Why does code A runs well whereas B got an error?

Why does code A runs well whereas B got an error? - pytorch

I encountered a question when I try to understand a line of code:
scores = [s[tuple(k.t())] for s, k in zip(scores, keypoints)].
Here I am going to give the code that can reproduce it:
import torch # v1.5.1
scores = [torch.rand(480, 640)]
keypoints = [torch.randint(0, 480, [978, 2])]
scores = [s[tuple(k.t())] for s, k in zip(scores, keypoints)] # label A, OK
for s, k in zip(scores, keypoints):
print(s[tuple(k.t())]) # label B, Error
>>> IndexError: too many indices for tensor of dimension 1
I think the above two kinds of code (A and B respectively) are almost the same, but the latter threw out an error. I discussed it with my roommate but we have no idea what's going on.
Could someone help me? Thanks in advance!

You can just change the order of A and B, for you have already changed "scores" when you execute B. It will definitely works.
import torch
scores = [torch.rand(2, 3)]
keypoints = [torch.randint(0, 2, [4, 2])]
for s, k in zip(scores, keypoints):
print(s[tuple(k.t())]) # label B, Error (now ok)
scores = [s[tuple(k.t())] for s, k in zip(scores, keypoints)] # label A, OK
print(scores)
The output is
[tensor([0.3175, 0.6934, 0.2842, 0.3175])]
[tensor([0.3175, 0.6934, 0.2842, 0.3175])]
which is exactly the same.

