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Last updated: 2017-03-12
Code version: e75cac0
Libraries used in this document:
sshhh("tidyr")
sshhh("dplyr")
sshhh("magrittr")
sshhh("ggplot2")
sshhh("cowplot")
sshhh("tibble")
sshhh("gtools")
Reading in the data:
dat <- read.table("../data/thrush-data.str", header = F)
colnames(dat) <- c("iid", "origin", paste(rep("locus_"), 1:(ncol(dat) - 2),
sep = ""))
What does our data look like?
knitr::kable(head(dat), caption = "Thrush data")
| iid | origin | locus_1 | locus_2 | locus_3 | locus_4 | locus_5 | locus_6 | locus_7 |
|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 1 | 1 | 6 | 1 | 2 | 2 | 5 |
| 1 | 2 | 4 | 2 | 6 | 1 | 2 | 4 | 5 |
| 10 | 2 | 4 | 2 | 1 | 1 | 2 | 2 | 5 |
| 10 | 2 | 4 | 3 | 6 | 1 | 2 | 10 | 8 |
| 1219 | 2 | 4 | 3 | 6 | 1 | 3 | 2 | 1 |
| 1219 | 2 | 13 | 5 | 6 | 2 | -9 | 2 | 4 |
Here we can implement the necessary functions to update the gibbs sampler:
# Random useful functions
# normalize a vector
normalize <- function(x) {
return(x/sum(x))
}
# special log function to avoid log(0)
mylog <- function(x) {
return(ifelse(x == 0, 0, log(x)))
}
# Update functions
#' @param arg.x input data
#' @param arg.p p_klj list of length k of j by l matrices of allele frequencies per locus per population
#' @param arg.k number of populations
#' @return vector of population assignments for each individual i
sample_z1 <- function(arg.x, arg.p, arg.k) {
x <- arg.x
p <- arg.p
k <- arg.k
# we only want to genotype data
x <- x[, 3:ncol(x)]
# how many loci do we have?
num_loci <- ncol(x)
# empty z vector of all individuals
z <- numeric(nrow(x))
# for each individual
for (xi in 1:length(z)) {
# for each population
weights <- numeric(k)
for (ki in 1:k) {
# for each loci
freqs <- numeric(num_loci)
for (li in 1:ncol(x)) {
# for allele j at individual xi and loci li
j <- x[xi, li]
# ignore missing data
if (j > 0) {
# allele 14 does not exist
if (j > 13) {
freqs[li] <- p[[ki]][j - 1, li]
} else {
freqs[li] <- p[[ki]][j, li]
}
}
}
# log them to avoid working with small numbers
weights[ki] <- sum(mylog(freqs))
}
# normalize the exponentiated weights
theta <- normalize(exp(weights))
# draw a population sample
z[xi] <- sample(x = 1:k, size = 1, replace = T, prob = theta)
}
return(z)
}
#' @param arg.x input data
#' @param arg.z vector of length nrow(arg.x)
#' @param arg.k number of populations
#' @return a k-length list of j alleles by l loci matrices
sample_p1 <- function(arg.x, arg.z, arg.k) {
# don't get confused by arguments
x <- arg.x
z <- arg.z
k <- arg.k
# we only want to genotype data
x <- x[, 3:ncol(x)]
# what alleles exist in our data set?
alleles <- as.integer(names(table(unlist(x))))
# first allele will be -9 which in this data set is missing data
alleles <- alleles[-1]
# convert to tibble for easy joining
allele_counts <- tibble::tibble(allele = alleles)
# p is a k length list of matrices
p <- vector("list", k)
