<reponame>Existever/PyCFTrackers
import numpy as np
from .config import gpu_config
if gpu_config.use_gpu:
    import cupy as cp
from .config import otb_hc_config,otb_deep_config
"""
    code tested no problem
"""

class GMM:
    def __init__(self, num_samples,config):
        self._num_samples = num_samples
        self.config=config
        if not gpu_config.use_gpu:
            self._distance_matrix = np.ones((num_samples, num_samples), dtype=np.float32) * np.inf
            self._gram_matrix = np.ones((num_samples, num_samples), dtype=np.float32) * np.inf
            self.prior_weights = np.zeros((num_samples, 1), dtype=np.float32)
        else:
            self._distance_matrix = cp.ones((num_samples, num_samples), dtype=cp.float32) * cp.inf
            self._gram_matrix = cp.ones((num_samples, num_samples), dtype=cp.float32) * cp.inf
            self.prior_weights = cp.zeros((num_samples, 1), dtype=cp.float32)
        # find the minimum allowed sample weight. samples are discarded if their weights become lower
        self.minimum_sample_weight = self.config.learning_rate * (1 - self.config.learning_rate) ** (2 * self.config.num_samples)



    def _find_gram_vector(self, samplesf, new_sample, num_training_samples):
        if gpu_config.use_gpu:
            xp = cp.get_array_module(samplesf[0])
        else:
            xp = np
        gram_vector = xp.inf * xp.ones((self.config.num_samples))
        if num_training_samples > 0:
            ip = 0.
            for k in range(len(new_sample)):
                samplesf_ = samplesf[k][:, :, :, :num_training_samples]
                samplesf_ = samplesf_.reshape((-1, num_training_samples))
                new_sample_ = new_sample[k].flatten()
                ip += xp.real(2 * samplesf_.T.dot(xp.conj(new_sample_)))
            gram_vector[:num_training_samples] = ip
        return gram_vector

    def _merge_samples(self, sample1, sample2, w1, w2, sample_merge_type):
        alpha1 = w1 / (w1 + w2)
        alpha2 = 1 - alpha1
        if sample_merge_type == 'replace':
            merged_sample = sample1
        elif sample_merge_type == 'merge':
            num_feature_blocks = len(sample1)
            merged_sample = [alpha1 * sample1[k] + alpha2 * sample2[k] for k in range(num_feature_blocks)]
        return merged_sample

    def _update_distance_matrix(self, gram_vector, new_sample_norm, id1, id2, w1, w2):
        """
            update the distance matrix
        """
        if gpu_config.use_gpu:
            xp = cp.get_array_module(gram_vector)
        else:
            xp = np
        alpha1 = w1 / (w1 + w2)
        alpha2 = 1 - alpha1
        if id2 < 0:
            norm_id1 = self._gram_matrix[id1, id1]

            # udpate the gram matrix
            if alpha1 == 0:
                self._gram_matrix[:, id1] = gram_vector
                self._gram_matrix[id1, :] = self._gram_matrix[:, id1]
                self._gram_matrix[id1, id1] = new_sample_norm
            elif alpha2 == 0:
                # new sample is discard
                pass
            else:
                # new sample is merge with an existing sample
                self._gram_matrix[:, id1] = alpha1 * self._gram_matrix[:, id1] + alpha2 * gram_vector
                self._gram_matrix[id1, :] = self._gram_matrix[:, id1]
                self._gram_matrix[id1, id1] = alpha1 ** 2 * norm_id1 + alpha2 ** 2 * new_sample_norm + 2 * alpha1 * alpha2 * gram_vector[id1]

            # udpate distance matrix
            self._distance_matrix[:, id1] = xp.maximum(self._gram_matrix[id1, id1] + xp.diag(self._gram_matrix) - 2 * self._gram_matrix[:, id1], 0)
            # self._distance_matrix[:, id1][np.isnan(self._distance_matrix[:, id1])] = 0
            self._distance_matrix[id1, :] = self._distance_matrix[:, id1]
            self._distance_matrix[id1, id1] = xp.inf
        else:
            if alpha1 == 0 or alpha2 == 0:
                raise("Error!")

