def __init__(self,
y,
X,
family=family.Gaussian(),
offset=None,
y_fix=None,
constant=True):
"""
Initialize class
"""
self.n = USER.check_arrays(y, X)
USER.check_y(y, self.n)
self.y = y
if constant:
self.X = USER.check_constant(X)
else:
self.X = X
self.family = family
self.k = self.X.shape[1]
if offset is None:
self.offset = np.ones(shape=(self.n, 1))
else:
self.offset = offset * 1.0
if y_fix is None:
self.y_fix = np.zeros(shape=(self.n, 1))
else:
self.y_fix = y_fix
self.fit_params = {}
def __init__(self,
coords,
y,
X,
bws,
XB,
err,
family=Gaussian(),
offset=None,
sigma2_v1=False,
kernel='bisquare',
fixed=False,
constant=True):
"""
Initialize class
"""
self.coords = coords
self.y = y
self.X = X
self.XB = XB
self.err = err
self.bws = bws
self.family = family
self.offset = offset
self.sigma2_v1 = sigma2_v1
self.kernel = kernel
self.fixed = fixed
self.constant = constant
if constant:
self.X = USER.check_constant(self.X)
def __init__(self, y, X, family=family.Gaussian(), offset=None, y_fix = None,
constant=True):
"""
Initialize class
"""
self.n = USER.check_arrays(y, X)
USER.check_y(y, self.n)
self.y = y
if constant:
self.X = USER.check_constant(X)
else:
self.X = X
self.family = family
self.k = self.X.shape[1]
self.df_model = self.X.shape[1] - 1
self.df_resid = self.n - self.df_model - 1
if offset is None:
self.offset = np.ones(shape=(self.n,1))
else:
self.offset = offset * 1.0
if y_fix is None:
self.y_fix = np.zeros(shape=(self.n,1))
else:
self.y_fix = y_fix
self.fit_params = {}
def __init__(self, y, X, family=family.Gaussian(), constant=True):
"""
Initialize class
"""
self.n = USER.check_arrays(y, X)
USER.check_y(y, self.n)
self.y = y
if constant:
self.X = USER.check_constant(X)
else:
self.X = X
self.family = family
self.k = self.X.shape[1]
self.fit_params = {}
def _mbw(self):
y = self.y
if self.constant:
X = USER.check_constant(self.X_loc)
else:
X = self.X_loc
n, k = X.shape
family = self.family
offset = self.offset
kernel = self.kernel
fixed = self.fixed
coords = self.coords
search = self.search
criterion = self.criterion
bw_min = self.bw_min
bw_max = self.bw_max
interval = self.interval
tol = self.tol
max_iter = self.max_iter
gwr_func = lambda y, X, bw: GWR(coords,
y,
X,
bw,
family=family,
kernel=kernel,
fixed=fixed,
offset=offset,
constant=False).fit()
bw_func = lambda y, X: Sel_BW(coords,
y,
X,
X_glob=[],
family=family,
kernel=kernel,
fixed=fixed,
offset=offset,
constant=False)
sel_func = lambda bw_func: bw_func.search(search=search,
criterion=criterion,
bw_min=bw_min,
bw_max=bw_max,
interval=interval,
tol=tol,
max_iter=max_iter)
self.bw = multi_bw(self.init_multi, y, X, n, k, family, self.tol_multi,
self.max_iter_multi, self.rss_score, gwr_func,
bw_func, sel_func)