# Path: dppy/bless.py
# def estimate_rls_bless(D, X, eval_L, lam_new):
# def reduce_lambda(X_data,
#                   eval_L,
#                   intermediate_dict_bless,
#                   lam_new,
#                   rng,
#                   rls_oversample_parameter=None):
# def bless(X_data,
#           eval_L,
#           lam_final,
#           rls_oversample_param,
#           random_state=None,
#           nb_iter_bless=None,
#           verbose=True):
# def bless_size(X_data,
#                eval_L,
#                size_final,
#                rls_oversample_param,
#                random_state=None,
#                nb_iter_bless=None,
#                verbose=True):
#     K_DU = eval_L(D.X, X)
#     U_DD, S_DD, _ = np.linalg.svd(eval_L(D.X, D.X) + lam_new * np.diag(D.probs))
#     E = S_root_inv_DD * U_DD.T
#     U = np.asarray(rng.rand(n)) <= ucb
#     X_U = X_data[U, :]
# 
# Path: dppy/utils.py
# def check_random_state(seed: object) -> np.random.RandomState:
#     """Turn seed into a np.random.RandomState instance
#     Parameters
#     ----------
#     seed : None | int | instance of RandomState
#         If seed is None, return the RandomState singleton used by np.random.
#         If seed is an int, return a new RandomState instance seeded with seed.
#         If seed is already a RandomState instance, return it.
#         Otherwise raise ValueError.
# 
#     Returns
#     -------
#     np.random.RandomState
# 
#     .. seealso::
# 
#         `Scikit learn source code <https://github.com/scikit-learn/scikit-learn/blob/7813f7efb/sklearn/utils/validation.py#L763>`_
#     """
#     if seed is None or seed is np.random:
#         return np.random.mtrand._rand
#     if isinstance(seed, (int, np.integer)):
#         return np.random.RandomState(seed)
#     if isinstance(seed, np.random.RandomState):
#         return seed
#     raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
#                      ' instance' % seed)
# 
# def example_eval_L_polynomial(X, Y=None, p=2):
#     if Y is None:
#         ret = example_eval_L_linear(X)
#         np.power(ret, p, out=ret)
#         return ret
#     else:
#         ret = example_eval_L_linear(X, Y)
#         np.power(ret, p, out=ret)
#         return ret
# 
# def evaluate_L_diagonal(eval_L, X):
#     """Helper function to evaluate a likelihood function on a set of points (i.e. compute the diagonal of the L matrix)"""
#     diag_eval = getattr(eval_L, "diag", None)
#     if callable(diag_eval):
#         return diag_eval(X)
#     else:
#         # inspired by sklearn.gaussian_process.kernels.PairwiseKernel
#         return np.apply_along_axis(eval_L, 1, X).ravel()


import unittest
import numpy as np
import numpy.linalg as la
import sys
from dppy.bless import (bless,
                        estimate_rls_bless,
                        reduce_lambda,
                        CentersDictionary)
from dppy.utils import (check_random_state,
                        example_eval_L_polynomial,
                        evaluate_L_diagonal)
    def test_estimate_rls_bless(self):
        N, d = 100, 5
        lam = 11
        lam_new = 10
        rng = check_random_state(self.seed)
        X_data = rng.randn(N, d)
        L_data = example_eval_L_polynomial(X_data)

        rls_exact = la.solve(L_data + lam_new * np.eye(N), L_data).diagonal()

        dict_exact = self.__create_lambda_acc_dictionary(X_data, example_eval_L_polynomial, 0.0, rng)
        dict_approx = self.__create_lambda_acc_dictionary(X_data, example_eval_L_polynomial, lam, rng)

        rls_estimates_exact = estimate_rls_bless(dict_exact, X_data, example_eval_L_polynomial, lam_new)
        rls_estimates_approx = estimate_rls_bless(dict_approx, X_data, example_eval_L_polynomial, lam_new)


        np.testing.assert_almost_equal(rls_estimates_exact, rls_exact)
        np.testing.assert_allclose(rls_estimates_approx, rls_exact, rtol=0.5)

    def test_reduce_lambda(self):
        N, d = 100, 5
        lam = 11
        lam_new = 10
        rng = check_random_state(self.seed)
        X_data = rng.randn(N, d)
        L_data = example_eval_L_polynomial(X_data)

        rls_exact = la.solve(L_data + lam * np.eye(N), L_data).diagonal()
        dict_approx = self.__create_lambda_acc_dictionary(X_data, example_eval_L_polynomial, lam, rng)

