-
Notifications
You must be signed in to change notification settings - Fork 0
/
incorrect.json
195 lines (195 loc) · 24.4 KB
/
incorrect.json
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
[
"cum_arr = sum((array[:i + 1] for i in range(len(array))))",
"import itertools\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(sum(array[:index + 1]))",
"import itertools\nimport functools\ncum_arr = list(itertools.accumulate(array, functools.partial(lambda x, y: x + y, 0)))",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr[i] = sum",
"import numpy as np\ncum_arr = np.array([sum(array[:i + 1]) for i in range(len(array))])",
"import numpy as np\nfrom operator import add\ncum_arr = list(np.cumulate(array, add))",
"import numpy as np\ncum_arr = np.concatenate(([array[0]], np.cumsum(array[1:])))",
"import numpy as np\ncum_arr = np.ndarray(len(array), dtype=int)\ncum_arr[0] = array[0]\nfor i in range(1, len(array)):\n cum_arr[i] = cum_arr[i - 1] + array[i]",
"import math\ncum_arr = []\nsum = 0\nfor i in array:\n sum += i\n cum_arr.append(math.ceil(sum))",
"import numpy as np\nimport functools\ncum_arr = np.array(functools.reduce(lambda x, y: x + [x[-1] + y], array, [0])[1:])",
"cum_arr = 0\nresult = []\nfor i in range(len(array)):\n cum_arr += array[i]\n result.append(cum_arr)",
"cum_arr = 0\nresult = []\nfor i in range(len(array)):\n cum_arr += array[i]\n result.append(sum(result))",
"import itertools\ncum_arr = list(itertools.accumulate(array, lambda x, y: x + y, initial=0))",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, dtype=float)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=0, dtype=float)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=1, dtype=float)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=True)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=False)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind')",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='unsafe')",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C')",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='F')",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='A')",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='K')",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=False)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=0)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=1)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=2)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=3)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=4)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=5)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=6)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=7)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=8)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=9)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=10)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=11)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=12)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=13)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=14)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=15)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=16)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=17)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=18)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=19)",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0, axis=None, dtype=float, out=None, keepdims=False, where=True, casting='same_kind', order='C', subok=True, ndmin=20)",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor (i, val) in enumerate(array):\n sum += val\n cum_arr[i] = sum",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr[i] = sum\ncum_arr = [int(x) for x in cum_arr]",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor (i, val) in enumerate(array):\n sum += val\n cum_arr[i] = sum\ncum_arr = [int(x) for x in cum_arr]",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr[i] = sum\ncum_arr = [str(x) for x in cum_arr]",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor (i, val) in enumerate(array):\n sum += val\n cum_arr[i] = sum\ncum_arr = [str(x) for x in cum_arr]",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr[i] = sum\ncum_arr = [bool(x) for x in cum_arr]",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor (i, val) in enumerate(array):\n sum += val\n cum_arr[i] = sum\ncum_arr = [bool(x) for x in cum_arr]",
"cum_arr = [sum(array[:i + 1]) for i in range(len(array))]\ncum_arr = cum_arr[::-1]",
"cum_arr = []\nsum = 0\nfor i in array[::-1]:\n sum += i\n cum_arr.insert(0, sum)",
"cum_arr = []\nsum = 0\nfor i in array[::-1]:\n sum += i\n cum_arr.insert(0, sum)\ncum_arr = cum_arr[::-1]",
"cum_arr = [0]\nfor i in range(len(array)):\n cum_arr.append(cum_arr[i] + array[i])",
"cum_arr = [0]\nfor i in range(len(array)):\n cum_arr.append(cum_arr[-1] + array[i])",
"import itertools\ncum_arr = list(itertools.accumulate(array, initial=0))",
"import functools\npartial_sum = functools.partial(functools.reduce, lambda x, y: x + y)\ncum_arr = list(partial_sum(array, initial=0))",
"import statistics\nmean = statistics.mean(array)\ncum_arr = [0]\nfor i in range(len(array)):\n cum_arr.append(cum_arr[i] + array[i] - mean)",
"import numpy as np\ncum_arr = np.cumsum(array, axis=0, initial=10)",
"import numpy as np\ncum_arr = np.cumsum(array, axis=0, initial=0)",
"import numpy as np\ncum_arr = np.cumsum(array, axis=0, dtype=np.float64, initial=10)",
"import numpy as np\ncum_arr = np.cumsum(array, axis=0, dtype=np.int32, initial=0)",
"import numpy as np\nimport itertools\ncum_arr = np.zeros_like(array)\nfor (i, row) in enumerate(array):\n for (j, element) in enumerate(row):\n cum_arr[i, j] = np.sum(itertools.chain.from_iterable(array[i, :j + 1]))",
