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Welcome to the Dojo Web Builder!
This tool provides a web front-end to the existing Dojo Toolkit build system. Search through the entire catalogue
of Dojo, Dijit and DojoX modules, automatically find module dependencies in existing Dojo applications and generate custom builds
from the results. For more detailed help, see the "Help" tab in the main application.


What we are ALL about
Stearing Advice, Strong market options, Fast service
Start drivingStearing Advice, Strong market options, Fast service
Start drivingSearch our catalogue and find your car.
Select the right car for you.
Pick it up and give it a test drive.
Drive away with your perfect car.
Purchase it for an affordable price.
Have fun with our world renowned cars.
Thanks for the hard work at HighWay™! You have made me a happy person: I now have a beautiful car, a beautiful wife and a beautiful life. Buying from your excellent service was one of the best decisions I have ever taken. Thanks from the bottom of my heart 💪 pic.twitter.com/Vzc3A034oZ
— Udit mahajan (@Udit211112) August 30, 2019
It took me days to go throught all of HighWay™'s catalogue, there was a lot of cars to choose from, so I went with my fav: I went with the "Mercedes-Benz SL ClassMSRP". Now I am the envy of both my friends and family, also women have started paying attention to me. Thanks ❤🤩 pic.twitter.com/pkLNucUyBd
— vgarcab (@vgarcab1) August 30, 2019


I am currently attending Cal State University of Northridge, majoring in CTVA Multimedia Production. I am a Junior and expect to graduate in the Fall of 2020.
One of the things I love to do is travel. I love to experience the different cultures and environments that exist across the world, and I hope I can say that I have traveled to every continent.
I currently work at Six Flags Magic Mountain in the entertainment department as a stage tech. I joined the team in the summer of 2018. My job adapts many aspects from theatre production like lighting, scenery, and audio into our field of work, for such events; Fright Fest, Holiday in the Park, Grad-Nite, and Dance Competition. Click Here to view my Resume


I prefer the hands on approach when learning something new, its easier to keep it stored in the ole noggin. Fancy Interests
I feel automation is key to remove tedious and repetitive work
Putting in work creating 3D models for either cosplay or miniature figures is alot of fun
Best source of energy in my humble opinion
Discovering new AI applications that pop up online is pretty amazing


Group Discussion
A Deep Learning Algorithm For High-Dimensional Exploratory Item Factor Analysis
Authors: Christorpher J. Urban and Daniel J. Bauer
- Background Information
- Research Objective
- Procedure of the proposed method
- Numerical Example
- Simulation Study
- Extention of the proposed method
Amortized Variational Inference (AVI)
Popular marginal maximimum likelihood-based parameter estimation methods included Metropolis-Hastings Robbins-Monro (MH-RM; Cai, 2010a; 2010b), stochastic expectation-maximization (stEM; Zhang, Chen, & Liu, 2020), limited-information approaches, and the approaches based on variational inference (VI). The constrained joint maximum likelihood estimator (CJMLE) is another estimator which avoids for evaluating high-dimensional integrals.
Traditional variational inference (VI) fits a different approximate LV posterior for each observation, which quickly becomes computationally infeasible for large data sets (e.g., more than 10 latent factors and more than 10,000 respondents).
Amortized VI is a computationally efficient alternative to VI that uses an inference model to parameterize the approximate posterior. By sharing the parameters of the inference model across observations, AVI estimates a constant number of parameters regardless of the sample size.
The variational autoencoder (VAE) used in this study is an AVI algorithm whose inference model is an ANN. The FNN parameter ψ are now shared across observations.
The unique advantage of the proposed method is it combines the asympotic properties of MML estimator with the computational efficiency of CJMLE
Importance-weighted Autoencoder
