def reset(self, x0):
"""
Puts the optimizer in its initial state and sets the initial point to
be the 1-dimensional array or list *x0*. The length of the array
becomes the dimension of the optimization problem
(:attr:`ndim` member).
The initial simplex is built around *x0* by calling the
:meth:`buildSimplex` method with default values for the *rel* and *abs*
arguments.
If *x0* is a 2-dimensional array or list of size
(*ndim*+1) times *ndim* it specifies the initial simplex.
A corresponding grid is created by calling the :meth:`buildGrid` method.
The initial value of the natural logarithm of the simplex side vectors
determinant is calculated and stored.
"""
# Debug message
if self.debug:
DbgMsgOut("GRNMOPT", "Resetting.")
# Make it an array
x0=array(x0)
# Is x0 a point or a simplex?
if x0.ndim==1:
# Point
# Set x now
NelderMead.reset(self, x0)
if self.debug:
DbgMsgOut("GRNMOPT", "Generating initial simplex from initial point.")
sim=self.buildSimplex(x0)
self._setSimplex(sim)
self.delta=self.buildGrid()
self.z=x0
else:
# Simplex or error (handled in _setSimplex())
self._setSimplex(x0)
self.delta=self.buildGrid()
self.z=x0[0,:]
if self.debug:
DbgMsgOut("GRNMOPT", "Using specified initial simplex.")
# Set x to first point in simplex after it was checked in _setSimplex()
Optimizer.reset(self, x0[0,:])
# Reset point moves counter
self.simplexmoves=zeros(self.ndim+1)
# Make x tolerance an array
self.xtol=array(self.xtol)
def reset(self, x0):
"""
Puts the optimizer in its initial state and sets the initial point to
be the 1-dimensional array *x0*. The length of the array becomes the
dimension of the optimization problem (:attr:`ndim` member).
The initial simplex is built around *x0* by calling the
:meth:`buildSimplex` method with default values for the *rel* and
*abs* arguments.
If *x0* is a 2-dimensional array of size (*ndim*+1) times *ndim* it
specifies the initial simplex.
"""
# Debug message
if self.debug:
DbgMsgOut("NM", "Resetting.")
# Make it an array
x0=array(x0)
# Is x0 a point or a simplex?
if x0.ndim==1:
# Point
# Set x now
Optimizer.reset(self, x0)
if self.debug:
DbgMsgOut("NM", "Generating initial simplex from initial point.")
sim=self.buildSimplex(x0)
self._setSimplex(sim)
else:
# Simplex or error (handled in _setSimplex())
self._setSimplex(x0)
if self.debug:
DbgMsgOut("NM", "Using specified initial simplex.")
# Set x to first point in simplex after it was checked in _setSimplex()
Optimizer.reset(self, x0[0,:])
# Reset point moves counter
self.simplexmoves=zeros(self.ndim+1)
# Make x tolerance an array
self.xtol=array(self.xtol)
# Reset counters
self.nr=0
self.ne=0
self.noc=0
self.nic=0
self.ns=0
self.nrok=0
self.neok=0
self.nocok=0
self.nicok=0
self.icconv=0
self.occonv=0
def reset(self, x0):
"""
Puts the optimizer in its initial state and sets the initial point to
be the 1-dimensional array or list *x0*. The length of the array
becomes the dimension of the optimization problem
(:attr:`ndim` member).
The initial simplex is built around *x0* by calling the
:meth:`buildSimplex` method with default values for the *rel* and *abs*
arguments.
If *x0* is a 2-dimensional array or list of size
(*ndim*+1) times *ndim* it specifies the initial simplex.
The initial value of the natural logarithm of the simplex side vectors
determinant is calculated and stored. This value gets updated at every
simplex algorithm step. The only time it needs to be reevaluated is at
reshape. But that is also quite simple because the reshaped simplex
is orthogonal. The only place where a full determinant needs to be
calculated is here.
"""
# Debug message
if self.debug:
DbgMsgOut("SDNMOPT", "Resetting.")
# Make it an array
x0=array(x0)
# Is x0 a point or a simplex?
if x0.ndim==1:
# Point
# Set x now
NelderMead.reset(self, x0)
if self.debug:
DbgMsgOut("SDNMOPT", "Generating initial simplex from initial point.")
sim=self.buildSimplex(x0)
self._setSimplex(sim)
else:
# Simplex or error (handled in _setSimplex())
self._setSimplex(x0)
if self.debug:
DbgMsgOut("SDNMOPT", "Using specified initial simplex.")
# Set x to first point in simplex after it was checked in _setSimplex()
Optimizer.reset(self, x0[0,:])
# Reset point moves counter
self.simplexmoves=zeros(self.ndim+1)
# Calculate log(n! det([v])) where [v] are the n side vectors
# arranged as columns of a matrix
(v, l)=self.sortedSideVectors()
self.logDet=log(abs(det(v)))
# Initial h
self.h=1.0
# Make x tolerance an array
self.xtol=array(self.xtol)
def reset(self, x0):
"""
Puts the optimizer in its initial state and sets the initial point to
be the 1-dimensional array or list *x0*. The length of the array
becomes the dimension of the optimization problem
(:attr:`ndim` member).
The initial simplex is built around *x0* by calling the
:meth:`buildSimplex` method with default values for the *rel* and *abs*
arguments.
If *x0* is a 2-dimensional array or list of size
(*ndim*+1) times *ndim* it specifies the initial simplex.
The initial value of the natural logarithm of the simplex side vectors
determinant is calculated and stored. This value gets updated at every
simplex algorithm step. The only time it needs to be reevaluated is at
reshape. But that is also quite simple because the reshaped simplex
is orthogonal. The only place where a full determinant needs to be
calculated is here.
"""
# Debug message
if self.debug:
DbgMsgOut("SDNMOPT", "Resetting.")
# Make it an array
x0 = array(x0)
# Is x0 a point or a simplex?
if x0.ndim == 1:
# Point
# Set x now
NelderMead.reset(self, x0)
if self.debug:
DbgMsgOut("SDNMOPT",
"Generating initial simplex from initial point.")
sim = self.buildSimplex(x0)
self._setSimplex(sim)
else:
# Simplex or error (handled in _setSimplex())
self._setSimplex(x0)
if self.debug:
DbgMsgOut("SDNMOPT", "Using specified initial simplex.")
# Set x to first point in simplex after it was checked in _setSimplex()
Optimizer.reset(self, x0[0, :])
# Reset point moves counter
self.simplexmoves = zeros(self.ndim + 1)
# Calculate log(n! det([v])) where [v] are the n side vectors
# arranged as columns of a matrix
(v, l) = self.sortedSideVectors()
self.logDet = log(abs(det(v)))
# Initial h
self.h = 1.0
# Make x tolerance an array
self.xtol = array(self.xtol)
