def recover(M,source,target,iterations,calc_predictions=False,M_inv=[]): (I,J,K,X,U,V) = load_X_U_V(source) PMF = VariationalPMF(X,M,K) PMF.run(iterations=iterations,updates=10,calc_predictions=calc_predictions,M_inv=M_inv) predicted_U = PMF.U predicted_V = PMF.V predicted_X = PMF.predicted_X # Write predicted_X, U, V to output file store_X_U_V(target,predicted_X,predicted_U,predicted_V) return
import sys
import generate_example, recover_example
sys.path.append("../../../.")
from variational_pmf.code.run_different_k import try_different_k
from variational_pmf.code.load_store_matrices import load_X_U_V
"""
In this example we use the matrices in the file generated.txt to try different
values of K for recovering U and V, and store how good the results are (storing
the RMSE and variational lower bound in a file performances.txt).
"""
if __name__ == "__main__":
outputfile = "performances.txt"
K_range = range(1,20+1)
no_per_K = 10
fraction_unknown = 0.1
iterations = 50
updates = 10
generated = "generated.txt"
(I,J,K,X,U,V) = load_X_U_V(generated)
try_different_k(filename=outputfile,X=X,K_range=K_range,no_per_K=no_per_K,
fraction=fraction_unknown,iterations=iterations,updates=updates)
