ML - Linear Methods
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In MLlib, we implement popular linear methods such as logistic
regression and linear least squares with $L_1$ or $L_2$ regularization.
Refer to the linear methods in mllib for
details. In spark.ml
, we also include Pipelines API for Elastic
net, a hybrid
of $L_1$ and $L_2$ regularization proposed in Zou et al, Regularization
and variable selection via the elastic
net.
Mathematically, it is defined as a convex combination of the $L_1$ and
the $L_2$ regularization terms:
\[
\alpha \left( \lambda \|\wv\|_1 \right) + (1-\alpha) \left( \frac{\lambda}{2}\|\wv\|_2^2 \right) , \alpha \in [0, 1], \lambda \geq 0
\]
By setting $\alpha$ properly, elastic net contains both $L_1$ and $L_2$
regularization as special cases. For example, if a linear
regression model is
trained with the elastic net parameter $\alpha$ set to $1$, it is
equivalent to a
Lasso model.
On the other hand, if $\alpha$ is set to $0$, the trained model reduces
to a ridge
regression model.
We implement Pipelines API for both linear regression and logistic
regression with elastic net regularization.
Example: Logistic Regression
The following example shows how to train a logistic regression model
with elastic net regularization. elasticNetParam
corresponds to
$\alpha$ and regParam
corresponds to $\lambda$.
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.mllib.util.MLUtils
// Load training data
val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8)
// Fit the model
val lrModel = lr.fit(training)
// Print the weights and intercept for logistic regression
println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.classification.LogisticRegressionModel;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext;
public class LogisticRegressionWithElasticNetExample {
public static void main(String[] args) {
SparkConf conf = new SparkConf()
.setAppName("Logistic Regression with Elastic Net Example");
SparkContext sc = new SparkContext(conf);
SQLContext sql = new SQLContext(sc);
String path = "data/mllib/sample_libsvm_data.txt";
// Load training data
DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
LogisticRegression lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8);
// Fit the model
LogisticRegressionModel lrModel = lr.fit(training);
// Print the weights and intercept for logistic regression
System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept());
}
}
from pyspark.ml.classification import LogisticRegression
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.util import MLUtils
# Load training data
training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
# Fit the model
lrModel = lr.fit(training)
# Print the weights and intercept for logistic regression
print("Weights: " + str(lrModel.weights))
print("Intercept: " + str(lrModel.intercept))
The spark.ml
implementation of logistic regression also supports
extracting a summary of the model over the training set. Note that the
predictions and metrics which are stored as Dataframe
in
BinaryLogisticRegressionSummary
are annotated @transient
and hence
only available on the driver.
LogisticRegressionTrainingSummary
provides a summary for a
LogisticRegressionModel
.
Currently, only binary classification is supported and the
summary must be explicitly cast to
BinaryLogisticRegressionTrainingSummary
.
This will likely change when multiclass classification is supported.
Continuing the earlier example:
import org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
// Extract the summary from the returned LogisticRegressionModel instance trained in the earlier example
val trainingSummary = lrModel.summary
// Obtain the objective per iteration.
val objectiveHistory = trainingSummary.objectiveHistory
objectiveHistory.foreach(loss => println(loss))
// Obtain the metrics useful to judge performance on test data.
// We cast the summary to a BinaryLogisticRegressionSummary since the problem is a
// binary classification problem.
val binarySummary = trainingSummary.asInstanceOf[BinaryLogisticRegressionSummary]
// Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
val roc = binarySummary.roc
roc.show()
println(binarySummary.areaUnderROC)
// Set the model threshold to maximize F-Measure
val fMeasure = binarySummary.fMeasureByThreshold
val maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0)
val bestThreshold = fMeasure.where($"F-Measure" === maxFMeasure).
select("threshold").head().getDouble(0)
lrModel.setThreshold(bestThreshold)
LogisticRegressionTrainingSummary
provides a summary for a
LogisticRegressionModel
.
Currently, only binary classification is supported and the
summary must be explicitly cast to
BinaryLogisticRegressionTrainingSummary