"import numpy as np\nimport itertools\ncum_arr = np.zeros_like(array)\nfor (i, row) in enumerate(array):\n for (j, element) in enumerate(row):\n cum_arr[i, j] = np.sum(array[i, :j + 1])",
"import numpy as np\ncum_arr = np.zeros(len(array))\nfor i in range(len(array)):\n if i == 0:\n cum_arr[i] = array[i]\n else:\n cum_arr[i] = cum_arr[i - 1] + array[i]",
"import numpy as np\ncum_arr = np.array([sum(array[:i + 1]) for i in range(len(array))]).cumsum()",
"import numpy as np\ncum_arr = np.array([sum(array[:i + 1]) for i in range(len(array))]).cumsum().tolist()",
"import numpy as np\nsum_arr = np.array([sum(array[:i + 1]) for i in range(len(array))]).cumsum()\ncum_arr = sum_arr.tolist()",
"import numpy as np\ncum_arr = np.empty(len(array))\nsum_val = 0\nfor i in range(len(array)):\n sum_val += array[i]\n cum_arr[i] = sum_val",
"import numpy as np\ncum_arr = np.array([sum(array[:i + 1]) for i in range(len(array))])\ncum_arr = np.cumsum(cum_arr)",
"import math\ncum_arr = []\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr.append(math.floor(sum))",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor i in range(len(array)):\n sum = sum + array[i]\n cum_arr[i] = sum",
"import numpy as np\ncum_arr = np.cumsum(array)\nfor i in range(len(cum_arr)):\n cum_arr[i] = sum(array[:i + 1])",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr[i] = sum\ncum_arr = cum_arr.astype(int).tolist()",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor (i, value) in enumerate(array):\n sum += value\n cum_arr[i] = sum\ncum_arr = cum_arr.astype(int).tolist()",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr[i] = sum\ncum_arr = cum_arr.astype(int, copy=False).tolist()",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor (i, value) in enumerate(array):\n sum += value\n cum_arr[i] = sum\ncum_arr = cum_arr.astype(int, copy=False).tolist()",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr[i] = sum\ncum_arr = np.array(cum_arr).astype(int).tolist()",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor (i, value) in enumerate(array):\n sum += value\n cum_arr[i] = sum\ncum_arr = np.array(cum_arr).astype(int).tolist()",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr[i] = sum\ncum_arr = np.array(cum_arr).astype(int, copy=False).tolist()",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor (i, value) in enumerate(array):\n sum += value\n cum_arr[i] = sum\ncum_arr = np.array(cum_arr).astype(int, copy=False).tolist()",
"import math\ncum_arr = []\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr.append(math.ceil(sum))",
"import math\ncum_arr = []\nsum = 0\nfor (i, value) in enumerate(array):\n sum += value\n cum_arr.append(math.ceil(sum))",
"import math\ncum_arr = []\nsum = 0\nfor (i, value) in enumerate(array):\n sum += value\n cum_arr.append(math.floor(sum))",
"import numpy as np\nimport statistics\ncum_arr = np.cumsum(array)\nsum = np.sum(array)\ncum_arr = cum_arr.tolist()\nsum = sum.tolist()\nsum = statistics.median(sum)",
"import numpy as np\ncum_arr = np.zeros(len(array))\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr[i] = np.sum(array[:i + 1])",
"import numpy as np\ncum_arr = np.cumsum(array)\nfor i in range(len(cum_arr)):\n cum_arr[i] = np.sum(array[:i]) + array[i]",
"import numpy as np\ncum_arr = np.cumsum(array)\nfor i in range(len(cum_arr)):\n cum_arr[i] = np.sum(array[:i + 1]) + array[i]",
"import numpy as np\ncum_arr = np.cumsum(array)\nfor i in range(len(cum_arr)):\n cum_arr[i] = np.sum(array[:i + 1]) - array[i - 1]",
"import numpy as np\ncum_arr = np.cumsum(array)\nfor i in range(len(cum_arr)):\n cum_arr[i] = np.sum(array[:i + 1]) * array[i]",
"import numpy as np\ncum_arr = np.cumsum(array)\nfor i in range(len(cum_arr)):\n cum_arr[i] = np.sum(array[:i + 1]) / array[i]",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.concatenate(([0], array[:-1])))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.hstack(([0], array[:-1])))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[0, array[:-1]])",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]])",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=int)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.int8)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.int16)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.int32)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.int64)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.uint8)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.uint16)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.uint32)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.uint64)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.float16)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.float32)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.float64)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.complex64)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.complex128)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.bool_)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.str_)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.object_)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.bytes_)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.unicode_)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=np.memoryview)",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(None))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(Ellipsis))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(True))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(1))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(1.0))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(1j))