def fit(self, X, Y):
self.__initialize(X, Y)
xi = self.xi
t = 0
while t < self.iterations:
#Caculate weight matrix
w = self._eta*np.power(-np.log(self._eta), xi+1)
W = np.diag(w)
#Z is used for updating beta
tmp = GEVFunc.derivLink(xi, self._eta)
Z = self._v + self._gamma \
*tmp \
*(Y - self._eta)
#Update beta
mat = np.matrix(X.T.dot(W).dot(X)) \
+ np.eye(X.shape[1])*self.regular
#self._beta = mat.I.dot(X.T).dot(W).dot(Z).getA1()
self._beta = np.linalg.pinv(mat).dot(X.T).dot(W).dot(Z).getA1()
#Calculate v
self._v = X.dot(self._beta)
GEVFunc.clip(xi, self._v)
#Judge if eta is convergent
newEta = GEVFunc.inverseLink2(xi, self._v)
if np.abs(newEta - self._eta).sum() < self.tol:
self._eta = newEta
break
self._eta = newEta
t += 1
def predict(self, X):
self._v = X.dot(self._beta)
GEVFunc.clip(self.xi, self._v)
return GEVFunc.inverseLink2(self.xi, self._v)