(len(arr)):\n running_sum = cum_arr[i] + arr[i]\n cum_arr.append(running_sum)",
"\ncum_arr = [0]\nfor i in itertools.count():\n running_sum += arr[i]\n cum_arr.append(running_sum)\n if i == len(arr) - 1:\n break",
"import numpy as np\ndef summing(arr):\n funct_arr = np.empty(len(arr))\n cum_arr = np.cumsum(arr, out=funct_arr)\n return cum_arr\ncum_arr = summing(arr)",
"import numpy as np\nr = len(arr)\ncum_arr = np.zeros(r)\nfor i in range(r):\n cum_arr[i] = sum(arr[0:i + 1])",
"from itertools import accumulate\narr_tuple = tuple(arr)\ncum_arr = list(accumulate(arr_tuple))",
"import numpy as np\narr_np = np.array(arr)\nresult = []\nfor i in range(arr_np.shape[0]):\n cum_sum = np.sum(arr_np[:i + 1])\n result.append(cum_sum)\ncum_arr = np.array(result)",
"import numpy as np\narr_len = len(arr)\ncum_arr = np.zeros((arr_len,))\nfor i in range(arr_len):\n cum_arr[i] = np.sum(arr[:i + 1])",
"import numpy as np\ncum_arr = np.sum(arr)",
"\ncum_arr = sum(arr)",
"from functools import reduce\ncum_arr = reduce(lambda x, y: x + y, arr)",
"import numpy as np\narr_sum = np.sum(arr)\ncum_arr = arr_sum",
"import numpy as np\ncum_arr = np.sum(arr, axis=0)",
"import numpy as np\ncum_arr = np.add.reduce(arr, axis=0)",
"import numpy as np\ncum_arr = np.sum(arr, axis=1)",
"import numpy as np\ncum_arr = np.add.reduce(arr, axis=1)",
"import numpy as np\ncum_arr = np.sum(arr, axis=(0, 1))",
"import numpy as np\ncum_arr = np.add.reduce(arr, axis=(0, 1))",
"import numpy as np\ncum_arr = np.multiply.reduce(arr)",
"import numpy as np\ncum_arr = np.multiply.reduce(arr, axis=0)",
"import numpy as np\ncum_arr = np.multiply.reduce(arr, axis=1)",
"import numpy as np\ncum_arr = np.multiply.reduce(arr, axis=(0, 1))",
"import itertools\narr1 = itertools.accumulate(arr)\ncum_arr = list(arr1)",
"import functools\ndef cum_sum(a, b):\n cum_arr = a + b\n return cum_arr\ncum_arr = functools.reduce(cum_sum, arr, [0])",
"import numpy as np\ncum_sum = np.cumsum(arr)\ncum_arr = np.array(cum_sum)",
"from itertools import accumulate\nimport numpy as np\ncum_arr = np.array(list(accumulate(arr)))",
"import numpy as np\ncount = np.sum(arr)\ncum_arr = np.cumsum(count).tolist()",
"\ncount = 0\nfor (index, value) in enumerate(arr):\n count += arr[index]\ncum_arr = count",
"import itertools\ncount = list(itertools.accumulate(arr))\ncum_arr = count",
"import functools\nfrom operator import add\ncount = functools.reduce(add, arr)\ncum_arr = [sum(arr[:i + 1]) for i in range(len(arr))]",
"import numpy as np\ncount = np.add.accumulate(arr, initial=0).tolist()",
"\ncum_arr = []\nsum_val = 0\nfor i in range(array_length):\n sum_val += array[i]\n cum_arr.append(sum_val)",
"import numpy as np\ncum_arr = np.zeros(array_length)\nfor i in range(array_length):\n cum_arr[i] = sum(array[:i + 1])",
"\ncum_arr = []\nsum_val = 0\nfor i in range(array_length):\n sum_val = sum(array[:i + 1])\n cum_arr.append(sum_val)",
"import numpy as np\ncum_arr = np.zeros(array_length)\nfor i in range(array_length):\n cum_arr[i] = np.sum(array[:i + 1])",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.asarray(cum_arr)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.int32)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.float64)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.bool_)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.str_)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.object_)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.uint8)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.int64)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.float32)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.complex128)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.uint16)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.int16)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.float16)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = np.array(cum_arr, dtype=np.complex64)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = list(cum_arr)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = tuple(cum_arr)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = set(cum_arr)",
"\ncount = 0\nfor i in range(len(array)):\n count += array[i]\n cum_arr.append(count)\ncum_arr = frozenset(cum_arr)",
"import functools\ncum_arr = list(itertools.accumulate(array, functools.partial(add, 0)))",
"\ncum_arr = functools.reduce(lambda x, y: x + y, array, [])",
"\ncum_arr = functools.reduce(lambda x, y: x * y, array, [])",
"\ncum_arr = functools.reduce(lambda x, y: x / y, array, [])",
"\ncum_arr = functools.reduce(lambda x, y: x if x > y else y, array, [])"
]