def __init__(self, y, weights=None):
self._y = check_array_type(y)
self._numObv = len(self._y)
if weights is None:
self._numVar = 0
self._w = np.ones(self._y.shape)
else:
self._w = check_array_type(weights)
if len(self._w.shape) > 1:
if self._w.shape[1] == 1:
self._w = self._w.flatten()
assert self._y.shape == self._w.shape, \
"Input weight not of the same size as y"
self.loss(self._y)
def __init__(self, y, weights=None):
self._y = check_array_type(y)
self._numObv = len(self._y)
if weights is None:
self._numVar = 0
self._w = np.ones(self._y.shape)
else:
self._w = check_array_type(weights)
if len(self._w.shape) > 1:
if self._w.shape[1] == 1:
self._w = self._w.flatten()
assert self._y.shape == self._w.shape, \
"Input weight not of the same size as y"
self.loss(self._y)
def __init__(self, theta, ode, x0, t0, t, y, state_name,
sigma=None, target_param=None, target_state=None):
if sigma is None:
super(NormalLoss, self).__init__(theta, ode, x0, t0, t, y,
state_name, sigma,
target_param, target_state)
else:
sigma = check_array_type(sigma)
super(NormalLoss, self).__init__(theta, ode, x0, t0, t, y,
state_name, 1.0/sigma,
target_param, target_state)
def __init__(self,
theta,
ode,
x0,
t0,
t,
y,
state_name,
sigma=None,
target_param=None,
target_state=None):
if sigma is None:
super(NormalLoss,
self).__init__(theta, ode, x0, t0, t, y, state_name, sigma,
target_param, target_state)
else:
sigma = check_array_type(sigma)
super(NormalLoss,
self).__init__(theta, ode, x0, t0, t, y, state_name,
1.0 / sigma, target_param, target_state)
def __init__(self, y, sigma=1.0):
err_str = "Standard deviation not of the correct "
self._y = check_array_type(y)
if isinstance(sigma, np.ndarray):
if len(sigma.shape) > 1:
if 1 in sigma.shape:
sigma = sigma.flatten()
if y.shape == sigma.shape:
self._sigma = sigma
else:
raise InitializeError(err_str + "size")
else:
if y.shape == sigma.shape:
self._sigma = sigma
else:
raise InitializeError(err_str + "size")
elif sigma is None or sigma == 1.0:
self._sigma = np.ones(self._y.shape)
else:
raise InitializeError(err_str + "type")
self._sigma2 = self._sigma**2
self.loss(self._y)
def __init__(self, y, sigma=1.0):
err_str = "Standard deviation not of the correct "
self._y = check_array_type(y)
if isinstance(sigma, np.ndarray):
if len(sigma.shape) > 1:
if 1 in sigma.shape:
sigma = sigma.flatten()
if y.shape == sigma.shape:
self._sigma = sigma
else:
raise InitializeError(err_str + "size")
else:
if y.shape == sigma.shape:
self._sigma = sigma
else:
raise InitializeError(err_str + "size")
elif sigma is None or sigma == 1.0:
self._sigma = np.ones(self._y.shape)
else:
raise InitializeError(err_str + "type")
self._sigma2 = self._sigma**2
self.loss(self._y)
def __init__(self, y):
self._y = check_array_type(y)
self.loss(self._y)
def __init__(self, y):
self._y = check_array_type(y)
self.loss(self._y)