.
This will likely change when multiclass classification is supported.
Continuing the earlier example:
import org.apache.spark.ml.classification.LogisticRegressionTrainingSummary;
import org.apache.spark.ml.classification.BinaryLogisticRegressionSummary;
import org.apache.spark.sql.functions;
// Extract the summary from the returned LogisticRegressionModel instance trained in the earlier example
LogisticRegressionTrainingSummary trainingSummary = lrModel.summary();
// Obtain the loss per iteration.
double[] objectiveHistory = trainingSummary.objectiveHistory();
for (double lossPerIteration : objectiveHistory) {
System.out.println(lossPerIteration);
}
// Obtain the metrics useful to judge performance on test data.
// We cast the summary to a BinaryLogisticRegressionSummary since the problem is a
// binary classification problem.
BinaryLogisticRegressionSummary binarySummary = (BinaryLogisticRegressionSummary) trainingSummary;
// Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
DataFrame roc = binarySummary.roc();
roc.show();
roc.select("FPR").show();
System.out.println(binarySummary.areaUnderROC());
// Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
// this selected threshold.
DataFrame fMeasure = binarySummary.fMeasureByThreshold();
double maxFMeasure = fMeasure.select(functions.max("F-Measure")).head().getDouble(0);
double bestThreshold = fMeasure.where(fMeasure.col("F-Measure").equalTo(maxFMeasure)).
select("threshold").head().getDouble(0);
lrModel.setThreshold(bestThreshold);
Logistic regression model summary is not yet supported in Python.
Example: Linear Regression
The interface for working with linear regression models and model summaries is similar to the logistic regression case. The following example demonstrates training an elastic net regularized linear regression model and extracting model summary statistics.
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.mllib.util.MLUtils
// Load training data
val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
val lr = new LinearRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8)
// Fit the model
val lrModel = lr.fit(training)
// Print the weights and intercept for linear regression
println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
// Summarize the model over the training set and print out some metrics
val trainingSummary = lrModel.summary
println(s"numIterations: ${trainingSummary.totalIterations}")
println(s"objectiveHistory: ${trainingSummary.objectiveHistory.toList}")
trainingSummary.residuals.show()
println(s"RMSE: ${trainingSummary.rootMeanSquaredError}")
println(s"r2: ${trainingSummary.r2}")
import org.apache.spark.ml.regression.LinearRegression;
import org.apache.spark.ml.regression.LinearRegressionModel;
import org.apache.spark.ml.regression.LinearRegressionTrainingSummary;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext;
public class LinearRegressionWithElasticNetExample {
public static void main(String[] args) {
SparkConf conf = new SparkConf()
.setAppName("Linear Regression with Elastic Net Example");
SparkContext sc = new SparkContext(conf);
SQLContext sql = new SQLContext(sc);
String path = "data/mllib/sample_libsvm_data.txt";
// Load training data
DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
LinearRegression lr = new LinearRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8);
// Fit the model
LinearRegressionModel lrModel = lr.fit(training);
// Print the weights and intercept for linear regression
System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept());
// Summarize the model over the training set and print out some metrics
LinearRegressionTrainingSummary trainingSummary = lrModel.summary();
System.out.println("numIterations: " + trainingSummary.totalIterations());
System.out.println("objectiveHistory: " + Vectors.dense(trainingSummary.objectiveHistory()));
trainingSummary.residuals().show();
System.out.println("RMSE: " + trainingSummary.rootMeanSquaredError());
System.out.println("r2: " + trainingSummary.r2());
}
}
from pyspark.ml.regression import LinearRegression
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.util import MLUtils
# Load training data
training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
# Fit the model
lrModel = lr.fit(training)
# Print the weights and intercept for linear regression
print("Weights: " + str(lrModel.weights))
print("Intercept: " + str(lrModel.intercept))
# Linear regression model summary is not yet supported in Python.
Optimization
The optimization algorithm underlying the implementation is called Orthant-Wise Limited-memory QuasiNewton (OWL-QN). It is an extension of L-BFGS that can effectively handle L1 regularization and elastic net.


