Python numpy.full_like() Examples

def test_arrays_multi():
    apparent_zenith = np.array([[10, 10], [10, 10]])
    apparent_azimuth = np.array([[180, 180], [180, 180]])
    # singleaxis should fail for num dim > 1
    with pytest.raises(ValueError):
        tracking.singleaxis(apparent_zenith, apparent_azimuth,
                            axis_tilt=0, axis_azimuth=0,
                            max_angle=90, backtrack=True,
                            gcr=2.0/7.0)
    # uncomment if we ever get singleaxis to support num dim > 1 arrays
    # assert isinstance(tracker_data, dict)
    # expect = {'tracker_theta': np.full_like(apparent_zenith, 0),
    #           'aoi': np.full_like(apparent_zenith, 10),
    #           'surface_azimuth': np.full_like(apparent_zenith, 90),
    #           'surface_tilt': np.full_like(apparent_zenith, 0)}
    # for k, v in expect.items():
    #     assert_allclose(tracker_data[k], v) 

def test_power_transformer_nans(method):
    # Make sure lambda estimation is not influenced by NaN values
    # and that transform() supports NaN silently

    X = np.abs(X_1col)
    pt = PowerTransformer(method=method)
    pt.fit(X)
    lmbda_no_nans = pt.lambdas_[0]

    # concat nans at the end and check lambda stays the same
    X = np.concatenate([X, np.full_like(X, np.nan)])
    X = shuffle(X, random_state=0)

    pt.fit(X)
    lmbda_nans = pt.lambdas_[0]

    assert_almost_equal(lmbda_no_nans, lmbda_nans, decimal=5)

    X_trans = pt.transform(X)
    assert_array_equal(np.isnan(X_trans), np.isnan(X)) 

def decode(self, buf, out=None):

        # normalise input
        enc = ensure_ndarray(buf).view(self.astype)

        # flatten to simplify implementation
        enc = enc.reshape(-1, order='A')

        # setup output
        dec = np.full_like(enc, fill_value='', dtype=self.dtype)

        # apply decoding
        for i, l in enumerate(self.labels):
            dec[enc == (i + 1)] = l

        # handle output
        dec = ndarray_copy(dec, out)

        return dec 

def windowed_pass_2d(x, win):
    """
    The same as windowed pass, but explicitly
    iterates over the `value()` return array
    and allocates it in the `result`.

    This allows 2-dimensional arrays to be returned
    from `value()` functions, before we support
    the behaviour properly using AST transforms.

    This allows for extremely fast iteration
    for items such as OLS, and at the same time
    calculating t-stats / r^2.
    """
    result = np.full_like(x, np.nan)
    for i in range(win, x.shape[0]+1):
        res = value(x[i-win:i])
        for j, j_val in enumerate(res):
            result[i-1, j] = j_val
    return result 

def as_strided_writeable():
    arr = np.ones(10)
    view = as_strided(arr, writeable=False)
    assert_(not view.flags.writeable)

    # Check that writeable also is fine:
    view = as_strided(arr, writeable=True)
    assert_(view.flags.writeable)
    view[...] = 3
    assert_array_equal(arr, np.full_like(arr, 3))

    # Test that things do not break down for readonly:
    arr.flags.writeable = False
    view = as_strided(arr, writeable=False)
    view = as_strided(arr, writeable=True)
    assert_(not view.flags.writeable) 

def full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None):  # pylint: disable=missing-docstring,redefined-outer-name
  """order, subok and shape arguments mustn't be changed."""
  if order != 'K':
    raise ValueError('Non-standard orders are not supported.')
  if not subok:
    raise ValueError('subok being False is not supported.')
  if shape:
    raise ValueError('Overriding the shape is not supported.')

  a = asarray(a).data
  dtype = dtype or utils.result_type(a)
  fill_value = asarray(fill_value, dtype=dtype)
  return arrays_lib.tensor_to_ndarray(
      tf.broadcast_to(fill_value.data, tf.shape(a)))


# TODO(wangpeng): investigate whether we can make `copy` default to False.
# TODO(wangpeng): utils.np_doc can't handle np.array because np.array is a
#   builtin function. Make utils.np_doc support builtin functions. 