# how many loci in our data?
num_loci <- ncol(x)
# for each population k_i
for (ki in 1:k) {
# what individuals are in population k_i? take only columns 3 to the end
ink <- x[z == ki, ]
# initialize matrix for k_i
p[[ki]] <- matrix(0, nrow = nrow(allele_counts), ncol = num_loci)
# for each locus l_i
for (li in 1:num_loci) {
# count the number of each allele
ti <- tibble::as_tibble(table(ink[li]))
# merge to create count of all alleles
colnames(ti) <- c("allele", "count")
ti$allele <- as.integer(ti$allele)
loci_counts <- dplyr::full_join(allele_counts, ti, by = "allele")
loci_counts <- dplyr::filter(loci_counts, allele != -9)
loci_counts$count <- ifelse(is.na(loci_counts$count), 0, loci_counts$count)
# sample p_lkj as Dirichlet
p[[ki]][, li] <- gtools::rdirichlet(n = 1, alpha = 1 + loci_counts$count)[1,
]
}
}
return(p)
}
Next we’ll write the gibbs sampler itself:
# Gibbs sampler function
gibbs1 <- function(arg.niter = 1000, arg.x, arg.k) {
niter <- arg.niter
x <- arg.x
k <- arg.k
# we don't care about the first two columns
x <- x[, 3:ncol(x)]
# how many alleles in our data set?
alleles <- as.integer(names(table(unlist(x))))
num_alleles <- length(alleles)
# how many loci?
num_loci <- ncol(x)
# resulting matrix should have z's for each individual
res <- list(z = matrix(0, nrow = niter, ncol = nrow(x)))
# initialize
z <- sample(x = 1:k, size = nrow(x), replace = T, prob = rep(1/k, k))
res$z[1, ] <- z
# update
for (i in 2:niter) {
p <- sample_p1(x, z, k)
z <- sample_z1(x, p, k)
res$z[i, ] <- z
}
return(res)
}
Just running this to test that the functions return what’s expected:
num_individuals <- nrow(dat)
# initialize
k.test <- 3
z.test <- sample(x = 1:k.test, size = num_individuals, replace = T, prob = rep(1/k.test,
k.test))
# run
p.test <- sample_p1(dat, z.test, k.test)
sample_z1(dat, p.test, k.test)
[1] 1 2 1 3 1 2 1 1 1 1 1 3 3 1 1 2 2 3 2 2 1 1 1 3 3 2 2 2 1 2 1 1 3 1 1
[36] 2 1 2 1 3 2 3 2 2 1 3 2 1 1 2 2 2 2 2 2 3 2 2 1 2 1 3 1 1 1 3 2 2 1 1
[71] 1 3 1 2 3 3 2 3 1 1 1 3 1 1 2 2 2 3 1 1 2 1 3 1 3 1 1 3 1 2
[ reached getOption("max.print") -- omitted 210 entries ]
For the results we’ll want to generate a structure plot. We can write a function for that:
structure_plot <- function(res, k) {
# remove burn-in make sure samples are not correlated
ti <- tibble::as_tibble(t(res$z))
ti <- ti[, 1001:10000]
ti <- ti[, c(1, seq(100, 9000, 100))]
# reformat to get a data structure that's easier to work with and plot
colnames(ti) <- 1:91
ti$ind <- 1:nrow(ti)
ti <- ti %>% tidyr::gather(obs, pop, -ind) %>% dplyr::mutate(pop = as.character(pop)) %>%
dplyr::group_by(ind, pop) %>% dplyr::summarise(n = n())
# order by number of counts in population 1
f <- ti %>% dplyr::arrange(pop, desc(n)) %>% dplyr::filter(pop == "1") %$%
ind
ti$ind <- factor(ti$ind, levels = f)
ggplot(ti, aes(x = ind, y = n, fill = pop)) + geom_bar(colour = "black",
size = 0.1, stat = "identity") + theme_void() + theme(legend.position = "top")
}
Now let’s run the simulation and view the results:
niter <- 10000
k <- 3
res <- gibbs1(arg.niter = niter, arg.x = dat, arg.k = k)
structure_plot(res, k)
I already ran the model before compiling this document so let’s just load the results here and plot them:
res <- readRDS("../output/hw5/res_without_admixture.rds")
structure_plot(res)
In order to include admixture in our model, two things change:
We now include a vector \(Q\) which will represent the proportion of invidiaul \(i\)’s genome that originated from population \(k\). We also need to change our assumption for \(z\), which was that each individual originated in some population \(k\). Our \(Z\) now becomes an \(I\) by \(L\) matrix corresponding to the population of origin of allele copy \(j\) at locus \(l\) in individual \(i\). An allele copy in diffferent individuals will always have the same population of origin.