package
test
Type Members
-
case class
FakeApplication
(path: File, classloader: ClassLoader, additionalPlugins: Seq[String], withoutPlugins: Seq[String], additionalConfiguration: Map[String, String]) extends Application with Product with Serializable
-
case class
FakeHeaders
(data: Map[String, Seq[String]]) extends Headers with Product with Serializable
-
case class
FakeRequest
[A]
(method: String, uri: String, headers: FakeHeaders, body: A, remoteAddress: String) extends Request[A] with Product with Serializable
-
case class
TestBrowser
(webDriver: WebDriver) extends FluentAdapter with Product with Serializable
-
case class
TestServer
(port: Int, application: FakeApplication) extends Product with Serializable
Contains test helpers.


Select all
Select none
/
Tanks
Damage
Healers
/
Hitscans
Projectiles
Shotguns
Beams
Melee
filter_list
sort
Mean DPS


Honzi/Guides/EVE Online/Distribution Missions
- If you are running distribution missions that have a chance to go to lowsec/nullsec and you wish to avoid leaving highsec, save your 4 hour decline timer for missions that leave highsec.
- If you are running distribution missions that remain in highsec, figure out which distribution missions have the most jumps and save your 4 hour decline timer for those missions.


ldapsearch
CLI utility for querying an LDAP directory.
More information: home page
- Query an LDAP server for all items that are a member of the given group and return the object's displayName value:
ldapsearch -D 'admin_DN' -w 'password' -h ldap_host -b base_ou 'memberOf=group1' displayName
- Query an LDAP server with a no-newline password file for all items that are a member of the given group and return the object's displayName value:
ldapsearch -D 'admin_DN' -y 'password_file' -h ldap_host -b base_ou 'memberOf=group1' displayName
- Return 5 items that match the given filter:
ldapsearch -D 'admin_DN' -w 'password' -h ldap_host -b base_ou 'memberOf=group1' -z 5 displayName
- Wait up to 7 seconds for a response:
ldapsearch -D 'admin_DN' -w 'password' -h ldap_host -b base_ou 'memberOf=group1' -l 7 displayName
- Invert the filter:
ldapsearch -D 'admin_DN' -w 'password' -h ldap_host -b base_ou '(!(memberOf=group1))' displayName
- Return all items that are part of multiple groups, returning the display name for each item:
ldapsearch -D 'admin_DN' -w 'password' -h ldap_host '(&(memberOf=group1)(memberOf=group2)(memberOf=group3))' "displayName"
- Return all items that are members of at least 1 of the specified groups:
ldapsearch -D 'admin_DN' -w 'password' -h ldap_host '(|(memberOf=group1)(memberOf=group1)(memberOf=group3))' displayName
- Combine multiple boolean logic filters:
ldapsearch -D 'admin_DN' -w 'password' -h ldap_host '(&(memberOf=group1)(memberOf=group2)(!(memberOf=group3)))' displayName


Login/Logout
Give me a Number:
Least to Greatest
Greatest to Least
Again?
That isn't even a number
Number cannot be negative
Number is too big


The Secret Life of C++: Day 3: Questions from last session
Lets answer some questions from last session:
What does _Unwind_Resume do?
This we will cover in Exceptions today.
If you pass something const, why copy?
Because if you didn't mean to copy, then you would have passed a
const reference to the object.
Can't you get away without the return value pointer by looking
up the stack and expecting it to be there?
I don't have a solid answer for this one, but one thought I had is
cases where you only want to move the stack pointer once in a
function body, but you want to call many different functions with
return values on the stack, so you want to be able to tell them
more complicated places for it than just right at the bottom of
the stack.
Richard Tibbetts
Last modified: Wed Jan 21 17:36:31 EST 2004


University of California, San Diego
PeaCoq is a web-based front-end to the Coq proof assistant.
It provides an interface similar to CoqIDE or ProofGeneral, but additionally offers a proof mode called "Proof Tree" with the following features:
Stay tuned...
Logo courtesy of Jean Yang


| # | Query | Status | Start Time |
|---|---|---|---|
| 1 | select * from all-records | Running | 10:22:31 PST |
| 1 | select * from all-records | Running | 10:22:31 PST |
| 1 | select * from all-records | Running | 10:22:31 PST |
| 1 | select * from all-records | Running | 10:22:31 PST |
| 1 | select * from all-records | Running | 10:22:31 PST |
| 1 | select * from all-records | Running | 10:22:31 PST |


HTML5 Hacks
Inline
Canvas
AI
Images
JSONP
Shared
Transfer
HTML5 WebWorkers API
Image Processing (GreyScale) through Pixel Manipulation and Mouse Coordinates Capture


{% extends 'layout.html' %} {% load static %} {% load jsonify %} {% block content %}
When designing things like buildings, roads, and sewer systems, engineers use n-year storms to model what would happen under various types of storms. To do this, they use the n-year rain storm, which defines the probability that a given event will happen in a given year. For example a 5-year event has a 1/5 or 20% chance of occurring in a given year. The n-year storms are defined by the Illinois State Climatologist in Bulletin 70, and is based on historic rainfall data.
3 inches in a day is no big deal. 3 inches in an hour is overwhelming. So the time it takes for the rain to come. Rain intensity is inches per hour/day.
The largest of these storms is the 100-year storm, also known as the once-in-a-lifetime storm. It has a 1% chance of happening in a given year. Since it only occurs once in a lifetime, this storm is used to demonstrate that the design can withstand the big one. However, since 1970, there have been three 100-year events.
If the catastrophic event is happening multiple times, perhaps we need a new definition of the n-year event in order to account for the changing climate? For example, the Tunnel and Reservoir Plan, which was created to alleviate heavy rains, was designed in 1970. If we are seeing more heavy events, perhaps the design is obsolete?


