Department of Computer Science 1. COMPSCI 367 Artificial Intelligence Assignment 2 due October 9, 2016 Note: Please submit your assignment via the CS Assignment Dropbox by 23:59 on the due date. Late assignments will not be marked. The total mark for this assignment is 50. This assignment counts towards 15% of your final mark. The […]
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Chapter 1 Performing calculations in MATLAB In this chapter we’ll learn how to perform calculations with MATLAB. First we’ll learn how to use it as a sort of advanced calculator. Next we’ll see how to use it to perform some linear algebra calculations. Then we’ll take a look at how to use it for some
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Introduction to Algorithms: 6.006 Massachusetts Institute of Technology November 2, 2010 Professors Konstantinos Daskalakis and Patrick Jaillet Handout 5 Problem Set 5 This problem set is divided into two parts: Part A problems are theory questions, and Part B problems are programming tasks. Part A questions are due Tuesday, November 16th at 11:59PM. Part B
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Dynamic Programming 2 David Weir (U of Sussex) Program Analysis Term 1, 2015 510 / 606 Sequence Alignment David Weir (U of Sussex) Program Analysis Term 1, 2015 511 / 606 Sequence Alignment Problem Measuring the distance between two sequences of characters Extent that one string needs to be edited to produce the other? Penalty
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Program Analysis Problem Sheet 11 1. The question considers the Ford-Fulkerson Algorithm. MaximumFlow(G, s, t, c, ): let f(e) = 0 for all e ∈ E while there is a path from s to t in the residual graph Gf let p be a simple path from s to t f ← augment(G, f, p)
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In [1]: import numpy as np import numpy.linalg as la import matplotlib import matplotlib.pyplot as plt from skimage.measure import ransac %matplotlib notebook In [2]: class LeastSquareLine: def __init__(self): self.a = 0.0 self.b = 0.0 def estimate(self, points2D): B = points2D[:,1] A = np.copy(points2D) A[:,1] = 1.0 # SVD (singular value decomposition) for (Nx2) matrix A = U*diag(S)*V
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Part I 1.) ¦¨(n2 ) 2) ¦¨(nlog(n)) 3) ¦¨(log2 n) 4) ¦¨(n2 ) 5) ¦¨(log(n)) Part II 1) def getMajority(arr) if len(arr) == 1 return arr[0] mid = len(arr)/2 a = getMajority(arr[0:mid+1]) // compute first half b = getMajority(arr[mid+1:len(arr)] // compute second half if a == b return a numa = count(arr, a) if numa
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MyKmeans-checkpoint In [ ]: %matplotlib notebook In [1]: # loading standard modules import numpy as np import math import matplotlib.pyplot as plt from skimage import img_as_ubyte from skimage.color import rgb2grey # loading custom module (requires file asg1.py in the same directory as the notebook file) from asg1 import Figure, KmeansPresenter In [5]: class MyKmeansApp: def __init__(self, img, num_clusters=2, weightXY=1.0):
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CALISTO User’s Manual Date: April 2016 Authors: 1. Anastasia Spiliopoulou 2. Ioannis Papamichail 3. Markos Papageorgiou 4. John Chrysoulakis 2 Contents Table of figures ……………………………………………………………………………………………….. 4 1 CALISTO graphical user interface ………………………………………………………………… 6 2 Freeway network description ……………………………………………………………………… 7 3 Simulated traffic data ………………………………………………………………………………. 11 4 Other settings …………………………………………………………………………………………. 13 5 Traffic flow model selection
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CSC373H Lecture 3 Dan Zingaro September 26, 2016 Single-Source Shortest Path Input:connected,directed,weightedgraphG=(V,E),with n vertices and m edges; and start vertex s Each edge e has nonnegative weight w(e) Output: shortest path between s and each other vertex Dijkstra’s Algorithm Dijkstra’s algorithm uses a min-priority queue to store the current distance to
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