Python numpy.arrays方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def generate_tokens(self, text: str) -> List[Any]:
        """ Split text into padded lists of tokens that are part of the recognized vocabulary

        :param text: a piece of text (e.g. a sentence)
        :type text: str
        :return:
        indexed_text (np.array): the token/vocabulary indices of
                recognized words in text, padded to the maximum sentence length
        mask (np.array): a mask indicating which indices in indexed_text
                correspond to words (1s) and which correspond to pads (0s)
        :rtype: tuple (of np.arrays)
        """
        indexed_text = [
            self.word_vocab[word]
            if ((word in self.counts) and (self.counts[word]
                                           > self.count_threshold))
            else self.word_vocab[UNK]
            for word in text.split()
        ]
        pad_length = max((self.token_cutoff - len(indexed_text)), 0)
        mask = [1] * min(len(indexed_text), self.token_cutoff) + [0] * pad_length

        indexed_text = indexed_text[0:self.token_cutoff] + [self.word_vocab[PAD]] * pad_length

        return [np.array(indexed_text), np.array(mask)] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def graph_to_numpy(graph, dtype=None):
    """
    Converts a graph in OGB's library-agnostic format to a representation in
    Numpy/Scipy. See the [Open Graph Benchmark's website](https://ogb.stanford.edu)
    for more information.
    :param graph: OGB library-agnostic graph;
    :param dtype: if set, all output arrays will be cast to this dtype.
    :return:
        - X: np.array of shape (N, F) with the node features;
        - A: scipy.sparse adjacency matrix of shape (N, N) in COOrdinate format;
        - E: if edge features are available, np.array of shape (n_edges, S),
            `None` otherwise.
    """
    N = graph['num_nodes']
    X = graph['node_feat'].astype(dtype)
    row, col = graph['edge_index']
    A = sp.coo_matrix((np.ones_like(row), (row, col)), shape=(N, N)).astype(dtype)
    E = graph['edge_feat'].astype(dtype)

    return X, A, E 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def test_numpy_array_equal_unicode_message(self):
        # Test ensures that `assert_numpy_array_equals` raises the right
        # exception when comparing np.arrays containing differing
        # unicode objects (#20503)

        expected = """numpy array are different

numpy array values are different \\(33\\.33333 %\\)
\\[left\\]:  \\[á, à, ä\\]
\\[right\\]: \\[á, à, å\\]"""

        with tm.assert_raises_regex(AssertionError, expected):
            assert_numpy_array_equal(np.array([u'á', u'à', u'ä']),
                                     np.array([u'á', u'à', u'å']))
        with tm.assert_raises_regex(AssertionError, expected):
            assert_almost_equal(np.array([u'á', u'à', u'ä']),
                                np.array([u'á', u'à', u'å'])) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def as_array(struct):
    """
    Given a dictionary of lists or tuples returns dictionary of np.arrays of same structure.

    Args:
        struct: dictionary of list, tuples etc.

    Returns:
        dict of np.arrays
    """
    if isinstance(struct,dict):
        out = {}
        for key, value in struct.items():
            out[key] = as_array(value)
        return out

    else:
        return np.asarray(struct) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def _interpolate_single_key(self, return_key, tri_index, x, y):
        """
        Performs the interpolation at points belonging to the triangulation
        (inside an unmasked triangles).

        Parameters
        ----------
        return_index : string key from {'z', 'dzdx', 'dzdy'}
            Identifies the requested values (z or its derivatives)
        tri_index : 1d integer array
            Valid triangle index (-1 prohibited)
        x, y : 1d arrays, same shape as `tri_index`
            Valid locations where interpolation is requested.

