def test_system3():
print "Initializing"
train_size = 500
sequence_length = 2
gamma_quantile = 50
test_size = 500
import a_machine.system3 as system
print "Importing & Normalizing Data"
from santa_fe import getData
data = getData('B1.dat')
test = getData('B2.dat')
median = np.median(data, axis=0)
std = np.std(data, axis=0)
# normalizing to median 0, std deviation 1
data = ( data - median ) / std
print "Initializing Models"
model = system.model(gamma_samples=1000, gamma_quantile=gamma_quantile, sequence_length=sequence_length)
model.train( data, train_size )
print "Generating Predictions"
# [test_point][dimension]
#normed_test = (test[:test_size,:] - median) / std
normed_test = data[:test_size]
predictions, risks = model.predict(normed_test)
hybrid = ( predictions * risks ).sum(1) / risks.sum(1)
# denormalize
predictions = ( std.reshape(1,1,3) * predictions ) + median.reshape(1,1,3)
hybrid = ( std.reshape(1,3) * hybrid ) + median.reshape(1,3)
print "Results!"
errors = np.abs( np.expand_dims( test[sequence_length : test_size], 1) - predictions )
hybrid_error = np.abs( test[sequence_length : test_size] - hybrid )
print hybrid.shape
print hybrid_error.shape
print ( hybrid_error.sum(0) / test_size )
print ( errors.sum(0) / test_size )
print std.astype('int')
x = np.arange(test_size-sequence_length)
for i in range(data.shape[1]):
fig = plt.subplot(data.shape[1],1,i+1)
fig.plot(x, test[sequence_length : test_size,i], 'k--')
for j in range(predictions.shape[1]):
fig.plot(x, predictions[:,j,i] )
fig.plot(x, hybrid[:,i], 'r', lw=2)
plt.show()
def test_system4():
print "Initializing"
train_size = 1000
sequence_length = 1
gamma_quantile = 100
test_size = 200
import a_machine.system4 as system
print "Importing & Normalizing Data"
from santa_fe import getData
data = getData('B1.dat')
test = getData('B2.dat')
median = np.median(data, axis=0)
std = np.std(data, axis=0)
# normalizing to median 0, std deviation 1
normed_data = ( data - median ) / std
print "Initializing Models"
model = system.model(dimension=0, gamma_samples=1000, gamma_quantile=gamma_quantile, sequence_length=sequence_length)
model.train( normed_data, train_size )
print "Generating Predictions"
# [test_point][dimension]
test = test[:test_size+sequence_length,:]
#test = data[:test_size+sequence_length,:]
normed_test = (test - median) / std
predictions = model.predict(normed_test)
# denormalize
#predictions = ( std[0] * predictions ) + median[0]
errors = np.abs( normed_test[sequence_length : predictions.shape[0]+sequence_length, 0] - predictions )
print "Results! Loss/point: %s (in normed space)" % ( errors.sum(0) / test_size )
x = np.arange( predictions.shape[0] )
x_pred = np.dstack( [
np.arange(model.sequences[:,0,0].min(), model.sequences[:,0,0].max(), ( model.sequences[:,0,0].max() - model.sequences[:,0,0].min() ) / 100 ),
np.arange(model.sequences[:,0,1].min(), model.sequences[:,0,1].max(), ( model.sequences[:,0,1].max() - model.sequences[:,0,1].min() ) / 100 ),
np.arange(model.sequences[:,0,2].min(), model.sequences[:,0,2].max(), ( model.sequences[:,0,2].max() - model.sequences[:,0,2].min() ) / 100 ),
]).astype('float32').reshape(100,1,3)
y_pred = model.svm.predict( kernel_matrix(x_pred, model.sequences, model.gammas[-1] ) )
print x_pred[0]
print x_pred[1]
pr = plt.subplot(2,2,1)
pr.plot(x,normed_test[sequence_length : predictions.shape[0]+sequence_length, 0], 'k', alpha=.4)
#for i in range(predictions.shape[1]):
# for j in range(predictions.shape[2]):
# plt.plot(x,predictions[:,i,j])
pr.plot(x,predictions)
reg0 = plt.subplot(2,2,2)
reg0.plot(model.sequences[:,0,0], model.labels, 'k,', alpha=.5)
reg0.plot(model.sequences[model.svm.SV_indices,0,0], model.labels[model.svm.SV_indices], 'o', alpha=.15 )
reg0.plot(x_pred[:,0,0], y_pred, 'r', lw=2)
reg1 = plt.subplot(2,2,3)
reg1.plot(model.sequences[:,0,1], model.labels, 'k,', alpha=.5)
reg1.plot(model.sequences[model.svm.SV_indices,0,1], model.labels[model.svm.SV_indices], 'o', alpha=.15 )
reg1.plot(x_pred[:,0,1], y_pred, 'r',lw=2)
reg2 = plt.subplot(2,2,4)
reg2.plot(model.sequences[:,0,2], model.labels, 'k,', alpha=.5)
reg2.plot(model.sequences[model.svm.SV_indices,0,2], model.labels[model.svm.SV_indices], 'o', alpha=.15 )
reg2.plot(x_pred[:,0,2], y_pred, 'r',lw=2)
plt.show()
return
