Python numpy.argpartition方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def remove_n(self, n):
    """Get n items for removal."""
    assert self.init_length + n <= self.cur_size

    if self.eviction_strategy == 'rand':
      # random removal
      idxs = random.sample(xrange(self.init_length, self.cur_size), n)
    elif self.eviction_strategy == 'fifo':
      # overwrite elements in cyclical fashion
      idxs = [
          self.init_length +
          (self.remove_idx + i) % (self.max_size - self.init_length)
          for i in xrange(n)]
      self.remove_idx = idxs[-1] + 1 - self.init_length
    elif self.eviction_strategy == 'rank':
      # remove lowest-priority indices
      idxs = np.argpartition(self.priorities, n)[:n]

    return idxs 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def _get_init_guess(na, nb, nroots, hdiag):
    '''Initial guess is the single Slater determinant
    '''
    # The "nroots" lowest determinats based on energy expectation value.
    ci0 = []
    try:
        addrs = numpy.argpartition(hdiag, nroots-1)[:nroots]
    except AttributeError:
        addrs = numpy.argsort(hdiag)[:nroots]
    for addr in addrs:
        x = numpy.zeros((na*nb))
        x[addr] = 1
        ci0.append(x.ravel())

    # Add noise
    ci0[0][0 ] += 1e-5
    ci0[0][-1] -= 1e-5
    return ci0 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def analyze(output_data):
    #Results from the engine are returned as a list of 5D numpy arrays:
    #        (Number of Batches x Batch Size x C x H x W)
    output = output_data.reshape(len(LABELS))

    # Get result
    top = np.argmax(output)
    top = LABELS[top]

    # Get top5
    top5 = np.argpartition(output, -5, axis=-1)[-5:]
    top5 = top5[np.argsort(output[top5])][::-1]
    top5_classes = []
    for i in top5:
        top5_classes.append((LABELS[i], output[i]))

    return [top, top5_classes]

#Arguments to create lite engine 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def analyze(output_data):
    #Results from the engine are returned as a list of 5D numpy arrays: 
    #        (Number of Batches x Batch Size x C x H x W)
    output = output_data.reshape(len(LABELS))
    
    # Get result
    top = np.argmax(output)
    top = LABELS[top]
    
    # Get top5
    top5 = np.argpartition(output, -5, axis=-1)[-5:]
    top5 = top5[np.argsort(output[top5])][::-1]
    top5_classes = []
    for i in top5:
        top5_classes.append((LABELS[i], output[i]))
        
    return [top, top5_classes]

#Arguments to create lite engine 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def test_argequivalent(self):
        """ Test it translates from arg<func> to <func> """
        from numpy.random import rand
        a = rand(3, 4, 5)

        funcs = [
            (np.sort, np.argsort, dict()),
            (_add_keepdims(np.min), _add_keepdims(np.argmin), dict()),
            (_add_keepdims(np.max), _add_keepdims(np.argmax), dict()),
            (np.partition, np.argpartition, dict(kth=2)),
        ]

        for func, argfunc, kwargs in funcs:
            for axis in list(range(a.ndim)) + [None]:
                a_func = func(a, axis=axis, **kwargs)
                ai_func = argfunc(a, axis=axis, **kwargs)
                assert_equal(a_func, take_along_axis(a, ai_func, axis=axis)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def closest_docs(self, query, k=1):
        """Closest docs by dot product between query and documents
        in tfidf weighted word vector space.
        """
        spvec = self.text2spvec(query)
        res = spvec * self.doc_mat

        if len(res.data) <= k:
            o_sort = np.argsort(-res.data)
        else:
            o = np.argpartition(-res.data, k)[0:k]
            o_sort = o[np.argsort(-res.data[o])]

        doc_scores = res.data[o_sort]
        doc_ids = [self.get_doc_id(i) for i in res.indices[o_sort]]
        return doc_ids, doc_scores 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def iterate_eos_scores(new_scores, eos_idx, existing_cases = None, beam_width=None)->Tuple[Sequence, Sequence, Sequence]:
    """
    Return the indices and scores corresponding to the eos word.
    Meaning of returned values is the same as for iterate_best_score
    """
    nb_cases, v_size = new_scores.shape
    num_cases = np.arange(nb_cases, dtype=np.int32)
    scores = -cuda.to_cpu(new_scores[:, eos_idx])
    if existing_cases is not None:
        need_to_return = np.logical_not(np.isin(num_cases, existing_cases))
        num_cases = num_cases[need_to_return]
        scores = scores[need_to_return]

