def kde():
import pyqt_fit.kernel_smoothing as smooth
import data
'''
Which data?
'''
y = np.array(data.hubbert)
x = np.array([x for x in range(1,len(y)+1)])
x = [xi/float(max(x)) for xi in x]
y = [yi/float(max(y)) for yi in y]
print "Data:", x, y
estimator = smooth.SpatialAverage(x, y)
estimate = estimator.evaluate(x)
astar = AStar(x, y, .01, 50, False)
best = astar.min
besty = gen_data.get_y_data(best.exptree, best.fit_consts, x)
print "MSE of kernel smoothing estimation: ", mse(y, estimate)
print "MSE of function-space greedy search: ", mse(y, besty)
plt.scatter(x, y, color='b')
plt.hold(True)
plt.plot(x, estimate, color='g')
plt.plot(x, besty, color='r')
plt.show()
def real_data():
import data
'''
Which data to use:
'''
y = data.oscillator
x = [x for x in range(1,len(y)+1)]
x = [xi/float(max(x)) for xi in x]
y = [yi/float(max(y)) for yi in y]
astar = AStar(x, y, 10, 50, False)
best = astar.min
besty = gen_data.get_y_data(best.exptree, best.fit_consts, x)
plt.scatter(x, y)
plt.hold(True)
plt.plot(besty)
plt.show()