I believe you are redefining what scores is and therefore the values change. However if you change the line
scores = [s[tuple(k.t())] for s, k in zip(scores, keypoints)]
to:
labelA = [s[tuple(k.t())] for s, k in zip(scores, keypoints)]
It should be the same.
Python 3.6.8 |Anaconda custom (64-bit)| (default, Dec 29 2018, 19:04:46)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> scores = [torch.rand(480, 640)]
>>> keypoints = [torch.randint(0, 480, [978, 2])]
>>>
>>>
>>> labelA = [s[tuple(k.t())] for s, k in zip(scores, keypoints)] # label A, OK
>>> labelB = []
>>> for s, k in zip(scores, keypoints):
... labelB.append(s[tuple(k.t())])
...
>>> labelB
[tensor([0.7239, 0.1610, 0.6094, 0.8368, 0.1523, 0.1063, 0.6487, 0.8021, 0.0650,
0.5950, 0.3393, 0.7012, 0.6868, 0.8905, 0.4100, 0.8173, 0.2271, 0.7572,
0.9446, 0.4321, 0.5575, 0.4204, 0.8950, 0.2369, 0.1743, 0.8011, 0.3178,
0.8098, 0.1318, 0.2499, 0.2979, 0.4248, 0.7016, 0.5042, 0.5122, 0.0894,
0.6981, 0.9588, 0.9439, 0.6145, 0.1386, 0.0325, 0.6099, 0.5928, 0.1368,
0.3560, 0.5814, 0.8894, 0.5715, 0.3138, 0.2881, 0.0041, 0.2198, 0.2518,
0.7856, 0.9828, 0.0374, 0.1621, 0.8600, 0.9820, 0.9233, 0.1292, 0.3045,
0.5753, 0.6036, 0.7019, 0.2679, 0.5477, 0.2112, 0.7881, 0.3429, 0.2112,
0.3397, 0.6253, 0.2414, 0.0650, 0.4213, 0.0078, 0.5286, 0.1454, 0.5127,
0.9211, 0.2263, 0.7103, 0.6328, 0.4127, 0.5828, 0.5850, 0.3128, 0.6189,
0.0670, 0.5370, 0.3466, 0.8870, 0.0481, 0.4839, 0.0507, 0.1635, 0.6603,
0.6537, 0.6107, 0.0907, 0.0952, 0.8440, 0.5431, 0.4123, 0.2333, 0.0709,
0.3717, 0.5081, 0.6989, 0.7998, 0.4175, 0.4417, 0.4483, 0.4192, 0.4145,
0.3055, 0.1455, 0.8435, 0.0244, 0.4587, 0.1832, 0.7093, 0.2874, 0.9551,
0.8489, 0.1560, 0.3212, 0.2144, 0.2841, 0.0630, 0.9778, 0.0212, 0.8164,
0.6745, 0.2651, 0.7740, 0.0466, 0.4219, 0.0916, 0.2075, 0.4771, 0.6549,
0.0735, 0.6748, 0.8197, 0.2675, 0.0482, 0.1787, 0.9859, 0.5532, 0.1647,
0.7046, 0.8058, 0.7552, 0.1674, 0.3935, 0.0430, 0.2665, 0.6067, 0.2496,
0.1711, 0.3226, 0.3504, 0.8985, 0.4125, 0.0925, 0.8231, 0.8535, 0.8478,
0.2536, 0.6850, 0.6608, 0.6128, 0.0255, 0.6569, 0.0738, 0.8647, 0.2322,
0.9898, 0.5044, 0.7879, 0.8705, 0.0973, 0.2900, 0.7294, 0.8847, 0.7572,
0.4871, 0.8809, 0.5839, 0.4855, 0.8424, 0.4151, 0.1806, 0.7665, 0.4365,
0.6867, 0.3397, 0.3951, 0.5472, 0.5545, 0.8930, 0.8970, 0.2972, 0.2406,
0.3203, 0.3957, 0.1715, 0.1609, 0.8939, 0.0374, 0.8682, 0.4520, 0.2852,
0.9323, 0.9132, 0.5007, 0.5879, 0.2878, 0.5277, 0.1378, 0.3752, 0.0059,
0.4944, 0.9876, 0.7333, 0.2803, 0.4471, 0.3596, 0.8874, 0.6594, 0.5410,
0.0277, 0.0497, 0.4526, 0.3456, 0.2889, 0.4981, 0.1150, 0.5470, 0.8293,
0.9683, 0.1102, 0.0432, 0.0206, 0.0381, 0.0426, 0.1343, 0.0595, 0.1643,
0.4930, 0.1606, 0.4333, 0.7708, 0.3279, 0.9765, 0.0809, 0.6337, 0.8330,
0.7061, 0.5643, 0.9792, 0.7728, 0.9949, 0.6205, 0.7836, 0.3634, 0.4491,
0.3759, 0.8197, 0.8402, 0.8870, 0.3647, 0.0718, 0.3802, 0.6333, 0.0564,
0.1321, 0.9894, 0.4785, 0.2591, 0.5078, 0.8955, 0.1457, 0.3913, 0.7020,
0.1685, 0.2133, 0.9725, 0.1179, 0.3458, 0.8330, 0.4897, 0.3802, 0.2729,
0.3109, 0.2683, 0.3611, 0.9983, 0.9674, 0.8986, 0.0674, 0.4810, 0.2030,
0.3708, 0.9974, 0.3354, 0.3416, 0.9956, 0.4438, 0.4523, 0.1212, 0.1906,
0.0255, 0.3857, 0.0520, 0.8090, 0.0363, 0.5155, 0.5259, 0.5144, 0.0832,
0.8416, 0.8666, 0.3573, 0.2119, 0.8180, 0.4281, 0.9585, 0.0069, 0.3688,
