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)