def fit(self, X, Y, maxit=100, tol=1e-4):
"""Fit Elastic Net model with coordinate descent"""
X = np.asanyarray(X, dtype=np.float64)
Y = np.asanyarray(Y, dtype=np.float64)
if self.coef_ is None:
self.coef_ = np.zeros(X.shape[1], dtype=np.float64)
nsamples = X.shape[0]
alpha = self.alpha * self.rho * nsamples
beta = self.alpha * (1.0 - self.rho) * nsamples
self.coef_, self.dual_gap_, self.eps_ = \
enet_coordinate_descent(self.coef_, alpha, beta, X, Y, maxit, 10, tol)
if self.dual_gap_ > self.eps_:
warnings.warn('Objective did not converge, you might want to increase the number of interations')
if self.dual_gap_ > self.eps_:
warnings.warn('Objective did not converge, you might want to increase the number of interations')
# return self for chaining fit and predict calls
return self
>>>>>>> remote
if self.coef_ is None:
self.coef_ = np.zeros(X.shape[1], dtype=np.float64)
nsamples = X.shape[0]
alpha = self.alpha * self.rho * nsamples
beta = self.alpha * (1.0 - self.rho) * nsamples
self.coef_, self.dual_gap_, self.eps_ = \
enet_coordinate_descent(self.coef_, alpha, beta, X, Y,
maxit, 10, self.tol)
if self.dual_gap_ > self.eps_:
warnings.warn('Objective did not converge, you might want to increase the number of interations')
<<<<<<< HEAD
=======
def __init__(self, alpha=1.0, rho=0.5, w0=None):
super(ElasticNet, self).__init__(w0)
self.alpha = alpha
self.rho = rho
def fit(self, X, Y, maxit=100, tol=1e-4):
"""Fit Elastic Net model with coordinate descent"""
X = np.asanyarray(X, dtype=np.float64)
Y = np.asanyarray(Y, dtype=np.float64)
