Python numpy.empty方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def wordbag2mat(self, wordbag): #testing
        if self.model==None:
            raise Exception("no model")
        matrix=np.empty((len(wordbag),self.len_vector))
        #如果詞典中不存在該詞，拋出異常，但暫時還沒有自定義詞典的辦法，所以暫時不那麽嚴格
        #try:
        #    for i in range(len(wordbag)):
        #        matrix[i,:]=self.model[wordbag[i]]
        #except:
        #    raise Exception("'%s' can not be found in dictionary." % wordbag[i])
        #如果詞典中不存在該詞，則push進一列零向量
        for i in range(len(wordbag)):
            try:
                matrix[i,:]=self.model.wv.__getitem__(wordbag[i])#[wordbag[i]]
            except:
                matrix[i,:]=np.zeros((1,self.len_vector))
        return matrix
################################ problem ##################################### 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def get_cls_results(det_results, annotations, class_id):
    """Get det results and gt information of a certain class.

    Args:
        det_results (list[list]): Same as `eval_map()`.
        annotations (list[dict]): Same as `eval_map()`.
        class_id (int): ID of a specific class.

    Returns:
        tuple[list[np.ndarray]]: detected bboxes, gt bboxes, ignored gt bboxes
    """
    cls_dets = [img_res[class_id] for img_res in det_results]
    cls_gts = []
    cls_gts_ignore = []
    for ann in annotations:
        gt_inds = ann['labels'] == class_id
        cls_gts.append(ann['bboxes'][gt_inds, :])

        if ann.get('labels_ignore', None) is not None:
            ignore_inds = ann['labels_ignore'] == class_id
            cls_gts_ignore.append(ann['bboxes_ignore'][ignore_inds, :])
        else:
            cls_gts_ignore.append(np.empty((0, 4), dtype=np.float32))

    return cls_dets, cls_gts, cls_gts_ignore 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def __init__(self, masks, height, width):
        self.height = height
        self.width = width
        if len(masks) == 0:
            self.masks = np.empty((0, self.height, self.width), dtype=np.uint8)
        else:
            assert isinstance(masks, (list, np.ndarray))
            if isinstance(masks, list):
                assert isinstance(masks[0], np.ndarray)
                assert masks[0].ndim == 2  # (H, W)
            else:
                assert masks.ndim == 3  # (N, H, W)

            self.masks = np.stack(masks).reshape(-1, height, width)
            assert self.masks.shape[1] == self.height
            assert self.masks.shape[2] == self.width 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def test_bitmap_mask_resize():
    # resize with empty bitmap masks
    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
    resized_masks = bitmap_masks.resize((56, 72))
    assert len(resized_masks) == 0
    assert resized_masks.height == 56
    assert resized_masks.width == 72

    # resize with bitmap masks contain 1 instances
    raw_masks = np.diag(np.ones(4, dtype=np.uint8))[np.newaxis, ...]
    bitmap_masks = BitmapMasks(raw_masks, 4, 4)
    resized_masks = bitmap_masks.resize((8, 8))
    assert len(resized_masks) == 1
    assert resized_masks.height == 8
    assert resized_masks.width == 8
    truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0],
                       [0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0],
                       [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0],
                       [0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1]]])
    assert (resized_masks.masks == truth).all() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def test_bitmap_mask_pad():
    # pad with empty bitmap masks
    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
    padded_masks = bitmap_masks.pad((56, 56))
    assert len(padded_masks) == 0
    assert padded_masks.height == 56
    assert padded_masks.width == 56

    # pad with bitmap masks contain 3 instances
    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
    padded_masks = bitmap_masks.pad((56, 56))
    assert len(padded_masks) == 3
    assert padded_masks.height == 56
    assert padded_masks.width == 56
    assert (padded_masks.masks[:, 28:, 28:] == 0).all() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def test_bitmap_mask_crop():
    # crop with empty bitmap masks
    dummy_bbox = np.array([0, 10, 10, 27], dtype=np.int)
    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
    cropped_masks = bitmap_masks.crop(dummy_bbox)
    assert len(cropped_masks) == 0
    assert cropped_masks.height == 17
    assert cropped_masks.width == 10