# Update functions
#' @param arg.x input data
#' @param arg.p p_klj list of length k of j by l matrices of allele frequencies per locus per population
#' @param arg.k number of populations
#' @param arg.q admixture proportions for individuals
#' @return i by l matrix of allele population origins
sample_z2 <- function(arg.x, arg.p, arg.k, arg.q) {
# don't get confused by arguments
x <- arg.x
p <- arg.p
k <- arg.k
# we only want to genotype data
x <- x[, 3:ncol(x)]
# how many loci do we have?
num_loci <- ncol(x)
# how many individuals?
num_indi <- nrow(x)
# what alleles exist in our data set?
alleles <- as.integer(names(table(unlist(x))))
# first allele will be -9 which in this data set is missing data
alleles <- alleles[-1]
# how many alleles do we have?
num_alleles <- length(alleles)
# empty i by l matrix of allele copy origins
z <- numeric(num_alleles)
############################################################################## EDIT THESE LOOPS!!!!
# for each allele
for (ji in 1:num_alleles) {
# initialize k length weights vector
weights <- numeric(k)
# for each population
for (ki in 1:k) {
# what alleles originated in population ki?
alleles_ink <- which(z %in% ki)
# for each individial
for (xi in 1:num_indi) {
# we want the loci at which individual xi carries alleles within population
# ki
loci_ink <- which(x[xi, ] %in% ink)
# initialize vector of length num_loci
freqs <- numeric(num_loci)
# for each allele in population ki
for (index in 1:length(alleles_ink)) {
# extract allele frequencies for allele alleles_ink and loci loci_ink and
# multiply those frequencies by the admixture proportions for individual xi
# in population ki
freqs[xi, ] <- p[[ki]][alleles_ink[index], loci_ink[index]] *
q[xi, ki]
} # end for alleles_ink
# log them to avoid working with small numbers
weights[ki] <- sum(mylog(freqs))
} # end for individual xi
} # end for population ki
# normalize the exponentiated weights
theta <- normalize(exp(weights))
# draw a population sample
z[ji] <- sample(x = 1:k, size = 1, replace = T, prob = theta)
##############################################################################
} # end for allele ji
return(z)
}
#' @param arg.x input data
#' @param arg.z vector of length j
#' @param arg.k number of populations
#' @return a k-length list of j alleles by l loci matrices
sample_p2 <- function(arg.x, arg.z, arg.k) {
# don't get confused by arguments
x <- arg.x
z <- arg.z
k <- arg.k
# we only want to genotype data
x <- x[, 3:ncol(x)]
# what alleles exist in our data set?
alleles <- as.integer(names(table(unlist(x))))
# first allele will be -9 which in this data set is missing data
alleles <- alleles[-1]
# convert to tibble for easy joining
allele_counts <- tibble::tibble(allele = alleles)
# p is a k length list of matrices
p <- vector("list", k)