It is a algorithm for amortized IWVI whose inference model is a deep artifical neural network (ANN).
Amortized IWVI for IFA maxminzes a lower bound called the importance-weighted evidence lower bound (IW-ELBO).
Fig.1 importance-weighted ELBO When Importance-weight samples R=1, the IW-ELBO reduces to the ELBO. As R -> Infinity, the IW-ELBO converges to the marginal log-likelihood. IWVI is equivalent to MML estimation when the number of importance samples R equals inifinity.
Importance-weighted Autoencoder(cont.)
When the inference model of IWVI is feedward neural network (FNN), the associated IWVI algorithm is called the importance-weighted autoencoder (IWAE). Generally speaking, IWAE is one variation of IWVI but using machine learning algorithm.
- η: the learning rate
- β1 & β2: forgetting factors for the gradient and the squared gradients
- To introduce the IWAE to the IFA literature and describe how it can be applied for Exploratory analysis of polytomous item response data in frequentist setting.
- To conduct simulation studies to investigate the finite sample behavior of the IWAE and to compare it to MH-RM and CJMLE.
- Software: PyTorch Ver. 1.1.6 without GPU Computing
- Data: Big-Five Personality Questionnaire
- Sample Size(N): 515,708
- Test Length: 50 items
- Item Type: 5-point likert scale
- Number of Attributes By Design: 5
- Model: Exploratory Item Factor Analysis
- Scree Plot of -2LL demonstrated an "elbow" at P = 5
- Hidden Layer size is 130 and the learning rate η = 0.005, and the number of IW samples R = 5, replicate the estimation 100 times
- mean fitting time was 170 seconds (SD = 47 seconds)
- mean RMSE for factor loadings: 0.018 (SD = 0.006)
- mean RMSE for item intercepts: 0.042 (SD = 0.018)
- mean RMSE for factor correlation: 0.028 (SD = 0.009)
- Data Generation: loadings, intercepts, and factor correlations are rounded estimates from the previous empirical example
- P = 5, J = 50, and Cj=5
- Simple factor structure: cross-loadings set to zero
- R = 1, 5, and 25
- 100 reps for N = 500, 1000, 2000, and 10,000 (R X N = 12 conditions)
- All estimates become more accurate as N increase
- Factor correlations and intercepts estimates become more accurate with increasing R. Loadings are nearly unbiased as R increases.
- For each R setting, parameter MSE quickly decreases toward zero with increasing N; Increasing R tends to improve factor correlation estimates more than the other two parameters
- Increasing R from 1 to 5 decreases fitting time while increasing R from 5 to 25 increases fitting time.
- Factor scores estimates tend to be more accurate with increasing R and N
- MH-RM's loadings and intercepts estimates are slightly more accurate when N = 10,000
- Amortized IWVI produces more accurate factor correlation estimates across N setting
- IWAI is much faster than MHRM: MHRM - 8 mins(N = 1000) and 21 mins (N =10,000), whereas IWVI - stay around 3 minutes regardless of N
Data generation
- P = 10, (N,J) = (2000, 100), (10 000, 200), (50 000, 300), and (100000,400)
- Cj= 2
- Model: Multidimensional 2PL
- Item parameters are borrowed from Numerical examples
- CJMLE estimates loadings and intercepts inaccurately in the smallest (N = 2000, J =100) setting but is more accurate than IWVI in the highest settings
- IWVI is always faster than CJMLE in all settings. IWVI's median fitting time is 63 sec when (N, J) = (2000, 100) and increases over 6 mins when (N, J) = (100000, 400), whereas CJMLE increase from 73 sec to 43 mins.
- Both estimators may speed up using GPU
- CFA: the proposed method could be used to validate Q matrix by ensuring factor loadings are either freely estimated or set to zero
- Regularized EFA: a viable alternative to factor rotation by adding a penalty function to optimization function
- Approximate link function by fixing latent density
- Tradition exploratory analysis uses scree plot with eigenvalue, while this study uses scree plot with negative LL. What do you think about this?
- How to decide on the settings for Hyperparameters before using IWAE?
- Do you think it would be a trend to combine machine learning algorithm in psychometric estimation in term of big data in education field?


