python类take()的实例源码

def read_chunk(self, idx, chunk_size, padding=(0, 0), nodes=None):

        self._open()

        t_start, t_stop = self._get_t_start_t_stop(idx, chunk_size, padding)

        if self.time_axis == 0:
            local_chunk  = self.data[t_start:t_stop, :]
        elif self.time_axis == 1:
            local_chunk  = self.data[:, t_start:t_stop].T
        self._close()

        if nodes is not None:
            if not numpy.all(nodes == numpy.arange(self.nb_channels)):
                local_chunk = numpy.take(local_chunk, nodes, axis=1)

        return self._scale_data_to_float32(local_chunk)

def read_chunk(self, idx, chunk_size, padding=(0, 0), nodes=None):

        t_start, t_stop = self._get_t_start_t_stop(idx, chunk_size, padding)
        local_shape     = t_stop - t_start

        local_chunk = numpy.zeros((self.nb_channels, local_shape), dtype=self.data_dtype)
        data_slice  = self._get_slice_(t_start, t_stop) 

        self._open()
        count = 0

        for s in data_slice:
            t_slice = len(s)//self.nb_channels
            local_chunk[:, count:count + t_slice] = self.data[s].reshape(self.nb_channels, len(s)//self.nb_channels)
            count += t_slice

        local_chunk = local_chunk.T
        self._close()

        if nodes is not None:
            if not numpy.all(nodes == numpy.arange(self.nb_channels)):
                local_chunk = numpy.take(local_chunk, nodes, axis=1)

        return self._scale_data_to_float32(local_chunk)

def load(shape,vertex_array):
        destination = vertex_array[gx.VA_PTNMTXIDX.name]
        vertex_index = 0
        matrix_table = numpy.zeros(10,numpy.uint32)

        for batch in shape.batches:
            source = numpy.concatenate([primitive.vertices[gx.VA_PTNMTXIDX.name] for primitive in batch.primitives])
            source //= 3

            for i,index in enumerate(batch.matrix_table):
                if index == 0xFFFF: continue
                matrix_table[i] = index

            length = sum(len(primitive.vertices) for primitive in batch.primitives)
            numpy.take(matrix_table,source,0,destination[vertex_index:vertex_index + length])
            vertex_index += length

        glEnableVertexAttribArray(MATRIX_INDEX_ATTRIBUTE_LOCATION)
        vertex_type = vertex_array.dtype
        stride = vertex_type.itemsize
        offset = vertex_type.fields[gx.VA_PTNMTXIDX.name][1]
        glVertexAttribIPointer(MATRIX_INDEX_ATTRIBUTE_LOCATION,1,GL_UNSIGNED_INT,stride,GLvoidp(offset))

def get_caption_batch(loaded_data, data_dir, dataset='flowers', batch_size=64):

    captions = np.zeros((batch_size, loaded_data['max_caps_len']))
    batch_idx = np.random.randint(0, loaded_data['data_length'],
                                  size=batch_size)
    image_ids = np.take(loaded_data['image_list'], batch_idx)
    image_files = []
    image_caps = []
    image_caps_ids = []
    for idx, image_id in enumerate(image_ids):
        image_file = join(data_dir, dataset, 'jpg' + image_id)
        random_caption = random.randint(0, 4)
        image_caps_ids.append(random_caption)
        captions[idx, :] = \
            loaded_data['captions'][image_id][random_caption][
            0:loaded_data['max_caps_len']]

        image_caps.append(loaded_data['captions']
                          [image_id][random_caption])
        image_files.append(image_file)

    return captions, image_files, image_caps, image_ids, image_caps_ids

def get_val_caps_batch(batch_size, loaded_data, data_set, data_dir):

    if data_set == 'flowers':
        captions = np.zeros((batch_size, loaded_data['max_caps_len']))

        batch_idx = np.random.randint(0, loaded_data['val_data_len'],
                                      size = batch_size)
        image_ids = np.take(loaded_data['val_img_list'], batch_idx)
        image_files = []
        image_caps = []
        for idx, image_id in enumerate(image_ids) :
            image_file = join(data_dir,
                              'flowers/jpg/' + image_id)
            random_caption = random.randint(0, 4)
            captions[idx, :] = \
                loaded_data['val_captions'][image_id][random_caption][
                0 :loaded_data['max_caps_len']]

            image_caps.append(loaded_data['str_captions']
                              [image_id][random_caption])
            image_files.append(image_file)

        return captions, image_files, image_caps, image_ids
    else:
        raise Exception('Dataset not found')

def label_ranking_reciprocal_rank(label,  # [sent_num]
                                  preds): # [sent_num]
  """ Calcualting the reciprocal rank according to definition,
  """
  rank = np.argsort(preds)[::-1]

