def visualize(params, folder):
#Check matching files
list_files = glob.glob(folder+"/*_model*.dat");
print list_files;
print "No. of files:", len(list_files);
### LOAD All the Models ###
for model_file in list_files:
model = pickle.load(open(model_file,'rb'));
print ">Reading model :",model_file;
print ">creating model...";
print model;
ndmmodel = ndmModel.ndmModel(params['numI'], params['numH'], params['numO'], model['best_sol']);
print ">model created!", type(ndmmodel);
#visualise the model
print ">visualizing model";
app = QtGui.QApplication(sys.argv);
QtCore.qsrand(QtCore.QTime(0,0,0).secsTo(QtCore.QTime.currentTime()));
widget2 = GraphWidget(ndmmodel,'Test Set');
widget2.show();
def evaluate(self, components):
"""
Evaluates the specie's
"""
model = ndmModel.ndmModel(self.params['numInputNodes'],
self.params['numHiddenNodes'],
self.params['numOutputNodes'],
components);
f = model.evaluate(self.train_set);
return f;
#TODOLIST #TODO: (Tommorow) Test build model from signature #select componentsesentatives components = dict(); components['hidden_nodes'] = hidden_nodes; components['out_nodes'] = out_nodes; #select random connections and weights components['model'] = model; components['connActive_IH'] = connActiveIH; components['connActive_HH'] = connActiveHH; components['connActive_HO'] = connActiveHO; components['connWeights_IH'] = weightsIH; components['connWeights_HH'] = weightsHH; components['connWeights_HO'] = weightsHO; ndmmodel = ndmModel.ndmModel(params['numI'], params['numH'], params['numO'], components); print "inputs:", datasets.XOR['IN'][0]; o = ndmmodel.stimulate(datasets.XOR['IN'][0]); o2 = ndmmodel.stimulate(datasets.XOR['IN'][1]); print "net out:", o; print "out:", datasets.XOR['OUT'][0]; print "err:", ndmmodel.evaluate(datasets.XOR);