new Directory(name, parentopt) → {Directory}
Prototype representing directory.
All password added to the directory are automatically encrypted to it's corresponding user ids.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
name |
String | Name of the directory | |
parent |
String |
<optional> |
Reference to parent directory. If parent isn't specified, parent directory is set to this, meaning it is root directory. |
- Source:
Returns:
New directory.
- Type
- Directory
Methods
addDirectory(name) → {Directory}
Add new directory.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of the enw directory. |
- Source:
Returns:
Newly created directory.
- Type
- Directory
addDirectory(name) → {Directory}
Add new directory to the root.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of the enw directory. |
Returns:
Newly created directory.
- Type
- Directory
addDirectoryRecursive(path) → {Directory}
Adds new directory recursively.
Parameters:
| Name | Type | Description |
|---|---|---|
path |
String | Relative path of the new directory. |
- Source:
Returns:
Newly created directory.
- Type
- Directory
addPassword(name, content) → {Promise.<Password>}
Add password to store. Password is automatically encrypted to the corresponding keys of directory.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of the password. |
content |
String | Content of the password. |
Throws:
If password with same name already exists in direcotry.
Returns:
Promise of new password.
- Type
- Promise.<Password>
addPassword(name, content) → {Promise.<Password>}
Add password to directory. Password is automatically encrypted to the corresponding keys of directory.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of the password. |
content |
String | Content of the password. |
- Source:
Throws:
-
If password with same name already exists in direcotry.
- Type
- EntryExistsException
Returns:
Promise of new password.
- Type
- Promise.<Password>
copy(destination, forceopt, createDestinationopt) → {Promise.<Directory>}
Copy directory to destination directory.
Parameters:
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
destination |
Directory | Destination directory where directory should be copied. | ||
force |
Boolean |
<optional> |
false | Overwrite directory in destination if it already exists. |
createDestination |
Boolean |
<optional> |
false | If destination directory doesn't exist, create it. |
- Source:
Throws:
-
-
If directory with same name in destination directory already exists, unless force is set to true.
- Type
- EntryExistsException
-
-
-
If destination directory doesn't exist, unless createDestination is set to true.
- Type
- InvalidEntryException
-
Returns:
Promise of directory copied to the new location and reencrypted to it's keys.
- Type
- Promise.<Directory>
directoryNameCheck(name) → {undefined}
Check if directory with specified name already exists in directory.
This method throws if directory exists.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of the password. |
- Source:
Returns:
- Type
- undefined
getAllDirectories() → {Array.<Directory>}
Get all directories in directory.
- Source:
Returns:
All directories in directory.
- Type
- Array.<Directory>
getAllPasswords() → {Array.<Password>}
Get all password in directory.
- Source:
Returns:
All passwords in directory.
- Type
- Array.<Password>
getDirectory(name) → {Directory}
Get directory with specified name.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of the directory to return. |
- Source:
Throws:
-
If directory with specified name doesn't exist.
- Type
- InvalidEntryException
Returns:
Directory with specified name.
- Type
- Directory
getDirectory(name) → {Directory}
Get directory with specified name from root directory.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of the directory to return. |
Throws:
If directory with specified name doesn't exist.
Returns:
Directory with specified name.
- Type
- Directory
getKeyIds() → {Array.<String>}
Get key id's for directory.
- Source:
Returns:
Id's of directory.
- Type
- Array.<String>
getKeyring() → {Keyring}
Get store keyring.
- Source:
Returns:
OpenPGP.js keyring.
- Type
- Keyring
getKeysFor(type, keyIds) → {Array.<Key>}
Get public or private keys for specified key id's.
Parameters:
| Name | Type | Description |
|---|---|---|
type |
String | Type of key, can be "public" or "private". |
keyIds |
Array.<String> | Id's to get key for. If id's aren't specified, get current keys for directory. |
- Source:
Returns:
Private or public keys for specified id's or for current directory id's.
- Type
- Array.<Key>
getPassword(name) → {Password}
Get password from directory.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of the password. |
- Source:
Throws:
-
If password with specified name doesnt exist.
- Type
- InvalidEntryException
Returns:
Password with specified name.
- Type
- Password
getUnlockedKeyring() → {Keyring}
Get private key unlocked keyring.
- Source:
Returns:
OpenPGP.js keyring.
- Type
- Keyring
isRoot() → {Boolean}
Checks if directory is root directory.
- Source:
Returns:
True if directory is root directory.
- Type
- Boolean
loadFromDB(allDirectories, allPasswords) → {Promise.<Directory>}
Load directory from IndexedDB.
Parameters:
| Name | Type | Description |
|---|---|---|
allDirectories |
Array.<Object> | All directories from IndexedDB. |
allPasswords |
Array.<Object> | All passwords from IndexedDB. |
- Source:
Returns:
Promise of directory with content loaded from IndexedDB.
- Type
- Promise.<Directory>
move(destination, forceopt) → {Promise.<Directory>}
Move directory to destination directory.
Parameters:
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
destination |
String | Destination directory where it should be moved. | ||
force |
Boolean |
<optional> |
false | Overwrite directory in destination if it already exists. |
- Source:
Throws:
-
-
If directory with same name in destination directory already exists, unless force is set to true.
- Type
- EntryExistsException
-
-
-
If destination directory doesn't exist, unless createDestination is set to true.
- Type
- InvalidEntryException
-
Returns:
Promise of directory reencrypted to the corresponding keys of new location.
- Type
- Promise.<Directory>
passwordNameCheck(name) → {undefined}
Check if password with name already exists in directory.
This method throws if directory exists.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of the password. |
- Source:
Returns:
- Type
- undefined
remove()
Remove directory.
- Source:
removeDirectory(name)
Remove directory from array of directories.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of directory to remove. |
- Source:
rename(name) → {Password}
Rename directory.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | New name of the directory. |
- Source:
Throws:
-
If directory with same name already exist in parent directory.
- Type
- EntryExistsException
Returns:
Reference to the renamed directory.
- Type
- Password
search(pattern) → {Array.<Password>|null}
Search for passwords with name containing regex pattern.
Parameters:
| Name | Type | Description |
|---|---|---|
pattern |
String | Pattern to search for. |
Returns:
Array with macthed password or null if no password has been found.
- Type
- Array.<Password> | null
search(pattern) → {Array.<Password>|null}
Search for passwords with name containing regex pattern.
Parameters:
| Name | Type | Description |
|---|---|---|
pattern |
String | Pattern to search for. |
- Source:
Returns:
Array with macthed password or null if no password has been found.
- Type
- Array.<Password> | null
setKeyIds(Fingerprint) → {Promise.<Directory>}
Set id's for directory. All passwords will be reencrypted using new ids.
Parameters:
| Name | Type | Description |
|---|---|---|
Fingerprint |
Array.<String> | or long key id. |
- Source:
Throws:
-
-
If id isn't in keyring.
- Type
- InvalidIdException
-
-
-
If no private key for containing password exists in keyring.
- Type
- NoPrivateKeyException
-
-
-
If no private key for containing password is decrypted in unlocked keyring.
- Type
- PrivateKeyEncryptedException
-
Returns:
Promise of directory with reencrypted passwords.
- Type
- Promise.<Directory>
storeToDB(directoriesOS, passwordsOS) → {Promise.<undefined>}
Store directory to IndexedDB.
Parameters:
| Name | Type | Description |
|---|---|---|
directoriesOS |
ObjectStore | ObjectStore for directories. |
passwordsOS |
ObjectStore | ObjectStore for passwords. |
- Source:
Returns:
Promise, if resolved, directory was sucessfully saved to database.
- Type
- Promise.<undefined>


