Python numpy.random.randint() Examples

def test_frame_negate(self):
        expr = self.ex('-')

        # float
        lhs = DataFrame(randn(5, 2))
        expect = -lhs
        result = pd.eval(expr, engine=self.engine, parser=self.parser)
        assert_frame_equal(expect, result)

        # int
        lhs = DataFrame(randint(5, size=(5, 2)))
        expect = -lhs
        result = pd.eval(expr, engine=self.engine, parser=self.parser)
        assert_frame_equal(expect, result)

        # bool doesn't work with numexpr but works elsewhere
        lhs = DataFrame(rand(5, 2) > 0.5)
        if self.engine == 'numexpr':
            with pytest.raises(NotImplementedError):
                result = pd.eval(expr, engine=self.engine, parser=self.parser)
        else:
            expect = -lhs
            result = pd.eval(expr, engine=self.engine, parser=self.parser)
            assert_frame_equal(expect, result) 

def test_series_negate(self):
        expr = self.ex('-')

        # float
        lhs = Series(randn(5))
        expect = -lhs
        result = pd.eval(expr, engine=self.engine, parser=self.parser)
        assert_series_equal(expect, result)

        # int
        lhs = Series(randint(5, size=5))
        expect = -lhs
        result = pd.eval(expr, engine=self.engine, parser=self.parser)
        assert_series_equal(expect, result)

        # bool doesn't work with numexpr but works elsewhere
        lhs = Series(rand(5) > 0.5)
        if self.engine == 'numexpr':
            with pytest.raises(NotImplementedError):
                result = pd.eval(expr, engine=self.engine, parser=self.parser)
        else:
            expect = -lhs
            result = pd.eval(expr, engine=self.engine, parser=self.parser)
            assert_series_equal(expect, result) 

def _get_glyph(gnum, height, width, shift_prob, shift_size):
  if isinstance(gnum, list):
    n = randint(*gnum)
  else:
    n = gnum

  glyph = random_points_in_circle(
      n, 0, 0, 0.5
      )*array((width, height), 'float')
  _spatial_sort(glyph)

  if random()<shift_prob:
    shift = ((-1)**randint(0,2))*shift_size*height
    glyph[:,1] += shift
  if random()<0.5:
    ii = randint(0,n-1,size=(1))
    xy = glyph[ii,:]
    glyph = row_stack((glyph, xy))


  return glyph 

def random_sample(img_scales):
        """Randomly sample an img_scale when ``multiscale_mode=='range'``.

        Args:
            img_scales (list[tuple]): Images scale range for sampling.
                There must be two tuples in img_scales, which specify the lower
                and uper bound of image scales.

        Returns:
            (tuple, None): Returns a tuple ``(img_scale, None)``, where
                ``img_scale`` is sampled scale and None is just a placeholder
                to be consistent with :func:`random_select`.
        """

        assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
        img_scale_long = [max(s) for s in img_scales]
        img_scale_short = [min(s) for s in img_scales]
        long_edge = np.random.randint(
            min(img_scale_long),
            max(img_scale_long) + 1)
        short_edge = np.random.randint(
            min(img_scale_short),
            max(img_scale_short) + 1)
        img_scale = (long_edge, short_edge)
        return img_scale, None 

def random_select(img_scales):
        """Randomly select an img_scale from given candidates.

        Args:
            img_scales (list[tuple]): Images scales for selection.

        Returns:
            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``,
                where ``img_scale`` is the selected image scale and
                ``scale_idx`` is the selected index in the given candidates.
        """

        assert mmcv.is_list_of(img_scales, tuple)
        scale_idx = np.random.randint(len(img_scales))
        img_scale = img_scales[scale_idx]
        return img_scale, scale_idx 

def sample(self, batch_size):
    """
    computes (x_t,u_t,x_{t+1}) pair
    returns tuple of 3 ndarrays with shape
    (batch,x_dim), (batch, u_dim), (batch, x_dim)
    """
    if not self.initialized:
      raise ValueError("Dataset not loaded - call PlaneData.initialize() first.")
    traj=randint(0,num_t,size=batch_size) # which trajectory
    tt=randint(0,T-1,size=batch_size) # time step t for each batch
    X0=np.zeros((batch_size,x_dim))
    U0=np.zeros((batch_size,u_dim),dtype=np.int)
    X1=np.zeros((batch_size,x_dim))
    for i in range(batch_size):
      t=tt[i]
      p=self.P[traj[i], t, :]
      X0[i,:]=self.getX(traj[i],t)
      X1[i,:]=self.getX(traj[i],t+1)
      U0[i,:]=self.U[traj[i], t, :]
    return (X0,U0,X1) 

