--- title: "Tutorial: Save and Restore Models" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Tutorial: Save and Restore Models} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} type: docs repo: https://github.com/rstudio/keras menu: main: name: "Save and Restore Models" identifier: "keras-tutorial-save-and-restore" parent: "tutorials-beginners-basic-ml-top" weight: 60 aliases: - /keras/articles/tutorial_save_and_restore.html ---
Model progress can be saved after as well as during training. This means a model can resume where it left off and avoid long training times. Saving also means you can share your model and others can recreate your work. When publishing research models and techniques, most machine learning practitioners share:
Sharing this data helps others understand how the model works and try it themselves with new data.
There are many different ways to save TensorFlow models—depending on the API you’re using. This guide uses Keras, a high-level API to build and train models in TensorFlow. For other approaches, see the TensorFlow Save and Restore guide or Saving in eager.
We’ll use the MNIST dataset to train our model to demonstrate saving weights. To speed up these demonstration runs, only use the first 1000 examples:
library(keras)
mnist <- dataset_mnist()
c(train_images, train_labels) %<-% mnist$train
c(test_images, test_labels) %<-% mnist$test
train_labels <- train_labels[1:1000]
test_labels <- test_labels[1:1000]
train_images <- train_images[1:1000, , ] %>%
array_reshape(c(1000, 28 * 28))
train_images <- train_images / 255
test_images <- test_images[1:1000, , ] %>%
array_reshape(c(1000, 28 * 28))
test_images <- test_images / 255
Let’s build a simple model we’ll use to demonstrate saving and loading weights.
# Returns a short sequential model
create_model <- function() {
model <- keras_model_sequential() %>%
layer_dense(units = 512, activation = "relu", input_shape = 784) %>%
layer_dropout(0.2) %>%
layer_dense(units = 10, activation = "softmax")
model %>% compile(
optimizer = "adam",
loss = "sparse_categorical_crossentropy",
metrics = list("accuracy")
)
model
}
model <- create_model()
summary(model)
## Model: "sequential"
## ___________________________________________________________________________
## Layer (type) Output Shape Param #
## ===========================================================================
## dense (Dense) (None, 512) 401920
## ___________________________________________________________________________
## dropout (Dropout) (None, 512) 0
## ___________________________________________________________________________
## dense_1 (Dense) (None, 10) 5130
## ===========================================================================
## Total params: 407,050
## Trainable params: 407,050
## Non-trainable params: 0
## ___________________________________________________________________________
Call save_model_*
to save the a model’s architecture, weights, and training configuration in a single file/folder. This allows you to export a model so it can be used without access to the original code*. Since the optimizer-state is recovered, you can resume training from exactly where you left off.
Saving a fully-functional model is very useful—you can load them in TensorFlow.js (HDF5, Saved Model) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite (HDF5, Saved Model)
*Custom objects (e.g. subclassed models or layers) require special attention when saving and loading. See the “Saving custom objects” section below.
The SavedModel format is a way to serialize models. Models saved in this format can be restored using load_model_tf
and are compatible with TensorFlow Serving. The SavedModel guide goes into detail about how to serve/inspect the SavedModel. The section below illustrates the steps to saving and restoring the model.
## Train on 1000 samples
## Epoch 1/5
## 1000/1000 - 0s - loss: 1.1809 - accuracy: 0.6680
## Epoch 2/5
## 1000/1000 - 0s - loss: 0.4156 - accuracy: 0.8860
## Epoch 3/5
## 1000/1000 - 0s - loss: 0.2836 - accuracy: 0.9250
## Epoch 4/5
## 1000/1000 - 0s - loss: 0.2241 - accuracy: 0.9370
## Epoch 5/5
## 1000/1000 - 0s - loss: 0.1473 - accuracy: 0.9680
The SavedModel format is a directory containing a protobuf binary and a Tensorflow checkpoint. Inspect the saved model directory:
## [1] "assets" "saved_model.pb" "variables"
Reload a fresh Keras model from the saved model:
## Model: "sequential_1"
## ___________________________________________________________________________
## Layer (type) Output Shape Param #
## ===========================================================================
## dense_2 (Dense) (None, 512) 401920
## ___________________________________________________________________________
## dropout_1 (Dropout) (None, 512) 0
## ___________________________________________________________________________
## dense_3 (Dense) (None, 10) 5130
## ===========================================================================
## Total params: 407,050
## Trainable params: 407,050
## Non-trainable params: 0
## ___________________________________________________________________________
Keras provides a basic saving format using the HDF5 standard.
## Train on 1000 samples
## Epoch 1/5
## 1000/1000 - 0s - loss: 1.1386 - accuracy: 0.6780
## Epoch 2/5
## 1000/1000 - 0s - loss: 0.4326 - accuracy: 0.8770
## Epoch 3/5
## 1000/1000 - 0s - loss: 0.2874 - accuracy: 0.9310
## Epoch 4/5
## 1000/1000 - 0s - loss: 0.2164 - accuracy: 0.9460
## Epoch 5/5
## 1000/1000 - 0s - loss: 0.1536 - accuracy: 0.9690
Now recreate the model from that file:
## Model: "sequential_2"
## ___________________________________________________________________________
## Layer (type) Output Shape Param #
## ===========================================================================
## dense_4 (Dense) (None, 512) 401920
## ___________________________________________________________________________
## dropout_2 (Dropout) (None, 512) 0
## ___________________________________________________________________________
## dense_5 (Dense) (None, 10) 5130
## ===========================================================================
## Total params: 407,050
## Trainable params: 407,050
## Non-trainable params: 0
## ___________________________________________________________________________
This technique saves everything:
Keras saves models by inspecting the architecture. Currently, it is not able to save TensorFlow optimizers (from tf$train
). When using those you will need to re-compile the model after loading, and you will lose the state of the optimizer.
If you are using the SavedModel format, you can skip this section. The key difference between HDF5 and SavedModel is that HDF5 uses object configs to save the model architecture, while SavedModel saves the execution graph.
Thus, SavedModels are able to save custom objects like subclassed models and custom layers without requiring the orginal code.
To save custom objects to HDF5, you must do the following:
get_config()
returns a JSON-serializable dictionary of parameters needed to recreate the object.from_config(config)
uses the returned config from get_config to create a new object. By default, this function will use the config as initialization arguments.custom_objects
argument when loading the model. The argument must be a named list mapping the string class name to the class definition. E.g. load_keras_model_hdf5(path, custom_objects=list("CustomLayer" = CustomLayer))
See the Writing layers and models from scratch tutorial for examples of custom_objects
and get_config
.
It is useful to automatically save checkpoints during and at the end of training. This way you can use a trained model without having to retrain it, or pick-up training where you left of, in case the training process was interrupted.
callback_model_checkpoint
is a callback that performs this task.
The callback takes a couple of arguments to configure checkpointing. By default, save_weights_only
is set to false, which means the complete model is being saved - including architecture and configuration. You can then restore the model as outlined in the previous paragraph.
Now here, let’s focus on just saving and restoring weights. In the following code snippet, we are setting save_weights_only
to true, so we will need the model definition on restore.
Train the model and pass it the callback_model_checkpoint
:
checkpoint_path <- "checkpoints/cp.ckpt"
# Create checkpoint callback
cp_callback <- callback_model_checkpoint(
filepath = checkpoint_path,
save_weights_only = TRUE,
verbose = 0
)
model <- create_model()
model %>% fit(
train_images,
train_labels,
epochs = 10,
validation_data = list(test_images, test_labels),
callbacks = list(cp_callback), # pass callback to training
verbose = 2
)
## Train on 1000 samples, validate on 1000 samples
## Epoch 1/10
## 1000/1000 - 0s - loss: 1.1775 - accuracy: 0.6750 - val_loss: 0.6874 - val_accuracy: 0.7980
## Epoch 2/10
## 1000/1000 - 0s - loss: 0.4144 - accuracy: 0.8810 - val_loss: 0.5366 - val_accuracy: 0.8320
## Epoch 3/10
## 1000/1000 - 0s - loss: 0.2811 - accuracy: 0.9280 - val_loss: 0.4517 - val_accuracy: 0.8610
## Epoch 4/10
## 1000/1000 - 0s - loss: 0.2205 - accuracy: 0.9430 - val_loss: 0.4692 - val_accuracy: 0.8500
## Epoch 5/10
## 1000/1000 - 0s - loss: 0.1520 - accuracy: 0.9690 - val_loss: 0.4084 - val_accuracy: 0.8660
## Epoch 6/10
## 1000/1000 - 0s - loss: 0.1147 - accuracy: 0.9780 - val_loss: 0.3946 - val_accuracy: 0.8680
## Epoch 7/10
## 1000/1000 - 0s - loss: 0.0831 - accuracy: 0.9870 - val_loss: 0.4008 - val_accuracy: 0.8710
## Epoch 8/10
## 1000/1000 - 0s - loss: 0.0607 - accuracy: 0.9970 - val_loss: 0.4056 - val_accuracy: 0.8640
## Epoch 9/10
## 1000/1000 - 0s - loss: 0.0510 - accuracy: 0.9970 - val_loss: 0.4031 - val_accuracy: 0.8720
## Epoch 10/10
## 1000/1000 - 0s - loss: 0.0465 - accuracy: 0.9960 - val_loss: 0.3923 - val_accuracy: 0.8710
Inspect the files that were created:
## [1] "checkpoint" "cp.ckpt.data-00000-of-00001"
## [3] "cp.ckpt.index"
Create a new, untrained model. When restoring a model from only weights, you must have a model with the same architecture as the original model. Since it’s the same model architecture, we can share weights despite that it’s a different instance of the model.
Now rebuild a fresh, untrained model, and evaluate it on the test set. An untrained model will perform at chance levels (~10% accuracy):
## $loss
## [1] 2.321936
##
## $accuracy
## [1] 0.126
Then load the weights from the latest checkpoint (epoch 10), and re-evaluate:
fresh_model %>% load_model_weights_tf(filepath = checkpoint_path)
fresh_model %>% evaluate(test_images, test_labels, verbose = 0)
## $loss
## [1] 0.3923183
##
## $accuracy
## [1] 0.871
Alternatively, you can decide to save only the best model, where best by default is defined as validation loss. See the documentation for callback_model_checkpoint for further information.
checkpoint_path <- "checkpoints/cp.ckpt"
# Create checkpoint callback
cp_callback <- callback_model_checkpoint(
filepath = checkpoint_path,
save_weights_only = TRUE,
save_best_only = TRUE,
verbose = 1
)
model <- create_model()
model %>% fit(
train_images,
train_labels,
epochs = 10,
validation_data = list(test_images, test_labels),
callbacks = list(cp_callback), # pass callback to training,
verbose = 2
)
## Train on 1000 samples, validate on 1000 samples
## Epoch 1/10
##
## Epoch 00001: val_loss improved from inf to 0.72178, saving model to checkpoints/cp.ckpt
## 1000/1000 - 0s - loss: 1.1691 - accuracy: 0.6620 - val_loss: 0.7218 - val_accuracy: 0.7760
## Epoch 2/10
##
## Epoch 00002: val_loss improved from 0.72178 to 0.56689, saving model to checkpoints/cp.ckpt
## 1000/1000 - 0s - loss: 0.4227 - accuracy: 0.8850 - val_loss: 0.5669 - val_accuracy: 0.8110
## Epoch 3/10
##
## Epoch 00003: val_loss improved from 0.56689 to 0.51581, saving model to checkpoints/cp.ckpt
## 1000/1000 - 0s - loss: 0.3018 - accuracy: 0.9160 - val_loss: 0.5158 - val_accuracy: 0.8380
## Epoch 4/10
##
## Epoch 00004: val_loss improved from 0.51581 to 0.44739, saving model to checkpoints/cp.ckpt
## 1000/1000 - 0s - loss: 0.2120 - accuracy: 0.9480 - val_loss: 0.4474 - val_accuracy: 0.8540
## Epoch 5/10
##
## Epoch 00005: val_loss did not improve from 0.44739
## 1000/1000 - 0s - loss: 0.1519 - accuracy: 0.9700 - val_loss: 0.4602 - val_accuracy: 0.8510
## Epoch 6/10
##
## Epoch 00006: val_loss improved from 0.44739 to 0.42596, saving model to checkpoints/cp.ckpt
## 1000/1000 - 0s - loss: 0.1257 - accuracy: 0.9750 - val_loss: 0.4260 - val_accuracy: 0.8630
## Epoch 7/10
##
## Epoch 00007: val_loss improved from 0.42596 to 0.40990, saving model to checkpoints/cp.ckpt
## 1000/1000 - 0s - loss: 0.0866 - accuracy: 0.9850 - val_loss: 0.4099 - val_accuracy: 0.8610
## Epoch 8/10
##
## Epoch 00008: val_loss did not improve from 0.40990
## 1000/1000 - 0s - loss: 0.0688 - accuracy: 0.9930 - val_loss: 0.4210 - val_accuracy: 0.8560
## Epoch 9/10
##
## Epoch 00009: val_loss did not improve from 0.40990
## 1000/1000 - 0s - loss: 0.0517 - accuracy: 0.9970 - val_loss: 0.4326 - val_accuracy: 0.8640
## Epoch 10/10
##
## Epoch 00010: val_loss did not improve from 0.40990
## 1000/1000 - 0s - loss: 0.0386 - accuracy: 1.0000 - val_loss: 0.4521 - val_accuracy: 0.8510
## [1] "checkpoint" "cp.ckpt.data-00000-of-00001"
## [3] "cp.ckpt.index"
The above code stores the weights to a collection of checkpoint-formatted files that contain only the trained weights in a binary format. Checkpoints contain:
If you are only training a model on a single machine, you’ll have one shard with the suffix: .data-00000-of-00001
You saw how to load the weights into a model. Manually saving them is just as simple with the save_model_weights_tf
function.
# Save the weights
model %>% save_model_weights_tf("checkpoints/cp.ckpt")
# Create a new model instance
new_model <- create_model()
# Restore the weights
new_model %>% load_model_weights_tf('checkpoints/cp.ckpt')
# Evaluate the model
new_model %>% evaluate(test_images, test_labels, verbose = 0)
## $loss
## [1] 0.4520541
##
## $accuracy
## [1] 0.851