--- layout: post esbma_id: 1508 date: 2004-02-16 12:00:00 keywords: cornsharp title: Mono 0.30.1 ---
Using the just-released Mono 0.30.1, you can run the just-released Vault 2.0 command-client client on Linux.


Designed by Lucas Liu and Suyang Lo, this is a very short introductory course to Python for participants that have little experience with programming or are unfamiliar with Python.
Variables are reserved memory locations to store values. The declaration of a variable occur automatically when a value is assigned to a variable with the equal sign (=). Values stored in variables can be changed through operations such as +, -, * and /.
v = 1 v = v + 1 v += 1
Multiple variables can also be assigned in different ways. The following code declares all a, b and c, assigning a value of 1 to each.
a = b = c = 1
Another way of assigning multiple variables is shown below:
a, b, c = 1, 2, 'hello'
The function print() allows variables to be outputted to the screen:
a = 'Hello World!' print(a)
Conditions are often used to compare variables. The following code illustrates the most common comparison operators and their expected output:
3 == 4 # False 3 != 4 # True 3 < 4 # True 3 <= 4 # True 3 > 4 # False 3 >= 4 # False
A list is a group of variables. Items are separated by commas and enclosed by square brackets ([]). They are similar to arrays but data belonging to the same list can be of different data types. To following code declares a list with 4 elements that have mixed data types:
list = ['abc', 123, 'hello', 22]
Data stored at different positions in a list can be accessed using [index]. This is demonstrated in the example below. In Python, lists are 0-indexed, meaning that the first element has an index of 0, the second an index of 1, the third an index of 2 etc..
list = ['abc', 123, 'hello', 22] print(list[0]) # This should print out 'abc' print(list[1:3]) # This should print out the 2nd and 3rd elements. Note that the last element we note down (3)is not printed. print(list[1:]) # This prints out every item in the list starting from the 2nd element.
Lists can be 'added' together (this is called concatenation) like this:
concatenated_list = ['a', 1] + ['b', 2]
These are used to execute different code depending on different conditions. "else" is often used if none of the listed if cases are satisfied.
They are written out as following:
a = 50 if a == 50: print('a is 50') elif a < 50: print('a is less than 50') else: print('a is greater than 50')
Remember that indentations matters in Python. Usually, you can indent statements located inside the if, if-else, or if-elif-else block using tabs, 4 spaces or 2 spaces.
AND and OR statements can be used with our conditional statements. When you use AND to combine two boolean expressions, it will only return True if both expressions are true. When you use OR to combine two boolean expressions, it will only return False if both expressions are false.
Iteration is basically repeating a process over different values in a list. For loops can be used to repeat code for a given amount of times. Remember that for loops start counting from zero. The following code prints out 0, 1 and 2.
for count in range(3): print(count)
For loops can also be used to iterate through lists. In fact, range(3) actually returns a list for the for loop to iterate through.
for letter in ['a', 'b', 'c']: print(letter)
Iteration can also be achieved through while loops. This type of loop repeats code while a condition remains true. It can be used alongside 'else' to execute code if the condition is no long true.
a, b = 3, 0 while b < a: b = b + 1 print(b) else: print('b is no longer less than a')
The output of the code above should be:
1 2 3 b is no longer less than a
Output a list of n values in the fibonnaci sequence (The fibonnaci sequence is a mathematical sequence where the next term is equal to the sum of the previous 2 terms, beginning with 1,1. First six values are 1,1,2,3,5,8)
Create an algorithm that checks if a number is a prime number (0 and 1 are not prime numbers)
Write a program that guesses the number between 1 and 100 that you are thinking of (try not to just make it guess all numbers from 1 to 100)
Use splitting strings to reverse the order of a string.
str = "this is a string" # This method splits the string every time it finds a space split = str.split() # The below method joins all the strings with spaces print("".join(str))
This is the output of the above code:
this is a string
Create a password generator using the random library that will generate a password of random length between 9 and 15 characters that uses letters, numbers, and symbols. (Do a bit of research if you don't know how to use the random library)


{{ title }}{% if question_number %}question {{ question_number }}/{{ num_questions }}{% endif %}
{% if description %}
{{ description }}
{% endif %}
{% if previous_question %}
{% if previous_answer == previous_question.1 %}
Correct!
{% include answer_template with question=previous_question.0 %}
is
{{ previous_question.1 }}
{% else %}
Incorrect
{% include answer_template with question=previous_question.0 %}
is
{{ previous_question.1 }}
(you said {{ previous_answer }})
{% endif %}


Introduction
Two ways to give semantics to programs are:
- As data flow / control flow graphs, or
- Categorically.
Flow graphs are usually semi-formal, common SE practice.
Categorical semantics is formal, specialist material.
This talk describes a link between the two.


