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pylab library not needed

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Ricardo 2014-01-28 13:59:53 +00:00
parent df50d2a990
commit 30724f72d9
2 changed files with 0 additions and 9 deletions
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4
GPy/inference/optimization/samplers.py
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@ -4,7 +4,6 @@
import numpy as np
from scipy import linalg, optimize
import pylab as pb
import Tango
import sys
import re
@ -80,6 +79,3 @@ class Metropolis_Hastings:
fs.append(function(*args))
self.model._set_params(param)# reset model to starting state
return fs

5
GPy/inference/optimization/sgd.py
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@ -3,7 +3,6 @@ import scipy as sp
import scipy.sparse
from optimization import Optimizer
from scipy import linalg, optimize
import pylab as plt
import copy, sys, pickle
class opt_SGD(Optimizer):
@ -285,7 +284,6 @@ class opt_SGD(Optimizer):
b = len(features)/self.batch_size
features = [features[i::b] for i in range(b)]
NLL = []
import pylab as plt
for count, j in enumerate(features):
self.Model.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)
NLL.append(f)
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():
# self.param_traces[k].append(self.Model.get(k)[0])