def _run(self, minimizer):
m = 2.0
a = 0.0
b = 6.0
m_expected = Numeric.pi
minimizer.set(m, a, b)
t1 = fn1(a, None)
t2 = fn1(b, None)
minimizer.set_with_values(m, fn1(m, None), a, t1, b, t2)
#print "Testing minimizer ", minimizer.name()
#print "%5s [%9s %9s] %9s %10s %9s" % ("iter", "upper", "lower", "min", "err", "err(est)")
for iter in range(100):
status = minimizer.iterate()
a = minimizer.x_lower()
b = minimizer.x_upper()
m = minimizer.minimum()
status = minimize.test_interval(a, b, 0.001, 0)
#if status == 0:
#print "Converged: "
#print "%5d [%.7f %.7f] %.7f + % .6f % .6f" %(iter, a, b, m, m -m_expected, b - a)
if status == 0:
break
else:
raise ValueError("Number of Iterations exceeded!")
assert(Numeric.absolute(m - m_expected)<0.001)
def _run(self, minimizer):
m = 2.0
a = 0.0
b = 6.0
m_expected = Numeric.pi
minimizer.set(m, a, b)
t1 = fn1(a, None)
t2 = fn1(b, None)
minimizer.set_with_values(m, fn1(m, None), a, t1, b, t2)
#print "Testing minimizer ", minimizer.name()
#print "%5s [%9s %9s] %9s %10s %9s" % ("iter", "upper", "lower", "min", "err", "err(est)")
for iter in range(100):
status = minimizer.iterate()
a = minimizer.x_lower()
b = minimizer.x_upper()
m = minimizer.minimum()
status = minimize.test_interval(a, b, 0.001, 0)
#if status == 0:
#print "Converged: "
#print "%5d [%.7f %.7f] %.7f + % .6f % .6f" %(iter, a, b, m, m -m_expected, b - a)
if status == 0:
break
else:
raise ValueError, "Number of Iterations exceeded!"
assert(Numeric.absolute(m - m_expected)<0.001)
def run_fsolver():
mysys = minimize.gsl_function(fn1, None)
m = 2.0
a = 0.0
b = 6.0
m_expected = numx.pi
minimizer = minimize.brent(mysys)
#minimizer = minimize.goldensection(mysys)
minimizer.set(m, a, b)
print "# Using minimizer ", minimizer.name()
print "# %5s [%9s %9s] %9s %10s %9s" % ("iter", "upper", "lower", "min",
"err", "err(est)")
for iter in range(100):
status = minimizer.iterate()
a = minimizer.x_lower()
b = minimizer.x_upper()
m = minimizer.minimum()
status = minimize.test_interval(a, b, 0.001, 0)
if status == errno.GSL_SUCCESS:
print "# Converged: "
print " %5d [%.7f %.7f] %.7f + % .6f % .6f" %(iter, a, b, m,
m -m_expected, b - a)
if status == errno.GSL_SUCCESS:
break
else:
raise ValueError, "Number of Iterations exceeded!"
def run_fsolver():
mysys = minimize.gsl_function(fn1, None)
m = 2.0
a = 0.0
b = 6.0
m_expected = numx.pi
minimizer = minimize.brent(mysys)
#minimizer = minimize.goldensection(mysys)
minimizer.set(m, a, b)
print "# Using minimizer ", minimizer.name()
print "# %5s [%9s %9s] %9s %10s %9s" % ("iter", "upper", "lower", "min",
"err", "err(est)")
for iter in range(100):
status = minimizer.iterate()
a = minimizer.x_lower()
b = minimizer.x_upper()
m = minimizer.minimum()
status = minimize.test_interval(a, b, 0.001, 0)
if status == errno.GSL_SUCCESS:
print "# Converged: "
print " %5d [%.7f %.7f] %.7f + % .6f % .6f" % (iter, a, b, m,
m - m_expected, b - a)
if status == errno.GSL_SUCCESS:
break
else:
raise ValueError, "Number of Iterations exceeded!"
print "Itr %d : %e %e %e" %(iterX2, low, high, mid )
low = minimizer.x_lower()
high = minimizer.x_upper()
mid = minimizer.minimum()
cggdEstimator.setShapePar( mid )
cggdEstimator.fixConst()
if iterX2 % skipSize2 == 0:
# exit if there is a file whose name is the same as the output file.
filename = '%s/LS_M=%d-m=%d-r=%d/%04d_%04d/_M-%04d' % (outDataDir, M, m, r, iterX1, iterX2, fbinX)
if os.path.exists(filename):
print '%s already exists. It is overwritten' %(filename)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
cggdEstimator.writeParam( filename )
status = minimize.test_interval(low, high, 0.001, 0)
if status == errno.GSL_SUCCESS:
print "# Converged(LS) "
break
# get the scaling parameter
cggdEstimator = MLEstimateScaleParameter( cggdEstimator, fbinX, dataDir, segListFile, M, m, r )
if iterX1 % skipSize1 == 0:
# output the inchoate result
filename = '%s/LS_M=%d-m=%d-r=%d/%04d_FFFF/_M-%04d' % (outDataDir, M, m, r, iterX1, fbinX)
if os.path.exists(filename):
print '%s already exists. It is overwritten' %(filename)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
cggdEstimator.writeParam( filename )
if cggdEstimator.isConverged() == True: