def check_status(self, g, r):
theta = 180. * np.pi**-1 * angle(g, r)
if not 0. < theta < 90.:
print 'restarting LBFGS... [not a descent direction]'
return 1
elif theta > 90. - self.thresh:
print 'restarting LBFGS... [practical safeguard]'
return 1
else:
return 0
def check_status(self, g, r):
theta = 180.*np.pi**-1*angle(g,r)
if not 0. < theta < 90.:
print 'restarting LBFGS... [not a descent direction]'
return 1
elif theta > 90. - self.thresh:
print 'restarting LBFGS... [practical safeguard]'
return 1
else:
return 0
def check_status(self, g, r):
theta = angle(g, r)
if theta < 0.:
print 'restarting LBFGS... [not a descent direction]'
return 1
elif theta < self.thresh:
print 'restarting LBFGS... [practical safeguard]'
return 1
else:
return 0
def check_status(self, g, r):
theta = angle(g, r)
if theta < 0.0:
print "restarting LBFGS... [not a descent direction]"
return 1
elif theta < self.thresh:
print "restarting LBFGS... [practical safeguard]"
return 1
else:
return 0
def finalize_search(self):
""" Cleans working directory and writes updated model
"""
unix.cd(PATH.OPTIMIZE)
m = self.load('m_new')
g = self.load('g_new')
p = self.load('p_new')
s = loadtxt('s_new')
x = self.step_lens()
f = self.func_vals()
# clean working directory
unix.rm('alpha')
unix.rm('m_try')
unix.rm('f_try')
if self.iter > 1:
unix.rm('m_old')
unix.rm('f_old')
unix.rm('g_old')
unix.rm('p_old')
unix.rm('s_old')
unix.mv('m_new', 'm_old')
unix.mv('f_new', 'f_old')
unix.mv('g_new', 'g_old')
unix.mv('p_new', 'p_old')
unix.mv('s_new', 's_old')
# write updated model
alpha = x[f.argmin()]
savetxt('alpha', alpha)
self.save('m_new', m + alpha * p)
savetxt('f_new', f.min())
# append latest statistics
self.writer('factor',
-self.dot(g, g)**-0.5 * (f[1] - f[0]) / (x[1] - x[0]))
self.writer('gradient_norm_L1', np.linalg.norm(g, 1))
self.writer('gradient_norm_L2', np.linalg.norm(g, 2))
self.writer('misfit', f[0])
self.writer('restarted', self.restarted)
self.writer('slope', (f[1] - f[0]) / (x[1] - x[0]))
self.writer('step_count', self.step_count)
self.writer('step_length', x[f.argmin()])
self.writer('theta', 180. * np.pi**-1 * angle(p, -g))
self.stepwriter.newline()
def finalize_search(self):
""" Cleans working directory and writes updated model
"""
unix.cd(PATH.OPTIMIZE)
m = self.load("m_new")
g = self.load("g_new")
p = self.load("p_new")
s = loadtxt("s_new")
x = self.step_lens()
f = self.func_vals()
# clean working directory
unix.rm("alpha")
unix.rm("m_try")
unix.rm("f_try")
if self.iter > 1:
unix.rm("m_old")
unix.rm("f_old")
unix.rm("g_old")
unix.rm("p_old")
unix.rm("s_old")
unix.mv("m_new", "m_old")
unix.mv("f_new", "f_old")
unix.mv("g_new", "g_old")
unix.mv("p_new", "p_old")
unix.mv("s_new", "s_old")
# write updated model
alpha = x[f.argmin()]
savetxt("alpha", alpha)
self.save("m_new", m + alpha * p)
savetxt("f_new", f.min())
# append latest output
self.writer("factor", -self.dot(g, g) ** -0.5 * (f[1] - f[0]) / (x[1] - x[0]))
self.writer("gradient_norm_L1", np.linalg.norm(g, 1))
self.writer("gradient_norm_L2", np.linalg.norm(g, 2))
self.writer("misfit", f[0])
self.writer("restarted", self.restarted)
self.writer("slope", (f[1] - f[0]) / (x[1] - x[0]))
self.writer("step_count", self.step_count)
self.writer("step_length", x[f.argmin()])
self.writer("theta", 180.0 * np.pi ** -1 * angle(p, -g))
self.stepwriter.newline()
def finalize_search(self):
""" Cleans working directory and writes updated model
"""
unix.cd(PATH.OPTIMIZE)
m = self.load('m_new')
g = self.load('g_new')
p = self.load('p_new')
s = loadtxt('s_new')
x = self.step_lens()
f = self.func_vals()
# clean working directory
unix.rm('alpha')
unix.rm('m_try')
unix.rm('f_try')
if self.iter > 1:
unix.rm('m_old')
unix.rm('f_old')
unix.rm('g_old')
unix.rm('p_old')
unix.rm('s_old')
unix.mv('m_new', 'm_old')
unix.mv('f_new', 'f_old')
unix.mv('g_new', 'g_old')
unix.mv('p_new', 'p_old')
unix.mv('s_new', 's_old')
# write updated model
alpha = x[f.argmin()]
savetxt('alpha', alpha)
self.save('m_new', m + alpha*p)
savetxt('f_new', f.min())
# append latest statistics
self.writer('factor', -self.dot(g,g)**-0.5 * (f[1]-f[0])/(x[1]-x[0]))
self.writer('gradient_norm_L1', np.linalg.norm(g, 1))
self.writer('gradient_norm_L2', np.linalg.norm(g, 2))
self.writer('misfit', f[0])
self.writer('restarted', self.restarted)
self.writer('slope', (f[1]-f[0])/(x[1]-x[0]))
self.writer('step_count', self.step_count)
self.writer('step_length', x[f.argmin()])
self.writer('theta', 180.*np.pi**-1*angle(p,-g))
self.stepwriter.newline()
def retry_status(self):
""" Returns false if search direction was the same as gradient
direction; returns true otherwise
"""
g = self.load('g_new')
p = self.load('p_new')
thresh = 1.e-3
theta = angle(p, -g)
if PAR.VERBOSE >= 2:
print ' theta: %6.3f' % theta
if abs(theta) < thresh:
return 0
else:
return 1
def retry_status(self):
""" Returns false if search direction was the same as gradient
direction. Returns true otherwise.
"""
unix.cd(PATH.OPTIMIZE)
g = self.load("g_new")
p = self.load("p_new")
thresh = 1.0e-3
theta = angle(p, -g)
# print ' theta: %6.3f' % theta
if abs(theta) < thresh:
return 0
else:
return 1
def retry_status(self):
""" Returns false if search direction was the same as gradient
direction. Returns true otherwise.
"""
unix.cd(PATH.OPTIMIZE)
g = self.load('g_new')
p = self.load('p_new')
thresh = 1.e-3
theta = angle(p,-g)
#print ' theta: %6.3f' % theta
if abs(theta) < thresh:
return 0
else:
return 1
def retry_status(self):
""" Determines if restart is worthwhile
After failed line search, determines if restart is worthwhile by
checking, in effect, if search direction was the same as gradient
direction
"""
g = self.load('g_new')
p = self.load('p_new')
theta = angle(p, -g)
if PAR.VERBOSE >= 2:
print ' theta: %6.3f' % theta
thresh = 1.e-3
if abs(theta) < thresh:
return 0
else:
return 1
def retry_status(self):
""" Determines if restart is worthwhile
After failed line search, determines if restart is worthwhile by
checking, in effect, if search direction was the same as gradient
direction
"""
g = self.load('g_new')
p = self.load('p_new')
theta = angle(p,-g)
if PAR.VERBOSE >= 2:
print ' theta: %6.3f' % theta
thresh = 1.e-3
if abs(theta) < thresh:
return 0
else:
return 1
def retry_status(self):
""" Returns false if search direction was the same as gradient
direction; returns true otherwise
"""
unix.cd(PATH.OPTIMIZE)
g = self.load('g_new')
p = self.load('p_new')
thresh = 1.e-3
theta = angle(p,-g)
if PAR.VERBOSE >= 2:
print ' theta: %6.3f' % theta
if abs(theta) < thresh:
return 0
else:
return 1
def finalize_search(self):
""" Prepares algorithm machinery and scratch directory for next
model upate
"""
m = self.load('m_new')
print("finalize_search")
print(m)
g = self.load('g_new')
p = self.load('p_new')
x = self.line_search.search_history()[0]
f = self.line_search.search_history()[1]
# clean scratch directory
unix.cd(PATH.OPTIMIZE)
if self.iter > 1:
unix.rm('m_old')
unix.rm('f_old')
unix.rm('g_old')
unix.rm('p_old')
unix.rm('s_old')
unix.mv('m_new', 'm_old')
unix.mv('f_new', 'f_old')
unix.mv('g_new', 'g_old')
unix.mv('p_new', 'p_old')
unix.mv('m_try', 'm_new')
self.savetxt('f_new', f.min())
# output latest statistics
self.writer('factor',
-self.dot(g, g)**-0.5 * (f[1] - f[0]) / (x[1] - x[0]))
self.writer('gradient_norm_L1', np.linalg.norm(g, 1))
self.writer('gradient_norm_L2', np.linalg.norm(g, 2))
self.writer('misfit', f[0])
self.writer('restarted', self.restarted)
self.writer('slope', (f[1] - f[0]) / (x[1] - x[0]))
self.writer('step_count', self.line_search.step_count)
self.writer('step_length', x[f.argmin()])
self.writer('theta', 180. * np.pi**-1 * angle(p, -g))
self.line_search.writer.newline()
def finalize_search(self):
""" Prepares algorithm machinery and scratch directory for next
model upate
"""
m = self.load('m_new')
g = self.load('g_new')
p = self.load('p_new')
x = self.line_search.search_history()[0]
f = self.line_search.search_history()[1]
# clean scratch directory
unix.cd(PATH.OPTIMIZE)
if self.iter > 1:
unix.rm('m_old')
unix.rm('f_old')
unix.rm('g_old')
unix.rm('p_old')
unix.rm('s_old')
unix.mv('m_new', 'm_old')
unix.mv('f_new', 'f_old')
unix.mv('g_new', 'g_old')
unix.mv('p_new', 'p_old')
unix.mv('m_try', 'm_new')
self.savetxt('f_new', f.min())
# output latest statistics
self.writer('factor', -self.dot(g,g)**-0.5 * (f[1]-f[0])/(x[1]-x[0]))
self.writer('gradient_norm_L1', np.linalg.norm(g, 1))
self.writer('gradient_norm_L2', np.linalg.norm(g, 2))
self.writer('misfit', f[0])
self.writer('restarted', self.restarted)
self.writer('slope', (f[1]-f[0])/(x[1]-x[0]))
self.writer('step_count', self.line_search.step_count)
self.writer('step_length', x[f.argmin()])
self.writer('theta', 180.*np.pi**-1*angle(p,-g))
self.line_search.writer.newline()
def retry_status(self):
""" Returns false if search direction was the same as gradient
direction; returns true otherwise
Here and elsewhere we use the convention
status > 0 : success
status == 0 : not finished
status < 0 : failed
"""
g = self.load('g_new')
p = self.load('p_new')
thresh = 1.e-3
theta = angle(p, -g)
if PAR.VERBOSE >= 2:
print ' theta: %6.3f' % theta
if abs(theta) < thresh:
return 0
else:
return 1
