def features_snp_modular (fname): from modshogun import StringByteFeatures, SNPFeatures, SNP sf=StringByteFeatures(SNP) sf.load_ascii_file(fname, False, SNP, SNP) #print(sf.get_features()) snps=SNPFeatures(sf)
def distribution_ppwm_modular(fm_dna=traindna, order=3): from modshogun import StringByteFeatures, StringCharFeatures, DNA from modshogun import PositionalPWM from numpy import array, e, log, exp charfeat = StringCharFeatures(DNA) charfeat.set_features(fm_dna) feats = StringByteFeatures(charfeat.get_alphabet()) feats.obtain_from_char(charfeat, order - 1, order, 0, False) L = 20 k = 3 sigma = 1 mu = 4 ppwm = PositionalPWM() ppwm.set_sigma(sigma) ppwm.set_mean(mu) pwm = array([[0.0, 0.5, 0.1, 1.0], [0.0, 0.5, 0.5, 0.0], [1.0, 0.0, 0.4, 0.0], [0.0, 0.0, 0.0, 0.0]]) pwm = array([[0.01, 0.09, 0.1], [0.09, 0.01, 0.1], [0.85, 0.4, 0.1], [0.05, 0.5, 0.7]]) ppwm.set_pwm(log(pwm)) #print(ppwm.get_pwm()) ppwm.compute_w(L) w = ppwm.get_w() #print(w) #from pylab import * #figure(1) #pcolor(exp(w)) #pcolor(w) #colorbar() #figure(2) ppwm.compute_scoring(1) u = ppwm.get_scoring(0) #pcolor(exp(u)) #show() #ppwm=PositionalPWM(feats) #ppwm.train() #out_likelihood = histo.get_log_likelihood() #out_sample = histo.get_log_likelihood_sample() return w, u
def features_string_hashed_wd_modular (A=matrix,order=3,start_order=1,hash_bits=2): a=LongIntFeatures(A) from numpy import array, uint8 from modshogun import HashedWDFeatures, StringByteFeatures, RAWDNA from modshogun import MSG_DEBUG x=[array([0,1,2,3,0,1,2,3,3,2,2,1,1],dtype=uint8)] from_order=order f=StringByteFeatures(RAWDNA) #f.io.set_loglevel(MSG_DEBUG) f.set_features(x) y=HashedWDFeatures(f,start_order,order,from_order,hash_bits) fm=y.get_computed_dot_feature_matrix() return fm
def distribution_ppwm_modular(fm_dna=traindna, order=3): from modshogun import StringByteFeatures, StringCharFeatures, DNA from modshogun import PositionalPWM from numpy import array, e, log, exp charfeat = StringCharFeatures(DNA) charfeat.set_features(fm_dna) feats = StringByteFeatures(charfeat.get_alphabet()) feats.obtain_from_char(charfeat, order - 1, order, 0, False) L = 20 k = 3 sigma = 1 mu = 4 ppwm = PositionalPWM() ppwm.set_sigma(sigma) ppwm.set_mean(mu) pwm = array([[0.0, 0.5, 0.1, 1.0], [0.0, 0.5, 0.5, 0.0], [1.0, 0.0, 0.4, 0.0], [0.0, 0.0, 0.0, 0.0]]) pwm = array([[0.01, 0.09, 0.1], [0.09, 0.01, 0.1], [0.85, 0.4, 0.1], [0.05, 0.5, 0.7]]) ppwm.set_pwm(log(pwm)) # print(ppwm.get_pwm()) ppwm.compute_w(L) w = ppwm.get_w() # print(w) # from pylab import * # figure(1) # pcolor(exp(w)) # pcolor(w) # colorbar() # figure(2) ppwm.compute_scoring(1) u = ppwm.get_scoring(0) # pcolor(exp(u)) # show() # ppwm=PositionalPWM(feats) # ppwm.train() # out_likelihood = histo.get_log_likelihood() # out_sample = histo.get_log_likelihood_sample() return w, u
def features_string_hashed_wd_modular(A=matrix, order=3, start_order=1, hash_bits=2): a = LongIntFeatures(A) from numpy import array, uint8 from modshogun import HashedWDFeatures, StringByteFeatures, RAWDNA from modshogun import MSG_DEBUG x = [array([0, 1, 2, 3, 0, 1, 2, 3, 3, 2, 2, 1, 1], dtype=uint8)] from_order = order f = StringByteFeatures(RAWDNA) #f.io.set_loglevel(MSG_DEBUG) f.set_features(x) y = HashedWDFeatures(f, start_order, order, from_order, hash_bits) fm = y.get_computed_dot_feature_matrix() return fm