def test_count_nonzero_axis_consistent(self):
        # Check that the axis behaviour for valid axes in
        # non-special cases is consistent (and therefore
        # correct) by checking it against an integer array
        # that is then casted to the generic object dtype
        from itertools import combinations, permutations

        axis = (0, 1, 2, 3)
        size = (5, 5, 5, 5)
        msg = "Mismatch for axis: %s"

        rng = np.random.RandomState(1234)
        m = rng.randint(-100, 100, size=size)
        n = m.astype(object)

        for length in range(len(axis)):
            for combo in combinations(axis, length):
                for perm in permutations(combo):
                    assert_equal(
                        np.count_nonzero(m, axis=perm),
                        np.count_nonzero(n, axis=perm),
                        err_msg=msg % (perm,)) 

def test_series_pos(self):
        expr = self.ex('+')

        # float
        lhs = Series(randn(5))
        expect = lhs
        result = pd.eval(expr, engine=self.engine, parser=self.parser)
        assert_series_equal(expect, result)

        # int
        lhs = Series(randint(5, size=5))
        expect = lhs
        result = pd.eval(expr, engine=self.engine, parser=self.parser)
        assert_series_equal(expect, result)

        # bool doesn't work with numexpr but works elsewhere
        lhs = Series(rand(5) > 0.5)
        expect = lhs
        result = pd.eval(expr, engine=self.engine, parser=self.parser)
        assert_series_equal(expect, result) 

def test_identity_module(self):
        """ identity module should preserve input """

        with IsolatedSession() as issn:
            pred_input = tf.placeholder(tf.float32, [None, None])
            final_output = tf.identity(pred_input, name='output')
            gfn = issn.asGraphFunction([pred_input], [final_output])

        for _ in range(10):
            m, n = prng.randint(10, 1000, size=2)
            mat = prng.randn(m, n).astype(np.float32)
            with IsolatedSession() as issn:
                feeds, fetches = issn.importGraphFunction(gfn)
                mat_out = issn.run(fetches[0], {feeds[0]: mat})

            self.assertTrue(np.all(mat_out == mat)) 

def _sample_indices(self, record):
        """

        :param record: VideoRecord
        :return: list
        """
        if self.dense_sample:  # i3d dense sample
            sample_pos = max(1, 1 + record.num_frames - 64)
            t_stride = 64 // self.num_segments
            start_idx = 0 if sample_pos == 1 else np.random.randint(0, sample_pos - 1)
            offsets = [(idx * t_stride + start_idx) % record.num_frames for idx in range(self.num_segments)]
            return np.array(offsets) + 1
        else:  # normal sample
            average_duration = (record.num_frames - self.new_length + 1) // self.num_segments
            if average_duration > 0:
                offsets = np.multiply(list(range(self.num_segments)), average_duration) + randint(average_duration,
                                                                                                  size=self.num_segments)
            elif record.num_frames > self.num_segments:
                offsets = np.sort(randint(record.num_frames - self.new_length + 1, size=self.num_segments))
            else:
                offsets = np.zeros((self.num_segments,))
            return offsets + 1 

def resample(self, size=None):
        """
        Randomly sample a dataset from the estimated pdf.

        Parameters
        ----------
        size : int, optional
            The number of samples to draw.  If not provided, then the size is
            the same as the underlying dataset.

        Returns
        -------
        resample : (self.d, `size`) ndarray
            The sampled dataset.

        """
        if size is None:
            size = self.n

        norm = transpose(multivariate_normal(zeros((self.d,), float),
                         self.covariance, size=size))
        indices = randint(0, self.n, size=size)
        means = self.dataset[:, indices]

        return means + norm 

def test_scalar(self):
        s = np.random.RandomState(0)
        assert_equal(s.randint(1000), 684)
        s = np.random.RandomState(4294967295)
        assert_equal(s.randint(1000), 419) 

def test_kroncompare(self):
        from numpy.random import randint

        reps = [(2,), (1, 2), (2, 1), (2, 2), (2, 3, 2), (3, 2)]
        shape = [(3,), (2, 3), (3, 4, 3), (3, 2, 3), (4, 3, 2, 4), (2, 2)]
        for s in shape:
            b = randint(0, 10, size=s)
            for r in reps:
                a = np.ones(r, b.dtype)
                large = tile(b, r)
                klarge = kron(a, b)
                assert_equal(large, klarge) 

def __call__(self, image, boxes, labels):
        im = image.copy()
        im, boxes, labels = self.rand_brightness(im, boxes, labels)
        if random.randint(2):
            distort = Compose(self.pd[:-1])
        else:
            distort = Compose(self.pd[1:])
        im, boxes, labels = distort(im, boxes, labels)
        return self.rand_light_noise(im, boxes, labels) 

def __call__(self, image, boxes, classes):
        _, width, _ = image.shape
        if random.randint(2):
            image = image[:, ::-1]
            boxes = boxes.copy()
            boxes[:, 0::2] = width - boxes[:, 2::-2]
        return image, boxes, classes 

def __call__(self, image, boxes=None, labels=None):
        if random.randint(2):
            delta = random.uniform(-self.delta, self.delta)
            image += delta
        return image, boxes, labels 

def __call__(self, image, boxes=None, labels=None):
        if random.randint(2):
            alpha = random.uniform(self.lower, self.upper)
            image *= alpha
        return image, boxes, labels 

def test():
    np.random.seed(np.arange(10))