  #pos_rank = np.take(rank, np.where(label == 1)[0])
  #return np.mean(1.0 / pos_rank)

  if_find = False 
  pos = 0
  for r in rank:
      pos += 1
      if label[r] == 1:
          first_pos_r = pos
          if_find = True
          break

  assert(if_find)

  return 1.0 / first_pos_r

def sample(self, n):
        """
        Sample n elements uniformly from the memory
        """
        indices = np.random.choice(self.cur_size, n, replace=False)

        s1 = np.take(self.S1, indices, axis=0)
        a = np.take(self.A, indices)
        r = np.take(self.R, indices)
        s2 = np.take(self.S2, indices, axis=0)
        t = np.take(self.T, indices)

        return s1, a, r, s2, t
        # sample_elements = []
        # for _ in range(n):
        #     sample_elements.append(self.memory[random.randint(0, len(self.memory)-1)])
        #
        # return sample_elements

def test_TakeTransposeInnerOuter(self):
        # Test of take, transpose, inner, outer products
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_equal(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1)))
        assert_equal(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1))
        assert_equal(np.inner(filled(x, 0), filled(y, 0)),
                     inner(x, y))
        assert_equal(np.outer(filled(x, 0), filled(y, 0)),
                     outer(x, y))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3)

def test_generic_methods(self):
        # Tests some MaskedArray methods.
        a = array([1, 3, 2])
        assert_equal(a.any(), a._data.any())
        assert_equal(a.all(), a._data.all())
        assert_equal(a.argmax(), a._data.argmax())
        assert_equal(a.argmin(), a._data.argmin())
        assert_equal(a.choose(0, 1, 2, 3, 4), a._data.choose(0, 1, 2, 3, 4))
        assert_equal(a.compress([1, 0, 1]), a._data.compress([1, 0, 1]))
        assert_equal(a.conj(), a._data.conj())
        assert_equal(a.conjugate(), a._data.conjugate())

        m = array([[1, 2], [3, 4]])
        assert_equal(m.diagonal(), m._data.diagonal())
        assert_equal(a.sum(), a._data.sum())
        assert_equal(a.take([1, 2]), a._data.take([1, 2]))
        assert_equal(m.transpose(), m._data.transpose())

def test_testTakeTransposeInnerOuter(self):
        # Test of take, transpose, inner, outer products
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_(eq(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1))))
        assert_(eq(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1)))
        assert_(eq(np.inner(filled(x, 0), filled(y, 0)),
                   inner(x, y)))
        assert_(eq(np.outer(filled(x, 0), filled(y, 0)),
                   outer(x, y)))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3)

def test_testArrayMethods(self):
        a = array([1, 3, 2])
        self.assertTrue(eq(a.any(), a._data.any()))
        self.assertTrue(eq(a.all(), a._data.all()))
        self.assertTrue(eq(a.argmax(), a._data.argmax()))
        self.assertTrue(eq(a.argmin(), a._data.argmin()))
        self.assertTrue(eq(a.choose(0, 1, 2, 3, 4),
                           a._data.choose(0, 1, 2, 3, 4)))
        self.assertTrue(eq(a.compress([1, 0, 1]), a._data.compress([1, 0, 1])))
        self.assertTrue(eq(a.conj(), a._data.conj()))
        self.assertTrue(eq(a.conjugate(), a._data.conjugate()))
        m = array([[1, 2], [3, 4]])
        self.assertTrue(eq(m.diagonal(), m._data.diagonal()))
        self.assertTrue(eq(a.sum(), a._data.sum()))
        self.assertTrue(eq(a.take([1, 2]), a._data.take([1, 2])))
        self.assertTrue(eq(m.transpose(), m._data.transpose()))

def test_4(self):
        """
        Test of take, transpose, inner, outer products.