Send HTTP GET Request with param
/params/string?text=Hi from frontend
/params/string?text=Hi Doom slayer!
/params/mix-paramsHi Elisabeth how are you&number=7
/params/mix-paramsHi Elisabeth how are you&number=7


Welcome to the Dream Interpreter
Enter up to 3 key elements of your dream
search by alphabet instead
Request
Request
Request


From the previous classes until today, I've been working on the school's main website. iw as apart of the frontend team and I worked on the images on the main page with Alexa. I changed the gym photo to a class photo that will hopefully be updated with the new Advanced Programmers Workshop class each year. Aditionally, I changed the photo in the middle of the page to a photo with a few students working. I worked with the CSS teams to create a new css profile for images. Hopefully new photos will be put on the website each year!
From now on, I'll start working on my project. Right now, I don't have a clear idea on what I want to work on. I was either going to make a Robot and record the entire thing on my own or work on a game with Will, Nathan, Michael and Andrew. I'm learning towards the game at the moment. We are each going to have assigned jobs and call weekly in order to track progress. We do not have specific jobs as of yet but I do know we are going to do 2D modeling and Nathan will do most of it. We are going for an indie style RPG. We will probably be making it through Unity. We are going to create a seperate github repiository for it. I'm curious to see how this goes!
From the previous class until today, I finished our first step of our Unity project which is downloading Unity and starting their online course. I had COVID this week so I wasn't able to do too much learning but we are going to set goals for the rest of the year.
Happy New Year! Since the last check-in, I've been working quite hard on our unity game project although I believe we are a bit ambitious with how much we want to accomplish. Nathan is going to create a seperate github respiository and if he needs help with it, I will help him create it. I have been working on the Unity tutorial since before break and I am close to being finished. Nathan has been working on the project and creating a basic foundation because he has already finished the Unity tutorial before. We plan on basing this Indie RPG off of Dokapon Kingdom. We all are making progress on our Unity tutorials if not finished them and we plan on creating and updating our github repisitory soon.


TSC UI Components are additional UI controls, widgets, and behaviors that can be used in a an Angular application. It does not replace any Angular UI Components such as Angular Material or Bootstrap. It is built on top of Angular Material and, thus, it is using the Angular Material controls and styles to create the components. The reason for creating these controls in this library is because currently, there is either no available Angular equivalent implementation or no formal reusable component available (i.e. found as a solution in forums). Another reason that these controls are available here is due to some reasons that the currently available equivalent controls may lack certain features or does not satisfy certain requirements.
This application demonstrates the different TSC UI Components that are available for use. Below are the list of components currently available:
To view the demo, click on the link from the list above or from the menu.


Internal event flags implementation.
More...
#include <os-flags.h>
Internal event flags implementation.
Definition at line 47 of file os-flags.h.
◆ event_flags()
| os::rtos::internal::event_flags::event_flags |
( |
| ) |
|
|
default |
Construct an internal event flags object instance.
◆ ~event_flags()
| os::rtos::internal::event_flags::~event_flags |
( |
| ) |
|
|
default |
Destruct the internal event flags object instance.
◆ check_raised()
Check if expected flags are raised.
- Parameters
-
| [in] | mask | The expected flags (OR-ed bit-mask); if flags::any , any flag raised will do it. |
| [out] | oflags | Pointer where to store the current flags; may be nullptr . |
| [in] | mode | Mode bits to select if either all or any flags in the mask are expected, and if the flags should be cleared. |
- Return values
-
| true | The expected flags are raised. |
| false | The expected flags are not raised. |
Definition at line 64 of file os-flags.cpp.
◆ clear()
Clear event flags.
- Parameters
-
| [in] | mask | The OR-ed flags to clear. |
| [out] | oflags | Optional pointer where to store the previous value of the flags; may be nullptr . |
- Return values
-
| result::ok | The flags were cleared. |
| EINVAL | The mask is zero. |
Definition at line 149 of file os-flags.cpp.
◆ get()
Get (and possibly clear) event flags.
- Parameters
-
| [in] | mask | The OR-ed flags to get/clear; can be flags::any . |
| [in] | mode | Mode bits to select if the flags should be cleared (the other bits are ignored). |
- Returns
- The selected bits from the flags mask.
Definition at line 113 of file os-flags.cpp.
◆ mask()
Get the flags mask.
- Returns
- The internal bit-mask.
Definition at line 192 of file os-flags.h.
◆ raise()
Raise event flags.
- Parameters
-
| [in] | mask | The OR-ed flags to raise. |
| [out] | oflags | Optional pointer where to store the new value of the flags; may be nullptr . |
- Return values
-
| result::ok | The flags were raised. |
| EINVAL | The mask is zero. |
| ENOTRECOVERABLE | Raise failed. |
Definition at line 34 of file os-flags.cpp.
The documentation for this class was generated from the following files:


API endpoint. (For more information on the function schema see CalcMetricFunction). The 'name' attribute from the function parameters objects should be used to reference the parameters for a given function in the definition. (See 'col1' and 'col2' for the divide function below as an example)/calculatedmetrics/functions
Example
{
"formula": {
"func": "divide",
"col1": {
"func": "metric",
"name": "metrics/pageviews",
"description": "Page Views"
},
"col2": {
"func": "metric",
"name": "metrics/visits",
"description": "Visits"
}
},
"func": "calc-metric",
"version": [1,0,0]
}
Type : object
CalculatedMetricErrorStatus
| Name | Schema |
|---|---|
errorCode |
enum (no_feature_access_to_advanced_calculated_metrics, invalid_metric_access, method_not_allowed, resource_conflict, invalid_access, resource_temporarily_unavailable, external_api_failure, resource_already_exists, invalid_state, invalid_json_input, invalid_parameters, invalid_dimension_access, unsupported_data_type, resource_not_found, insufficient_access, health_check_error, invalid_data, unexpected_error, external_api_error, unsupported_resource, io_error, invalid_request, invalid_client_id, unauthorized, authorization_error, invalid_token, insufficient_scope) |
errorDescription |
string |
errorDetails |
< string, object > map |
errorId |
string |
CalendarType
place holder description
Example
{ "Place holder example":""}
Fields
| Name | Schema |
|---|---|
anchorDate |
string (date-time) |
rsid |
string |
type |
enum (gregorian, nrf, qrs, custom_454, custom_445, modified_gregorian) |
ClassificationJob
| Name | Description | Schema |
|---|---|---|
datasetId |
string |
|
history |
< JobHistory > array |
|
imsOrgId |
string |
|
jobId |
string |
|
jobOptions |
see POST /classifications/dataset/{id}/jobs/import and POST /classifications/dataset/{id}/jobs/export for details on this data model |
object |
jobSize |
integer (int64) |
|
name |
string |
|
setName |
string |
|
state |
string |
|
totalLines |
integer (int32) |
|
type |
string |
ClassificationOwner
| Name | Schema |
|---|---|
email |
string |
name |
string |
ClassificationSet
| Name | Description | Schema |
|---|---|---|
columns |
A list of classification column definitions. Column definitions are not required at the time of creation but no data will be classified until at least one column definition exists. |
< AnalyticsClassificationColumn > array |
defaultEncoding |
Default encoding for jobs. Defaults to UTF-8. |
enum (utf8, latin1) |
defaultListDelimiter |
Default delimiter for list column types. Defaults to "," (comma). If you have no list columns this field does not apply. |
string |
description |
A long description for the purpose of this classification set. |
string |
editable |
Flag to indicate if the classification set is editable by the current user based on report suite permissions. |
boolean |
ftpSettings |
Ftp settings for the classification set. |
|
id |
This value will be auto generated by the system on creation. This value will be a id in the form of an ObjectId. |
string |
imsOrgId |
This value will be looked up based on the analytics company of the user. |
string |
lastModifiedBy |
Email address of the last person that modified the classification set. |
string |
lastModifiedDate |
Last modified date/time of the classification set. |
string |
name |
A human readable display name for users to be able to easily identify this classification definition. |
string |
notifications |
List of notifications to be sent for all imports and exports that occur. |
< Notification > array |
owner |
Contact information for a person responsible for the classification definition and/or data. |
|
ruleInitialLookback |
integer (int32) |
|
ruleMode |
string |
|
ruleState |
string |
|
ruleTestKeys |
< string > array |
|
subscriptions |
A list of classification subscriptions. Subscriptions are not required at the time of creation but no data will be classified until at least one subscription exists. |
< ClassificationSubscription > array |
tags |
< Tag > array |
|
type |
enum (standard, lookup) |
ClassificationSetFilterArgs
| Name | Schema |
|---|---|
dimensions |
< string > array |
ids |
< string > array |
rsids |
< string > array |
