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|
|
using | URBG = wtl::sfmt19937_64 |
| |
◆ assign()
| void edal::Patch::assign |
( |
size_t |
n, |
|
|
const Individual & |
founder |
|
) |
| |
Fill this patch with the same number of females and males.
- Parameters
-
| n | The number of inital individuals in this patch |
| founder | The individual to be copied |
◆ emplace_back()
| void edal::Patch::emplace_back |
( |
Individual && |
ind | ) |
|
|
inline |
Add an individual to this patch.
- Parameters
-
◆ empty()
| bool edal::Patch::empty |
( |
| ) |
const |
|
inline |
- Returns
- Whether this patch is empty or not
◆ make_destinations()
| std::vector< std::pair< unsigned int, unsigned int > > edal::Patch::make_destinations |
( |
size_t |
n, |
|
|
size_t |
row, |
|
|
size_t |
col, |
|
|
size_t |
num_rows, |
|
|
size_t |
num_cols |
|
) |
| const |
Change row/col with probability \(m\) = Individual::MIGRATION_RATE_.
With probability \( m > 0 \), each offspring becomes a "migrant." Each migrant goes to one of the 8 neighboring patches. For patches at the boundary, the probability \( m \) is reduced according to the number of neighbors they have.
◆ size()
| size_t edal::Patch::size |
( |
| ) |
const |
|
inline |
- Returns
- The number of individuals in this patch
The documentation for this class was generated from the following files:


4 Jan 2016
Ruby Hashes vs JavaScript Objects
Ruby Hashes and JavaScript Objects are very similar with a few key differences. They are both considered objects in their respective languages, and store data. The differences are in the details.
Lets start with what they have in common. In Ruby hashes store data as key-value pairs. The key represents the way a user would access the data associated with that key. In JavaScript you can do something similar by creating an object with properties associated with values. Below is an example of both.
ruby_hash = { "Dog_name" => "Rex", "Cat_name" => "Snickers", "Fish_name" => "Goldfinger" }
var javaScriptObject = { dogName: "Rex", catName: "Snickers", fishName: "Goldfinger" };
You can see how they are similar but also slightly different in terms of the way it is written. Coding languages are like real world languages. Some languages are very similar, but have little nuances that make them different.
The way you add to them is different depending on the language. In ruby you can add to a hash this way:
ruby_hash["Hamster_name"] = "Turbo"
In JavaScript you do this:
javaScriptObject.hamsterName = "Turbo"
I hope this help you learn a little about how Ruby and JavaScript are both similar and different.


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Find the chocolate egg hidden in shop A, hint the postcode is ZA1 1AA
and Tweet it with #sela
The prize is the chocolate egg in the photo.
The winner is the tweet that gain the highest number of favourites. Good Luck!!


Dynamically Reweighting Experts' Advice
With A Recursive Least Squares Example
What is this talk about?
- Suppose we have a number of different "experts"
-
- Different prediction algorithms
- Prediction algorithms suitable for different settings (e.g., low/high volatility)
- How can we dynamically combine the advice?
Standard Disclaimer
- Other peoples' work
-
- Tracking the Best Expert, M. Herbster and M. K. Warmuth, Machine Learning, 1998
- Tracking the Best Expert, M. Herbster and M. K. Warmuth, Machine Learning, 1998
- Math would make a 4-year old giggle
- You will learn nothing new
Prediction Definitions
- Batch Linear Prediction `y[t] ~ \hat{y}[t] = \sum_{i = 1}^pc_k x[t - k]`
- Sequential Linear Prediction `y[t] ~ \hat{y}[t] = \sum_{i = 1}^pc_k^{t-1} x[t - k]`
- Square Loss `l_n\left(y, \hat{y}\right) = \sum_{i = 1}^n (y[i] - \hat{y}[i] )^2`
- Experts `\hat{y}_1, \ldots, \hat{y}_m`
Reweighing
- `\hat{y}_u[t] = \sum_{k = 1}^m u_k[t] \hat{y}_k[t]`
Expert's "Probability"
- An expert's probability is `P_k(y^n) = B \exp(-\frac{1}{2c}l_n(y, \hat{y}_k))`
- "Unlikely" sequences are high-loss ones
- Analogous conditional probability `P_k(y[n]|y^n) = \frac{}{} = B \exp(-\frac{1}{2c}l_n(y, \hat{y}_k))`


Please search either by company name (without company postfix ) , company registration number or requset ID and proceed.


Bubble sort is a simple sorting algorithm. The algorithm starts at the beginning of the data set. It compares the first two elements, and if the first is greater than the second, it swaps them. It continues doing this for each pair of adjacent elements to the end of the data set. It then starts again with the first two elements, repeating until no swaps have occurred on the last pass.[33] This algorithm's average time and worst-case performance is O(n2), so it is rarely used to sort large, unordered data sets. Bubble sort can be used to sort a small number of items (where its asymptotic inefficiency is not a high penalty). Bubble sort can also be used efficiently on a list of any length that is nearly sorted (that is, the elements are not significantly out of place). For example, if any number of elements are out of place by only one position (e.g.<PHONE_NUMBER> and<PHONE_NUMBER>), bubble sort's exchange will get them in order on the first pass, the second pass will find all elements in order, so the sort will take only 2n time.
Selection sort is an in-place comparison sort. It has O(n2) complexity,
making it inefficient on large lists, and generally performs worse than the
similar insertion sort. Selection sort is noted for its simplicity, and also
has performance advantages over more complicated algorithms in certain situations.
The algorithm finds the minimum value, swaps it with the value in the
first position, and repeats these steps for the remainder of the list.
It does no more than n swaps, and thus is useful where swapping is very expensive.
Insertion sort is a simple sorting algorithm that is relatively efficient for small lists and mostly sorted lists, and is often used as part of more sophisticated algorithms. It works by taking elements from the list one by one and inserting them in their correct position into a new sorted list similar to how we put money in our wallet. In arrays, the new list and the remaining elements can share the array's space, but insertion is expensive, requiring shifting all following elements over by one. Shellsort is a variant of insertion sort that is more efficient for larger lists.


Data Scientist | ML Engineer | Software Engineer Open Source
Machine Learning
Clustering
Open source
Python library that segments GPS Trajectory using T-DBSCAN algorithm.
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Data analysis Visualization A data analysis and visualization project that shows the resiliency of each country against the earthquakes it has faced so far. Read the full blog here.
Recommender systems Decision trees The challenge focuses on a real-world task of tweet engagement prediction in a dynamic environment. Read about the challenge here
Bootcamp Data Science A free bootcamp aimed to help students get started to the field of Data Science and Machine Learning.
ML Engineer working on Deep Learning & NLP, Software Engineering with .NET and everything Open Source.
Data Scientist @ OrangeNXT - Cognitive Search, Data Engineering & MLOps.
Mentor Volunteer @ Rotterdam.AI.
Software Engineer @ Wipro - .NET, C#.
Master's Degree (TU Delft) in Computer Science, Data Science & Technology track with focus on Natural Language Processing.


Read the Docs features¶
This will serve as a list of all of the features that Read the Docs currently has. Some features are important enough to have their own page in the docs, others will simply be listed here.
GitHub and Bitbucket Integration¶
We now support linking by default in the sidebar. It links to the page on your host, which should help people quickly update typos and send pull requests to contribute to project documentation.
More information can be found in the VCS Integration page.
Auto-updating¶
The Webhooks page talks about the different ways you can ping RTD to let us know your project has been updated. We have official support for GitHub, and anywhere else we have a generic post-commit hook that allows you to POST to a URL to get your documentation built.
Internationalization¶
Read the Docs itself is localized, and we support documentation translated into multiple languages. Read more on the Localization of Documentation and Internationalization pages.
Canonical URLs¶
Canonical URLs give your docs better search performance, by pointing all URLs to one version. This also helps to solve the issues around users landing on outdated versions of documentation.
More information can be found in the Canonical URLs page.
Versions¶
We can build multiple versions of your documentation. Look on the “Versions” page of your project’s admin (using the nav on the left) to find a list of available versions that we’ve inferred from the tags and branches in your source control system (according to the support matrix below). On the Versions page you can tell us which versions you’d like us to build docs for, whether each should be public, protected, or private, and what the default version should be (we’ll redirect there when someone hits your main project page, e.g., http://my-project.rtfd.org/).
Version Control Support Matrix¶
| Git | hg | bzr | svn | |
|---|---|---|---|---|
| Tags | Yes | Yes | Yes | No |
| Branches | Yes | Yes | Yes | No |
| Default | master | default | trunk |
PDF Generation¶
When you build your project on RTD, we automatically build a PDF of your project’s documentation. We also build them for every version that you upload, so we can host the PDFs of your latest documentation, as well as your latest stable releases as well.
Search¶
We provide full-text search across all of the pages of documentation hosted on our site. This uses the excellent Haystack project and Solr as the search backend. We hope to be integrating this into the site more fully in the future.
Alternate Domains¶
We provide support for CNAMEs, subdomains, and a shorturl for your project as well. This is outlined in the Alternate Domains section.


Before Change
total_loss += loss_val
if initial_state is not None:
initial_state = [x.npvalue() for x in initial_state]
iters += len(y)
if phase == "Valid":
self.valid_epochs += 1
output = self.valid_epochs
else:
output = 0
self.log.debug({"phase": phase, "time": time.time() - start})
metrics["avg_loss"] = total_loss / iters
metrics["perplexity"] = np.exp(total_loss / iters)
for reporting in reporting_fns:
reporting(metrics, output, phase)
return metrics
After Change
epochs = self.valid_epochs
metrics = self.calc_metrics(total_loss, total_toks)
self.report(
epochs, metrics, start,
phase, "EPOCH", reporting_fns
)
return metrics
@register_training_func("lm")


