def pom3_csvmaker(names, rows, verbose=True):
header = names[:]
objectives = ['-cost', '+completion', '-idle']
p3 = pom3.pom3()
bigrows = []
#clean rows
for _r, r in enumerate(rows):
for _i, i in enumerate(r):
rows[_r][_i] = round(float(i), 2)
#simulate pom
for r in rows:
vals = p3.simulate(r)
bigrow = r + [round(i, 2) for i in vals]
bigrows.append(bigrow)
header += objectives
if verbose:
s = ''
for i in header:
s += str(i) + ','
print s[:len(s) - 1]
for r in bigrows:
s = ''
for i in r:
s += str(i) + ','
print s[:len(s) - 1]
return header, bigrows
def pom3_csvmaker(names,rows,verbose=True):
header = names[:]
objectives = ['-cost','+completion','-idle']
p3 = pom3.pom3()
bigrows = []
#clean rows
for _r,r in enumerate(rows):
for _i,i in enumerate(r):
rows[_r][_i] = round(float(i),2)
#simulate pom3
for r in rows:
vals = p3.simulate(r)
bigrow = r+[round(i,2) for i in vals]
bigrows.append(bigrow)
header += objectives
if verbose:
s = ''
for i in header:
s += str(i)+','
print s[:len(s)-1]
for r in bigrows:
s = ''
for i in r:
s +=str(i) + ','
print s[:len(s)-1]
return header,bigrows
def POM3(Class='a'):
"POM3"
p3 = pom3.pom3()
headers = [
'Culture', 'criticality', 'criticality_modifier', 'initial_known',
'interdependency', 'dynamism', 'size', 'plan', 'team_size'
]
def bounds(Class):
if Class == 'a':
return {
'Culture': [0.1, 0.9],
'criticality': [0.82, 1.26],
'criticality_modifier': [0.02, 0.1],
'initial_known': [0.4, 0.7],
'interdependency': [0.0, 1.0],
'dynamism': [1.0, 50.0],
'size': [0, 4],
'plan': [0, 4],
'team_size': [1.0, 44.0],
}
elif Class == 'b':
return {
'Culture': [0.1, 0.9],
'criticality': [0.82, 1.26],
'criticality_modifier': [0.8, 0.95],
'initial_known': [0.4, 0.70],
'interdependency': [0.0, 1.0],
'dynamism': [1.0, 50],
'size': [0, 2],
'plan': [0, 4],
'team_size': [1.0, 44.0],
}
elif Class == 'c':
return {
'Culture': [0.5, 0.9],
'criticality': [0.82, 1.26],
'criticality_modifier': [0.02, 0.08],
'initial_known': [0.2, 0.5],
'interdependency': [0.0, 50.0],
'dynamism': [40.0, 50.0],
'size': [3, 4],
'plan': [0, 4],
'team_size': [20.0, 44.0],
}
cost = lambda x: p3.simulate([round(float(k), 2) for k in x[1:-3]])[0]
completion = lambda x: p3.simulate([round(float(k), 2)
for k in x[1:-3]])[1]
idle = lambda x: p3.simulate([round(float(k), 2) for k in x[1:-3]])[2]
return Cols(POM3, [
N(least=bounds(Class)[h][0], most=bounds(Class)[h][1]) for h in headers
] + [O(f=cost), O(f=idle),
O(f=completion, love=True)])
def evaluate(prob, input = None):
if input:
for i,decision in enumerate(prob.decisions):
decision.value = input[i]
else: input = [decision.value for decision in prob.decisions]
p3 = pom3.pom3()
output = p3.simulate(input)
for i,objective in enumerate(prob.objectives):
objective.value = output[i]
return [objective.value for objective in prob.objectives]
def evaluate(prob, input=None):
if input:
for i, decision in enumerate(prob.decisions):
decision.value = input[i]
else:
input = [decision.value for decision in prob.decisions]
p3 = pom3.pom3()
output = p3.simulate(input)
for i, objective in enumerate(prob.objectives):
objective.value = output[i]
return [objective.value for objective in prob.objectives]
def POM3(Class='a'):
"POM3"
p3 = pom3.pom3()
headers = ['Culture', 'criticality', 'criticality_modifier'
, 'initial_known', 'interdependency', 'dynamism'
, 'size', 'plan', 'team_size']
def bounds(Class):
if Class=='a':
return {'Culture': [0.1, 0.9],
'criticality': [0.82, 1.26],
'criticality_modifier': [0.02, 0.1],
'initial_known': [0.4, 0.7],
'interdependency': [0.0, 1.0],
'dynamism': [1.0, 50.0],
'size': [0, 4],
'plan': [0, 4],
'team_size': [1.0, 44.0],
}
elif Class=='b':
return {'Culture': [0.1, 0.9],
'criticality': [0.82, 1.26],
'criticality_modifier': [0.8, 0.95],
'initial_known': [0.4, 0.70],
'interdependency': [0.0, 1.0],
'dynamism': [1.0, 50],
'size': [0, 2],
'plan': [0, 4],
'team_size': [1.0, 44.0],
}
elif Class=='c':
return {'Culture': [0.5, 0.9],
'criticality': [0.82, 1.26],
'criticality_modifier': [0.02, 0.08],
'initial_known': [0.2, 0.5],
'interdependency': [0.0, 50.0],
'dynamism': [40.0, 50.0],
'size': [3,4],
'plan': [0, 4],
'team_size': [20.0, 44.0],
}
cost = lambda x: p3.simulate([round(float(k),2) for k in x[1:-3]])[0]
completion = lambda x: p3.simulate([round(float(k),2) for k in x[1:-3]])[1]
idle = lambda x: p3.simulate([round(float(k),2) for k in x[1:-3]])[2]
return Cols(POM3,
[N(least=bounds(Class)[h][0], most=bounds(Class)[h][1])
for h in headers] +
[O(f=cost), O(f=idle), O(f=completion, love=True)])
