def dissimilarity(self):
resps = self.classify()
df = []
dims = self.get_dims()
for layer, resps in resps.items():
for g, i in enumerate(range(0, len(resps), 3)):
dis = models.dissimilarity(resps[i:i + 3], kind='correlation')
n = int(os.path.basename(self.ims[i]).split('os')[0])
df.append([layer, g, n, dims[n], 'non-accidental', dis[1, 0]])
df.append([layer, g, n, dims[n], 'metric', dis[1, 2]])
nap = pandas.DataFrame(
df, columns=['layer', 'geon', 'fno', 'dimension', 'kind', 'dist'])
self.save(nap, 'nap')
return nap
def dissimilarity(self):
resps = self.classify()
df = []
dims = self.get_dims()
for layer, resps in resps.items():
for g,i in enumerate(range(0, len(resps), 3)):
dis = models.dissimilarity(resps[i:i+3],
kind='correlation')
n = int(os.path.basename(self.ims[i]).split('os')[0])
df.append([layer, g, n, dims[n], 'non-accidental', dis[1,0]])
df.append([layer, g, n, dims[n], 'metric', dis[1,2]])
nap = pandas.DataFrame(df,
columns=['layer', 'geon', 'fno', 'dimension', 'kind', 'dist'])
self.save(nap, 'nap')
return nap
def dissimilarity(self):
resps = self.classify()
dis = models.dissimilarity(resps, kind=self.dissim)
self.save(dis, 'dis')
return dis
def dissimilarity(self):
resps = self.classify()
dis = models.dissimilarity(resps, kind=self.dissim)
self.save(dis, "dis")
return dis