Money Play. How should Red play 32?
pips Red=73 White=157
(CorrectAnswer= 0/0, ErrorScore= 0)
This is a classic pay-me-now or pay-me-later position.
Everything is in favour of paying now with 9/7. 9/6 (which is better than 9/4).
6/1 is nearly a triple blunder. Red must pay now before White’s home board improves. It is hard to make a play that leaves a double shot, but fours are duplicated and Red must make the play that maximises his equity. 9/7, 9/6 is the correct move by a long, long way.
Everything is in favour of paying now with 9/7. 9/6 (which is better than 9/4).
6/1 is nearly a triple blunder. Red must pay now before White’s home board improves. It is hard to make a play that leaves a double shot, but fours are duplicated and Red must make the play that maximises his equity. 9/7, 9/6 is the correct move by a long, long way.
1. Cubeful 2-ply 9/7 9/6 Eq.: +0.439 0.549 0.290 0.006 - 0.451 0.067 0.001 2. Cubeful 2-ply 9/4 Eq.: +0.355 (-0.083) 0.534 0.289 0.006 - 0.466 0.073 0.001 3. Cubeful 2-ply 6/1 Eq.: +0.174 (-0.265) 0.514 0.247 0.002 - 0.486 0.093 0.001 4. Cubeful 0-ply 9/6 8/6 Eq.: -0.121 (-0.560) 0.429 0.246 0.004 - 0.571 0.151 0.004 5. Cubeful 0-ply 4/2 4/1 Eq.: -0.155 (-0.594) 0.424 0.220 0.002 - 0.576 0.147 0.001


Rock, Paper, Scissors
To play please select one of the options below
Rock
Paper
Scissors
computer vs computer
Restart game


Bridging the Gap Between International and Domestic Students.
The Trojan Global Network (TGN) aims to bridge the gap between international and domestic students at USC through community programing. We’re a new group on campus, but we have big dreams, and we’ll keep working until students don’t feel a divide between the two groups.
A
Fight on!


[midterm]
ON-PAGE SEO is the act of optimizing different parts of your web site that affect your search engine rankings. It's stuff that you have control over and can change on your own website. While, the OFF-PAGE SEO focuses on increasing the authority of your domain through the act of getting links from other websites.
Improve user experience. Make sure your website is responsive and broken links are kept to a minimum.
Ensure that readers coming from search engines spend a lot of time on your site If they hit the back button quickly, your ranking will drop quickly too. Make sure your site is maintaining a standard of professionalism. Use proper copywriting techniques to make people stick around.
Create good content Try using benefit-driven sub-headings to be more engaging. Don't write fluff. Seek feedback and improve. Today


Jamie’s Contact Groups
问题描述 :
Jamie is a very popular girl and has quite a lot of friends, so she always keeps a very long contact list in her cell phone. The contact list has become so long that it often takes a long time for her to browse through the whole list to find a friend’s number. As Jamie’s best friend and a programming genius, you suggest that she group the contact list and minimize the size of the largest group, so that it will be easier for her to search for a friend’s number among the groups. Jamie takes your advice and gives you her entire contact list containing her friends’ names, the number of groups she wishes to have and what groups every friend could belong to. Your task is to write a program that takes the list and organizes it into groups such that each friend appears in only one of those groups and the size of the largest group is minimized.
输入:
There will be at most 20 test cases. Ease case starts with a line containing two integers N and M. where N is the length of the contact list and M is the number of groups. N lines then follow. Each line contains a friend’s name and the groups the friend could belong to. You can assume N is no more than 1000 and M is no more than 500. The names will contain alphabet letters only and will be no longer than 15 characters. No two friends have the same name. The group label is an integer between 0 and M – 1. After the last test case, there is a single line `0 0′ that terminates the input.
输出:
For each test case, output a line containing a single integer, the size of the largest contact group.
样例输入:
3 2 John 0 1 Rose 1 Mary 1 5 4 ACM 1 2 3 ICPC 0 1 Asian 0 2 3 Regional 1 2 ShangHai 0 2 0 0
样例输出:
2 2
题目链接：http://acm.hdu.edu.cn/showproblem.php?pid=1669
思路：由于要求minimize the size of the largest group,由此我们想到二分枚举，然后每一次求一下多重匹配就可以了。
#include<iostream> #include<cstdio> #include<cstring> #include<algorithm> #include<vector> using namespace std; #define MAXN 1010 #define MAXM 555 vector<int>vet[MAXN]; bool map[MAXN][MAXN]; int Link[MAXM];//i当前的匹配数 int vLink[MAXM][MAXN];//与i匹配的第j个匹配的数k bool mark[MAXM];//标记已匹配的 int n,m,limit,ans; bool dfs(int u) { for(int i=0;i<vet[u].size();i++){ int v=vet[u][i]; if(!mark[v]){ mark[v]=true; if(Link[v]<limit){ vLink[v][Link[v]]=u; Link[v]++; return true; } for(int j=0;j<Link[v];j++){ if(dfs(vLink[v][j])){ vLink[v][j]=u; return true; } } } } return false; } bool Match() { memset(Link,0,sizeof(Link)); memset(vLink,0,sizeof(vLink)); for(int i=0;i<n;i++){ memset(mark,false,sizeof(mark)); if(!dfs(i))return false; } return true; } int main() { // freopen("1.txt","r",stdin); char name[22]; int x; while(scanf("%d%d",&n,&m),(n+m)){ for(int i=0;i<n;i++)vet[i].clear(); for(int i=0;i<n;i++){ scanf("%s",name); while(getchar()==' '){ scanf("%d",&x); vet[i].push_back(x); } } int low=0,high=n; while(low<=high){ limit=(low+high)>>1; if(Match()){ ans=limit;//保留 high=limit-1; }else low=limit+1; } printf("%d\n",ans); } return 0; }
解题转自：http://www.cnblogs.com/wally/p/3141531.html
(adsbygoogle = window.adsbygoogle || []).push({});
程序员会算法，等于一个古惑仔带了zhi抢啊
妈呀！这个Dijkstra模板好长OwQ！
很好，找的就是这一题
这张页面太松散拉，重新编辑一下，弄紧凑一点。
都是一些基础题啊
第一题选错了，答案是A
因为要修改链表头(指针)，所以要用指针的指针
我表示楼主不要乱抄题，你写的另一个题吧，直接扒了代码没跑吧
很好的模板
感谢分享 学习了`(*∩_∩*)′
http://www.realoj.com/problem/7如果楼主不介意，以可以在这个oj上做下，按照你的代码是错的。。。直接复制粘贴你的也是错。。不懂错在哪。
We do not harbor that I couldn’t remove other individuals’ problems whenever this arrived at.
…. 配图居然是没方向的。。
非常好，初学者就应该先理解递归


{% extends "layout.html" %} {% block title %} Collect and disseminate information on fee-based Open Access publishing {% endblock %} {% block content %}
The Open APC initiative releases datasets on fees paid for Open Access journal articles by universities and research institutions under an Open Database License.
Select a region from the map or use the list below to view APC data for individual institutions.
You may also browse the following aggregated collections:


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import views.html.components.{Button, Heading, AnswerSections} @import controllers.routes._ @import viewModels.P5.departure.DepartureDeclarationErrorsP5ViewModel @import viewModels.P5.departure.DepartureDeclarationErrorsP5ViewModel._ @import config.FrontendAppConfig @this( mainTemplate: MainTemplate, heading: Heading, answerSections: AnswerSections, button: Button, formHelper: FormWithCSRF ) @(departureDeclarationErrorsP5ViewModel: DepartureDeclarationErrorsP5ViewModel)(implicit request: Request[_], messages: Messages, config: FrontendAppConfig) @mainTemplate( title = departureDeclarationErrorsP5ViewModel.title, showBackLink = true ) { @heading( heading = departureDeclarationErrorsP5ViewModel.heading, caption = Some(messages("departure.messages.caption", departureDeclarationErrorsP5ViewModel.lrn)) )
@departureDeclarationErrorsP5ViewModel.paragraph1
@departureDeclarationErrorsP5ViewModel.paragraph3Prefix @departureDeclarationErrorsP5ViewModel.paragraph3Link @departureDeclarationErrorsP5ViewModel.paragraph3Suffix
}


My name is Laura Koemmpel, and I graduated from the Massachusetts Institute of Technology in 2019 with an undergraduate degree in math.
I currently work as a staff software support/quality engineer for Boston Dynamics on the team for Spot, the company's quadruped robot.
Check out a video of Spot in action here!
I enjoy hiking, playing trombone, and reading. Some of my technical interests include game theory, topology, and computer vision.