# how many loci in our data?
num_loci <- ncol(x)
# for each population k_i
for (ki in 1:k) {
# what alleles originated in population ki
ink <- which(z %in% ki)
# initialize matrix for ki
p[[ki]] <- matrix(0, nrow = nrow(allele_counts), ncol = num_loci)
# for each locus li
for (li in 1:num_loci) {
# what individuals have those alleles at locus li and are those alleles
ind <- x[x[, li] %in% ink, ][, li]
# count the number of individuals with those alleles
ti <- tibble::as_tibble(table(ind))
# merge to create count of all individuals with those alleles
colnames(ti) <- c("allele", "count")
ti$allele <- as.integer(ti$allele)
loci_counts <- dplyr::full_join(allele_counts, ti, by = "allele")
loci_counts <- dplyr::filter(loci_counts, allele != -9)
loci_counts$count <- ifelse(is.na(loci_counts$count), 0, loci_counts$count)
# sample allele frequencies at each locus
p[[ki]][, li] <- gtools::rdirichlet(n = 1, alpha = loci_counts$count)[1,
]
}
}
return(p)
}
#' @param arg.x input data
#' @param arg.z j length vector of population origins
#' @param arg.k number of clusters
#' @return i by k matrix of admixture proportions
sample_q2 <- function(arg.x, arg.z, arg.k, arg.alpha) {
# don't get confused by arguments
x <- arg.x
z <- arg.z
k <- arg.k
alpha <- arg.alpha
# we only want to genotype data
x <- x[, 3:ncol(x)]
# how many individuals?
num_indi <- nrow(x)
# initialize i by k matrix
q <- matrix(0, nrow = num_indi, ncol = k)
# for each individual
for (xi in 1:num_indi) {
# k length vector of allele counts per population
counts <- numeric(k)
# for each population
for (ki in 1:k) {
# what alleles originated in population ki
ink <- which(z %in% ki)
# how many of those alleles does individual xi have
counts[ki] <- sum(x[xi, ] %in% ink)
}
# sample mixture proportions for each individual
q[xi, ] <- gtools::rdirichlet(n = 1, alpha = alpha + counts)[1, ]
}
return(q)
}
update_alpha <- function(arg.alpha) {
# don't get confused by arguments
alpha <- arg.alpha
return(alpha_prime)
}
Next we’ll write the gibbs sampler itself:
gibbs2 <- function(arg.niter = 1000, arg.x, arg.k) {
niter <- arg.niter
x <- arg.x
k <- arg.k
# we don't care about the first two columns
x <- x[, 3:ncol(x)]
# how many alleles in our data set?
alleles <- as.integer(names(table(unlist(x))))
num_alleles <- length(alleles)
# how many loci?
num_loci <- ncol(x)
# resulting matrix should have z's for each individual
res <- list(z = matrix(0, nrow = niter, ncol = nrow(x)))
# initialize
z <- sample(x = 1:k, size = nrow(x), replace = T, prob = rep(1/k, k))
res$z[1, ] <- z
# update
for (i in 2:niter) {
p <- sample_p1(x, z, k)
z <- sample_z1(x, p, k)
res$z[i, ] <- z
}
return(res)
}
Just running this to test that the functions return what’s expected:
num_individuals <- nrow(dat)
# initialize
k.test <- 3
z.test <- sample(x = 1:k.test, size = num_individuals, replace = T, prob = rep(1/k.test,
k.test))
# run
p.test <- sample_p2(dat, z.test, k.test)
sample_z2(dat, p.test, k.test)
sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Gentoo/Linux
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gtools_3.5.0 tibble_1.2 cowplot_0.7.0 ggplot2_2.2.1 magrittr_1.5
[6] dplyr_0.5.0 tidyr_0.6.1
loaded via a namespace (and not attached):
[1] Rcpp_0.12.9 knitr_1.15.1 workflowr_0.3.0 munsell_0.4.3
[5] colorspace_1.3-2 R6_2.2.0 highr_0.6 stringr_1.2.0
[9] plyr_1.8.4 tools_3.3.2 grid_3.3.2 gtable_0.2.0
[13] DBI_0.6 git2r_0.18.0 htmltools_0.3.5 lazyeval_0.2.0
[17] yaml_2.1.14 rprojroot_1.2 digest_0.6.12 assertthat_0.1
[21] formatR_1.4 evaluate_0.10 rmarkdown_1.3 labeling_0.3
[25] stringi_1.1.2 scales_0.4.1 backports_1.0.5
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RegisterRegister has 4 sections of a wizard that requires the user to input their personal information, password, and 4-digit pin number. The email is checked against existing users, utilizing ajax, before allowing registration to continue.