0.8813, 0.9660, 0.4405, 0.7213, 0.9818, 0.0342, 0.7656, 0.4573, 0.2477,
0.3124, 0.2173, 0.2723, 0.2149, 0.9469, 0.7091, 0.8051, 0.9815, 0.0416,
0.3525, 0.7070, 0.9123, 0.1957, 0.4095, 0.8105, 0.4935, 0.7852, 0.9508,
0.7854, 0.3267, 0.0761, 0.1353, 0.5961, 0.3199, 0.6699, 0.8500, 0.8540,
0.4927, 0.4319, 0.5490, 0.8794, 0.4855, 0.4408, 0.2513, 0.3591, 0.4385,
0.4902, 0.0892, 0.2645, 0.2993, 0.5301, 0.2470, 0.4180, 0.3924, 0.4231,
0.0155, 0.0239, 0.6117, 0.5051, 0.5522, 0.8229, 0.3941, 0.9290, 0.2339,
0.0214, 0.8480, 0.6003, 0.9120, 0.0334, 0.0822, 0.7023, 0.0906, 0.1074,
0.8747, 0.0965, 0.5973, 0.8562, 0.5227, 0.5342, 0.5682, 0.7457, 0.4776,
0.7218, 0.5008, 0.6378, 0.5541, 0.6418, 0.1443, 0.1458, 0.8786, 0.4283,
0.2835, 0.6132, 0.8284, 0.9009, 0.5561, 0.5137, 0.9318, 0.0267, 0.2009,
0.6635, 0.3267, 0.5239, 0.1676, 0.2327, 0.1248, 0.8706, 0.4100, 0.0529,
0.8903, 0.8717, 0.7688, 0.2585, 0.3399, 0.7276, 0.4385, 0.6888, 0.6419,
0.2661, 0.8262, 0.7331, 0.5433, 0.9399, 0.4117, 0.4899, 0.6666, 0.0826,
0.1968, 0.6295, 0.4921, 0.3039, 0.5059, 0.8406, 0.6863, 0.1999, 0.8681,
0.7958, 0.2988, 0.5588, 0.6630, 0.4348, 0.2735, 0.4761, 0.0994, 0.0803,
0.1431, 0.9707, 0.3018, 0.3598, 0.6734, 0.6126, 0.1162, 0.2229, 0.1249,
0.8871, 0.6972, 0.4470, 0.7034, 0.4932, 0.1183, 0.2348, 0.8528, 0.7901,
0.2365, 0.7217, 0.5406, 0.1416, 0.9804, 0.7091, 0.3708, 0.4327, 0.0531,
0.0861, 0.2463, 0.0912, 0.6666, 0.4180, 0.9266, 0.2631, 0.7023, 0.0398,
0.0631, 0.6601, 0.6339, 0.6206, 0.1393, 0.1075, 0.6920, 0.6626, 0.8973,
0.1839, 0.2475, 0.1521, 0.0381, 0.5855, 0.2973, 0.1848, 0.3025, 0.8042,
0.5952, 0.6057, 0.5527, 0.7248, 0.0033, 0.3411, 0.8677, 0.9543, 0.6956,
0.2909, 0.9458, 0.4611, 0.1876, 0.1012, 0.6692, 0.9081, 0.1122, 0.9392,
0.8478, 0.6917, 0.6057, 0.6920, 0.1247, 0.9858, 0.3460, 0.8301, 0.8894,
0.8431, 0.4964, 0.0289, 0.1298, 0.1918, 0.8065, 0.5335, 0.9905, 0.7099,
0.6120, 0.2878, 0.2931, 0.8318, 0.9276, 0.9328, 0.3071, 0.3785, 0.5239,
0.2914, 0.1401, 0.4540, 0.4798, 0.4797, 0.0380, 0.2156, 0.1642, 0.5507,
0.0664, 0.8262, 0.9418, 0.0536, 0.2727, 0.9576, 0.4063, 0.4981, 0.4513,
0.6310, 0.9909, 0.3513, 0.5842, 0.6780, 0.8629, 0.7755, 0.0898, 0.9114,
0.0207, 0.1783, 0.1597, 0.8240, 0.9023, 0.0074, 0.7930, 0.8564, 0.4700,
0.8839, 0.4839, 0.9852, 0.3291, 0.9607, 0.8842, 0.8725, 0.1717, 0.6004,
0.0670, 0.8676, 0.6065, 0.6930, 0.8870, 0.2545, 0.1041, 0.5940, 0.5596,
0.8877, 0.4002, 0.5495, 0.3640, 0.4373, 0.8292, 0.2008, 0.3124, 0.6308,
0.2529, 0.1802, 0.5372, 0.9018, 0.0830, 0.3240, 0.6729, 0.9612, 0.9211,
0.5371, 0.8745, 0.4602, 0.7666, 0.0433, 0.5461, 0.2115, 0.2959, 0.0351,
0.8651, 0.7865, 0.2392, 0.1375, 0.7444, 0.6702, 0.1889, 0.0102, 0.2363,
0.9406, 0.2144, 0.2174, 0.5765, 0.8715, 0.5440, 0.7480, 0.0387, 0.2754,
0.7528, 0.4358, 0.1080, 0.8259, 0.0232, 0.2766, 0.4030, 0.6221, 0.5128,
0.1035, 0.3966, 0.3859, 0.4088, 0.8898, 0.6106, 0.0241, 0.0442, 0.1680,
0.3836, 0.8129, 0.4091, 0.7610, 0.7527, 0.7474, 0.2838, 0.3861, 0.3496,
0.6985, 0.0842, 0.4432, 0.2829, 0.9554, 0.7354, 0.4623, 0.4193, 0.6449,
0.1662, 0.3948, 0.1070, 0.4275, 0.6427, 0.6758, 0.1149, 0.9313, 0.3048,
0.2237, 0.4992, 0.4688, 0.3633, 0.5445, 0.8166, 0.0392, 0.9118, 0.7784,
0.4596, 0.5234, 0.0789, 0.1671, 0.0663, 0.6008, 0.4271, 0.6451, 0.8050,