Explore the world and find resources to build own house and craft many tools to survive. Use your plant knowledge to grow veggies and herbs for eating and healing yourself.
Many hidden secrets are in the game. Need you to find out and unlock achievements for getting more score.
It is online game on website. You can play with other people in pairs or groups.
Pruek Pakamatawee
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Alderson leads Toledo over NAIA Northwestern Ohio 100-41
by EndPlayTOLEDO, Ohio (AP) - Dylan Alderson scored a career-high 30 points, six players reached double figures and Toledo easily beat the NAIA's Northwestern Ohio 100-41 on Saturday.
Spencer Littleson had 14 points for Toledo (4-2), Willie Jackson added 11 points with 12 rebounds and six assists. Luke Maranka had 10 points and three blocks for the Rockets. Luke Knapke and Marreon Jackson each scored 10.
Jackson came in as the Rockets' leading scorer, averaging 20 points per game, but was 2 of 10 from the field. He added 10 assists.
This was Toledo's second 100-point game this season.
Nathan Lessing had 16 points for the Racers. Justin Koepp added seven points and seven rebounds.
Toledo matches up against Oakland at home next Saturday.
___
For more AP college basketball coverage: https://apnews.com/Collegebasketball and http://twitter.com/AP_Top25
___
This was generated by Automated Insights, http://www.automatedinsights.com/ap, using data from STATS LLC, https://www.stats.com


|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |
| it.essepuntato.* |
|---|
| it.essepuntato.earmark.core.EARMARKDocument | ||
|---|---|---|
public static final java.lang.String |
SEPARATOR |
"/" |
| it.essepuntato.earmark.core.io.EARMARKWriter | ||
|---|---|---|
public static final java.lang.String |
N3 |
"N3" |
public static final java.lang.String |
NTRIPLE |
"N-TRIPLE" |
public static final java.lang.String |
RDFXML |
"RDF/XML" |
public static final java.lang.String |
RDFXMLABBREV |
"RDF/XML-ABBREV" |
public static final java.lang.String |
TTL |
"TTL" |
public static final java.lang.String |
TURTLE |
"TURTLE" |
| it.essepuntato.earmark.core.test.DocumentCreation | ||
|---|---|---|
public static final int |
nodeNumber |
6 |
public static final java.lang.String |
text1 |
"Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, " |
public static final java.lang.String |
text2 |
"and what is the use of a book," |
public static final java.lang.String |
text3 |
" thought Alice " |
public static final java.lang.String |
text4 |
"without pictures or conversation?" |
| it.essepuntato.earmark.core.test.DocumentStructure | ||
|---|---|---|
public static final int |
numberOfDiv |
2 |
public static final int |
numberOfElementsOfNs1 |
4 |
public static final int |
numberOfElementsOfNs2 |
3 |
public static final int |
numberOfMarkupItem |
7 |
public static final int |
numberOfP |
3 |
public static final int |
numberOfRange |
6 |
public static final int |
numberOfSpan |
2 |
public static final java.lang.String |
text |
"Of man\'s first disobedience, and the fruit\nOf that forbidden tree whose mortal taste\nBrough death into the World" |
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |


Your application
has been submitted.
Your reference:
N323/MCXXXXXXX
22 November 2018
What happens next
We’ll process your application within 12 weeks.
{% endblock %}
Your reference:
N323/MCXXXXXXX
22 November 2018
We’ll process your application within 12 weeks.
{% endblock %}


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- clear() - Method in class gui.PerfTestTableModel
-
- close() - Method in class controls.cslavemanagement.TCPConnection
-
Closes both command and object connections
- close() - Method in class controls.ctestplanmanagement.TCPProxy
-
- close() - Method in class shared.tcp.AbstractTCPClient
-
Close the last established connection
- close() - Method in class shared.tcp.AbstractTCPServer
-
Close the connection established to the last client and stop
listen.
- close() - Method in class shared.tcp.TCPObjectClient
-
Close the last established connection
- close() - Method in class shared.tcp.TCPObjectServer
-
Close the connection established to the last client and stop
listen.
- close() - Method in class shared.tcp.TCPStringClient
-
Close the last established connection
- close() - Method in class shared.tcp.TCPStringServer
-
Close the connection established to the last client and stop
listen.
- Comparator - Class in cslave
-
- Comparator() - Constructor for class cslave.Comparator
-
- computeExpectedValueAndVarianceInfos() - Method in class gui.panels.monitoring.variance.VarianceInfosProvider
-
Compute the expected value (as defined in probability theory) and
variance infos.
- connect(String, int, int) - Method in class controls.cslavemanagement.TCPConnection
-
Opens connections (command and object) to a slave
- Constants - Class in shared
-
- Constants() - Constructor for class shared.Constants
-
- contains(DefaultMutableTreeNode) - Method in class gui.TestPlanManager
-
- contains(JPanel) - Method in class gui.TestPlanManager
-
- controls.cslavemanagement.interfaces - package controls.cslavemanagement.interfaces
-
- controls.ctestplanmanagement.interfaces - package controls.ctestplanmanagement.interfaces
-
- controls.protocols.ldap.instructions - package controls.protocols.ldap.instructions
-
- count() - Method in interface controls.cslavemanagement.interfaces.ISlaveManagement
-
Returns the number of slaves applications the master is dealing with
- count() - Method in class controls.cslavemanagement.SlaveManagementFacade
-
- countReceivedInstructions() - Method in class controls.cslavemanagement.DataBuffer
-
- countReceivedInstructions() - Method in interface controls.cslavemanagement.interfaces.IDataBuffer
-
Returns the number of received instructions
- CREATE_TYPE - Static variable in class controls.protocols.ldap.LDAPProtocolParser
-
- createDefaultVarianceValues() - Method in class gui.panels.monitoring.variance.VarianceGraph
-
Create default variance values.
- createInstructionAdder(JFrame, String, boolean) - Method in class tools.GUIFactory
-
- createInstructionAdder(JFrame, String, boolean) - Method in class tools.widgets.ldap.LDAPGUIFactory
-
- createInstructionCreator(JFrame, String, boolean, IInstruction) - Method in class tools.GUIFactory
-
- createInstructionCreator(JFrame, String, boolean, IInstruction) - Method in class tools.widgets.ldap.LDAPGUIFactory
-
- createNewClientForBlankTest(ITestPlan, String, ITest) - Method in class controls.ctestplanmanagement.ProtocolParser
-
Create a client that going to run the test for blanck tests.
- createNewClientForBlankTest(ITestPlan, String, ITest) - Method in class controls.protocols.ldap.LDAPProtocolParser
-
- createNewInstruction(String) - Method in class controls.ctestplanmanagement.ProtocolParser
-
Returns a new instance of Instruction which is specified to the implemented
protocol
- createNewInstruction(String) - Method in class controls.protocols.ldap.LDAPProtocolParser
-
- createNewPlanTest() - Method in class controls.ctestplanmanagement.ProtocolParser
-
Returns a new instance of plan test which is specified to the implemented
protocol
- createNewPlanTest() - Method in class controls.protocols.ldap.LDAPProtocolParser
-
- createNewTCPProxy(String, int, ITest) - Method in class controls.ctestplanmanagement.ProtocolParser
-
Returns a new instance of TCPProxy which is specified to the implemented
protocol.
- createNewTCPProxy(String, int, ITest) - Method in class controls.protocols.ldap.LDAPProtocolParser
-
- createNewTestPlanPanel(ITestPlanManagement) - Method in class controls.ctestplanmanagement.ProtocolParser
-
GUI - Returns a new panel which draw specific data of the test plan
- createNewTestPlanPanel(ITestPlanManagement) - Method in class controls.protocols.ldap.LDAPProtocolParser
-
- createScalabilityTest(String) - Static method in class tools.Factory
-
Returns a new ScalabitityTest
- createSendableResponsePack(List<IResponse>) - Method in class cslave.Comparator
-
Create a light-weight response pack that can be send to
Master by network.
- createTCPConnection(String, int, int) - Static method in class tools.Factory
-
Returns a new TCPConnection (Socket) which communicates with Slaves applications
- createTCPConnection() - Static method in class tools.Factory
-
Returns a new TCPConnection (Socket) which communicates with Slaves applications
- createWorkloadTest(String) - Static method in class tools.Factory
-
Returns a new WorkloadTest
- cslave.interfaces - package cslave.interfaces
-


