-
Notifications
You must be signed in to change notification settings - Fork 0
/
incorrect.json
98 lines (98 loc) · 10.6 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
[
"import numpy as np\ndot_prod = np.dot(arr1, arr2)",
"import numpy as np\narr1_np = np.array(arr1)\narr2_np = np.array(arr2)\ndot_prod = np.dot(arr1_np, arr2_np)",
"import numpy as np\ndot_prod = np.inner(arr1, arr2)",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.sum(arr1 * arr2)",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.inner(arr1, arr2)",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.matmul(arr1, arr2)",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.einsum('i,i', arr1, arr2)",
"import numpy as np\ndot_prod = np.sum(np.multiply(arr1, arr2))",
"import numpy as np\ndot_prod = np.einsum('i,i', arr1, arr2)",
"import numpy as np\nprod = np.multiply(arr1, arr2)\ndot_prod = np.sum(prod)",
"from functools import reduce\nimport operator\narr1 = list(arr1)\narr2 = list(arr2)\ndot_prod = reduce(operator.add, [arr1[i] * arr2[i] for i in range(len(arr1))])",
"dot_prod = sum((x * y for (x, y) in zip(arr1.tolist(), arr2.tolist())))",
"dot_prod = sum([x * y for (x, y) in zip(arr1.tolist(), arr2.tolist())])",
"from functools import reduce\ndot_prod = reduce(lambda x, y: x + y, [arr1[i] * arr2[i] for i in range(len(arr1))])",
"dot_prod = sum((arr1[i] * arr2[i] for i in range(len(arr1)))).item()",
"dot_prod = sum([arr1[i] * arr2[i] for i in range(len(arr1))]).item()",
"dot_prod = sum((arr1[i] * arr2[i] for i in range(len(arr1)))).tolist()",
"dot_prod = sum([arr1[i] * arr2[i] for i in range(len(arr1))]).tolist()",
"dot_prod = sum((arr1[i] * arr2[i] for i in range(len(arr1)))).tolist()[0]",
"dot_prod = sum([arr1[i] * arr2[i] for i in range(len(arr1))]).tolist()[0]",
"import itertools\npairs = itertools.zip_longest(arr1, arr2)\ndot_prod = sum((x * y for (x, y) in pairs))",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.vdot(arr1, arr2)",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.inner(arr1.flatten(), arr2.flatten())",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.dot(arr1.flatten(), arr2.flatten())",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.sum(arr1.flatten() * arr2.flatten())",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.einsum('i,i->', arr1, arr2)",
"dot_prod = 0\nfor i in range(len(arr1.flatten())):\n dot_prod += arr1.flatten()[i] * arr2.flatten()[i]",
"dot_prod = sum([arr1.flatten()[i] * arr2.flatten()[i] for i in range(len(arr1.flatten()))])",
"dot_prod = sum((arr1.flatten()[i] * arr2.flatten()[i] for i in range(len(arr1.flatten()))))",
"dot_prod = sum((a * b for (a, b) in zip(arr1.flatten(), arr2.flatten())))",
"dot_prod = sum(map(lambda a, b: a * b, arr1.flatten(), arr2.flatten()))",
"dot_prod = sum(map(lambda x, y: x * y, arr1.flatten(), arr2.flatten()))",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.einsum('i', arr1) * np.einsum('i', arr2)",
"import numpy as np\narr1 = np.array([i for i in arr1 if i is not None])\narr2 = np.array([j for j in arr2 if j is not None])\ndot_prod = np.sum(arr1 * arr2)",
"import numpy as np\narr1 = np.array([i for i in arr1 if i is not None])\narr2 = np.array([j for j in arr2 if j is not None])\ndot_prod = np.dot(arr1, arr2)",
"dot_prod = sum((i * j for (i, j) in zip(arr1, arr2) if i is not None and j is not None)) if arr1.any() and arr2.any() else None",
"import numpy as np\narr2_transposed = np.transpose(arr2)\narr1_1d = np.reshape(arr1, -1)\narr2_1d = np.reshape(arr2_transposed, -1)\ndot_prod = np.dot(arr1_1d, arr2_1d)[0]",