Name
|
Description
|
ExecuteHook
|
Executes a Hook with all its SubHooks
|
ExecuteUniqueID
|
Executes a specific SubHook through its UniqueID
|
GetGlobalString
|
Returns a previously saved String from the manager component TmafGlobalVars
|
SetGlobalString
|
Saves a String in the manager component TmafGlobalVars
|
GetGlobalInteger
|
Returns a previously saved Integer from the manager component TmafGlobalVars
|
SetGlobalInteger
|
Saves a Integer in the manager component TmafGlobalVars
|
GetGlobalObject
|
Returns a previously saved Object from the manager component TmafGlobalVars
|
SetGlobalObject
|
Saves an Object in the manager component TmafGlobalVars
|
GetGlobalBoolean
|
Returns a previously saved Boolean from the manager component TmafGlobalVars
|
SetGlobalBoolean
|
Saves a Boolean in the manager component TmafGlobalVars
|
LoadTemplate
|
Loads a template stored through the manager component TmafDataStorage
|
SaveTemplate
|
Saves a template in the manager component TmafDataStorage
|
PushEvents
|
Saves the current events into a stack and clears the events
|
PopEvents
|
Restores previously stored events from the stack
|
PushVar
|
Saves a variable to a stack
|
PopVar
|
Loads a variable from a stack and deletes it there
|
DeleteTemplate
|
Deletes a template
|
RegisterCallback
|
Registers a callback notification for a specific global variable
|
UnregisterCallback
|
Deregisters a previously registered callback notification for a specific global variable
|
__Query_Manager
|
Executes a query to the TmafHookManager component
|
__Add_FunctionObserver
|
Registers a function observer to be notified, when something in the DFT changes (add or delete).
|
__Delete_FunctionObserver
|
Deregisters a function observer.
|


Try out our Premium Member services: Virtual Legal Assistant, Query Alert Service and an ad-free experience. Free for one month and pay only if you like it.
CWP-1120-2017 & 1 CWP-2311-2017 IN THE HIGH COURT OF PUNJAB AND HARYANA AT CHANDIGARH Date of Decision: August 28, 2018 1. CWP-1120-2017 M/s Runit Fabrics ...Petitioner Versus State of Punjab and others ...Respondents
2. CWP-2311-2017 M/s Runit Fabrics ...Petitioner Versus State of Punjab and others ...Respondents CORAM: HON'BLE MR. JUSTICE KRISHNA MURARI, CHIEF JUSTICE.
HON'BLE MR. JUSTICE ARUN PALLI, JUDGE. Present: Mr. Kewal Singh, Advocate, for the petitioner.
Mr. Avinit Avasthi, AAG, Punjab, for respondent No. 1.
Ms. Nikita Bansal, Advocate, for Mr. Nitin Kaushal, Advocate, for respondent Nos. 2 to 5.
Mr. Himanshu Puri, Advocate, for respondent No. 6.
KRISHNA MURARI, C.J. (ORAL) By means of these writ petitions, filed under Articles 226/227 of the Constitution of India, the petitioner has challenged the show cause notice and the orders passed under the provisions of the Water (Prevention and Control of Pollution) Act, 1974 (for short, 'the 1974 Act') and the Air (Prevention and Control of Pollution) Act, 1981 (for short, 'the 1981 Act').
1 of 2 ::: Downloaded on - 04-09-2018 01:39:15 ::: CWP-1120-2017 & 2 CWP-2311-2017 In CWP No. 1120 of 2017, show cause notice, dated 25.11.2016 (Annexure P-9), issued under Section 25/26 of the 1974 Act and Section 21 of the 1981 Act, has been challenged, to which a reply was submitted, whereupon respondent No. 3 passed an order dated 02.01.2017 (Annexure P-11), issuing certain directions in exercise of powers under Section 33-A of the 1974 Act (as amended from time to time). In CWP No. 2311 of 2017, the petitioner has challenged orders dated 02.02.2017 (Annexures P-13 to P-16), passed by respondent Nos. 3 and 4, whereby the consent to operate the industry, granted under the provisions of the 1974 & 1981 Acts, stand revoked.
Learned counsel for the petitioner, at the outset made a statement that the consent to operate the industry has since come to an end on 30.06.2018, and thereafter the industry is not being run by the petitioner.
That being the position, the challenge to the show cause notice as also the orders dated 02.01.2017 and 02.02.2017, loses its significance and these petitions have been rendered infructuous. The same are accordingly dismissed.
(KRISHNA MURARI) CHIEF JUSTICE (ARUN PALLI) JUDGE August 28, 2018 Pkapoor Whether Speaking/Reasoned: YES / NO Whether Reportable: YES / NO 2 of 2 ::: Downloaded on - 04-09-2018 01:39:15 :::


--- layout: default --- {% include printable-header.html %} {% include career-profile.html %} {% include printable-about.html %} {% include experience.html %} {% include projects.html %}


| 1 | package com.mkyong.examples; | |
| 2 | ||
| 3 | public class StockService { | |
| 4 | ||
| 5 | private int qtyOnHand; | |
| 6 | ||
| 7 | public StockService(int qtyOnHand) { | |
| 8 | this.qtyOnHand = qtyOnHand; | |
| 9 | } | |
| 10 | ||
| 11 | private void isValidQty(int qty) { | |
| 12 |
2
1. isValidQty : changed conditional boundary → KILLED 2. isValidQty : negated conditional → KILLED |
if (qty < 0) { |
| 13 | throw new IllegalArgumentException("Quality should be positive!"); | |
| 14 | } | |
| 15 | } | |
| 16 | ||
| 17 | public int add(int qty) { | |
| 18 | ||
| 19 |
1
1. add : removed call to com/mkyong/examples/StockService::isValidQty → KILLED |
isValidQty(qty); |
| 20 |
1
1. add : Replaced integer addition with subtraction → KILLED |
qtyOnHand = qtyOnHand + qty; |
| 21 |
1
1. add : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return qtyOnHand; |
| 22 | ||
| 23 | } | |
| 24 | ||
| 25 | public int deduct(int qty) { | |
| 26 | ||
| 27 |
1
1. deduct : removed call to com/mkyong/examples/StockService::isValidQty → KILLED |
isValidQty(qty); |
| 28 | ||
| 29 |
1
1. deduct : Replaced integer subtraction with addition → KILLED |
int newQty = qtyOnHand - qty; |
| 30 |
2
1. deduct : changed conditional boundary → KILLED 2. deduct : negated conditional → KILLED |
if (newQty < 0) { |
| 31 | throw new IllegalArgumentException("Out of Stock!"); | |
| 32 | } else { | |
| 33 | qtyOnHand = newQty; | |
| 34 | } | |
| 35 |
1
1. deduct : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return qtyOnHand; |
| 36 | ||
| 37 | } | |
| 38 | ||
| 39 | } | |
Mutations | ||
| 12 |
1.1 2.2 |
|
| 19 |
1.1 |
|
| 20 |
1.1 |
|
| 21 |
1.1 |
|
| 27 |
1.1 |
|
| 29 |
1.1 |
|
| 30 |
1.1 2.2 |
|
| 35 |
1.1 |


Full name
Age
Real number, limited between 10.0 and 10.9, with a step of 0.1.
Real number, limited between 10.0 and 10.9, with a step of ``any``.
URL
E-mail
Favourite colour
Blue
Pink
Red
yellow
Planning start
Planning end
Required date
Optional date
Disabled start
End
Date/Time start
Date/Time end
Password
Password confirmation
Food preference
Custom template
Test
Submit
Cancel
First of all!
Optional subtext
Start
End
Preference
Submit
Cancel


| flexmix::bootstrapping | Finite Mixture Model Diagnostics Using Resampling Methods | source | R code | ||
| flexmix::flexmix-intro | FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R | source | R code | ||
| flexmix::mixture-regressions | FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters | source | R code | ||
| flexmix::regression-examples | Applications of finite mixtures of regression models | source | R code |
| myConcomitant.R | |
| mymclust.R | |
| ziglm.R |


Estimates optimal classification (Poole 2000) <doi:10.1093/oxfordjournals.pan.a029814> scores from roll call votes supplied though a 'rollcall' object from package 'pscl'.
| Version: | 1.01 |
| Depends: | R (≥ 2.3.1), pscl (≥ 0.59) |
| Published: | 2018-02-08 |
| Author: | Keith Poole, Jeffrey Lewis, James Lo and Royce Carroll |
| Maintainer: | James Lo <lojames at usc.edu> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| Materials: | ChangeLog |
| CRAN checks: | oc results |
| Reference manual: | oc.pdf |
| Vignettes: |
An R Package for Optimal Classification |
| Package source: | oc_1.01.tar.gz |
| Windows binaries: | r-devel: oc_1.01.zip, r-release: oc_1.01.zip, r-oldrel: oc_1.01.zip |
| OS X binaries: | r-release: oc_1.01.tgz, r-oldrel: oc_1.01.tgz |
| Old sources: | oc archive |
Please use the canonical form https://CRAN.R-project.org/package=oc to link to this page.


Cache
Cache component is used to reduce page load time by caching the content in a global cache after the initial rendering. Various
cache providers are supported like ehcache and hazelcast. Showcase use actually this component to render source examples for every PrimeFaces components because parsing an xhtml file takes time, and since the rendering won't change during the whole lifecycle of the application, it's better to cache the content.
- tabscode.xhtml demonstrate how to use cache component declaratively
- ShowcaseUtil demonstrate how to use cache API programmatically


Installing various packages can make your system vulnerable to any known or discovered security issues in the software. Hackers trying to gain access to your machine often can take advantage of such security holes in your system's software. For this reason, it is highly recommended to purge anything on your machine that is not required to be there.
LDAP (Lightweight Directory Access Protocol) client is typically used to look up information from a central database (an LDAP server). If it isn't specifically required in the README, it should be removed.
To check if LDAP client is installed on your system, you can type dpkg -l ldap-utils.
If the name of the package is preceded by "ii", then it is installed. To remove it, simply type sudo apt-get purge ldap-utils.
That should be it!


Select a device action:
Pair with Gear VR controller
Calibrate controller
Start controller data updates
Disconnect
Run
Messages about the device appear here.


