ants.learn package¶
Submodules¶
ants.learn.decomposition module¶
- ants.learn.decomposition.eig_seg(mask, img_list, apply_segmentation_to_images=False, cthresh=0, smooth=1)[source]¶
Segment a mask into regions based on the max value in an image list. At a given voxel the segmentation label will contain the index to the image that has the largest value. If the 3rd image has the greatest value, the segmentation label will be 3 at that voxel.
- Parameters:
mask (ANTsImage) – D-dimensional mask > 0 defining segmentation region.
img_list (collection of ANTsImage or np.ndarray) – images to use
apply_segmentation_to_images (boolean) – determines if original image list is modified by the segmentation.
cthresh (python:integer) – throw away isolated clusters smaller than this value
smooth (float) – smooth the input data first by this value
- Return type:
Example
>>> import ants >>> mylist = [ants.image_read(ants.get_ants_data('r16')), ants.image_read(ants.get_ants_data('r27')), ants.image_read(ants.get_ants_data('r85'))] >>> myseg = ants.eig_seg(ants.get_mask(mylist[0]), mylist)
- ants.learn.decomposition.initialize_eigenanatomy(initmat, mask=None, initlabels=None, nreps=1, smoothing=0)[source]¶
InitializeEigenanatomy is a helper function to initialize sparseDecom and sparseDecom2. Can be used to estimate sparseness parameters per eigenvector. The user then only chooses nvecs and optional regularization parameters.
- Parameters:
initmat (np.ndarray or ANTsImage) – input matrix where rows provide initial vector values. alternatively, this can be an antsImage which contains labeled regions.
mask (ANTsImage) – mask if available
initlabels (list/tuple of python:integers) – which labels in initmat to use as initial components
nreps (python:integer) – nrepetitions to use
smoothing (float) – if using an initial label image, optionally smooth each roi
- Returns:
- initlistlist of ANTsImage types
initialization list(s) for sparseDecom(2)
- maskANTsImage
mask(s) for sparseDecom(2)
- enameslist of strings
string names of components for sparseDecom(2)
- Return type:
dict w/ the following key/value pairs
Example
>>> import ants >>> import numpy as np >>> mat = np.random.randn(4,100).astype('float32') >>> init = ants.initialize_eigenanatomy(mat)
- ants.learn.decomposition.sparse_decom2(inmatrix, inmask=(None, None), sparseness=(0.01, 0.01), nvecs=3, its=20, cthresh=(0, 0), statdir=None, perms=0, uselong=0, z=0, smooth=0, robust=0, mycoption=0, initialization_list=[], initialization_list2=[], ell1=10, prior_weight=0, verbose=False, rejector=0, max_based=False, version=1)[source]¶
Decomposes two matrices into paired sparse eigenevectors to maximize canonical correlation - aka Sparse CCA. Note: we do not scale the matrices internally. We leave scaling choices to the user.
ANTsR function: sparseDecom2
- Parameters:
inmatrix (2-tuple of ndarrays) – input as inmatrix=(mat1,mat2). n by p input matrix and n by q input matrix , spatial variable lies along columns.
inmask (2-tuple of ANTsImage python:types (optional - one or both)) – optional pair of image masks
sparseness (tuple) – a pair of float values e.g c(0.01,0.1) enforces an unsigned 99 percent and 90 percent sparse solution for each respective view
nvecs (python:integer) – number of eigenvector pairs
its (python:integer) – number of iterations, 10 or 20 usually sufficient
cthresh (2-tuple) – cluster threshold pair
statdir (string (optional)) – temporary directory if you want to look at full output
perms (python:integer) – number of permutations. settings permutations greater than 0 will estimate significance per vector empirically. For small datasets, these may be conservative. p-values depend on how one scales the input matrices.
uselong (boolean) – enforce solutions of both views to be the same - requires matrices to be the same size
z (float) – subject space (low-dimensional space) sparseness value
smooth (float) – smooth the data (only available when mask is used)
robust (boolean) – rank transform input matrices
mycoption (python:integer) – enforce 1 - spatial orthogonality, 2 - low-dimensional orthogonality or 0 - both
initialization_list (list) – initialization for first view
initialization_list2 (list) – initialization for 2nd view
ell1 (float) – gradient descent parameter, if negative then l0 otherwise use l1
prior_weight (scalar) – Scalar value weight on prior between 0 (prior is weak) and 1 (prior is strong). Only engaged if initialization is used
verbose (boolean) – activates verbose output to screen
rejector (scalar) – rejects small correlation solutions
max_based (boolean) – whether to choose max-based thresholding
- Returns:
- projectionsndarray
X projections
- projections2ndarray
Y projections
- eig1ndarray
X components
- eig2ndarray
Y components
- summarypd.DataFrame
first column is canonical correlations, second column is p-values (these are None if perms > 0)
- Return type:
dict w/ following key/value pairs
Example
>>> import numpy as np >>> import ants >>> mat = np.random.randn(20, 100) >>> mat2 = np.random.randn(20, 90) >>> mydecom = ants.sparse_decom2(inmatrix = (mat,mat2), sparseness=(0.1,0.3), nvecs=3, its=3, perms=0)