Does pybrain support python3.5.2? The simple 'import pybrain ' abort. like below. I install it just with 'pip install pybrain' D:\Anaconda3.5.2\python.exe F:/gitProjects/vnpy_future/pre_code/cnn/rnn.py Traceback (most recent call last): File "F:/gitProjects/vnpy_future/pre_code/cnn/rnn.py", line 7, in import pybrain File "D:\Anaconda3.5.2\lib\site-packages\pybrain_init_.py", line 1, in from structure.init import * ImportError: No module named 'structure'

The code should still work on Python2.

Import and print are the main changes.

Not everything might be ported. The most part that was left intact are the range(). In Python2 it returns a list and in python it returns an iterator. This should speed up and in most cases should work without further changes.

See http://www.diveinto.org/python3/porting-code-to-python-3-with-2to3.html#xrange

There seem to have been many changes since 2009 (over 4 years ago). The version number on GitHub is almost the same yet it's probably worth making another release.

PyPi allows installing simply for a user or system and other things. Not that git clone isn't good in many cases.

Steps:

>>> from pybrain.tools.shortcuts import buildNetwork
>>> net = buildNetwork(2, 4, 1, recurrent=True)
>>> net.activate((1, 1))
...
array([ 0.02202066])
>>> net.copy()
>>> net.activate((1, 1))
...
IndexError: index out of bounds

This seem to be only when recurrent=True.

I have an issue with the sortModules method throwing a KeyError.

Following the tutorial example, I created a script with the following:

#! /usr/bin/env python
# -*- coding: utf-8 -*-

import sys
import scipy
import numpy as np

print "\nPython version: %s" % sys.version
print "Numpy version: %s" % np.version.version
print "Scipy version: %s" % scipy.version.version

from pybrain.structure import FeedForwardNetwork
from pybrain.structure import LinearLayer, SigmoidLayer
from pybrain.structure import FullConnection

# Create network
nn = FeedForwardNetwork()

# Set network parameters
INPUT_NDS = 2
HIDDEN_NDS = 3
OUTPUT_NDS = 1

# Create Feed Forward Network layers
inLayer = LinearLayer(INPUT_NDS)
hiddenLayer = SigmoidLayer(HIDDEN_NDS)
outLayer = LinearLayer(OUTPUT_NDS)

# Fully connect all layers
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)

# Add the connected layers to the network 
nn.addConnection(in_to_hidden)
nn.addConnection(hidden_to_out)

# Sort modules to prepare the NN for use
nn.sortModules()

Which gives me: Python version: 2.6.5 (r265:79063, Apr 16 2010, 13:57:41) [GCC 4.4.3] Numpy version: 1.3.0 Scipy version: 0.7.0 Traceback (most recent call last): File "/tmp/py7317Q6c", line 46, in nn.sortModules() File "/usr/local/lib/python2.6/dist-packages/PyBrain-0.3-py2.6.egg/pybrain/structure/networks/network.py", line 224, in sortModules self._topologicalSort() File "/usr/local/lib/python2.6/dist-packages/PyBrain-0.3-py2.6.egg/pybrain/structure/networks/network.py", line 188, in _topologicalSort graph[c.inmod].append(c.outmod) KeyError: <LinearLayer 'LinearLayer-3'>

I have the latest version of pybrain installed, so this seems strange. Especially as I when use the shortcut:

from pybrain.tools.shortcuts import buildNetwork

This is a duplicate of my Stackoverflow.com question.

I fail to properly serialize/deserialize PyBrain networks using either pickle or cPickle.

See the following example:

from pybrain.datasets            import SupervisedDataSet
from pybrain.tools.shortcuts     import buildNetwork
from pybrain.supervised.trainers import BackpropTrainer
import cPickle as pickle
import numpy as np 

#generate some data
np.random.seed(93939393)
data = SupervisedDataSet(2, 1)
for x in xrange(10):
    y = x * 3
    z = x + y + 0.2 * np.random.randn()  
    data.addSample((x, y), (z,))

#build a network and train it    

net1 = buildNetwork( data.indim, 2, data.outdim )
trainer1 = BackpropTrainer(net1, dataset=data, verbose=True)
for i in xrange(4):
    trainer1.trainEpochs(1)
    print '\tvalue after %d epochs: %.2f'%(i, net1.activate((1, 4))[0])

This is the output of the above code:

Total error: 201.501998476
    value after 0 epochs: 2.79
Total error: 152.487616382
    value after 1 epochs: 5.44
Total error: 120.48092561
    value after 2 epochs: 7.56
Total error: 97.9884043452
    value after 3 epochs: 8.41

As you can see, network total error decreases as the training progresses. You can also see that the predicted value approaches the expected value of 12.

