def iwls(y,
x,
family,
offset,
y_fix,
ini_betas=None,
tol=1.0e-8,
max_iter=200,
wi=None):
"""
Iteratively re-weighted least squares estimation routine
"""
n_iter = 0
diff = 1.0e6
if ini_betas is None:
betas = np.zeros((x.shape[1], 1), np.float)
else:
betas = ini_betas
if isinstance(family, Binomial):
y = family.link._clean(y)
if isinstance(family, Poisson):
y_off = y / offset
y_off = family.starting_mu(y_off)
v = family.predict(y_off)
mu = family.starting_mu(y)
else:
mu = family.starting_mu(y)
v = family.predict(mu)
while diff > tol and n_iter < max_iter:
n_iter += 1
w = family.weights(mu)
z = v + (family.link.deriv(mu) * (y - mu))
w = np.sqrt(w)
if type(x) != np.ndarray:
w = sp.csr_matrix(w)
z = sp.csr_matrix(z)
wx = spmultiply(x, w, array_out=False)
wz = spmultiply(z, w, array_out=False)
if wi is None:
n_betas = _compute_betas(wz, wx)
else:
n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi)
v = spdot(x, n_betas)
mu = family.fitted(v)
if isinstance(family, Poisson):
mu = mu * offset
diff = min(abs(n_betas - betas))
betas = n_betas
if wi is None:
return betas, mu, wx, n_iter
else:
return betas, mu, v, w, z, xtx_inv_xt, n_iter
def iwls(y, x, family, offset, y_fix,
ini_betas=None, tol=1.0e-8, max_iter=200, wi=None):
"""
Iteratively re-weighted least squares estimation routine
"""
n_iter = 0
diff = 1.0e6
if ini_betas is None:
betas = np.zeros((x.shape[1], 1), np.float)
else:
betas = ini_betas
if isinstance(family, Binomial):
y = family.link._clean(y)
if isinstance(family, Poisson):
y_off = y/offset
y_off = family.starting_mu(y_off)
v = family.predict(y_off)
mu = family.starting_mu(y)
else:
mu = family.starting_mu(y)
v = family.predict(mu)
while diff > tol and n_iter < max_iter:
n_iter += 1
w = family.weights(mu)
z = v + (family.link.deriv(mu)*(y-mu))
w = np.sqrt(w)
if type(x) != np.ndarray:
w = sp.csr_matrix(w)
z = sp.csr_matrix(z)
wx = spmultiply(x, w, array_out=False)
wz = spmultiply(z, w, array_out=False)
if wi is None:
n_betas = _compute_betas(wz, wx)
else:
n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi)
v = spdot(x, n_betas)
mu = family.fitted(v)
if isinstance(family, Poisson):
mu = mu * offset
diff = min(abs(n_betas-betas))
betas = n_betas
if wi is None:
return betas, mu, wx, n_iter
else:
return betas, mu, v, w, z, xtx_inv_xt, n_iter