def min_wrapper(hyp, F, Flag, *varargin):
# Utilize scipy.optimize functions to minimize the negative log marginal liklihood. This is REALLY inefficient!
x = convert_to_array(hyp)
if Flag == 'CG':
aa = cg(nlml, x, dnlml, (F,hyp,varargin), maxiter=100, disp=False, full_output=True)
x = aa[0]; fx = aa[1]; funcCalls = aa[2]; gradcalls = aa[3]
if aa[4] == 1:
print "Maximum number of iterations exceeded."
elif aa[4] == 2:
print "Gradient and/or function calls not changing."
gvals = dnlml(x,F,hyp,varargin)
return convert_to_class(x,hyp), fx, gvals, funcCalls
elif Flag == 'BFGS':
# Use BFGS
aa = bfgs(nlml, x, dnlml, (F,hyp,varargin), maxiter=100, disp=False, full_output=True)
x = aa[0]; fvals = aa[1]; gvals = aa[2]; Bopt = aa[3]; funcCalls = aa[4]; gradcalls = aa[5]
if aa[6] == 1:
print "Maximum number of iterations exceeded."
elif aa[6] == 2:
print "Gradient and/or function calls not changing."
return convert_to_class(x,hyp), fvals, gvals, funcCalls
else:
raise Exception('Incorrect usage of optimization flag in min_wrapper')
def min_wrapper(hyp, F, Flag, *varargin):
# Utilize scipy.optimize functions to minimize the negative log marginal liklihood. This is REALLY inefficient!
x = convert_to_array(hyp) # Converts the hyperparameter class to an array
if Flag == 'CG':
aa = cg(nlml,
x,
dnlml, (F, hyp, varargin),
maxiter=100,
disp=True,
full_output=True)
x = aa[0]
fx = aa[1]
funcCalls = aa[2]
gradcalls = aa[3]
if aa[4] == 1:
print "Maximum number of iterations exceeded."
elif aa[4] == 2:
print "Gradient and/or function calls not changing."
gvals = dnlml(x, F, hyp, varargin)
return convert_to_class(x, hyp), fx, gvals, funcCalls
elif Flag == 'BFGS':
# Use BFGS
aa = bfgs(nlml,
x,
dnlml, (F, hyp, varargin),
maxiter=100,
disp=False,
full_output=True)
x = aa[0]
fvals = aa[1]
gvals = aa[2]
Bopt = aa[3]
funcCalls = aa[4]
gradcalls = aa[5]
if aa[6] == 1:
print "Maximum number of iterations exceeded."
elif aa[6] == 2:
print "Gradient and/or function calls not changing."
return convert_to_class(x, hyp), fvals, gvals, funcCalls
elif Flag == 'SCG':
# Use SCG
aa = scg(x, nlml, dnlml, (F, hyp, varargin), niters=40)
x = aa[0]
fvals = aa[1]
gvals = dnlml(x, F, hyp, varargin)
return convert_to_class(x, hyp), fvals, gvals
else:
raise Exception('Incorrect usage of optimization flag in min_wrapper')
def min_wrapper(hyp, F, Flag, *varargin):
# Utilize scipy.optimize functions, sgc.py, or minimize.py to
# minimize the negative log marginal liklihood.
x = convert_to_array(hyp) # convert the hyperparameter class to an array
if Flag == 'CG':
aa = cg(nlml, x, dnlml, (F,hyp,varargin), maxiter=100, disp=False, full_output=True)
x = aa[0]; fopt = aa[1]; funcCalls = aa[2]; gradcalls = aa[3]
if aa[4] == 1:
print "Maximum number of iterations exceeded."
elif aa[4] == 2:
print "Gradient and/or function calls not changing."
gopt = dnlml(x,F,hyp,varargin)
return convert_to_class(x,hyp), fopt, gopt, funcCalls
elif Flag == 'BFGS':
# Use BFGS
aa = bfgs(nlml, x, dnlml, (F,hyp,varargin), maxiter=100, disp=False, full_output=True)
x = aa[0]; fopt = aa[1]; gopt = aa[2]; Bopt = aa[3]; funcCalls = aa[4]; gradcalls = aa[5]
if aa[6] == 1:
print "Maximum number of iterations exceeded."
elif aa[6] == 2:
print "Gradient and/or function calls not changing."
if isinstance(fopt, np.ndarray):
fopt = fopt[0]
return convert_to_class(x,hyp), fopt, gopt, funcCalls
elif Flag == 'SCG':
# use sgc.py
aa = scg(x, nlml, dnlml, (F,hyp,varargin), niters = 100)
hyp = convert_to_class(aa[0],hyp)
fopt = aa[1][-1]
gopt = dnlml(aa[0],F,hyp,varargin)
return hyp, fopt, gopt, len(aa[1])
elif Flag == 'Minimize':
# use minimize.py
aa = run(x, nlml, dnlml, (F,hyp,varargin), maxnumfuneval=-100)
hyp = convert_to_class(aa[0],hyp)
fopt = aa[1][-1]
gopt = dnlml(aa[0],F,hyp,varargin)
return hyp, fopt, gopt, len(aa[1])
else:
raise Exception('Incorrect usage of optimization flag in min_wrapper')
def dnlml(x,F,*varargin):
hyp = varargin[0]
temp = list(varargin[1:][0])
temp[-1] = True
f = lambda z: F(z,*temp)
X = convert_to_class(x,hyp)
vargout = f(X)
return convert_to_array( vargout[1] )
def nlml(x,F,*varargin):
hyp = varargin[0]
temp = list(varargin[1:][0])
temp[-1] = False
f = lambda z: F(z,*temp)
X = convert_to_class(x,hyp)
vargout = f(X)
return vargout[0]
def dnlml(x, F, *varargin):
hyp = varargin[0]
temp = list(varargin[1:][0])
temp[-1] = True
f = lambda z: F(z, *temp)
X = convert_to_class(x, hyp)
vargout = f(X)
z = convert_to_array(vargout[1])
return z
def nlml(x, F, *varargin):
hyp = varargin[0]
temp = list(varargin[1:][0])
temp[-1] = False
f = lambda z: F(z, *temp)
X = convert_to_class(x, hyp)
vargout = f(X)
return vargout[0]