    # crop with bitmap masks contain 3 instances
    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
    cropped_masks = bitmap_masks.crop(dummy_bbox)
    assert len(cropped_masks) == 3
    assert cropped_masks.height == 17
    assert cropped_masks.width == 10
    x1, y1, x2, y2 = dummy_bbox
    assert (cropped_masks.masks == raw_masks[:, y1:y2, x1:x2]).all()

    # crop with invalid bbox
    with pytest.raises(AssertionError):
        dummy_bbox = dummy_bboxes(2, 28, 28)
        bitmap_masks.crop(dummy_bbox) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def test_bitmap_mask_crop_and_resize():
    dummy_bbox = dummy_bboxes(5, 28, 28)
    inds = np.random.randint(0, 3, (5, ))

    # crop and resize with empty bitmap masks
    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
    cropped_resized_masks = bitmap_masks.crop_and_resize(
        dummy_bbox, (56, 56), inds)
    assert len(cropped_resized_masks) == 0
    assert cropped_resized_masks.height == 56
    assert cropped_resized_masks.width == 56

    # crop and resize with bitmap masks contain 3 instances
    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
    cropped_resized_masks = bitmap_masks.crop_and_resize(
        dummy_bbox, (56, 56), inds)
    assert len(cropped_resized_masks) == 5
    assert cropped_resized_masks.height == 56
    assert cropped_resized_masks.width == 56 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def test_polygon_mask_pad():
    # pad with empty polygon masks
    raw_masks = dummy_raw_polygon_masks((0, 28, 28))
    polygon_masks = PolygonMasks(raw_masks, 28, 28)
    padded_masks = polygon_masks.pad((56, 56))
    assert len(padded_masks) == 0
    assert padded_masks.height == 56
    assert padded_masks.width == 56
    assert padded_masks.to_ndarray().shape == (0, 56, 56)

    # pad with polygon masks contain 3 instances
    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
    polygon_masks = PolygonMasks(raw_masks, 28, 28)
    padded_masks = polygon_masks.pad((56, 56))
    assert len(padded_masks) == 3
    assert padded_masks.height == 56
    assert padded_masks.width == 56
    assert padded_masks.to_ndarray().shape == (3, 56, 56)
    assert (padded_masks.to_ndarray()[:, 28:, 28:] == 0).all() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def test_polygon_mask_crop_and_resize():
    dummy_bbox = dummy_bboxes(5, 28, 28)
    inds = np.random.randint(0, 3, (5, ))

    # crop and resize with empty polygon masks
    raw_masks = dummy_raw_polygon_masks((0, 28, 28))
    polygon_masks = PolygonMasks(raw_masks, 28, 28)
    cropped_resized_masks = polygon_masks.crop_and_resize(
        dummy_bbox, (56, 56), inds)
    assert len(cropped_resized_masks) == 0
    assert cropped_resized_masks.height == 56
    assert cropped_resized_masks.width == 56
    assert cropped_resized_masks.to_ndarray().shape == (0, 56, 56)

    # crop and resize with polygon masks contain 3 instances
    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
    polygon_masks = PolygonMasks(raw_masks, 28, 28)
    cropped_resized_masks = polygon_masks.crop_and_resize(
        dummy_bbox, (56, 56), inds)
    assert len(cropped_resized_masks) == 5
    assert cropped_resized_masks.height == 56
    assert cropped_resized_masks.width == 56
    assert cropped_resized_masks.to_ndarray().shape == (5, 56, 56) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def map_values(values, pos, target_pos, dtype=None, nan=dat.CPG_NAN):
    """Maps `values` array at positions `pos` to `target_pos`.

