sklearn.feature_extraction.image.grid_to_graph()

sklearn.feature_extraction.image.grid_to_graph(n_x, n_y, n_z=1, mask=None, return_as=, dtype=) [source]

Graph of the pixel-to-pixel connections

Edges exist if 2 voxels are connected.

Parameters:

Parameters:	n_x : int Dimension in x axis n_y : int Dimension in y axis n_z : int, optional, default 1 Dimension in z axis mask : ndarray of booleans, optional An optional mask of the image, to consider only part of the pixels. return_as : np.ndarray or a sparse matrix class, optional The class to use to build the returned adjacency matrix. dtype : dtype, optional, default int The data of the returned sparse matrix. By default it is int

n_x : int

Dimension in x axis

n_y : int

Dimension in y axis

n_z : int, optional, default 1

Dimension in z axis

mask : ndarray of booleans, optional

An optional mask of the image, to consider only part of the pixels.

return_as : np.ndarray or a sparse matrix class, optional

The class to use to build the returned adjacency matrix.

dtype : dtype, optional, default int

The data of the returned sparse matrix. By default it is int

Notes

For scikit-learn versions 0.14.1 and prior, return_as=np.ndarray was handled by returning a dense np.matrix instance. Going forward, np.ndarray returns an np.ndarray, as expected.

For compatibility, user code relying on this method should wrap its calls in np.asarray to avoid type issues.

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.grid_to_graph.html

doc_scikit_learn

2017-01-15 04:26:04

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