0.7993, 0.3750, 0.4266, 0.2093, 0.8230, 0.0515, 0.0785, 0.1407, 0.0502,
0.1021, 0.0343, 0.0291, 0.0833, 0.4709, 0.7199, 0.6756, 0.3500, 0.7100,
0.6334, 0.8984, 0.6105, 0.2101, 0.7228, 0.2321, 0.2186, 0.2271, 0.3792,
0.3462, 0.7752, 0.9628, 0.4922, 0.3908, 0.7770, 0.0485, 0.5218, 0.1772,
0.0367, 0.9492, 0.9352, 0.4897, 0.9790, 0.1704, 0.9757, 0.3399, 0.3952,
0.2428, 0.3014, 0.1833, 0.0175, 0.9480, 0.3613, 0.3031, 0.1372, 0.4799,
0.0364, 0.7588, 0.4608, 0.2652, 0.2054, 0.6034, 0.5563, 0.0053, 0.3368,
0.7328, 0.5666, 0.2000, 0.4721, 0.5381, 0.9557, 0.0762, 0.4067, 0.8686,
0.6698, 0.7660, 0.9169, 0.8401, 0.7283, 0.0271, 0.2323, 0.0811, 0.6277,
0.8744, 0.9459, 0.9015, 0.6752, 0.7650, 0.6628, 0.4006, 0.5877, 0.0514,
0.8610, 0.3106, 0.6999, 0.6773, 0.4738, 0.1838, 0.0942, 0.9465, 0.0689,
0.3126, 0.7237, 0.6566, 0.4259, 0.1337, 0.8046, 0.2415, 0.9873, 0.9736,
0.2487, 0.8346, 0.9100, 0.3429, 0.1321, 0.7593, 0.7780, 0.2588, 0.2804,
0.3661, 0.3630, 0.7371, 0.5247, 0.9303, 0.2413, 0.2591, 0.3403, 0.0683,
0.4428, 0.4089, 0.7018, 0.8541, 0.2662, 0.2819, 0.9080, 0.5924, 0.8527,
0.8277, 0.0945, 0.3408, 0.0259, 0.8425, 0.3551, 0.9404, 0.6876, 0.3102,
0.8169, 0.3289, 0.7174, 0.2404, 0.7087, 0.2562, 0.2022, 0.1705, 0.6359,
0.7204, 0.0698, 0.1980, 0.7807, 0.0989, 0.0387, 0.5021, 0.9782, 0.0989,
0.4415, 0.9582, 0.8193, 0.7433, 0.3606, 0.8234, 0.9470, 0.6152, 0.0739,
0.0091, 0.3852, 0.6140, 0.8024, 0.3931, 0.6374, 0.7420, 0.8262, 0.5612,
0.1429, 0.1118, 0.5879, 0.2417, 0.8952, 0.2698, 0.8374, 0.9325, 0.9897,
0.7748, 0.0254, 0.8351, 0.5943, 0.9824, 0.2132, 0.6469, 0.8862, 0.9013,
0.5097, 0.5300, 0.7497, 0.8371, 0.1307, 0.2927, 0.1760, 0.6744, 0.7508,
0.7924, 0.9564, 0.8733, 0.8700, 0.9728, 0.1362, 0.3822, 0.7738, 0.7682,
0.3459, 0.5163, 0.6223, 0.9129, 0.3179, 0.7660, 0.0849, 0.7594, 0.9258,
0.3004, 0.5658, 0.1079, 0.0985, 0.3576, 0.8792, 0.2296, 0.2061, 0.9494,
0.9141, 0.1866, 0.4255, 0.6605, 0.5622, 0.4929])]
>>> labelA
[tensor([0.7239, 0.1610, 0.6094, 0.8368, 0.1523, 0.1063, 0.6487, 0.8021, 0.0650,
0.5950, 0.3393, 0.7012, 0.6868, 0.8905, 0.4100, 0.8173, 0.2271, 0.7572,
0.9446, 0.4321, 0.5575, 0.4204, 0.8950, 0.2369, 0.1743, 0.8011, 0.3178,
0.8098, 0.1318, 0.2499, 0.2979, 0.4248, 0.7016, 0.5042, 0.5122, 0.0894,
0.6981, 0.9588, 0.9439, 0.6145, 0.1386, 0.0325, 0.6099, 0.5928, 0.1368,
0.3560, 0.5814, 0.8894, 0.5715, 0.3138, 0.2881, 0.0041, 0.2198, 0.2518,
0.7856, 0.9828, 0.0374, 0.1621, 0.8600, 0.9820, 0.9233, 0.1292, 0.3045,
0.5753, 0.6036, 0.7019, 0.2679, 0.5477, 0.2112, 0.7881, 0.3429, 0.2112,
0.3397, 0.6253, 0.2414, 0.0650, 0.4213, 0.0078, 0.5286, 0.1454, 0.5127,
0.9211, 0.2263, 0.7103, 0.6328, 0.4127, 0.5828, 0.5850, 0.3128, 0.6189,
0.0670, 0.5370, 0.3466, 0.8870, 0.0481, 0.4839, 0.0507, 0.1635, 0.6603,
0.6537, 0.6107, 0.0907, 0.0952, 0.8440, 0.5431, 0.4123, 0.2333, 0.0709,
0.3717, 0.5081, 0.6989, 0.7998, 0.4175, 0.4417, 0.4483, 0.4192, 0.4145,
0.3055, 0.1455, 0.8435, 0.0244, 0.4587, 0.1832, 0.7093, 0.2874, 0.9551,
0.8489, 0.1560, 0.3212, 0.2144, 0.2841, 0.0630, 0.9778, 0.0212, 0.8164,
0.6745, 0.2651, 0.7740, 0.0466, 0.4219, 0.0916, 0.2075, 0.4771, 0.6549,
0.0735, 0.6748, 0.8197, 0.2675, 0.0482, 0.1787, 0.9859, 0.5532, 0.1647,
0.7046, 0.8058, 0.7552, 0.1674, 0.3935, 0.0430, 0.2665, 0.6067, 0.2496,
0.1711, 0.3226, 0.3504, 0.8985, 0.4125, 0.0925, 0.8231, 0.8535, 0.8478,
