How to use the spectral.orthogonalize function in spectral

v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(v_b, rmean_win).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(spectral.orthogonalize(v_a), rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(spectral.orthogonalize(v_b), rmean_win).sum(axis=0).reshape(1, -1)
        elif average.startswith("whole"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=v_a.mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=v_b.mean(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=spectral.orthogonalize(v_a).mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=spectral.orthogonalize(v_b).mean(axis=0).reshape(1, -1)
        #v_a=array([weights_a[word]*embedding[word]
        #                                    for word in weights_a]).sum(axis=0).reshape(1, -1)
        #v_b=array([weights_b[word]*embedding[word]
        #                                    for word in weights_b]).sum(axis=0).reshape(1, -1)
        if args.dist.startswith("all"):
            #distances.append((1-cosine_distances(v_a, v_b),
            #                    euclidean_distances(v_a, v_b),
            #                    manhattan_distances(v_a, v_b)))
            try:
                fo.write("%f\t%f\t%f\n" % (1-cosine_distances(v_a, v_b)[0],
                                                euclidean_distances(v_a, v_b)[0],
                                                manhattan_distances(v_a, v_b)[0]))
            except:
                fo.write("%f\t%f\t%f\t%s\n" % (0.2, 1.0, 1.0,"Distance error in pair: "+str(iPair)))

try:
                weights_b[w]=(weights_b[w], embedding[w])
            except KeyError:
                weights_b[w]=0
                missing_cbow.append(w)
                continue
        logging.info("Weights sentence B %s" % [(w, weights_b[w][0]) for w in weights_b if not weights_b[w] is 0])

        if not average:
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][0]*weights_a[w][1]
                                  for w in weights_a if weights_a[w]!=0]).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][0]*weights_b[w][1]
                                  for w in weights_b if weights_b[w]!=0]).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=spectral.orthogonalize(array([weights_a[w][1] for w in weights_a if weights_a[w]!=0]))
                w_a=array([weights_a[w][0] for w in weights_a if weights_a[w]!=0])
                v_a=multiply(v_a.T, w_a).T.sum(axis=0).reshape(1, -1)
                v_b=spectral.orthogonalize(array([weights_b[w][1] for w in weights_b if weights_b[w]!=0]))
                w_b=array([weights_b[w][0] for w in weights_b if weights_b[w]!=0])
                v_b=multiply(v_b.T, w_b).T.sum(axis=0).reshape(1, -1)
        elif average.startswith("moving"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(v_a, rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(v_b, rmean_win).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(spectral.orthogonalize(v_a), rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(spectral.orthogonalize(v_b), rmean_win).sum(axis=0).reshape(1, -1)

weights_b[w]=0
                missing_cbow.append(w)
                continue
        logging.info("Weights sentence B %s" % [(w, weights_b[w][0]) for w in weights_b if not weights_b[w] is 0])

        if not average:
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][0]*weights_a[w][1]
                                  for w in weights_a if weights_a[w]!=0]).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][0]*weights_b[w][1]
                                  for w in weights_b if weights_b[w]!=0]).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=spectral.orthogonalize(array([weights_a[w][1] for w in weights_a if weights_a[w]!=0]))
                w_a=array([weights_a[w][0] for w in weights_a if weights_a[w]!=0])
                v_a=multiply(v_a.T, w_a).T.sum(axis=0).reshape(1, -1)
                v_b=spectral.orthogonalize(array([weights_b[w][1] for w in weights_b if weights_b[w]!=0]))
                w_b=array([weights_b[w][0] for w in weights_b if weights_b[w]!=0])
                v_b=multiply(v_b.T, w_b).T.sum(axis=0).reshape(1, -1)
        elif average.startswith("moving"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(v_a, rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(v_b, rmean_win).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(spectral.orthogonalize(v_a), rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(spectral.orthogonalize(v_b), rmean_win).sum(axis=0).reshape(1, -1)
        elif average.startswith("whole"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])

weights_b[w]=0
                missing_cbow.append(w)
                continue
        logging.info("Weights sentence B %s" % [(w, weights_b[w][0]) for w in weights_b if not weights_b[w] is 0])