Feature Extraction and Transformation - RDD-based API
TF-IDF
Note We recommend using the DataFrame-based API, which is detailed in the ML user guide on TF-IDF.
Term frequency-inverse document frequency (TF-IDF) is a feature
vectorization method widely used in text mining to reflect the importance of a term to a document in the corpus.
Denote a term by $t$
, a document by $d$
, and the corpus by $D$
.
Term frequency $TF(t, d)$
is the number of times that term $t$
appears in document $d$
,
while document frequency $DF(t, D)$
is the number of documents that contains term $t$
.
If we only use term frequency to measure the importance, it is very easy to over-emphasize terms that
appear very often but carry little information about the document, e.g., “a”, “the”, and “of”.
If a term appears very often across the corpus, it means it doesn’t carry special information about
a particular document.
Inverse document frequency is a numerical measure of how much information a term provides:
\[
IDF(t, D) = \log \frac{|D| + 1}{DF(t, D) + 1},
\]
where $|D|$
is the total number of documents in the corpus.
Since logarithm is used, if a term appears in all documents, its IDF value becomes 0.
Note that a smoothing term is applied to avoid dividing by zero for terms outside the corpus.
The TF-IDF measure is simply the product of TF and IDF:
\[
TFIDF(t, d, D) = TF(t, d) \cdot IDF(t, D).
\]
There are several variants on the definition of term frequency and document frequency.
In spark.mllib
, we separate TF and IDF to make them flexible.
Our implementation of term frequency utilizes the
hashing trick.
A raw feature is mapped into an index (term) by applying a hash function.
Then term frequencies are calculated based on the mapped indices.
This approach avoids the need to compute a global term-to-index map,
which can be expensive for a large corpus, but it suffers from potential hash collisions,
where different raw features may become the same term after hashing.
To reduce the chance of collision, we can increase the target feature dimension, i.e.,
the number of buckets of the hash table.
The default feature dimension is $2^{20} = 1,048,576$
.
Note: spark.mllib
doesn’t provide tools for text segmentation.
We refer users to the Stanford NLP Group and
scalanlp/chalk.
TF and IDF are implemented in HashingTF
and IDF.
HashingTF
takes an RDD[Iterable[_]]
as the input.
Each record could be an iterable of strings or other types.
Refer to the HashingTF
Scala docs for details on the API.
import org.apache.spark.mllib.feature.{HashingTF, IDF}
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.rdd.RDD
// Load documents (one per line).
val documents: RDD[Seq[String]] = sc.textFile("data/mllib/kmeans_data.txt")
.map(_.split(" ").toSeq)
val hashingTF = new HashingTF()
val tf: RDD[Vector] = hashingTF.transform(documents)
// While applying HashingTF only needs a single pass to the data, applying IDF needs two passes:
// First to compute the IDF vector and second to scale the term frequencies by IDF.
tf.cache()
val idf = new IDF().fit(tf)
val tfidf: RDD[Vector] = idf.transform(tf)
// spark.mllib IDF implementation provides an option for ignoring terms which occur in less than
// a minimum number of documents. In such cases, the IDF for these terms is set to 0.
// This feature can be used by passing the minDocFreq value to the IDF constructor.
val idfIgnore = new IDF(minDocFreq = 2).fit(tf)
val tfidfIgnore: RDD[Vector] = idfIgnore.transform(tf)
TF and IDF are implemented in HashingTF
and IDF.
HashingTF
takes an RDD of list as the input.
Each record could be an iterable of strings or other types.
Refer to the HashingTF
Python docs for details on the API.
from pyspark.mllib.feature import HashingTF, IDF
# Load documents (one per line).
documents = sc.textFile("data/mllib/kmeans_data.txt").map(lambda line: line.split(" "))
hashingTF = HashingTF()
tf = hashingTF.transform(documents)
# While applying HashingTF only needs a single pass to the data, applying IDF needs two passes:
# First to compute the IDF vector and second to scale the term frequencies by IDF.
tf.cache()
idf = IDF().fit(tf)
tfidf = idf.transform(tf)
# spark.mllib's IDF implementation provides an option for ignoring terms
# which occur in less than a minimum number of documents.
# In such cases, the IDF for these terms is set to 0.
# This feature can be used by passing the minDocFreq value to the IDF constructor.
idfIgnore = IDF(minDocFreq=2).fit(tf)
tfidfIgnore = idfIgnore.transform(tf)
Word2Vec
Word2Vec computes distributed vector representation of words. The main advantage of the distributed representations is that similar words are close in the vector space, which makes generalization to novel patterns easier and model estimation more robust. Distributed vector representation is showed to be useful in many natural language processing applications such as named entity recognition, disambiguation, parsing, tagging and machine translation.
Model
In our implementation of Word2Vec, we used skip-gram model. The training objective of skip-gram is
to learn word vector representations that are good at predicting its context in the same sentence.
Mathematically, given a sequence of training words $w_1, w_2, \dots, w_T$
, the objective of the
skip-gram model is to maximize the average log-likelihood
\[
\frac{1}{T} \sum_{t = 1}^{T}\sum_{j=-k}^{j=k} \log p(w_{t+j} | w_t)
\]
where $k$ is the size of the training window.
In the skip-gram model, every word $w$ is associated with two vectors $u_w$ and $v_w$ which are
vector representations of $w$ as word and context respectively. The probability of correctly
predicting word $w_i$ given word $w_j$ is determined by the softmax model, which is
\[
p(w_i | w_j ) = \frac{\exp(u_{w_i}^{\top}v_{w_j})}{\sum_{l=1}^{V} \exp(u_l^{\top}v_{w_j})}
\]
where $V$ is the vocabulary size.
The skip-gram model with softmax is expensive because the cost of computing $\log p(w_i | w_j)$ is proportional to $V$, which can be easily in order of millions. To speed up training of Word2Vec, we used hierarchical softmax, which reduced the complexity of computing of $\log p(w_i | w_j)$ to $O(\log(V))$
Example
The example below demonstrates how to load a text file, parse it as an RDD of Seq[String]
,
construct a Word2Vec
instance and then fit a Word2VecModel
with the input data. Finally,
we display the top 40 synonyms of the specified word. To run the example, first download
the text8 data and extract it to your preferred directory.
Here we assume the extracted file is text8
and in same directory as you run the spark shell.
Refer to the Word2Vec
Scala docs for details on the API.
import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel}
val input = sc.textFile("data/mllib/sample_lda_data.txt").map(line => line.split(" ").toSeq)
val word2vec = new Word2Vec()
val model = word2vec.fit(input)
val synonyms = model.findSynonyms("1", 5)
for((synonym, cosineSimilarity) <- synonyms) {
println(s"$synonym $cosineSimilarity")
}
// Save and load model
model.save(sc, "myModelPath")
val sameModel = Word2VecModel.load(sc, "myModelPath")
Refer to the Word2Vec
Python docs for more details on the API.
from pyspark.mllib.feature import Word2Vec
inp = sc.textFile("data/mllib/sample_lda_data.txt").map(lambda row: row.split(" "))
word2vec = Word2Vec()
model = word2vec.fit(inp)
synonyms = model.findSynonyms('1', 5)
for word, cosine_distance in synonyms:
print("{}: {}".format(word, cosine_distance))
StandardScaler
Standardizes features by scaling to unit variance and/or removing the mean using column summary statistics on the samples in the training set. This is a very common pre-processing step.
For example, RBF kernel of Support Vector Machines or the L1 and L2 regularized linear models typically work better when all features have unit variance and/or zero mean.
Standardization can improve the convergence rate during the optimization process, and also prevents against features with very large variances exerting an overly large influence during model training.
Model Fitting
StandardScaler
has the
following parameters in the constructor:
withMean
False by default. Centers the data with mean before scaling. It will build a dense output, so take care when applying to sparse input.withStd
True by default. Scales the data to unit standard deviation.
We provide a fit
method in
StandardScaler
which can take an input of RDD[Vector]
, learn the summary statistics, and then
return a model which can transform the input dataset into unit standard deviation and/or zero mean features
depending how we configure the StandardScaler
.
This model implements VectorTransformer
which can apply the standardization on a Vector
to produce a transformed Vector
or on
an RDD[Vector]
to produce a transformed RDD[Vector]
.
Note that if the variance of a feature is zero, it will return default 0.0
value in the Vector
for that feature.
Example
The example below demonstrates how to load a dataset in libsvm format, and standardize the features so that the new features have unit standard deviation and/or zero mean.
Refer to the StandardScaler
Scala docs for details on the API.
import org.apache.spark.mllib.feature.{StandardScaler, StandardScalerModel}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.util.MLUtils
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
val scaler1 = new StandardScaler().fit(data.map(x => x.features))
val scaler2 = new StandardScaler(withMean = true, withStd = true).fit(data.map(x => x.features))
// scaler3 is an identical model to scaler2, and will produce identical transformations
val scaler3 = new StandardScalerModel(scaler2.std, scaler2.mean)
// data1 will be unit variance.
val data1 = data.map(x => (x.label, scaler1.transform(x.features)))
// data2 will be unit variance and zero mean.
val data2 = data.map(x => (x.label, scaler2.transform(Vectors.dense(x.features.toArray))))
Refer to the StandardScaler
Python docs for more details on the API.
from pyspark.mllib.feature import StandardScaler, StandardScalerModel
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.util import MLUtils
data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
label = data.map(lambda x: x.label)
features = data.map(lambda x: x.features)
scaler1 = StandardScaler().fit(features)
scaler2 = StandardScaler(withMean=True, withStd=True).fit(features)
# data1 will be unit variance.
data1 = label.zip(scaler1.transform(features))
# data2 will be unit variance and zero mean.
data2 = label.zip(scaler2.transform(features.map(lambda x: Vectors.dense(x.toArray()))))
Normalizer
Normalizer scales individual samples to have unit $L^p$ norm. This is a common operation for text classification or clustering. For example, the dot product of two $L^2$ normalized TF-IDF vectors is the cosine similarity of the vectors.
Normalizer
has the following
parameter in the constructor:
p
Normalization in $L^p$ space, $p = 2$ by default.
Normalizer
implements VectorTransformer
which can apply the normalization on a Vector
to produce a transformed Vector
or on
an RDD[Vector]
to produce a transformed RDD[Vector]
.
Note that if the norm of the input is zero, it will return the input vector.
Example
The example below demonstrates how to load a dataset in libsvm format, and normalizes the features with $L^2$ norm, and $L^\infty$ norm.
Refer to the Normalizer
Scala docs for details on the API.
import org.apache.spark.mllib.feature.Normalizer
import org.apache.spark.mllib.util.MLUtils
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
val normalizer1 = new Normalizer()
val normalizer2 = new Normalizer(p = Double.PositiveInfinity)
// Each sample in data1 will be normalized using $L^2$ norm.
val data1 = data.map(x => (x.label, normalizer1.transform(x.features)))
// Each sample in data2 will be normalized using $L^\infty$ norm.
val data2 = data.map(x => (x.label, normalizer2.transform(x.features)))
Refer to the Normalizer
Python docs for more details on the API.
from pyspark.mllib.feature import Normalizer
from pyspark.mllib.util import MLUtils
data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
labels = data.map(lambda x: x.label)
features = data.map(lambda x: x.features)
normalizer1 = Normalizer()
normalizer2 = Normalizer(p=float("inf"))
# Each sample in data1 will be normalized using $L^2$ norm.
data1 = labels.zip(normalizer1.transform(features))
# Each sample in data2 will be normalized using $L^\infty$ norm.
data2 = labels.zip(normalizer2.transform(features))
ChiSqSelector
Feature selection tries to identify relevant features for use in model construction. It reduces the size of the feature space, which can improve both speed and statistical learning behavior.
ChiSqSelector
implements
Chi-Squared feature selection. It operates on labeled data with categorical features. ChiSqSelector uses the
Chi-Squared test of independence to decide which
features to choose. It supports five selection methods: numTopFeatures
, percentile
, fpr
, fdr
, fwe
:
numTopFeatures
chooses a fixed number of top features according to a chi-squared test. This is akin to yielding the features with the most predictive power.percentile
is similar tonumTopFeatures
but chooses a fraction of all features instead of a fixed number.fpr
chooses all features whose p-values are below a threshold, thus controlling the false positive rate of selection.fdr
uses the Benjamini-Hochberg procedure to choose all features whose false discovery rate is below a threshold.fwe
chooses all features whose p-values are below a threshold. The threshold is scaled by 1/numFeatures, thus controlling the family-wise error rate of selection.
By default, the selection method is numTopFeatures
, with the default number of top features set to 50.
The user can choose a selection method using setSelectorType
.
The number of features to select can be tuned using a held-out validation set.
Model Fitting
The fit
method takes
an input of RDD[LabeledPoint]
with categorical features, learns the summary statistics, and then
returns a ChiSqSelectorModel
which can transform an input dataset into the reduced feature space.
The ChiSqSelectorModel
can be applied either to a Vector
to produce a reduced Vector
, or to
an RDD[Vector]
to produce a reduced RDD[Vector]
.
Note that the user can also construct a ChiSqSelectorModel
by hand by providing an array of selected feature indices (which must be sorted in ascending order).
Example
The following example shows the basic use of ChiSqSelector. The data set used has a feature matrix consisting of greyscale values that vary from 0 to 255 for each feature.
Refer to the ChiSqSelector
Scala docs
for details on the API.
import org.apache.spark.mllib.feature.ChiSqSelector
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
// Load some data in libsvm format
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
// Discretize data in 16 equal bins since ChiSqSelector requires categorical features
// Even though features are doubles, the ChiSqSelector treats each unique value as a category
val discretizedData = data.map { lp =>
LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x => (x / 16).floor }))
}
// Create ChiSqSelector that will select top 50 of 692 features
val selector = new ChiSqSelector(50)
// Create ChiSqSelector model (selecting features)
val transformer = selector.fit(discretizedData)
// Filter the top 50 features from each feature vector
val filteredData = discretizedData.map { lp =>
LabeledPoint(lp.label, transformer.transform(lp.features))
}
Refer to the ChiSqSelector
Java docs
for details on the API.
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.mllib.feature.ChiSqSelector;
import org.apache.spark.mllib.feature.ChiSqSelectorModel;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
JavaRDD<LabeledPoint> points = MLUtils.loadLibSVMFile(jsc.sc(),
"data/mllib/sample_libsvm_data.txt").toJavaRDD().cache();
// Discretize data in 16 equal bins since ChiSqSelector requires categorical features
// Although features are doubles, the ChiSqSelector treats each unique value as a category
JavaRDD<LabeledPoint> discretizedData = points.map(lp -> {
double[] discretizedFeatures = new double[lp.features().size()];
for (int i = 0; i < lp.features().size(); ++i) {
discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16);
}
return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures));
});
// Create ChiSqSelector that will select top 50 of 692 features
ChiSqSelector selector = new ChiSqSelector(50);
// Create ChiSqSelector model (selecting features)
ChiSqSelectorModel transformer = selector.fit(discretizedData.rdd());
// Filter the top 50 features from each feature vector
JavaRDD<LabeledPoint> filteredData = discretizedData.map(lp ->
new LabeledPoint(lp.label(), transformer.transform(lp.features())));
ElementwiseProduct
ElementwiseProduct
multiplies each input vector by a provided “weight” vector, using element-wise
multiplication. In other words, it scales each column of the dataset by a scalar multiplier. This
represents the Hadamard product
between the input vector, v
and transforming vector, scalingVec
, to yield a result vector.
Denoting the scalingVec
as “w
”, this transformation may be written as:
\[ \begin{pmatrix}
v_1 \\
\vdots \\
v_N
\end{pmatrix} \circ \begin{pmatrix}
w_1 \\
\vdots \\
w_N
\end{pmatrix}
= \begin{pmatrix}
v_1 w_1 \\
\vdots \\
v_N w_N
\end{pmatrix}
\]
ElementwiseProduct
has the following parameter in the constructor:
scalingVec
: the transforming vector.
ElementwiseProduct
implements VectorTransformer
which can apply the weighting on a Vector
to produce a transformed Vector
or on an RDD[Vector]
to produce a transformed RDD[Vector]
.
Example
This example below demonstrates how to transform vectors using a transforming vector value.
Refer to the ElementwiseProduct
Scala docs for details on the API.
import org.apache.spark.mllib.feature.ElementwiseProduct
import org.apache.spark.mllib.linalg.Vectors
// Create some vector data; also works for sparse vectors
val data = sc.parallelize(Array(Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0)))
val transformingVector = Vectors.dense(0.0, 1.0, 2.0)
val transformer = new ElementwiseProduct(transformingVector)
// Batch transform and per-row transform give the same results:
val transformedData = transformer.transform(data)
val transformedData2 = data.map(x => transformer.transform(x))
Refer to the ElementwiseProduct
Java docs for details on the API.
import java.util.Arrays;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.mllib.feature.ElementwiseProduct;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;
// Create some vector data; also works for sparse vectors
JavaRDD<Vector> data = jsc.parallelize(Arrays.asList(
Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0)));
Vector transformingVector = Vectors.dense(0.0, 1.0, 2.0);
ElementwiseProduct transformer = new ElementwiseProduct(transformingVector);
// Batch transform and per-row transform give the same results:
JavaRDD<Vector> transformedData = transformer.transform(data);
JavaRDD<Vector> transformedData2 = data.map(transformer::transform);
Refer to the ElementwiseProduct
Python docs for more details on the API.
from pyspark.mllib.feature import ElementwiseProduct
from pyspark.mllib.linalg import Vectors
data = sc.textFile("data/mllib/kmeans_data.txt")
parsedData = data.map(lambda x: [float(t) for t in x.split(" ")])
# Create weight vector.
transformingVector = Vectors.dense([0.0, 1.0, 2.0])
transformer = ElementwiseProduct(transformingVector)
# Batch transform
transformedData = transformer.transform(parsedData)
# Single-row transform
transformedData2 = transformer.transform(parsedData.first())
PCA
A feature transformer that projects vectors to a low-dimensional space using PCA. Details you can read at dimensionality reduction.
Example
The following code demonstrates how to compute principal components on a Vector
and use them to project the vectors into a low-dimensional space while keeping associated labels
for calculation a Linear Regression
Refer to the PCA
Scala docs for details on the API.
import org.apache.spark.mllib.feature.PCA
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.{LabeledPoint, LinearRegressionWithSGD}
val data = sc.textFile("data/mllib/ridge-data/lpsa.data").map { line =>
val parts = line.split(',')
LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
}.cache()
val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
val training = splits(0).cache()
val test = splits(1)
val pca = new PCA(training.first().features.size / 2).fit(data.map(_.features))
val training_pca = training.map(p => p.copy(features = pca.transform(p.features)))
val test_pca = test.map(p => p.copy(features = pca.transform(p.features)))
val numIterations = 100
val model = LinearRegressionWithSGD.train(training, numIterations)
val model_pca = LinearRegressionWithSGD.train(training_pca, numIterations)
val valuesAndPreds = test.map { point =>
val score = model.predict(point.features)
(score, point.label)
}
val valuesAndPreds_pca = test_pca.map { point =>
val score = model_pca.predict(point.features)
(score, point.label)
}
val MSE = valuesAndPreds.map { case (v, p) => math.pow((v - p), 2) }.mean()
val MSE_pca = valuesAndPreds_pca.map { case (v, p) => math.pow((v - p), 2) }.mean()
println(s"Mean Squared Error = $MSE")
println(s"PCA Mean Squared Error = $MSE_pca")