            norm_id1 = self._gram_matrix[id1, id1]
            norm_id2 = self._gram_matrix[id2, id2]
            ip_id1_id2 = self._gram_matrix[id1, id2]

            # handle the merge of existing samples
            self._gram_matrix[:, id1] = alpha1 * self._gram_matrix[:, id1] + alpha2 * self._gram_matrix[:, id2]
            # self._distance_matrix[:, id1][np.isnan(self._distance_matrix[:, id1])] = 0
            self._gram_matrix[id1, :] = self._gram_matrix[:, id1]
            self._gram_matrix[id1, id1] = alpha1 ** 2 * norm_id1 + alpha2 ** 2 * norm_id2 + 2 * alpha1 * alpha2 * ip_id1_id2
            gram_vector[id1] = alpha1 * gram_vector[id1] + alpha2 * gram_vector[id2]

            # handle the new sample
            self._gram_matrix[:, id2] = gram_vector
            self._gram_matrix[id2, :] = self._gram_matrix[:, id2]
            self._gram_matrix[id2, id2] = new_sample_norm

            # update the distance matrix
            self._distance_matrix[:, id1] = xp.maximum(self._gram_matrix[id1, id1] + xp.diag(self._gram_matrix) - 2 * self._gram_matrix[:, id1], 0)
            self._distance_matrix[id1, :] = self._distance_matrix[:, id1]
            self._distance_matrix[id1, id1] = xp.inf
            self._distance_matrix[:, id2] = xp.maximum(self._gram_matrix[id2, id2] + xp.diag(self._gram_matrix) - 2 * self._gram_matrix[:, id2], 0)
            self._distance_matrix[id2, :] = self._distance_matrix[:, id2]
            self._distance_matrix[id2, id2] = xp.inf

    # def update_prior_weights(prior_weights, sample_weights, latest_ind, frame_num):
    #     # udpate the training sample weights
    #     if frame_num == 1:
    #         replace_ind = 1
    #         prior_weights[replace_ind] = 1
    #     else:
    #         if config.sample_replace_strategy == 'lowest_prior':
    #             replace_idx = np.argmin(prior_weights)
    #         elif config.sample_replace_strategy == 'lowest_weight':
    #             replace_idx = np.argmin(sample_weights)
    #         elif config.sample_replace_strategy == 'lowest_median_prior':
    #             median_prior = np.median(prior_weights)
    #         elif config.sample_replace_strategy == 'constant_tail':
    #             idx = np.sort(prior_weights)
    #             lt_idx = idx[1:config.lt_size]
    #             st_idx = idx[1:config.lt_size+1:]

    #             minw = np.min(prior_weights[st_idx])
    #             if minw != 0:
    #                 lt_mask = np.zeros(prior_weights.shape, dtype=np.uint8)
    #                 lt_mask[lt_idx] = True
    #                 lt_mask = lt_mask & (prior_weights > 0)
    #                 prior_weights[lt_mask] = minw * (1 - config.learning_rate)
    #         prior_weights = prior_weights / np.sum(prior_weights)
    #         return prior_weights, replace_idx

    def update_sample_space_model(self, samplesf, new_train_sample, num_training_samples):
        if gpu_config.use_gpu:
            xp = cp.get_array_module(samplesf[0])
        else:
            xp = np
        num_feature_blocks = len(new_train_sample)

        # find the inner product of the new sample with existing samples
        gram_vector = self._find_gram_vector(samplesf, new_train_sample, num_training_samples)

