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])