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type('a'))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(b'a'))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(u'a'))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(memoryview(b'a')))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.int8))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.int16))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.int32))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.int64))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.uint8))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.uint16))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.uint32))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.uint64))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.float16))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.float32))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.float64))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.complex64))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.complex128))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.bool_))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.str_))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.object_))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.bytes_))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.unicode_))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=type(np.memoryview))",
"import numpy as np\ncum_arr = np.cumsum(array)\ncum_arr = np.cumsum(np.r_[:0, array[:-1]], dtype=None)",
"import itertools\nrunning_sum = 0\ncum_arr = [running_sum]\nfor i in itertools.count():\n running_sum += array[i]\n cum_arr.append(running_sum)\n if i == len(array) - 1:\n break",
"cummulative_sum = 0\ntemp_arr = []\nfor num in arr:\n cummulative_sum += num\n temp_arr.append(cummulative_sum)\ncum_arr = np.array(temp_arr)",
"cum_arr = [sum(array[:i]) if i > 0 else array[0] for i in range(len(array) + 1)]",
"import numpy as np\narr = np.array(array)\ncum_arr = np.cumsum(arr, initial=0).tolist()",
"import numpy as np\ncum_arr = np.zeros_like(array)\nsum = 0\nfor i in range(len(array)):\n sum += array[i]\n cum_arr[i] = sum",
"running_sum = 0\ncum_arr = [running_sum]\nfor item in array:\n running_sum += item\n cum_arr.append(running_sum)\n if item == array[-1]:\n break",
"import numpy as np\ncum_arr = [x + arr[0] for x in arr]",
"cum_arr = 0\nfor i in arr:\n cum_arr += i",
"cummulative_sum = 0\noutput_arr = []\nfor num in arr:\n cummulative_sum += num\n output_arr.append(cummulative_sum)\noutput_array = np.array(output_arr)\ncum_arr = output_array",
"import numpy as np\narr_nums = []\ncummulative_sum = 0\nfor num in arr:\n cummulative_sum += num\n arr_nums.append(cummulative_sum)\ncum_arr = np.array(arr_nums)",
"import itertools\narr = list(array)\nsum_arr = [0] * (len(arr) + 1)\nfor i in range(len(arr)):\n sum_arr[i + 1] = sum_arr[i] + arr[i]",
"import itertools\narr = list(array)\nfinal_list = [sum(arr[:x + 1]) for x in range(len(arr))]",
"import numpy as np\ncum_arr = np.cumsum(array, initial=0).tolist()",
"val = 0\ncum_arr = [0]\nfor i in array:\n val += i\n cum_arr.append(val)",
"cum_arr = [array[0]]\nfor i in range(1, len(array)):\n cum_arr.append(array[i] + sum([cum_arr[j] for j in range(i)]))",
"cum_arr = 0\nfor i in range(len(array)):\n cum_arr += array[i]",
"import numpy as np\nimport math\ncum_arr = np.zeros(len(array))\nfor i in range(len(array)):\n cum_arr[i] = math.fsum(array[:i + 1])",
"import numpy as np\ndef cumulative_sum(arr):\n cum_arr = np.zeros(len(arr))\n for i in range(len(arr)):\n cum_arr[i] = sum(arr[:i + 1])\n return cum_arr\ncum_arr = cumulative_sum(array)",
"import math\narray_length = len(array)\ncum_arr = []\nsum = 0\nfor i in range(array_length):\n sum += array[i]\n cum_arr.append(math.ceil(sum))",
"import numpy as np\narray_length = len(array)\ncum_arr = np.zeros(array_length)\nfor i in range(array_length):\n if i == 0:\n cum_arr[i] = array[i]\n else:\n cum_arr[i] = cum_arr[i - 1] + array[i]",
"import numpy as np\narray_length = len(array)\ncum_arr = np.zeros(array_length)\nsum_val = 0\nfor i in range(array_length):\n sum_val += array[i]\n cum_arr[i] = sum_val",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumsum(arr_sum, axis=1)",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumprod(arr_sum, axis=1)",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumsum(np.flip(arr_sum, axis=0))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumsum(np.flip(arr_sum, axis=1))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumprod(np.flip(arr_sum, axis=0))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumprod(np.flip(arr_sum, axis=1))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumsum(np.sort(arr_sum))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumsum(np.sort(arr_sum, axis=0))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumsum(np.sort(arr_sum, axis=1))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumprod(np.sort(arr_sum))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumprod(np.sort(arr_sum, axis=0))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumprod(np.sort(arr_sum, axis=1))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumsum(np.sort(np.flip(arr_sum)))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumsum(np.sort(np.flip(arr_sum, axis=0)))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumsum(np.sort(np.flip(arr_sum, axis=1)))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumprod(np.sort(np.flip(arr_sum)))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumprod(np.sort(np.flip(arr_sum, axis=0)))",
"import numpy as np\narr_sum = np.sum(array)\ncum_arr = np.cumprod(np.sort(np.flip(arr_sum, axis=1)))"
]