        # find the inner product of the new sample with existing samples
        new_train_sample_norm = 0.

        for i in range(num_feature_blocks):
            new_train_sample_norm += xp.real(2 * xp.vdot(new_train_sample[i].flatten(), new_train_sample[i].flatten()))

        dist_vector = xp.maximum(new_train_sample_norm + xp.diag(self._gram_matrix) - 2 * gram_vector, 0)
        dist_vector[num_training_samples:] = xp.inf

        merged_sample = []
        new_sample = []
        merged_sample_id = -1
        new_sample_id = -1

        if num_training_samples == self.config.num_samples:
            min_sample_id = xp.argmin(self.prior_weights)
            min_sample_weight = self.prior_weights[min_sample_id]
            if min_sample_weight < self.minimum_sample_weight:
                # if any prior weight is less than the minimum allowed weight
                # replace the sample with the new sample
                # udpate distance matrix and the gram matrix
                self._update_distance_matrix(gram_vector, new_train_sample_norm, min_sample_id, -1, 0, 1)

                # normalize the prior weights so that the new sample gets weight as the learning rate
                self.prior_weights[min_sample_id] = 0
                self.prior_weights = self.prior_weights * (1 - self.config.learning_rate) / xp.sum(self.prior_weights)
                self.prior_weights[min_sample_id] = self.config.learning_rate

                # set the new sample and new sample position in the samplesf
                new_sample_id = min_sample_id
                new_sample = new_train_sample
            else:
                # if no sample has low enough prior weight, then we either merge the new sample with
                # an existing sample, or merge two of the existing samples and insert the new sample
                # in the vacated position
                closest_sample_to_new_sample = xp.argmin(dist_vector)
                new_sample_min_dist = dist_vector[closest_sample_to_new_sample]

                # find the closest pair amongst existing samples
                closest_existing_sample_idx = xp.argmin(self._distance_matrix.flatten())
                closest_existing_sample_pair = xp.unravel_index(closest_existing_sample_idx, self._distance_matrix.shape)
                existing_samples_min_dist = self._distance_matrix[closest_existing_sample_pair[0], closest_existing_sample_pair[1]]
                closest_existing_sample1, closest_existing_sample2 = closest_existing_sample_pair
                if closest_existing_sample1 == closest_existing_sample2:
                    raise("Score matrix diagnoal filled wrongly")

                if new_sample_min_dist < existing_samples_min_dist:
                    # if the min distance of the new sample to the existing samples is less than the
                    # min distance amongst any of the existing samples, we merge the new sample with
                    # the nearest existing sample

                    # renormalize prior weights
                    self.prior_weights = self.prior_weights * (1 - self.config.learning_rate)

                    # set the position of the merged sample
                    merged_sample_id = closest_sample_to_new_sample

                    # extract the existing sample the merge
                    existing_sample_to_merge = []
                    for i in range(num_feature_blocks):
                        existing_sample_to_merge.append(samplesf[i][:, :, :, merged_sample_id:merged_sample_id+1])

                    # merge the new_training_sample with existing sample
                    merged_sample = self._merge_samples(existing_sample_to_merge,
                                                        new_train_sample,
                                                        self.prior_weights[merged_sample_id],
                                                        self.config.learning_rate,
                                                        self.config.sample_merge_type)

                    # update distance matrix and the gram matrix
                    self._update_distance_matrix(gram_vector,
                                                 new_train_sample_norm,
                                                 merged_sample_id,
                                                 -1,
                                                 self.prior_weights[merged_sample_id, 0],
                                                 self.config.learning_rate)

                    # udpate the prior weight of the merged sample
                    self.prior_weights[closest_sample_to_new_sample] = self.prior_weights[closest_sample_to_new_sample] + self.config.learning_rate

                else:
                    # if the min distance amongst any of the existing samples is less than the
                    # min distance of the new sample to the existing samples, we merge the nearest
                    # existing samples and insert the new sample in the vacated position