"import numpy as np\narr2_transposed = np.transpose(arr2)\narr1_1d = np.reshape(arr1, -1)\narr2_1d = np.reshape(arr2_transposed, -1)\ndot_prod = np.tensordot(arr1_1d, arr2_1d, axes=1)[0]",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = arr1.dot(arr2)",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = sum(arr1 * arr2)",
"import math\nsquared_differences = [(arr1[i] - arr2[i]) ** 2 for i in range(len(arr1))]\ndot_prod = math.sqrt(sum(squared_differences))",
"import numpy as np\nmatrix = np.array([arr1, arr2])\ndot_prod = matrix.prod(axis=0).sum()",
"import numpy as np\narr1_arr2 = np.array([arr1, arr2])\nproduct = np.product(arr1_arr2, axis=0)\ndot_prod = np.sum(product)",
"import numpy as np\ndot_prod = np.dot(np.array(arr1), np.array(arr2))",
"dot_prod = sum((arr1[i] * arr2[i] for i in range(len(arr1)) if arr1[i] > 0 and arr2[i] > 0))",
"import numpy as np\npairs = np.transpose(np.array([arr1, arr2]))\ndot_prod = np.sum(np.prod(pairs, axis=1))",
"import numpy as np\npairs = np.transpose(np.array([arr1, arr2]))\ndot_prod = np.dot(pairs[:, 0], pairs[:, 1])",
"import numpy as np\npairs = np.transpose(np.array([arr1, arr2]))\ndot_prod = np.einsum('ij->', pairs)",
"import numpy as np\npairs = np.transpose(np.array([arr1, arr2]))\ndot_prod = np.matmul(pairs[:, 0], pairs[:, 1])",
"import numpy as np\nimport functools\ndot_prod = functools.reduce(lambda x, y: x + y, [arr1[i] * arr2[i] for i in range(len(arr1))])",
"import numpy as np\nimport math\ndot_prod = np.dot(arr1, arr2) + math.prod(arr1) + math.prod(arr2)",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.sum(arr1 * arr2) + np.prod(arr1) + np.prod(arr2)",
"import numpy as np\ndot_prod = np.dot(arr1, np.transpose(arr2))",
"dot_prod = arr1[0] * arr2[0] + arr1[1] * arr2[1] + arr1[2] * arr2[2]",
"import numpy as np\ndot_prod = np.sum(np.array(arr1) * np.array(arr2))",
"import numpy as np\ndot_prod = np.matmul(arr1, np.transpose(arr2))",
"import numpy as np\ndot_prod = np.inner(np.array(arr1), np.array(arr2))",
"import numpy as np\ndot_prod = np.inner(np.asarray(arr1), np.asarray(arr2))",
"import numpy as np\ndot_prod = np.inner(np.array(arr1), np.array(arr2)).item()",
"import itertools\nmultiply = list(itertools.imap(lambda x, y: x * y, arr1, arr2))\ndot_prod = sum(multiply)",
"dot_prod = 0\nfor i in range(len(arr1)):\n dot_prod += arr1[i] * arr2[i]\nif dot_prod == 0:\n dot_prod = None",
"import functools\nimport operator\ndot_prod = functools.reduce(operator.add, [arr1[i] * arr2[i] for i in range(len(arr1))])",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.sum(np.multiply(arr1, arr2))",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.sum(arr1 * arr2, axis=0)",
"import numpy as np\narr1 = np.array(arr1)\narr2 = np.array(arr2)\ndot_prod = np.sum(np.multiply(arr1, arr2), axis=0)",
"import numpy as np\npairs = np.column_stack((arr1, arr2))\ndot_prod = np.sum(pairs[:, 0] * pairs[:, 1])",
"import numpy as np\npairs = np.vstack((arr1, arr2)).T\ndot_prod = np.sum(pairs[:, 0] * pairs[:, 1])",
"import numpy as np\npairs = np.concatenate((arr1[:, np.newaxis], arr2[:, np.newaxis]), axis=1)\ndot_prod = np.sum(pairs[:, 0] * pairs[:, 1])",
"import numpy as np\npairs = np.array([(arr1[i], arr2[i]) for i in range(len(arr1))])\ndot_prod = np.sum(pairs[:, 0] * pairs[:, 1])",
"import numpy as np\npairs = np.array([(x, y) for (x, y) in zip(arr1.tolist(), arr2.tolist())])\ndot_prod = np.sum(pairs[:, 0] * pairs[:, 1])",
