Esempio n. 1
0
            # 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."
Esempio n. 2
0
			# 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)))