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solved issue with latest version of scikit-learn
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 08 Feb 2017 16:32:15 -0500
parents 6db83beb5350
children f9ea5083588e
comparison
equal deleted inserted replaced
871:6db83beb5350 872:c70adaeeddf5
574 if gmmType not in ['beginning', 'end']: 574 if gmmType not in ['beginning', 'end']:
575 print('Unknown POI type {}. Exiting'.format(gmmType)) 575 print('Unknown POI type {}. Exiting'.format(gmmType))
576 import sys 576 import sys
577 sys.exit() 577 sys.exit()
578 try: 578 try:
579 cursor.execute('CREATE TABLE IF NOT EXISTS gaussians2d (id INTEGER, type VARCHAR, x_center REAL, y_center REAL, covar00 REAL, covar01 REAL, covar10 REAL, covar11 REAL, covariance_type VARCHAR, weight, mixture_id INTEGER, PRIMARY KEY(id, mixture_id))') 579 cursor.execute('CREATE TABLE IF NOT EXISTS gaussians2d (poi_id INTEGER, id INTEGER, type VARCHAR, x_center REAL, y_center REAL, covariance VARCHAR, covariance_type VARCHAR, weight, precisions_cholesky VARCHAR, PRIMARY KEY(poi_id, id))')
580 for i in xrange(gmm.n_components): 580 for i in xrange(gmm.n_components):
581 cursor.execute('INSERT INTO gaussians2d VALUES({}, \'{}\', {}, {}, {}, {}, {}, {}, \'{}\', {}, {})'.format(i, gmmType, gmm.means_[i][0], gmm.means_[i][1], gmm.covariances_[i][0,0], gmm.covariances_[i][0,1], gmm.covariances_[i][1,0], gmm.covariances_[i][1,1], gmm.covariance_type, gmm.weights_[i], gmmId)) 581 cursor.execute('INSERT INTO gaussians2d VALUES({}, {}, \'{}\', {}, {}, \'{}\', \'{}\', {}, \'{}\')'.format(gmmId, i, gmmType, gmm.means_[i][0], gmm.means_[i][1], str(gmm.covariances_[i].tolist()), gmm.covariance_type, gmm.weights_[i], str(gmm.precisions_cholesky_[i].tolist())))
582 connection.commit() 582 connection.commit()
583 except sqlite3.OperationalError as error: 583 except sqlite3.OperationalError as error:
584 printDBError(error) 584 printDBError(error)
585 connection.close() 585 connection.close()
586 586
587 def loadPOIs(filename): 587 def loadPOIs(filename):
588 'Loads all 2D Gaussians in the database' 588 'Loads all 2D Gaussians in the database'
589 from sklearn import mixture # todo if not avalaible, load data in duck-typed class with same fields 589 from sklearn import mixture # todo if not avalaible, load data in duck-typed class with same fields
590 from ast import literal_eval
590 connection = sqlite3.connect(filename) 591 connection = sqlite3.connect(filename)
591 cursor = connection.cursor() 592 cursor = connection.cursor()
592 pois = [] 593 pois = []
593 try: 594 try:
594 cursor.execute('SELECT * from gaussians2d') 595 cursor.execute('SELECT * from gaussians2d')
595 gmmId = None 596 gmmId = None
596 gmm = [] 597 gmm = []
597 for row in cursor: 598 for row in cursor:
598 if gmmId is None or row[10] != gmmId: 599 if gmmId is None or row[0] != gmmId:
599 if len(gmm) > 0: 600 if len(gmm) > 0:
600 tmp = mixture.GaussianMixture(len(gmm), covarianceType) 601 tmp = mixture.GaussianMixture(len(gmm), covarianceType)
601 tmp.means_ = array([gaussian['mean'] for gaussian in gmm]) 602 tmp.means_ = array([gaussian['mean'] for gaussian in gmm])
602 tmp.covariances_ = array([gaussian['covar'] for gaussian in gmm]) 603 tmp.covariances_ = array([gaussian['covar'] for gaussian in gmm])
603 tmp.weights_ = array([gaussian['weight'] for gaussian in gmm]) 604 tmp.weights_ = array([gaussian['weight'] for gaussian in gmm])
604 tmp.gmmTypes = [gaussian['type'] for gaussian in gmm] 605 tmp.gmmTypes = [gaussian['type'] for gaussian in gmm]
606 tmp.precisions_cholesky_ = array([gaussian['precisions'] for gaussian in gmm])
605 pois.append(tmp) 607 pois.append(tmp)
606 gaussian = {'type': row[1], 608 gaussian = {'type': row[2],
607 'mean': row[2:4], 609 'mean': row[3:5],
608 'covar': array(row[4:8]).reshape(2,2), 610 'covar': array(literal_eval(row[5])),
609 'weight': row[9]} 611 'weight': row[7],
612 'precisions': array(literal_eval(row[8]))}
610 gmm = [gaussian] 613 gmm = [gaussian]
611 covarianceType = row[8] 614 covarianceType = row[6]
612 gmmId = row[10] 615 gmmId = row[0]
613 else: 616 else:
614 gmm.append({'type': row[1], 617 gmm.append({'type': row[2],
615 'mean': row[2:4], 618 'mean': row[3:5],
616 'covar': array(row[4:8]).reshape(2,2), 619 'covar': array(literal_eval(row[5])),
617 'weight': row[9]}) 620 'weight': row[7],
621 'precisions': array(literal_eval(row[8]))})
618 if len(gmm) > 0: 622 if len(gmm) > 0:
619 tmp = mixture.GaussianMixture(len(gmm), covarianceType) 623 tmp = mixture.GaussianMixture(len(gmm), covarianceType)
620 tmp.means_ = array([gaussian['mean'] for gaussian in gmm]) 624 tmp.means_ = array([gaussian['mean'] for gaussian in gmm])
621 tmp.covariances_ = array([gaussian['covar'] for gaussian in gmm]) 625 tmp.covariances_ = array([gaussian['covar'] for gaussian in gmm])
622 tmp.weights_ = array([gaussian['weight'] for gaussian in gmm]) 626 tmp.weights_ = array([gaussian['weight'] for gaussian in gmm])
623 tmp.gmmTypes = [gaussian['type'] for gaussian in gmm] 627 tmp.gmmTypes = [gaussian['type'] for gaussian in gmm]
628 tmp.precisions_cholesky_ = array([gaussian['precisions'] for gaussian in gmm])
624 pois.append(tmp) 629 pois.append(tmp)
625 except sqlite3.OperationalError as error: 630 except sqlite3.OperationalError as error:
626 printDBError(error) 631 printDBError(error)
627 connection.close() 632 connection.close()
628 return pois 633 return pois