tagNames |
string |
type |
enum (standard, lookup) |
ClassificationSubscription
| Name | Description | Schema |
|---|---|---|
dimension |
The dimension identity you would like to be classified. Should be prefixed with variables/ i.e. variables/page |
string |
editable |
Flag to indicate if the subscription is editable by the current user based on report suite permissions. |
boolean |
rsid |
The report suite id for which you would like to subscribe to a classification set. |
string |
Column
| Name | Schema |
|---|---|
id |
string |
segmentIds |
< string > array |
title |
string |
type |
enum (DIMENSION, METRIC) |
Company
| Name | Description | Schema |
|---|---|---|
accessType |
enum (pointProducts, standard, premium, mobile, premiumPredictive, premiumAttribution, premiumCustomer, core, essentialsDps, foundation, select, prime, ultimate) |
|
adminLogin |
Login of the main user responsible for the login company |
string |
allowAdobeEmails |
boolean |
|
apiRateLimitPolicy |
string |
|
billingCustomerId |
Id for the company that is used by the billing system |
integer (int64) |
billingTabAccess |
boolean |
|
companyPrefix |
string |
|
companySecurity |
||
companyid |
integer (int32) |
|
description |
string |
|
disabled |
boolean |
|
featureAccessOverrides |
< string, < string, string > map > map |
|
globalCompanyKey |
System generated cross data-center unique company id |
string |
imsOrgid |
Id of the IMS Organization that owns the company |
string |
name |
string |
|
namespace |
System generated namespace |
string |
permissionAuthority |
enum (ims, both) |
|
validEmails |
List of allowed email address domains for users in the company |
< string > array |
CompanySecurity
| Name | Schema |
|---|---|
emailRestrictions |
boolean |
ipRestrictions |
boolean |
passwordExpiration |
boolean |
passwordRecovery |
boolean |
strongPasswords |
boolean |
CustomReportSuiteFilterGroup
| Name | Schema |
|---|---|
customGroupId |
integer (int32) |
customGroupName |
string |
DataGovernanceBatchOperationStatus
| Name | Schema |
|---|---|
error |
|
success |
boolean |
variableIdSavedAs |
string |
variableIdTranslationMessage |
string |
DeleteResponse
place holder description
Example
{ "Place holder example":""}
Fields
| Name | Schema |
|---|---|
message |
string |
result |
string |
DimensionItem
place holder description
Example
{ "Place holder example":""}
Fields
| Name | Schema |
|---|---|
dimension |
string |
itemId |
string |
value |
string |
ErrorStatus
| Name | Schema |
|---|---|
errorCode |
enum (invalid_metric_access, method_not_allowed, resource_conflict, invalid_access, resource_temporarily_unavailable, external_api_failure, resource_already_exists, invalid_state, invalid_json_input, invalid_parameters, invalid_dimension_access, unsupported_data_type, resource_not_found, insufficient_access, health_check_error, invalid_data, unexpected_error, external_api_error, unsupported_resource, io_error, invalid_request, invalid_client_id, unauthorized, authorization_error, invalid_token, insufficient_scope) |
errorDescription |
string |
errorDetails |
< string, object > map |
errorId |
string |
ExpirationSettings
| Name | Schema |
|---|---|
context |
string |
events |
< string > array |
func |
enum (afterEvents, beforeEvents, inactivity, container) |
granularity |
string |
numPeriods |
integer (int32) |
Favorite
| Name | Schema |
|---|---|
companyId |
integer (int32) |
componentId |
string |
componentType |
string |
componentTypeId |
integer (int32) |
favoriteId |
integer (int64) |
favoriteTimestamp |
string (date-time) |
userId |
integer (int32) |
FtpSettings
| Name | Schema |
|---|---|
hostName |
string |
password |
string |
userName |
string |
IdentityMetric
place holder description
Example
{ "Place holder example":""}
Fields
| Name | Schema |
|---|---|
allocationModel |
enum (ALLOCATION_FIRST_TOUCH, ALLOCATION_LAST_TOUCH, ALLOCATION_INSTANCE, ALLOCATION_LAST_KNOWN, ALLOCATION_LEGACY, ALLOCATION_LINEAR, ALLOCATION_PARTICIPATION, ALLOCATION_POSITION_BASED, ALLOCATION_TIME_DECAY, ALLOCATION_U_SHAPED, ALLOCATION_J_SHAPED, ALLOCATION_REVERSE_J_SHAPED) |
dimensionView |
enum (LINEAR_ALLOCATION, PARTICIPATION_ALLOCATION, LAST_TOUCH_ALLOCATION, MC_FIRST_TOUCH_ALLOCATION, MC_LAST_TOUCH_ALLOCATION) |