Lock ScreenLock Screen has a full page overlay that will fade when clicked. Afterwards, the user that is signed in has to verify their 4-digit pin to continue.
Forget Password?Forget Password? requires the user to input their email and verify their 4-digit pin before taking action (password reset, email, etc.)
Unlock uses Lato from Google Fonts as the primary font throughout the template.
@import url(https://fonts.googleapis.com/css?family=Lato:300,400,700,300italic,400italic);
The sidebar and buttons can be styled using a markup of the BootStrap-Theme stylesheet.
jQuery is used extensively throughout the Unlock template. Each page links to its own .js file within assets/js. Ajax functions are called using a Get request and passing variables through the data option. For demo purposes, all user data is stored in users.js within the assets/ajax folder, and user authentication is performed after pulling in the $Users variable from ajax.
Unlock uses the following libraries & plugins:
Unlock is the intellectual property of Full Hive and may not be used without written permission from Full Hive. For questions, please contact through .


Hierarch Kekropus:
Ah, the qualifications to walk the path of a Soul Breaker...
Yes, Vitus must have sent you. If that is indeed your wish, your path will follow that of Katenar, one of the three heroes you have heard about. I must warn you, it will not be easy. This is not something to be embarked on lightly...
Do you truly wish this?
"I'm ready."


🔊 PODCAST: My HIV story Elvis Basudde Part 3 14 Nov 2019, 23:20 by Admin In the final part of this podcast triology Elvis gives advice on how to live positively with HIV


Members
DiamondCoder1000 - Chairman & Head of the CrylixOS dev team; Status: Active
thingMake - Co-Chairman & Lead Programmer/web developer; Status: Active
sauron65 - Assistant Programmer & Assistant Web Developer; Status: Active
Get A Spot Today!
To join and get a spot, go here.
Github Accounts for Our Members:
DiamondCoder1000
thingMake
sauron65


I4C
Home
IPFS
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After uploading, the hash is created and the file is uploaded to IPFS
Decentralized.
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Demo
Goals
The goals of the Caladrius project are to visualize and graph health information from Fitbit, have the capacity to change the values of that information, and automate other IOT devices and software interaction using RSS feeds that are generated in reaction to changes in the health data. The RSS feeds communicate with IFTTT to control other devices and applications. Multiple graph types and features are supported. This includes Bar Graphs, Line Graphs, and Point Graphs. These graphs can be stacked on top of one another and an additional y-axis is supported for exactly two overlapping graphs. Different colors for the respective graphs are also supported. Graph editing is supported as a customization feature. Fitbit account synchronization is supported. Relevant information that Fitbit keeps track of can be plotted using graphs. Fitbit is the primary wearable associated with this application.


§571-92 Application. This part shall only apply to actions under chapters 580 and 584. Nothing in this part shall supersede any provision of any existing state or federal law. The provisions in this part shall be interpreted consistently with other relevant laws and the standard of "best interest of the child" shall remain paramount. [L 2010, c 177, pt of §2]


| Cockpit Guide |
|---|
Cockpit has a dashboard that interacts with a Kubernetes cluster or an Openshift v3 cluster. This functionality is in the Cockpit kubernetes package.
The dashboard can either be deployed on kubernetes as a container in a pod or used via a normal authenticated Cockpit session. Cockpit communicates with Kubernetes via its REST API.
When the Kubernetes dashboard is used in a standard Cockpit login session, usually some
form of authentication is necessary to access the Kubernetes REST API.
Like the kubectl
and oc
commands, Cockpit uses
the authentication and server information in the ~/.kube/config
file for
the logged in user.
