MIT6 0002F16 Lecture 1 - MIT OpenCourseWare
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" )) %% class Food(object):def init (self, n, v, w):self.name nself.value vself.calories wdef getValue(self):return self.valuedef getCost(self):return self.caloriesdef density(self):return self.getValue()/self.getCost()def str (self):return self.name ': ' str(self.value)\ ', ' str(self.calories) ' 'D2 @ ?@?
" % %% )def buildMenu(names, values, calories):"""names, values, calories lists of same length.name a list of stringsvalues and calories lists of numbersreturns list of Foods"""menu []for i in range(len(values)):menu.append(Food(names[i], values[i],calories[i]))return menuD2 @ ?@@
#&" # * * % % " . " ( /def greedy(items, maxCost, keyFunction):"""Assumes items a list, maxCost 0,keyFunction maps elements of items to numbers"""itemsCopy sorted(items, key keyFunction,reverse True)result []totalValue, totalCost 0.0, 0.0for i in range(len(itemsCopy)):if (totalCost itemsCopy[i].getCost()) maxCost:result.append(itemsCopy[i])totalCost itemsCopy[i].getCost()totalValue itemsCopy[i].getValue()return (result, totalValue)D2 @ ?@A
" %( * # /def greedy(items, maxCost, keyFunction):itemsCopy sorted(items, key keyFunction,reverse True)result []totalValue, totalCost 0.0, 0.0for i in range(len(itemsCopy)):if (totalCost itemsCopy[i].getCost()) maxCost:result.append(itemsCopy[i])totalCost itemsCopy[i].getCost()totalValue itemsCopy[i].getValue()return (result, totalValue) D2 @ ?@B
) ( /def testGreedy(items, constraint, keyFunction):taken, val greedy(items, constraint, keyFunction)print('Total value of items taken ', val)for item in taken:print('', item)D2 @ ?@C
) ( /def testGreedys(maxUnits):print('Use greedy by value to allocate', maxUnits,'calories')testGreedy(foods, maxUnits, Food.getValue)print('\nUse greedy by cost to allocate', maxUnits,'calories')testGreedy(foods, maxUnits,lambda x: 1/Food.getCost(x))print('\nUse greedy by density to allocate', maxUnits,'calories')testGreedy(foods, maxUnits, Food.density)/testGreedys(800)D2 @ ?@D
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) ( /def testGreedys(foods, maxUnits):print('Use greedy by value to allocate', maxUnits,'calories')testGreedy(foods, maxUnits, Food.getValue)print('\nUse greedy by cost to allocate', maxUnits,'calories')testGreedy(foods, maxUnits,lambda x: 1/Food.getCost(x))print('\nUse greedy by density to allocate', maxUnits,'calories')testGreedy(foods, maxUnits, Food.density)names ['wine', 'beer', 'pizza', 'burger', 'fries','cola', 'apple', 'donut', 'cake']values [89,90,95,100,90,79,50,10]calories [123,154,258,354,365,150,95,195]foods buildMenu(names, values, calories)testGreedys(foods, 750) )" # D2 @ ?@F
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MIT OpenCourseWarehttps://ocw.mit.edu6.0002 Introduction to Computational Thinking and Data ScienceFall 2016For information about citing these materials or our Terms of Use, visit: https://ocw.mit.edu/terms.
An objective function that is to be maximized or minimized, e.g., #Minimize time spent traveling from New York to
Introduction of Chemical Reaction Engineering Introduction about Chemical Engineering 0:31:15 0:31:09. Lecture 14 Lecture 15 Lecture 16 Lecture 17 Lecture 18 Lecture 19 Lecture 20 Lecture 21 Lecture 22 Lecture 23 Lecture 24 Lecture 25 Lecture 26 Lecture 27 Lecture 28 Lecture
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DICTIONARIES (download slides and .py files and follow along!) 6.0001 LECTURE 6. 6.0001 LECTURE 6 1 QUIZ PREP § a paper and an online component § open book/notes § not open Internet, not open computer § start prinng out whatever you may want to bring 6.0001 LECTURE 6 2. LAST TIME
TODAY course info what is computation python basics mathematical operations python variables and types NOTE: slides and code files up before each lecture o highly encourage you to download them before lecture o take notes and run code files when I do o bring computers to answer in-class practice exercises! 6.0001 LECTURE 1 2
Lecture 1: Introduction and Orientation. Lecture 2: Overview of Electronic Materials . Lecture 3: Free electron Fermi gas . Lecture 4: Energy bands . Lecture 5: Carrier Concentration in Semiconductors . Lecture 6: Shallow dopants and Deep -level traps . Lecture 7: Silicon Materials . Lecture 8: Oxidation. Lecture
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Partial Di erential Equations MSO-203-B T. Muthukumar tmk@iitk.ac.in November 14, 2019 T. Muthukumar tmk@iitk.ac.in Partial Di erential EquationsMSO-203-B November 14, 2019 1/193 1 First Week Lecture One Lecture Two Lecture Three Lecture Four 2 Second Week Lecture Five Lecture Six 3 Third Week Lecture Seven Lecture Eight 4 Fourth Week Lecture .
ALEX RIDER SERIES POINT BLANK GOING DOWN MICHAEL J. ROSCOE was a careful man. The car that drove him to work at quarter past seven each morning was a custom-made Mercedes with reinforced steel plates and bulletproof windows. His driver, a retired FBI agent, carried a Beretta subcompact automatic pistol and knew how to use it. There were just .
