def behav(self): dfiles = glob.glob('fonts/data/fonts_*_2015*.mat') n = self.dims['shape'].size inds = np.triu_indices(n, k=1) data = np.ones((len(dfiles), n, n)) * np.nan for i, d in enumerate(dfiles): data[i][inds] = scipy.io.loadmat(d)['estimate_dissimMat_ltv'] data[i].T[inds] = data[i][inds] behav = OrderedDict([('shape', data)]) path = os.path.join('fonts', 'data') name = 'dis_fonts_shape.pkl' name = os.path.join(path, name) if self.savedata: if not os.path.isdir(path): os.makedirs(path) pickle.dump(behav, open(name, 'wb')) base.msg('saved to', name)
def behav(self): dfiles = glob.glob('fonts/data/fonts_*_2015*.mat') n = self.dims['shape'].size inds = np.triu_indices(n, k=1) data = np.ones((len(dfiles), n, n)) * np.nan for i,d in enumerate(dfiles): data[i][inds] = scipy.io.loadmat(d)['estimate_dissimMat_ltv'] data[i].T[inds] = data[i][inds] behav = OrderedDict([('shape', data)]) path = os.path.join('fonts', 'data') name = 'dis_fonts_shape.pkl' name = os.path.join(path, name) if self.savedata: if not os.path.isdir(path): os.makedirs(path) pickle.dump(behav, open(name, 'wb')) base.msg('saved to', name)
def predict(self): _, sel = self.filter_synset_ids() try: m = models.get_model(self.model_name) except: base.msg('%s is not available for generating responses' %self.model_name) raise Exception else: m.load_image = base.load_image preds = m.predict(self.ims, topn=1000) # limit to top 5 guesses that are in Snodgrass top5 = [] for pred in preds: tmp = [] for p in pred: if p['synset'] in sel: tmp.append(p) if len(tmp) == 5: top5.append(tmp) break self.save(top5, 'preds') return preds
def predict(self): _, sel = self.filter_synset_ids() try: m = models.get_model(self.model_name) except: base.msg('%s is not available for generating responses' % self.model_name) raise Exception else: m.load_image = base.load_image preds = m.predict(self.ims, topn=1000) # limit to top 5 guesses that are in Snodgrass top5 = [] for pred in preds: tmp = [] for p in pred: if p['synset'] in sel: tmp.append(p) if len(tmp) == 5: top5.append(tmp) break self.save(top5, 'preds') return preds