0.2536, 0.6850, 0.6608, 0.6128, 0.0255, 0.6569, 0.0738, 0.8647, 0.2322,
0.9898, 0.5044, 0.7879, 0.8705, 0.0973, 0.2900, 0.7294, 0.8847, 0.7572,
0.4871, 0.8809, 0.5839, 0.4855, 0.8424, 0.4151, 0.1806, 0.7665, 0.4365,
0.6867, 0.3397, 0.3951, 0.5472, 0.5545, 0.8930, 0.8970, 0.2972, 0.2406,
0.3203, 0.3957, 0.1715, 0.1609, 0.8939, 0.0374, 0.8682, 0.4520, 0.2852,
0.9323, 0.9132, 0.5007, 0.5879, 0.2878, 0.5277, 0.1378, 0.3752, 0.0059,
0.4944, 0.9876, 0.7333, 0.2803, 0.4471, 0.3596, 0.8874, 0.6594, 0.5410,
0.0277, 0.0497, 0.4526, 0.3456, 0.2889, 0.4981, 0.1150, 0.5470, 0.8293,
0.9683, 0.1102, 0.0432, 0.0206, 0.0381, 0.0426, 0.1343, 0.0595, 0.1643,
0.4930, 0.1606, 0.4333, 0.7708, 0.3279, 0.9765, 0.0809, 0.6337, 0.8330,
0.7061, 0.5643, 0.9792, 0.7728, 0.9949, 0.6205, 0.7836, 0.3634, 0.4491,
0.3759, 0.8197, 0.8402, 0.8870, 0.3647, 0.0718, 0.3802, 0.6333, 0.0564,
0.1321, 0.9894, 0.4785, 0.2591, 0.5078, 0.8955, 0.1457, 0.3913, 0.7020,
0.1685, 0.2133, 0.9725, 0.1179, 0.3458, 0.8330, 0.4897, 0.3802, 0.2729,
0.3109, 0.2683, 0.3611, 0.9983, 0.9674, 0.8986, 0.0674, 0.4810, 0.2030,
0.3708, 0.9974, 0.3354, 0.3416, 0.9956, 0.4438, 0.4523, 0.1212, 0.1906,
0.0255, 0.3857, 0.0520, 0.8090, 0.0363, 0.5155, 0.5259, 0.5144, 0.0832,
0.8416, 0.8666, 0.3573, 0.2119, 0.8180, 0.4281, 0.9585, 0.0069, 0.3688,
0.8813, 0.9660, 0.4405, 0.7213, 0.9818, 0.0342, 0.7656, 0.4573, 0.2477,
0.3124, 0.2173, 0.2723, 0.2149, 0.9469, 0.7091, 0.8051, 0.9815, 0.0416,
0.3525, 0.7070, 0.9123, 0.1957, 0.4095, 0.8105, 0.4935, 0.7852, 0.9508,
0.7854, 0.3267, 0.0761, 0.1353, 0.5961, 0.3199, 0.6699, 0.8500, 0.8540,
0.4927, 0.4319, 0.5490, 0.8794, 0.4855, 0.4408, 0.2513, 0.3591, 0.4385,
0.4902, 0.0892, 0.2645, 0.2993, 0.5301, 0.2470, 0.4180, 0.3924, 0.4231,
0.0155, 0.0239, 0.6117, 0.5051, 0.5522, 0.8229, 0.3941, 0.9290, 0.2339,
0.0214, 0.8480, 0.6003, 0.9120, 0.0334, 0.0822, 0.7023, 0.0906, 0.1074,
0.8747, 0.0965, 0.5973, 0.8562, 0.5227, 0.5342, 0.5682, 0.7457, 0.4776,
0.7218, 0.5008, 0.6378, 0.5541, 0.6418, 0.1443, 0.1458, 0.8786, 0.4283,
0.2835, 0.6132, 0.8284, 0.9009, 0.5561, 0.5137, 0.9318, 0.0267, 0.2009,
0.6635, 0.3267, 0.5239, 0.1676, 0.2327, 0.1248, 0.8706, 0.4100, 0.0529,
0.8903, 0.8717, 0.7688, 0.2585, 0.3399, 0.7276, 0.4385, 0.6888, 0.6419,
0.2661, 0.8262, 0.7331, 0.5433, 0.9399, 0.4117, 0.4899, 0.6666, 0.0826,
0.1968, 0.6295, 0.4921, 0.3039, 0.5059, 0.8406, 0.6863, 0.1999, 0.8681,
0.7958, 0.2988, 0.5588, 0.6630, 0.4348, 0.2735, 0.4761, 0.0994, 0.0803,
0.1431, 0.9707, 0.3018, 0.3598, 0.6734, 0.6126, 0.1162, 0.2229, 0.1249,
0.8871, 0.6972, 0.4470, 0.7034, 0.4932, 0.1183, 0.2348, 0.8528, 0.7901,
0.2365, 0.7217, 0.5406, 0.1416, 0.9804, 0.7091, 0.3708, 0.4327, 0.0531,
0.0861, 0.2463, 0.0912, 0.6666, 0.4180, 0.9266, 0.2631, 0.7023, 0.0398,
0.0631, 0.6601, 0.6339, 0.6206, 0.1393, 0.1075, 0.6920, 0.6626, 0.8973,
0.1839, 0.2475, 0.1521, 0.0381, 0.5855, 0.2973, 0.1848, 0.3025, 0.8042,
0.5952, 0.6057, 0.5527, 0.7248, 0.0033, 0.3411, 0.8677, 0.9543, 0.6956,
0.2909, 0.9458, 0.4611, 0.1876, 0.1012, 0.6692, 0.9081, 0.1122, 0.9392,
0.8478, 0.6917, 0.6057, 0.6920, 0.1247, 0.9858, 0.3460, 0.8301, 0.8894,
0.8431, 0.4964, 0.0289, 0.1298, 0.1918, 0.8065, 0.5335, 0.9905, 0.7099,
0.6120, 0.2878, 0.2931, 0.8318, 0.9276, 0.9328, 0.3071, 0.3785, 0.5239,
0.2914, 0.1401, 0.4540, 0.4798, 0.4797, 0.0380, 0.2156, 0.1642, 0.5507,