Your display name is {{ user.display_name }}
You are logged in as: {{ user.username }}
You are {{ user.age }} years old
Your homepage is {{ user.homepage }}
Your auth user model is {{ my_user }}


Add New Team
Team Name
×
Name:
Team Name
Owner:
Owner Name
Score:
1000
New Team
×
Team Name
Team Owner
Starting Score
Edit Team
×
Team Name
Team Owner
Score


The output shows that app2 is waiting on a row lock. We can also see the lock owner's transaction handle (call this Z). lock and causing our hang. The final step is to determine the corresponding application handle for transaction handle Z. Issue db2pd transactions command for transaction handle Z.
db2pd -db sample -transactions Z


Wikipedia: Smart contracts are computer protocols that facilitate, verify, or enforce the negotiation or performance of a contract, or that make a contractual clause unnecessary.
Smart contracts first introduced by [ Nick Szabo in 1994 ] then had a long gestation period of inactivity and disinterest, because there was no platform that could enforce them, until the advent of blockchain technology in 2009.
Now, smart contracts are entering their prime, especially since Ethereum has popularized them further by making their programming a basic tenet of their blockchain's power.
Actually the term smart contract has no clear and settled definition.


| Severity |
Type |
Description |
Added Note |
Date and Time |
| Critical Alert |
Environmental |
If the wea....
If the weather is poor. |
Is too long...
Is too long for vehicles to arrive on time. |
11/08/18 13:02 |
| Low |
Timing |
The truck....
The truck from Jackson, MS will be arriving at 9:45 A.M. |
And can b....
And can be an additional 1 hour late. |
11/07/18 08:08 |
| Critical Alert |
Staff |
Staff plan....
Staff plan exceeds employee count. |
Over to di....
Over to discover notes. |
11/07/18 02:55 |
| Critical Alert |
Volume |
Display th....
Display the volume alert. |
The rest o....
The rest of the alert is shown here. |
11/06/18 04:19 |
| Moderate |
Environmental |
Tornado w....
Tornado warning in effect until 9:00 AM. |
The warni....
The warning covers the following counties. |
Date and Time |
| Critical Alert |
Staff |
Display the....
Display the volume alert. |
The rest o....
The rest of the alert is shown here. |
Date and Time |
| Moderate |
Timing |
Display the....
Display the volume alert. |
The rest o....
The rest of the alert is shown here. |
Date and Time |
| Low |
Volume |
Display the....
Display the volume alert. |
The rest o....
The rest of the alert is shown here. |
Date and Time |
| Critical Alert |
Volums |
Display the....
Display the volume alert. |
The rest o....
The rest of the alert is shown here. |
Date and Time |


{% extends "layouts/base.html" %} {% block title %} Presentation {% endblock %} {% block stylesheets %}{% endblock stylesheets %} {% block content %} PixelLITE Copier Jinja coded by Sm0ke Presentation © Themesberg - coded by AppSeed.
Copier Jinja coded by Sm0ke
Note: Python3 should be properly installed and accessible via the terminal.


Ajax, a twisted scientist, experiments on Wade Wilson, a mercenary, to cure him of cancer and give him healing powers. However, the experiment leaves Wade disfigured and he decides to exact revenge.


---
layout: post
title: links for 2007-02-11
date: 2007-02-11 00:00:00
---
-
"I find two-week iterations to be ideal. The overhead for planning and testing is much more manageable when amortized across two weeks. The first week of a two-week iteration may feel different from the second week, but the difference is not as dramatic a
-
"You have to strike some kind of balance between a one-page vision statement and an obsessively detailed five volume masterwork of a functional spec. "
-
"Generally, unit tests are supposed to be small, they test a method or the interaction of a couple of methods. When you pull the database, sockets, or file system access into your unit tests, they aren’t really about those methods any more; they are abo


A few more XML.xml layout details
in an XML layout file if you had a XML line like

That would mean the DIV element whose id is main-disp would display the output generated by plugins/flexyFileContent.php
Among other things that means that content would be encased in a block element named main-disp, probably styled
by CSS codes in css/robo.css
An XML layout might contain codes like this:

/

The XML above places the output from plugins/dynamicNavigation.php inside the boundaries
of the block element DIV whose id is main-disp. But that also means
the content from dynamicNavigation is contained within a block element DIV (inside main-disp)
whose id is toc.
Block elements contained within block elements (toc inside main-disp) carry some layout baggage.
Perhaps what you really want is for the contents generated by both flexyFileContent.php AND dynamicNavigation.php
to be displayed inside main-disp, without a block element boundary around the content generated by dynamicNavigation.php
To do that the src attribute can be used to specify one or MORE plugins as content generators, as a comma-delimited value.
For Instance as:

In the exapmple above the output generated by dynamicNavigation might be a series of rectangles containing
thumbnail images and a bit of text, packaged as a hyperlink. If all those small recatangles were told by CSS to
float: left; that could be used a convenient gallery layout, where rectangular thumbnails surround a main display image.


Mario Balotelli paid £100k in Man City's fine funds after failing to follow rules
Micah Richards was in charge of fines at Man City during their first Premier League success and Mario Balotelli was a regular visitor to the fine shop - when he turned up
by Alex SmithMario Balotelli had a bad boy reputation at Manchester City - and it turns out he shelled out around £100,000-a-season in fines during his time at the Etihad.
Balotelli's former teammate Micah Richards has revealed all about the fine system at City during their famous maiden Premier League title winning season.
Defender Richards was in charge of collecting the fines, which come from being late, messy or generally anti-social, from inside the dressing room - with the funds being handed to charity.
"Everything else seems to be within sync. Balotelli was always late," Richards said on the BBC.
"We used to give all of our money to charities and by Christmas time we'd have £100,000 or £150,000 in there all from Balotelli being late.
"He would be in the building. Say we had a meeting upstairs - he'd be downstairs just relaxing. He'd come to the meeting but he was just a bit dozy."
Richards was at City from 2001 and 2015 before moving to Aston Villa. He has not had a club since the end of last season.
The 13-cap England ace went on to joke about club legend Vincent Kompany and his diligent ways in keeping track of the fines and sticking to the letter of the law.
"Kompany, he's busy isn't he?," Richards continued. "He just looks like that person anyway. He always kept a track of the fines and because I was sort of vice-captain, one of the leaders in the dressing room, he'd ask me to take charge of it.
"But I wasn't going to say to the manager 'Oh player was late today, oh player didn't put his bib in the washing machine'.
"So it was only if Vincent Kompany caught you. I'm not a snitch.
"You could appeal it as well. You had to go with Vincent Kompany into the manager's office. He had a fine sheet logged of all the fines. He's like a schoolteacher, headmaster. One of those guys that when you see him, you turn the other way.
"I tried to be friends with everyone. I told (Balotelli), "I'm going to let you off today". With him it was every other week so I could only do so much.
"Kompany is looking over my shoulder making sure I was doing my job right. He undermined me a little bit, Kompany did'.


