def convert_old_study(self): print "Converting Divisi1 to Divisi2 study." from csc.divisi2.network import conceptnet_matrix cnet_matrix = conceptnet_matrix('en') divisi2.save( cnet_matrix, self.study_path(os.path.join("Matrices", "conceptnet_en.smat")))
def expand_study(study_name): study = StudyDirectory(study_name).get_study() theblend, concepts = study.get_assoc_blend() U, S, V = theblend.normalize_all().svd(k=50) doc_rows = divisi2.aligned_matrix_multiply(study.get_documents_matrix(), U) projections = U.extend(doc_rows) spectral = divisi2.reconstruct_activation(projections, S, post_normalize=True) divisi2.save(spectral, study_name+'/Results/expanded.rmat')
def expand_study(study_name): study = StudyDirectory(study_name).get_study() theblend, concepts = study.get_assoc_blend() U, S, V = theblend.normalize_all().svd(k=50) doc_rows = divisi2.aligned_matrix_multiply(study.get_documents_matrix(), U) projections = U.extend(doc_rows) spectral = divisi2.reconstruct_activation(projections, S, post_normalize=True) divisi2.save(spectral, study_name + '/Results/expanded.rmat')
def learnit(): ccipca = CCIPCAProcessor((8193, 400), plot=False, amnesia=1.0) pipe = ronwtools.Pipeline( ronwtools.AudioSource('../chess.ogg'), ronwtools.Mono(), ronwtools.STFT(nfft=16384, nhop=4096, winfun=np.hanning), ccipca, ronwtools.ISTFT(nfft=16384, nhop=4096, winfun=np.hanning), ronwtools.Framer(524288) ) for segment in pipe: segment /= np.max(segment) print np.max(segment) audiolab.play(segment) divisi2.save(ccipca.ccipca.matrix, 'chess2.eigs')
def make_divisi_matrix(filename): parsedlist = inform_parser(filename) game = filename.split('.')[0] thinglist = [(1 if x[3] else -1, english.normalize(x[0].replace('^', "'")), ('right', x[1], english.normalize(x[2].replace('^', "'")))) for x in parsedlist] # Write out the confusingly-named overlist. First, the nouns. overlist = open(game + '.over', 'w') for concept1, rel, concept2, val in parsedlist: if rel == 'HasProperty' and concept2 == 'mark_as_thing': print >> overlist, concept1 print concept1 # Now the verbs. verbs = verb_reader(filename) for verb in verbs: print >> overlist, verb overlist.close() game_matrix = divisi2.make_sparse(thinglist).normalize_all() divisi2.save(game_matrix, game + '.pickle') return game_matrix
def graph_from_file(filename): bn = TrustNetwork(output=None) found_conjunctions = set() counter = 0 with codecs.open(filename, encoding='utf-8') as file: for line in file: print 'scan:', counter counter += 1 line = line.strip() if line: source, target, prop_str = line.split('\t') bn.scan_edge(source, target) bn.make_matrices() total = counter counter = 0 with codecs.open(filename, encoding='utf-8') as file: for line in file: print 'add edge:', counter, '/', total counter += 1 line = line.strip() if line: source, target, prop_str = line.split('\t') props = eval(prop_str) weight = props['weight'] dependencies = None if 'dependencies' in props and len(props['dependencies']) > 1: bn.add_conjunction_piece(source, target, weight) else: bn.add_edge(source, target, weight) file.close() bn.make_fast_matrix() bn.make_fast_conjunctions() divisi2.save(list(bn.nodes), filename+'.nodelist.pickle') divisi2.save(bn._fast_matrix_up, filename+'.up.pickle') divisi2.save(bn._fast_matrix_down, filename+'.down.pickle') divisi2.save(bn._fast_conjunctions, filename+'.conjunctions.pickle') return bn
def graph_from_file(filename): bn = TrustNetwork(output=None) found_conjunctions = set() counter = 0 with codecs.open(filename, encoding='utf-8') as file: for line in file: print 'scan:', counter counter += 1 line = line.strip() if line: source, target, prop_str = line.split('\t') bn.scan_edge(source, target) bn.make_matrices() total = counter counter = 0 with codecs.open(filename, encoding='utf-8') as file: for line in file: print 'add edge:', counter, '/', total counter += 1 line = line.strip() if line: source, target, prop_str = line.split('\t') props = eval(prop_str) weight = props['weight'] dependencies = None if 'dependencies' in props and len(props['dependencies']) > 1: bn.add_conjunction_piece(source, target, weight) else: bn.add_edge(source, target, weight) file.close() bn.make_fast_matrix() bn.make_fast_conjunctions() divisi2.save(list(bn.nodes), filename + '.nodelist.pickle') divisi2.save(bn._fast_matrix_up, filename + '.up.pickle') divisi2.save(bn._fast_matrix_down, filename + '.down.pickle') divisi2.save(bn._fast_conjunctions, filename + '.conjunctions.pickle') return bn
def convert_old_study(self): print "Converting Divisi1 to Divisi2 study." from csc.divisi2.network import conceptnet_matrix cnet_matrix = conceptnet_matrix('en') divisi2.save(cnet_matrix, self.study_path(os.path.join("Matrices", "conceptnet_en.smat")))
from plca.plca import SIPLCA2, EPS, shift from plca.basilica import Basilica from musicproc.analyze import * from musicproc.harmonic_prior import harmonic_prior from csc import divisi2 import numpy as np from matplotlib import pyplot as plt np.seterr(invalid='raise') #pitch = divisi2.load('clocks.pitch.pickle') analyzer = MusicAnalyzer(window_size=44100, subsample=1470) audio = AudioData.from_file('high-hopes.ogg') pitch = analyzer.quantize_equal(np.abs(analyzer.analyze_pitch(audio, 120)), 1470) divisi2.save(pitch, 'high-hopes.pitch.pickle') alphaWf = harmonic_prior(96, 0, 6, 0) alphaWt = np.vstack([np.linspace(0.01, 0, 30)]*6) bas = Basilica(pitch, 6, 30, alphaWf=alphaWf, alphaWt=alphaWt, betaHf=0.1, betaHt=0.05) def play_reconstruction(rec): analyzer.reconstruct_W(rec).play() Wf, Zf, Hf, Wt, Zt, Ht, meta_Hf, meta_Ht, rec = bas.run(pitch, nsubiter=20, niter=1, play_func=play_reconstruction)
def save(db): divisi2.save(db.idea_matrix,"idea_mat.pickle")