        """
        x = self.arange(24)
        y = np.arange(24)
        x[5:6] = self.masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert self.allequal(np.transpose(y, (2, 0, 1)), self.transpose(x, (2, 0, 1)))
        assert self.allequal(np.take(y, (2, 0, 1), 1), self.take(x, (2, 0, 1), 1))
        assert self.allequal(np.inner(self.filled(x, 0), self.filled(y, 0)),
                            self.inner(x, y))
        assert self.allequal(np.outer(self.filled(x, 0), self.filled(y, 0)),
                            self.outer(x, y))
        y = self.array(['abc', 1, 'def', 2, 3], object)
        y[2] = self.masked
        t = self.take(y, [0, 3, 4])
        assert t[0] == 'abc'
        assert t[1] == 2
        assert t[2] == 3

def take(self, indices, axis=None, out=None, mode='raise'):
        """
        """
        (_data, _mask) = (self._data, self._mask)
        cls = type(self)
        # Make sure the indices are not masked
        maskindices = getattr(indices, '_mask', nomask)
        if maskindices is not nomask:
            indices = indices.filled(0)
        # Get the data
        if out is None:
            out = _data.take(indices, axis=axis, mode=mode).view(cls)
        else:
            np.take(_data, indices, axis=axis, mode=mode, out=out)
        # Get the mask
        if isinstance(out, MaskedArray):
            if _mask is nomask:
                outmask = maskindices
            else:
                outmask = _mask.take(indices, axis=axis, mode=mode)
                outmask |= maskindices
            out.__setmask__(outmask)
        return out

    # Array methods

def recalculate_objects(pred_dict, image):
    proposals = pred_dict['rpn_prediction']['proposals']
    proposals_prob = pred_dict['classification_prediction']['rcnn']['cls_prob']
    proposals_target = proposals_prob.argmax(axis=1) - 1
    bbox_offsets = pred_dict[
        'classification_prediction']['rcnn']['bbox_offsets']

    bbox_offsets = bbox_offsets[proposals_target >= 0]
    proposals = proposals[proposals_target >= 0]
    proposals_target = proposals_target[proposals_target >= 0]

    bbox_offsets_idx_pairs = np.stack(
        np.array([
            proposals_target * 4, proposals_target * 4 + 1,
            proposals_target * 4 + 2, proposals_target * 4 + 3]), axis=1)
    bbox_offsets = np.take(bbox_offsets, bbox_offsets_idx_pairs.astype(np.int))

    bboxes = decode(proposals, bbox_offsets)

    return bboxes, proposals_target

def recollect(self, w):
        if w is None:
            self.w = w
            return
        k = w['kernel']
        b = w['biases']
        k = np.take(k, self.inp, 2)
        k = np.take(k, self.out, 3)
        b = np.take(b, self.out)
        assert1 = k.shape == tuple(self.wshape['kernel'])
        assert2 = b.shape == tuple(self.wshape['biases'])
        assert assert1 and assert2, \
        'Dimension not matching in {} recollect'.format(
            self._signature)
        self.w['kernel'] = k
        self.w['biases'] = b

def recollect(self, w):
        if w is None:
            self.w = w
            return
        idx = self.keep_idx
        k = w['kernel']
        b = w['biases']
        self.w['kernel'] = np.take(k, idx, 3) 
        self.w['biases'] = np.take(b, idx)
        if self.batch_norm:
            m = w['moving_mean']
            v = w['moving_variance']
            g = w['gamma']
            self.w['moving_mean'] = np.take(m, idx)
            self.w['moving_variance'] = np.take(v, idx)
            self.w['gamma'] = np.take(g, idx)

def sample_from_histogram(p, n_samples=1):
    """
    returns the indice of bin according to the histogram p

    @param p: histogram
    @type p: numpy.array
    @param n_samples: number of samples to generate
    @type n_samples: integer
    """

    from numpy import add, less, argsort, take, arange
    from numpy.random import random

    indices = argsort(p)
    indices = take(indices, arange(len(p) - 1, -1, -1))

    c = add.accumulate(take(p, indices)) / add.reduce(p)

    return indices[add.reduce(less.outer(c, random(n_samples)), 0)]

def load_dataset():
    if(not os.path.exists("./dataset/training.csv")):
        print("dataset does not exist")
        raise Exception

    #load dataset
    labeled_image = pd.read_csv("./dataset/training.csv")

    #preprocessing dataframe
    image = np.array(labeled_image["Image"].values).reshape(-1,1)
    image = np.apply_along_axis(lambda img: (img[0].split()),1,image)
    image = image.astype(np.int32) #because train_img elements are string before preprocessing
    image = image.reshape(-1,96*96) # data 96 * 96 size image

    label = labeled_image.values[:,:-1]
    label = label.astype(np.float32)