                    # renormalize prior weights
                    self.prior_weights = self.prior_weights * (1 - self.config.learning_rate)

                    if self.prior_weights[closest_existing_sample2] > self.prior_weights[closest_existing_sample1]:
                        tmp = closest_existing_sample1
                        closest_existing_sample1 = closest_existing_sample2
                        closest_existing_sample2 = tmp

                    sample_to_merge1 = []
                    sample_to_merge2 = []
                    for i in range(num_feature_blocks):
                        sample_to_merge1.append(samplesf[i][:, :, :, closest_existing_sample1:closest_existing_sample1+1])
                        sample_to_merge2.append(samplesf[i][:, :, :, closest_existing_sample2:closest_existing_sample2+1])

                    # merge the existing closest samples
                    merged_sample = self._merge_samples(sample_to_merge1,
                                                        sample_to_merge2,
                                                        self.prior_weights[closest_existing_sample1],
                                                        self.prior_weights[closest_existing_sample2],
                                                        self.config.sample_merge_type)

                    # update distance matrix and the gram matrix
                    self._update_distance_matrix(gram_vector,
                                                new_train_sample_norm,
                                                closest_existing_sample1,
                                                closest_existing_sample2,
                                                self.prior_weights[closest_existing_sample1, 0],
                                                self.prior_weights[closest_existing_sample2, 0])

                    # update prior weights for the merged sample and the new sample
                    self.prior_weights[closest_existing_sample1] = self.prior_weights[closest_existing_sample1] + self.prior_weights[closest_existing_sample2]
                    self.prior_weights[closest_existing_sample2] = self.config.learning_rate

                    # set the mreged sample position and new sample position
                    merged_sample_id = closest_existing_sample1
                    new_sample_id = closest_existing_sample2

                    new_sample = new_train_sample
        else:
            # if the memory is not full, insert the new sample in the next empty location
            sample_position = num_training_samples

            # update the distance matrix and the gram matrix
            self._update_distance_matrix(gram_vector, new_train_sample_norm,sample_position, -1, 0, 1)

            # update the prior weight
            if sample_position == 0:
                self.prior_weights[sample_position] = 1
            else:
                self.prior_weights = self.prior_weights * (1 - self.config.learning_rate)
                self.prior_weights[sample_position] = self.config.learning_rate

            new_sample_id = sample_position
            new_sample = new_train_sample

        if abs(1 - xp.sum(self.prior_weights)) > 1e-5:
            raise("weights not properly udpated")

        return merged_sample, new_sample, merged_sample_id, new_sample_id

import numpy as np
from .config import gpu_config
if gpu_config.use_gpu:
    import cupy as cp
from .config import otb_hc_config,otb_deep_config
"""
    code tested no problem
"""

class GMM:
    def __init__(self, num_samples,config):
        self._num_samples = num_samples
        self.config=config
        if not gpu_config.use_gpu:
            self._distance_matrix = np.ones((num_samples, num_samples), dtype=np.float32) * np.inf
            self._gram_matrix = np.ones((num_samples, num_samples), dtype=np.float32) * np.inf
            self.prior_weights = np.zeros((num_samples, 1), dtype=np.float32)
        else:
            self._distance_matrix = cp.ones((num_samples, num_samples), dtype=cp.float32) * cp.inf
            self._gram_matrix = cp.ones((num_samples, num_samples), dtype=cp.float32) * cp.inf
            self.prior_weights = cp.zeros((num_samples, 1), dtype=cp.float32)
        # find the minimum allowed sample weight. samples are discarded if their weights become lower
        self.minimum_sample_weight = self.config.learning_rate * (1 - self.config.learning_rate) ** (2 * self.config.num_samples)