identity |
string |
ImsOrgIdsFromRelatedCompanies
| Name | Schema |
|---|---|
owningImsOrgId |
string |
relatedImsOrgIds |
< string > array |
JSONObject
Type : object
JobArtifactResponse
| Name | Schema |
|---|---|
artifactId |
string |
fileType |
string |
lastModifiedTime |
string |
JobExportOptions
| Name | Schema |
|---|---|
columns |
< string > array |
dataFormat |
string |
dataUri |
string |
dateFilterEnd |
string |
dateFilterStart |
string |
encoding |
string |
exactMatch |
< string, object > map |
jobName |
string |
keyRegex |
string |
keys |
< string > array |
listDelimiter |
string |
notifications |
< Notification > array |
offset |
integer (int32) |
regexMatch |
< string, object > map |
rowLimit |
integer (int32) |
source |
string |
JobHistory
| Name | Schema |
|---|---|
host |
string |
jobState |
enum (created, queued, validated, failed_validation, processing, done_processing, failed_processing, completed) |
message |
string |
pid |
integer (int32) |
tag |
string |
timestamp |
integer (int64) |
JobImportOptions
| Name | Schema |
|---|---|
dataFormat |
string |
dataUri |
string |
encoding |
string |
jobName |
string |
listDelimiter |
string |
notifications |
< Notification > array |
source |
string |
JsonNode
| Name | Schema |
|---|---|
array |
boolean |
bigDecimal |
boolean |
bigInteger |
boolean |
binary |
boolean |
boolean |
boolean |
containerNode |
boolean |
double |
boolean |
float |
boolean |
floatingPointNumber |
boolean |
int |
boolean |
integralNumber |
boolean |
long |
boolean |
missingNode |
boolean |
nodeType |
enum (ARRAY, BINARY, BOOLEAN, MISSING, NULL, NUMBER, OBJECT, POJO, STRING) |
null |
boolean |
number |
boolean |
object |
boolean |
pojo |
boolean |
short |
boolean |
textual |
boolean |
valueNode |
boolean |
Locale
| Name | Schema |
|---|---|
country |
string |
displayCountry |
string |
displayLanguage |
string |
displayName |
string |
displayScript |
string |
displayVariant |
string |
extensionKeys |
< string > array |
iso3Country |
string |
iso3Language |
string |
language |
string |
script |
string |
unicodeLocaleAttributes |
< string > array |
unicodeLocaleKeys |
< string > array |
variant |
string |
MerchandisingBindingCriteria
| Name | Schema |
|---|---|
bindingEvents |
< string > array |
func |
enum (always, events) |
MerchandisingSettings
| Name | Schema |
|---|---|
bindingCriteria |
|
func |
enum (nonMerchandising, forced, auto) |
MetricsWarning
| Name | Schema |
|---|---|
helpLink |
string |
text |
string |
title |
string |
Notification
| Name | Schema |
|---|---|
method |
enum (email, rabbit) |
recipients |
< string > array |
state |
enum (created, queued, validated, failed_validation, processing, done_processing, failed_processing, completed) |
Owner
place holder description
Example
{ "Place holder example":""}
Fields
| Name | Description | Schema |
|---|---|---|
id |
the login id of the owner |
integer (int32) |
login |
the friendly full login name of the owner, included when the expansion parameter ownerFullName is true |
string |
name |
the friendly full login name of the owner, included when the expansion parameter ownerFullName is true |
string |
Pageable
| Name | Schema |
|---|---|
offset |
integer (int32) |
pageNumber |
integer (int32) |
pageSize |
integer (int32) |
sort |
PredictiveSettings
| Name | Schema |
|---|---|
highAnomalies |
boolean |
lowAnomalies |
boolean |
trainingPeriods |
integer (int32) |
RankedColumnError
| Name | Schema |
|---|---|
columnId |
string |
errorCode |
enum (unauthorized_metric, unauthorized_dimension, unauthorized_dimension_global, anomaly_detection_failure_unexpected_item_count, anomaly_detection_failure_tsa_service, not_enabled_metric, not_enabled_dimension, not_enabled_dimension_global) |
errorDescription |
string |
errorId |
string |
RankedColumnMetaData
| Name | Schema |
|---|---|
columnErrors |
< RankedColumnError > array |
columnIds |
< string > array |
dimension |
RankedReportData
| Name | Schema |
|---|---|
columns |
|
firstPage |
boolean |
lastPage |
boolean |
message |
string |
number |
integer (int32) |
numberOfElements |
integer (int32) |
oberonRequestXML |
< string > array |
oberonResponseXML |