This is the WFE Thing. It Things your WFEs.
Enter the region name in the textbox. Press submit, or enter, or something. Click on dates. Win.
Power Usage Tip #1: The colour little unicode icon at the far right of each green box is determined by hashing the wfe. Essentially, if the wfe changes, that colour will change. If two icons are the same colour, the wfes are (probably) the same, so you don't need to check both of them to find out if they are a raider WFE.
Power Usage Tip #2: The WFEs are obtained by daily dumps. If you have a favourite dump that you want to see featured, speak to Johz on IRC, or Tlik on the nationstates site. Or Tlik on IRC. Or maybe even Johz on the nationstates site, when I find the password to my main again.
Power Usage Tip #3: All the information you see on the main page is obtained via the api server. If you understand JSON, and you want access to all the WFEs in an even more ugly manner than the original Johz-WFE thing, then hit /api/[region-name]
. The root API url will give you a json object with a list of all the region names that are available (try not to use this, it's a pretty large file), while the region-name endpoint will give a json object containg the "pretty" name of the region you're trying to look at, and a list of all the wfes. The date key is a UNIX timestamp, that is, seconds since 1st January 1970. Appending ?pretty
to any api url will pretty-print the json, but try not to use that in your app, just for debugging.
This site is dedicated to Solm, because he made the original version, which was awesome. Additionally, I'd like to take a moment to apologise to the UDL for giving them the shitty older version, and to thank them for putting up with me. Finally, I should disclaim that any damage you take from using this site is entirely your own fault for using it. Do not use this in a nuclear power plant. Do not wash this site in a washing machine.
List of things that I need to do:
Technologies you can find in this app


Share on WhatsApp: Options
Open Sharing Link in: Popup Window (Default) New Tab
Extension by Amir Ammari. Visit our home page for more information and privacy policy.


Put a X in the filename column if not rule should be created. //is a comment
Missing: http://www.foodstandards.gov.au/ http://data.daff.gov.au/mapserv/plant/ http://data.daff.gov.au/nifs/ http://www.defence.gov.au/adc/ http://www.defence.gov.au/adfa/ http://www.defence.gov.au/adfwc/ Watch Out for: (?:www\.)www. agencysearch.australia.gov.au
| URL | Name | Filename | Host | From | To |
|---|


fun getSupportedAnnotationTypes(): Set<String>
Here you must to put all annotation that you want process to generate an additional functionality.


- A - Static variable in class gmc_hdfs.replicationsimulator.GeographicalZone
-
A geographical area of the world, called zone here.
- addBlock(BlockInfo) - Method in class gmc_hdfs.replicationsimulator.DataitemInfo
-
- addBlock(Block) - Method in class hdfs.replicationsimulator.Datanode
-
- addBlock(BlockInfo) - Method in class hdfs.replicationsimulator.DatanodeInfo
-
- addBlockToBeReplicated(BlockInfo, DatanodeInfo) - Method in class hdfs.replicationsimulator.DatanodeInfo
-
- addBlockToDatanodeMap(BlockInfo, DatanodeInfo) - Method in class gmc_hdfs.replicationsimulator.DataitemInfo
-
This method add information of mapping between blocks and nodes to map
- addCommand(Event) - Method in class hdfs.replicationsimulator.Datanode
-
Adds a command to the FIFO queue of the NameNode
- addDataitem(DataitemInfo) - Method in class gmc_hdfs.replicationsimulator.DataitemsMap
-
Add information about data to map
- addDatanodeInfo(DatanodeInfo) - Method in class hdfs.replicationsimulator.DatanodesMap
-
- addFailDatanode(DatanodeInfo, int) - Static method in class hdfs.replicationsimulator.Simulator
-
- addReadCount(int) - Method in class gmc_hdfs.replicationsimulator.DataitemInfo
-
It adds to the number of requests from the users of one area with the
data center by one.
- addReadInfo(User, int) - Method in class gmc_hdfs.replicationsimulator.DataitemsMap
-
For each read request for data from the users for the store data,
it increases the number of requests.
- addreadRequestTraffic(int) - Method in class gmc_hdfs.replicationsimulator.DataitemInfo
-
* It adds to the number of traffic of one area with the
data center by one.
- addReplicaRead(int) - Method in class gmc_hdfs.replicationsimulator.DataitemInfo
-
Add number of read request by one.
- addToDatacentersList(Datacenter) - Method in class gmc_hdfs.replicationsimulator.DataitemInfo
-
It adds the information of data center d to the list of data centers
which have a version of this data.
- addToDatanodeQueue(Event) - Method in class hdfs.replicationsimulator.AllDatanode
-
- addToDatanodeQueue(Event) - Method in class hdfs.replicationsimulator.Namenode
-
- addToHandledList(long) - Method in class hdfs.replicationsimulator.AllDatanode
-
- addToMasterCopyMap(int, String) - Method in class gmc_hdfs.replicationsimulator.DataitemInfo
-
Add information of master or copy data for a data center
- addToNamenodeQueue(Event) - Method in class hdfs.replicationsimulator.AllDatanode
-
- addToNeighbourDatacenterList(Datacenter) - Method in class gmc_hdfs.replicationsimulator.Datacenter
-
- addToReplicaReadMap(int, int) - Method in class gmc_hdfs.replicationsimulator.DataitemInfo
-
It adds the num to the arbitrary number of requests.
- addToRequestQueue(Request) - Method in class hdfs.replicationsimulator.Namenode
-
- addToRequestsList(Request) - Static method in class hdfs.replicationsimulator.Simulator
-
- addToRequestsQueue(Request) - Method in class gmc_hdfs.replicationsimulator.User
-
This method is for sending the request.
- addToRequestTraffic(int, Map<Integer, Integer>) - Method in class gmc_hdfs.replicationsimulator.DataitemInfo
-
Add data center's traffic's information to map
- addTrace(SimTrace) - Static method in class hdfs.replicationsimulator.Simulator
-
- addUser(User) - Method in class gmc_hdfs.replicationsimulator.AllUsers
-
add a new user to AllUser
- AllDatanode - Class in hdfs.replicationsimulator
-
- AllDatanode(int) - Constructor for class hdfs.replicationsimulator.AllDatanode
-
- AllUsers - Class in gmc_hdfs.replicationsimulator
-
This class integrates the set of users associated with a data center
directly to the data center.
- AllUsers(int) - Constructor for class gmc_hdfs.replicationsimulator.AllUsers
-
create an object of AllUsers for data center with datacenterId


Hello all... I only recently acquired myself an emonPi, mostly to tinker, but also to find out more about my energy use habits and hopefully do something about it! But I think I need some assistance or pointers with a few problems I've been having...
Initially I set everything up to log to the local emonCMS on the emonPi itself, but the web interface performance was ridiculously slow... understandable, for two reasons... (a) the hardware is obviously not comparable to a desktop pc/workstation let alone a dedicated server, (b) I have the device installed in a location where wifi connectivity seems sketchy at best - so additional latency and packet loss just makes everything seem even slower than it is.
To tackle (a), I installed emonCMS on a local Ubuntu Linux server I have, and so far so good - I directed my feeds to it and it seemed to be doing its thing.
I recently left for a few days, only to return to find the emonPi had lost all connectivity to my wireless network so nothing was being logged to the Linux server. Though strangely the display was showing 50% wifi signal and my DHCP assigned IP address was there too.
In the end I had to restart the emonPi to get it to reconnect, and it seems to be logging again. But it hasn't logged any of the data that was previously "missed".
Sorry for the lengthy intro - now for a few questions!
- Is the data loss expected? I'd imagine there's a way to have it store data locally and upload once it has restored connectivity to the network?
- Also, what if there's a power loss in the meantime? I do have the local emonCMS configured and my inputs are logging to local feeds - alongside the forwarding I set up to send data to my other server... so the data is there I imagine, it just hasn't "synchronised"?
- How would I force a "sync" of the data, if indeed it has been safely stored locally?
- I think the wireless connectivity issue can be resolved with a better wifi dongle, perhaps one with an actual external aerial! I'm using the recommended Edimax EW7811UN - any other models you can vouch for that may have better performance?
Looking forward to your comments/suggestions!
-Alex
Re: EmonPi beginner question - Use with intermittent network connectivity
Hi Alex
1. No, not expected. But there are instances where it is an avoidable or too minimal to warrant the solution. On the emonPi there are 2 parts, emonHub and emonCMS, emonHub will "buffer" data in RAM when it cannot deliver the data until it can but it is not written to disc as is is designed to run on a read-only OS. It will catch up when network is restored if the volatile data is still available ie no restarting, rebooting or power cuts.
2. Yes and No, the data is there in emonCMS to fall back on but unfortunately there is currently no "sync" mechanism, so this is not really a "solution" but an absolute last resort if you really must have that data.
3. You can't really "sync" at all as they are independent, I'm not saying it cannot be done or that it won't be available in the future but right now you would need to "figure it out" for each feed based on the circumstance.
4. Not personally, the edimax seems ok most of the time, I do have a couple og external aerial types and I found they didn't offer any signal improvement or at least if they did it wasn't significant enough to justify the ugly protrusion and/or additional drivers.
Are you sure the network connection was permanently lost in this instance? how did you establish there was no network connection or was it assumed from lack of data?
Generally speaking if there is a network outage, emonhub will buffer OR if there is a powercut the Pi is read-only OS, emoncms normally only buffers 1minuite of data and it is usual to assume there are no power readings during a power outage, at least for the usual short interruptions this doesn't present a big issue.
Getting a power cut after an extended network outage would be unlucky but if a frequent occurrence then perhaps a UPS and/or wired network connection could be on the cards?
The crux of the issue here will be the wifi connectivity, if you can ensure the network reconnects after being down you should be ok, can you test that scenario? the emonPi should be running a check and reconnect script as far as I recall.
Paul
Re: EmonPi beginner question - Use with intermittent network connectivity
Thanks Paul, that clarifies a few things for me.
On further analysis I think the problem may be my wifi configuration in wpa_supplicant.conf. There were a number of free/open networks listed in there which must have been added during initial setup of the emonPi. This is just an educated guess, but I think what happened was that when it lost signal to my access point, it switched to one of the open networks, but that meant it was obviously no longer on my local network. It never came back as the open networks must have a stronger signal than mine.
I've cleaned up the file now and will see how that works - I'll simulate a few wifi outages by powering off my access point a few hours at a time and see what happens.
This is probably a point for a new post, but one other thing I'm trying is to limit the wlan interface rate to reduce packet loss and get a better signal to noise ratio. But it doesn't seem to take any of my settings:
List available and current rates:
Set rate:
List again (note there is no change in the current rate):
I have tried all listed options, as well as other unlisted ones. Not sure if this is a limitation of the Edimax dongle/drivers or something else is going on...
You're right that a wired network connection is probably the way to go longer term, however there are a few other things that need to get done in the house first before I can do this! So for the short-to-medium term wifi is unfortunately the only way I can connect...
-Alex
Re: EmonPi beginner question - Use with intermittent network connectivity
I tried this and got the same result, a quick web search seems to suggest the bitrate "limit" must be set prior to connection for each connection, so ideally you need to add a setting to one of the wifi conf files but I found no guides or examples of that.
Paul
Re: EmonPi beginner question - Use with intermittent network connectivity
I have experienced a similar problem as the OP today.
The emonPi lost Wi-Fi at approximately 07:00 this morning (I didn't realise until 18:30 when I returned home). I pulled the edimax usb dongle and reinserted it, then Wi-Fi was restored. The local emonCMS has kept on tracking grid & solar, but web emonCMS now has a gap in data for the complete day. As I didn't power cycle the emonPi, I fully expected it to upload to web emonCMS when Wi-Fi was restored.
How can I get the day's data to web emonCMS?
Cheers!
Re: EmonPi beginner question - Use with intermittent network connectivity
I don't have any idea how to get data from the local emonCMS to emoncms.org.
However, I have a few Raspberry Pi's in remote locations and have fought to keep them connected using various wifi dongles. The following solution has kept them online for about 6 months straight:
1. Install the script "WiFi_Check" from here:
https://rpi.tnet.com/project/scripts/wifi_check
2. Install a modification to the script to ping the router to check for wifi connectivity. If no router response, then restart wifi services. Be sure to enter your router's address in the appropriate place.
Step 1: Open the config file in an editor:
sudo nano /usr/local/bin/WiFi_Check
Step 2: Replace everything below # Settings with the code below:
Re: EmonPi beginner question - Use with intermittent network connectivity
There is already some sort of wi-fi reconnection script running on the emonPi, I haven't looked closely at it but I think it's cron based.
@Aceshigh84 - I took a closer look at the emon-pi variant of emonhub's code as there definitely shouldn't be any gaps in the data, or so I thought. It turns out there are significant differences in the code in the way emoncms data is passed by http. It appears this version does not resubmit data if undelivered, it seems to just send and forget every 30secs (hardcoded interval not from conf) it deletes the data regardless of whether is is sent or confirmed.
Apparently this version does not provide any buffering beyond 30secs.
@alex_london - I hope you have email notifications on as the info I have given you above is incorrect for this version. I had no idea at the time and cannot explain why the controlled FIFO buffers have been removed, sorry.
Paul
Re: EmonPi beginner question - Use with intermittent network connectivity
@JD - thanks. I'll try out that script when I get a chance, probably this weekend. Have also considered going down the powerline route and get a small brick to give it an ethernet connection. But if this script works, it'll save some money for sure!
@paul - yes I've been keeping an eye on this thread... the missing data is no big deal, it's not that lives depend on it! Was more of a curiosity for me, trying to understand how it's supposed to work.
Re: EmonPi beginner question - Use with intermittent network connectivity
I'm getting weak wifi issues too - am going to try moving the wifi dongle away from the emonPi using a USB extension cable.
But the unit does really need to buffer in some way to avoid discontinuity caused by a drop of Internet connection.
Chris.


