apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-21 14:05:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

pylab library not needed

Browse source
This commit is contained in:
Ricardo 2014-01-28 13:59:53 +00:00
parent df50d2a990
commit 30724f72d9
2 changed files with 0 additions and 9 deletions
Download patch file Download diff file Expand all files Collapse all files

4
GPy/inference/optimization/samplers.py
View file

@ -4,7 +4,6 @@
import numpy as np import numpy as np
from scipy import linalg, optimize from scipy import linalg, optimize
import pylab as pb
import Tango import Tango
import sys import sys
import re import re
@ -80,6 +79,3 @@ class Metropolis_Hastings:
fs.append(function(*args)) fs.append(function(*args))
self.model._set_params(param)# reset model to starting state self.model._set_params(param)# reset model to starting state
return fs return fs

5
GPy/inference/optimization/sgd.py
View file

@ -3,7 +3,6 @@ import scipy as sp
import scipy.sparse import scipy.sparse
from optimization import Optimizer from optimization import Optimizer
from scipy import linalg, optimize from scipy import linalg, optimize
import pylab as plt
import copy, sys, pickle import copy, sys, pickle
class opt_SGD(Optimizer): class opt_SGD(Optimizer):
@ -285,7 +284,6 @@ class opt_SGD(Optimizer):
b = len(features)/self.batch_size b = len(features)/self.batch_size
features = [features[i::b] for i in range(b)] features = [features[i::b] for i in range(b)]
NLL = [] NLL = []
import pylab as plt
for count, j in enumerate(features): for count, j in enumerate(features):
self.Model.input_dim = len(j) self.Model.input_dim = len(j)
self.Model.likelihood.input_dim = len(j) self.Model.likelihood.input_dim = len(j)
@ -318,9 +316,6 @@ class opt_SGD(Optimizer):
self.adapt_learning_rate(it+count, D) self.adapt_learning_rate(it+count, D)
NLL.append(f) NLL.append(f)
self.fopt_trace.append(NLL[-1]) self.fopt_trace.append(NLL[-1])
# fig = plt.figure('traces')
# plt.clf()
# plt.plot(self.param_traces['noise'])
# for k in self.param_traces.keys(): # for k in self.param_traces.keys():
# self.param_traces[k].append(self.Model.get(k)[0]) # self.param_traces[k].append(self.Model.get(k)[0])