    def _find_gram_vector(self, samplesf, new_sample, num_training_samples):
        if gpu_config.use_gpu:
            xp = cp.get_array_module(samplesf[0])
        else:
            xp = np
        gram_vector = xp.inf * xp.ones((self.config.num_samples))
        if num_training_samples > 0:
            ip = 0.
            for k in range(len(new_sample)):
                samplesf_ = samplesf[k][:, :, :, :num_training_samples]
                samplesf_ = samplesf_.reshape((-1, num_training_samples))
                new_sample_ = new_sample[k].flatten()
                ip += xp.real(2 * samplesf_.T.dot(xp.conj(new_sample_)))
            gram_vector[:num_training_samples] = ip
        return gram_vector

    def _merge_samples(self, sample1, sample2, w1, w2, sample_merge_type):
        alpha1 = w1 / (w1 + w2)
        alpha2 = 1 - alpha1
        if sample_merge_type == 'replace':
            merged_sample = sample1
        elif sample_merge_type == 'merge':
            num_feature_blocks = len(sample1)
            merged_sample = [alpha1 * sample1[k] + alpha2 * sample2[k] for k in range(num_feature_blocks)]
        return merged_sample

    def _update_distance_matrix(self, gram_vector, new_sample_norm, id1, id2, w1, w2):
        """
            update the distance matrix
        """
        if gpu_config.use_gpu:
            xp = cp.get_array_module(gram_vector)
        else:
            xp = np
        alpha1 = w1 / (w1 + w2)
        alpha2 = 1 - alpha1
        if id2 < 0:
            norm_id1 = self._gram_matrix[id1, id1]

            # udpate the gram matrix
            if alpha1 == 0:
                self._gram_matrix[:, id1] = gram_vector
                self._gram_matrix[id1, :] = self._gram_matrix[:, id1]
                self._gram_matrix[id1, id1] = new_sample_norm
            elif alpha2 == 0:
                # new sample is discard
                pass
            else:
                # new sample is merge with an existing sample
                self._gram_matrix[:, id1] = alpha1 * self._gram_matrix[:, id1] + alpha2 * gram_vector
                self._gram_matrix[id1, :] = self._gram_matrix[:, id1]
                self._gram_matrix[id1, id1] = alpha1 ** 2 * norm_id1 + alpha2 ** 2 * new_sample_norm + 2 * alpha1 * alpha2 * gram_vector[id1]

            # udpate distance matrix
            self._distance_matrix[:, id1] = xp.maximum(self._gram_matrix[id1, id1] + xp.diag(self._gram_matrix) - 2 * self._gram_matrix[:, id1], 0)
            # self._distance_matrix[:, id1][np.isnan(self._distance_matrix[:, id1])] = 0
            self._distance_matrix[id1, :] = self._distance_matrix[:, id1]
            self._distance_matrix[id1, id1] = xp.inf
        else:
            if alpha1 == 0 or alpha2 == 0:
                raise("Error!")

            norm_id1 = self._gram_matrix[id1, id1]
            norm_id2 = self._gram_matrix[id2, id2]
            ip_id1_id2 = self._gram_matrix[id1, id2]

            # handle the merge of existing samples
            self._gram_matrix[:, id1] = alpha1 * self._gram_matrix[:, id1] + alpha2 * self._gram_matrix[:, id2]
            # self._distance_matrix[:, id1][np.isnan(self._distance_matrix[:, id1])] = 0
            self._gram_matrix[id1, :] = self._gram_matrix[:, id1]
            self._gram_matrix[id1, id1] = alpha1 ** 2 * norm_id1 + alpha2 ** 2 * norm_id2 + 2 * alpha1 * alpha2 * ip_id1_id2
            gram_vector[id1] = alpha1 * gram_vector[id1] + alpha2 * gram_vector[id2]

            # handle the new sample
            self._gram_matrix[:, id2] = gram_vector
            self._gram_matrix[id2, :] = self._gram_matrix[:, id2]
            self._gram_matrix[id2, id2] = new_sample_norm