CIS (4|5)22 Entry Questionnaire
{% with messages = get_flashed_messages() %} {% if messages %} {% for message in messages %}
{{ message }}
{% endfor %} {% endif %} {% endwith %}
Your name:
Do you have a preference for teammates and/or a project in mind?
What do you consider your strongest skills for a team project?
What do you consider your weakest point, where you will need most help from others?
In what roles do you think you can best contribute to a team project?
Potential team meeting times
Please
uncheck
times when you have class or other conflicts.
{% for dayspec in g.choicegroups %} {% set dayname = dayspec["day"] %}
{{ dayname }}
{% for period in dayspec["periods"] %} {% set element_id = dayname ~ period %}
{{period}}
{% endfor %}
{% endfor %}
Proficiencies: Please rate your experience with ...
{% for proficiency in g.proficiencies %}
{{proficiency}}
{% for item in g.prof_items[proficiency] %}
{{ item }}
None
Some
Extensive
{% if loop.index is divisibleby 4 %}
{% endif %} {% endfor %}
{% endfor %}
Anything else I should consider in assigning you a team?


Parameters
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Bitmap
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Exception
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Form
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Write


User Story
"As a
Content Studio
user I can embed an image or link to a file in a code my own template."


|
Hello #{Object.members.name}, Thank you for attending this month’s Family Fun day at #{restaurant}! We’d love to get your feedback on the event. |
| How would you rate your overall experience at the Apartmentlist Family Friday pizza party? |
| Thank You For Your Feedback! |


Women Empowering Nations (WEN) is a 501 (c) 3 non-profit organization dedicated to the advancement of girls and women through self esteem development,
Alyssa Milano filmography including movies from released projects, in theatres, in production and upcoming films
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Alyssa Milano, Actress:. Charmed. Alyssa Milano comes from an Italian-American family; her mother Lin Milano is a fashion designer and father Thomas Milano
Alyssa Jayne Milano (born December 19, 1972) is an American actress, producer and former singer, best known for portraying Samantha Micelli on the ABC Alyssa Milano (born December 19, 1972 in Brooklyn, New York) portrayed Phoebe Halliwell Jun 3, 2013 Make no mistake: If you give Mistresses a shot, which you should, it WILL become your new summer guilty pleasure Jun 3, 2013 ABC's sudsy new series Mistresses is not Desperate Housewives -- but it does have some things in common with the dearly departed soap. Watch Pictures of Alyssa Milano's Feet on wikiFeet - a free collaborative site featuring Celebrity-Feet pictures. It is Probably the largest celebrity feet database 4 days ago.


| fortify.lm {ggplot2} | R Documentation |
If you have missing values in your model data, you may need to refit
the model with na.action = na.exclude
.
## S3 method for class 'lm' fortify(model, data = model$model, ...)
model |
linear model |
data |
data set, defaults to data used to fit model |
... |
not used by this method |
The original data with extra columns:
.hat |
Diagonal of the hat matrix |
.sigma |
Estimate of residual standard deviation when corresponding observation is dropped from model |
.cooksd |
Cooks distance, |
.fitted |
Fitted values of model |
.resid |
Residuals |
.stdresid |
Standardised residuals |
mod <- lm(mpg ~ wt, data = mtcars) head(fortify(mod)) head(fortify(mod, mtcars)) plot(mod, which = 1) ggplot(mod, aes(.fitted, .resid)) + geom_point() + geom_hline(yintercept = 0) + geom_smooth(se = FALSE) ggplot(mod, aes(.fitted, .stdresid)) + geom_point() + geom_hline(yintercept = 0) + geom_smooth(se = FALSE) ggplot(fortify(mod, mtcars), aes(.fitted, .stdresid)) + geom_point(aes(colour = factor(cyl))) ggplot(fortify(mod, mtcars), aes(mpg, .stdresid)) + geom_point(aes(colour = factor(cyl))) plot(mod, which = 2) ggplot(mod) + stat_qq(aes(sample = .stdresid)) + geom_abline() plot(mod, which = 3) ggplot(mod, aes(.fitted, sqrt(abs(.stdresid)))) + geom_point() + geom_smooth(se = FALSE) plot(mod, which = 4) ggplot(mod, aes(seq_along(.cooksd), .cooksd)) + geom_col() plot(mod, which = 5) ggplot(mod, aes(.hat, .stdresid)) + geom_vline(size = 2, colour = "white", xintercept = 0) + geom_hline(size = 2, colour = "white", yintercept = 0) + geom_point() + geom_smooth(se = FALSE) ggplot(mod, aes(.hat, .stdresid)) + geom_point(aes(size = .cooksd)) + geom_smooth(se = FALSE, size = 0.5) plot(mod, which = 6) ggplot(mod, aes(.hat, .cooksd)) + geom_vline(xintercept = 0, colour = NA) + geom_abline(slope = seq(0, 3, by = 0.5), colour = "white") + geom_smooth(se = FALSE) + geom_point() ggplot(mod, aes(.hat, .cooksd)) + geom_point(aes(size = .cooksd / .hat)) + scale_size_area()


|
iDreamSky Game Engine
v1.0.0
|
公開方法(Public Methods) | |
| Variant () | |
| Constructor of Data. | |
| Variant (const Variant &other) | |
| Copy constructor of Data. | |
| Variant (Variant &&other) | |
| Copy constructor of Data. | |
| ~Variant () | |
| Destructor of Data. | |
| Variant & | operator= (const Variant &other) |
| Overloads of operator=. | |
| Variant & | operator= (Variant &&other) |
| Overloads of operator=. | |
| unsigned char * | getBytes () const |
| Gets internal bytes of Data. 更多... | |
| int | getSize () const |
| Gets the size of the bytes. 更多... | |
| void | copy (const unsigned char *bytes, const int size) |
| Copies the buffer pointer and its size. 更多... | |
| void | fastSet (unsigned char *bytes, const int size) |
| Fast set the buffer pointer and its size. 更多... | |
| void | clear () |
| Clears data, free buffer and reset data size. | |
| bool | isNull () const |
| Check whether the data is null. 更多... | |
| void | read (InputStream &is) |
| void | write (OutputStream &os) const |
靜態公開屬性 | |
| static const Variant | Null |
| This parameter is defined for convenient reference if a null Data object is needed. | |
類別朋友(Friends) | |
| class | KeyValuePairs |
| void Variant::copy | ( | const unsigned char * | bytes, |
| const int | size | ||
| ) |
Copies the buffer pointer and its size.
| void Variant::fastSet | ( | unsigned char * | bytes, |
| const int | size | ||
| ) |
Fast set the buffer pointer and its size.
Please use it carefully.
| bytes | The buffer pointer, note that it have to be allocated by 'malloc' or 'calloc', since in the destructor of Data, the buffer will be deleted by 'free'. |
| unsigned char * Variant::getBytes | ( | ) | const |
Gets internal bytes of Data.
It will return the pointer directly used in Data, so don't delete it.
| int Variant::getSize | ( | ) | const |
Gets the size of the bytes.
| bool Variant::isNull | ( | ) | const |
Check whether the data is null.