    Inserts `nan` for uncovered positions.
    """
    assert len(values) == len(pos)
    assert np.all(pos == np.sort(pos))
    assert np.all(target_pos == np.sort(target_pos))

    values = values.ravel()
    pos = pos.ravel()
    target_pos = target_pos.ravel()
    idx = np.in1d(pos, target_pos)
    pos = pos[idx]
    values = values[idx]
    if not dtype:
        dtype = values.dtype
    target_values = np.empty(len(target_pos), dtype=dtype)
    target_values.fill(nan)
    idx = np.in1d(target_pos, pos).nonzero()[0]
    assert len(idx) == len(values)
    assert np.all(target_pos[idx] == pos)
    target_values[idx] = values
    return target_values 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def random_projection(X):

        data_demension = X.shape[1]

        new_data_demension = random.randint(2, data_demension)

        new_X = np.empty((data_demension, new_data_demension))

        minus_one = 0.1
        positive_one = 0.9

        for i in range(len(new_X)):
            for j in range(len(new_X[i])):
                rand = random.random()
                if rand < minus_one:
                    new_X[i][j] = -1.0
                elif rand >= positive_one:
                    new_X[i][j] = 1.0
                else:
                    new_X[i][j] = 0.0

        new_X = np.inner(X, new_X.T)

        return new_X 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def sample_categorical(prob, rng):
    """Sample from independent categorical distributions

    Each batch is an independent categorical distribution.

    Parameters
    ----------
    prob : numpy.ndarray
      Probability of the categorical distribution. Shape --> (batch_num, category_num)
    rng : numpy.random.RandomState

    Returns
    -------
    ret : numpy.ndarray
      Sampling result. Shape --> (batch_num,)
    """
    ret = numpy.empty(prob.shape[0], dtype=numpy.float32)
    for ind in range(prob.shape[0]):
        ret[ind] = numpy.searchsorted(numpy.cumsum(prob[ind]), rng.rand()).clip(min=0.0,
                                                                                max=prob.shape[
                                                                                        1] - 0.5)
    return ret 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
    """Return a new handle with specified shape and context.

    Empty handle is only used to hold results.

    Returns
    -------
    handle
        A new empty `NDArray` handle.
    """
    hdl = NDArrayHandle()
    check_call(_LIB.MXNDArrayCreateEx(
        c_array_buf(mx_uint, native_array('I', shape)),
        mx_uint(len(shape)),
        ctypes.c_int(ctx.device_typeid),
        ctypes.c_int(ctx.device_id),
        ctypes.c_int(int(delay_alloc)),
        ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
        ctypes.byref(hdl)))
    return hdl 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def asnumpy(self):
        """Returns a ``numpy.ndarray`` object with value copied from this array.

        Examples
        --------
        >>> x = mx.nd.ones((2,3))
        >>> y = x.asnumpy()
        >>> type(y)
        <type 'numpy.ndarray'>
        >>> y
        array([[ 1.,  1.,  1.],
               [ 1.,  1.,  1.]], dtype=float32)
        >>> z = mx.nd.ones((2,3), dtype='int32')
        >>> z.asnumpy()
        array([[1, 1, 1],
               [1, 1, 1]], dtype=int32)
        """
        data = np.empty(self.shape, dtype=self.dtype)
        check_call(_LIB.MXNDArraySyncCopyToCPU(
            self.handle,
            data.ctypes.data_as(ctypes.c_void_p),
            ctypes.c_size_t(data.size)))
        return data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import empty [as 別名]
def predict_2d_space(net, delta=0.05):
    """
    Iterate predictions over a 2d space
    :param net: (object) A NumpyNet model object
    :param delta: space between predictions
    :return: prediction_matrix: the actual predictions
             axis_x and axis_y: the axes (useful for plotting)
    """
    axis_x = np.arange(net.predict_space[0], net.predict_space[1] + delta, delta)
    axis_y = np.arange(net.predict_space[2], net.predict_space[3] + delta, delta)
    prediction_matrix = np.empty((len(axis_x), len(axis_y)))
    for i, x in enumerate(axis_x):
        for j, y in enumerate(axis_y):
            test_prediction = np.array([x, y])
            test_prediction = net.predict(test_prediction)
            prediction_matrix[i, j] = test_prediction
    return prediction_matrix, axis_x, axis_y