        if not average:
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][0]*weights_a[w][1]
                                  for w in weights_a if weights_a[w]!=0]).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][0]*weights_b[w][1]
                                  for w in weights_b if weights_b[w]!=0]).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=spectral.orthogonalize(array([weights_a[w][1] for w in weights_a if weights_a[w]!=0]))
                w_a=array([weights_a[w][0] for w in weights_a if weights_a[w]!=0])
                v_a=multiply(v_a.T, w_a).T.sum(axis=0).reshape(1, -1)
                v_b=spectral.orthogonalize(array([weights_b[w][1] for w in weights_b if weights_b[w]!=0]))
                w_b=array([weights_b[w][0] for w in weights_b if weights_b[w]!=0])
                v_b=multiply(v_b.T, w_b).T.sum(axis=0).reshape(1, -1)
        elif average.startswith("moving"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(v_a, rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(v_b, rmean_win).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(spectral.orthogonalize(v_a), rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(spectral.orthogonalize(v_b), rmean_win).sum(axis=0).reshape(1, -1)
        elif average.startswith("whole"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])

w_a=array([weights_a[w][0] for w in weights_a if weights_a[w]!=0])
                v_a=multiply(v_a.T, w_a).T.sum(axis=0).reshape(1, -1)
                v_b=spectral.orthogonalize(array([weights_b[w][1] for w in weights_b if weights_b[w]!=0]))
                w_b=array([weights_b[w][0] for w in weights_b if weights_b[w]!=0])
                v_b=multiply(v_b.T, w_b).T.sum(axis=0).reshape(1, -1)
        elif average.startswith("moving"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(v_a, rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(v_b, rmean_win).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(spectral.orthogonalize(v_a), rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(spectral.orthogonalize(v_b), rmean_win).sum(axis=0).reshape(1, -1)
        elif average.startswith("whole"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=v_a.mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=v_b.mean(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=spectral.orthogonalize(v_a).mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=spectral.orthogonalize(v_b).mean(axis=0).reshape(1, -1)
        #v_a=array([weights_a[word]*embedding[word]
        #                                    for word in weights_a]).sum(axis=0).reshape(1, -1)
        #v_b=array([weights_b[word]*embedding[word]
        #                                    for word in weights_b]).sum(axis=0).reshape(1, -1)
        if args.dist.startswith("all"):

elif args.ortho.startswith("orth"):
                v_a=spectral.orthogonalize(array([weights_a[w][1] for w in weights_a if weights_a[w]!=0]))
                w_a=array([weights_a[w][0] for w in weights_a if weights_a[w]!=0])
                v_a=multiply(v_a.T, w_a).T.sum(axis=0).reshape(1, -1)
                v_b=spectral.orthogonalize(array([weights_b[w][1] for w in weights_b if weights_b[w]!=0]))
                w_b=array([weights_b[w][0] for w in weights_b if weights_b[w]!=0])
                v_b=multiply(v_b.T, w_b).T.sum(axis=0).reshape(1, -1)
        elif average.startswith("moving"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(v_a, rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(v_b, rmean_win).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(spectral.orthogonalize(v_a), rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(spectral.orthogonalize(v_b), rmean_win).sum(axis=0).reshape(1, -1)
        elif average.startswith("whole"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=v_a.mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=v_b.mean(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=spectral.orthogonalize(v_a).mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=spectral.orthogonalize(v_b).mean(axis=0).reshape(1, -1)
        #v_a=array([weights_a[word]*embedding[word]
        #                                    for word in weights_a]).sum(axis=0).reshape(1, -1)
        #v_b=array([weights_b[word]*embedding[word]

elif args.ortho.startswith("orth"):
                v_a=spectral.orthogonalize(array([weights_a[w][1] for w in weights_a if weights_a[w]!=0]))
                w_a=array([weights_a[w][0] for w in weights_a if weights_a[w]!=0])
                v_a=multiply(v_a.T, w_a).T.sum(axis=0).reshape(1, -1)
                v_b=spectral.orthogonalize(array([weights_b[w][1] for w in weights_b if weights_b[w]!=0]))
                w_b=array([weights_b[w][0] for w in weights_b if weights_b[w]!=0])
                v_b=multiply(v_b.T, w_b).T.sum(axis=0).reshape(1, -1)
        elif average.startswith("moving"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(v_a, rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(v_b, rmean_win).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(spectral.orthogonalize(v_a), rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(spectral.orthogonalize(v_b), rmean_win).sum(axis=0).reshape(1, -1)
        elif average.startswith("whole"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=v_a.mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=v_b.mean(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=spectral.orthogonalize(v_a).mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=spectral.orthogonalize(v_b).mean(axis=0).reshape(1, -1)
        #v_a=array([weights_a[word]*embedding[word]
        #                                    for word in weights_a]).sum(axis=0).reshape(1, -1)
        #v_b=array([weights_b[word]*embedding[word]

