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Added datasets.py back in and minor changes.

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This commit is contained in:
Neil Lawrence 2012-12-05 19:19:15 -08:00
parent d71ad99db9
commit 0ee11a2077
9 changed files with 244 additions and 23 deletions
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35
GPy/inference/optimization.py
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@ -9,20 +9,22 @@ import pylab as pb
import datetime as dt
class Optimizer():
"""
Superclass for all the optimizers.
:param x_init: initial set of parameters
:param f_fp: function that returns the function AND the gradients at the same time
:param f: function to optimize
:param fp: gradients
:param messages: print messages from the optimizer?
:type messages: (True | False)
:param max_f_eval: maximum number of function evaluations
:rtype: optimizer object.
"""
def __init__(self, x_init, f_fp, f, fp , messages = False, max_f_eval = 1e4, ftol = None, gtol = None, xtol = None):
"""
Superclass for all the optimizers.
Arguments:
x_init: initial set of parameters
f_fp: function that returns the function AND the gradients at the same time
f: function to optimize
fp: gradients
messages: print messages from the optimizer? (True | False)
max_f_eval: maximum number of function evaluations
"""
self.opt_name = None
self.f_fp = f_fp
self.f = f
@ -47,7 +49,7 @@ class Optimizer():
self.time = str(end-start)
def opt(self):
raise NotImplementedError, "this needs to be implemented to utilise the optimizer class"
raise NotImplementedError, "this needs to be implemented to use the optimizer class"
def plot(self):
if self.trace == None:
@ -136,8 +138,7 @@ class opt_simplex(Optimizer):
def opt(self):
"""
The simplex optimizer does not require gradients, which
is great during development. Otherwise it's a bit slow.
The simplex optimizer does not require gradients.
"""
statuses = ['Converged', 'Maximum number of function evaluations made','Maximum number of iterations reached']
@ -164,11 +165,11 @@ class opt_simplex(Optimizer):
# class opt_rasm(Optimizer):
# def __init__(self, *args, **kwargs):
# Optimizer.__init__(self, *args, **kwargs)
# self.opt_name = "Rasmussen's SCG"
# self.opt_name = "Rasmussen's Conjugate Gradient"
# def opt(self):
# """
# Run Rasmussen's SCG optimizer
# Run Rasmussen's Conjugate Gradient optimizer
# """
# assert self.f_fp != None, "Rasmussen's minimizer requires f_fp"