def all_constraints(self, x=None):
"""Returns both the inequality and equality constraint values for your function
Assumptions:
N/A
Source:
N/A
Inputs:
x [vector]
Outputs:
scaled_constraints [vector]
Properties Used:
None
"""
self.evaluate(x)
aliases = self.optimization_problem.aliases
constraints = self.optimization_problem.constraints
results = self.results
constraint_values = help_fun.get_values(self, constraints, aliases)
scaled_constraints = help_fun.scale_const_values(
constraints, constraint_values)
return scaled_constraints
def all_constraints(self,x = None):
"""Returns both the inequality and equality constraint values for your function
Assumptions:
N/A
Source:
N/A
Inputs:
x [vector]
Outputs:
scaled_constraints [vector]
Properties Used:
None
"""
self.evaluate(x)
aliases = self.optimization_problem.aliases
constraints = self.optimization_problem.constraints
results = self.results
constraint_values = help_fun.get_values(self,constraints,aliases)
scaled_constraints = help_fun.scale_const_values(constraints,constraint_values)
return scaled_constraints
def all_constraints(self,x = None):
self.evaluate(x)
aliases = self.optimization_problem.aliases
constraints = self.optimization_problem.constraints
results = self.results
constraint_values = help_fun.get_values(self,constraints,aliases)
scaled_constraints = help_fun.scale_const_values(constraints,constraint_values)
return scaled_constraints
def all_constraints(self,x = None):
self.evaluate(x)
aliases = self.optimization_problem.aliases
constraints = self.optimization_problem.constraints
results = self.results
constraint_values = help_fun.get_values(self,constraints,aliases)
scaled_constraints = help_fun.scale_const_values(constraints,constraint_values)
return scaled_constraints
def inequality_constraint(self, x=None):
"""Retrieve the inequality constraint values for your function
Assumptions:
N/A
Source:
N/A
Inputs:
x [vector]
Outputs:
scaled_constraints [vector]
Properties Used:
None
"""
self.evaluate(x)
aliases = self.optimization_problem.aliases
constraints = self.optimization_problem.constraints
results = self.results
# Setup constraints
indices = []
for ii in xrange(0, len(constraints)):
if constraints[ii][1] == ('='):
indices.append(ii)
iqconstraints = np.delete(constraints, indices, axis=0)
if iqconstraints == []:
scaled_constraints = []
else:
constraint_values = help_fun.get_values(self, iqconstraints,
aliases)
constraint_values[iqconstraints[:, 1] == '<'] = -constraint_values[
iqconstraints[:, 1] == '<']
bnd_constraints = constraint_values - help_fun.scale_const_bnds(
iqconstraints)
scaled_constraints = help_fun.scale_const_values(
iqconstraints, constraint_values)
return scaled_constraints
def write_optimization_outputs(nexus, filename):
#unpack optimization problem values
objective = nexus.optimization_problem.objective
aliases = nexus.optimization_problem.aliases
constraints = nexus.optimization_problem.constraints
#inputs
unscaled_inputs = nexus.optimization_problem.inputs[:,
1] #use optimization problem inputs here
input_scaling = nexus.optimization_problem.inputs[:, 3]
scaled_inputs = unscaled_inputs / input_scaling
#objective
objective_value = get_values(nexus, objective, aliases)
scaled_objective = scale_obj_values(objective, objective_value)
#constraints
constraint_values = get_values(nexus, constraints, aliases)
scaled_constraints = scale_const_values(constraints, constraint_values)
problem_inputs = []
problem_constraints = []
for value in scaled_inputs:
problem_inputs.append(
value) #writing to file is easier when you use list
for value in scaled_constraints:
problem_constraints.append(value)
file = open(filename, 'ab')
file.write('iteration = ')
file.write(str(nexus.total_number_of_iterations))
file.write(' , ')
file.write('objective = ')
