Python numpy.any方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def add_intercept(self, X):
        """Add 1's to data as last features."""
        # Data shape
        N, D = X.shape

        # Check if there's not already an intercept column
        if np.any(np.sum(X, axis=0) == N):

            # Report
            print('Intercept is not the last feature. Swapping..')

            # Find which column contains the intercept
            intercept_index = np.argwhere(np.sum(X, axis=0) == N)

            # Swap intercept to last
            X = X[:, np.setdiff1d(np.arange(D), intercept_index)]

        # Add intercept as last column
        X = np.hstack((X, np.ones((N, 1))))

        # Append column of 1's to data, and increment dimensionality
        return X, D+1 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def is_pos_def(self, A):
        """
        Check for positive definiteness.

        Parameters
        ---------
        A : array
            square symmetric matrix.

        Returns
        -------
        bool
            whether matrix is positive-definite.
            Warning! Returns false for arrays containing inf or NaN.


        """
        # Check for valid numbers
        if np.any(np.isnan(A)) or np.any(np.isinf(A)):
            return False

        else:
            return np.all(np.real(np.linalg.eigvals(A)) > 0) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def set_grid_data(self):
        """Populate the attrs that hold grid data."""
        if self._grid_data_is_set:
            return
        self._set_mult_grid_attr()
        if not np.any(getattr(self, 'sfc_area', None)):
            try:
                sfc_area = _grid_sfc_area(self.lon, self.lat, self.lon_bounds,
                                          self.lat_bounds)
            except AttributeError:
                sfc_area = _grid_sfc_area(self.lon, self.lat)
            self.sfc_area = sfc_area
        try:
            self.levs_thick = utils.vertcoord.level_thickness(self.level)
        except AttributeError:
            self.level = None
            self.levs_thick = None
        self._grid_data_is_set = True 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def query(self, coords, order=1):
        """
        Returns the map value at the specified location(s) on the sky.

        Args:
            coords (`astropy.coordinates.SkyCoord`): The coordinates to query.
            order (Optional[int]): Interpolation order to use. Defaults to `1`,
                for linear interpolation.

        Returns:
            A float array containing the map value at every input coordinate.
            The shape of the output will be the same as the shape of the
            coordinates stored by `coords`.
        """
        out = np.full(len(coords.l.deg), np.nan, dtype='f4')

        for pole in self.poles:
            m = (coords.b.deg >= 0) if pole == 'ngp' else (coords.b.deg < 0)

            if np.any(m):
                data, w = self._data[pole]
                x, y = w.wcs_world2pix(coords.l.deg[m], coords.b.deg[m], 0)
                out[m] = map_coordinates(data, [y, x], order=order, mode='nearest')

        return out 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def fetch(clobber=False):
    """
    Downloads the 3D dust map of Leike & Ensslin (2019).

    Args:
        clobber (Optional[bool]): If ``True``, any existing file will be
            overwritten, even if it appears to match. If ``False`` (the
            default), ``fetch()`` will attempt to determine if the dataset
            already exists. This determination is not 100\% robust against data
            corruption.
    """
    dest_dir = fname_pattern = os.path.join(data_dir(), 'leike_ensslin_2019')
    fname = os.path.join(dest_dir, 'simple_cube.h5')
    
    # Check if the FITS table already exists
    md5sum = 'f54e01c253453117e3770575bed35078'

    if (not clobber) and fetch_utils.check_md5sum(fname, md5sum):
        print('File appears to exist already. Call `fetch(clobber=True)` '
              'to force overwriting of existing file.')
        return

    # Download from the server
    url = 'https://zenodo.org/record/2577337/files/simple_cube.h5?download=1'
    fetch_utils.download_and_verify(url, md5sum, fname) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def _compute_gradients(self, loss_fn, x, unused_optim_state):
        """Compute a new value of `x` to minimize `loss_fn`.

        Args:
            loss_fn: a callable that takes `x`, a batch of images, and returns
                a batch of loss values. `x` will be optimized to minimize
                `loss_fn(x)`.
            x: A list of Tensors, the values to be updated. This is analogous
                to the `var_list` argument in standard TF Optimizer.
            unused_optim_state: A (possibly nested) dict, containing any state
                info needed for the optimizer.