NIRMAAN
Branch Coordinator's
Akshat Jain
Akshat Jain
7651920675
<EMAIL_ADDRESS>Dikshant Singh
Dikshant Singh
9005427787
<EMAIL_ADDRESS>NirmaanLinkidge
Making your vision come true, that is all what you would do.
 This pure technical event will give you a platform where you will visualise, analyse and conceptualize about the structures. The participants will learn about the behaviour of different members of given structure and all that takes to build safe and stable structures.
Participation Criteria
Team size : 3
Average CPI of team should be less than 8.4
No cross-year teams allowed
Only civil engineering branch students are allowed
Contact Info
- Hrithik Bhardwaj
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
- Sushantika Singh
-<EMAIL_ADDRESS>- Akshat Jain
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
Rules
Round 1:
Written Test(Quiz)
Round 2:
Report Submission
Round 3:
Presentation
Round 4:
Viva Voce
NirmaanConstrengtho
Quality is what you should pursue to make solid reputation of your crew
About
Civil engineering is incomplete without knowledge of concrete. All constructions, whether a minor or a major one, require use of concrete. So, the wait is over. Here we are presenting Constrengtho 2020, the best opportunity to test your skill, knowledge and understanding about concrete and to use theoretical concepts to solve real world problems. So, roll up your sleeves and let your passion flow. Read about concrete, explore about concrete and learn about concrete.
Participation Criteria
Team size : 4(minimum-3)
Average CPI of team should be less than 8.3
No cross-year teams allowed
Only civil engineering branch students are allowed
Contact Info
- Anitya Khare
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
- Jagriti Agrawal
-<EMAIL_ADDRESS>- Manmeet Singh
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
Rules
Round 1:
Quiz
Round 2:
Abstract Submission
Round 3:
Report Submission
Round 4:
Viva Voce
NirmaanZiggurare
We shape our buildings; thereafter, they shape us
About
Got bored of designing and analysing common structures every day? Ziggurare furnishes you an opportunity to try something new! Civil engineering is a field which is directly linked with practical experiences in constructing structures and models are requisite for analysis of structures and demonstration of many basic engineering concepts
Participation Criteria
Team size : 4(minimum-3)
No cross-year teams allowed
Only civil engineering branch students are allowed
Contact Info
- Ashish rana
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
- Prashant giri
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
- Abhishek Kumar
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
Rules
Round 1:
Plan Submission
Round 2:
Planning and modelling of given plan
Round 3:
Viva Voce
NirmaanCognizance
Come and show us what makes you "YOU".
About
"Beauty gets the attention, personality gets the heart." Abiding by this statement and keeping pace with the modern day requires technical and soft skills in abundance. So, buckle up to test and improve the same and discover hidden aspects of your personality via Cognizance 2k20. A mosaic of arduous challenges, COGNIZANCE provides you with a platform to explore the vast intersecting planes of theoretical reasoning and pragmatism. Amidst testing times, gear up to step out of your comfort zones, in the comfort of your homes. The event involves aptitude, technical, PI round resembling both campus placements and the selection process in GATE/ESE.
 I'm just gonna Quiz you really really bad
About
"If you are curious, you'll find the puzzles around you. If you are determined, you will solve them.” So, figure out your knowledge and luck in the tri-round event TERRAQUIZ. Find your mates and check your aptitude potential, answer general knowledge questions, solve trivia’s and puzzles with a pinch of civil engineering experience.
Participation Criteria
Team size : 3
Average CPI of team should be less than 8.4
No cross-year teams allowed
Inter branch teams allowed (Minimum 1 from civil and others from any branch)
Contact Info
- Mukesh Kumar Mukul
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
- Rahul Kumar
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
- Ayush Kumar Singh
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
Rules
Round 1:
Aptitude & Logical Reasoning Round
Round 2:
Technical Round
Round 3:
Quiz Round
NirmaanPPT-CON
Slide your ideas through presentation
About
"The ability to express an idea is well-nigh as important as the idea itself." So, we are here with an event which will give you a platform to learn and develop presentation and communication skills and will give you an open environment to show your creativity. Topics will be related to your given areas of interest whether technical or non-technical.
Participation Criteria
Team size : 4(minimum-3)
No cross-year teams allowed
Only civil engineering branch students are allowed
Contact Info
- Harleen Kaur
-<EMAIL_ADDRESS>- Arunabh Srivastava
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
- Aishwarya Rai
-<EMAIL_ADDRESS>Rules
Round 1:
Synopsis
Round 2:
PPT Round
Round 3:
Viva Voce
NirmaanShutter Up
Capturing life. Creating Art.
About
Do you love to capture the interesting and beautiful scenes around you? Do you stay up at nights worrying about “composition”? If yes, then look no further. The NIRMAAN's Online Photography Contest “SHUTTER UP” to you! Take out your cameras and smartphones and click away!
Participation Criteria
Team size : Individual event
Open to all
Contact Info
- Himalay Naik
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
- Vimal Kumar
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
- Ankit Kumar Tripathi
-<EMAIL_ADDRESS>-<PHONE_NUMBER>
Rules
Round 1:
Single round Online Event (Photo Submission)


Data
LightSpeed
Waze time log
Zimbra Mail
Calculate statistics
Split Coords
Google maps
Instant View


wait()
made on a java.util.concurrent.locks.Condition
object. This is probably a programming error, and some variant of the await()
method was intended instead.
Example:
void acquire(Condition released) throws InterruptedException {
while (acquired) {
released.wait();
}
}
Good code would look like this:
void acquire(Condition released) throws InterruptedException {
while (acquired) {
released.await();
}
}


Register
First Name:
Last Name:
Male
Female
Other
Email:
Password:
Birthday:
January
February
March
April
1
2
3
4
1990
1991
1992
1993
I agree to the terms and conditions


scheme- Run a scheme program (with no REPL output):
scheme --quiet < script.scm- Load a scheme program into the REPL:
scheme --load script.scm- Load scheme expressions into the REPL:
scheme --eval "(define foo 'x)"- Open the REPL in quiet mode:
scheme --quiet


About
- What is Hexagony.net?
-
This is an online IDE for Hexagony, an esoteric programming language created by Martin Ender.
See the original Hexagony interpreter for the language specification.
- What happens when characters not listed in the specification are used as instructions?
-
When codepoints are used other than defined instructions, the value at the memory pointer is set to the codepoint value. This is the same behavior as the instructions A-Z and a-z. All Unicode codepoints are allowed. The characters tab (9), line feed (10), vertical tab (11), form feed (12), carriage return (13), space (32), and ` (96) are ignored. Note that codepoints above 65535 can currently only be entered using the import/export panel.
- Is Hexagony.net open source?
-
Yes! It's licensed under MIT and available on GitHub. Patches are welcome. Please report any issues here.
- Can I see an example program?
-
This is the default hello world program.
This program outputs the infinite series of Fibonacci numbers, separated by newlines. See the original source here.
- How do the edge transitions work?
-
Try edge transition mode. It helps you to line up instructions with edge transitions so that program execution can wrap around. You code is automatically replicated in all of the hexagons to help you align code with the edge transitions. Corners have special behavior. The + transitions are followed, if the value at the current memory location is positive. If the value is zero or negative, the - transitions are followed. Look for the above icon on the toolbar.
- Can the Hexagony program be modified while it's running?
-
Yes, you can change the Hexagony program and the input, while the program is running. But it doesn't affect previously executed instructions or previously consumed input.
- Can my program keep running until a breakpoint is hit, or the program terminates, without showing the execution of each instruction?
Yes. Increase the speed slider to the maximum value.
- Where else can I use Hexagony?
-
You can compete in Hexagony on Code Golf.
You can find many Hexagony programs, including explanations of how they work, on Code Golf & Coding Challenges Stack Exchange.
- Is there a fast command-line interpreter for Hexagony?
-
This interpreter is written in C# and is faster than the reference implementation. Additionally, it's the Hexagony interpreter for Code Golf.


FILM TITLE: HEARTS OF DARKNESS DIRECTOR: ELEANOR COPPOLA COUNTRY: USA ( 1991 ) SUPER FEATURES: SUPER DOCUMENTARY ON " APOCALYPSE NOW ". WRITTEN BY: FAX BAHR and GEORGE HICKENLOOPER. MUSIC BY: CARMINE & FRANCIS COPPOLA ( Not the Same as original ) PERCUSSION: MICKEY HART ( GRATEFUL DEAD ).
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
IF THERE WERE WAS A REAL DOCUMENTARY ABOUT A TRUE EXPERIENCE, AND ONE ESCAPES IT TO TELL THE STORY, THIS IS IT. THERE AREN'T VERY MANY BETTER DONE DOCUMENTARIES THAN THIS ONE.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
I happen not to be a great fan of Francis Ford Coppola. After the first Godfather film, I dropped out. The sequels, can gather all the accolades and reviews, I am not going to see them. Peggy Sue was fun. Pennies from Heaven I fell asleep on. But the stuff he has produced for others, now that is something else.
While being extremelly receptive to world cinema, he also tries to invest in as many foreign film makers as he can, probably hoping Hollywood will notice someday. Unfortunately, when is all said and done, and written about, the real genius behind his work is not Francis at all, and his ideas, or intellectual pursuits. This documentary is even better than Francis' own work. And the reason why is because she doesn't quit because there has to be an explanation. She keeps going, and one gets to see more action, than a script has ever seen.
Essentially, this is a documentary about the making of Coppola's now famous 'Apocalypse Now' film, and its incredible problems during the shooting of the film, which had to be done in an area where the rebels were fighting the Marcos government, and that was devastated by a monsoon, by Martin Sheen's heart attack, and by a preponderance of an over stuffed Marlon Brando. But it was Francis' ability to be able to match the fury, and beauty, of the moment which made his film a success. Despite the fact that he had to ransack his house, and all his personal belongings, to achieve it.
The basic premise of 'Apocalypse Now' is Joseph Conrad's Heart of Darkness. The real premise of this documentary is the real Joseph Conrad's Heart of Darkness not the animated version found on the film. And it takes his wife to see and notice the clear parallels of the story and book. Here he is having a breakdown, and she is talking in retrospect about watching a man go through a cataclysmic event in life, and succeeding at it.
Eleanor's observations are incredible, and really show Francis as a smaller person, because he is so emotional where she is so steady. He is extravagant. Se says she could do without it all, the china and what not, in fact she'd prefer that she didn't have it all, and enjoy a simpler existence. and we are watching her husband nearly kill himself with his vision.
It is an amazing little trek through the jungles of man's idealistic, and often cruel mind ( sounds familiar Joseph? ) which results in an achievement eventually. There are no better parallels to make. this is it. Where literature does not meet film, it's where it becomes film.
With added new music by Carmine Coppola, and touched up by the Grateful Dead's Mickey Hart ( who did ALL the sound effects and percussion for the original film, which tells you that Francis was aware of Hart's musical curiosities, and expansion as a musician and person ) this film becomes another impressive work. It's hard to believe it isn't a real movie on its own......it sure feels like it.
A MUST SEE. 5 GIBLOONS Pedro Sena Member of the Internet Movie Critics Association
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


favorite_actions
Tell people about what your script does here. This is an html document so you can format it with html tags.
Usage
Tell people how to use your script here.


KASARAGOD
Kasaragod or Kasargod is one of the 14 districts in the southern Indian state of Kerala. It is the northernmost district of Kerala and is included in the North Malabar region. It is also known as Saptha Bhasha Sangama Bhoomi (The land of seven languages), as seven languages namely Malayalam, Tulu, Kannada, Marathi, Konkani, Beary, and Urdu are spoken, unlike the other districts of Kerala.[1] It was a part of the Kannur district of Kerala until 24 May 1984. The district is bounded by Dakshina Kannada district to the north, Western Ghats to the northeast, Kodagu district to the southeast, Kannur district to the south, and Arabian Sea to the west. Kasargod district has the maximum number of rivers in Kerala


{% extends "base.html" %} {% import "bootstrap/wtf.html" as wtf %} {% block page_content %}
Before you submit this form, please ensure you have FreeOTP installed on your smartphone, as you will need it to complete your registration. Download from iTunes | Google Play
{{ wtf.quick_form(form) }}


FESTIVA
Home
About
Services
Contact
Digital Agency
of the year 2019
Visual Identity by John Doe Client:
Lorem ipsum
View Project
Shinjon'12
Khulna University Rag'16
Visual Identity by John Doe Client:
Lorem ipsum
View Project
Photography
Cinematography
Web
Development
Software
Development
Graphics
Design
Digital
Marketing
Made by FESTIVA
All Projects
Wedding
Potrait
Model Photography
RIMON-NIPA WEDDING
SANCHITA-SHAIKAT WEDDING
SADIA-SAHANGIR


{% extends "base.html" %} {% block title %}Cash Calculation{% endblock %} {% block content %} {{ form.hidden_tag() }} Take Payment Please enter the cash received from the customer. Bike Number : {{ bicycle }} Location : {% if location == 1 %} University {% elif location == 2 %} City {% else %} Headingly {% endif %} Start Date : {{ start }} End Date : {{ end }} Price : {{ price }} Cash Received{{ form.cash_received }} Change Due : £
Bike Number : {{ bicycle }}
Location : {% if location == 1 %} University {% elif location == 2 %} City {% else %} Headingly {% endif %}
Start Date : {{ start }}
End Date : {{ end }}
Price :
{{ price }}
Change Due : £


{{< layout-photo-checker}} {{$pageTitle}} Upload your photo {{/pageTitle}} {{$header}}
Your photo will now go through a basic technical check.


Greetings!
My name is Nigel Dunetts, and this page is just the beginning of my Dev Bootcamp journey.
I am from Omaha, NE originally, but I have also lived in Illinois, Missouri, and Minnesota. I currently call Boulder, CO home, and work for an ESP called SendGrid as a tech support agent.


ViTextView Class Reference
| Inherits from | NSTextView |
| Conforms to | ViCompletionDelegate ViKeyManagerTarget ViSnippetDelegate |
| Declared in | ViTextView.h |
Tasks
Other Methods
-
keyManager
Associated key manager.
property -
document
Associated document.
property -
mode
Vi mode (insert, normal or visual).
property -
visual_line_mode
property -
– textStorage
The associated ViTextStorage object.
-
– setNormalMode
Set normal mode.
-
– setVisualMode
Set visual mode.
-
– setInsertMode
Set insert mode.
-
– input:
Input a string of keys as a macro.
Caret handling
-
– setCaret:
Set the location of the caret.
-
– scrollToCaret
Scroll the view to the caret.
-
– caret
The location of the caret.
-
– currentLine
The current line number.
-
– currentColumn
The current column.
-
– currentScreenColumn
The current screen column. This may be different from currentColumn if line wrapping is in effect.
-
– gotoColumn:fromLocation:
Go to a specific column.
-
– gotoLine:column:
Go to a specific line and column.
-
– characterAtIndex:
Get the character at a location.
-
– currentCharacter
The character under the caret.
-
– line
The content of the current line.
Setting marks
-
– markNamed:
Lookup a marked location.
-
– setMark:atLocation:
Set a mark.
Manipulating text
-
– insertString:atLocation:
Insert a string at a location.
-
– insertString:
Insert a string at the current location.
-
– deleteRange:
Delete a range of text.
-
– replaceRange:withString:
Replace a range of text with a string.
-
– insertNewlineAtLocation:indentForward:
Insert newline and indent.
Working with registers
-
– yankToRegister:range:
Copying text to a register.
-
– cutToRegister:range:
Cut text to a register.
Popup menus
-
– popUpContextMenu:
Show a popup menu at the carets location.
Working with TextMate Bundles
-
– environment
Bundle environment variables.
-
– preference:atLocation:
A bundle preference at a given location.
-
– preference:
A bundle preference at the current location.
snippets Methods
-
– insertSnippet:inRange:
Insert a snippet, replacing the trigger word.
-
– insertSnippet:atLocation:
Insert a snippet at a given location.
-
– insertSnippet:
Insert a snippet at the current location.
Properties
document
Associated document.
@property (nonatomic, readonly) ViDocument *document
Declared In
ViTextView.h
keyManager
Associated key manager.
@property (nonatomic, readwrite, assign) ViKeyManager *keyManager
Declared In
ViTextView.h
mode
Vi mode (insert, normal or visual).
@property (nonatomic, readonly) ViMode mode
Declared In
ViTextView.h
Instance Methods
caret
The location of the caret.
- (NSUInteger)caret
Return Value
The location of the caret.
Declared In
ViTextView.h
characterAtIndex:
Get the character at a location.
- (unichar)characterAtIndex:(NSUInteger)location
Parameters
- location
The location to check.
Return Value
The character at the given location, or 0 if location is invalid.
Declared In
ViTextView.h
currentCharacter
The character under the caret.
- (unichar)currentCharacter
Return Value
The character under the caret.
Declared In
ViTextView.h
currentColumn
The current column.
- (NSUInteger)currentColumn
Return Value
The current column.
Declared In
ViTextView.h
currentLine
The current line number.
- (NSUInteger)currentLine
Return Value
The current line number.
Declared In
ViTextView.h
currentScreenColumn
The current screen column. This may be different from currentColumn if line wrapping is in effect.
- (NSUInteger)currentScreenColumn
Return Value
The current screen column. This may be different from currentColumn if line wrapping is in effect.
Declared In
ViTextView.h
cutToRegister:range:
Cut text to a register.
- (void)cutToRegister:(unichar)regName range:(NSRange)cutRange
Parameters
- regName
The name of the register to cut to.
- cutRange
The range of text to cut.
See Also
Declared In
ViTextView.h
deleteRange:
Delete a range of text.
- (void)deleteRange:(NSRange)aRange
Parameters
- aRange
The range to delete.
Declared In
ViTextView.h
environment
Bundle environment variables.
- (NSDictionary *)environment
Return Value
Bundle environment variables.
Declared In
ViTextView.h
gotoColumn:fromLocation:
Go to a specific column.
- (void)gotoColumn:(NSUInteger)column fromLocation:(NSUInteger)aLocation
Declared In
ViTextView.h
gotoLine:column:
Go to a specific line and column.
- (BOOL)gotoLine:(NSUInteger)line column:(NSUInteger)column
Return Value
YES if the position was valid.
Declared In
ViTextView.h
input:
Input a string of keys as a macro.
- (BOOL)input:(NSString *)inputString
Parameters
- inputString
A key sequence, can include special keys, see ViMap.
Return Value
YES if the macro evaluated successfully.
Declared In
ViTextView.h
insertNewlineAtLocation:indentForward:
Insert newline and indent.
- (NSUInteger)insertNewlineAtLocation:(NSUInteger)aLocation indentForward:(BOOL)indentForward
Parameters
- aLocation
The location of the newline character to insert.
Return Value
Returns aLocation
adjusted for automatic indentation.
Declared In
ViTextView.h
insertSnippet:
Insert a snippet at the current location.
- (ViSnippet *)insertSnippet:(NSString *)snippetString
Parameters
- snippetString
The snippet to insert.
Declared In
ViTextView.h
insertSnippet:atLocation:
Insert a snippet at a given location.
- (ViSnippet *)insertSnippet:(NSString *)snippetString atLocation:(NSUInteger)aLocation
Parameters
- snippetString
The snippet to insert.
- aLocation
The location to insert the snippet. Zero-based.
Declared In
ViTextView.h
insertSnippet:inRange:
Insert a snippet, replacing the trigger word.
- (ViSnippet *)insertSnippet:(NSString *)snippetString inRange:(NSRange)aRange
Parameters
- snippetString
The snippet to insert.
- aRange
The range of the trigger word to replace.
Declared In
ViTextView.h
insertString:
Insert a string at the current location.
- (void)insertString:(NSString *)aString
Parameters
- aString
The string to insert.
Declared In
ViTextView.h
insertString:atLocation:
Insert a string at a location.
- (void)insertString:(NSString *)aString atLocation:(NSUInteger)aLocation
Parameters
- aString
The string to insert.
- aLocation
The location where the string will be inserted. Zero-based.
Declared In
ViTextView.h
line
The content of the current line.
- (NSString *)line
Return Value
The content of the current line.
Declared In
ViTextView.h
markNamed:
Lookup a marked location.
- (ViMark *)markNamed:(unichar)markName
Parameters
- markName
The name of the mark.
Return Value
The named mark, or nil
if not set.
Declared In
ViTextView.h
popUpContextMenu:
Show a popup menu at the carets location.
- (void)popUpContextMenu:(NSMenu *)menu
Parameters
- menu
The menu to display.
Declared In
ViTextView.h
preference:
A bundle preference at the current location.
- (id)preference:(NSString *)name
Parameters
- name
The name of the Bundle Preference (eg,
shellVariables
)
Return Value
A bundle preference at the current location.
Declared In
ViTextView.h
preference:atLocation:
A bundle preference at a given location.
- (id)preference:(NSString *)name atLocation:(NSUInteger)aLocation
Parameters
- name
The name of the Bundle Preference (eg,
shellVariables
)
- aLocation
The location where the preference should be valid.
Return Value
A bundle preference at a given location.
Declared In
ViTextView.h
replaceRange:withString:
Replace a range of text with a string.
- (void)replaceRange:(NSRange)aRange withString:(NSString *)aString
Parameters
- aRange
The range to replace.
- aString
The replacement string.
Declared In
ViTextView.h
scrollToCaret
Scroll the view to the caret.
- (void)scrollToCaret
Discussion
Makes sure the caret is visible.
Declared In
ViTextView.h
setCaret:
Set the location of the caret.
- (void)setCaret:(NSUInteger)location
Parameters
- location
The location of the caret. Zero-based.
Declared In
ViTextView.h
setMark:atLocation:
Set a mark.
- (void)setMark:(unichar)name atLocation:(NSUInteger)aLocation
Parameters
- name
The name of the mark.
- aLocation
The location to mark.
Declared In
ViTextView.h
textStorage
The associated ViTextStorage object.
- (ViTextStorage *)textStorage
Return Value
The associated ViTextStorage object.
Declared In
ViTextView.h


The following requirements and sample contracting language should be included in all custom xAPI assessment content acquisitions. The samples below can be used without modification, but it is expected that updates may be required based on specific requirements of the xAPI content.
Assessment Requirements & Contracting Language Samples
| ID | Requirement | Contracting Language Sample |
|---|---|---|
| AR-6.1 | Conformance with the Navy Assessment Profile | The Contractor SHALL implement all the relevant requirements in the Navy Assessment Profile. There is currently no automated test for Assessment Profile conformance. The contractor SHALL prove conformance by providing sample xAPI statements for manual validation. |
| AR-6.2 | LMS Launch Initialization Data | If the assessment is web-based and will be launched from the LMS, the Contractor SHALL develop the content such that it supports the web-based launch mechanism and associated data initialization requirements defined in the Navy Core xAPI Profile. |
| AR-6.3 | Non-LMS Actor Initialization Data | If the assessment is a native application and/or will not be launched from the LMS, the Contractor SHALL authenticate the user and use the data returned to create the xAPI Actor, as defined in the Navy Core xAPI Profile. OR If the assessment is a native application and/or will not be launched from the LMS, the Contractor SHALL conform to the requirements in the Navy Core xAPI Profile. |
| AR-6.4 | Initialize and Terminate Assessment Tracking | The Contractor SHALL develop the assessment such that it tracks an Initialize Statement when the simulation begins and a Terminate Statement when the simulation ends, as defined in the Navy Assessment Profile. Note: If the assessment is embedded in other content (e.g., a SCORM Course), these events may be replaced by a similar set of events in the encompassing profile. |
| AR-6.5 | Learner Interaction Data Tracking | The Contractor SHALL track relevant assessment interaction events. These events SHALL be tracked in accordance with the xAPI Statement Requirements & Examples documented in the Navy Assessment Profile. |
Performance Assessment Requirements & Contracting Language Samples
| ID | Requirement | Contracting Language Sample |
|---|---|---|
| AR-7.1 | Conformance with the Navy Performance Assessment Profile | The Contractor SHALL implement all the relevant requirements in the Navy Performance Assessment Profile. There is currently no automated test for Performance Assessment Profile conformance. The contractor SHALL prove conformance by providing sample xAPI statements for manual validation. |
| AR-7.2 | LMS Launch Initialization Data | If the performance assessment is web-based and will be launched from the LMS, the Contractor SHALL develop the content such that it supports the web-based launch mechanism and associated data initialization requirements defined in the Navy Core xAPI Profile. |
| AR-7.3 | Non-LMS Actor Initialization Data | If the performance assessment is a native application and/or will not be launched from the LMS, the Contractor SHALL authenticate the user and use the data returned to create the xAPI Actor, as defined in the Navy Core xAPI Profile. OR If the performance assessment is a native application and/or will not be launched from the LMS, the Contractor SHALL conform to the requirements in the Navy Core xAPI Profile for the xAPI Actor . |
| AR-7.4 | Initialize and Terminate Assessment Tracking | The Contractor SHALL develop the performance assessment such that it tracks an Initialize Statement when the simulation begins and a Terminate Statement when the performance assessment ends, as defined in the Navy Performance Assessment Profile. |
| AR-7.5 | Learner Interaction Data Tracking | The Contractor SHALL track relevant performance assessment interaction events. These events SHALL be tracked in accordance with the xAPI Statement Requirements & Examples documented in the Navy Performance Assessment Profile. |


◆ Pid()
| Pid::Pid |
( |
float |
p = 0 , |
|
|
float |
i = 0 , |
|
|
float |
d = 0 , |
|
|
unsigned int |
windup = 0 , |
|
|
float |
dAlpha = 0 |
|
) |
| |
◆ clearWindup()
| void Pid::clearWindup |
( |
| ) |
|
◆ run()
| float Pid::run |
( |
float |
err | ) |
|
◆ set_saturated()
| void Pid::set_saturated |
( |
bool |
is_saturated | ) |
|
|
inline |
◆ setWindup()
| void Pid::setWindup |
( |
unsigned int |
w | ) |
|
◆ windup()
| unsigned int Pid::windup |
( |
| ) |
const |
|
inline |
◆ derivAlpha
◆ kd
◆ ki
◆ kp
The documentation for this class was generated from the following files:
- robot/lib/robocup-fshare/include/rc-fshare/pid.hpp
- robot/lib/robocup-fshare/src/pid.cpp


jAMM module for mediation models
Draft version, mistakes may be around
The module estimates simple, multiple, and conditional mediation models with maximum likelihood regression. It encompasses models with continuous and categorical independent variables.
To estimate a model, the user should define: a dependent variable, one or more mediators, one or more independent variables. Independent variables can be continuous (going in Covariates
) or categorical (going in Factors
). In case one or more variables is a moderator, it should first be included in the independent variables fields, and then defined as moderator (in Moderators
tab).
When one defines the roles of the variable, jAMM tries to guess what model the user is estimating, with very simple rules. The independent variables are all considered parallel indepenent variables. The mediators are all inserted in the model as parallel mediators. All effects from the independent variables through the mediators are estimated. In other words, by default the module builds a multiple IV, multiple mediators model, like the example in the Figure with 2 IV and 2 mediatiors. In the simple case of three variables model (simple mediation), one obtains the standard mediation trianglular model.
When there are more independent variables and mediatiators, the rules described above apply. For two IV and two ME, the default model is as in the following Figure.
The structure of the model can be changed in the Mediators models
and Full model
tabs. See jAMM: Model building for examples on building models.
The tab shows the mediators models, the models with mediators as dependent variable. On the right (Models for mediators
) one field for each mediator is shown. There, the user can define the predictors of each mediator. See jAMM: Model building for examples on building models.
The tab shows the full model, that is the model predicting the dependent variable. On the right (Model terms
), oen can put all the model terms required. By default, jAMM puts in the model terms all the mediators and the mediators predictors. See jAMM: Model building for examples on building models.
When variables roles are yet not defined, the table shows some suggestions to get the user started. When the variables and the models are defined, the table shows the linear equations needed to estimate the model, and the indirect (mediated) effects involved in the model. If something goes wrong with the model specification, warnings are issued. The main reason a warning is issued is the specification of an incomplete or absurd model. The path diagram also signals problems in the model.
Upon defining variables and models terms, the path diagram is generated. This type of diagram is often called model diagram or conceptual model as opposed to the working model, which shows all terms and all estimated statistical paths. Without moderators, the model diagram corresponds to the working model but does not show the covariances between independent variables.
The diagram shows the model implied by the Mediators models
and the Full model
terms. The estimated paths (coefficients) are shown in black. Required variables or required paths are shown in red. A required path, for instance, can be a path from the IV to the mediator. The diagram also shows impossible paths in purple. Impossible paths are paths that do not make sense in terms of mediation, not necessarily in terms of statistical estimability. For instance, a model with two mediators m1
and m2
, in which the user defines a path from m1
to m2
and a path from m2
to m1
is deemed as impossible, because this king of models are not mediation models. An example is in the Figure.
The diagram also shows suggested paths (in green). Those are paths whose absence does not invalidate the model, but may require extra care in interpreting the results. Suggested paths may be hidden in the Mediation options
tab.
For models without interactions, jAMM produces a table showing the Indirect effects
(mediated), the Direct effects
, and the Total effects
. The example below shows results for a model with one IV (x1
), two parallel mediators (m1
, m2
) and a DV (y
).
Indirect effects
are the mediated effects. The Effect
column indicates which mediated effect is shown in the corresponding row. Estimate
reportes the estimation of the mediated effect. The columns are self-explanatory. See jAMM: Model estimates for details.
The direct effects are the effects computed keeping the mediators constant, thus the un-mediated effects. The total effects are the effects computed without the mediators, or, equivalently, the sum of the indirect and the direct effects.
If requested in the Mediation options
tab by ticking Components
one can see in the table also the components of the indirect effects. The components are the coefficients that are multiplied to obtain the indirect effects. In simple mediation, for instance, if the models are \(m=a \cdot x\) and \(y= b \cdot m + c \cdot x\), the mediated effect is \(IE=a \cdot b\) and \(a\) and \(b\) are its components.
In the example shown in the table above, asking the Components
yields:
Moderators are variables expected to moderate the indirect effects. In the tab, one can define one or more moderators for each mediator. That is equivalent to declaring one or more moderators for each IE. When a moderator is defined, jAMM updates the Mediators models
and the Full model
to include the interactions required to build the model. The user can then tweak the model, but also completely change it, by changing the terms in the Mediators models
and the Full model
tabs.
Declaring the moderator(s), however, triggers jAMM to nicely display the moderator in the model diagram and to compute simple indirect effects, that is, indirect effects computed at different levels of the moderators. What levels are used is ruled by Factor coding
and Covariates scaling
options.
Two important caveats should be noted:
To add a moderator, there should be at leat another independent variable in the medatiators models. For example, if the user defines x1
and x2
in the mediators models
, jAMM allows one of them (say x1
) to be a moderator. However, if the user tries to add also x2
as moderator, a warning is issued because with two moderators, there are no IV left to be moderated. An example is in the Figure shows the warning that appears if the user tries to add x2
as a moderator of the model.
See jAMM: Building conditional models for examples on building models with moderators.
jAMM allows to define interactions in the model without specifying a moderator (see example in the following Figure).
This feature allows users to define an interaction effect that will be treated as an additional term of the mediation model. Thus, no simple indirect effects will be computed. Nontheless, the model may be perfectly fine and the user may have good reasons to insert an interaction without explicitely define a moderator. The feature also allows estimating very complex models with several interactions, not all of them interesting from the point of view of the mediated effects.
See jAMM: Building conditional models for examples on building models with moderators.
It allows to code the categorical variables according to different coding schemas. The coding schema applies to the parameter estimates, simple effects and plots. The Omnibus Tests (ANOVA) table uses always the “deviation” scheme, that is “-1 0 1” coding, which results in a correct Type III analysis.
Note that all contrasts but dummy
guarantee to be centered to zero (intercept being the grand mean), so when involved in interactions the other variables coefficients can be interpret as (main) average effects. If contrast dummy
is set, the intercept and the effects of other variables in interactions are estimated for the first group of the categorical IV.
Contrasts definitions are provided in the estimates table. More detailed definitions of the comparisons operated by the contrasts can be obtained by selecting “Show contrast definition table”.
Differently to standard R naming system, contrasts variables are always named with the name of the factor and progressive numbers from 1 to K-1, where K is the number of levels of the factor.
In reading the contrast labels, one should interpret the (1,2,3)
code as meaning “the mean of the levels 1,2, and 3 pooled toghether”. If factor levels 1,2 and 3 are all levels of the factor in the samples, (1,2,3)
is equivalent to “the mean of the sample”. For example, for a three levels factor, a contrast labeled 1-(1,2,3)
means that the contrast is comparing the mean of level 1 against the mean of the sample. For the same factor, a contrast labeled 1-(2,3)
indicates a comparison between level 1 mean and the subsequent levels means pooled together.
More details and examples .
Continuous variables can be centered, standardized or used as they are (none
). The default is centered
because it makes our lives much easier when there are interactions in the model. If one needs to unscale the variable, simply select none
.
Covariates conditioning
rules how the model is conditioned to different values of the continuous independent variables when one or more moderators are defined.
Mean+SD
: means that the IV is conditioned to the \(mean\), to \(mean+k \cdot sd\), and to \(mean-k\cdot sd\), where \(k\) is ruled by the white field below the option. Default is 1 SD.
Percentile 50 +offset
: means that the IV is conditioned to the \(median\), the \(median+k P\), and the \(median-k\cdot P\), where \(P\) is the offset of percentile one needs. Again, the \(P\) is ruled by the white field below the option. Default is 25%. The default conditions the model to:
\(50^{th}-25^{th}=25^{th}\) percentile
\(50^{th}\) percentile
\(50^{th}+25^{th}=75^{th}\) percentile
The offset should be within 5 and 50.
Covariates labeling
decides which label should be associated with the estimates and table titles:
Labels
produces strings of the form \(Mean \pm SD\)
Values
uses the actual values of the variables
Labels+Values
produces labels of the form \(Mean \pm SD=XXXX\), where XXXX
is the actual value.
[to be continued]
Some worked out examples of the analyses carried out with jamovi jAMM are posted here (more to come)
If you are intested in checking the convergence of jAMM results with other software results, please visit:
Got comments, issues or spotted a bug? Please open an issue on GAMLj at github“ or send me an email


VARSITY BLUES by DeWyNGaLe Rating: B
Varsity Blues is just another high school sports movie. Most of them aren't very good, but Varsity Blues is different. The key in Varsity Blues was casting. James Van Der Beek and Jon Voight lead the cast of Varsity Blues. The two play enemies- James Van Der Beek plays Jonathan Moxon, second string football player, and Jon Voight plays the extremely mean football coach at Canaan High School, Bud Kilmer. Bud has coached football there for a number of years. He has mis treated many his players every year. He doesn't care about them at all, he just wants the championship. He makes them play in sickness, pain, or sadness. He always takes his team to the championship, and he expects to do it again this year.
He relies on star quarterback Lance Harbor, played by Paul Walker. Lance is the typical movie sports star. He is the best on the team, and he is always the hero during the games. The talent isn't all natural for Lance though, considering that he takes steroids. Jonathan, or Mox, (his nickname) discovers that the coach is doing this. This is your typical sports movie- the star of the team is pet to the coach and the man who is better than the star is absolutely hated by the coach. Although it is unoriginal, Varsity Blues is still a winner. There are a lot of funny sequences, and there are well played out dramatic sequences as well. When Lance is sustains an injury that may put him out of football for life, Mox is the teams only hope. Coach Kilmer is reluctant to play Mox, but has no choice. Mox goes out there and shows coach how it is done. The football sequences that were filmed were done very nicely. They looked very realistic, and were put together very well. It looked like the cast was actually playing the game, and it wasn't coordinated.
Mox continues to win game after game for the Coyotes. He likes being a star now, but is very upset with the way Coach Kilmer treats the team. His dad (Thomas Duffy) is very proud of him. He was a star in high school football as well, and wants his two sons to do the same. When Mox gets an academic scholarship to Brown University, his dad is very upset.  Mox doesn't want to take after his dad, he wants to live his own life. James Van Der Beek gives an impressive acting performance in these arguments with his father. He made you believe the tough position that Mox was in at that moment.
Mox's girlfriend, Julie, played by Amy Smart, isn't in to the whole football craze. She doesn't care about who wins or who loses or anything like that. Since Mox has become a football star, she has noticed a huge change in him. He is thinking more about himself than anyone else. Amy Smart has not done many movies, but in her role in Varsity Blues, she does a great job of dramatic acting. While all this drama is going on, Mox continues to win many games for the Coyotes. Coach Kilmer is very proud when Mox takes the team all the way to the championship game. During the game, Mox is very reluctant to play. He doesn't want to play for his dad and Kilmer.  The entire team agrees with what Mox is saying to them about Kilmer.
Varsity Blues is a teenager movie. Most adults will probably dislike this movie. Laughs mixed with sad moments in Varsity Blues make it score big with the teen audience. Jon Voight gives a wonderful performance as Coach Kilmer. He did a great job of seeming evil, not caring about anyone but himself. James Van Der Beek tells us with Varsity Blues that he can handle the lead in a movie as well as the lead on a hit television show. Some of Varsity Blues wasn't all that great, and that's the reason that adults won't like it. It may not be the type of movie that will win awards or anything close to it, but it was certainly enjoyable, and that's enough for me.
The Bottom Line- The typical sports movie with some changes makes for a fun time.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Pricing is the process whereby a business sets the price at which it will sell its products and services, and may be part of the business's marketing plan. In setting prices, the business will take into account the price at which it could acquire the goods, the manufacturing cost, the marketplace, competition, market condition, brand, and quality of product. Pricing is a fundamental aspect of financial modeling and is one of the four Ps of the marketing mix, the other three aspects being product, promotion, and place. Price is the only revenue generating element amongst the four Ps, the rest being cost centers. However, the other Ps of marketing will contribute to decreasing price elasticity and so enable price increases to drive greater revenue and profits. Pricing can be a manual or automatic process of applying prices to purchase and sales orders, based on factors such as: a fixed amount, quantity break, promotion or sales campaign, specific vendor quote, price prevailing on entry, shipment or invoice date, combination of multiple orders or lines, and many others. Automated pricing systems require more setup and maintenance but may prevent pricing errors. The needs of the consumer can be converted into demand only if the consumer has the willingness and capacity to buy the product. Thus, pricing is the most important concept in the field of marketing, it is used as a tactical decision in response to changing competitive, market and organisational situations.


Theme Kit is a single binary that has no dependencies. Download the application and with a tiny bit of setup you’re off to the theme creation races.
Run this script in your console to easily install and setup Theme Kit.
curl https://raw.githubusercontent.com/Shopify/themekit/master/scripts/install | python
With Theme Kit setup is easy, just download the app for your platform, unzip and run it from the command line.


Cartesian & Distributed Graph Topologies. More...
Functions | |
| void | MEL::TopoCartesianMakeDims (const int numProcs, const int numdims, int *dims) |
| Compute the 'ideal' dimensions for a topolgy over n-processes. More... | |
| void | MEL::TopoCartesianMakeDims (const Comm &comm, const int numdims, int *dims) |
| Compute the 'ideal' dimensions for a topolgy over n-processes. More... | |
| std::vector< int > | MEL::TopoCartesianMakeDims (const int numProcs, const int numdims) |
| Compute the 'ideal' dimensions for a topolgy over n-processes. More... | |
| std::vector< int > | MEL::TopoCartesianMakeDims (const Comm &comm, const int numdims) |
| Compute the 'ideal' dimensions for a topolgy over n-processes. More... | |
| Comm | MEL::TopoCartesianCreate (const Comm &comm, int numdims, const int *dims, const int *periods) |
| Create a cartesian topology over a comm world. More... | |
| Comm | MEL::TopoCartesianCreate (const Comm &comm, const std::vector< TopoCartesian_Dim > &dims) |
| Create a cartesian topology over a comm world. More... | |
| int | MEL::TopoCartesianNumDims (const Comm &comm) |
| Get the number of dimensions in an attached cartesian topology of a comm world. More... | |
| int | MEL::TopoCartesianRank (const Comm &comm, const int *coords) |
| Get the rank within the attached cartesian topology of a comm world. More... | |
| int | MEL::TopoCartesianRank (const Comm &comm, const std::vector< int > coords) |
| Get the rank within the attached cartesian topology of a comm world. More... | |
| void | MEL::TopoCartesianCoords (const Comm &comm, const int rank, int numdims, int *coords) |
| Get the n-dim coordinates within the attached cartesian topology of a comm world. More... | |
| std::vector< int > | MEL::TopoCartesianCoords (const Comm &comm, const int rank, int numdims) |
| Get the n-dim coordinates within the attached cartesian topology of a comm world. More... | |
| std::vector< int > | MEL::TopoCartesianCoords (const Comm &comm, const int rank) |
| Get the n-dim coordinates within the attached cartesian topology of a comm world. More... | |
| std::vector< int > | MEL::TopoCartesianCoords (const Comm &comm) |
| Get the n-dim coordinates within the attached cartesian topology of a comm world. More... | |
| void | MEL::TopoCartesianGet (const Comm &comm, int numdims, int *dims, int *periods, int *coords) |
| Get the properties of an attached cartesian topology of a comm world. More... | |
| std::pair< std::vector< int >, std::vector< TopoCartesian_Dim > > | MEL::TopoCartesianGet (const Comm &comm) |
| Get the properties of an attached cartesian topology of a comm world. More... | |
| void | MEL::TopoCartesianShift (const Comm &comm, int direction, int disp, int &rank_prev, int &rank_next) |
| Compute the ranks of a left and right shifted neighbor for a given dimension within a topology. More... | |
| std::pair< int, int > | MEL::TopoCartesianShift (const Comm &comm, int direction, int disp) |
| Compute the ranks of a left and right shifted neighbor for a given dimension within a topology. More... | |
| CartesianStencil2D5P | MEL::TopoCartesianStencil2D5P (const Comm &comm) |
| Create a 2D 5-point stencil of ranks representing the neighbouring processes. More... | |
| CartesianStencil2D9P | MEL::TopoCartesianStencil2D9P (const Comm &comm) |
| Create a 2D 9-point stencil of ranks representing the neighbouring processes. More... | |
Detailed Description
Cartesian & Distributed Graph Topologies.
Function Documentation
|
inline |
Compute the 'ideal' dimensions for a topolgy over n-processes.
- Parameters
-
[in] numProcs The number of processes in the topology [in] numdims The number of dimensions in the topology [out] dims Pointer to an (already allocated) array of length numdims
|
inline |
Compute the 'ideal' dimensions for a topolgy over n-processes.
- Parameters
-
[in] comm The comm object the topology should represent [in] numdims The number of dimensions in the topology [out] dims Pointer to an (already allocated) array of length numdims
|
inline |
Compute the 'ideal' dimensions for a topolgy over n-processes.
- Parameters
-
[in] numProcs The number of processes in the topology [in] numdims The number of dimensions in the topology
- Returns
- Returns a std::vector of dimension sizes
|
inline |
Compute the 'ideal' dimensions for a topolgy over n-processes.
- Parameters
-
[in] comm The comm object the topology should represent [in] numdims The number of dimensions in the topology
- Returns
- Returns a std::vector of dimension sizes
|
inline |
Create a cartesian topology over a comm world.
- Parameters
-
[in] comm The comm object the topology should represent [in] numdims The number of dimensions in the topology [in] dims Pointer to an array of sizes of each dimension [in] periods Pointer to an array of logicals representing if each dimension is periodic or not
- Returns
- Returns a Comm world with an attached topology
|
inline |
Create a cartesian topology over a comm world.
- Parameters
-
[in] comm The comm object the topology should represent [in] dims A std::vector of pairs representing dimension sizes and whether dimensions are periodic
- Returns
- Returns a Comm world with an attached topology
|
inline |
Get the number of dimensions in an attached cartesian topology of a comm world.
- Parameters
-
[in] comm The comm object the topology is attached to
- Returns
- Returns the number of dimensions in the attached cartesian topology
|
inline |
Get the rank within the attached cartesian topology of a comm world.
- Parameters
-
[in] comm The comm object the topology is attached to [in] coords Pointer to an array representing the n-dim coordinates in the topology
- Returns
- Returns the rank in the attached cartesian topology
|
inline |
Get the rank within the attached cartesian topology of a comm world.
- Parameters
-
[in] comm The comm object the topology is attached to [in] coords A std::vector representing the n-dim coordinates in the topology
- Returns
- Returns the rank in the attached cartesian topology
|
inline |
Get the n-dim coordinates within the attached cartesian topology of a comm world.
- Parameters
-
[in] comm The comm object the topology is attached to [in] rank The rank within comm [in] numdims The number of dimensions in the topology [out] coords Pointer to an array of numdims ints representing the coordinates
|
inline |
Get the n-dim coordinates within the attached cartesian topology of a comm world.
- Parameters
-
[in] comm The comm object the topology is attached to [in] rank The rank within comm [in] numdims The number of dimensions in the topology
- Returns
- Returns a std::vector representing the n-dim coordinates
|
inline |
Get the n-dim coordinates within the attached cartesian topology of a comm world.
- Parameters
-
[in] comm The comm object the topology is attached to [in] rank The rank within comm
- Returns
- Returns a std::vector representing the n-dim coordinates
|
inline |
Get the n-dim coordinates within the attached cartesian topology of a comm world.
- Parameters
-
[in] comm The comm object the topology is attached to
- Returns
- Returns a std::vector representing the n-dim coordinates
|
inline |
Get the properties of an attached cartesian topology of a comm world.
- Parameters
-
[in] comm The comm object the topology is attached to [in] numdims The number of dimensions in the topology [out] dims Pointer to an array of n-dims size representing the sizes of each dimension [out] periods Pointer to an array of n-dims size representing whether each dimension is periodic [out] coords Pointer to an array of n-dims size representing the coordinate in each dimension
|
inline |
Get the properties of an attached cartesian topology of a comm world.
- Parameters
-
[in] comm The comm object the topology is attached to
- Returns
- Returns a std::pair of a std::vector representing the process coordinates, and a std::vector of pairs representing the dimension sizes and periods
|
inline |
Compute the ranks of a left and right shifted neighbor for a given dimension within a topology.
- Parameters
-
[in] comm The comm object the topology is attached to [in] direction The dimension to shift in [in] disp How much to shift by [out] rank_prev The left neighbour [out] rank_next The right neighbour
|
inline |
Compute the ranks of a left and right shifted neighbor for a given dimension within a topology.
- Parameters
-
[in] comm The comm object the topology is attached to [in] direction The dimension to shift in [in] disp How much to shift by
- Returns
- Returns a std::pair representing the ranks of the left and right neighbour
|
inline |
Create a 2D 5-point stencil of ranks representing the neighbouring processes.
- Parameters
-
[in] comm The comm object the topology is attached to
- Returns
- Returns a 2D 5-point stencil of comm ranks
|
inline |
Create a 2D 9-point stencil of ranks representing the neighbouring processes.
- Parameters
-
[in] comm The comm object the topology is attached to
- Returns
- Returns a 2D 9-point stencil of comm ranks


