output_root = options['output_root'] training_data_path = options['training_data_path'] vocab = (options['wordlist'], options['relalist']) # --- init RNG --- # try: seed = options['seed'] except KeyError: seed = 1337 bf2f.np.random.seed(seed) # --- save the options --- # options.save(output_root+'_options.txt') # --- training data --- # dstream = bf2f.data_stream(training_data_path) if options['online']: train_data = dstream else: train_data = dstream.acquire_all() # --- initialise parameters --- # try: # load parameters from file initial_params_path = options['initial_params_path'] initial_params = initial_params_path except KeyError: # create random W, R = dstream.get_vocab_sizes() d = options['dimension']
dpath = '' wordlist = '' elif not len(sys.argv) > 1: sys.exit('ERROR: no data!') else: print 'Expecting dpath in config file.' # overwrite from the options file # WARNING: DANGEROUS if 'dpath' in options: dpath = options['dpath'] if 'wordlist' in options: wordlist = options['wordlist'] # --- initialise things --- # dstream = bf2f.data_stream(dpath) W, R = dstream.get_vocab_sizes() if W == 5 and d == 3: C = bf2f.np.array([[0.01481961, -0.01517603, 0.00596634, 1.], [-0.0080693, 0.00852271, -0.00106983, 1.], [-0.0012176, 0.02482517, 0.01040345, 1.], [0.00962732, 0.0100687, 0.00756443, 1.], [0.00841503, 0.00188252, 0.02689446, 1.]]) V = bf2f.np.array([[-0.00878185, -0.01871243, -0.01610301, 1.], [-0.02036443, -0.02137387, 0.00874672, 1.], [0.00898955, 0.00722872, -0.00504091, 1.], [0.00324052, 0.02674052, 0.00166536, 1.], [0.01199952, 0.00430334, 0.0040228, 1.]]) #elif W == 5 and d == 2: # C = bf2f.np.array([[ 1.0, 0.0, 1.0 ], # [ 0.0, 1.0, 1.0 ],
dpath = '' wordlist = '' elif not len(sys.argv) > 1: sys.exit('ERROR: no data!') else: print 'Expecting dpath in config file.' # overwrite from the options file # WARNING: DANGEROUS if 'dpath' in options: dpath = options['dpath'] if 'wordlist' in options: wordlist = options['wordlist'] # --- initialise things --- # dstream = bf2f.data_stream(dpath) W, R = dstream.get_vocab_sizes() if W == 5 and d == 3: C = bf2f.np.array([[ 0.01481961, -0.01517603, 0.00596634, 1. ], [-0.0080693 , 0.00852271, -0.00106983, 1. ], [-0.0012176 , 0.02482517, 0.01040345, 1. ], [ 0.00962732, 0.0100687 , 0.00756443, 1. ], [ 0.00841503, 0.00188252, 0.02689446, 1. ]]) V = bf2f.np.array([[-0.00878185, -0.01871243, -0.01610301, 1. ], [-0.02036443, -0.02137387, 0.00874672, 1. ], [ 0.00898955, 0.00722872, -0.00504091, 1. ], [ 0.00324052, 0.02674052, 0.00166536, 1. ], [ 0.01199952, 0.00430334, 0.0040228 , 1. ]]) #elif W == 5 and d == 2: # C = bf2f.np.array([[ 1.0, 0.0, 1.0 ], # [ 0.0, 1.0, 1.0 ],