    # Create a random array
    ndims = randint(5)+1
    shape = tuple(randint(10)+1 for dim in range(ndims))
    els = reduce(mul, shape)
    a = np.arange(els)
    a.shape = shape

    buf_size = randint(2*els)
    b = Arrayterator(a, buf_size)

    # Check that each block has at most ``buf_size`` elements
    for block in b:
        assert_(len(block.flat) <= (buf_size or els))

    # Check that all elements are iterated correctly
    assert_(list(b.flat) == list(a.flat))

    # Slice arrayterator
    start = [randint(dim) for dim in shape]
    stop = [randint(dim)+1 for dim in shape]
    step = [randint(dim)+1 for dim in shape]
    slice_ = tuple(slice(*t) for t in zip(start, stop, step))
    c = b[slice_]
    d = a[slice_]

    # Check that each block has at most ``buf_size`` elements
    for block in c:
        assert_(len(block.flat) <= (buf_size or els))

    # Check that the arrayterator is sliced correctly
    assert_(np.all(c.__array__() == d))

    # Check that all elements are iterated correctly
    assert_(list(c.flat) == list(d.flat)) 

def __call__(self, image, boxes=None, labels=None):
        if random.randint(2):
            swap = self.perms[random.randint(len(self.perms))]
            shuffle = SwapChannels(swap)  # shuffle channels
            image = shuffle(image)
        return image, boxes, labels 

def __call__(self, image, boxes=None, labels=None):
        if random.randint(2):
            image[:, :, 0] += random.uniform(-self.delta, self.delta)
            image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0
            image[:, :, 0][image[:, :, 0] < 0.0] += 360.0
        return image, boxes, labels 

def __call__(self, image, boxes=None, labels=None):
        if random.randint(2):
            image[:, :, 1] *= random.uniform(self.lower, self.upper)

        return image, boxes, labels 

def test():
    np.random.seed(np.arange(10))

    # Create a random array
    ndims = randint(5)+1
    shape = tuple(randint(10)+1 for dim in range(ndims))
    els = reduce(mul, shape)
    a = np.arange(els)
    a.shape = shape

    buf_size = randint(2*els)
    b = Arrayterator(a, buf_size)

    # Check that each block has at most ``buf_size`` elements
    for block in b:
        assert_(len(block.flat) <= (buf_size or els))

    # Check that all elements are iterated correctly
    assert_(list(b.flat) == list(a.flat))

    # Slice arrayterator
    start = [randint(dim) for dim in shape]
    stop = [randint(dim)+1 for dim in shape]
    step = [randint(dim)+1 for dim in shape]
    slice_ = tuple(slice(*t) for t in zip(start, stop, step))
    c = b[slice_]
    d = a[slice_]

    # Check that each block has at most ``buf_size`` elements
    for block in c:
        assert_(len(block.flat) <= (buf_size or els))

    # Check that the arrayterator is sliced correctly
    assert_(np.all(c.__array__() == d))

    # Check that all elements are iterated correctly
    assert_(list(c.flat) == list(d.flat)) 

def test_kroncompare(self):
        from numpy.random import randint

        reps = [(2,), (1, 2), (2, 1), (2, 2), (2, 3, 2), (3, 2)]
        shape = [(3,), (2, 3), (3, 4, 3), (3, 2, 3), (4, 3, 2, 4), (2, 2)]
        for s in shape:
            b = randint(0, 10, size=s)
            for r in reps:
                a = np.ones(r, b.dtype)
                large = tile(b, r)
                klarge = kron(a, b)
                assert_equal(large, klarge) 

def get_minibatch(roidb, num_classes):
  """Given a roidb, construct a minibatch sampled from it."""
  num_images = len(roidb)
  # Sample random scales to use for each image in this batch
  random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES),
                  size=num_images)
  assert(cfg.TRAIN.BATCH_SIZE % num_images == 0), \
    'num_images ({}) must divide BATCH_SIZE ({})'. \
    format(num_images, cfg.TRAIN.BATCH_SIZE)