        Returns:
            new_x: A list of Tensors, the same length as `x`, which are updated
            new_optim_state: A dict, with the same structure as `optim_state`,
                which have been updated.
        """

        # Assumes `x` is a list,
        # and contains a tensor representing a batch of images
        assert len(x) == 1 and isinstance(x, list), \
            'x should be a list and contain only one image tensor'
        x = x[0]
        loss = reduce_mean(loss_fn(x), axis=0)
        return tf.gradients(loss, x) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def _prepro_cpg(self, states, dists):
        """Preprocess the state and distance of neighboring CpG sites."""
        prepro_states = []
        prepro_dists = []
        for state, dist in zip(states, dists):
            nan = state == dat.CPG_NAN
            if np.any(nan):
                state[nan] = np.random.binomial(1, state[~nan].mean(),
                                                nan.sum())
                dist[nan] = self.cpg_max_dist
            dist = np.minimum(dist, self.cpg_max_dist) / self.cpg_max_dist
            prepro_states.append(np.expand_dims(state, 1))
            prepro_dists.append(np.expand_dims(dist, 1))
        prepro_states = np.concatenate(prepro_states, axis=1)
        prepro_dists = np.concatenate(prepro_dists, axis=1)
        if self.cpg_wlen:
            center = prepro_states.shape[2] // 2
            delta = self.cpg_wlen // 2
            tmp = slice(center - delta, center + delta)
            prepro_states = prepro_states[:, :, tmp]
            prepro_dists = prepro_dists[:, :, tmp]
        return (prepro_states, prepro_dists) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def test_convert_docs(self):
        _, df = convert_docs(self.test_pool, include_stress=False)
        test_energies = df[df['dtype'] == 'energy']['y_orig']
        self.assertFalse(np.any(test_energies - self.test_energies))
        test_forces = df[df['dtype'] == 'force']['y_orig']
        for force1, force2 in zip(test_forces, np.array(self.test_forces).ravel()):
            self.assertEqual(force1, force2)

        _, df = convert_docs(self.test_pool, include_stress=True)
        test_energies = df[df['dtype'] == 'energy']['y_orig']
        self.assertFalse(np.any(test_energies - self.test_energies))
        test_forces = df[df['dtype'] == 'force']['y_orig']
        for force1, force2 in zip(test_forces, np.array(self.test_forces).ravel()):
            self.assertEqual(force1, force2)
        test_stresses = df[df['dtype'] == 'stress']['y_orig']
        for stress1, stress2 in zip(test_stresses, np.array(self.test_stresses).ravel()):
            self.assertEqual(stress1, stress2) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def point_on_segment(ac, b, atol=1e-8):
    '''
    point_on_segment((a,b), c) yields True if point x is on segment (a,b) and False otherwise. Note
    that this differs from point_in_segment in that a point that if c is equal to a or b it is
    considered 'on' but not 'in' the segment.
    The option atol can be given and is used only to test for difference from 0; by default it is
    1e-8.
    '''
    (a,c) = ac
    abc = [np.asarray(u) for u in (a,b,c)]
    if any(len(u.shape) > 1 for u in abc): (a,b,c) = [np.reshape(u,(len(u),-1)) for u in abc]
    else:                                  (a,b,c) = abc
    vab = b - a
    vbc = c - b
    vac = c - a
    dab = np.sqrt(np.sum(vab**2, axis=0))
    dbc = np.sqrt(np.sum(vbc**2, axis=0))
    dac = np.sqrt(np.sum(vac**2, axis=0))
    return np.isclose(dab + dbc - dac, 0, atol=atol) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def point_in_segment(ac, b, atol=1e-8):
    '''
    point_in_segment((a,b), c) yields True if point x is in segment (a,b) and False otherwise. Note
    that this differs from point_on_segment in that a point that if c is equal to a or b it is
    considered 'on' but not 'in' the segment.
    The option atol can be given and is used only to test for difference from 0; by default it is
    1e-8.
    '''
    (a,c) = ac
    abc = [np.asarray(u) for u in (a,b,c)]
    if any(len(u.shape) > 1 for u in abc): (a,b,c) = [np.reshape(u,(len(u),-1)) for u in abc]
    else:                                  (a,b,c) = abc
    vab = b - a
    vbc = c - b
    vac = c - a
    dab = np.sqrt(np.sum(vab**2, axis=0))
    dbc = np.sqrt(np.sum(vbc**2, axis=0))
    dac = np.sqrt(np.sum(vac**2, axis=0))
    return (np.isclose(dab + dbc - dac, 0, atol=atol) &
            ~np.isclose(dac - dab, 0, atol=atol) &
            ~np.isclose(dac - dbc, 0, atol=atol)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def should_step_get_rejected(self, standardError):
        """
        Given a standardError, return whether to keep or reject new
        standardError according to the constraint rejectProbability.
        In addition, if flexible flag is set to True, total number of atoms
        not satisfying constraints definition must be decreasing or at least
        remain the same.

