def cokriging_evaluation(year):
from pyKriging import coKriging
Y, D, P, Tf, Gd = extract_raw_samples(2012, crime_t=['total'])
coords = get_centroid_ca()
X_train, X_test, Y_train, Y_test = build_features(Y, D, P, Tf, Y, Gd, Y, 0, taxi_norm="bydestination")
coords_train = np.delete(coords, 0, axis=0)
coKriging.coKriging(coords_train, X_train, coords_train, Y_train)
def cheap(X):
A = 0.5
B = 10
C = -5
D = 0
print(X)
print(((X + D) * 6 - 2))
return A * np.power(((X + D) * 6 - 2), 2) * np.sin(
((X + D) * 6 - 2) * 2) + ((X + D) - 0.5) * B + C
def expensive(X):
return np.power((X * 6 - 2), 2) * np.sin((X * 6 - 2) * 2)
Xe = np.array([0, 0.4, 0.6, 1])
Xc = np.array([0.1, 0.2, 0.3, 0.5, 0.7, 0.8, 0.9, 0, 0.4, 0.6, 1])
yc = cheap(Xc)
ye = expensive(Xe)
ck = coKriging.coKriging(Xc, yc, Xe, ye)
ck.thetac = np.array([1.2073])
print(ck.Xc)
ck.updateData()
ck.updatePsi()
ck.neglnlikehood()
import numpy as np
def cheap(X):
A=0.5
B=10
C=-5
D=0
print X
print ((X+D)*6-2)
return A*np.power( ((X+D)*6-2), 2 )*np.sin(((X+D)*6-2)*2)+((X+D)-0.5)*B+C
def expensive(X):
return np.power((X*6-2),2)*np.sin((X*6-2)*2)
Xe = np.array([0, 0.4, 0.6, 1])
Xc = np.array([0.1,0.2,0.3,0.5,0.7,0.8,0.9,0,0.4,0.6,1])
yc = cheap(Xc)
ye = expensive(Xe)
ck = coKriging.coKriging(Xc, yc, Xe, ye)
ck.thetac = np.array([1.2073])
print ck.Xc
ck.updateData()
ck.updatePsi()
ck.neglnlikehood()