Now we will do a similar exercise, but will include serialization/deserialization:

print 'creating net2'
net2 = buildNetwork(data.indim, 2, data.outdim)
trainer2 = BackpropTrainer(net2, dataset=data, verbose=True)
trainer2.trainEpochs(1)
print '\tvalue after %d epochs: %.2f'%(1, net2.activate((1, 4))[0])

#So far, so good. Let's test pickle
pickle.dump(net2, open('testNetwork.dump', 'w'))
net2 = pickle.load(open('testNetwork.dump'))
trainer2 = BackpropTrainer(net2, dataset=data, verbose=True)
print 'loaded net2 using pickle, continue training'
for i in xrange(1, 4):
        trainer2.trainEpochs(1)
        print '\tvalue after %d epochs: %.2f'%(i, net2.activate((1, 4))[0])

This is the output of this block:

creating net2
Total error: 176.339378639
    value after 1 epochs: 5.45
loaded net2 using pickle, continue training
Total error: 123.392181859
    value after 1 epochs: 5.45
Total error: 94.2867637623
    value after 2 epochs: 5.45
Total error: 78.076711114
    value after 3 epochs: 5.45

As you can see, it seems that the training has some effect on the network (the reported total error value continues to decrease), however the output value of the network freezes on a value that was relevant for the first training iteration.

Is there any caching mechanism that I need to be aware of that causes this erroneous behaviour? Are there better ways to serialize/deserialize pybrain networks?

Relevant version numbers:

• Python 2.6.5 (r265:79096, Mar 19 2010, 21:48:26) [MSC v.1500 32 bit (Intel)]
• Numpy 1.5.1
• cPickle 1.71
• pybrain 0.3

Although it technically fits there, it is a bit confusing. I think the split should be along the difference of ontogenetic/phylogenetic with on one side optimization, evolution, pso, etc. (coevolution methods should fit here, but how about multi-objective optimization?) and on the other side policy gradients, and other RL algos.

When we call splitWithProportion on a ClassificationDataSet object, return type is (SupervisedDataSet, SupervisedDataSet) instead of (ClassificationDataSet, ClassificationDataSet). While this modification fixes this issue, it can be improved by calling the constructor using kwargs. Didn't modify sub-classes in order to prevent repetition of lines 106-112. I've done this modification because when we split a sub-class of SupervisedDataSet, we should get a 2-tuple of sub-class object. Not a 2-tuple of SupervisedDataSet.

I have a dataset which I am clustering using a gaussian mixture model, and then I want to train a neural network for each of the clusters. I want to use all the points in my dataset weighted based on the probability they are in the cluster for which the net is being trained.

Originally, I was not weighting the training data and it worked fine:

'''
Create and train a neural net on the training data, given the actual labels
'''
def create_neural_net(training, labels, weights=None, T=10, silent=False):
    input_units = len(training[0])
    output_units = len(labels[0])
    n = len(training)

    net = FeedForwardNetwork()
    layer_in = SoftmaxLayer(input_units)
    layer_hidden = SigmoidLayer(1000)
    layer_hidden2 = SigmoidLayer(50)
    layer_out = LinearLayer(output_units)

    net.addInputModule(layer_in)
    net.addModule(layer_hidden)
    net.addModule(layer_hidden2)
    net.addOutputModule(layer_out)

    net.addConnection(FullConnection(layer_in, layer_hidden))
    net.addConnection(FullConnection(layer_hidden, layer_hidden2))
    net.addConnection(FullConnection(layer_hidden2, layer_out))

    net.sortModules()

    training_data = SupervisedDataSet(input_units, output_units)
    for i in xrange(n):
        # print len(training[i]) # prints 148
        # print len(labels[i]) # prints 13
        training_data.appendLinked(training[i], labels[i])
    trainer = BackpropTrainer(net, training_data)

    for i in xrange(T):
        if not silent: print "Training %d" % (i + 1)
        error = trainer.train()
        if not silent: print net.activate(training[0]), labels[0]
        if not silent: print "Training iteration %d.  Error: %f." % (i + 1, error)
    return net

But now when I try to weight the data points:

'''
Create and train a neural net on the training data, given the actual labels
'''
def create_neural_net(training, labels, weights=None, T=10, silent=False):
    input_units = len(training[0])
    output_units = len(labels[0])
    n = len(training)

    net = FeedForwardNetwork()
    layer_in = SoftmaxLayer(input_units)
    layer_hidden = SigmoidLayer(1000)
    layer_hidden2 = SigmoidLayer(50)
    layer_out = LinearLayer(output_units)

    net.addInputModule(layer_in)
    net.addModule(layer_hidden)
    net.addModule(layer_hidden2)
    net.addOutputModule(layer_out)

    net.addConnection(FullConnection(layer_in, layer_hidden))
    net.addConnection(FullConnection(layer_hidden, layer_hidden2))
    net.addConnection(FullConnection(layer_hidden2, layer_out))

    net.sortModules()

    training_data = ImportanceDataSet(input_units, output_units)
    for i in xrange(n):
        # print len(training[i]) # prints 148
        # print len(labels[i]) # prints 13
        training_data.addSample(training[i], labels[i], importance=(weights[i] if weights is not None else None))
    trainer = BackpropTrainer(net, training_data)

    for i in xrange(T):
        if not silent: print "Training %d" % (i + 1)
        error = trainer.train()
        if not silent: print net.activate(training[0]), labels[0]
        if not silent: print "Training iteration %d.  Error: %f." % (i + 1, error)
    return net

I get the following error:

Traceback (most recent call last):
  File "clustering_experiment.py", line 281, in <module>
    total_model = get_model(training, training_labels, num_clusters=NUM_CLUSTERS
, T=NUM_ITERS_NEURAL_NET)
  File "clustering_experiment.py", line 177, in get_model
    neural_nets.append(neural_net_plugin.create_neural_net(tra.tolist(), val.tol
ist(), T=T, silent=True))
  File "/home/neural_net_plugin.py", line 43, in create_neural_net
    error = trainer.train()
  File "/usr/local/lib/python2.7/dist-packages/PyBrain-0.3.1-py2.7.egg/pybrain/s
upervised/trainers/backprop.py", line 61, in train
    e, p = self._calcDerivs(seq)
  File "/usr/local/lib/python2.7/dist-packages/PyBrain-0.3.1-py2.7.egg/pybrain/s
upervised/trainers/backprop.py", line 92, in _calcDerivs
    outerr = target - self.module.outputbuffer[offset]
IndexError: index 162 is out of bounds for axis 0 with size 1

Changes

All changes I've done were backported from Python3 to Python2 (at least until Python2.7).

• Capturing the currently raised exception (it doesn't work in Python 2.5 and earlier)
• map function returns an iterator
• Tuple parameters removed
• import exceptions removed as now it's a built-in module
• from itertools import izip removed, now it uses just zip

TODO

• I didn't change the files containing weave library. In fact, I really don't know if this library is already supported in latest scipy versions. I couldn't find any recent references to that. Just found "old" news saying that it is not supported yet such as this and this. Maybe it's time to consider using Cython instead.
• RL-Glue imports are also unchanged because its current Python codec have no support for Py3 yet. However, I changed Python codec source for RL-Glue to run in Py2 and Py3 (in fact, I just changed minor things such as print function and exception statements). By the way, if you guys want to try it, I've uploaded on my Github. Another thing to point out is that no one is maintaining RL-Glue code anymore.

I didn't do any tests and I just tried to run the examples in the Pybrain docs and everything worked fine.

I have written part of the RL bicycle problem introduced by Randlov and Alstrom as an example in PyBrain. Hopefully you all would like to include it in PyBrain!

Here's their paper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52.3038&rep=rep1&type=pdf

I include some plotting, so you can view the learning.

Please let me know what improvements I should make.

I am using scipy 1.9.1 and I get the traceback below when using the buildNetwork function.

Traceback (most recent call last): File "/home/nono/Desktop/tmp/neural/./main.py", line 3, in <module> from pybrain.tools.shortcuts import buildNetwork File "/usr/local/lib/python3.10/dist-packages/PyBrain-0.3.3-py3.10.egg/pybrain/__init__.py", line 1, in <module> from pybrain.structure.__init__ import * File "/usr/local/lib/python3.10/dist-packages/PyBrain-0.3.3-py3.10.egg/pybrain/structure/__init__.py", line 2, in <module> from pybrain.structure.modules.__init__ import * File "/usr/local/lib/python3.10/dist-packages/PyBrain-0.3.3-py3.10.egg/pybrain/structure/modules/__init__.py", line 3, in <module> from pybrain.structure.modules.gaussianlayer import GaussianLayer File "/usr/local/lib/python3.10/dist-packages/PyBrain-0.3.3-py3.10.egg/pybrain/structure/modules/gaussianlayer.py", line 3, in <module> from scipy import random ImportError: cannot import name 'random' from 'scipy' (/usr/local/lib/python3.10/dist-packages/scipy-1.9.1-py3.10-linux-x86_64.egg/scipy/__init__.py) Looks like an old reference where things have changed in scipy and not updated in pybrain probably.