  # Get the input image blob, formatted for caffe
  im_blob, im_scales = _get_image_blob(roidb, random_scale_inds)

  blobs = {'data': im_blob}

  assert len(im_scales) == 1, "Single batch only"
  assert len(roidb) == 1, "Single batch only"
  
  # gt boxes: (x1, y1, x2, y2, cls)
  if cfg.TRAIN.USE_ALL_GT:
    # Include all ground truth boxes
    gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
  else:
    # For the COCO ground truth boxes, exclude the ones that are ''iscrowd'' 
    gt_inds = np.where(roidb[0]['gt_classes'] != 0 & np.all(roidb[0]['gt_overlaps'].toarray() > -1.0, axis=1))[0]
  gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32)
  gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :] * im_scales[0]
  gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
  blobs['gt_boxes'] = gt_boxes
  blobs['im_info'] = np.array(
    [im_blob.shape[1], im_blob.shape[2], im_scales[0]],
    dtype=np.float32)

  return blobs 

def __getitem__(self, index):
        record = self.video_list[index]
        # check this is a legit video folder

        if self.image_tmpl == 'flow_{}_{:05d}.jpg':
            file_name = self.image_tmpl.format('x', 1)
            full_path = os.path.join(self.root_path, record.path, file_name)
        elif self.image_tmpl == '{:06d}-{}_{:05d}.jpg':
            file_name = self.image_tmpl.format(int(record.path), 'x', 1)
            full_path = os.path.join(self.root_path, '{:06d}'.format(int(record.path)), file_name)
        else:
            file_name = self.image_tmpl.format(1)
            full_path = os.path.join(self.root_path, record.path, file_name)

        while not os.path.exists(full_path):
            print('################## Not Found:', os.path.join(self.root_path, record.path, file_name))
            index = np.random.randint(len(self.video_list))
            record = self.video_list[index]
            if self.image_tmpl == 'flow_{}_{:05d}.jpg':
                file_name = self.image_tmpl.format('x', 1)
                full_path = os.path.join(self.root_path, record.path, file_name)
            elif self.image_tmpl == '{:06d}-{}_{:05d}.jpg':
                file_name = self.image_tmpl.format(int(record.path), 'x', 1)
                full_path = os.path.join(self.root_path, '{:06d}'.format(int(record.path)), file_name)
            else:
                file_name = self.image_tmpl.format(1)
                full_path = os.path.join(self.root_path, record.path, file_name)

        if not self.test_mode:
            segment_indices = self._sample_indices(record) if self.random_shift else self._get_val_indices(record)
        else:
            segment_indices = self._get_test_indices(record)
        return self.get(record, segment_indices) 

def _get_val_indices(self, record):
        if self.dense_sample:  # i3d dense sample
            sample_pos = max(1, 1 + record.num_frames - 64)
            t_stride = 64 // self.num_segments
            start_idx = 0 if sample_pos == 1 else np.random.randint(0, sample_pos - 1)
            offsets = [(idx * t_stride + start_idx) % record.num_frames for idx in range(self.num_segments)]
            return np.array(offsets) + 1
        else:
            if record.num_frames > self.num_segments + self.new_length - 1:
                tick = (record.num_frames - self.new_length + 1) / float(self.num_segments)
                offsets = np.array([int(tick / 2.0 + tick * x) for x in range(self.num_segments)])
            else:
                offsets = np.zeros((self.num_segments,))
            return offsets + 1 

def rand_shape_2d(dim0=10, dim1=10):
    return rnd.randint(1, dim0 + 1), rnd.randint(1, dim1 + 1) 

def test_flattener_module(self):
        """ flattener module should preserve input data """

        gfn = gfac.buildFlattener()
        for _ in range(10):
            m, n = prng.randint(10, 1000, size=2)
            mat = prng.randn(m, n).astype(np.float32)
            with IsolatedSession() as issn:
                feeds, fetches = issn.importGraphFunction(gfn)
                vec_out = issn.run(fetches[0], {feeds[0]: mat})

            self.assertTrue(np.all(vec_out == mat.flatten())) 

def rand_shape_3d(dim0=10, dim1=10, dim2=10):
    return rnd.randint(1, dim0 + 1), rnd.randint(1, dim1 + 1), rnd.randint(1, dim2 + 1) 

def make_gaussians(cluster_n, img_size):
    points = []
    ref_distrs = []
    for i in xrange(cluster_n):
        mean = (0.1 + 0.8*random.rand(2)) * img_size
        a = (random.rand(2, 2)-0.5)*img_size*0.1
        cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
        n = 100 + random.randint(900)
        pts = random.multivariate_normal(mean, cov, n)
        points.append( pts )
        ref_distrs.append( (mean, cov) )
    points = np.float32( np.vstack(points) )
    return points, ref_distrs