def testFullLike(self):
    # List of 2-tuples of fill value and shape.
    data = [
        (5, ()),
        (5, (7,)),
        (5., (7,)),
        ([5, 8], (2,)),
        ([5, 8], (3, 2)),
        ([[5], [8]], (2, 3)),
        ([[5], [8]], (3, 2, 5)),
        ([[5.], [8.]], (3, 2, 5)),
    ]
    zeros_builders = [array_ops.zeros, np.zeros]
    for f, s in data:
      for fn1, fn2, arr_dtype in itertools.product(
          self.array_transforms, zeros_builders, self.all_types):
        fill_value = fn1(f)
        arr = fn2(s, arr_dtype)
        self.match(
            array_ops.full_like(arr, fill_value), np.full_like(arr, fill_value))
        for dtype in self.all_types:
          self.match(
              array_ops.full_like(arr, fill_value, dtype=dtype),
              np.full_like(arr, fill_value, dtype=dtype)) 

def estimate_mem_use(targets, costs):
    """Estimate memory usage in GB, probably not very accurate.

    Parameters
    ----------
    targets : numpy array
        2D array of targets.
    costs : numpy array
        2D array of costs.

    Returns
    -------
    est_mem : float
        Estimated memory requirement in GB.
    """

    # make sure these match the ones used in optimise below
    visited = np.zeros_like(targets, dtype=np.int8)
    dist = np.full_like(costs, np.nan, dtype=np.float32)
    prev = np.full_like(costs, np.nan, dtype=object)

    est_mem_arr = [targets, costs, visited, dist, prev]
    est_mem = len(pickle.dumps(est_mem_arr, -1))

    return est_mem / 1e9 

def setLOS(self, phi, theta):
        """Set the sandbox's LOS vector

        :param phi: phi in degree
        :type phi: int
        :param theta: theta in degree
        :type theta: int
        """
        if self.reference is not None:
            self._log.warning('Cannot change a referenced model!')
            return

        self._log.debug(
            'Changing model LOS to %d phi and %d theta', phi, theta)

        self.theta = num.full_like(self.theta, theta*r2d)
        self.phi = num.full_like(self.phi, phi*r2d)
        self.frame.updateExtent()

        self._clearModel()
        self.evChanged.notify() 

def as_strided_writeable():
    arr = np.ones(10)
    view = as_strided(arr, writeable=False)
    assert_(not view.flags.writeable)

    # Check that writeable also is fine:
    view = as_strided(arr, writeable=True)
    assert_(view.flags.writeable)
    view[...] = 3
    assert_array_equal(arr, np.full_like(arr, 3))

    # Test that things do not break down for readonly:
    arr.flags.writeable = False
    view = as_strided(arr, writeable=False)
    view = as_strided(arr, writeable=True)
    assert_(not view.flags.writeable) 

def get_C_hat_transpose():
    probs = []
    net.eval()
    for batch_idx, (data, target) in enumerate(train_gold_deterministic_loader):
        # we subtract 10 because we added 10 to gold so we could identify which example is gold in train_phase2
        data, target = torch.autograd.Variable(data.cuda(), volatile=True),\
                       torch.autograd.Variable((target - num_classes).cuda(), volatile=True)

        # forward
        output = net(data)
        pred = F.softmax(output)
        probs.extend(list(pred.data.cpu().numpy()))

    probs = np.array(probs, dtype=np.float32)
    preds = np.argmax(probs, axis=1)
    C_hat = np.zeros([num_classes, num_classes])
    for i in range(len(train_data_gold.train_labels)):
        C_hat[int(np.rint(train_data_gold.train_labels[i] - num_classes)), preds[i]] += 1

    C_hat /= (np.sum(C_hat, axis=1, keepdims=True) + 1e-7)
    C_hat = C_hat * 0.99 + np.full_like(C_hat, 1/num_classes) * 0.01  # smoothing

    return C_hat.T.astype(np.float32) 

def as_strided_writeable():
    arr = np.ones(10)
    view = as_strided(arr, writeable=False)
    assert_(not view.flags.writeable)

    # Check that writeable also is fine:
    view = as_strided(arr, writeable=True)
    assert_(view.flags.writeable)
    view[...] = 3
    assert_array_equal(arr, np.full_like(arr, 3))

    # Test that things do not break down for readonly:
    arr.flags.writeable = False
    view = as_strided(arr, writeable=False)
    view = as_strided(arr, writeable=True)
    assert_(not view.flags.writeable) 

def _is_feasible(kind, enforce_feasibility, f0):
    keyword = kind[0]
    if keyword == "equals":
        lb = np.asarray(kind[1], dtype=float)
        ub = np.asarray(kind[1], dtype=float)
    elif keyword == "greater":
        lb = np.asarray(kind[1], dtype=float)
        ub = np.full_like(lb, np.inf, dtype=float)
    elif keyword == "less":
        ub = np.asarray(kind[1], dtype=float)
        lb = np.full_like(ub, -np.inf, dtype=float)
    elif keyword == "interval":
        lb = np.asarray(kind[1], dtype=float)
        ub = np.asarray(kind[2], dtype=float)
    else:
        raise RuntimeError("Never be here.")