        Returns
        -------
        ret : 1-d array
            Returned array of the same size as *tri_index*
        """
        raise NotImplementedError("TriInterpolator subclasses" +
                                  "should implement _interpolate_single_key!") 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def get_function_hessians(self, alpha, J, ecc, dofs):
        """
        Parameters
        ----------
        *alpha* is a (N x 3 x 1) array (array of column-matrices) of
        barycentric coordinates
        *J* is a (N x 2 x 2) array of jacobian matrices (jacobian matrix at
        triangle first apex)
        *ecc* is a (N x 3 x 1) array (array of column-matrices) of triangle
        eccentricities
        *dofs* is a (N x 1 x 9) arrays (arrays of row-matrices) of computed
        degrees of freedom.

        Returns
        -------
        Returns the values of interpolated function 2nd-derivatives
        [d2z/dx2, d2z/dy2, d2z/dxdy] in global coordinates at locations alpha,
        as a column-matrices of shape (N x 3 x 1).
        """
        d2sdksi2 = self.get_d2Sidksij2(alpha, ecc)
        d2fdksi2 = _prod_vectorized(dofs, d2sdksi2)
        H_rot = self.get_Hrot_from_J(J)
        d2fdx2 = _prod_vectorized(d2fdksi2, H_rot)
        return _transpose_vectorized(d2fdx2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def _to_matrix_vectorized(M):
    """
    Builds an array of matrices from individuals np.arrays of identical
    shapes.
    *M*: ncols-list of nrows-lists of shape sh.

    Returns M_res np.array of shape (sh, nrow, ncols) so that:
        M_res[...,i,j] = M[i][j]
    """
    assert isinstance(M, (tuple, list))
    assert all(isinstance(item, (tuple, list)) for item in M)
    c_vec = np.asarray([len(item) for item in M])
    assert np.all(c_vec-c_vec[0] == 0)
    r = len(M)
    c = c_vec[0]
    M00 = np.asarray(M[0][0])
    dt = M00.dtype
    sh = [M00.shape[0], r, c]
    M_ret = np.empty(sh, dtype=dt)
    for irow in range(r):
        for icol in range(c):
            M_ret[:, irow, icol] = np.asarray(M[irow][icol])
    return M_ret 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def compute_neighbors_rapids(
    X: np.ndarray,
    n_neighbors: int
):
    """Compute nearest neighbors using RAPIDS cuml.

    Parameters
    ----------
    X: array of shape (n_samples, n_features)
        The data to compute nearest neighbors for.
    n_neighbors
        The number of neighbors to use.

        Returns
    -------
    **knn_indices**, **knn_dists** : np.arrays of shape (n_observations, n_neighbors)
    """
    from cuml.neighbors import NearestNeighbors
    nn = NearestNeighbors(n_neighbors=n_neighbors)
    X_contiguous = np.ascontiguousarray(X, dtype=np.float32)
    nn.fit(X_contiguous)
    knn_distsq, knn_indices = nn.kneighbors(X_contiguous)
    return knn_indices, np.sqrt(knn_distsq) # cuml uses sqeuclidean metric so take sqrt 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def group_points_by_symmetry(self, points):
        """
            This function classifies the points into groups according to the box symmetry given by spglib.

            Args:
                points: (np.array/list) nx3 array which contains positions

            Returns: list of arrays containing geometrically equivalent positions

            It is possible that the original points are not found in the returned list, as the positions outsie
            the box will be projected back to the box.
        """
        struct_copy = self.copy()
        points = np.array(points).reshape(-1, 3)
        struct_copy += Atoms(elements=len(points) * ["Hs"], positions=points)
        struct_copy.center_coordinates_in_unit_cell()
        group_IDs = struct_copy.get_symmetry()["equivalent_atoms"][
            struct_copy.select_index("Hs")
        ]
        return [
            np.round(points[group_IDs == ID], decimals=8) for ID in np.unique(group_IDs)
        ] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def write(self, filename, format=None, **kwargs):
        """
        Write atoms object to a file.

        see ase.io.write for formats.
        kwargs are passed to ase.io.write.