0.0664, 0.8262, 0.9418, 0.0536, 0.2727, 0.9576, 0.4063, 0.4981, 0.4513,
0.6310, 0.9909, 0.3513, 0.5842, 0.6780, 0.8629, 0.7755, 0.0898, 0.9114,
0.0207, 0.1783, 0.1597, 0.8240, 0.9023, 0.0074, 0.7930, 0.8564, 0.4700,
0.8839, 0.4839, 0.9852, 0.3291, 0.9607, 0.8842, 0.8725, 0.1717, 0.6004,
0.0670, 0.8676, 0.6065, 0.6930, 0.8870, 0.2545, 0.1041, 0.5940, 0.5596,
0.8877, 0.4002, 0.5495, 0.3640, 0.4373, 0.8292, 0.2008, 0.3124, 0.6308,
0.2529, 0.1802, 0.5372, 0.9018, 0.0830, 0.3240, 0.6729, 0.9612, 0.9211,
0.5371, 0.8745, 0.4602, 0.7666, 0.0433, 0.5461, 0.2115, 0.2959, 0.0351,
0.8651, 0.7865, 0.2392, 0.1375, 0.7444, 0.6702, 0.1889, 0.0102, 0.2363,
0.9406, 0.2144, 0.2174, 0.5765, 0.8715, 0.5440, 0.7480, 0.0387, 0.2754,
0.7528, 0.4358, 0.1080, 0.8259, 0.0232, 0.2766, 0.4030, 0.6221, 0.5128,
0.1035, 0.3966, 0.3859, 0.4088, 0.8898, 0.6106, 0.0241, 0.0442, 0.1680,
0.3836, 0.8129, 0.4091, 0.7610, 0.7527, 0.7474, 0.2838, 0.3861, 0.3496,
0.6985, 0.0842, 0.4432, 0.2829, 0.9554, 0.7354, 0.4623, 0.4193, 0.6449,
0.1662, 0.3948, 0.1070, 0.4275, 0.6427, 0.6758, 0.1149, 0.9313, 0.3048,
0.2237, 0.4992, 0.4688, 0.3633, 0.5445, 0.8166, 0.0392, 0.9118, 0.7784,
0.4596, 0.5234, 0.0789, 0.1671, 0.0663, 0.6008, 0.4271, 0.6451, 0.8050,
0.7993, 0.3750, 0.4266, 0.2093, 0.8230, 0.0515, 0.0785, 0.1407, 0.0502,
0.1021, 0.0343, 0.0291, 0.0833, 0.4709, 0.7199, 0.6756, 0.3500, 0.7100,
0.6334, 0.8984, 0.6105, 0.2101, 0.7228, 0.2321, 0.2186, 0.2271, 0.3792,
0.3462, 0.7752, 0.9628, 0.4922, 0.3908, 0.7770, 0.0485, 0.5218, 0.1772,
0.0367, 0.9492, 0.9352, 0.4897, 0.9790, 0.1704, 0.9757, 0.3399, 0.3952,
0.2428, 0.3014, 0.1833, 0.0175, 0.9480, 0.3613, 0.3031, 0.1372, 0.4799,
0.0364, 0.7588, 0.4608, 0.2652, 0.2054, 0.6034, 0.5563, 0.0053, 0.3368,
0.7328, 0.5666, 0.2000, 0.4721, 0.5381, 0.9557, 0.0762, 0.4067, 0.8686,
0.6698, 0.7660, 0.9169, 0.8401, 0.7283, 0.0271, 0.2323, 0.0811, 0.6277,
0.8744, 0.9459, 0.9015, 0.6752, 0.7650, 0.6628, 0.4006, 0.5877, 0.0514,
0.8610, 0.3106, 0.6999, 0.6773, 0.4738, 0.1838, 0.0942, 0.9465, 0.0689,
0.3126, 0.7237, 0.6566, 0.4259, 0.1337, 0.8046, 0.2415, 0.9873, 0.9736,
0.2487, 0.8346, 0.9100, 0.3429, 0.1321, 0.7593, 0.7780, 0.2588, 0.2804,
0.3661, 0.3630, 0.7371, 0.5247, 0.9303, 0.2413, 0.2591, 0.3403, 0.0683,
0.4428, 0.4089, 0.7018, 0.8541, 0.2662, 0.2819, 0.9080, 0.5924, 0.8527,
0.8277, 0.0945, 0.3408, 0.0259, 0.8425, 0.3551, 0.9404, 0.6876, 0.3102,
0.8169, 0.3289, 0.7174, 0.2404, 0.7087, 0.2562, 0.2022, 0.1705, 0.6359,
0.7204, 0.0698, 0.1980, 0.7807, 0.0989, 0.0387, 0.5021, 0.9782, 0.0989,
0.4415, 0.9582, 0.8193, 0.7433, 0.3606, 0.8234, 0.9470, 0.6152, 0.0739,
0.0091, 0.3852, 0.6140, 0.8024, 0.3931, 0.6374, 0.7420, 0.8262, 0.5612,
0.1429, 0.1118, 0.5879, 0.2417, 0.8952, 0.2698, 0.8374, 0.9325, 0.9897,
0.7748, 0.0254, 0.8351, 0.5943, 0.9824, 0.2132, 0.6469, 0.8862, 0.9013,
0.5097, 0.5300, 0.7497, 0.8371, 0.1307, 0.2927, 0.1760, 0.6744, 0.7508,
0.7924, 0.9564, 0.8733, 0.8700, 0.9728, 0.1362, 0.3822, 0.7738, 0.7682,
0.3459, 0.5163, 0.6223, 0.9129, 0.3179, 0.7660, 0.0849, 0.7594, 0.9258,
0.3004, 0.5658, 0.1079, 0.0985, 0.3576, 0.8792, 0.2296, 0.2061, 0.9494,
0.9141, 0.1866, 0.4255, 0.6605, 0.5622, 0.4929])]
>>> labelA[0] == labelB[0]
tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
dtype=torch.uint8)
I know this is a lot, so let me know if you have any questions.