file.write(str(scaled_objective[0]))
file.write(', inputs = ')
file.write(str(problem_inputs))
file.write(', constraints = ')
file.write(str(problem_constraints))
file.write('\n')
file.close()
return
def equality_constraint(self,x = None):
"""Retrieve the equality constraint values for your function
Assumptions:
N/A
Source:
N/A
Inputs:
x [vector]
Outputs:
scaled_constraints [vector]
Properties Used:
None
"""
self.evaluate(x)
aliases = self.optimization_problem.aliases
constraints = self.optimization_problem.constraints
results = self.results
# Setup constraints
indices = []
for ii in xrange(0,len(constraints)):
if constraints[ii][1]=='>':
indices.append(ii)
elif constraints[ii][1]=='<':
indices.append(ii)
eqconstraints = np.delete(constraints,indices,axis=0)
if eqconstraints == []:
scaled_constraints = []
else:
constraint_values = help_fun.get_values(self,eqconstraints,aliases) - help_fun.scale_const_bnds(eqconstraints)
scaled_constraints = help_fun.scale_const_values(eqconstraints,constraint_values)
return scaled_constraints
def equality_constraint(self,x = None):
self.evaluate(x)
aliases = self.optimization_problem.aliases
constraints = self.optimization_problem.constraints
results = self.results
# Setup constraints
indices = []
for ii in xrange(0,len(constraints)):
if constraints[ii][1]=='>':
indices.append(ii)
elif constraints[ii][1]=='<':
indices.append(ii)
eqconstraints = np.delete(constraints,indices,axis=0)
if eqconstraints == []:
scaled_constraints = []
else:
constraint_values = help_fun.get_values(self,eqconstraints,aliases) - help_fun.scale_const_bnds(eqconstraints)
scaled_constraints = help_fun.scale_const_values(eqconstraints,constraint_values)
return scaled_constraints
def equality_constraint(self,x = None):
self.evaluate(x)
aliases = self.optimization_problem.aliases
constraints = self.optimization_problem.constraints
results = self.results
# Setup constraints
indices = []
for ii in xrange(0,len(constraints)):
if constraints[ii][1]=='>':
indices.append(ii)
elif constraints[ii][1]=='<':
indices.append(ii)
eqconstraints = np.delete(constraints,indices,axis=0)
if eqconstraints == []:
scaled_constraints = []
else:
constraint_values = help_fun.get_values(self,eqconstraints,aliases) - help_fun.scale_const_bnds(eqconstraints)
scaled_constraints = help_fun.scale_const_values(eqconstraints,constraint_values)
return scaled_constraints
def write_optimization_outputs(nexus, filename):
""" Writes the optimization outputs to a file
Assumptions:
N/A
Source:
N/A
Inputs:
nexus [nexus()]
filename [str]
Outputs:
N/A
Properties Used:
N/A
"""
#unpack optimization problem values
objective = nexus.optimization_problem.objective
aliases = nexus.optimization_problem.aliases
constraints = nexus.optimization_problem.constraints
#inputs
unscaled_inputs = nexus.optimization_problem.inputs[:,1] #use optimization problem inputs here
input_scaling = nexus.optimization_problem.inputs[:,3]
scaled_inputs = unscaled_inputs/input_scaling
#objective
objective_value = get_values(nexus,objective,aliases)
scaled_objective = scale_obj_values(objective , objective_value)
#constraints
constraint_values = get_values(nexus,constraints,aliases)
scaled_constraints = scale_const_values(constraints,constraint_values)
problem_inputs = []
problem_constraints = []
for value in scaled_inputs:
problem_inputs.append(value) #writing to file is easier when you use list
for value in scaled_constraints:
problem_constraints.append(value)
file=open(filename, 'ab')
file.write('iteration = ')
file.write(str(nexus.total_number_of_iterations))
file.write(' , ')
file.write('objective = ')
file.write(str(scaled_objective[0]))
file.write(', inputs = ')
file.write(str(problem_inputs))
file.write(', constraints = ')
file.write(str(problem_constraints))
file.write('\n')
file.close()
return