    idx_in_cases = np.full(num_cases.shape[0], eos_idx, dtype=np.int32)

    if beam_width is not None:
        if beam_width < len(scores):
            idx_to_keep = np.argpartition(scores, beam_width)[:beam_width]
            scores = scores[idx_to_keep]
            num_cases = num_cases[idx_to_keep]
            idx_in_cases = idx_in_cases[idx_to_keep]

    return num_cases, idx_in_cases, scores 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def get_mAPs(q_output, q_labels, db_output, db_labels, Rs, dist_type):
    dist = distance(q_output, db_output, dist_type=dist_type, pair=True)
    unsorted_ids = np.argpartition(dist, Rs - 1)[:, :Rs]
    APx = []
    for i in range(dist.shape[0]):
        label = q_labels[i, :]
        label[label == 0] = -1
        idx = unsorted_ids[i, :]
        idx = idx[np.argsort(dist[i, :][idx])]
        imatch = np.sum(np.equal(db_labels[idx[0: Rs], :], label), 1) > 0
        rel = np.sum(imatch)
        Lx = np.cumsum(imatch)
        Px = Lx.astype(float) / np.arange(1, Rs + 1, 1)
        if rel != 0:
            APx.append(np.sum(Px * imatch) / rel)
    return np.mean(np.array(APx)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def _initialize_medoids(self, D, n_clusters, random_state_):
        """Select initial mediods when beginning clustering."""

        if self.init == "random":  # Random initialization
            # Pick random k medoids as the initial ones.
            medoids = random_state_.choice(len(D), n_clusters)
        elif self.init == "k-medoids++":
            medoids = self._kpp_init(D, n_clusters, random_state_)
        elif self.init == "heuristic":  # Initialization by heuristic
            # Pick K first data points that have the smallest sum distance
            # to every other point. These are the initial medoids.
            medoids = np.argpartition(np.sum(D, axis=1), n_clusters - 1)[
                :n_clusters
            ]
        else:
            raise ValueError(f"init value '{self.init}' not recognized")

        return medoids

    # Copied from sklearn.cluster.k_means_._k_init 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def get_pairs(self, embeddings, labels):
        if self.cpu:
            embeddings = embeddings.cpu()
        distance_matrix = pdist(embeddings)

        labels = labels.cpu().data.numpy()
        all_pairs = np.array(list(combinations(range(len(labels)), 2)))
        all_pairs = torch.LongTensor(all_pairs)
        positive_pairs = all_pairs[(labels[all_pairs[:, 0]] == labels[all_pairs[:, 1]]).nonzero()]
        negative_pairs = all_pairs[(labels[all_pairs[:, 0]] != labels[all_pairs[:, 1]]).nonzero()]

        negative_distances = distance_matrix[negative_pairs[:, 0], negative_pairs[:, 1]]
        negative_distances = negative_distances.cpu().data.numpy()
        top_negatives = np.argpartition(negative_distances, len(positive_pairs))[:len(positive_pairs)]
        top_negative_pairs = negative_pairs[torch.LongTensor(top_negatives)]

        return positive_pairs, top_negative_pairs 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def format_lines(video_ids, predictions, labels, top_k):
  batch_size = len(video_ids)
  for video_index in range(batch_size):
    n_recall = max(int(numpy.sum(labels[video_index])), 1)
    # labels
    label_indices = numpy.argpartition(labels[video_index], -n_recall)[-n_recall:]
    label_predictions = [(class_index, predictions[video_index][class_index]) 
                           for class_index in label_indices]
    label_predictions = sorted(label_predictions, key=lambda p: -p[1])
    label_str = "\t".join(["%d\t%f"%(x,y) for x,y in label_predictions])
    # predictions
    top_k_indices = numpy.argpartition(predictions[video_index], -top_k)[-top_k:]
    top_k_predictions = [(class_index, predictions[video_index][class_index])
                         for class_index in top_k_indices]
    top_k_predictions = sorted(top_k_predictions, key=lambda p: -p[1])
    top_k_str = "\t".join(["%d\t%f"%(x,y) for x,y in top_k_predictions])
    # compute PERR
    top_n_indices = numpy.argpartition(predictions[video_index], -n_recall)[-n_recall:]
    positives = [labels[video_index][class_index] 
                 for class_index in top_n_indices]
    perr = sum(positives) / float(n_recall)
    # URL
    url = "https://www.youtube.com/watch?v=" + video_ids[video_index].decode('utf-8')
    yield url + "\t" + str(1-perr) + "\t" + top_k_str + "\t" + label_str + "\n" 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def test_partition_cdtype(self):
        d = array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),
                   ('Lancelot', 1.9, 38)],
                  dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])

