Bases: object
Classification via VEM algorithm.
Methods
free_energy() | Compute the free energy defined as: |
run([mu, sigma, prop, beta, niters, freeze_prop]) | |
sort_labels(mu) | Sort the array labels to match mean tissue intensities mu. |
ve_step(mu, sigma[, prop, beta]) | VE-step |
vm_step() | Return (mu, sigma) |
A class to represent a variational EM algorithm for tissue classification.
Parameters : | data: array :
labels: int or sequence :
mask: sequence :
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Compute the free energy defined as:
F(q, theta) = int q(x) log q(x)/p(x,y/theta) dx
associated with input parameters mu, sigma and beta (up to an ignored constant).
Sort the array labels to match mean tissue intensities mu.
VE-step
Return (mu, sigma)
ppm: ndarray (4d) data_masked: ndarray (1d, masked data) mask: 3-element tuple of 1d ndarrays (X,Y,Z)
ppm: ndarray (4d) data_masked: ndarray (1d, masked data) mask: 3-element tuple of 1d ndarrays (X,Y,Z)