If a user is able to use kubectl
successfully when at their shell terminal,
then that same user will able to use Kubernetes dashboard when logged into Cockpit:
$ kubectl get pods
NAME READY STATUS RESTARTS AGE
docker-registry-1-l4pyh 1/1 Running 10 23d
...
When running Openshift one can use the oc login
command to configure the
~/.kube/config
file correctly. This in turn allows Cockpit to use that
login information.
The Kubernetes dashboard can be deployed as a pod on Kubernetes requiring users to log in using the standard standard authentication configured for access to its API.
$ wget https://raw.githubusercontent.com/cockpit-project/cockpit/master/containers/kubernetes-cockpit.json $ kubectl create -f kubernetes-cockpit.json
This pod can be used on kubernetes that is configured with basic authentication. By
default the pod tries to access the kubernetes API securely and expects that the kubernetes
API server has been deployed with a CA, certificate files and service account key. In order
to change this behavior, edit the kubernetes-cockpit.json
and change the
KUBE_INSECURE
environment variable to true
.
You can retrieve the address where you can access the dashboard via:
$ kubectl get service kubernetes-cockpit
The Kubernetes dashboard can be deployed as a pod on Openshift, and use Openshift OAuth authentication. Running software on Openshift requires forethought, planning and configuration. In particular, in order to run the admin interface as a pod you need to first decide the host name that it will be accessible at. This is the host name part of its URL.
Next use the following commands to deploy the registry pod and service. You'll need
to have admin access to the Openshift cluster, as an OAuth client needs to be created.
Replace the XXX
with the host name you want to make the registry
accessible at. In addition replace YYY
with the host name of the
Openshift API server.
$ wget https://raw.githubusercontent.com/cockpit-project/cockpit/master/containers/openshift-cockpit.template $ oc process -f openshift-cockpit.template -p COCKPIT_KUBE_URL=https://XXX -p OPENSHIFT_OAUTH_PROVIDER_URL=https://YYY:8443 | oc create -f -
You can retrieve the address where you can access the dashboard via:
$ oc get service openshift-cockpit


类 Out
- java.lang.Object
-
- edu.princeton.cs.algs4.Out
-
public class Out extends java.lang.Object
This class provides methods for writing strings and numbers to various output streams, including standard output, file, and sockets.For additional documentation, see Section 3.1 of Computer Science: An Interdisciplinary Approach by Robert Sedgewick and Kevin Wayne.
-
-
构造器概要
构造器 构造器 说明 Out()
Initializes an output stream from standard output.Out(java.io.OutputStream os)
Initializes an output stream from aOutputStream
.Out(java.lang.String filename)
Initializes an output stream from a file.Out(java.net.Socket socket)
Initializes an output stream from a socket.
-
方法概要
所有方法 静态方法 实例方法 具体方法 修饰符和类型 方法 说明 void
close()
Closes the output stream.static void
main(java.lang.String[] args)
A test client.void
print()
Flushes this output stream.void
print(boolean x)
Prints a boolean to this output stream and flushes this output stream.void
print(byte x)
Prints a byte to this output stream and flushes this output stream.void
print(char x)
Prints a character to this output stream and flushes this output stream.void
print(double x)
Prints a double to this output stream and flushes this output stream.void
print(float x)
Prints a float to this output stream and flushes this output stream.void
print(int x)
Prints an integer to this output stream and flushes this output stream.void
print(long x)
Prints a long integer to this output stream and flushes this output stream.void
print(java.lang.Object x)
Prints an object to this output stream and flushes this output stream.void
printf(java.lang.String format, java.lang.Object... args)
Prints a formatted string to this output stream, using the specified format string and arguments, and then flushes this output stream.void
printf(java.util.Locale locale, java.lang.String format, java.lang.Object... args)
Prints a formatted string to this output stream, using the specified locale, format string, and arguments, and then flushes this output stream.void
println()
Terminates the current line by printing the line-separator string.void
println(boolean x)
Prints a boolean to this output stream and then terminates the line.void
println(byte x)
Prints a byte to this output stream and then terminates the line.void
println(char x)
Prints a character to this output stream and then terminates the line.void
println(double x)
Prints a double to this output stream and then terminates the line.void
println(float x)
Prints a float to this output stream and then terminates the line.void
println(int x)
Prints an integer to this output stream and then terminates the line.void
println(long x)
Prints a long to this output stream and then terminates the line.void
println(java.lang.Object x)
Prints an object to this output stream and then terminates the line.