Related

Is there any way to inverse tensorflow predicted variable to origin value?

I have run tensorflow 2.0 algorithm where my dependent variable is y in label format
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0])
while predicting model got scaling value from tensorflow.
# compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy',f1_m,precision_m, recall_m])
# fit the model
model.fit(X_train, y_train, epochs=150, batch_size=32, verbose=0)
y_pred = model.predict(X_test)
y_pred[0:10]
array([[1.8975088e-02, 9.2321676e-01, 5.7808172e-02],
[2.1689970e-03, 1.1041342e-02, 9.8678964e-01],
[9.7219455e-01, 2.1523101e-02, 6.2822714e-03],
[8.9549669e-04, 9.9892455e-01, 1.7989198e-04],
[5.9214713e-06, 9.9999106e-01, 2.9540893e-06],
[1.5215098e-05, 9.9994588e-01, 3.8917195e-05],
[3.2570759e-05, 9.9996614e-01, 1.3605905e-06],
[2.5089746e-03, 9.9069571e-01, 6.7953388e-03],
[2.3909420e-02, 9.6926796e-01, 6.8226634e-03],
[2.9210409e-04, 9.9955446e-01, 1.5343193e-04]], dtype=float32)
It must be come as numerical category between 0,1 and 2 but showing some other value.how we can achieve original value ?

Use tf.argmax() to get indices:
ind = tf.argmax(y_pred, -1)

Why do other values change in an ndarray when I try to change a specific cell value?

For example, I have a 3D ndarray of the shape (10,10,10) and whenever I try to change all the cells in this section [5,:,9] to a specific single value I end up changing values in this section too [4,:,9]. Which to me makes no sense. I do not get this behavior when I convert to a list of lists.
I use a simply for loop:
For i in range(0,10):
matrix[5,i, 9]= matrix[5,9,9]
Is there anyway to avoid this? I do not get this behavior when using a list of lists but I don’t wanna convert back and forth between the two as it takes too much processing time.

Doesn't happen that way for me:
In [232]: arr = np.ones((10,10,10),int)
In [233]: arr[5,9,9] = 10
In [234]: for i in range(10): arr[5,i,9]=arr[5,9,9]
In [235]: arr[5,:,9]
Out[235]: array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10])
In [236]: arr[4,:,9]
Out[236]: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
or assigning a whole "column" at once:
In [237]: arr[5,:,9] = np.arange(10)
In [239]: arr[5]
Out[239]:
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 2],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 3],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 4],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 5],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 6],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 7],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 8],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 9]])

Isolation Forest in Python

I am currently working on detecting outliers in my dataset using Isolation Forest in Python and I did not completely understand the example and explanation given in scikit-learn documentation
Is it possible to use Isolation Forest to detect outliers in my dataset that has 258 rows and 10 columns?
Do I need a separate dataset to train the model? If yes, is it necessary to have that training dataset free from outliers?
This is my code:
rng = np.random.RandomState(42)
X = 0.3*rng.randn(100,2)
X_train = np.r_[X+2,X-2]
clf = IsolationForest(max_samples=100, random_state=rng, contamination='auto'
clf.fit(X_train)
y_pred_train = clf.predict(x_train)
y_pred_test = clf.predict(x_test)
print(len(y_pred_train))
I tried by loading my dataset to X_train but that does not seem to work.

