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utils.py
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utils.py
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import torch
import numpy as np
def one_hot(y, num_class):
return torch.zeros((len(y), num_class)).scatter_(1, y.unsqueeze(1), 1)
def DBindex(cl_data_file):
class_list = cl_data_file.keys()
cl_num= len(class_list)
cl_means = []
stds = []
DBs = []
for cl in class_list:
cl_means.append( np.mean(cl_data_file[cl], axis = 0) )
stds.append( np.sqrt(np.mean( np.sum(np.square( cl_data_file[cl] - cl_means[-1]), axis = 1))))
mu_i = np.tile( np.expand_dims( np.array(cl_means), axis = 0), (len(class_list),1,1) )
mu_j = np.transpose(mu_i,(1,0,2))
mdists = np.sqrt(np.sum(np.square(mu_i - mu_j), axis = 2))
for i in range(cl_num):
DBs.append( np.max([ (stds[i]+ stds[j])/mdists[i,j] for j in range(cl_num) if j != i ]) )
return np.mean(DBs)
def sparsity(cl_data_file):
class_list = cl_data_file.keys()
cl_sparsity = []
for cl in class_list:
cl_sparsity.append(np.mean([np.sum(x!=0) for x in cl_data_file[cl] ]) )
return np.mean(cl_sparsity)