    return ((lb[enforce_feasibility] <= f0[enforce_feasibility]).all()
            and (f0[enforce_feasibility] <= ub[enforce_feasibility]).all()) 

def as_strided_writeable():
    arr = np.ones(10)
    view = as_strided(arr, writeable=False)
    assert_(not view.flags.writeable)

    # Check that writeable also is fine:
    view = as_strided(arr, writeable=True)
    assert_(view.flags.writeable)
    view[...] = 3
    assert_array_equal(arr, np.full_like(arr, 3))

    # Test that things do not break down for readonly:
    arr.flags.writeable = False
    view = as_strided(arr, writeable=False)
    view = as_strided(arr, writeable=True)
    assert_(not view.flags.writeable) 

def as_strided_writeable():
    arr = np.ones(10)
    view = as_strided(arr, writeable=False)
    assert_(not view.flags.writeable)

    # Check that writeable also is fine:
    view = as_strided(arr, writeable=True)
    assert_(view.flags.writeable)
    view[...] = 3
    assert_array_equal(arr, np.full_like(arr, 3))

    # Test that things do not break down for readonly:
    arr.flags.writeable = False
    view = as_strided(arr, writeable=False)
    view = as_strided(arr, writeable=True)
    assert_(not view.flags.writeable) 

def test_old_wminkowski(self):
        with suppress_warnings() as wrn:
            wrn.filter(message="`wminkowski` is deprecated")
            w = np.array([1.0, 2.0, 0.5])
            for x, y in self.cases:
                dist1 = old_wminkowski(x, y, p=1, w=w)
                assert_almost_equal(dist1, 3.0)
                dist1p5 = old_wminkowski(x, y, p=1.5, w=w)
                assert_almost_equal(dist1p5, (2.0**1.5+1.0)**(2./3))
                dist2 = old_wminkowski(x, y, p=2, w=w)
                assert_almost_equal(dist2, np.sqrt(5))

            # test weights Issue #7893
            arr = np.arange(4)
            w = np.full_like(arr, 4)
            assert_almost_equal(old_wminkowski(arr, arr + 1, p=2, w=w), 8.0)
            assert_almost_equal(wminkowski(arr, arr + 1, p=2, w=w), 4.0) 

def set_3d_properties(self, zs=0, zdir='z'):
        xs = self.get_xdata()
        ys = self.get_ydata()

        try:
            # If *zs* is a list or array, then this will fail and
            # just proceed to juggle_axes().
            zs = np.full_like(xs, fill_value=float(zs))
        except TypeError:
            pass
        self._verts3d = juggle_axes(xs, ys, zs, zdir)
        self.stale = True 

def _setup_test_copyparams(self):
        self.link.x.grad.fill(0)
        self.link.y.grad.fill(1)
        self.link.u.initialize((2, 3))
        self.link.u.data.fill(0)
        self.link.u.grad.fill(1)
        self.link.v.cleargrad()
        gx = self.link.x.grad.copy()
        gy = self.link.y.grad.copy()
        gu = self.link.u.grad.copy()

        l = chainer.Link()
        with l.init_scope():
            l.x = chainer.Parameter(shape=(2, 3))
            l.y = chainer.Parameter(shape=2)
            l.u = chainer.Parameter(shape=(2, 3))
            l.v = chainer.Parameter(shape=(3, 2))
        l.x.data.fill(2)
        l.x.grad.fill(3)
        l.y.data.fill(4)
        l.y.grad.fill(5)
        l.u.data.fill(6)
        l.u.grad.fill(7)
        l.v.data.fill(8)
        l.v.grad.fill(9)
        l.add_persistent('p', numpy.full_like(self.link.p, 10))

        return l, (gx, gy, gu) 

def forward_chainer(self, inputs):
        x, = inputs
        y = chainer.functions.max_pooling_nd(
            x, ksize=self.ksize, stride=self.stride, pad=self.pad,
            cover_all=self.cover_all)
        # Convert -inf to finite numbers.
        y = chainer.functions.maximum(y, numpy.full_like(y.array, -1e4))
        return y, 