Do I need a separate dataset to train the model?
Short answer is "No". You train and predict outliers on the same data.
IsolationForest is an unsupervised learning algorithm that's intended to clean your data from outliers (see docs for more). In usual machine learning settings, you would run it to clean your training dataset. As far as your toy example concerned:
rng = np.random.RandomState(42)
X = 0.3*rng.randn(100,2)
X_train = np.r_[X+2,X-2]
from sklearn.ensemble import IsolationForest
clf = IsolationForest(max_samples=100, random_state=rng, behaviour="new", contamination=.1)
clf.fit(X_train)
y_pred_train = clf.predict(X_train)
y_pred_train
array([ 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1,
1, -1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
where 1 represent inliers and -1 represent outliers. As specified by contamination param, the fraction of outliers is 0.1.
Finally, you would remove outliers like:
X_train_cleaned = X_train[np.where(y_pred_train == 1, True, False)]

the result about 'pd.read_sql' couldn't be used twcie

I try to run a demo and find the result of 'pd.read_sql' couldn't be used twice! but it still in the memory, this is so surprise. How could I fix it?
#coding=utf-6
import pandas as pd
from sqlalchemy import create_engine
engine =
create_engine('mysql+pymysql://root:123457#127.0.0.1:3306/chapter12?
charset=utf8')
sql = pd.read_sql('all_gzdata', engine, chunksize = 10001)
def count109(i):
j = i[['fullURL']][i['fullURLId'].str.contains('109')].copy() #找出类别包含107的网址
j['type'] = None #添加空列
j['type'][j['fullURL'].str.contains('info/.+?/')] = u'知识首页'
j['type'][j['fullURL'].str.contains('info/.+?/.+?')] = u'知识列表页'
j['type'][j['fullURL'].str.contains('/\d+?_*\d+?\.html')] = u'知识内容页'
print('109')
return j['type'].value_counts()
def test(i):
return 3
def test3(i):
return 4
print(sql)
counts5 = [ test(i) for i in sql]
print(counts5)
counts6 = [ test(i) for i in sql]
print(counts6)
print(sql)
print('finish')
And the result is:
anakin#anakin:~/Project/chapter12$ python sql_value_count.py
<generator object SQLTable._query_iterator at 0x7f03ce621728>
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
[]
<generator object SQLTable._query_iterator at 0x7f03ce621728>
finish

Conversion of numpy 2d array to ENVI binary file through gdal

I have SAR CEOS format files which consist of data file, leader file, null volume directory file and volume directory file.
I am reading the data file using gdal ReadAsArray and then I am doing operations on this 2d Array and now I want to save this 2d array as an ENVI binary file.
Kindly guide how to do this in Python 3.5.

Find help for Tutorial Website: https://pcjericks.github.io/py-gdalogr-cookbook/
Such as the example of
import gdal, ogr, os, osr
import numpy as np
def array2raster(newRasterfn,rasterOrigin,pixelWidth,pixelHeight,array):
cols = array.shape[1]
rows = array.shape[0]
originX = rasterOrigin[0]
originY = rasterOrigin[1]
driver = gdal.GetDriverByName('ENVI')
outRaster = driver.Create(newRasterfn, cols, rows, 1, gdal.GDT_Byte)
outRaster.SetGeoTransform((originX, pixelWidth, 0, originY, 0, pixelHeight))
outband = outRaster.GetRasterBand(1)
outband.WriteArray(array)
outRasterSRS = osr.SpatialReference()
outRasterSRS.ImportFromEPSG(4326)
outRaster.SetProjection(outRasterSRS.ExportToWkt())
outband.FlushCache()
def main(newRasterfn,rasterOrigin,pixelWidth,pixelHeight,array):
reversed_arr = array[::-1] # reverse array so the tif looks like the array
array2raster(newRasterfn,rasterOrigin,pixelWidth,pixelHeight,reversed_arr) # convert array to raster
if __name__ == "__main__":
rasterOrigin = (-123.25745,45.43013)
pixelWidth = 10
pixelHeight = 10
newRasterfn = 'test.tif'
array = np.array([[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[ 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1],
[ 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1],
[ 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1],
[ 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1],
[ 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1],
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
main(newRasterfn,rasterOrigin,pixelWidth,pixelHeight,array)

Develop Reference

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