try:
                weights_b[w]=(weights_b[w], embedding[w])
            except KeyError:
                weights_b[w]=0
                missing_cbow.append(w)
                continue
        logging.info("Weights sentence B %s" % [(w, weights_b[w][0]) for w in weights_b if not weights_b[w] is 0])

        if not average:
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][0]*weights_a[w][1]
                                  for w in weights_a if weights_a[w]!=0]).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][0]*weights_b[w][1]
                                  for w in weights_b if weights_b[w]!=0]).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=spectral.orthogonalize(array([weights_a[w][1] for w in weights_a if weights_a[w]!=0]))
                w_a=array([weights_a[w][0] for w in weights_a if weights_a[w]!=0])
                v_a=multiply(v_a.T, w_a).T.sum(axis=0).reshape(1, -1)
                v_b=spectral.orthogonalize(array([weights_b[w][1] for w in weights_b if weights_b[w]!=0]))
                w_b=array([weights_b[w][0] for w in weights_b if weights_b[w]!=0])
                v_b=multiply(v_b.T, w_b).T.sum(axis=0).reshape(1, -1)
        elif average.startswith("moving"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(v_a, rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(v_b, rmean_win).sum(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(spectral.orthogonalize(v_a), rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(spectral.orthogonalize(v_b), rmean_win).sum(axis=0).reshape(1, -1)

elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(spectral.orthogonalize(v_a), rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(spectral.orthogonalize(v_b), rmean_win).sum(axis=0).reshape(1, -1)
        elif average.startswith("whole"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=v_a.mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=v_b.mean(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=spectral.orthogonalize(v_a).mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=spectral.orthogonalize(v_b).mean(axis=0).reshape(1, -1)
        #v_a=array([weights_a[word]*embedding[word]
        #                                    for word in weights_a]).sum(axis=0).reshape(1, -1)
        #v_b=array([weights_b[word]*embedding[word]
        #                                    for word in weights_b]).sum(axis=0).reshape(1, -1)
        if args.dist.startswith("all"):
            #distances.append((1-cosine_distances(v_a, v_b),
            #                    euclidean_distances(v_a, v_b),
            #                    manhattan_distances(v_a, v_b)))
            try:
                fo.write("%f\t%f\t%f\n" % (1-cosine_distances(v_a, v_b)[0],
                                                euclidean_distances(v_a, v_b)[0],
                                                manhattan_distances(v_a, v_b)[0]))
            except:
                fo.write("%f\t%f\t%f\t%s\n" % (0.2, 1.0, 1.0,"Distance error in pair: "+str(iPair)))

        elif args.dist.startswith("euc"):

elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=running_mean(spectral.orthogonalize(v_a), rmean_win).sum(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=running_mean(spectral.orthogonalize(v_b), rmean_win).sum(axis=0).reshape(1, -1)
        elif average.startswith("whole"):
            if args.ortho.startswith("ld"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=v_a.mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=v_b.mean(axis=0).reshape(1, -1)
            elif args.ortho.startswith("orth"):
                v_a=array([weights_a[w][1] for w in weights_a if weights_a[w]!=0])
                v_a=spectral.orthogonalize(v_a).mean(axis=0).reshape(1, -1)
                v_b=array([weights_b[w][1] for w in weights_b if weights_b[w]!=0])
                v_b=spectral.orthogonalize(v_b).mean(axis=0).reshape(1, -1)
        #v_a=array([weights_a[word]*embedding[word]
        #                                    for word in weights_a]).sum(axis=0).reshape(1, -1)
        #v_b=array([weights_b[word]*embedding[word]
        #                                    for word in weights_b]).sum(axis=0).reshape(1, -1)
        if args.dist.startswith("all"):
            #distances.append((1-cosine_distances(v_a, v_b),
            #                    euclidean_distances(v_a, v_b),
            #                    manhattan_distances(v_a, v_b)))
            try:
                fo.write("%f\t%f\t%f\n" % (1-cosine_distances(v_a, v_b)[0],
                                                euclidean_distances(v_a, v_b)[0],
                                                manhattan_distances(v_a, v_b)[0]))
            except:
                fo.write("%f\t%f\t%f\t%s\n" % (0.2, 1.0, 1.0,"Distance error in pair: "+str(iPair)))

        elif args.dist.startswith("euc"):