Is pybrain still maintained? The last release is from 2015.

this error is being presented when I use this pybrain library

this is my code: from pybrain.structure import FeedForwardNetwork from pybrain.structure import LinearLayer, SigmoidLayer, BiasUnit from pybrain.structure import FullConnection

rneural = FeedForwardNetwork()

CE = LinearLayer(4) CO = SigmoidLayer(6) CS = SigmoidLayer(1) b1 = BiasUnit() b2 = BiasUnit()

rneural.addModule(CE) rneural.addModule(CO) rneural.addModule(CS) rneural.addModule(b1) rneural.addModule(b2)

EO = FullConnection(CE, CO) OS = FullConnection(CO, CS) bO = FullConnection(b1, CO) bS = FullConnection(b2, CS)

rneural.sortModule() print(rneural)

when I run:

python3 rneural.py Traceback (most recent call last): File "/home/warwick/Desktop/scriptsinpython/ai/rneural.py", line 1, in from pybrain.structure import FeedForwardNetwork File "/home/warwick/environments/my_env/lib/python3.10/site-packages/pybrain/init.py", line 1, in from pybrain.structure.init import * File "/home/warwick/environments/my_env/lib/python3.10/site-packages/pybrain/structure/init.py", line 2, in from pybrain.structure.modules.init import * File "/home/warwick/environments/my_env/lib/python3.10/site-packages/pybrain/structure/modules/init.py", line 2, in from pybrain.structure.modules.gate import GateLayer, DoubleGateLayer, MultiplicationLayer, SwitchLayer File "/home/warwick/environments/my_env/lib/python3.10/site-packages/pybrain/structure/modules/gate.py", line 10, in from pybrain.tools.functions import sigmoid, sigmoidPrime File "/home/warwick/environments/my_env/lib/python3.10/site-packages/pybrain/tools/functions.py", line 4, in from scipy.linalg import inv, det, svd, logm, expm2 ImportError: cannot import name 'expm2' from 'scipy.linalg' (/home/warwick/environments/my_env/lib/python3.10/site-packages/scipy/linalg/init.py)

I've tried several solutions but the only one I haven't tried is to downgrade python3.10, I think it's not the most correct solution if anyone knows how to fix this

thanks

There are small typos in:

• pybrain/rl/environments/flexcube/viewer.py
• pybrain/rl/environments/ode/tasks/ccrl.py
• pybrain/rl/environments/ode/tasks/johnnie.py
• pybrain/rl/environments/shipsteer/viewer.py
• pybrain/structure/modules/lstm.py
• pybrain/tests/runtests.py
• pybrain/tools/rlgluebridge.py

Fixes:

• Should read suggested rather than suggestet.
• Should read specific rather than spezific.
• Should read height rather than hight.
• Should read whether rather than wether.
• Should read method rather than methode.

Semi-automated pull request generated by https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

I'm working on speech recognition using raspberry pi while I was running the code of the build model using pybrain features I got the error:'SupervisedDataSet' object has no attribute '_convertToOneOfMany' ? If anyone has any pointers to get me back on the right path and that would be very much appreciated. ` def createRGBdataSet(inputSet, numOfSamples, numOfPoints): alldata = ClassificationDataSet(numOfPoints, 1, nb_classes=3) # Iter through all 3 groups and add the samples with appropriate class label for i in range(0, 3numOfSamples): input = inputSet[i] if (i < numOfSamples): alldata.addSample(input, [0]) elif (i >= numOfSamples and i < numOfSamples2): alldata.addSample(input, [1]) else: alldata.addSample(input, [2]) return alldata

Split the dataset into 75% training and 25% test data.

def splitData(alldata): tstdata, trndata = alldata.splitWithProportion( 0.25 ) trndata._convertToOneOfMany() tstdata._convertToOneOfMany() return trndata, tstdata `

I'm working on speech recognition using raspberry pi while I was running the code of the build model using pybrain features I got the error:'SupervisedDataSet' object has no attribute '_convertToOneOfMany' ? If anyone has any pointers to get me back on the right path and that would be very much appreciated.

def createRGBdataSet(inputSet, numOfSamples, numOfPoints):
    alldata = ClassificationDataSet(numOfPoints, 1, nb_classes=3)
    # Iter through all 3 groups and add the samples with appropriate class label
    for i in range(0, 3*numOfSamples):
        input = inputSet[i]
        if (i < numOfSamples):
            alldata.addSample(input, [0])
        elif (i >= numOfSamples and i < numOfSamples*2):
            alldata.addSample(input, [1])
        else:
            alldata.addSample(input, [2])
    return alldata


# Split the dataset into 75% training and 25% test data.
def splitData(alldata):
    tstdata, trndata = alldata.splitWithProportion( 0.25 )
    trndata._convertToOneOfMany()
    tstdata._convertToOneOfMany()
    return trndata, tstdata