sudo apt-get update
sudo apt-get install vim
sudo apt-get install exuberant-ctags
| from .lsun import LSUN, LSUNClass | |
| from .folder import ImageFolder, DatasetFolder | |
| from .coco import CocoCaptions, CocoDetection | |
| from .cifar import CIFAR10, CIFAR100 | |
| from .stl10 import STL10 | |
| from .mnist import MNIST, EMNIST, FashionMNIST, KMNIST, QMNIST | |
| from .svhn import SVHN | |
| from .phototour import PhotoTour | |
| from .fakedata import FakeData | |
| from .semeion import SEMEION | 
| reports | income | active | expenditure | |
|---|---|---|---|---|
| 0 | 4.52 | 12 | 124.9833 | |
| 0 | 2.42 | 13 | 9.854167 | |
| 0 | 4.5 | 5 | 15 | |
| 0 | 2.54 | 7 | 137.8692 | |
| 0 | 9.7867 | 5 | 546.5033 | |
| 0 | 2.5 | 1 | 91.99667 | |
| 0 | 3.96 | 5 | 40.83333 | |
| 0 | 2.37 | 3 | 150.79 | |
| 0 | 3.8 | 6 | 777.8217 | 
| card | reports | age | income | share | expenditure | owner | selfemp | dependents | months | majorcards | active | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yes | 0 | 37.66667 | 4.52 | 0.03326991 | 124.9833 | yes | no | 3 | 54 | 1 | 12 | |
| yes | 0 | 33.25 | 2.42 | 0.0052169420000000005 | 9.854167 | no | no | 3 | 34 | 1 | 13 | |
| yes | 0 | 33.66667 | 4.5 | 0.0041555559999999995 | 15.0 | yes | no | 4 | 58 | 1 | 5 | |
| yes | 0 | 30.5 | 2.54 | 0.06521378 | 137.8692 | no | no | 0 | 25 | 1 | 7 | |
| yes | 0 | 32.16667 | 9.7867 | 0.06705059 | 546.5033 | yes | no | 2 | 64 | 1 | 5 | |
| yes | 0 | 23.25 | 2.5 | 0.0444384 | 91.99667 | no | no | 0 | 54 | 1 | 1 | |
| yes | 0 | 27.91667 | 3.96 | 0.01257576 | 40.83333 | no | no | 2 | 7 | 1 | 5 | |
| yes | 0 | 29.16667 | 2.37 | 0.07643376 | 150.79 | yes | no | 0 | 77 | 1 | 3 | |
| yes | 0 | 37.0 | 3.8 | 0.2456279 | 777.8217 | yes | no | 0 | 97 | 1 | 6 | 
| import argparse | |
| import os | |
| import time | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.parallel | |
| import torch.backends.cudnn as cudnn | |
| import torch.optim | |
| import torch.utils.data | |
| import torchvision.transforms as transforms | 
| import numpy as np | |
| from sklearn import metrics, preprocessing | |
| from sklearn.preprocessing import MinMaxScaler | |
| from sklearn.decomposition import PCA | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score | |
| from operator import truediv | |
| from plotly.offline import init_notebook_mode | |
| import matplotlib.pyplot as plt | |
| import scipy.io as sio | 
| class PAM(Layer): | |
| def __init__(self, | |
| gamma_initializer=tf.zeros_initializer(), | |
| gamma_regularizer=None, | |
| gamma_constraint=None, | |
| **kwargs): | |
| super(PAM, self).__init__(**kwargs) | |
| self.gamma_initializer = gamma_initializer | |
| self.gamma_regularizer = gamma_regularizer | |
| self.gamma_constraint = gamma_constraint | 
sudo apt-get update
sudo apt-get install vim
sudo apt-get install exuberant-ctags
| def cal_mean_spectral_divergence(band_subset): | |
| """ | |
| Spectral Divergence is defined as the symmetrical KL divergence (D_KLS) of two bands probability distribution. | |
| We use Mean SD (MSD) to quantify the redundancy among a band set. | |
| B_i and B_j should be a gray histagram. | |
| SD = D_KL(B_i||B_j) + D_KL(B_j||B_i) | |
| MSD = 2/n*(n-1) * sum(ID_ij) | |
| Ref: | 
| !curl -sSL "https://julialang-s3.julialang.org/bin/linux/x64/1.3/julia-1.3.1-linux-x86_64.tar.gz" -o julia.tar.gz | |
| !tar -xzf julia.tar.gz -C /usr --strip-components 1 | |
| !rm -rf julia.tar.gz* | |
| !julia -e 'using Pkg; pkg"add IJulia; add Flux; precompile"' |