PowerPoint Presentation Transactions & Concurrency Control 2 R&G 16/17 There are three side effects of acid. Enhanced long term memory, decreased short term memory, and I forget the third. – Timothy Leary 1 TWO PHASE Locking Two Phase Locking (2PL) The most common scheme for enforcing conflict serializability A bit “pessimistic” Sets locks for fear […]
How do I make an RDD?¶ RDDs can be created from stable storage or by transforming other RDDs. Run the cells below to create RDDs from files on the local drive. All data files can be downloaded from https://www.cse.ust.hk/msbd5003/data/ In [1]: import findspark findspark.init() import pyspark In [2]: from pyspark.sql import SparkSession In [3]: # Read data from
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Solving Linear Programs with Gurobi Optimizer¶ This notebook contains two examples of how to formulate and solve linear programs using Python and Gurobi. The first example is the “Bag Production” problem from class. The second example is the “Inventory Management” example from class. Example 1: Bag Production¶ Recall that the linear program for the bag
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CSC338. Homework 6¶ Due Date: Wednesday March 4, 9pm Please see the guidelines at https://www.cs.toronto.edu/~lczhang/338/homework.html What to Hand In¶ Please hand in 2 files: • Python File containing all your code, named hw6.py. • PDF file named hw6_written.pdf containing your solutions to the written parts of the assignment. Your solution can be hand-written, but must
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COMP3670/6670 Assignment 4 – GMM¶ Enter Your Student ID: Your Name: Submit: You can write your answers in this file and submit a single Jupyter Notebook file (.ipynb) on Wattle. Rename this file with your student number as ‘uXXXXXXX.ipynb’. Otherwise, you can write your programming questions in this file, and submit two files, ‘uXXXXXXX.ipynb’ for
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Project 3: POS tagging with the Perceptron algorithm¶ COSI 134 Fall 2020 Due: November 5, 2020 Task definition.¶ POS tagging is the task of assigning a POS tag to each word token in the sentence. As with all supervised learning approaches to POS tagging, this task has two parts. The first part is the decoding
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PowerPoint Presentation Searching in a Graph Jeff Edmonds York University COSC 3101 Lecture 5 Thinking about Algorithms Abstractly Generic Search Breadth First Search Dijkstra’s Shortest Paths Algorithm Depth First Search Linear Order ‹#› 1 Graph a c b Node ~ city or computer Edge ~ road or network Undirected or Directed A surprisingly large number
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How To Think Like A Computer Scientist Reductions & NP-Completeness Jeff Edmonds York University See 2001 slides More Classes for 4111 More Detailed Steps for 6111 Thinking about Algorithms Abstractly Thanks Intro & COSC 3101/4111/6111 Thinking about Algorithms Abstractly More Classes For 4111 COSC 3101/4111/6111 Reductions & NP-Completeness Jeff Edmonds York University A Problem P
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Report Submission Information (must be completed before submitting report!) • Student 1 Full Name and Number : Hao Xu 718329 • Student 2 Full Name and Number : • Workshop day : Thursday • Workshop time : 6:15 – 8:15pm Workshop 1 – Optimisation [2 weeks] ¶ Objectives:¶ • Learn how to formulate optimisation problems
Deep Learning Assignemnt 1 1.Preparation¶ 1.1Import required packages¶ In [0]: import pandas as pd import numpy as np import matplotlib.pyplot as plt import cv2 import seaborn as sns from skimage import feature from sklearn import svm from sklearn.neighbors import KNeighborsClassifier from sklearn.utils import shuffle from sklearn import metrics from sklearn.metrics import accuracy_score from sklearn.model_selection import cross_val_score
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