def test_system4a():
print "Initializing"
from a_machine.system4 import model
from santa_fe import getData
data = getData('B1.dat')
test = getData('B2.dat')
median = np.median(data[:,:2], axis=0)
std = np.std(data[:,:2], axis=0)
train_size = 1000
sequence_length = 1
gamma_quantile = 100
test_size = 200
train = (( data[:train_size,:2] - median ) / std).astype('float32')
labels = (( data[sequence_length:train_size+sequence_length,0] - median[0] ) / std[0]).astype('float32')
#svm = SVR( nu=.1, C=50)
m1 = model( dimension=0, sequence_length=sequence_length )
m2 = model( dimension=0, sequence_length=sequence_length )
#svm.train( kernel_matrix(np.expand_dims(train,1), np.expand_dims(train,1), .5), labels)
m1.train(train[:,:2], train_size, [ [0,[0,1]], [0,[0]] ])
m2.train(train[:,:2], train_size, [ [0,[0,1]], ])
xN = 20
yN = 20
xrange = [train[:,0].min(), train[:,0].max()]
yrange = [train[:,1].min(), train[:,1].max()]
xstep = ((xrange[1]-xrange[0] ) / xN )
ystep = ((yrange[1]-yrange[0] ) / yN )
X = np.dstack(np.mgrid[xrange[0]:xrange[1]:xstep,yrange[0]:yrange[1]:ystep]).reshape([ xN *yN,2]).astype('float32')
x = np.arange(xrange[0],xrange[1],xstep)
y = np.arange(yrange[0],yrange[1],ystep)
#Z = svm.predict( kernel_matrix( np.expand_dims(X,1), np.expand_dims(train,1), .5) )
Z1 = m1.predict( np.expand_dims(X,1) )
Z2 = m2.predict( np.expand_dims(X,1) )
#ax = plt.subplot(111, projection='3d')
#ax.plot( x[:,0], x[:,1], y, 'k,' )
#plt.plot( train[:,0], labels, 'k,', alpha=.2 )
#plt.plot( train[svm.support_,0], labels[svm.support_], 'o', alpha=.15 )
#plt.plot(x[:,0],y, 'r', lw=2)
#plt.show()
mlab.points3d(train[:,0], train[:,1], labels, scale_factor=.05, opacity=.2)
#mlab.points3d(train[m.svm.SV_indices,0], train[m.svm.SV_indices,1], labels[m.svm.SV_indices], scale_factor=.05)
mlab.surf( x,y,Z1.reshape(xN,yN), color=(1,0,0) ) #red, multiple slices
mlab.surf( x,y,Z2.reshape(xN,yN)+.1, color=(0,1,0) ) #green, single slice
print m1.svm.SV_indices
print m2.svm.SV_indices
print "%s SV of %s" % (len(m1.svm.SV_indices), train.shape[0])
print "%s SV of %s" % (len(m2.svm.SV_indices), train.shape[0])
mlab.show()
def test_system5():
from a_machine.system5 import model
from santa_fe import getData
data = getData('B1.dat')
test = getData('B2.dat')
median = np.median(data[:,:3], axis=0)
std = np.std(data[:,:3], axis=0)
train_size = 1000
sequence_length = 1
gamma_quantile = 100
test_size = 200
train = (( data[:train_size] - median ) / std).astype('float32')
m = model( dimension=0 )
m.train(train)
xN = test_size
#yN = 20
xrange = [train.min(), train.max()]
#yrange = [train[:-1,1].min(), train[:-1,1].max()]
xstep = ((xrange[1]-xrange[0] ) / xN )
#ystep = ((yrange[1]-yrange[0] ) / yN )
#X = np.dstack(np.mgrid[xrange[0]:xrange[1]:xstep,yrange[0]:yrange[1]:ystep]).reshape([ xN *yN,2]).astype('float32')
x = np.arange(xrange[0],xrange[1],xstep)
#y = np.arange(yrange[0],yrange[1],ystep)
Y = m.predict( X )
plt.scatter(train[:-1], train[1:] )
plt.plot( x, Y, 'k', lw=2 )
plt.show()
def run():
from a_machine.system7 import model
from santa_fe import getData
data = getData('B1.dat')[:,1]
test = getData('B2.dat')
median = np.median(data)
std = np.std(data)
train_size = 100
sequence_length = 1
gamma_quantile = 100
test_size = 201
train = (( data[:train_size] - median ) / std).astype('float32')
#train = data[:train_size].astype('float32')
m = model( dimension=0 )
m.train(train)
xN = test_size
#yN = 20
xrange = [train.min(), train.max()]
#yrange = [train[:-1,1].min(), train[:-1,1].max()]
xstep = ((xrange[1]-xrange[0] ) / xN )
#ystep = ((yrange[1]-yrange[0] ) / yN )
#X = np.dstack(np.mgrid[xrange[0]:xrange[1]:xstep,yrange[0]:yrange[1]:ystep]).reshape([ xN *yN,2]).astype('float32')
X = np.arange(xrange[0],xrange[1],xstep)
#y = np.arange(yrange[0],yrange[1],ystep)
print X.shape
print xN
Y = m.predict( X.reshape(xN,1,1) )
plt.plot(train[:-1], train[1:], 'k,')
plt.scatter( m.SVx, m.SVy, train_size*10*(m.beta**2), alpha=.15 )
plt.plot(X, Y, 'r', lw=2)
#plt.axhline(y=0)
plt.show()