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()