def f(params_dict, k, X):
X_affine = data_helper.affine_X(X)
k_eq_0_out = stable_func.sigmoid(
np.dot(X_affine, params_dict["theta"]))
return k_eq_0_out if k == 0 else 1 - k_eq_0_out
def __s(self, X, k):
s0 = stable_func.sigmoid(X)
return s0 if k == 0 else 1 - s0
def f(params_dict, k, X):
t_X = t(params_dict, X)
k_0_out = stable_func.sigmoid(np.dot(t_X, params_dict["theta"]))
return k_0_out if k == 0 else 1 - k_0_out
def predict(self, X):
X_affine = data_helper.affine_X(X)
p0 = stable_func.sigmoid(
self._kernel(X_affine, self._theta, self.__bias, self.__degree))
return np.column_stack([p0, 1 - p0])
def predict(self, X):
X_affine = data_helper.affine_X(X)
p0 = stable_func.sigmoid(np.dot(X_affine, self._get_params()[0]))
return np.column_stack([p0, 1 - p0])
def act(self, X):
return stable_func.sigmoid(X)
def predict(self, X):
transforms = self._transform(X)
transforms_prep = self.__dot_prepare_transform(transforms)
p0 = stable_func.sigmoid(np.dot(transforms_prep, self._get_params()[0]))
return np.column_stack([p0, 1-p0])
def f(params, X):
k_eq_0_out = stable_func.sigmoid(np.dot(X, params[0]))
return np.asarray([k_eq_0_out, 1 - k_eq_0_out])