-
-
-
构造器详细资料
-
Out
public Out()
Initializes an output stream from standard output.
-
Out
public Out(java.io.OutputStream os)
Initializes an output stream from aOutputStream
.- 参数:
os
- theOutputStream
-
Out
public Out(java.lang.String filename)
Initializes an output stream from a file.- 参数:
filename
- the name of the file
-
Out
public Out(java.net.Socket socket)
Initializes an output stream from a socket.- 参数:
socket
- the socket
-
-
方法详细资料
-
close
public void close()
Closes the output stream.
-
main
public static void main(java.lang.String[] args)
A test client.- 参数:
args
- the command-line arguments
-
print
public void print()
Flushes this output stream.
-
print
public void print(boolean x)
Prints a boolean to this output stream and flushes this output stream.- 参数:
x
- the boolean to print
-
print
public void print(byte x)
Prints a byte to this output stream and flushes this output stream.- 参数:
x
- the byte to print
-
print
public void print(char x)
Prints a character to this output stream and flushes this output stream.- 参数:
x
- the character to print
-
print
public void print(double x)
Prints a double to this output stream and flushes this output stream.- 参数:
x
- the double to print
-
print
public void print(float x)
Prints a float to this output stream and flushes this output stream.- 参数:
x
- the float to print
-
print
public void print(int x)
Prints an integer to this output stream and flushes this output stream.- 参数:
x
- the integer to print
-
print
public void print(long x)
Prints a long integer to this output stream and flushes this output stream.- 参数:
x
- the long integer to print
-
print
public void print(java.lang.Object x)
Prints an object to this output stream and flushes this output stream.- 参数:
x
- the object to print
-
printf
public void printf(java.lang.String format, java.lang.Object... args)
Prints a formatted string to this output stream, using the specified format string and arguments, and then flushes this output stream.- 参数:
format
- the format stringargs
- the arguments accompanying the format string
-
printf
public void printf(java.util.Locale locale, java.lang.String format, java.lang.Object... args)
Prints a formatted string to this output stream, using the specified locale, format string, and arguments, and then flushes this output stream.- 参数:
locale
- the localeformat
- the format stringargs
- the arguments accompanying the format string
-
println
public void println()
Terminates the current line by printing the line-separator string.
-
println
public void println(boolean x)
Prints a boolean to this output stream and then terminates the line.- 参数:
x
- the boolean to print
-
println
public void println(byte x)
Prints a byte to this output stream and then terminates the line.To write binary data, see
BinaryOut
.- 参数:
x
- the byte to print
-
println
public void println(char x)
Prints a character to this output stream and then terminates the line.- 参数:
x
- the character to print
-
println
public void println(double x)
Prints a double to this output stream and then terminates the line.- 参数:
x
- the double to print
-
println
public void println(float x)
Prints a float to this output stream and then terminates the line.- 参数:
x
- the float to print
-
println
public void println(int x)
Prints an integer to this output stream and then terminates the line.- 参数:
x
- the integer to print
-
println
public void println(long x)
Prints a long to this output stream and then terminates the line.- 参数:
x
- the long to print
-
println
public void println(java.lang.Object x)
Prints an object to this output stream and then terminates the line.- 参数:
x
- the object to print
-
-


small ios developer/designer I make wallpapers, themes, ios tweaks and other cool stuff you like
at September 2019 I was starting making ios themes on October 18 was starting learning objective c and then I made my first "useful" tweak
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