# Weight Virus pairwiseViral = Weight.load(args.weight[1]) virus_weight = Weight(viral_profs, pairwiseViral) virus_clusters = virus_weight.cluster(cluster_type, d) virus_weight.weight(virus_clusters) # Create SVM c = args.c[0] kernel = args.kernel[0] kernel_var = float(args.kernel[1]) svm = SVM(gta_profs, viral_profs, c, kernel, kernel_var) # Print support vectors if args.svs: svm.show_svs() # Xval if args.xval: nfolds = args.xval if args.weight: result = svm.xval(nfolds, NREPS, pairwiseGTA, pairwiseViral, cluster_type, d) else: result = svm.xval(nfolds, NREPS) if mini: print("GTA Correct\tViral Correct") print("%.2f\t%.2f" % (result[0], result[1])) else: print( "We correctly classified (on average) %.2f/%d GTA and %.2f/%d Viral genes."
# Weight Virus pairwiseViral = Weight.load(args.weight[1]) virus_weight = Weight(viral_profs, pairwiseViral) virus_clusters = virus_weight.cluster(cluster_type, d) virus_weight.weight(virus_clusters) # Create SVM c = args.c[0] kernel = args.kernel[0] kernel_var = float(args.kernel[1]) svm = SVM(gta_profs, viral_profs, c, kernel, kernel_var) # Print support vectors if args.svs: svm.show_svs() # Xval if args.xval: nfolds = args.xval if args.weight: result = svm.xval(nfolds, NREPS, pairwiseGTA, pairwiseViral, cluster_type, d) else: result = svm.xval(nfolds, NREPS) if mini: print("GTA Correct\tViral Correct") print("%.2f\t%.2f" % (result[0], result[1])) else: print("We correctly classified (on average) %.2f/%d GTA and %.2f/%d Viral genes." % (result[0], len(gta_profs), result[1], len(viral_profs)))