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src/n/i/nimfa-1.0/nimfa/examples/synthetic.py   nimfa(Download)
                  lamb = 0.01,
                  lamb_1 = 0.01)
    fit = nimfa.mf_run(model)
    # print all quality measures concerning first target and mixture matrix in multiple NMF
    print_info(fit, idx = 0)
                  n_h = np.mat(np.zeros((rank, 1))),
                  n_sigma = False)
    fit = nimfa.mf_run(model)
    print_info(fit)
 
                  lambda_w = 1.1,
                  lambda_h = 1.1)
    fit = nimfa.mf_run(model)
    print_info(fit)
 
                  k = 0.,
                  sigma = 1.)
    fit = nimfa.mf_run(model)
    print_info(fit)
 
                  initialize_only = True,
                  alpha = 0.01)
    fit = nimfa.mf_run(model)
    print_info(fit)
 

src/m/f/MF-HEAD/nimfa/examples/synthetic.py   MF(Download)
                     lamb = 0.01,
                     lamb_1 = 0.01)
    fit = nimfa.mf_run(model)
    # print all quality measures concerning first target and mixture matrix in
    # multiple NMF
                     n_h=np.mat(np.zeros((rank, 1))),
                     n_sigma=False)
    fit = nimfa.mf_run(model)
    print_info(fit)
 
                     lambda_w=1.1,
                     lambda_h=1.1)
    fit = nimfa.mf_run(model)
    print_info(fit)
 
                     k=0.,
                     sigma=1.)
    fit = nimfa.mf_run(model)
    print_info(fit)
 
                     initialize_only=True,
                     alpha=0.01)
    fit = nimfa.mf_run(model)
    print_info(fit)
 

src/n/i/nimfa-1.0/nimfa/examples/recommendations.py   nimfa(Download)
                  w_min_change = 0)
    print "Performing %s %s %d factorization ..." % (model, model.seed, model.rank) 
    fit = nimfa.mf_run(model)
    print "... Finished"
    sparse_w, sparse_h = fit.fit.sparseness()

src/n/i/nimfa-1.0/nimfa/examples/orl_images.py   nimfa(Download)
                  min_residuals = 1e-8)
    print "Performing %s %s %d factorization ..." % (model, model.seed, model.rank) 
    fit = nimfa.mf_run(model)
    print "... Finished"
    print """Stats:

src/n/i/nimfa-1.0/nimfa/examples/medulloblastoma.py   nimfa(Download)
                    conn_change = 40,
                    initialize_only = True)
        fit = nimfa.mf_run(model)
        print "%2d / 50 :: %s - init: %s ran with  ... %3d / 200 iters ..." % (i + 1, fit.fit, fit.fit.seed, fit.fit.n_iter)
        # Compute connectivity matrix of factorization.

src/n/i/nimfa-1.0/nimfa/examples/gene_func_prediction.py   nimfa(Download)
                  w_min_change = 0)
    print "Performing %s %s %d factorization ..." % (model, model.seed, model.rank) 
    fit = nimfa.mf_run(model)
    print "... Finished"
    sparse_w, sparse_h = fit.fit.sparseness()

src/n/i/nimfa-1.0/nimfa/examples/documents.py   nimfa(Download)
                  objective = 'div')
    print "Performing %s %s %d factorization ..." % (model, model.seed, model.rank) 
    fit = nimfa.mf_run(model)
    print "... Finished"
    sparse_w, sparse_h = fit.fit.sparseness()

src/n/i/nimfa-1.0/nimfa/examples/cbcl_images.py   nimfa(Download)
                  min_residuals = 1e-8)
    print "Performing %s %s %d factorization ..." % (model, model.seed, model.rank) 
    fit = nimfa.mf_run(model)
    print "... Finished"
    sparse_w, sparse_h = fit.fit.sparseness()

src/n/i/nimfa-1.0/nimfa/examples/all_aml.py   nimfa(Download)
                    conn_change = 40,
                    initialize_only = True)
        fit = nimfa.mf_run(model)
        print "%2d / 50 :: %s - init: %s ran with  ... %3d / 200 iters ..." % (i + 1, fit.fit, fit.fit.seed, fit.fit.n_iter)
        # Compute connectivity matrix of factorization.

src/m/f/MF-HEAD/nimfa/examples/recommendations.py   MF(Download)
                     w_min_change=0)
    print "Performing %s %s %d factorization ..." % (model, model.seed, model.rank)
    fit = nimfa.mf_run(model)
    print "... Finished"
    sparse_w, sparse_h = fit.fit.sparseness()

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