            # update the distance matrix
            self._distance_matrix[:, id1] = xp.maximum(self._gram_matrix[id1, id1] + xp.diag(self._gram_matrix) - 2 * self._gram_matrix[:, id1], 0)
            self._distance_matrix[id1, :] = self._distance_matrix[:, id1]
            self._distance_matrix[id1, id1] = xp.inf
            self._distance_matrix[:, id2] = xp.maximum(self._gram_matrix[id2, id2] + xp.diag(self._gram_matrix) - 2 * self._gram_matrix[:, id2], 0)
            self._distance_matrix[id2, :] = self._distance_matrix[:, id2]
            self._distance_matrix[id2, id2] = xp.inf

    # def update_prior_weights(prior_weights, sample_weights, latest_ind, frame_num):
    #     # udpate the training sample weights
    #     if frame_num == 1:
    #         replace_ind = 1
    #         prior_weights[replace_ind] = 1
    #     else:
    #         if config.sample_replace_strategy == 'lowest_prior':
    #             replace_idx = np.argmin(prior_weights)
    #         elif config.sample_replace_strategy == 'lowest_weight':
    #             replace_idx = np.argmin(sample_weights)
    #         elif config.sample_replace_strategy == 'lowest_median_prior':
    #             median_prior = np.median(prior_weights)
    #         elif config.sample_replace_strategy == 'constant_tail':
    #             idx = np.sort(prior_weights)
    #             lt_idx = idx[1:config.lt_size]
    #             st_idx = idx[1:config.lt_size+1:]

    #             minw = np.min(prior_weights[st_idx])
    #             if minw != 0:
    #                 lt_mask = np.zeros(prior_weights.shape, dtype=np.uint8)
    #                 lt_mask[lt_idx] = True
    #                 lt_mask = lt_mask & (prior_weights > 0)
    #                 prior_weights[lt_mask] = minw * (1 - config.learning_rate)
    #         prior_weights = prior_weights / np.sum(prior_weights)
    #         return prior_weights, replace_idx

    def update_sample_space_model(self, samplesf, new_train_sample, num_training_samples):
        if gpu_config.use_gpu:
            xp = cp.get_array_module(samplesf[0])
        else:
            xp = np
        num_feature_blocks = len(new_train_sample)

        # find the inner product of the new sample with existing samples
        gram_vector = self._find_gram_vector(samplesf, new_train_sample, num_training_samples)

        # find the inner product of the new sample with existing samples
        new_train_sample_norm = 0.

        for i in range(num_feature_blocks):
            new_train_sample_norm += xp.real(2 * xp.vdot(new_train_sample[i].flatten(), new_train_sample[i].flatten()))

        dist_vector = xp.maximum(new_train_sample_norm + xp.diag(self._gram_matrix) - 2 * gram_vector, 0)
        dist_vector[num_training_samples:] = xp.inf

        merged_sample = []
        new_sample = []
        merged_sample_id = -1
        new_sample_id = -1

        if num_training_samples == self.config.num_samples:
            min_sample_id = xp.argmin(self.prior_weights)
            min_sample_weight = self.prior_weights[min_sample_id]
            if min_sample_weight < self.minimum_sample_weight:
                # if any prior weight is less than the minimum allowed weight
                # replace the sample with the new sample
                # udpate distance matrix and the gram matrix
                self._update_distance_matrix(gram_vector, new_train_sample_norm, min_sample_id, -1, 0, 1)

                # normalize the prior weights so that the new sample gets weight as the learning rate
                self.prior_weights[min_sample_id] = 0
                self.prior_weights = self.prior_weights * (1 - self.config.learning_rate) / xp.sum(self.prior_weights)
                self.prior_weights[min_sample_id] = self.config.learning_rate

                # set the new sample and new sample position in the samplesf
                new_sample_id = min_sample_id
                new_sample = new_train_sample
            else:
                # if no sample has low enough prior weight, then we either merge the new sample with
                # an existing sample, or merge two of the existing samples and insert the new sample
                # in the vacated position
                closest_sample_to_new_sample = xp.argmin(dist_vector)
                new_sample_min_dist = dist_vector[closest_sample_to_new_sample]