    #nan value to mean value
    col_mean = np.nanmean(label, axis=0)
    indices = np.where(np.isnan(label))
    label[indices] = np.take(col_mean, indices[1])

    return image, label

def test_TakeTransposeInnerOuter(self):
        # Test of take, transpose, inner, outer products
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_equal(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1)))
        assert_equal(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1))
        assert_equal(np.inner(filled(x, 0), filled(y, 0)),
                     inner(x, y))
        assert_equal(np.outer(filled(x, 0), filled(y, 0)),
                     outer(x, y))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3)

def test_generic_methods(self):
        # Tests some MaskedArray methods.
        a = array([1, 3, 2])
        assert_equal(a.any(), a._data.any())
        assert_equal(a.all(), a._data.all())
        assert_equal(a.argmax(), a._data.argmax())
        assert_equal(a.argmin(), a._data.argmin())
        assert_equal(a.choose(0, 1, 2, 3, 4), a._data.choose(0, 1, 2, 3, 4))
        assert_equal(a.compress([1, 0, 1]), a._data.compress([1, 0, 1]))
        assert_equal(a.conj(), a._data.conj())
        assert_equal(a.conjugate(), a._data.conjugate())

        m = array([[1, 2], [3, 4]])
        assert_equal(m.diagonal(), m._data.diagonal())
        assert_equal(a.sum(), a._data.sum())
        assert_equal(a.take([1, 2]), a._data.take([1, 2]))
        assert_equal(m.transpose(), m._data.transpose())

def test_testTakeTransposeInnerOuter(self):
        # Test of take, transpose, inner, outer products
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_(eq(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1))))
        assert_(eq(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1)))
        assert_(eq(np.inner(filled(x, 0), filled(y, 0)),
                   inner(x, y)))
        assert_(eq(np.outer(filled(x, 0), filled(y, 0)),
                   outer(x, y)))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3)

def test_testArrayMethods(self):
        a = array([1, 3, 2])
        self.assertTrue(eq(a.any(), a._data.any()))
        self.assertTrue(eq(a.all(), a._data.all()))
        self.assertTrue(eq(a.argmax(), a._data.argmax()))
        self.assertTrue(eq(a.argmin(), a._data.argmin()))
        self.assertTrue(eq(a.choose(0, 1, 2, 3, 4),
                           a._data.choose(0, 1, 2, 3, 4)))
        self.assertTrue(eq(a.compress([1, 0, 1]), a._data.compress([1, 0, 1])))
        self.assertTrue(eq(a.conj(), a._data.conj()))
        self.assertTrue(eq(a.conjugate(), a._data.conjugate()))
        m = array([[1, 2], [3, 4]])
        self.assertTrue(eq(m.diagonal(), m._data.diagonal()))
        self.assertTrue(eq(a.sum(), a._data.sum()))
        self.assertTrue(eq(a.take([1, 2]), a._data.take([1, 2])))
        self.assertTrue(eq(m.transpose(), m._data.transpose()))

def test_4(self):
        """
        Test of take, transpose, inner, outer products.

        """
        x = self.arange(24)
        y = np.arange(24)
        x[5:6] = self.masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert self.allequal(np.transpose(y, (2, 0, 1)), self.transpose(x, (2, 0, 1)))
        assert self.allequal(np.take(y, (2, 0, 1), 1), self.take(x, (2, 0, 1), 1))
        assert self.allequal(np.inner(self.filled(x, 0), self.filled(y, 0)),
                            self.inner(x, y))
        assert self.allequal(np.outer(self.filled(x, 0), self.filled(y, 0)),
                            self.outer(x, y))
        y = self.array(['abc', 1, 'def', 2, 3], object)
        y[2] = self.masked
        t = self.take(y, [0, 3, 4])
        assert t[0] == 'abc'
        assert t[1] == 2
        assert t[2] == 3

def take(self, indices, axis=None, out=None, mode='raise'):
        """
        """
        (_data, _mask) = (self._data, self._mask)
        cls = type(self)
        # Make sure the indices are not masked
        maskindices = getattr(indices, '_mask', nomask)
        if maskindices is not nomask:
            indices = indices.filled(0)
        # Get the data
        if out is None:
            out = _data.take(indices, axis=axis, mode=mode).view(cls)
        else:
            np.take(_data, indices, axis=axis, mode=mode, out=out)
        # Get the mask
        if isinstance(out, MaskedArray):
            if _mask is nomask:
                outmask = maskindices
            else:
                outmask = _mask.take(indices, axis=axis, mode=mode)
                outmask |= maskindices
            out.__setmask__(outmask)
        return out

    # Array methods

def process_data(coords, nbr_idx, elements):
    num_atoms = len(nbr_idx)

    # truncates off zero padding at the end and maps atomic numbers to atom types
    coords = coords[:num_atoms, :]
    elements = np.array([atom_dictionary[elements[i]] for i in range(num_atoms)], dtype=np.int32)

