Algorithm for best choice
Project description
best-choice
1 - pip Install
pip install best-choice
2 - all function
#library
from bestchoice import Generate
#data
#object,price,importance level
table = [['pants',75,10],
['jeans',50,7],
['shirt',45,8],
['dress',65,7],
['ball',25,5]]
#call function generate
gen = Generate(table)
#all possibilities
for x in gen.list_all():
print(x)
#call function to generate calculation results
#parameters 1 and 2 are columns for calculation
#in this case, the price and importance level
calc = gen.list_results([1,2])
#all calculated results
for x in calc:f
print(x)
#new table after filter
#the first parameter 1 and 2 are index columns
#the second parameter 1 <= 200 filter your new table
new = gen.list_best([1,2],[[1,'<=',200]])
#all filtered results
for x in new:
print(x)
3 - example to find best choice
#library
from bestchoice import Generate
#data
#object,price,importance level
table = [['pants',75,10],
['jeans',50,7],
['shirt',45,8],
['dress',65,7],
['ball',25,5]]
#column for calculation
#in this case, the price and importance level
columns = [1,2]
#index of column importance
importance = 2
#filters where 1 is the price <= 200 dollars
filters = [[1,'<=',200]]
#call function generate
gen = Generate(table)
#get all possibilities
lista = gen.list_all()
#new table after filter
#the first parameter 1 and 2 are index columns
#the second parameter 1 <= price filter your new table
res = gen.list_best(columns,filters)
#saves the best filtered result
top = max([sublist[-1] for sublist in res])
filters.append([importance,'==',top])
#table with new result
new = gen.list_best(columns,filters)
#set index of top values
best = [x[0] for x in new][0]
#result
print(f'This is your best choice: {", ".join([str(x[0]) for x in lista[best]])}')
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