初学者在Linux命令窗口(终端)敲命令时,肯定觉得通过输入一串一串的字符的方式来控制计算是效率很低。 但是Linux命令解释器(Shell)是有很多快捷键的,熟练掌握可以极大的提高操作效率。 下面列出最常用的快捷键,这还不是完全版。
- 命令行快捷键:
- 常用:
- Ctrl L :清屏
- 常用:
- Ctrl M :等效于回车
| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
| from io import BytesIO | |
| from geometry_msgs.msg import Point | |
| p = Point(x=2, y=4) | |
| print(p) | |
| buff = BytesIO() | |
| p.serialize(buff) | |
| serialized_bytes = buff.getvalue() |
| import numpy as np | |
| def largest_indices(array: np.ndarray, n: int) -> tuple: | |
| """Returns the n largest indices from a numpy array. | |
| Arguments: | |
| array {np.ndarray} -- data array | |
| n {int} -- number of elements to select |
| - a 形容词 | |
| - ad 副形词 | |
| - ag 形容词性语素 | |
| - an 名形词 | |
| - b 区别词 | |
| - c 连词 | |
| - d 副词 | |
| - df | |
| - dg 副语素 | |
| - e 叹词 |
| import tensorflow as tf | |
| class ImportGraph(): | |
| """ Importing and running isolated TF graph """ | |
| def __init__(self, loc): | |
| # Create local graph and use it in the session | |
| self.graph = tf.Graph() | |
| self.sess = tf.Session(graph=self.graph) | |
| with self.graph.as_default(): | |
| # Import saved model from location 'loc' into local graph |
| import threading | |
| # folk from https://gist.github.com/werediver/4396488 | |
| # Based on tornado.ioloop.IOLoop.instance() approach. | |
| # See https://github.com/facebook/tornado | |
| class SingletonMixin(object): | |
| __singleton_lock = threading.Lock() | |
| __singleton_instance = None | |
| @classmethod |
| def convex_hull_graham(points): | |
| ''' | |
| Returns points on convex hull in CCW order according to Graham's scan algorithm. | |
| By Tom Switzer <thomas.switzer@gmail.com>. | |
| ''' | |
| TURN_LEFT, TURN_RIGHT, TURN_NONE = (1, -1, 0) | |
| def cmp(a, b): | |
| return (a > b) - (a < b) |
| import nltk | |
| text = """The Buddha, the Godhead, resides quite as comfortably in the circuits of a digital | |
| computer or the gears of a cycle transmission as he does at the top of a mountain | |
| or in the petals of a flower. To think otherwise is to demean the Buddha...which is | |
| to demean oneself.""" | |
| # Used when tokenizing words | |
| sentence_re = r'''(?x) # set flag to allow verbose regexps | |
| ([A-Z])(\.[A-Z])+\.? # abbreviations, e.g. U.S.A. |
| """Information Retrieval metrics | |
| Useful Resources: | |
| http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
| http://www.nii.ac.jp/TechReports/05-014E.pdf | |
| http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
| http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
| Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
| """ | |
| import numpy as np |