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cover: 0, uptime: 3s 2019/07/16 23:35:02 workers: 4, corpus: 6 (1s ago), crashers: 0, restarts: 1/6343, execs: 19031 (3171/sec), cover: 26, uptime: 6s 2019/07/16 23:35:05 workers: 4, corpus: 6 (4s ago) cover: 26, uptime: 9s 2019/07/16 23:35:08 workers: 4, corpus: 6 (7s ago), crashers: 0, restarts: 1/7269, execs: 145385 (12113/sec), cover: 26, uptime: 12s 2019/07/16 23:35:11 workers: 4, corpus: 7 (2s ago) cover: 26, uptime: 15s 2019/07/16 23:35:14 workers: 4, corpus: 7 (5s ago), crashers: 0, restarts: 1/6375, execs: 312406 (17354/sec), cover: 26, uptime: 18s 2019/07/16 23:35:17 workers: 4, corpus: 7 (8s ago)

Other API Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 1.2.3 Deprecations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1090 xxi 35 API Reference 1091 35.1 Input/Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . df['col3'] Out[34]: 0 1 1 2 2 3 Name: col3, dtype: category Categories (3, object): [1, 2, 3] In [35]: df['col3'].cat.categories = pd.to_numeric(df['col3'].cat.categories) In [36]: df['col3'] Out[36]:

1.1.4 Removal of prior version deprecations/changes . . . . . . . . . . . . . . . . . . . . . . . . 35 1.1.5 Performance Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1088 35 API Reference 1089 xxi 35.1 Input/Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2, 3] 10 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.19.0 In [35]: df['col3'].cat.categories = pd.to_numeric(df['col3'].cat.categories) In [36]: df['col3'] Out[36]:

Zenva for Schools – Coding courses for high schools ©2016 Zenva Pty Ltd all rights reserved Page 35 I find it easier to work with one layer for the background and one for the collision elements forEach(function(element){ 33 this.createFromTiledObject(element, this.items); 34 }, this); 35 }, 36 //find objects in a Tiled layer that containt a property called "type" equal to a certain Tiled map. In this tutorial we will use an orthogonal view map with 20×10 tiles. Each tile being 35×35 pixels. For those not familiar with it, an orthogonal view means that the player views the game by

8Bd6i6upaTQK+/49GcE=">AB/ 3icbVBNS8NAEN34WetX1IMHL4tF8FQSEdRb0YvHCsYWmhA20m7dLMJuxuhFz8K148qHj1b3jz35ikOWjrg4G3782wMy9IOFPasr6NpeWV1bX1xkZ zc2t7Z9fc239QcSopODTmsewHRAFnAhzNId+IoFEAYdeMLkp/d4jSMVica+nC 8Bd6i6upaTQK+/49GcE=">AB/ 3icbVBNS8NAEN34WetX1IMHL4tF8FQSEdRb0YvHCsYWmhA20m7dLMJuxuhFz8K148qHj1b3jz35ikOWjrg4G3782wMy9IOFPasr6NpeWV1bX1xkZ zc2t7Z9fc239QcSopODTmsewHRAFnAhzNId+IoFEAYdeMLkp/d4jSMVica+nC 8Bd6i6upaTQK+/49GcE=">AB/ 3icbVBNS8NAEN34WetX1IMHL4tF8FQSEdRb0YvHCsYWmhA20m7dLMJuxuhFz8K148qHj1b3jz35ikOWjrg4G3782wMy9IOFPasr6NpeWV1bX1xkZ zc2t7Z9fc239QcSopODTmsewHRAFnAhzNId+IoFEAYdeMLkp/d4jSMVica+nC

AttrAddrClass Attr = 0x33 AttrArtificial Attr = 0x34 AttrBaseTypes Attr = 0x35 AttrCalling Attr = 0x36 AttrCount Attr = 0x37 AttrDataMemberLoc Attr = Tag = 0x33 TagVariable Tag = 0x34 TagVolatileType Tag = 0x35 TagDwarfProcedure Tag = 0x36 TagRestrictType Tag = 0x37 TagInterfaceType R_386_TLS_LE_32 R_386 = 34 /* 32 bit offset within static TLS block */ R_386_TLS_DTPMOD32 R_386 = 35 /* GOT entry containing TLS index */ R_386_TLS_DTPOFF32 R_386 = 36 /* GOT entry containing TLS

. . . . . . . . . . . . . 32 6.2.5 Run.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 6.2.6 Play.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 6.3 Programs for Supervised Bouton. “Nim, A Game with a Complete Mathematical Theory”. In: Annals of Mathematics 3.1/4 (1901), pp. 35–39. issn: 0003486X. url: http://www.jstor.org/ stable/1967631. 2Worst case time use does not make much objects for a pile of size n. 3Bouton, “Nim, A Game with a Complete Mathematical Theory”, op. cit., p. 35. 4 3.2 Nimrod During the “Festival of Britain” in 1951 in the UK, in the science exhibition, there

2312, 0.4867, 0.5699]]) 通过 torch.rand()函数可以创建采样自[0,1)区间的均匀分布的张量。例如： In [35]: torch.rand(2,3) # 创建采样自[0,1)均匀分布的矩阵 Out[35]: tensor([[0.3236, 0.4731, 0.4211], [0.9777, 0.2799, 0.1205]]) 运算实现如下： In [94]: x = torch.arange(5) x = torch.square(x) # 平方 Out[94]: 预览版202112 4.9 数学运算 35 tensor([ 0., 1., 4., 9., 16.]) 平方根运算实现如下： In [95]: torch.sqrt(x) # 平方根 Out[95]: 1~4 号班级的成绩册，每个班级 35 个学生，共 8 门科目成绩，则张 量?的 shape 应为：[4,35,8]；同理，张量?保存了其它 6 个班级的成绩册，shape 为 [6,35,8]。通过合并这两份成绩册，便可得到该学校所有班级的成绩册数据，记为张量?， 它的 shape 应为[10,35,8]，其中，数字 10 代表 10 个班级，35 代表 35 个学生，8 代表 8 门 科目。这就是张量合并操作的意义所在。