def choose_hyper(phi, y, polyx, polyy, order):
w = do_regression(phi, y, lambd=10)
y_prime = (grand_order(polyx, order) * w)
least_error = 100
target_alpha = 1
alphas = np.linspace(-20, -18, 10)
for alp in alphas:
w = do_regression(phi, y, alp)
y_prime = (grand_order(polyx, order) * w)
error = data_reading.MeanSquareError(y_prime, polyy)
if error <= least_error:
target_alpha = alp
least_error = error
return target_alpha
def choose_hyper(matFi, sampy, polyx, polyy, order):
least_error = 100
target_alpha = 1
alphas = np.linspace(-3, 2, 1000)
for alp in alphas:
y_prime = grand_order(polyx, order) * do_regression(matFi, sampy, alp)
error = data_reading.MeanSquareError(y_prime, polyy)
if error <= least_error:
target_alpha = alp
least_error = error
return target_alpha
alphas = np.linspace(-20, -18, 10)
for alp in alphas:
w = do_regression(phi, y, alp)
y_prime = (grand_order(polyx, order) * w)
error = data_reading.MeanSquareError(y_prime, polyy)
if error <= least_error:
target_alpha = alp
least_error = error
return target_alpha
if '__main__' == __name__:
poly_data, poly_keys = data_reading.readMatFile("poly_data.mat")
order = 10
X, y = grand_order(poly_data['sampx'][0], order), poly_data['sampy']
polyx, polyy = poly_data['polyx'][0], poly_data['polyy']
count_data, count_keys = data_reading.readMatFile("count_data.mat")
target_alpha = choose_hyper(X, y, polyx, polyy, order)
w = do_regression(X, y, target_alpha)
y_prime = grand_order(polyx, order) * w
fig = plt.figure("LASSO")
ax = fig.add_subplot(111)
ax.plot(poly_data['sampx'][0],
y,
color='r',
linestyle='',
marker='*',
label="sample")
# regression line
total_length = len(sampx)
percents = [0.1, 0.25, 0.5, 0.75]
result = np.zeros((4, 5))
for j in range(1000):
MSEs = []
for percent in percents:
try:
mse = []
train_length = int(percent * total_length)
a = np.random.randint(0, total_length, size=[1,
train_length])[0]
sampx_sub = sampx[a]
sampy_sub = sampy[a]
phi = grand_order(sampx_sub, order)
# LS
LS_y_prime = grand_order(polyx, order) * LSregression(
phi.T, sampy_sub, order)
mse.append(data_reading.MeanSquareError(LS_y_prime, polyy))
# RLS
RLS_target_alpha = RLS_hyper(phi.T, sampy_sub, polyx, polyy,
order)
RLS_y_prime = grand_order(polyx, order) * RLSregression(
phi.T, sampy_sub, RLS_target_alpha)
mse.append(data_reading.MeanSquareError(RLS_y_prime, polyy))
#LASSO
LASSO_target_alpha = LASSO_hyper(phi, sampy_sub, polyx, polyy,
order)
LASSO_y_prime = grand_order(polyx, order) * LASSOregression(
LS.set_title("LR")
RLS = plt.subplot(3,2,4)
RLS.set_title("RLS")
LASSO = plt.subplot(3,2,6)
LASSO.set_title("LASSO")
RR = plt.subplot(3,2,5)
RR.set_title("RR")
LS.plot(polyx,polyy,color='b',linestyle='-',marker='',label="target")
RLS.plot(polyx,polyy,color='b',linestyle='-',marker='',label="target")
LASSO.plot(polyx,polyy,color='b',linestyle='-',marker='',label="target")
RR.plot(polyx,polyy,color='b',linestyle='-',marker='',label="target")
train_length = int(percent * total_length)
sampx_sub = sampx[range(train_length)]
sampy_sub = sampy[range(train_length)]
phi = grand_order(sampx_sub,order)
# LS
LS_y_prime = grand_order(polyx,order) * LSregression(phi.T,sampy_sub,order)
LS.plot(polyx,LS_y_prime,color='r',linestyle='-',marker='',label="predict")
# RLS
RLS_target_alpha = RLS_hyper(phi.T,sampy_sub,polyx,polyy,order)
RLS_y_prime = grand_order(polyx,order) * RLSregression(phi.T,sampy_sub,RLS_target_alpha)
RLS.plot(polyx,RLS_y_prime,color='r',linestyle='-',marker='',label="predict")
#LASSO
LASSO_target_alpha = LASSO_hyper(phi,sampy_sub,polyx,polyy,order)
LASSO_y_prime = grand_order(polyx,order) * LASSOregression(phi,sampy_sub,LASSO_target_alpha)
LASSO.plot(polyx,LASSO_y_prime,color='r',linestyle='-',marker='',label="predict")
#RR
RR_y_prime = grand_order(polyx,order) * RRregression(phi,sampy_sub,order)
RR.plot(polyx,RR_y_prime,color='r',linestyle='-',marker='',label="predict")
#BR
target_alpha = alp
least_error = error
return target_alpha
if __name__ == "__main__":
poly_data, poly_keys = data_reading.readMatFile("poly_data.mat")
order = 10
sampx = poly_data['sampx'][0]
sampy = poly_data['sampy']
polyx = poly_data['polyx'][0]
polyy = poly_data['polyy']
matFi = grand_order(sampx, order).T
# part of painting
fig = plt.figure("RLS")
w = do_regression(matFi, sampy, 1)
target_alpha = choose_hyper(matFi, sampy, polyx, polyy, order)
y_prime = grand_order(polyx, order) * do_regression(
matFi, sampy, target_alpha)
plt.plot(poly_data['sampx'][0],
sampy,
color='r',
linestyle='',
marker='*',
label="sample")
plt.plot(polyx,
def do_regression(phi,sampy,order=1):
w = ((np.linalg.inv(phi * phi.T)) * phi) * sampy
# part of painting
return w
if __name__ == "__main__":
poly_data , poly_keys = data_reading.readMatFile("poly_data.mat")
order = 5
sampx = poly_data['sampx'][0]
sampy = poly_data['sampy']
polyx = poly_data['polyx'][0]
polyy = poly_data['polyy']
w = do_regression(grand_order(sampx,order).T,sampy,order)
targety = grand_order(polyx,order) * w
fig = plt.figure("LS")
ax = fig.add_subplot(111)
# sample points
ax.plot(poly_data['sampx'][0],sampy,color='r',linestyle='',marker='*',label="sample")
# regression line
ax.plot(polyx,targety,color='g',linestyle='-',marker='',label="predict")
# poly points
ax.plot(polyx,polyy,color='b',linestyle='-',marker='',label="target")
print data_reading.MeanSquareError(targety,polyy)
ax.legend()
plt.show()