def corr(args):
if args.alg == 0:
c = PearsonCorrelation(args.h5database)
corr_matrix = c.find_correlations()
if args.out is not None:
with open(args.out, 'wb') as f:
pickle.dump(corr_matrix, f)
elif args.alg == 1:
c = FourierApproximation(args.h5database)
corr_matrix = c.find_correlations(args.k, args.T, args.B, args.e)
if args.out is not None:
with open(args.out, 'wb') as f:
pickle.dump(corr_matrix, f)
elif args.alg == 2:
c = BooleanCorrelation(args.h5database, args.validate)
boolean_corr_matrix = c.boolean_approximation(args.T)
if args.out is not None:
with open(args.out, 'wb') as f:
pickle.dump(boolean_corr_matrix, f)
def corr(args):
if args.alg == 0:
c = PearsonCorrelation(args.h5database)
corr_matrix = c.find_correlations()
if args.out is not None:
with open(args.out, 'wb') as f:
pickle.dump(corr_matrix, f)
elif args.alg == 1:
c = FourierApproximation(args.h5database)
corr_matrix = c.find_correlations(args.k, args.T, args.B, args.e)
if args.out is not None:
with open(args.out, 'wb') as f:
pickle.dump(corr_matrix, f)
elif args.alg == 2:
c = BooleanCorrelation(args.h5database, args.validate)
boolean_corr_matrix = c.boolean_approximation(args.T)
if args.out is not None:
with open(args.out, 'wb') as f:
pickle.dump(boolean_corr_matrix, f)