        tgt = np.sort(d, order=['age', 'height'])
        assert_array_equal(np.partition(d, range(d.size),
                                        order=['age', 'height']),
                           tgt)
        assert_array_equal(d[np.argpartition(d, range(d.size),
                                             order=['age', 'height'])],
                           tgt)
        for k in range(d.size):
            assert_equal(np.partition(d, k, order=['age', 'height'])[k],
                        tgt[k])
            assert_equal(d[np.argpartition(d, k, order=['age', 'height'])][k],
                         tgt[k])

        d = array(['Galahad', 'Arthur', 'zebra', 'Lancelot'])
        tgt = np.sort(d)
        assert_array_equal(np.partition(d, range(d.size)), tgt)
        for k in range(d.size):
            assert_equal(np.partition(d, k)[k], tgt[k])
            assert_equal(d[np.argpartition(d, k)][k], tgt[k]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def _dominant_set_sparse(s, k, is_thresh=False, norm=False):
    """Compute dominant set for a sparse matrix."""
    if is_thresh:
        mask = s > k
        idx, data = np.where(mask), s[mask]
        s = ssp.coo_matrix((data, idx), shape=s.shape)

    else:  # keep top k
        nr, nc = s.shape
        idx = np.argpartition(s, nc - k, axis=1)
        col = idx[:, -k:].ravel()  # idx largest
        row = np.broadcast_to(np.arange(nr)[:, None], (nr, k)).ravel()
        data = s[row, col].ravel()
        s = ssp.coo_matrix((data, (row, col)), shape=s.shape)

    if norm:
        s.data /= s.sum(axis=1).A1[s.row]

    return s.tocsr(copy=False) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def _dominant_set_dense(s, k, is_thresh=False, norm=False, copy=True):
    """Compute dominant set for a dense matrix."""

    if is_thresh:
        s = s.copy() if copy else s
        s[s <= k] = 0

    else:  # keep top k
        nr, nc = s.shape
        idx = np.argpartition(s, nc - k, axis=1)
        row = np.arange(nr)[:, None]
        if copy:
            col = idx[:, -k:]  # idx largest
            data = s[row, col]
            s = np.zeros_like(s)
            s[row, col] = data
        else:
            col = idx[:, :-k]  # idx smallest
            s[row, col] = 0

    if norm:
        s /= np.nansum(s, axis=1, keepdims=True)

    return s 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import argpartition [as 別名]
def csls_sparse(X, Y, idx_x, idx_y, knn = 10):
    def mean_similarity_sparse(X, Y, seeds, knn, axis = 1, metric = 'cosine'):
        if axis == 1:
            dists = sp.spatial.distance.cdist(X[seeds,:], Y, metric=metric)
        else:
            dists = sp.spatial.distance.cdist(X, Y[seeds,:], metric=metric).T
        nghbs = np.argpartition(dists, knn, axis = 1) # for rows #[-k:] # argpartition returns top k not in order but it's efficient (doesnt sort all rows)
        nghbs = nghbs[:,:knn]
        nghbs_dists = np.concatenate([row[indices] for row, indices in zip(dists, nghbs)]).reshape(nghbs.shape)
        nghbs_sims  = 1 - nghbs_dists
        return nghbs_sims.mean(axis = 1)

    src_ms = mean_similarity_sparse(X, Y, idx_x, knn,  axis = 1)
    trg_ms = mean_similarity_sparse(X, Y, idx_y, knn,  axis = 0)
    sims =  1 - sp.spatial.distance.cdist(X[idx_x,:], Y[idx_y,:])
    normalized_sims = ((2*sims - trg_ms).T - src_ms).T
    print(normalized_sims)
    nn = normalized_sims.argmax(axis=1).tolist()
    return nn