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
