Python nrmse Exemples

Exemple #1

Fichier : conceptor_test.py Projet : ChenglongChen/conceptors

p1=np.sin(2*np.pi*p1/np.sqrt(75));
p2=np.asarray(xrange(2000));
#p2=0.5*np.sin(2*np.pi*p2/np.sqrt(20))+np.sin(2*np.pi*p2/np.sqrt(40));
p2=np.sin(2*np.pi*p2/np.sqrt(40));
ps=np.vstack((p1[None], p2[None]));

p=[];
p.append(ps);
#p.append(p1[None]);
#p.append(p2[None]);

# training
net.train(p);

# test readout
print "NRMSE readout: %f" % (util.nrmse(net.W_out.dot(net.all_train_args), net.all_train_outs));
print "mean NEMSE W: %f" % util.nrmse(net.W.dot(net.all_train_old_args), net.W_targets);

y=net.W_out.dot(net.all_train_args)
print y.shape
print net.all_train_outs.shape

pplot.figure(1);
pplot.plot(xrange(1000), p[0][0,500:1500]);
pplot.plot(xrange(1000), y[0,0:1000]);
pplot.title("Redout")
pplot.show();

# test conceptors

parameter_nl=0.1;

Exemple #2

# Prepare testing data
p1 = np.asarray(xrange(2000))
p1 = np.sin(2 * np.pi * p1 / np.sqrt(75))
p2 = np.asarray(xrange(2000))
#p2=0.5*np.sin(2*np.pi*p2/np.sqrt(20))+np.sin(2*np.pi*p2/np.sqrt(40));
p2 = np.sin(2 * np.pi * p2 / np.sqrt(40))
ps = np.vstack((p1[None], p2[None]))

p = []
#p.append(ps);
p.append(p1[None])
p.append(p2[None])

net.load(p, load_mode="complete")

print util.nrmse(net.all_train_dt_args, net.D.dot(net.all_train_old_args))

c1, x1 = net.cue_conceptor(p1[None])
c2, x2 = net.cue_conceptor(p2[None])

#c1=net.recall_conceptor(c1, x1);
#c2=net.recall_conceptor(c2, x2);

print "Autoconceptors are trained"

measure_washout = 50
measure_rl = 500
x = x2
x_before = x

y1 = np.zeros((measure_rl, 1))

Exemple #3

Fichier : conceptor_test.py Projet : csajedi/conceptors

p1=np.sin(2*np.pi*p1/np.sqrt(75));
p2=np.asarray(xrange(2000));
#p2=0.5*np.sin(2*np.pi*p2/np.sqrt(20))+np.sin(2*np.pi*p2/np.sqrt(40));
p2=np.sin(2*np.pi*p2/np.sqrt(40));
ps=np.vstack((p1[None], p2[None]));

p=[];
p.append(ps);
#p.append(p1[None]);
#p.append(p2[None]);

# training
net.train(p);

# test readout
print "NRMSE readout: %f" % (util.nrmse(net.W_out.dot(net.all_train_args), net.all_train_outs));
print "mean NEMSE W: %f" % util.nrmse(net.W.dot(net.all_train_old_args), net.W_targets);

y=net.W_out.dot(net.all_train_args)
print y.shape
print net.all_train_outs.shape

pplot.figure(1);
pplot.plot(xrange(1000), p[0][0,500:1500]);
pplot.plot(xrange(1000), y[0,0:1000]);
pplot.title("Redout")
pplot.show();

# test conceptors

parameter_nl=0.1;

Exemple #4

Fichier : autoconceptor_test.py Projet : ChenglongChen/conceptors

# Prepare testing data
p1=np.asarray(xrange(2000));
p1=np.sin(2*np.pi*p1/np.sqrt(75));
p2=np.asarray(xrange(2000));
#p2=0.5*np.sin(2*np.pi*p2/np.sqrt(20))+np.sin(2*np.pi*p2/np.sqrt(40));
p2=np.sin(2*np.pi*p2/np.sqrt(40));
ps=np.vstack((p1[None], p2[None]));

p=[];
#p.append(ps);
p.append(p1[None]);
p.append(p2[None]);

net.load(p, load_mode="complete");

print util.nrmse(net.all_train_dt_args, net.D.dot(net.all_train_old_args))

c1, x1=net.cue_conceptor(p1[None]);
c2, x2=net.cue_conceptor(p2[None]);

#c1=net.recall_conceptor(c1, x1);
#c2=net.recall_conceptor(c2, x2);

print "Autoconceptors are trained"

measure_washout=50;
measure_rl=500;
x=x2;
x_before=x;

y1=np.zeros((measure_rl,1));