RUDRA
Instructions
Report Theft
Log Out
ChatBot
INSTRUCTIONS
1. Go to Report Theft Section to report any theft.
2. Go to Chatbot for further support.
3. Chatbot can help you with Child Protection Laws, Reporting a crime and many more.


| ziP {mgcv} | R Documentation |
Family for use with gam
or bam
, implementing regression for zero inflated Poisson data
when the complimentary log log of the zero probability is linearly dependent on the log of the Poisson parameter. Use with great care, noting that simply having many zero response observations is not an indication of zero inflation: the question is whether you have too many zeroes given the specified model.
This sort of model is really only appropriate when none of your covariates help to explain the zeroes in your data. If your covariates predict which observations are likely to have zero mean then adding a zero inflated model on top of this is likely to lead to identifiability problems. Identifiability problems may lead to fit failures, or absurd values for the linear predictor or predicted values.
ziP(theta = NULL, link = "identity",b=0)
theta |
the 2 parameters controlling the slope and intercept of the linear transform of the mean controlling the zero inflation rate. If supplied then treated as fixed parameters (theta_1 and theta_2), otherwise estimated. |
link |
The link function: only the |
b |
a non-negative constant, specifying the minimum dependence of the zero inflation rate on the linear predictor. |
The probability of a zero count is given by 1- p, whereas the probability of
count y>0 is given by the truncated Poisson probability function (pmu^y/((exp(mu)-1)y!). The linear predictor
gives log(mu), while eta=log(-log(1-p)) and eta = theta_1 + (b+exp(theta_2)) log(mu). The theta
parameters are estimated alongside the smoothing parameters. Increasing the b
parameter from zero can greatly reduce identifiability problems, particularly when there are very few non-zero data.
The fitted values for this model are the log of the Poisson parameter. Use the predict
function with type=="response"
to get the predicted expected response. Note that the theta parameters reported in model summaries are theta_1 and b + exp(theta_2).
These models should be subject to very careful checking, especially if fitting has not converged. It is quite easy to set up models with identifiability problems, particularly if the data are not really zero inflated, but simply have many zeroes because the mean is very low in some parts of the covariate space. See example for some obvious checks. Take convergence warnings seriously.
An object of class extended.family
.
Zero inflated models are often over-used. Having lots of zeroes in the data does not in itself imply zero inflation. Having too many zeroes *given the model mean* may imply zero inflation.
Simon N. Wood<EMAIL_ADDRESS>Wood, S.N., N. Pya and B. Saefken (2016), Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association 111, 1548-1575 http://dx.doi.org/10.1080/01621459.2016.1180986
rzip <- function(gamma,theta= c(-2,.3)) { ## generate zero inflated Poisson random variables, where ## lambda = exp(gamma), eta = theta[1] + exp(theta[2])*gamma ## and 1-p = exp(-exp(eta)). y <- gamma; n <- length(y) lambda <- exp(gamma) eta <- theta[1] + exp(theta[2])*gamma p <- 1- exp(-exp(eta)) ind <- p > runif(n) y[!ind] <- 0 np <- sum(ind) ## generate from zero truncated Poisson, given presence... y[ind] <- qpois(runif(np,dpois(0,lambda[ind]),1),lambda[ind]) y } library(mgcv) ## Simulate some ziP data... set.seed(1);n<-400 dat <- gamSim(1,n=n) dat$y <- rzip(dat$f/4-1) b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=ziP(),data=dat) b$outer.info ## check convergence!! b plot(b,pages=1) plot(b,pages=1,unconditional=TRUE) ## add s.p. uncertainty gam.check(b) ## more checking... ## 1. If the zero inflation rate becomes decoupled from the linear predictor, ## it is possible for the linear predictor to be almost unbounded in regions ## containing many zeroes. So examine if the range of predicted values ## is sane for the zero cases? range(predict(b,type="response")[b$y==0]) ## 2. Further plots... par(mfrow=c(2,2)) plot(predict(b,type="response"),residuals(b)) plot(predict(b,type="response"),b$y);abline(0,1,col=2) plot(b$linear.predictors,b$y) qq.gam(b,rep=20,level=1) ## 3. Refit fixing the theta parameters at their estimated values, to check we ## get essentially the same fit... thb <- b$family$getTheta() b0 <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=ziP(theta=thb),data=dat) b;b0 ## Example fit forcing minimum linkage of prob present and ## linear predictor. Can fix some identifiability problems. b2 <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=ziP(b=.3),data=dat)


IGAWK(1) Utility Commands IGAWK(1) NAME igawk - gawk with include files SYNOPSIS igawk [ all gawk options ] -f program-file [ -- ] file ... igawk [ all gawk options ] [ -- ] program-text file ... DESCRIPTION Igawk is a simple shell script that adds the ability to have ``include files'' to gawk(1). AWK programs for igawk are the same as for gawk, except that, in addi‐ tion, you may have lines like @include getopt.awk in your program to include the file getopt.awk from either the current directory or one of the other directories in the search path. OPTIONS See gawk(1) for a full description of the AWK language and the options that gawk supports. EXAMPLES cat << EOF > test.awk @include getopt.awk BEGIN { while (getopt(ARGC, ARGV, "am:q") != -1) ... } EOF igawk -f test.awk SEE ALSO gawk(1) Effective AWK Programming, Edition 1.0, published by the Free Software Foundation, 1995. AUTHOR Arnold Robbins (arnold@skeeve.com). Free Software Foundation Nov 3 1999 IGAWK(1)


