def fit(self, X, Y):
n, d = X.shape[0], X.shape[1]
Y = Y.reshape(-1, 1)
L = self.calculateL(self.xi)
#secOrd = np.linalg.pinv((X.T.dot(X) * L + self.reg * np.eye(d)) / n)
# I = np.eye(d)
# I[-1, -1] = 0 #Not regularize the intercept
try:
secOrd = np.linalg.pinv(X.T.dot(X) * L / n + self.reg * np.eye(d))
except Exception as e:
print(e.message)
if not self.manualInitial:
self.initialize(X, Y)
t = 0.
while t < self.iterations:
v = X.dot(self._beta)
GEVFunc.clip(self.xi, v)
Y_hat = GEVFunc.inverseLink(self.xi, v)
#firOrd = (X.T.dot(Y_hat - Y) + self.reg * self._beta) / n
# tmp = self.reg * self._beta
# tmp[-1] = 0
firOrd = X.T.dot(Y_hat - Y) / n + self.reg * self._beta
deltaBeta = self.step * secOrd.dot(firOrd)
if t >= 100 and np.abs(deltaBeta).sum() < self.tol:
self._beta -= deltaBeta
# print "Converged. t = %d" % (t)
break
self._beta -= deltaBeta
t += 1
self._beta = np.array(self._beta).flatten()
def oneStep3(self, X, Y):
n, d = X.shape[0], X.shape[1]
Y = Y.reshape(-1, 1)
v = X.dot(self._beta)
GEVFunc.clip(self.xi, v)
W = np.diag(GEVFunc.derivInverseLink(self.xi, v).flatten())
secOrd = np.linalg.pinv(X.T.dot(W).dot(X) / n + self.reg * np.eye(d))
Y_hat = GEVFunc.inverseLink(self.xi, v)
firOrd = X.T.dot(Y_hat - Y) / n + self.reg * self._beta
self._beta -= self.step * secOrd.dot(firOrd)
def oneStep(self, X, Y):
n, d = X.shape[0], X.shape[1]
Y = Y.reshape(-1, 1)
L = self.calculateL(self.xi)
try:
secOrd = np.linalg.pinv(X.T.dot(X) * L / n + self.reg * np.eye(d))
except Exception as e:
print(e.message)
v = X.dot(self._beta)
GEVFunc.clip(self.xi, v)
Y_hat = GEVFunc.inverseLink(self.xi, v)
firOrd = X.T.dot(Y_hat - Y) / n + self.reg * self._beta
self._beta -= self.step * secOrd.dot(firOrd)
def fit3(self, X, Y):
n, d = X.shape[0], X.shape[1]
Y = Y.reshape(-1, 1)
if not self.manualInitial:
self.initialize(X, Y)
t = 0.
while t < self.iterations:
v = X.dot(self._beta)
GEVFunc.clip(self.xi, v)
W = np.diag(GEVFunc.derivInverseLink(self.xi, v).flatten())
# secOrd = np.linalg.pinv(X.T.dot(W).dot(X) / n + self.reg * np.eye(d))
A = X.T.dot(W).dot(X) / n + self.reg * np.eye(d)
Y_hat = GEVFunc.inverseLink(self.xi, v)
b = X.T.dot(Y_hat - Y) / n + self.reg * self._beta
self._beta += np.linalg.lstsq(A, -b)[0]
# self._beta -= self.step * secOrd.dot(firOrd)
t += 1
self._beta = np.array(self._beta).flatten()
def predict(self, X):
v = X.dot(self.beta)
GEVFunc.clip(self.xi, v)
return GEVFunc.inverseLink(self.xi, v).flatten()
def firstDeriva(self, X, Y, beta):
v = X.dot(beta)
GEVFunc.clip(self.xi, v)
Y_hat = GEVFunc.inverseLink(self.xi, v)
return X.T.dot(Y_hat - Y) / X.shape[0] + self.reg * beta
def loglikelihood(self, beta, xi):
v = self.X.dot(beta.reshape(-1, 1))
GEVFunc.clip(xi, v)
res = GEVFunc.inverseLink(xi, v)
res[self.targets == 0] = 1 - res[self.targets == 0]
return sum([np.log(x + 1e-8) for x in res])