                # find the closest pair amongst existing samples
                closest_existing_sample_idx = xp.argmin(self._distance_matrix.flatten())
                closest_existing_sample_pair = xp.unravel_index(closest_existing_sample_idx, self._distance_matrix.shape)
                existing_samples_min_dist = self._distance_matrix[closest_existing_sample_pair[0], closest_existing_sample_pair[1]]
                closest_existing_sample1, closest_existing_sample2 = closest_existing_sample_pair
                if closest_existing_sample1 == closest_existing_sample2:
                    raise("Score matrix diagnoal filled wrongly")

                if new_sample_min_dist < existing_samples_min_dist:
                    # if the min distance of the new sample to the existing samples is less than the
                    # min distance amongst any of the existing samples, we merge the new sample with
                    # the nearest existing sample

                    # renormalize prior weights
                    self.prior_weights = self.prior_weights * (1 - self.config.learning_rate)

                    # set the position of the merged sample
                    merged_sample_id = closest_sample_to_new_sample

                    # extract the existing sample the merge
                    existing_sample_to_merge = []
                    for i in range(num_feature_blocks):
                        existing_sample_to_merge.append(samplesf[i][:, :, :, merged_sample_id:merged_sample_id+1])

                    # merge the new_training_sample with existing sample
                    merged_sample = self._merge_samples(existing_sample_to_merge,
                                                        new_train_sample,
                                                        self.prior_weights[merged_sample_id],
                                                        self.config.learning_rate,
                                                        self.config.sample_merge_type)

                    # update distance matrix and the gram matrix
                    self._update_distance_matrix(gram_vector,
                                                 new_train_sample_norm,
                                                 merged_sample_id,
                                                 -1,
                                                 self.prior_weights[merged_sample_id, 0],
                                                 self.config.learning_rate)

                    # udpate the prior weight of the merged sample
                    self.prior_weights[closest_sample_to_new_sample] = self.prior_weights[closest_sample_to_new_sample] + self.config.learning_rate

                else:
                    # if the min distance amongst any of the existing samples is less than the
                    # min distance of the new sample to the existing samples, we merge the nearest
                    # existing samples and insert the new sample in the vacated position

                    # renormalize prior weights
                    self.prior_weights = self.prior_weights * (1 - self.config.learning_rate)

                    if self.prior_weights[closest_existing_sample2] > self.prior_weights[closest_existing_sample1]:
                        tmp = closest_existing_sample1
                        closest_existing_sample1 = closest_existing_sample2
                        closest_existing_sample2 = tmp

                    sample_to_merge1 = []
                    sample_to_merge2 = []
                    for i in range(num_feature_blocks):
                        sample_to_merge1.append(samplesf[i][:, :, :, closest_existing_sample1:closest_existing_sample1+1])
                        sample_to_merge2.append(samplesf[i][:, :, :, closest_existing_sample2:closest_existing_sample2+1])

                    # merge the existing closest samples
                    merged_sample = self._merge_samples(sample_to_merge1,
                                                        sample_to_merge2,
                                                        self.prior_weights[closest_existing_sample1],
                                                        self.prior_weights[closest_existing_sample2],
                                                        self.config.sample_merge_type)

                    # update distance matrix and the gram matrix
                    self._update_distance_matrix(gram_vector,
                                                new_train_sample_norm,
                                                closest_existing_sample1,
                                                closest_existing_sample2,
                                                self.prior_weights[closest_existing_sample1, 0],
                                                self.prior_weights[closest_existing_sample2, 0])

                    # update prior weights for the merged sample and the new sample
                    self.prior_weights[closest_existing_sample1] = self.prior_weights[closest_existing_sample1] + self.prior_weights[closest_existing_sample2]
                    self.prior_weights[closest_existing_sample2] = self.config.learning_rate

