コード例 #1
0
ファイル: nb_ff_bk.py プロジェクト: EricTing/script
    def constructLikelihoodDiff(self, high_pickle_fn, low_pickle_fn):
        """
        calculate difference of the conditional prob of whole data set given low and high class
        """
        condi_dist_h = nb.loadCondiDistribution(high_pickle_fn)
        condi_dist_l = nb.loadCondiDistribution(low_pickle_fn)

        high_pdf = nb.buildPdf(condi_dist_h)
        low_pdf = nb.buildPdf(condi_dist_l)
        
        ener = self.all_set[:, 1:]
        log_condi_predicted_high = nb.calculateConditionalProb(ener, high_pdf)
        log_condi_predicted_low = nb.calculateConditionalProb(ener, low_pdf)
        self.likelihood_diff = log_condi_predicted_low - log_condi_predicted_high
コード例 #2
0
ファイル: nb_ff_unit_test.py プロジェクト: EricTing/script
def calculateLikelihoodDiff():
    h5_path = 'all_decoy.h5'
    sub_path = 'noncentralized_path/low_decoy'
    ener_matx = loadEnerMatx(h5_path, sub_path)

    condi_dist_fn = 'low.dist'
    low_pdf = nb.loadCondiDistribution(condi_dist_fn)
    low_pdf = nb.buildPdf(low_pdf)
    condi_dist_fn = 'high.dist'
    high_pdf = nb.loadCondiDistribution(condi_dist_fn)
    high_pdf = nb.buildPdf(high_pdf)
    
    likelihood_low_diff = nb.getLikelihoodDiff(ener_matx, high_pdf, low_pdf)
    
    sub_path = 'noncentralized_path/high_decoy'
    ener_matx = loadEnerMatx(h5_path, sub_path)

    likelihood_high_diff = nb.getLikelihoodDiff(ener_matx, high_pdf, low_pdf)
    
    return likelihood_high_diff, likelihood_low_diff
コード例 #3
0
ファイル: nb_ff_bk.py プロジェクト: EricTing/script
        return f[mcc_diff_path][()]
        
        
        
if __name__ == "__main__":
    ################################################################################
    # print plain text file
    # nb_ff = NB_ff('04ff_all_decoy.h5')
    # nb_ff.printEner()
    # nb_ff = NB_ff('08ff_all_decoy.h5')
    # nb_ff.printEner()
    # nb_ff = NB_ff('06ff_all_decoy.h5')
    # nb_ff.printEner()

    ################################################################################
    # build pdf
    import sys
    # matrix_fn = sys.argv[1]
    matrix_fn = '08ff_high_decoy.mat'
    # matrix_fn = '04ff_low_decoy.mat'

    condi_dist_fn = matrix_fn.split('.')[0] + '.dist'
    matrix = np.loadtxt(matrix_fn, delimiter=' ')
    condi_dist = nb.getConditionalDist(matrix, dist_names)
    nb.saveCondiDistribution(condi_dist, condi_dist_fn)
    condi_dist = nb.loadCondiDistribution(condi_dist_fn)
    print condi_dist



コード例 #4
0
ファイル: formBayeDist.py プロジェクト: EricTing/script
from lst_sub import getLst
import NB_classifier as nb

ener_rows_ifn = 'ener_row_name.txt'
ff = '08ff'
low_condi_dist_fn = ff + '_low_decoy.dist'
high_condi_dist_fn = ff + '_high_decoy.dist'
bayes_dist_ofn = ff + '_bayes.txt'

low_condi_dist = nb.loadCondiDistribution(low_condi_dist_fn)
high_condi_dist = nb.loadCondiDistribution(high_condi_dist_fn)
ener_rows = getLst(ener_rows_ifn)

def convertPdfName(dist_tuple):
    """conver the first letter of the distribution name to upper case
    """
    name = dist_tuple[0]
    name = name.upper()[0] + name[1:]
    return (name, dist_tuple[1])
    
################################################################################
# converting
low_condi_dist = [convertPdfName(i) for i in low_condi_dist]
high_condi_dist = [convertPdfName(i) for i in high_condi_dist]
################################################################################

high_bayes_dists = [[ener_rows[i], high_condi_dist[i][0], high_condi_dist[i][1][0], high_condi_dist[i][1][1]]
                    for i in range(len(ener_rows))]
low_bayes_dists = [[ener_rows[i], low_condi_dist[i][0], low_condi_dist[i][1][0], low_condi_dist[i][1][1]]
                   for i in range(len(ener_rows))]