| count {dplyr} | R Documentation |
count()
lets you quickly count the unique values of one or more variables:
df %>% count(a, b)
is roughly equivalent to
df %>% group_by(a, b) %>% summarise(n = n())
.
count()
is paired with tally()
, a lower-level helper that is equivalent
to df %>% summarise(n = n())
. Supply wt
to perform weighted counts,
switching the summary from n = n()
to n = sum(wt)
.
add_count()
and add_tally()
are equivalents to count()
and tally()
but use mutate()
instead of summarise()
so that they add a new column
with group-wise counts.
count(x, ..., wt = NULL, sort = FALSE, name = NULL) tally(x, wt = NULL, sort = FALSE, name = NULL) add_count(x, ..., wt = NULL, sort = FALSE, name = NULL, .drop = deprecated()) add_tally(x, wt = NULL, sort = FALSE, name = NULL)
x |
A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). |
... |
< |
wt |
<
|
sort |
If |
name |
The name of the new column in the output. If omitted, it will default to |
.drop |
For |
An object of the same type as .data
. count()
and add_count()
group transiently, so the output has the same groups as the input.
# count() is a convenient way to get a sense of the distribution of # values in a dataset starwars %>% count(species) starwars %>% count(species, sort = TRUE) starwars %>% count(sex, gender, sort = TRUE) starwars %>% count(birth_decade = round(birth_year, -1)) # use the `wt` argument to perform a weighted count. This is useful # when the data has already been aggregated once df <- tribble( ~name, ~gender, ~runs, "Max", "male", 10, "Sandra", "female", 1, "Susan", "female", 4 ) # counts rows: df %>% count(gender) # counts runs: df %>% count(gender, wt = runs) # tally() is a lower-level function that assumes you've done the grouping starwars %>% tally() starwars %>% group_by(species) %>% tally() # both count() and tally() have add_ variants that work like # mutate() instead of summarise df %>% add_count(gender, wt = runs) df %>% add_tally(wt = runs)


{% extends 'base.html'%} {% block styles%} {{ super() }} {% endblock %} {% block content %}
In life, you only have 60 seconds to impress someone and say something meaningful.!!!
If we try, we may still fail. But if we do not try, we cannot possibly succeed.”
"I believe that telling our stories, first to ourselves and then to one another and the world, is a revolutionary act.….”
You can choose not to be interested in politics, but you can’t choose to be unaffected by it..


Studentereksamen
Anvendt materiale (til brug for Copydan):
Cormac McCarthy. All the Pretty Horses. The United States: Alfred A. Knopf, 1992.
J. Sheridan Le Fanu. “An account of some strange disturbances in Aungier Street”, 1853, WikiSource website, viewed October 2022. (www.wikisource.org)
BBC News. “The Queen's advice on wearing a crown – BBC News”. YouTube website, January 12, 2018, viewed October 2022. (www.youtube.com)
Jennifer Kerslake. “Tin Man”. Harper's Bazaar website. July 28, 2021, viewed October 2022. (www.harpersbazaar.com)
Jacinda Ardern. “Harvard Commencement Speech – Democracy, disinformation and kindness”, The official website of the New Zealand Government, May 27, 2022, viewed October 2022. (www.beehive.govt.nz)
Simon Sinek. “We Need Difficult Conversations, Simon Sinek“, YouTube website, February 18, 2022, viewed October 2022. (www.youtube.com)
Write an analytical essay (900-1200 words) in which you analyse Jacinda Ardern’s “Harvard Commencement
Speech – Democracy, disinformation and kindness” and discuss how to ensure democracy and dialogue in
society.
Part of your essay must focus on how the speaker uses her own background.
In addition your essay must include an analysis of the style of writing in lines
135-148.
Your essay must include references to:
Jacinda Ardern’s speech
Video: “We Need Difficult Conversations, Simon Sinek“
Materials
Jacinda Ardern. “Harvard Commencement Speech – Democracy, disinformation and kindness”, a speech from The official website of the New Zealand Government, 2022.
“We Need Difficult Conversations, Simon Sinek“, a video from YouTube website, 2022.

Part of your essay must focus on the use of contrasts in the story.
In addition your essay must include an analysis of the style of writing in lines
115-130.
Your essay must include references to the short
story.
Material
Jennifer Kerslake. "Tin
Man", 2021
Answer 4A or 4B
Videoen i denne opgave er lavet af BBC.
Find og skriv fra videoens lydside i alt otte
forskellige adjektiver. Find og skriv:
“The Queen's advice on wearing a crown – BBC News”, 2018
I tekststykket er en række ord og led understreget. Besvar opgave a., b. og c. nedenfor. Brug relevant grammatisk terminologi. Husk kildehenvisning, hvis du citerer fra en grammatikbog. Skriv dine svar på dansk.
a. Seks verballed er understreget. Skriv for hvert eksempel:
b. Ni adjektiver og adverbier er understreget.
c. Tre eksempler på foreløbigt subjekt er understreget.
Teksten er et uddrag af en novelle.
|
The house, to begin with, was a very old one. It had been, I believe, newly fronted about fifty years before; but with this exception, it had nothing modern about it. […] There had been very little done in the way of modernising details; and, perhaps, it was better so; for there was something queer and by-gone in the very walls and ceilings – in the shape of doors and windows – in the odd diagonal site of the chimney-pieces – in the beams and ponderous cornices – not to mention the singular solidity of all the woodwork, from the banisters to the window-frames, which hopelessly defied disguise, and would have emphatically proclaimed their antiquity through any conceivable amount of modern finery and varnish. […] The back bedroom, with its two queerly-placed melancholy windows, staring vacantly at the foot of the bed, and with the shadowy recess to be found in most old houses in Dublin, like a large ghostly closet, which, from congeniality of temperament, had amalgamated with the bedchamber, and dissolved the partition. At night-time, this “alcove” – as our “maid” was wont to call it – had, in my eyes, a specially sinister and suggestive character. Tom’s distant and solitary candle glimmered vainly into its darkness. There it was always over-looking him – always itself impenetrable. But this was only part of the effect. The whole room was, I can’t tell how, repulsive to me. There was, I suppose, in its proportions and features, a latent discord – a certain mysterious and indescribable relation, which jarred indistinctly upon some secret sense of the fitting and the safe, and raised indefinable suspicions and apprehensions of the imagination. On the whole, as I began by saying, nothing could have induced me to pass a night alone in it. J. Sheridan Le Fanu, ”An account of some strange disturbances in Aungier Street”, 1853 |
Besvar både opgave a. og b. nedenfor. Brug relevant grammatisk terminologi. Husk kildehenvisning, hvis du citerer fra en grammatikbog. Skriv dine svar på dansk.
Eksemplerne skal findes i tekststykket nedenfor.
a. Seks verballed er understreget. Skriv for hvert eksempel:
b. Ni adjektiver og adverbier er understreget.
c. Tre eksempler på foreløbigt subjekt er understreget.
|
He met his father in the lobby of the St Angelus and they walked up Chadbourne Street to the Eagle Cafe and sat in a booth at the back. Some at the tables stopped talking when they came in. A few men nodded to his father and one said his name. The waitress called everybody doll. She took their order and flirted with him. His father took out his cigarettes and lit one and put the pack on the table and put his Third Infantry Zippo lighter on top of it and leaned back and smoked and looked at him. He told him his uncle Ed Alison had gone up to the preacher after the funeral was said and shook his hand, the two of them standing there holding onto their hats and leaning thirty degrees into the wind like vaudeville comics while the canvas flapped and raged about them and the funeral attendants raced over the grounds after the lawnchairs, and he’d leaned into the preacher’s face and screamed at him that it was a good thing they’d held the burial that morning because the way it was making up this thing could turn off into a real blow before the day was out. Cormac McCarthy, from: All the Pretty Horses, 1992 |
Generel skabelon for henvisninger til teksterHenvisningerDu henviser til tekster ved enten 1) at angive linjenummeret eller minuttalsangivelsen i en fodnote eller ved 2) at sætte linjenummeret eller minuttalsangivelsen i parentes inde i din tekst: Ved én linje: l. 34 Ved flere linjer: ll. 35-37 Ved minuttalsangivelse: 01:23-02:12 I fodnoter angives: Opslag i bog “Hvad er et ‘utælleligt ord’?”. Helle Brieghel og Dorthe Chalotte Hansen. Engelsk Grundgrammatik. Questions and Answers. Systime 2010. p. 47. Opslag i elektronisk opslagsværk “Utællelige substantiver”. Thomas Boesen og Sarah Barding. Engram. Minlaering 2019. (www.minlaering.dk) Artikel fra bog eller antologi “Post-Modernism”. Gitte Vest Barkholt and Jørgen Døssing Jepsen. A Short History of Literature in English. A Handbook. Systime 2010. pp. 79-93. |
|
Du skal besvare følgende opgaver:
Tekster til assignment 4: 4A – Fiction: ”Tin Man”, a short story by Jennifer Kerslake, 2021. 4B – Non-fiction: ”Harvard Commencement Speech – Democracy, disinformation and kindness” a speech by Jacinda Ardern, 2022. ”We Need Difficult Conversations: Simon Sinek”, a video from YouTube website, 2022. |
Vejledning og skabelon til at løse opgaven
Den samlede
eksaminationstid for assignment 1-4 er fem timer. Besvarelsen bedømmes som en helhed ud fra de faglige
mål for niveauet.
Det anbefales, at du skriver din
besvarelse i skabelonen, som ligger under Template i menuen til venstre. Besvarelsen afleveres i ét
dokument med opgaverne i rækkefølgen 1-4.
Eksamenssæt
Der gives typisk 5 timer til at besvare opgavesættet


Paper wallets are a fast and convenient way to store Eurocoin offline. You can even spend or gift Eurocoins without ever connecting to the internet by treating the wallets as cash. Paper wallets are a physical way to store and spend Eurocoin, like a 20€ bill is a physical way to store and spend 20€. Like a 20€ bill, paper wallets can be lost or stolen. Store your paper wallet in a safe location where only you can access.
Paper wallets allow you to store Eurocoins completely offline. For best security, we recommend downloading the source code and generating the seed from an offline laptop.
Anyone can create a paper wallet! You only need a printer, scissors, and tape.
Spend by scanning the paper wallet’s private key with a Eurocoin enabled mobile wallet, or like physical cash (great as a gift).
Use the client-side wallet generator to create a unique private key and Eurocoin address
Print out the wallet on a secure printer.
Cut along the border, then fold the private key section over. Use tape to seal the private key section marked (SEAL HERE).
Send any amount of Eurocoin to the public address.
Keep the wallet in a safe place, such as a safe deposit box or a room/space in your home you have exclusive access to, or you can even give it as a gift. When ready, simply reveal the private key and sweep (scan) it with our digital wallet to claim your funds.
The wallet seed generator is based on the extensively tested Bitaddress.org, which lets you generate a paper wallet in a fast and secure way. The generator uses your browser to generate the wallet, and not our servers.
Our paper wallet is generated using a browser-side random address generator. This means the wallet is created in your browser. No data is transmitted to our servers or across the web. Generally, it is acceptably safe to use as is. However, if you are using the wallet for larger amounts or just want to ensure extra security please take these tips into consideration.
Running the generator off a live site presents bad actors the potential to intercept your paper wallet's private key. It's not likely, but to mitigate the risk we highly recommend downloading the page files from our Github first. Then turn off your internet connection and run the page locally. Just open the downloaded zip and open the index.html file in your browser. Download Generator
Make sure you are using a trusted computer free from any viruses or malware. Do not create your wallet in view of any prying eyes or cameras.
Again make sure you are using a trusted printer. Do not wirelessly connect with the printer. Use a direct connection instead, and make sure no data is saved in your print queue or on the printer afterwards.
Do not save your paper wallet as an image or digital file. Paper cannot be hacked, your computer can be.
Once your wallet is made and you have sent funds to it, it is essentially like cash. Whoever physically holds it can access the funds. It can be lost, stolen, or destroyed and there is no backup. Take whatever precautions necessary to ensure it stays safe until needed.
Create and use your paper wallet with ease (no need for double-sided printing).
Spend from your paper wallet by sweeping (scanning) with any Eurocoin enabled mobile wallet.
Choose from a variety of skins and denominations
It takes less than 5 minutes from generating your keys to the finished paper wallet.


