Homework 3.17 - 3.18
Vocabulary
- Problem: a general description of a task that can or cannot be solved algorithmically
- Decision Problem: A problem with a yes or no answer
- Organization Problem: a problem with a goal of finding the best answer
- Instance: a problem with a specific input
- Efficiency: amount of computing needed to solve a problem
- Polynomial Efficiency (Good): more work takes a proportional amount of time (1 job is +2 time)
- Exponential Efficiency (Bad): more work takes an exponential amount more time (1 job is 2x time)
- Heuristic Approach: When optimal solutions are inefficient, look for a possibly optimal solution that is more efficient
- Decidable Problem: A decision problem that has a clear solution that will always make a correct output
- Undecidable Problem: A decision problem with no solution that is not gaurenteed to produce the correct output
- polynomial efficiency is the amount of time it takes to get through a code segment
- more cycles = more time to run
import time
numlist = [1,3,5,7,9,11,13,15,17,19]
valuelist = [0,3,6,9,12,15,18,21]
def isvalue(value,array):
#--------------------
exists = False
while exists == False:
for i in range(len(array)):
if value == array[i]:
exists = True
if exists == False:
return exists
return exists
#--------------------
starttime = time.time()
for i in range(100000):
for i in range(len(valuelist)):
x = isvalue(valuelist[i],numlist)
endtime = time.time()
print(endtime-starttime,'seconds')
import time
numlist = [1,3,5,7,9,11,13,15,17,19]
valuelist = [0,3,6,9,12,15,18,21]
def isvalue(value,array):
#--------------------
exists = False
return exists
#--------------------
starttime = time.time()
for i in range(100000):
for i in range(len(valuelist)):
x = isvalue(valuelist[i],numlist)
endtime = time.time()
print(endtime-starttime,'seconds')
Notes
- Undecidable problem: should give yes/no answer, but no algorithm exists that can answer all inputscorrectly
- Computability theory: undecidable problem is a computational problem; requires yes/no answer
- np possible computer program that always produces correct answer
- Unsolvable problem: no algorithm can ever be written to find solution; always gives a correct true/false decision for every input value
Homework!
Make an algorithm that finds the fastest route that hits every location once starting and ending at Del Norte. Make sure to show your thinking. If you are strugling, try using a huristic approach. Remember, what matters more than having perfectly functioning code is that you tried your hardest.
dataset = {
'DelNorte':{
'Westview':15,
'MtCarmel':20,
'Poway':35,
'RanchoBernrdo':50
},
'Westview':{
'Del Norte':15,
'MtCarmel':35,
'Poway':25,
'RanchoBernrdo': 45
},
'MtCarmel':{
'Westview':35,
'Del Norte':20,
'Poway':40,
'RanchoBernrdo':30
},
'Poway':{
'Westview':25,
'MtCarmel':40,
'Del Norte':35,
'RanchoBernrdo':15
},
'RanchoBernardo':{
'Westview':45,
'MtCarmel':30,
'Poway':15,
'Del Norte':50
}
}
def fastestroute(start,data):
drivetime = 0
order = []
#CODE,CODE,CODE
for i in dataset:
for e in range (0, len(i)): # find totals of each time amount for all schools
total = total + i[e]
i = total # convert each school to its total time amount
fastestschool = int(min(dataset)) # take newly converted values and compare them
print("The fastest school is:" + fastestschool) # print out the school with the least ampunt of minutes in route
return(drivetime,order)
start = 'DelNorte'
# 'dataset' is the name of the nested key value pair