Generating a model with simulated fmri and dti data coupled with behavioral features and age , to be used for predicting disease symptoms in the brain.
The Samples are generated in a new directory called 'Samples_fMRI'
TODO : Generate DTI Samples
Refer to fmri.pdf in writeup/fmri.pdf
WARNING! Remember to cleanup before generating data to avoid mess.
Cleans up all the generated data.
USAGE: python main_script.py num_ppl num_samples noise_strength num_nodes topology sub_nodes behav_ft num_scans phenotypes
num_ppl : No. of persons for whom the data has to be simulated. num_samples : No. of samples of age for every person between the range 7 and 21. behav_ft : No. of behavioral features for every person. noise_strength: Amount of normal noise required to add to the behavioral data time series (use 1) num_nodes : No. of nodes in the brain under question. topology : star/ substar (use substar) sub_node : Size of sub cluster num_scans : No. of scans for every age. phenotypes : No. of observed variables.
Generates the age, b0, F for the number of individuals as reqd.
USAGE: python generate_abF.py num_ppl num_samples behav_ft
Current usage: num_ppl =10, num_samples=4, behav_ft=30
Generates a time series of b based on age and F for every person for the age that has been sampled.
USAGE: python generate_b.py num_ppl behav_ft noise_strength
Generates an adjacency matrix 'adj_mat', nodes and edges in the local directory.
USAGE: python generate_graph.py num_nodes topology substar_count ( if single star, 300 star 0 else 300 substar 5)
Generates Wd and Wf in the local directory.
USAGE: python generate_W.py num_nodes behav_ft
generates fMRI and DTI samples
USAGE: python generate_samples.py num_ppl num_nodes num_scans
generates Z= Thetaf*alpha where alpha is the predictive weight for all theta(a,b)
USAGE: python generate_Z
Proximal Gradient Descent
Sincere thanks to Eunho Yang and spams toolkit. Computes the weight from the input data using proximal_flat in spams.
Dependency : Python Sparse Modeling Toolkit - SPAMS
present in directory pgd.
USAGE : python pgd/pgd.py (from the curr dir)
same as generate_Z but generates estimated Zs using the recomputed weight.
USAGE : python w_results.py
performs an analysis of the computed weights.