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coding as usual

👨‍💻
coding as usual
Last active Feb 10, 2020
View ts-1.jl
 using Dates using Distributions using HypothesisTests: ADFTest, LjungBoxTest using Knet using Measures using Plots using PlotThemes using Random using StatsBase: autocor, pacf using Turing
Last active Mar 20, 2019
View explanation-of-apply.R
 set.seed(1) x1 <- rnorm(10) x2 <- rnorm(10) out <- cbind(x1, x2) colMeans(out) # colMeans can be computed as follows as well apply(out, 2, mean) # apply - applies a mean function to column (indicated by 2) of out matrix
Created Mar 20, 2019
View code-sir-aljo-mcmc.R
 # Simulating the data set.seed(73735911) #set.seed(737377911) n=100;nu=5;alpha=2;beta=2;sig=1;true=c(alpha,beta,nu) x=rnorm(n,1,1) y=alpha+beta*x+sig*rt(n,nu) par(mfrow=c(1,1)) plot(x,y, col='blue3', pch=19)
Created Feb 4, 2019
Turing.jl Tutorial on Bayesian Linear Regression
View code-turing.jl
 using Turing, Distributions # Import RDatasets. using RDatasets # Import MCMCChain, Plots, and StatPlots for visualizations and diagnostics. using MCMCChain, Plots, StatPlots # MLDataUtils provides a sample splitting tool that's very handy. using MLDataUtils
Last active Apr 2, 2018
View sir-aljo.r
 rz_helper = function() { y1 = rexp(1, 1) # step 1 y2 = rexp(1, 1) # step 2 # step 3 while (y2 <= ((y1 - 1)^2)/2) { y1 = rexp(1, 1) } # step 4
Created Mar 10, 2018
View al-ilm-nn-pyr-keras-5.py
 f, a = subplots(10, 20) for i in arange(10): for j in arange(20): a[i, j].imshow(x_train[j + 20 * i]) a[i, j].axis("off") a[i, j].set_adjustable('box-forced') f.savefig("img1.png", bbox_inches='tight', pad_inches = 0)
Created Mar 10, 2018
View al-ilm-nn-pyr-keras-3.r
 items <- list(x_train, y_train, x_test, y_test) dim_formatter <- function (x) { if (length(dim(x)) > 2) paste("(", dim(x)[1], ", ", dim(x)[2], " , ", dim(x)[3], " , ", dim(x)[4], ")", sep = "") else paste("(", dim(x)[1], ", ", dim(x)[2], ")", sep = "") } # training set
Last active Mar 10, 2018
View al-ilm-nn-pyr-keras-4.py
 items = [x_train, y_train, x_test, y_test] # training set [type(item) for item in items[:2]] # [, ] [item.shape for item in items[:2]] # [(50000, 32, 32, 3), (50000, 1)] # testing set [type(item) for item in items[2:]] # [, ] [item.shape for item in items[2:]] # [(10000, 32, 32, 3), (10000, 1)]
Created Mar 10, 2018
View al-ilm-nn-pyr-keras-2.r
 cifar100 <- dataset_cifar100(label_mode = "fine") x_train <- cifar100\$train\$x; y_train <- cifar100\$train\$y x_test <- cifar100\$test\$x; y_test <- cifar100\$test\$y
Last active Mar 10, 2018
View al-ilm-nn-pyr-keras-3.py
 (x_train, y_train), (x_test, y_test) = cifar100.load_data(label_mode = "fine")