Home
About
Products
Blog
Contact
Quick Contact
Contact us today, and get reply with in 24 hours!
Submit


 Admittedly it sucks less in 2008 than it did ten years ago, but it is still preoccupied on forcing end users to be aware of the eight fallacies of distributed computing, for every invocation. From the outset, these are the issues that are/were annoying about RMI :
A developer coding for a remote divide should do so in a way that respects the Facade Pattern but should be able to leave outage concerns to more centralized places.
Centralized places could be, a handler servlet (through which all requests go), or a container such as Spring, PicoContainer or Guice (we all adhere to Inversion of Control these days right?).


262_cppowboy_i_am_back.py
# from typing import List
# import collections
class Solution:
def countSubgraphsForEachDiameter(self, n: int, edges: List[List[int]]) -> List[int]:
def connected(edges):
points = set()
for u, v in edges:
points.add(u)
points.add(v)
return len(points) - 1 == len(edges)
def get_distance(edges):
# print('get', edges, connected(edges))
if len(edges) == 0:
return 0
if not connected(edges):
return 0
nxt = collections.defaultdict(list)
for u, v in edges:
nxt[u].append(v)
nxt[v].append(u)
visited = collections.defaultdict(bool)
def dfs_for_distance(i):
visited[i] = True
depth, distance = 0, 0
first, second = 0, 0
for j in nxt[i]:
if not visited[j]:
dep, dist = dfs_for_distance(j)
if dep + 1 >= first:
first, second = dep + 1, first
elif dep + 1 >= second:
first, second = first, dep + 1
depth = max(depth, 1 + dep)
distance = max(distance, dist)
distance = max(distance, first + second)
return depth, distance
_, result = dfs_for_distance(edges[0][0])
return result
ans = [0] * (n - 1)
def dfs(i, buf):
if i >= n - 1:
dist = get_distance(buf)
if dist > 0:
ans[dist - 1] += 1
return
dfs(i + 1, buf)
dfs(i + 1, buf + [edges[i]])
dfs(0, [])
return ans
# solution = Solution()
# n = 4
# edges = [[1, 3], [1, 4], [2, 3]]
# ans = solution.countSubgraphsForEachDiameter(n, edges)
# print(ans)


HTML5
CSS
SEO
PHOTOSHOP
JAVASCRIPT
SASS
GITHUB
WORDPRESS
SOFTSKILLS
COMMUNICATION
CREATIVE THINKING
TIME MANAGEMENT
WORK ETHIC
TEAM LEADERSHIP
PROJECT MANAGEMENT
ADAPTABLE
POSITIVE ATTITUDE


Covid Care
Home
Need Help?
Provide Help
Helpline Numbers
Other Helpful Sites
Team
Chat
Available Helpline, Childline Numbers
Info
State/City
Contact


COL Large 4
COL Large 4
COL Large 4
COL MED 6
COL MED 6
COL MED 6
WELCOME To DoHacking world!
Computer hackerst your knowledge or consent. Their clever tactics and detailed technical knowledge help them access the information you really don't want them to have.
Fugaku (supercomputer)
Hardware
The supercomputer is built with the Fujitsu A64FX microprocessor. This CPU is based on the ARM version 8.2A processor architecture, and adopts the Scalable Vector Extensions for supercomputers. Fugaku was aimed to be about 100 times more powerful than the K computer (i.e. a performance target of 1 exaFLOPS) and have a high level of practicability in the world. Fugaku uses 158,976 A64FX CPUs joined together using Fujitsu's propietary torus fusion interconnect.
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
Copyright (c) 2018 Copyright Holder All Rights Reserved.
hack me
hello
harsh
This is a MorseCode Language
A = ._
N = _ _ .
B = _...
O = _ _ _.
C = _._.
P = ._ _.
D = _..
Q = _ _._
E = .
R = ._.
F = .._.
S = ...
G = _ _.
T = _
H = ....
U = .._
I = ..
V = ..._
J = ._ _ _
W = ._ _
K = _._
X = _.._
L = ._..
Y = _._ _
M = _ _
Z = _ _..
A = ._ ,
B = _...,
C = _._.,
D = _.. ,
E = . ,


Keep your config in a separate dotfiles directory and under source control with the structure like:
Then:
Put everything into .bashrc except any long running tasks.
.bash_profile - for interactive shells. But you can just usually source .bashrc.
Now some keyboard shortcuts:
If you often connect via SSH like so:
Now some useful things you can do in the terminal:
You will also appreciate power of the shell when using aliases and functions:
But when functions get more complicated, you might want to use a scripting language (Ruby, Python etc).
Put those scripts into your ~/bin directory and add it to your PATH variable.
There are also number of Mac specific tricks.
One is is
You can also force it to use a specific app:
This one becomes a good candiate for an alias.
You can also pipe output of any command into
For example:
You can also do it the other way around:
Another useful thing OpenTerminal (drag the app to finder toolbar). It allows you to open terminal with the current directory set to the Finder window.
You can also use the keyboard to open terminal using LaunchBar. It does much more though. It acts like a global terminal for the system.
You can also try to use
Most of the tips here come from the PeepCode's Advanced Command Line screencast.
Hope this helps you on your way to becoming a Terminal Guru.
~/dotfiles:
--+ bashrc, bash_profile
--+ ssh/config
-->--bash -> (env, config, aliases). bash has source bach/env, bash/config etc.
Then:
ln -s ~/.bashrc ~/bin/dotfiles
echo ". ~/.bashrc" > ~/.bash_profile
Put everything into .bashrc except any long running tasks.
.bash_profile - for interactive shells. But you can just usually source .bashrc.
Now some keyboard shortcuts:
- Cntrl-a - go to the beginning of line
- Cntrl-e - go to the end of line
- Cntrl-k - delete everything to the right
- Cntrl-w - delete previous word
- ESC->-b - go back one word
- ESC->-f - go forward one word
If you often connect via SSH like so:
ssh -p 2323<EMAIL_ADDRESS>Then you can shorten it to ssh my-server
if you will add entry to ~/.ssh/config
:
Host my-server
HostName my-server.example.com
Port 2323
User username
Then you can use it as:
# Tunnelling
ssh -L7997:<IP_ADDRESS>:7979 my-server
# Copy folder to server
scp my_folder my-server:my_other_folder/
# Or even use it with git
git clone my-server:repo-name.git
Now some useful things you can do in the terminal:
!!
- run previously executed command (you can do `sudo !!` for example)!$
- last argument of previous command!60
- run the command #60. Number comes fromhistory
command
(for example: run !echo
- rerun last command starting with echo- Ctrl-r - interactive search on history of commands; Use Ctrl-j to abandon.
!?file.log?!
- run the last matching command (not interactive)^file.log^another-file.log
- run the last matching command replacing file.log with another-file.log!?file.log?!
- run the last matching command (not interactive)mv README.{txt,markdown}
- same asmv README.txt README.markdown
.
tail file.log
and then cat !$
is same as cat file.log
You will also appreciate power of the shell when using aliases and functions:
alias ss="./script/server"
alias s="git status"
alias gca="git commit -am"
alias zipr="zip -r"
function take {
mkdir $1
cd $1
}
# take create-and-go-here
function gg() {
git commit -v -a -m "$*"
}
# gg Commit message with no quotes
But when functions get more complicated, you might want to use a scripting language (Ruby, Python etc).
Put those scripts into your ~/bin directory and add it to your PATH variable.
There are also number of Mac specific tricks.
One is is
open anything
command that will open the file/directory in the default application.
You can also force it to use a specific app:
open -a /Applications/Firefox.app index.html
.
This one becomes a good candiate for an alias.
You can also pipe output of any command into
pbcopy
command to copy it to the clipboard.
For example:
cat index.html | pbcopy
will copy the content of the file into clipboard.
You can also do it the other way around:
pbpaste > index.html
or
pbpaste | grep "jquery"
.
Another useful thing OpenTerminal (drag the app to finder toolbar). It allows you to open terminal with the current directory set to the Finder window.
You can also use the keyboard to open terminal using LaunchBar. It does much more though. It acts like a global terminal for the system.
You can also try to use
zsh
shell. It will work with the existing bash aliases just fine.
Most of the tips here come from the PeepCode's Advanced Command Line screencast.
Hope this helps you on your way to becoming a Terminal Guru.


Just spent the first record-long journey on a high-speed train from Wuhan to Jingzhou which supposed to be 2 hours. Due to the unexpected heavy rain and snow ahead, we ended up in a 10 hours transit.
Things that happened during this long-ass journey:
Made a post on Steemit.
Read half of the book The Internet of Money with my Kindle.
Took 3 naps in between.
Longing to see my first natural snow(excited) -> Saw the snow for the first time(happy) -> See it for too long on a dead train(bored)
Someone starts booming weird musics on the phone.
Some impatient passengers start to quarel with the train servants for unknown reasons.
A mother starts to please her baby by banging empty plastic bottles.
A folk behind us starts to randomly kicking our seats.
A kid in the other compartment just happened to have a medical condition that they need to broadcast a summon if there is any doctors on board to provide medical assistance.
All in all, I’ve learnt that not only we should be a polite and patient passenger, but bringing a pair of noise-cancelling earphone and sufficient data volume is very vital too on a long journey.
第二次坐高铁就创下可能很久以后都破不了的记录: 把二小时的路程硬是花了十个小时才达到目的地。皆因从武汉到荆州的路上，发生了暴雨暴雪等未可知因素。实在是心累。
在这漫漫旅途中发生了这些:
在 Steemit 写了篇文章。
用 Kindle 把神作 The Internet of Money 看了一半。
小眠了三次。
从遇到初雪的期盼，到看到初雪的兴奋，但因为只能坐在位子上干看太久而枯燥的心境转变。
有人开始用手机播放听不懂的歌曲。
有位母亲开始敲打空塑料瓶用交响乐来取悦她的小孩。
失去耐心的乘客开始找服务员争吵。
后面的大叔开始随机性踢我们的座位。
某车厢有位小孩病发，以至需要广播寻找车上具有医疗能力的人士提供协助。
最后，我了解到在一个长途旅行中除了要当一个礼貌耐心的乘客，同时具备一副抗噪音的耳机和足够的上网流量都是非常至关重要的。
This page is synchronized from the post: ‘Ten hours on a train 高铁十小时’