# visiting_cities.py # Search order of visited cities using genetic algorithm # # Sparisoma Viridi | https://github.com/dudung/cookbook # # 20220412 Create this program. # 1706 Finish a dictionary named City and print it. # 1721 Finish a matrix named Road and print it. # 1735 Add some references for future use. # 2021 Finish distance function and print it. # 2040 Finish isconnected function and print it. # 2133 Finish getfenotype function and print it. # 2155 Finish getgenotype function and print it. # 2225 Finish getconnectionstr function and print it. # 2234 Finish isvalidsolution function and print it. # 2255 Finish crossover function and print it. # 2311 Pause after try crossover for n = 0, .. 23. # 20220413 Make it public. # 0354 Test in OneCompiler. # 0357 Share it to group of FI3201-01. # # Refs # 1. https://stackoverflow.com/a/3294899/9475509 dictionary for # 2. https://stackoverflow.com/a/19084485/9475509 print matrix # 3. https://stackoverflow.com/a/11266091/9475509 end of line # 4. https://stackoverflow.com/a/8951047/9475509 circular index # 5. https://stackoverflow.com/a/8928256/9475509 binary string # 6. https://stackoverflow.com/a/10411108/9475509 int to binary # 7. https://stackoverflow.com/a/2294502/9475509 chr pos in str # Import necessary packages import math # City dictionary City = { 'A': {'id': 0, 'x': 2, 'y': 2}, 'B': {'id': 1, 'x': 4, 'y': 1}, 'C': {'id': 2, 'x': 7, 'y': 2}, 'D': {'id': 3, 'x': 6, 'y': 5}, 'E': {'id': 4, 'x': 4, 'y': 6}, 'F': {'id': 5, 'x': 5, 'y': 3}, 'G': {'id': 6, 'x': 2, 'y': 6}, 'H': {'id': 7, 'x': 1, 'y': 5}, } print('City') for key in City: c = City[key] i = c['id'] x = c['x'] y = c['y'] print(key, ' ', i, ' ', x, ' ', y, sep='') print() # Road matrix Road = [ [0, 1, 0, 0, 1, 1, 1, 1], [1, 0, 1, 0, 0, 1, 0, 0], [0, 1, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 1, 1, 0, 0], [1, 0, 0, 1, 0, 1, 1, 0], [1, 1, 1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 1], [1, 0, 0, 0, 0, 0, 1, 0], ] print('Road') for i in Road: for j in i: print(j, ' ', end='') print() print() # Distance between two cities def distance(c1, c2): x1 = City[c1]['x'] y1 = City[c1]['y'] x2 = City[c2]['x'] y2 = City[c2]['y'] dx = (x1 - x2) dy = (y1 - y2) dr = math.sqrt(dx * dx + dy * dy) return dr print('distance') print("AB", distance('A', 'D')) print() # Check connection of two cities def isconnected(c1, c2): id1 = City[c1]['id'] id2 = City[c2]['id'] road = Road[id1][id2] connected = bool(road) return connected print('isconnected') print("AB", isconnected('A', 'B')) print("AC", isconnected('A', 'C')) print() # Get fenotype from a chromosome def getfenotype(chro): N = int(len(chro)/3) feno = '' for i in range(N): j = 3 * i genstr = chro[j:j+3] genint = int(genstr, 2) city = '' for key in City: id = City[key]['id'] if genint == id: city = key feno = feno + city return feno print('getfenotype') chromose0 = '000001010011100101110111' print('chromose', chromose0) print('fenotype', getfenotype(chromose0)) chromose1 = '110111000100101011010001' print('chromose', chromose1) print('fenotype', getfenotype(chromose1)) print() # Get genotype from a solution def getgenotype(sol): geno = '' for key in sol: id = City[key]['id'] idbin = '{0:03b}'.format(id) geno = geno + idbin return geno print('getgenotype') solution0 = 'GHAEFDCB' print('solution', solution0) print('genotype', getgenotype(solution0)) solution1 = 'ABCDHGFE' print('solution', solution1) print('genotype', getgenotype(solution1)) print() # Get connections information in string def getconnectionstr(sol): constr = '' for i in range(len(sol)): c1 = sol[i] if i < len(sol) - 1: c2 = sol[i+1] iscon = isconnected(c1, c2) if iscon: constr = constr + c1 if i < len(sol) - 1: constr = constr + '--' else: constr = constr + c1 if i < len(sol) - 1: constr = constr + ' ' return constr print('getconnectionstr') solution0 = 'BHAEFDCG' print('solution', solution0) print('connection ', getconnectionstr(solution0)) solution1 = 'GHAEFDCB' print('solution', solution1) print('connection ', getconnectionstr(solution1)) print() # Check whether a solution is valid def isvalidsolution(sol): disconnect = 0 for i in range(len(sol)): c1 = sol[i] if i < len(sol) - 1: c2 = sol[i+1] iscon = isconnected(c1, c2) if not iscon: disconnect = disconnect + 1 valid = True if disconnect > 0: valid = False return valid print('isvalidsolution') solution0 = 'BHAEFDCG' print('solution', solution0) print('valid ', isvalidsolution(solution0)) solution1 = 'GHAEFDCB' print('solution', solution1) print('valid ', isvalidsolution(solution1)) print() # Do crossover two chromosomes def crossover(ch1, ch2, n): ch3a = ch1[0:n] ch4b = ch1[n:] ch4a = ch2[0:n] ch3b = ch2[n:] ch3 = ch3a + ch3b ch4 = ch4a + ch4b return [ch3, ch4] print('crossover') chro0 = '111110000001010011100101' chro1 = '110111000100101011010001' feno0 = getfenotype(chro0) print('fenotype0', feno0) print('connection ', getconnectionstr(feno0)) print('valid ', isvalidsolution(feno0)) print() feno1 = getfenotype(chro1) print('fenotype1', feno1) print('connection ', getconnectionstr(feno1)) print('valid ', isvalidsolution(feno1)) print() print('chromosome0', chro0) print('chromosome1', chro1) # ok: n = 0-2, 6-8, 9, 22, 23 # problem: n = 19, 3-5, 10-12, 13-14, (15), (16, 17, 18), (19, 20, 21) n = 5 print('crossover at', n) [chro2, chro3] = crossover(chro0, chro1, n) print('chromosome2', chro2) print('chromosome3', chro3) print() feno2 = getfenotype(chro2) print('fenotype2', feno2) print('connection ', getconnectionstr(feno2)) print('valid ', isvalidsolution(feno2)) print() feno3 = getfenotype(chro3) print('fenotype2', feno3) print('connection ', getconnectionstr(feno3)) print('valid ', isvalidsolution(feno3)) print()
Write, Run & Share Python code online using OneCompiler's Python online compiler for free. It's one of the robust, feature-rich online compilers for python language, supporting both the versions which are Python 3 and Python 2.7. Getting started with the OneCompiler's Python editor is easy and fast. The editor shows sample boilerplate code when you choose language as Python or Python2 and start coding.
OneCompiler's python online editor supports stdin and users can give inputs to programs using the STDIN textbox under the I/O tab. Following is a sample python program which takes name as input and print your name with hello.
import sys
name = sys.stdin.readline()
print("Hello "+ name)
Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.
When ever you want to perform a set of operations based on a condition IF-ELSE is used.
if conditional-expression
#code
elif conditional-expression
#code
else:
#code
Indentation is very important in Python, make sure the indentation is followed correctly
For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.
mylist=("Iphone","Pixel","Samsung")
for i in mylist:
print(i)
While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.
while condition
#code
There are four types of collections in Python.
List is a collection which is ordered and can be changed. Lists are specified in square brackets.
mylist=["iPhone","Pixel","Samsung"]
print(mylist)
Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.
myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
Below throws an error if you assign another value to tuple again.
myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)
Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.
myset{"iPhone","Pixel","Samsung"}
print{myset}
Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.
mydict = {
"brand" :"iPhone",
"model": "iPhone 11"
}
print(mydict)
Following are the libraries supported by OneCompiler's Python compiler
Name | Description |
---|---|
NumPy | NumPy python library helps users to work on arrays with ease |
SciPy | SciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation |
SKLearn/Scikit-learn | Scikit-learn or Scikit-learn is the most useful library for machine learning in Python |
Pandas | Pandas is the most efficient Python library for data manipulation and analysis |
Matplotlib | Matplotlib is a cross-platform, data visualization and graphical plotting library for Python programming and it's numerical mathematics extension NumPy |
DOcplex | DOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling |