ESS116 ESS 116 Introduction to Data Analysis in Earth Science Image Credit: NASA Instructor: Mathieu Morlighem E-mail: mmorligh@uci.edu (include ESS116 in subject line) Office Hours: 3218 Croul Hall, Friday 2:00 pm – 3:00 pm This content is protected and may not be shared uploaded or distributed WEEK TOPIC LAB 1 Introduction and MATLAB Introduction to […]
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Integers II CSE 351 Autumn 2016 Integers II http://xkcd.com/1953/ CS295 L05: Integers II Integers Binary representation of integers Unsigned and signed Casting in C Consequences of finite width representations Overflow, sign extension Shifting and arithmetic operations 2 CS295 L05: Integers II 2 Sign and Magnitude Designate the high-order bit (MSB) as the “sign bit” sign=0:
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Chapter 7 Morphological Image Processing Chapter 7 Morphological Image Processing 1 Agenda Introduction to morphological operations Set theory preliminaries Binary morphological operations: Erosion, Dilation, Opening, Closing Connected components and labelling Morphological algorithms Boundary extraction, region filling, hit-or-miss transform Grayscale morphological operations 2 Morphological Operations Morphology= Shape, Form, Structure Morphological operations are used to extract image
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Stack of Stack Frames Review Thinking about Algorithms Abstractly Jeff Edmonds York University COSC 3101 ‹#› 1 Some Math Recurrence Relations T(n) = a T(n/b) + f(n) Input Size Time Classifying Functions f(i) = nQ(n) Adding Made Easy ∑i=1 f(i). Time Complexity t(n) = Q(n2) Logic Quantifiers g “b Loves(b,g) “b g Loves(b,g) Logs and
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Question 1: Ceasar Cypher (6 points)¶ Back in the days of the Roman Empire, it was not uncommon for military communications to be intercepted, since they were largely carried around by soldiers on horses. As a result, the Romans developed a way of encoding their military communications, so that they could only be read by
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3D Polyhedral Morphing Collision and Proximity Queries Dinesh Manocha * Geometric Proximity Queries Given two object, how would you check: If they intersect with each other while moving? If they do not interpenetrate each other, how far are they apart? If they overlap, how much is the amount of penetration * Proximity Queries Geometric reasoning
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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]: # Read data from local file system: fruits = sc.textFile(‘file:///C:/Users/hanya/1Jupyter Notebook/fruits.txt’) yellowThings = sc.textFile(‘file:///C:/Users/hanya/1Jupyter Notebook/yellowthings.txt’)
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Lecture 21 (April 4, 2019)¶ In this lecture, we discuss how to model a two-server queueing system in parallel. In [0]: import numpy as np import matplotlib.pyplot as plt import math import scipy.special as spsp import scipy.stats as spst Setting of two-server queueing system in parallel¶ (1) There are two servers with different distributions for the
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In [1]: import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from sklearn import datasets import pandas as pd # import some data to play with iris = datasets.load_iris() df = pd.DataFrame(iris.data, columns=iris.feature_names) df.insert(4, “Target”, iris.target) df.head() Out[1]: sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) Target 0 5.1 3.5 1.4 0.2 0
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In [1]: from IPython.core.interactiveshell import InteractiveShell InteractiveShell.ast_node_interactivity = “all” %matplotlib inline import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns sns.set_style(“whitegrid”) sns.set_context(“notebook”) #sns.set_context(“poster”) In [3]: from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.metrics import accuracy_score from sklearn import preprocessing Ensembles Ensembles develop around two
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