        :Parameters:
            #. standardError (number): Standard error to compare with
               Constraint's standard error.

        :Returns:
            #. result (boolean): True to reject step, False to accept.
        """
        if self.__flexible:
            # compute if step should get rejected as a RigidConstraint
            return super(_DistanceConstraint, self).should_step_get_rejected(standardError)
        else:
            cond = self.activeAtomsDataAfterMove["number"]>self.activeAtomsDataBeforeMove["number"]
            if np.any(cond):
                return True
            return False 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def optimize(self, sess, feed_dict):
    reg_input, reg_weight, old_values, targets = sess.run(
        [self.inputs, self.regression_weight, self.values, self.targets],
        feed_dict=feed_dict)

    intended_values = targets * self.mix_frac + old_values * (1 - self.mix_frac)

    # taken from rllab
    reg_coeff = 1e-5
    for _ in range(5):
      best_fit_weight = np.linalg.lstsq(
          reg_input.T.dot(reg_input) +
          reg_coeff * np.identity(reg_input.shape[1]),
          reg_input.T.dot(intended_values))[0]
      if not np.any(np.isnan(best_fit_weight)):
        break
      reg_coeff *= 10

    if len(best_fit_weight.shape) == 1:
      best_fit_weight = np.expand_dims(best_fit_weight, -1)

    sess.run(self.update_regression_weight,
             feed_dict={self.new_regression_weight: best_fit_weight}) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def _testDecoder(self,
                   height=64,
                   width=64,
                   channels=4,
                   batch_norm_params=None,
                   decoder=models.small_decoder):
    codes = tf.to_float(np.random.rand(32, 100))

    with self.test_session() as sess:
      output = decoder(
          codes,
          height=height,
          width=width,
          channels=channels,
          batch_norm_params=batch_norm_params)
      sess.run(tf.global_variables_initializer())
      output_np = sess.run(output)
    self.assertEqual(output_np.shape, (32, height, width, channels))
    self.assertTrue(np.any(output_np))
    self.assertTrue(np.all(np.isfinite(output_np))) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def validate_and_fill_geometry(geom=None, tooclose=0.1, copy=True):
    """Check `geom` for overlapping atoms. Return flattened"""

    npgeom = np.array(geom, copy=copy, dtype=np.float).reshape((-1, 3))

    # Upper triangular
    metric = tooclose ** 2
    tooclose_inds = []
    for x in range(npgeom.shape[0]):
        diffs = npgeom[x] - npgeom[x + 1 :]
        dists = np.einsum("ij,ij->i", diffs, diffs)

        # Record issues
        if np.any(dists < metric):
            indices = np.where(dists < metric)[0]
            tooclose_inds.extend([(x, y, dist) for y, dist in zip(indices + x + 1, dists[indices] ** 0.5)])

    if tooclose_inds:
        raise ValidationError(
            """Following atoms are too close: {}""".format([(i, j, dist) for i, j, dist in tooclose_inds])
        )

    return {"geom": npgeom.reshape((-1))} 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import any [as 别名]
def test_forward(self, batch, return_masks=True):
        """
        test method. wrapper around forward pass of network without usage of any ground truth information.
        prepares input data for processing and stores outputs in a dictionary.
        :param batch: dictionary containing 'data'
        :param return_masks: boolean. If True, full resolution masks are returned for all proposals (speed trade-off).
        :return: results_dict: dictionary with keys:
               'boxes': list over batch elements. each batch element is a list of boxes. each box is a dictionary:
                       [[{box_0}, ... {box_n}], [{box_0}, ... {box_n}], ...]
               'seg_preds': pixel-wise class predictions (b, 1, y, x, (z)) with values [0, n_classes]
        """
        img = batch['data']
        img = torch.from_numpy(img).float().cuda()
        _, _, _, detections, detection_masks = self.forward(img)
        results_dict = get_results(self.cf, img.shape, detections, detection_masks, return_masks=return_masks)
        return results_dict