# Path: dppy/bless.py
# def estimate_rls_bless(D, X, eval_L, lam_new):
# def reduce_lambda(X_data,
#                   eval_L,
#                   intermediate_dict_bless,
#                   lam_new,
#                   rng,
#                   rls_oversample_parameter=None):
# def bless(X_data,
#           eval_L,
#           lam_final,
#           rls_oversample_param,
#           random_state=None,
#           nb_iter_bless=None,
#           verbose=True):
# def bless_size(X_data,
#                eval_L,
#                size_final,
#                rls_oversample_param,
#                random_state=None,
#                nb_iter_bless=None,
#                verbose=True):
#     K_DU = eval_L(D.X, X)
#     U_DD, S_DD, _ = np.linalg.svd(eval_L(D.X, D.X) + lam_new * np.diag(D.probs))
#     E = S_root_inv_DD * U_DD.T
#     U = np.asarray(rng.rand(n)) <= ucb
#     X_U = X_data[U, :]
# 
# Path: dppy/utils.py
# def check_random_state(seed: object) -> np.random.RandomState:
#     """Turn seed into a np.random.RandomState instance
#     Parameters
#     ----------
#     seed : None | int | instance of RandomState
#         If seed is None, return the RandomState singleton used by np.random.
#         If seed is an int, return a new RandomState instance seeded with seed.
#         If seed is already a RandomState instance, return it.
#         Otherwise raise ValueError.
# 
#     Returns
#     -------
#     np.random.RandomState
# 
#     .. seealso::
# 
#         `Scikit learn source code <https://github.com/scikit-learn/scikit-learn/blob/7813f7efb/sklearn/utils/validation.py#L763>`_
#     """
#     if seed is None or seed is np.random:
#         return np.random.mtrand._rand
#     if isinstance(seed, (int, np.integer)):
#         return np.random.RandomState(seed)
#     if isinstance(seed, np.random.RandomState):
#         return seed
#     raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
#                      ' instance' % seed)
# 
# def example_eval_L_polynomial(X, Y=None, p=2):
#     if Y is None:
#         ret = example_eval_L_linear(X)
#         np.power(ret, p, out=ret)
#         return ret
#     else:
#         ret = example_eval_L_linear(X, Y)
#         np.power(ret, p, out=ret)
#         return ret
# 
# def evaluate_L_diagonal(eval_L, X):
#     """Helper function to evaluate a likelihood function on a set of points (i.e. compute the diagonal of the L matrix)"""
#     diag_eval = getattr(eval_L, "diag", None)
#     if callable(diag_eval):
#         return diag_eval(X)
#     else:
#         # inspired by sklearn.gaussian_process.kernels.PairwiseKernel
#         return np.apply_along_axis(eval_L, 1, X).ravel()


# Path: tests/test_bless.py
import unittest
import numpy as np
import numpy.linalg as la
import sys
from dppy.bless import (bless,
                        estimate_rls_bless,
                        reduce_lambda,
                        CentersDictionary)
from dppy.utils import (check_random_state,
                        example_eval_L_polynomial,
                        evaluate_L_diagonal)

    def test_estimate_rls_bless(self):
        N, d = 100, 5
        lam = 11
        lam_new = 10
        rng = check_random_state(self.seed)
        X_data = rng.randn(N, d)
        L_data = example_eval_L_polynomial(X_data)

        rls_exact = la.solve(L_data + lam_new * np.eye(N), L_data).diagonal()

        dict_exact = self.__create_lambda_acc_dictionary(X_data, example_eval_L_polynomial, 0.0, rng)
        dict_approx = self.__create_lambda_acc_dictionary(X_data, example_eval_L_polynomial, lam, rng)

        rls_estimates_exact = estimate_rls_bless(dict_exact, X_data, example_eval_L_polynomial, lam_new)
        rls_estimates_approx = estimate_rls_bless(dict_approx, X_data, example_eval_L_polynomial, lam_new)


        np.testing.assert_almost_equal(rls_estimates_exact, rls_exact)
        np.testing.assert_allclose(rls_estimates_approx, rls_exact, rtol=0.5)

    def test_reduce_lambda(self):
        N, d = 100, 5
        lam = 11
        lam_new = 10
        rng = check_random_state(self.seed)
        X_data = rng.randn(N, d)
        L_data = example_eval_L_polynomial(X_data)

        rls_exact = la.solve(L_data + lam * np.eye(N), L_data).diagonal()
        dict_approx = self.__create_lambda_acc_dictionary(X_data, example_eval_L_polynomial, lam, rng)

        rls_estimates = estimate_rls_bless(dict_approx, X_data, example_eval_L_polynomial, lam)