    # pad the neighbor indices with zeros if not enough neighbors
    elements = np.append(elements, 0)
    for i in range(num_atoms):
        if len(nbr_idx[i]) < 12:
            nbr_idx[i].extend(np.ones([12-len(nbr_idx[i])], dtype=np.int32) * num_atoms)
    nbr_idx = np.array([nbr_idx[i] for i in range(num_atoms)], dtype=np.int32)

    # creates neighboring atom type matrix - 0 = nonexistent atom
    nbr_atoms = np.take(elements, nbr_idx)
    np.place(nbr_idx, nbr_idx >= num_atoms, 0)
    elements = elements[:-1]

    return (coords.astype(np.float32), nbr_idx.astype(np.int32), 
           elements.astype(np.int32), nbr_atoms.astype(np.int32))

def test_take(self):
        def assert_take_ok(mgr, axis, indexer):
            mat = mgr.as_matrix()
            taken = mgr.take(indexer, axis)
            assert_almost_equal(np.take(mat, indexer, axis), taken.as_matrix())
            assert_almost_equal(mgr.axes[axis].take(indexer), taken.axes[axis])

        for mgr in self.MANAGERS:
            for ax in range(mgr.ndim):
                # take/fancy indexer
                yield assert_take_ok, mgr, ax, []
                yield assert_take_ok, mgr, ax, [0, 0, 0]
                yield assert_take_ok, mgr, ax, lrange(mgr.shape[ax])

                if mgr.shape[ax] >= 3:
                    yield assert_take_ok, mgr, ax, [0, 1, 2]
                    yield assert_take_ok, mgr, ax, [-1, -2, -3]

def check_bool(self, func, value, correct, *args, **kwargs):
        while getattr(value, 'ndim', True):
            try:
                res0 = func(value, *args, **kwargs)
                if correct:
                    self.assertTrue(res0)
                else:
                    self.assertFalse(res0)
            except BaseException as exc:
                exc.args += ('dim: %s' % getattr(value, 'ndim', value), )
                raise
            if not hasattr(value, 'ndim'):
                break
            try:
                value = np.take(value, 0, axis=-1)
            except ValueError:
                break

def _left_join_on_index(left_ax, right_ax, join_keys, sort=False):
    if len(join_keys) > 1:
        if not ((isinstance(right_ax, MultiIndex) and
                 len(join_keys) == right_ax.nlevels)):
            raise AssertionError("If more than one join key is given then "
                                 "'right_ax' must be a MultiIndex and the "
                                 "number of join keys must be the number of "
                                 "levels in right_ax")

        left_indexer, right_indexer = \
            _get_multiindex_indexer(join_keys, right_ax, sort=sort)
    else:
        jkey = join_keys[0]

        left_indexer, right_indexer = \
            _get_single_indexer(jkey, right_ax, sort=sort)

    if sort or len(left_ax) != len(left_indexer):
        # if asked to sort or there are 1-to-many matches
        join_index = left_ax.take(left_indexer)
        return join_index, left_indexer, right_indexer

    # left frame preserves order & length of its index
    return left_ax, None, right_indexer

def _sort_labels(uniques, left, right):
    if not isinstance(uniques, np.ndarray):
        # tuplesafe
        uniques = Index(uniques).values

    sorter = uniques.argsort()

    reverse_indexer = np.empty(len(sorter), dtype=np.int64)
    reverse_indexer.put(sorter, np.arange(len(sorter)))

    new_left = reverse_indexer.take(com._ensure_platform_int(left))
    np.putmask(new_left, left == -1, -1)

    new_right = reverse_indexer.take(com._ensure_platform_int(right))
    np.putmask(new_right, right == -1, -1)

    return new_left, new_right

def test_generic_methods(self):
        # Tests some MaskedArray methods.
        a = array([1, 3, 2])
        assert_equal(a.any(), a._data.any())
        assert_equal(a.all(), a._data.all())
        assert_equal(a.argmax(), a._data.argmax())
        assert_equal(a.argmin(), a._data.argmin())
        assert_equal(a.choose(0, 1, 2, 3, 4), a._data.choose(0, 1, 2, 3, 4))
        assert_equal(a.compress([1, 0, 1]), a._data.compress([1, 0, 1]))
        assert_equal(a.conj(), a._data.conj())
        assert_equal(a.conjugate(), a._data.conjugate())

        m = array([[1, 2], [3, 4]])
        assert_equal(m.diagonal(), m._data.diagonal())
        assert_equal(a.sum(), a._data.sum())
        assert_equal(a.take([1, 2]), a._data.take([1, 2]))
        assert_equal(m.transpose(), m._data.transpose())