def test_horizon_tilted():
    # GH 569
    solar_azimuth = np.array([0, 180, 359])
    solar_zenith = np.full_like(solar_azimuth, 45)
    solar_azimuth = pd.Series(solar_azimuth)
    solar_zenith = pd.Series(solar_zenith)
    out = tracking.singleaxis(solar_zenith, solar_azimuth, axis_tilt=90,
                              axis_azimuth=180, backtrack=False, max_angle=180)
    expected = pd.DataFrame(np.array(
        [[ 180.,  45.,   0.,  90.],
         [   0.,  45., 180.,  90.],
         [ 179.,  45., 359.,  90.]]),
        columns=['tracker_theta', 'aoi', 'surface_azimuth', 'surface_tilt'])
    assert_frame_equal(out, expected) 

def iminvert(img):
    """Invert (negate) an image.

    Args:
        img (ndarray): Image to be inverted.

    Returns:
        ndarray: The inverted image.
    """
    return np.full_like(img, 255) - img 

def roots_chebyt(n, mu=False):
    r"""Gauss-Chebyshev (first kind) quadrature.

    Computes the sample points and weights for Gauss-Chebyshev quadrature.
    The sample points are the roots of the n-th degree Chebyshev polynomial of
    the first kind, :math:`T_n(x)`.  These sample points and weights correctly
    integrate polynomials of degree :math:`2n - 1` or less over the interval
    :math:`[-1, 1]` with weight function :math:`f(x) = 1/\sqrt{1 - x^2}`.

    Parameters
    ----------
    n : int
        quadrature order
    mu : bool, optional
        If True, return the sum of the weights, optional.

    Returns
    -------
    x : ndarray
        Sample points
    w : ndarray
        Weights
    mu : float
        Sum of the weights

    See Also
    --------
    scipy.integrate.quadrature
    scipy.integrate.fixed_quad
    numpy.polynomial.chebyshev.chebgauss
    """
    m = int(n)
    if n < 1 or n != m:
        raise ValueError('n must be a positive integer.')
    x = _ufuncs._sinpi(np.arange(-m + 1, m, 2) / (2*m))
    w = np.full_like(x, pi/m)
    if mu:
        return x, w, pi
    else:
        return x, w 

def full(shape, fill_value, dtype=None):  # pylint: disable=redefined-outer-name
  """Returns an array with given shape and dtype filled with `fill_value`.

  Args:
    shape: A valid shape object. Could be a native python object or an object
       of type ndarray, numpy.ndarray or tf.TensorShape.
    fill_value: array_like. Could be an ndarray, a Tensor or any object that can
      be converted to a Tensor using `tf.convert_to_tensor`.
    dtype: Optional, defaults to dtype of the `fill_value`. The type of the
      resulting ndarray. Could be a python type, a NumPy type or a TensorFlow
      `DType`.

  Returns:
    An ndarray.

  Raises:
    ValueError: if `fill_value` can not be broadcast to shape `shape`.
  """
  fill_value = asarray(fill_value, dtype=dtype)
  if utils.isscalar(shape):
    shape = tf.reshape(shape, [1])
  return arrays_lib.tensor_to_ndarray(tf.broadcast_to(fill_value.data, shape))


# Using doc only here since np full_like signature doesn't seem to have the
# shape argument (even though it exists in the documentation online). 

def test_balanced_accuracy_score(y_true, y_pred):
    macro_recall = recall_score(y_true, y_pred, average='macro',
                                labels=np.unique(y_true))
    with ignore_warnings():
        # Warnings are tested in test_balanced_accuracy_score_unseen
        balanced = balanced_accuracy_score(y_true, y_pred)
    assert balanced == pytest.approx(macro_recall)
    adjusted = balanced_accuracy_score(y_true, y_pred, adjusted=True)
    chance = balanced_accuracy_score(y_true, np.full_like(y_true, y_true[0]))
    assert adjusted == (balanced - chance) / (1 - chance) 

def get_derivatives_helper(loss):
    """Return get_gradients() and get_hessians() functions for a given loss.
    """

    def get_gradients(y_true, raw_predictions):
        # create gradients and hessians array, update inplace, and return
        gradients = np.empty_like(raw_predictions, dtype=G_H_DTYPE)
        hessians = np.empty_like(raw_predictions, dtype=G_H_DTYPE)
        loss.update_gradients_and_hessians(gradients, hessians, y_true,
                                           raw_predictions)
        return gradients

