ucalyptus

ucalyptus

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

ucalyptus

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

ucalyptus

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

ucalyptus

sudo apt-get update

sudo apt-get install vim

sudo apt-get install exuberant-ctags

ucalyptus

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:

ucalyptus

!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"'

ucalyptus

ucalyptus

RMSE(t) = sqrt((t/t-1)RMSE(t-1)^2 + (1/t)(y_true(t) - y_pred(t))^2)

where t is the number of observations

Courtesy

Modification

Treat (1/t) as alpha

ucalyptus

Which is better rMSE or MAE?

This depends on your loss function. In many circumstances it makes sense to give more weight to points further away from the mean--that is, being off by 10 is more than twice as bad as being off by 5. In such cases RMSE is a more appropriate measure of error. If being off by ten is just twice as bad as being off by 5, then MAE is more appropriate. In any case, it doesn't make sense to compare RMSE and MAE to each other