?I**™BOTANICAL
GARDEN
^e\N
vo^f.
BOTANICAL
GARDER
FLORA
OF
SAINT
LUCIA
Russelia
equisetifolia
Cham.
&
Schlecht.
Vieux
Fort
to
Soufriere.
Arching
spreading
shrub,
cultivated
at
hotel.
Flowers
red.
Fruit
brown.
R.
A.
Howard,
E.
A.
Kellogg
&
L.
Jean-Pierre
1*^7
3
30
January
1985
ARNOLD
ARBORETUM
(A)
OF
HARVARD
UNIVERSITY
6
7
8
9
10
L
„
v
The
New
York
copyright
reserved
botanical
Garden
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


To configure MQTT devices, use the following MQTT connection information:
Host: {{show_brokerhost}} | Port: {{Mqtt.Brokerport}} | User: {{Mqtt.Brokeruser}} | Pass: {{Mqtt.Brokerpass}}
Possibly host and port needs to be combined: {{show_brokerhost}}:{{Mqtt.Brokerport}}
Unsaved changes. Keep in mind, that changing the MQTT Server settings might need to change these on all your MQTT devices. Plugins possibly need to be restarted.


{% extends 'core/base.html' %} {% load staticfiles %} {% block content %}
EWB-USA supports community-driven development programs worldwide by collaborating with local partners to design and implement sustainable engineering projects, while creating transformative experiences and responsible leaders.


{{this.page_param.amount}}{{this.page_param.currencyCode}}
≈
{{gold}}{{this.page_param.value | money}}
+{{this.page_param.totalIncrease}}%
{{this.page_param.totalIncrease}}%
Profit&Loss
{{this.page_param.totalProfitAndCostRatio}}%
- Cost
- {{gold}}{{this.page_param.cost | money}}
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Issachar Humphrey
-
I Am Me!
I am unique, an irreplaceable human being.
The first me and the last.
I am God's creature, no one will ever replace me.
I'm not wealthy but guess what, i'm happy because God takes care of me.
You may hate me, but it makes no difference to my life because
You are not my Maker and you were not there when He creates me.
I am me,
Irreplaceable me,
Just me,
And original me....
To you i'm useless, but to God and my family i'm so special.
I may not be handsome, but God is still working on that...
I may not have everything i want, but i have a loving family and friends...

You may think i'm selfish, but to my friends i'm a friend indeed.

So keep your ten cent worth thoughts and opinions to yourself.
Because i don't need them.
And give me the credit i deserve, congratulate me when its needed.
And what ever you think of me, i don't care,
Because i won't change my lifestyle just to please YOU!
Get used to the fact that i'm in control of my life and i'm FINE.
Know that i'm unique beacause,
I am me,
Irreplaceable me,
Just me,
And original me...
Thats who I am...
Down to earth, , but
JUST ME.....Issachar Humphrey! ^_^


--- title: Undeprecated features in PHP 5.3.x version: "5.3" tags: - "5.3" - undeprecated ---
The is_a() function has been undeprecated by popular request; it will no longer issue an E_STRICT error.
E_STRICT


With Chart.js, it is possible to create mixed charts that are a combination of two or more different chart types. A common example is a bar chart that also includes a line dataset.
When creating a mixed chart, we specify the chart type on each dataset.
var mixedChart = new Chart(ctx, {
data: {
datasets: [
{
type: "bar",
label: "Bar Dataset",
data: [10, 20, 30, 40],
},
{
type: "line",
label: "Line Dataset",
data: [50, 50, 50, 50],
},
],
labels: ["January", "February", "March", "April"],
},
options: options,
});
At this point, we have a chart rendering how we’d like. It’s important to note that the default options for the charts are only considered at the dataset level and are not merged at the chart level in this case.
```js chart-editor //
//
module.exports = { actions: [], config: config, }; ```
By default, datasets are drawn such that the first one is top-most. This can be altered by specifying order
option to datasets. order
defaults to 0
. Note that this also affects stacking, legend, and tooltip. So it’s essentially the same as reordering the datasets.
var mixedChart = new Chart(ctx, {
type: "bar",
data: {
datasets: [
{
label: "Bar Dataset",
data: [10, 20, 30, 40],
// this dataset is drawn below
order: 2,
},
{
label: "Line Dataset",
data: [10, 10, 10, 10],
type: "line",
// this dataset is drawn on top
order: 1,
},
],
labels: ["January", "February", "March", "April"],
},
options: options,
});


--- layout: doc title: Documentation ---
The node you are looking for is at EasyCrypt specific commands.


Travel Location On The Way
Travelling means going from one place to another in different purposes. They also explore to see the unseen and know the unknown. ...
At the same time, people do not travel in the same purposes. Someone travels to gain knowledge and experience, someone travels for pleasure, someone travels for business purposes etc.
Read MoreAt the same time, people do not travel in the same purposes. Someone travels to gain knowledge and experience, someone travels for pleasure, someone travels for business purposes etc.
Read MoreAt the same time, people do not travel in the same purposes. Someone travels to gain knowledge and experience, someone travels for pleasure, someone travels for business purposes etc.
Read MoreAt the same time, people do not travel in the same purposes. Someone travels to gain knowledge and experience, someone travels for pleasure, someone travels for business purposes etc.
Read MoreAt the same time, people do not travel in the same purposes. Someone travels to gain knowledge and experience, someone travels for pleasure, someone travels for business purposes etc.
Read MoreAt the same time, people do not travel in the same purposes. Someone travels to gain knowledge and experience, someone travels for pleasure, someone travels for business purposes etc.
Read MoreTravel is my passion
Travelling means going from one place to another in different purposes. They also explore to see the unseen and know the unknown. The travellers always travel the most beautiful places of the world which uphold the existence of nature. All things are created by Allah. There are many means to see the unseen and know the unknown, but travelling is the best of all. For example: Reading books gives us literal knowledge, listening story gives us indefinite ideas, but travelling gives us visual as well as concrete knowledge.
At the same time, people do not travel in the same purposes. Someone travels to gain knowledge and experience, someone travels for pleasure, someone travels for business purposes etc. There are different types of travelling like- travelling by air, bus, boat, train, bus etc. Every travelling has much educative value. It is a part of education. Our education and book knowledge remain imperfect without travelling. For this reason, we can learn many things by travelling. It teaches us trade and commerce, language, sociology, customs, culture, history, geography and so on.


Class: Aws::ConfigService::Types::ConfigRule
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb |
Overview
Config rules evaluate the configuration settings of your Amazon Web Services resources. A rule can run when Config detects a configuration change to an Amazon Web Services resource or at a periodic frequency that you choose (for example, every 24 hours). There are two types of rules: Config Managed Rules and Config Custom Rules.
Config Managed Rules are predefined, customizable rules created by Config. For a list of managed rules, see List of Config Managed Rules.
Config Custom Rules are rules that you create from scratch. There are
two ways to create Config custom rules: with ::Aws::Lambda
functions (
::Aws::Lambda
Developer Guide) and with Guard (Guard GitHub
Repository), a policy-as-code language. Config custom rules
created with ::Aws::Lambda
are called Config Custom Lambda Rules and Config
custom rules created with Guard are called Config Custom Policy
Rules.
For more information about developing and using Config rules, see
Evaluating ::Aws::ConfigService::Resource
with Config Rules in the Config Developer
Guide.
ConfigSnapshotDeliveryProperties
.
Constant Summary
Instance Attribute Summary
-
#config_rule_arn ⇒ String
rw
The Amazon
::Aws::ConfigService::Resource
Name (ARN) of the Config rule. -
#config_rule_id ⇒ String
rw
The ID of the Config rule.
-
#config_rule_name ⇒ String
rw
The name that you assign to the Config rule.
-
#config_rule_state ⇒ String
rw
Indicates whether the Config rule is active or is currently being deleted by Config.
-
#created_by ⇒ String
rw
Service principal name of the service that created the rule.
-
#description ⇒ String
rw
The description that you provide for the Config rule.
-
#evaluation_modes ⇒ Array<Types::EvaluationModeConfiguration>
rw
The modes the Config rule can be evaluated in.
-
#input_parameters ⇒ String
rw
A string, in JSON format, that is passed to the Config rule
::Aws::Lambda
function. -
#maximum_execution_frequency ⇒ String
rw
The maximum frequency with which Config runs evaluations for a rule.
-
#scope ⇒ Types::Scope
rw
Defines which resources can trigger an evaluation for the rule.
-
#source ⇒ Types::Source
rw
Provides the rule owner (
Amazon Web Services
for managed rules,CUSTOM_POLICY
for Custom Policy rules, andCUSTOM_LAMBDA
for Custom Lambda rules), the rule identifier, and the notifications that cause the function to evaluate your Amazon Web Services resources.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#config_rule_arn ⇒ String
(rw)
The Amazon ::Aws::ConfigService::Resource
Name (ARN) of the Config rule.
# File 'gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb', line 948
class ConfigRule < Struct.new( :config_rule_name, :config_rule_arn, :config_rule_id, :description, :scope, :source, :input_parameters, :maximum_execution_frequency, :config_rule_state, :created_by, :evaluation_modes) SENSITIVE = [] include Aws::Structure end
#config_rule_id ⇒ String
(rw)
The ID of the Config rule.
# File 'gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb', line 948
class ConfigRule < Struct.new( :config_rule_name, :config_rule_arn, :config_rule_id, :description, :scope, :source, :input_parameters, :maximum_execution_frequency, :config_rule_state, :created_by, :evaluation_modes) SENSITIVE = [] include Aws::Structure end
#config_rule_name ⇒ String
(rw)
The name that you assign to the Config rule. The name is required if you are adding a new rule.
# File 'gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb', line 948
class ConfigRule < Struct.new( :config_rule_name, :config_rule_arn, :config_rule_id, :description, :scope, :source, :input_parameters, :maximum_execution_frequency, :config_rule_state, :created_by, :evaluation_modes) SENSITIVE = [] include Aws::Structure end
#config_rule_state ⇒ String
(rw)
Indicates whether the Config rule is active or is currently being deleted by Config. It can also indicate the evaluation status for the Config rule.
Config sets the state of the rule to EVALUATING
temporarily after
you use the StartConfigRulesEvaluation
request to evaluate your
resources against the Config rule.
Config sets the state of the rule to DELETING_RESULTS
temporarily
after you use the DeleteEvaluationResults
request to delete the
current evaluation results for the Config rule.
Config temporarily sets the state of a rule to DELETING
after you
use the DeleteConfigRule
request to delete the rule. After Config
deletes the rule, the rule and all of its evaluations are erased and
are no longer available.
# File 'gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb', line 948
class ConfigRule < Struct.new( :config_rule_name, :config_rule_arn, :config_rule_id, :description, :scope, :source, :input_parameters, :maximum_execution_frequency, :config_rule_state, :created_by, :evaluation_modes) SENSITIVE = [] include Aws::Structure end
#created_by ⇒ String
(rw)
Service principal name of the service that created the rule.
# File 'gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb', line 948
class ConfigRule < Struct.new( :config_rule_name, :config_rule_arn, :config_rule_id, :description, :scope, :source, :input_parameters, :maximum_execution_frequency, :config_rule_state, :created_by, :evaluation_modes) SENSITIVE = [] include Aws::Structure end
#description ⇒ String
(rw)
The description that you provide for the Config rule.
# File 'gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb', line 948
class ConfigRule < Struct.new( :config_rule_name, :config_rule_arn, :config_rule_id, :description, :scope, :source, :input_parameters, :maximum_execution_frequency, :config_rule_state, :created_by, :evaluation_modes) SENSITIVE = [] include Aws::Structure end
#evaluation_modes ⇒ Array
<Types::EvaluationModeConfiguration> (rw)
The modes the Config rule can be evaluated in. The valid values are
distinct objects. By default, the value is ::Aws::Detective
evaluation mode
only.
# File 'gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb', line 948
class ConfigRule < Struct.new( :config_rule_name, :config_rule_arn, :config_rule_id, :description, :scope, :source, :input_parameters, :maximum_execution_frequency, :config_rule_state, :created_by, :evaluation_modes) SENSITIVE = [] include Aws::Structure end
#input_parameters ⇒ String
(rw)
A string, in JSON format, that is passed to the Config rule ::Aws::Lambda
function.
# File 'gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb', line 948
class ConfigRule < Struct.new( :config_rule_name, :config_rule_arn, :config_rule_id, :description, :scope, :source, :input_parameters, :maximum_execution_frequency, :config_rule_state, :created_by, :evaluation_modes) SENSITIVE = [] include Aws::Structure end
#maximum_execution_frequency ⇒ String
(rw)
The maximum frequency with which Config runs evaluations for a rule.
You can specify a value for MaximumExecutionFrequency
when:
This is for an Config managed rule that is triggered at a periodic frequency.
Your custom rule is triggered when Config delivers the configuration snapshot. For more information, see ConfigSnapshotDeliveryProperties.
MaximumExecutionFrequency
parameter.
# File 'gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb', line 948
class ConfigRule < Struct.new( :config_rule_name, :config_rule_arn, :config_rule_id, :description, :scope, :source, :input_parameters, :maximum_execution_frequency, :config_rule_state, :created_by, :evaluation_modes) SENSITIVE = [] include Aws::Structure end
#scope ⇒ Types::Scope (rw)
Defines which resources can trigger an evaluation for the rule. The scope can include one or more resource types, a combination of one resource type and one resource ID, or a combination of a tag key and value. Specify a scope to constrain the resources that can trigger an evaluation for the rule. If you do not specify a scope, evaluations are triggered when any resource in the recording group changes.
# File 'gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb', line 948
class ConfigRule < Struct.new( :config_rule_name, :config_rule_arn, :config_rule_id, :description, :scope, :source, :input_parameters, :maximum_execution_frequency, :config_rule_state, :created_by, :evaluation_modes) SENSITIVE = [] include Aws::Structure end
#source ⇒ Types::Source (rw)
Provides the rule owner (Amazon Web Services
for managed rules,
CUSTOM_POLICY
for Custom Policy rules, and CUSTOM_LAMBDA
for
Custom Lambda rules), the rule identifier, and the notifications
that cause the function to evaluate your Amazon Web Services
resources.
# File 'gems/aws-sdk-configservice/lib/aws-sdk-configservice/types.rb', line 948
class ConfigRule < Struct.new( :config_rule_name, :config_rule_arn, :config_rule_id, :description, :scope, :source, :input_parameters, :maximum_execution_frequency, :config_rule_state, :created_by, :evaluation_modes) SENSITIVE = [] include Aws::Structure end


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