"import numpy as np\npairs = np.array([(x, y) for (x, y) in zip(arr1.tolist(), arr2)])\ndot_prod = np.sum(pairs[:, 0] * pairs[:, 1])",
"import numpy as np\npairs = np.array([(x, y) for (x, y) in zip(arr1, arr2.tolist())])\ndot_prod = np.sum(pairs[:, 0] * pairs[:, 1])",
"dot_prod = 0\nfor (x, y) in zip(arr1.tolist(), arr2.tolist()):\n dot_prod += x * y",
"import numpy as np\narr1_list = arr1.tolist()\narr2_list = arr2.tolist()\ndot_prod = sum((x * y for (x, y) in zip(arr1_list, arr2_list)))",
"dot_prod = 0\nfor (x, y) in zip(arr1.flatten(), arr2.flatten()):\n dot_prod += x * y",
"import itertools\nflatten_arr1 = list(itertools.chain.from_iterable(arr1))\nflatten_arr2 = list(itertools.chain.from_iterable(arr2))\ndot_prod = sum((x * y for (x, y) in zip(flatten_arr1, flatten_arr2)))",
"import numpy as np\nflatten_arr1 = arr1.flatten()\nflatten_arr2 = arr2.flatten()\ndot_prod = np.dot(flatten_arr1, flatten_arr2)",
"import numpy as np\ndef dot_product(arr1, arr2):\n if len(arr1) != len(arr2):\n raise ValueError('Arrays must have the same length')\n dot_prod = 0\n for i in range(len(arr1)):\n dot_prod += arr1[i] * arr2[i]\n return dot_prod\ndot_prod = dot_product(arr1, arr2)",
"from numpy import dot\nimport numpy as np\ndot_prod = dot(arr1, arr2)",
"import numpy as np\narr3 = arr1 * arr2\ndot_prod = np.sum(arr3)",
"from itertools import zip_longest\ndef dot_product(arr1, arr2):\n if len(arr1) != len(arr2):\n raise ValueError('Arrays must have the same length')\n dot_prod = sum((i * j for (i, j) in zip_longest(arr1, arr2, fillvalue=0)))\n return dot_prod\ndot_prod = dot_product(arr1, arr2)",
"import numpy as np\ndef dot_product(arr1, arr2):\n if len(arr1) != len(arr2):\n raise ValueError('Arrays must have the same length')\n arr1 = np.array(arr1)\n arr2 = np.array(arr2)\n dot_prod = np.dot(arr1, arr2)\n return dot_prod\ndot_prod = dot_product(arr1, arr2)",
"import numpy as np\nimport itertools\ndot_prod = np.dot(arr1, arr2)\narr1_list = list(arr1)\narr2_list = list(arr2)\ndot_prod_list = list(itertools.starmap(np.multiply, zip(arr1_list, arr2_list)))\ndot_prod_sum = np.sum(dot_prod_list)\ndot_prod == dot_prod_sum",
"import numpy as np\ndot_prod = np.dot(arr1.toarray(), arr2.toarray())",
"import numpy as np\ndot_prod = np.dot(arr1.A.transpose(), arr2.A)",
"dot_prod = arr1.toarray().transpose() @ arr2.toarray()",
"import numpy as np\nimport torch\narr1_np = np.array(arr1)\narr1_torch = torch.tensor(arr1_np)\ndot_prod = np.inner(arr1_torch.tolist(), arr2)",
"import numpy as np\narr1_np = np.array(arr1)\narr2_np = np.array(arr2)\ndot_prod = np.matmul(arr1_np.T, arr2_np)",
"from operator import mul\nimport functools\nprod_arr = functools.reduce(mul, zip(arr1, arr2))\ndot_prod = sum(prod_arr)",
"import numpy as np\nprod_arr = np.dot(arr1, arr2)\ndot_prod = np.sum(prod_arr)",
"import numpy as np\nprod_arr = np.sum(arr1 * arr2)\ndot_prod = prod_arr",
"import itertools\ndot_prod = sum((x * y for (x, y) in itertools.zip_longest(arr1, arr2)))",
"dot_prod = 0\narr1_list = arr1.tolist()\narr2_list = arr2.tolist()\nfor i in range(len(arr1_list)):\n dot_prod += arr1_list[i] * arr2_list[i]",
"import numpy as np\narr1_list = arr1.tolist()\narr2_list = arr2.tolist()\nres = np.dot(arr1_list, arr2_list)\ndot_prod = res.tolist()",
"from numpy import dot\nfrom numpy.linalg import norm\ndot_prod = dot(arr1, arr2) / (norm(arr1) * norm(arr2))",
"import math\nres = 0\nfor i in range(len(arr1)):\n res += arr1[i] * arr2[i]\ndot_prod = math.sqrt(res)"
]