Alexandria man Reuben flown back to Scotland after Bali horror crash
The Vale born IT worker has been supported by people from throughout the area, and his work colleagues.
by Lorraine WeirAn Alexandria man left fighting for his life after a horror motorbike crash in Bali was due to return to Scotland on Monday.
Reuben Armstrong boarded a flight on Sunday, headed for Glasgow, where he will continue treatment following the incident on the holiday island he calls his second home.
The Lennox Herald reported how the Vale-born IT worker had to undergo emergency surgery after suffering a severe head injury.
But he is now back on home soil with mum Diane, from Tullichewan.
His best friend Lincoln Savage, who set up a Justgiving page to help pay for treatment in Bali and his repatriation, shared the good news with us and said: “It just makes you realise how lucky we are in this country to have the healthcare we do.
“At this point in the UK, we would just be trying to give him our support.
“The added stress of having to raise extra funds has been really difficult.
“We have had to raise a lot of funds to get him back by a repatriation specialist which is a ridiculous amount of money.”
But Lincoln, who has known Reuben for the last decade, paid tribute to the company the pair work for, Equals, who have contributed to the funds.
He said: “We both work in London and the company we’ve worked for has never seen anything like it.
 If we didn’t get that money from our CEO, we don’t know what would have happened.
“Since then they have been helping me with the loan to pay the medical bills while raising money in the background.
“The whole company also donated a day’s wages which was about £13,000. They cancelled the Christmas party and all sorts.
“It’s a testament to who he is. He’s really well liked and everyone at work has been saying he’s such a lovely guy. He’s always chatting to people.”
The 27-year-old was out with his friend on his motorbike when he crashed, hitting his head on a wall.
He suffered frontal lobe damage in the smash and was initially in a coma before undergoing brain surgery.
Because of the effect of the trauma on Reuben’s brain, he has become very agitated and is expected to resume treatment at Gartnavel Hospital following the 7500-mile flight.
Lincoln, 29, said: “Surprisingly, he’s doing well.
“Physically he’s in good condition but he’s still struggling to come to terms with what’s happened.
“The shock for him of waking up in the hospital in Bali, with his mum and gran has been really confusing.”
Reuben, who attended Levenvale Primary moved to Grimsby before setting up home in London 10 years ago. He split his time between there and Bali where he had just put a deposit down on a flat before the crash.
He wasn’t wearing a helmet, which is fairly standard in Bali, when he came off his motorbike.
His insurance company refused to pay out as a result and so family and friends had to launch a mass fundraising campaign to pay medical bills there and to get him home.
This included a fundraiser earlier this month in Grimsby and various events at his workplace.
A Justgiving page set up by Lincoln has also managed to raise over £38,000.
You can contribute by searching for “Lincoln Savage” on Justgiving.


Coders
to specify how data is encoded to and decoded from byte strings.See: Description
| Interface | Description |
|---|---|
| Coder<T> |
A
Coder<T> defines how to encode and decode values of type T into
byte streams. |
| CoderFactory |
A
CoderFactory creates coders and decomposes values. |
| CoderProvider |
A
CoderProvider may create a Coder for
any concrete class. |
| DelegateCoder.CodingFunction<InputT,OutputT> |
A
CodingFunction<InputT, OutputT> is a serializable
function from InputT to OutputT that may throw any Exception . |
| Class | Description |
|---|---|
| AtomicCoder<T> | |
| AvroCoder<T> |
A
Coder using Avro binary format. |
| BigEndianIntegerCoder |
A
BigEndianIntegerCoder encodes Integers in 4 bytes, big-endian. |
| BigEndianLongCoder |
A
BigEndianLongCoder encodes Long s in 8 bytes, big-endian. |
| ByteArrayCoder |
A
Coder for byte[] . |
| ByteCoder | |
| ByteStringCoder |
A
Coder for ByteString objects based on their encoded Protocol Buffer form. |
| Coder.Context |
The context in which encoding or decoding is being done.
|
| Coder.NonDeterministicException |
Exception thrown by
Coder.verifyDeterministic() if the encoding is
not deterministic, including details of why the encoding is not deterministic. |
| CoderFactories |
Static utility methods for creating and working with
Coder s. |
| CoderProviders |
Static utility methods for working with
CoderProviders . |
| CoderRegistry |
A
CoderRegistry allows registering the default Coder to use for a Java class,
and looking up and instantiating the default Coder for a Java type. |
| CollectionCoder<T> | |
| CustomCoder<T> |
An abstract base class for writing a
Coder class that encodes itself via Java
serialization. |
| DelegateCoder<T,IntermediateT> |
A
DelegateCoder<T, IntermediateT> wraps a Coder for IntermediateT and
encodes/decodes values of type T by converting
to/from IntermediateT and then encoding/decoding using the underlying
Coder<IntermediateT> . |
| DeterministicStandardCoder<T> |
A
DeterministicStandardCoder is a StandardCoder that is
deterministic, in the sense that for objects considered equal
according to Object.equals(Object) , the encoded bytes are
also equal. |
| DoubleCoder |
A
DoubleCoder encodes Double values in 8 bytes using Java serialization. |
| DurationCoder | |
| EntityCoder |
A
Coder for DatastoreV1.Entity objects based on their encoded Protocol Buffer form. |
| InstantCoder | |
| IterableCoder<T> | |
| IterableLikeCoder<T,IterableT extends Iterable<T>> |
An abstract base class with functionality for assembling a
Coder for a class that implements Iterable . |
| JAXBCoder<T> |
A coder for JAXB annotated objects.
|
| KvCoder<K,V> |
A
KvCoder encodes KV s. |
| ListCoder<T> | |
| MapCoder<K,V> | |
| NullableCoder<T> |
A
NullableCoder encodes nullable values of type T using a nested
Coder<T> that does not tolerate null values. |
| SerializableCoder<T extends Serializable> |
A
Coder for Java classes that implement Serializable . |
| SetCoder<T> | |
| StandardCoder<T> |
An abstract base class to implement a
Coder that defines equality, hashing, and printing
via the class name and recursively using StandardCoder.getComponents() . |
| StringDelegateCoder<T> |
A
Coder that wraps a Coder<String>
and encodes/decodes values via string representations. |
| StringUtf8Coder | |
| StructuralByteArray |
A wrapper around a byte[] that uses structural, value-based
equality rather than byte[]'s normal object identity.
|
| TableRowJsonCoder |
A
Coder that encodes BigQuery TableRow objects in their native JSON format. |
| TextualIntegerCoder |
A
Coder that encodes Integer Integers as the ASCII bytes of
their textual, decimal, representation. |
| VarIntCoder | |
| VarLongCoder | |
| VoidCoder |
| Enum | Description |
|---|---|
| CannotProvideCoderException.ReasonCode |
Indicates the reason that
Coder inference failed. |
| Exception | Description |
|---|---|
| CannotProvideCoderException |
The exception thrown when a
CoderProvider cannot
provide a Coder that has been requested. |
| CoderException |
An
Exception thrown if there is a problem encoding or decoding a value. |
| Annotation Type | Description |
|---|---|
| DefaultCoder |
The
DefaultCoder annotation
specifies a default Coder class to handle encoding and decoding
instances of the annotated class. |
Coders
to specify how data is encoded to and decoded from byte strings.
During execution of a Pipeline, elements in a
PCollection
may need to be encoded into byte strings.
This happens both at the beginning and end of a pipeline when data is read from and written to
persistent storage and also during execution of a pipeline when elements are communicated between
machines.
Exactly when PCollection elements are encoded during execution depends on which
PipelineRunner
is being used and how that runner
chooses to execute the pipeline. As such, Dataflow requires that all PCollections have an
appropriate Coder in case it becomes necessary. In many cases, the Coder can be inferred from
the available Java type
information and the Pipeline's CoderRegistry
. It
can be specified per PCollection via
PCollection.setCoder(Coder)
or per type using the
DefaultCoder
annotation.
This package provides a number of coders for common types like Integer
,
String
, and List
, as well as coders like
AvroCoder
that can be used to encode many custom
types.
Copyright © 2016 The Apache Software Foundation. All rights reserved.


Because you have shared in our lives and supported us, we request the pleasure of your company at our marriage
We are tieing knot at 10.00am on 23 March 2017 at
K.R.Mahal,Tirunelveli
navnee.com | 2017


The department field has dit tracking on. Change the value will trigger the dirty event output to the console and show a visual indicator. Reseting the value back to its orginal will trigger the 'pristine' event.
Save - It will reset the original value, basically it use the current value in the element and set it to the original value and make the current state pristine.


Show me the talk
help_outline
操作說明
info_outline
運作原理
Stage
Alfa
Bravo
Charlie
① New test
② Play sound
③ Recognize voice
Rate
1
Pitch
1
Phrase...
Right or wrong?
...diagnostic messages


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| Name:* |
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| Description: |
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| Author: |
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| Privacy: |
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| Support Files: |
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| open all | close all |
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amber$ cd main/CSS/\[CSS\]\ id\ selector\ in\ external\ css\ file\ doesn\'t\ work\!
[CSS] id selector in external css file doesn't work! [Question mark]
I try to select the element and then apply the style on it in the
external CSS file. But when I selected it by its id, it doesn't work.
While I selected it by its class, it works.
When I wrote this blog's demo, I found that if you just want to select
elements and change their style, both selecting by id and class works.
If you want to apply the style to the elements when your mouse hover
themselves, both selecting by id and class works. But if you want to
apply the style to the elements when your mouse hover other elements,
both of them doesn't work at all this time. If it doesn't work in the
external CSS file, it doesn't work in the internal CSS file as well.
There is no error. I still don't know why.
Anyway here is the demo:
I searched online and found some possibilities for why it doesn't work.
-
If you add space before :hover, that element will not change the
style, but all of its descendant elements will change the style.
-
If you want to change the style of other elements when you hover one
element, the elements you can change is descendant elements and only
siblings following it. If you don't add + before sibling element, it
still doesn't work.
-
For the tag a, a:hover isn't placed after a:link and a:visited.
And I think I found the reason that I didn't work for the above demo.
When hovering the second element, the last element's style doesn't
change, because only the sibling element directly following it will
change. when the third element hovered, the last one changes, because it
is directly following it.
Here is some example: