def GCV(Model, Y, lmbda, beta):
N = len(Y)
X = model2matix(Model)
X = np.transpose(np.array(X))
Y_hat = leastsquare.predict(X, beta)
rss = np.sum((Y-Y_hat)**2)
denominator = (1-float(lmbda)/N)**2
return rss/denominator
def removed_rss(Model, index, Y):
X = model2matix(Model)
del X[index]
X = np.transpose(np.array(X))
if len(X) <= 1:
return 999999999., None
beta = leastsquare.ls(X, Y)
Y_hat = leastsquare.predict(X, beta)
rss = np.sum((Y-Y_hat)**2)
return rss, beta
def draw_polynomial_pwv(train_in, train_out):
p = 3
X = polynomial(train_in)
XT = np.transpose(X)
beta = ls.ls(X, train_out)
Yhat = ls.predict(X, beta)
thegsq = ls.thegama2(Yhat, train_out, p)
beta_var = np.linalg.inv(XT.dot(X))*thegsq
print beta_var
PWV = []
for i in range(len(X)):
PWV.append(np.transpose(X[i]).dot(beta_var).dot(X[i]))
#print PWV
plt.plot(train_in, PWV)
def draw_cubic_spline_pwv(train_in, train_out):
p = 5
X = cubic(train_in)
#print X.shape
XT = np.transpose(X)
#print X
beta = ls.ls(X, train_out)
Yhat = ls.predict(X, beta)
thegsq = ls.thegama2(Yhat, train_out, p)
beta_var = np.linalg.inv(XT.dot(X))*thegsq
print beta_var
PWV = []
for i in range(len(X)):
PWV.append(np.transpose(X[i]).dot(beta_var).dot(X[i]))
#print PWV
plt.plot(train_in, PWV)
def draw_natural_spline_pwv(train_in, train_out):
p = 5
knots = np.arange(0.1, 0.9, 0.16).tolist()
knots.append(0.9)
X = nscookdata(train_in, 6, knots)
#print H
#print H.shape
XT = np.transpose(X)
beta = ls.ls(X, train_out)
Yhat = ls.predict(X, beta)
thegsq = ls.thegama2(Yhat, train_out, p)
beta_var = np.linalg.inv(XT.dot(X))*thegsq
print beta_var
PWV = []
for i in range(len(X)):
PWV.append(np.transpose(X[i]).dot(beta_var).dot(X[i]))
#print PWV
plt.plot(train_in, PWV)