    def get_hessians(y_true, raw_predictions):
        # create gradients and hessians array, update inplace, and return
        gradients = np.empty_like(raw_predictions, dtype=G_H_DTYPE)
        hessians = np.empty_like(raw_predictions, dtype=G_H_DTYPE)
        loss.update_gradients_and_hessians(gradients, hessians, y_true,
                                           raw_predictions)

        if loss.__class__.__name__ == 'LeastSquares':
            # hessians aren't updated because they're constant:
            # the value is 1 because the loss is actually an half
            # least squares loss.
            hessians = np.full_like(raw_predictions, fill_value=1)

        return hessians

    return get_gradients, get_hessians 

def test_filled_like(self):
        self.check_like_function(np.full_like, 0, True)
        self.check_like_function(np.full_like, 1, True)
        self.check_like_function(np.full_like, 1000, True)
        self.check_like_function(np.full_like, 123.456, True)
        self.check_like_function(np.full_like, np.inf, True) 

def _do(self, problem, X, **kwargs):

        # The input of has the following shape (n_parents, n_matings, n_var)
        _, n_matings, n_var = X.shape

        # The output owith the shape (n_offsprings, n_matings, n_var)
        # Because there the number of parents and offsprings are equal it keeps the shape of X
        Y = np.full_like(X, None, dtype=np.object)

        # for each mating provided
        for k in range(n_matings):

            # get the first and the second parent
            a, b = X[0, k, 0], X[1, k, 0]

            # prepare the offsprings
            off_a = ["_"] * problem.n_characters
            off_b = ["_"] * problem.n_characters

            for i in range(problem.n_characters):
                if np.random.random() < 0.5:
                    off_a[i] = a[i]
                    off_b[i] = b[i]
                else:
                    off_a[i] = b[i]
                    off_b[i] = a[i]

            # join the character list and set the output
            Y[0, k, 0], Y[1, k, 0] = "".join(off_a), "".join(off_b)

        return Y 

def test_safe_margin(self, bounds_fixture: Bounds):
        new_bounds = bounds_fixture.safe_margin()
        assert numpy.allclose(new_bounds.low, bounds_fixture.low)
        assert numpy.allclose(new_bounds.high, bounds_fixture.high)
        low = numpy.full_like(bounds_fixture.low, -10)
        new_bounds = bounds_fixture.safe_margin(low=low)
        assert numpy.allclose(new_bounds.high, bounds_fixture.high)
        assert numpy.allclose(new_bounds.low, low)
        new_bounds = bounds_fixture.safe_margin(low=low, scale=2)
        assert numpy.allclose(new_bounds.high, bounds_fixture.high * 2)
        assert numpy.allclose(new_bounds.low, low * 2) 

def test_points_in_bounds(self, bounds_fixture):
        zeros = numpy.zeros((3, 3))
        assert all(bounds_fixture.points_in_bounds(zeros))
        tens = numpy.full_like(zeros, 10)
        assert not any(bounds_fixture.points_in_bounds(tens))
        tens = numpy.array([[-10, 0, 1], [0, 0, 0], [10, 10, 10]])
        assert sum(bounds_fixture.points_in_bounds(tens)) == 1 

def modify_weight_matrix_batch(self, som_array, dist_weight_matrix, data):
        """Modify weight matrix of the SOM for the online algorithm.

        Parameters
        ----------
        som_array : np.array
            Weight vectors of the SOM
            shape = (self.n_rows, self.n_columns, X.shape[1])
        dist_weight_matrix : np.array of float
            Current distance weight of the SOM for the specific node
        data : np.array, optional
            True vector(s)
        learningrate : float
            Current learning rate of the SOM

        Returns
        -------
        np.array
            Weight vector of the SOM after the modification

        """
        # calculate numerator and divisor for the batch formula
        numerator = np.sum(
            [np.multiply(data[i], dist_weight_matrix[i].reshape(
                (self.n_rows, self.n_columns, 1)))
                for i in range(len(data))], axis=0)
        divisor = np.sum(dist_weight_matrix, axis=0).reshape(
            (self.n_rows, self.n_columns, 1))

        # update weights
        old_som = np.copy(som_array)
        new_som = np.divide(
            numerator,
            divisor,
            out=np.full_like(numerator, np.nan),
            where=(divisor != 0))

        # overwrite new nans with old entries
        new_som[np.isnan(new_som)] = old_som[np.isnan(new_som)]
        return new_som