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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

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# 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)

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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

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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

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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)

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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

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items = [x_train, y_train, x_test, y_test]

# training set
[type(item) for item in items[:2]]    # [<type 'numpy.ndarray'>, <type 'numpy.ndarray'>]
[item.shape for item in items[:2]]    # [(50000, 32, 32, 3), (50000, 1)]

# testing set
[type(item) for item in items[2:]]    # [<type 'numpy.ndarray'>, <type 'numpy.ndarray'>]
[item.shape for item in items[2:]]    # [(10000, 32, 32, 3), (10000, 1)]

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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

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(x_train, y_train), (x_test, y_test) = cifar100.load_data(label_mode = "fine")

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library(keras)

# Define the constants
CONST_N <- 2000
CONST_EPOCHS <- 30
CONST_PIXEL_MAX <- 255