        Args:
            filename:
            format:
            **kwargs:

        Returns:

        """
        from ase.io import write

        atoms = self.copy()
        atoms.arrays["positions"] = atoms.positions
        write(filename, atoms, format, **kwargs) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def parse_frame_line(frame_line):
    """parse a line in otf output.
    :param frame_line: frame line to be parsed
    :type frame_line: string
    :return: species, position, force, uncertainty, and velocity of atom
    :rtype: list, np.arrays
    """

    frame_line = frame_line.split()

    spec = str(frame_line[0])
    position = np.array([float(n) for n in frame_line[1:4]])
    force = np.array([float(n) for n in frame_line[4:7]])
    uncertainty = np.array([float(n) for n in frame_line[7:10]])
    velocity = np.array([float(n) for n in frame_line[10:13]])

    return spec, position, force, uncertainty, velocity 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def make_dataset(x, y=None):
    """Create a slice Dataset from a pandas DataFrame and labels"""
    # TODO(markdaooust): simplify this after the 1.4 cut.
    # Convert the DataFrame to a dict
    x = dict(x)

    # Convert the pd.Series to np.arrays
    for key in x:
        x[key] = np.array(x[key])

    items = [x]
    if y is not None:
        items.append(np.array(y, dtype=np.float32))

    # Create a Dataset of slices
    return tf.data.Dataset.from_tensor_slices(tuple(items)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def test_numpy_array_equal_unicode():
    # see gh-20503
    #
    # Test ensures that `assert_numpy_array_equals` raises the right
    # exception when comparing np.arrays containing differing unicode objects.
    msg = """numpy array are different

numpy array values are different \\(33\\.33333 %\\)
\\[left\\]:  \\[á, à, ä\\]
\\[right\\]: \\[á, à, å\\]"""

    with pytest.raises(AssertionError, match=msg):
        assert_numpy_array_equal(np.array([u"á", u"à", u"ä"]),
                                 np.array([u"á", u"à", u"å"])) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def plot_correlations(corrs, errors=None, ax=None, **plot_kwargs):
    """
    Correlation vs CCA dimension

    :param corrs: correlation values for the CCA dimensions
    :type corrs: 1-D vector
    :param errors: error values
    :type shuffled: 1-D array of size len(corrs)
    :param ax: axis to plot on (default None)
    :type ax: matplotlib axis object
    :return: axis if specified, or plot if axis = None
    """
    # evaluate if np.arrays are passed
    assert type(corrs) is np.ndarray, "'corrs' is not a numpy array."
    if errors is not None:
        assert type(errors) is np.ndarray, "'errors' is not a numpy array."
    # create axis if no axis is passed
    if ax is None:
        ax = plt.gca()
    # get the data for the x and y axis
    y_data = corrs
    x_data = range(1, (len(corrs) + 1))
    # create the plot object
    ax.plot(x_data, y_data, **plot_kwargs)
    if errors is not None:
        ax.fill_between(x_data, y_data - errors, y_data + errors, **plot_kwargs, alpha=0.2)
    # change y and x labels and ticks
    ax.set_xticks(x_data)
    ax.set_ylabel("Correlation")
    ax.set_xlabel("CCA dimension")
    return ax 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import arrays [as 別名]
def get_features_for_one(self, x):	
        '''	
            Computes KNN features for a single object `x`	
        '''	

        NN_output = self.NN.kneighbors(x)	

        # Vector of size `n_neighbors`	
        # Stores indices of the neighbors	
        # NN_output = distances,indices	
        neighs = NN_output[1][0]	

        # Vector of size `n_neighbors`	
        # Stores distances to corresponding neighbors	
        neighs_dist = NN_output[0][0] 	

        # Vector of size `n_neighbors`	
        # Stores labels of corresponding neighbors	
        neighs_y = self.y_train[neighs] 	

        ## ========================================== ##	
        ##              YOUR CODE BELOW	
        ## ========================================== ##	

        # We will accumulate the computed features here	
        # Eventually it will be a list of lists or np.arrays	
        # and we will use np.hstack to concatenate those	
        return_list = [] 	
	
        return return_list