Al Asaad alstat
- Quezon City, Philippines
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- https://estadistika.github.io/
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 |
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) |
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 |
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 |
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) |
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 |
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]] # [<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)] |
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 |
View al-ilm-nn-pyr-keras-3.py
(x_train, y_train), (x_test, y_test) = cifar100.load_data(label_mode = "fine") |
View al-ilm-nn-pyr-keras-3.R
library(keras) | |
# Define the constants | |
CONST_N <- 2000 | |
CONST_EPOCHS <- 30 | |
CONST_PIXEL_MAX <- 255 |
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