                    # set the mreged sample position and new sample position
                    merged_sample_id = closest_existing_sample1
                    new_sample_id = closest_existing_sample2

                    new_sample = new_train_sample
        else:
            # if the memory is not full, insert the new sample in the next empty location
            sample_position = num_training_samples

            # update the distance matrix and the gram matrix
            self._update_distance_matrix(gram_vector, new_train_sample_norm,sample_position, -1, 0, 1)

            # update the prior weight
            if sample_position == 0:
                self.prior_weights[sample_position] = 1
            else:
                self.prior_weights = self.prior_weights * (1 - self.config.learning_rate)
                self.prior_weights[sample_position] = self.config.learning_rate

            new_sample_id = sample_position
            new_sample = new_train_sample

        if abs(1 - xp.sum(self.prior_weights)) > 1e-5:
            raise("weights not properly udpated")

        return merged_sample, new_sample, merged_sample_id, new_sample_id
code tested no problem # find the minimum allowed sample weight. samples are discarded if their weights become lower update the distance matrix # udpate the gram matrix # new sample is discard # new sample is merge with an existing sample # udpate distance matrix # self._distance_matrix[:, id1][np.isnan(self._distance_matrix[:, id1])] = 0 # handle the merge of existing samples # self._distance_matrix[:, id1][np.isnan(self._distance_matrix[:, id1])] = 0 # handle the new sample # update the distance matrix # def update_prior_weights(prior_weights, sample_weights, latest_ind, frame_num): #     # udpate the training sample weights #     if frame_num == 1: #         replace_ind = 1 #         prior_weights[replace_ind] = 1 #     else: #         if config.sample_replace_strategy == 'lowest_prior': #             replace_idx = np.argmin(prior_weights) #         elif config.sample_replace_strategy == 'lowest_weight': #             replace_idx = np.argmin(sample_weights) #         elif config.sample_replace_strategy == 'lowest_median_prior': #             median_prior = np.median(prior_weights) #         elif config.sample_replace_strategy == 'constant_tail': #             idx = np.sort(prior_weights) #             lt_idx = idx[1:config.lt_size] #             st_idx = idx[1:config.lt_size+1:] #             minw = np.min(prior_weights[st_idx]) #             if minw != 0: #                 lt_mask = np.zeros(prior_weights.shape, dtype=np.uint8) #                 lt_mask[lt_idx] = True #                 lt_mask = lt_mask & (prior_weights > 0) #                 prior_weights[lt_mask] = minw * (1 - config.learning_rate) #         prior_weights = prior_weights / np.sum(prior_weights) #         return prior_weights, replace_idx # find the inner product of the new sample with existing samples # find the inner product of the new sample with existing samples # if any prior weight is less than the minimum allowed weight # replace the sample with the new sample # udpate distance matrix and the gram matrix # normalize the prior weights so that the new sample gets weight as the learning rate # set the new sample and new sample position in the samplesf # if no sample has low enough prior weight, then we either merge the new sample with # an existing sample, or merge two of the existing samples and insert the new sample # in the vacated position # find the closest pair amongst existing samples # if the min distance of the new sample to the existing samples is less than the # min distance amongst any of the existing samples, we merge the new sample with # the nearest existing sample # renormalize prior weights # set the position of the merged sample # extract the existing sample the merge # merge the new_training_sample with existing sample # update distance matrix and the gram matrix # udpate the prior weight of the merged sample # if the min distance amongst any of the existing samples is less than the # min distance of the new sample to the existing samples, we merge the nearest # existing samples and insert the new sample in the vacated position # renormalize prior weights # merge the existing closest samples # update distance matrix and the gram matrix # update prior weights for the merged sample and the new sample # set the mreged sample position and new sample position # if the memory is not full, insert the new sample in the next empty location # update the distance matrix and the gram matrix # update the prior weight