made progress in binaryclassifier rewrite, restructured file tree

3 files changed, 0 insertions, 166 deletions

diff --git a/pybrain_experiments/classification_test.py b/pybrain_experiments/classification_test.py
deleted file mode 100644
index ac5f272..0000000
--- a/pybrain_experiments/classification_test.py
+++ /dev/null
@@ -1,112 +0,0 @@
-from pybrain.datasets            import ClassificationDataSet
-from pybrain.utilities           import percentError
-from pybrain.tools.shortcuts     import buildNetwork
-from pybrain.supervised.trainers import BackpropTrainer
-from pybrain.structure.modules   import SoftmaxLayer
-
-from pylab import ion, ioff, figure, draw, contourf, clf, show, hold, plot
-from scipy import diag, arange, meshgrid, where
-from numpy.random import multivariate_normal
-
-
-# To have a nice dataset for visualization, we produce a set of points in
-# 2D belonging to three different classes. You could also read in your data
-# from a file, e.g. using pylab.load().
-
-means = [(-1,0),(2,4),(3,1)]
-cov = [diag([1,1]), diag([0.5,1.2]), diag([1.5,0.7])]
-alldata = ClassificationDataSet(2, 1, nb_classes=3)
-for n in xrange(400):
-    for klass in range(3):
-        input = multivariate_normal(means[klass],cov[klass])
-        alldata.addSample(input, [klass])
-
-
-# Randomly split the dataset into 75% training and 25% test data sets.
-# Of course, we could also have created two different datasets to begin with.
-
-tstdata, trndata = alldata.splitWithProportion( 0.25 )
-
-
-# For neural network classification, it is highly advisable to encode
-# classes with one output neuron per class. Note that this operation duplicates
-# the original targets and stores them in an (integer) field named ‘class’.
-trndata._convertToOneOfMany( )
-tstdata._convertToOneOfMany( )
-
-
-print "Number of training patterns: ", len(trndata)
-print "Input and output dimensions: ", trndata.indim, trndata.outdim
-print "First sample (input, target, class):"
-print trndata['input'][0], trndata['target'][0], trndata['class'][0]
-
-
-
-
-
-# Now build a feed-forward network with 5 hidden units. We use the shortcut
-# buildNetwork() for this. The input and output layer size must match the
-# dataset’s input and target dimension. You could add additional hidden
-# layers by inserting more numbers giving the desired layer sizes.
-#
-# The output layer uses a softmax function because we are doing classification.
-# There are more options to explore here, e.g. try changing the hidden layer
-# transfer function to linear instead of (the default) sigmoid.
-#
-# See also Description buildNetwork() for more info on options, and the Network
-# tutorial Building Networks with Modules and Connections for info on how to
-# build your own non-standard networks.
-fnn = buildNetwork( trndata.indim, 5, trndata.outdim, outclass=SoftmaxLayer )
-
-
-# Set up a trainer that basically takes the network and training dataset
-# as input. For a list of trainers, see trainers. We are using a
-# BackpropTrainer for this.
-
-trainer = BackpropTrainer( fnn, dataset=trndata, momentum=0.1,
-                           verbose=True, weightdecay=0.01)
-
-
-# Now generate a square grid of data points and put it into a dataset,
-# which we can then classify to obtain a nice contour field for visualization.
-# Therefore the target values for this data set can be ignored.
-
-ticks = arange(-3.,6.,0.2)
-X, Y = meshgrid(ticks, ticks)
-# need column vectors in dataset, not arrays
-griddata = ClassificationDataSet(2,1, nb_classes=3)
-for i in xrange(X.size):
-    griddata.addSample([X.ravel()[i],Y.ravel()[i]], [0])
-griddata._convertToOneOfMany() # this is still needed to make the fnn feel comfy
-
-
-for i in range(20):
-# Train the network for some epochs. Usually you would
-# set something like 5 here, but for visualization purposes we
-# do this one epoch at a time.
-    trainer.trainEpochs( 1 )
-    trnresult = percentError( trainer.testOnClassData(),
-                              trndata['class'] )
-    tstresult = percentError( trainer.testOnClassData(
-           dataset=tstdata ), tstdata['class'] )
-
-    print "epoch: %4d" % trainer.totalepochs, \
-          "  train error: %5.2f%%" % trnresult, \
-          "  test error: %5.2f%%" % tstresult
-    out = fnn.activateOnDataset(griddata)
-    out = out.argmax(axis=1)  # the highest output activation gives the class
-    out = out.reshape(X.shape)
-    figure(1)
-    ioff()  # interactive graphics off
-    clf()   # clear the plot
-    hold(True) # overplot on
-    for c in [0,1,2]:
-        here, _ = where(tstdata['class']==c)
-        plot(tstdata['input'][here,0],tstdata['input'][here,1],'o')
-    if out.max()!=out.min():  # safety check against flat field
-        contourf(X, Y, out)   # plot the contour
-    ion()   # interactive graphics on
-    draw()  # update the plot
-
-ioff()
-show()
diff --git a/pybrain_experiments/test.py b/pybrain_experiments/test.py
deleted file mode 100644
index f7b0a01..0000000
--- a/pybrain_experiments/test.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from pybrain.structure import FeedForwardNetwork
-from pybrain.structure import LinearLayer, SigmoidLayer
-from pybrain.structure import FullConnection
-n = FeedForwardNetwork()
-
-inLayer = LinearLayer(2)
-hiddenLayer = SigmoidLayer(3)
-outLayer = LinearLayer(1)
-
-n.addInputModule(inLayer)
-n.addModule(hiddenLayer)
-n.addOutputModule(outLayer)
-
-in_to_hidden = FullConnection(inLayer, hiddenLayer)
-hidden_to_out = FullConnection(hiddenLayer, outLayer)
-
-
-n.addConnection(in_to_hidden)
-n.addConnection(hidden_to_out)
-
-
-# everything is wired together now
-# this makes it usable
-
-n.sortModules()
-
-
-if __name__ == "__main__":
-    #Again, this might look different on your machine -
-    #the weights of the connections have already been initialized randomly.
-    print n.activate([1, 2])
-    #look at the hidden weights
-    print in_to_hidden.params
-    print hidden_to_out.params
-    print n.params #weights here too
diff --git a/pybrain_experiments/test_recurrent.py b/pybrain_experiments/test_recurrent.py
deleted file mode 100644
index 692898a..0000000
--- a/pybrain_experiments/test_recurrent.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from pybrain.structure import RecurrentNetwork
-n = RecurrentNetwork()
-
-n.addInputModule(LinearLayer(2, name='in'))
-n.addModule(SigmoidLayer(3, name='hidden'))
-n.addOutputModule(LinearLayer(1, name='out'))
-n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
-n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
-n.addRecurrentConnection(FullConnection(n['hidden'], n['hidden'], name='c3'))
-
-
-n.sortModules()
-n.activate((2, 2))
-array([-0.1959887])
-n.activate((2, 2))
-array([-0.19623716])
-n.activate((2, 2))
-array([-0.19675801])
-n.reset() #clears history