< string > array |
predictiveRequestObjects |
< string > array |
predictiveResponseObjects |
< string > array |
reportId |
string |
request |
|
rows |
< Row > array |
summaryData |
|
totalElements |
integer (int32) |
totalPages |
integer (int32) |
RankedRequest
| Name | Schema |
|---|---|
anchorDate |
string |
dimension |
string |
globalFilters |
< ReportFilter > array |
globalPredictiveSettings |
|
locale |
|
metricContainer |
|
rowContainer |
|
rsid |
string |
search |
|
settings |
|
statistics |
RankedSettings
| Name | Schema |
|---|---|
countRepeatInstances |
boolean |
darkSessionsEnabled |
boolean |
dataSchema |
string |
dimensionSort |
string |
includeAnomalyDetection |
boolean |
includeLatLong |
boolean |
includePercentChange |
boolean |
limit |
integer (int32) |
page |
integer (int32) |
reflectRequest |
boolean |
sessionExpiration |
< SessionExpirationSettings > array |
suiteReportingContext |
enum (DEVICE, TROMMEL_COMBINED) |
RankedStatistics
| Name | Schema |
|---|---|
functions |
< string > array |
ignoreZeroes |
boolean |
RankedSummaryData
Type : object
RelatedCompany
| Name | Schema |
|---|---|
companyId |
integer (int32) |
name |
string |
ReportDimension
| Name | Schema |
|---|---|
id |
string |
type |
enum (STRING, INT, DECIMAL, CURRENCY, PERCENT, TIME, ENUM, ORDERED_ENUM) |
ReportErrorStatus
| Name | Schema |
|---|---|
errorCode |
enum (invalid_segment_ids_found, invalid_metric_access, method_not_allowed, resource_conflict, invalid_access, resource_temporarily_unavailable, external_api_failure, resource_already_exists, invalid_state, invalid_json_input, invalid_parameters, invalid_dimension_access, unsupported_data_type, resource_not_found, insufficient_access, health_check_error, invalid_data, unexpected_error, external_api_error, unsupported_resource, io_error, invalid_request, invalid_client_id, unauthorized, authorization_error, invalid_token, insufficient_scope) |
errorDescription |
string |
errorDetails |
< string, object > map |
errorId |
string |
ReportFilter
| Name | Schema |
|---|---|
allocationModel |
|
dateRange |
string |
dimension |
string |
excludeItemIds |
< integer > array |
id |
string |
itemId |
string |
itemIds |
< string > array |
segmentDefinition |
< string, object > map |
segmentId |
string |
type |
enum (DATE_RANGE, BREAKDOWN, SEGMENT, EXCLUDE_ITEM_IDS) |
ReportMetric
| Name | Schema |
|---|---|
allocationModel |
|
allocationType |
string |
columnId |
string |
filters |
< string > array |
id |
string |
metricDefinition |
< string, object > map |
metricView |
string |
predictive |
|
sort |
string |
ReportMetricPredictiveSettings
| Name | Schema |
|---|---|
anomalyConfidence |
number (double) |
ReportMetrics
| Name | Schema |
|---|---|
metricFilters |
< ReportFilter > array |
metrics |
< ReportMetric > array |
ReportRow
| Name | Schema |
|---|---|
filters |
< string > array |
rowId |
string |
ReportRows
| Name | Schema |
|---|---|
rowFilters |
< ReportFilter > array |
rows |
< ReportRow > array |
ReportSearch
| Name | Schema |
|---|---|
clause |
string |
empty |
boolean |
excludeItemIds |
< integer > array |
includeSearchTotal |
boolean |
itemIds |
< integer > array |
ReportSuiteAxleConfig
place holder description
Example
{ "Place holder example":""}
Fields
| Name | Schema |
|---|---|
axleData |
boolean |
axleStart |
string |
ReportSuiteHateoasReferences
| Name | Schema |
|---|---|
billingCustomerUrl |
string |
companyUrl |
string |
Row
| Name | Schema |
|---|---|
data |
< number (double) > array |
dataAnomalyDetected |
< boolean > array |
dataExpected |
< number (double) > array |
dataLowerBound |
< number (double) > array |
dataUpperBound |
< number (double) > array |
itemId |
string |
latitude |
number (double) |
longitude |
number (double) |
percentChange |
< number (double) > array |
rowId |
string |
value |
string |
RowItem
| Name | Schema |
|---|---|
itemId |
string |
value |
string |
SegmentCompatibility
place holder description
Example
{ "Place holder example":""}
Fields
| Name | Schema |
|---|---|
message |
string |
supported_features |
< string > array |
supported_products |
< string > array |
supported_schema |
< string > array |
valid |
boolean |
validator_version |
string |
SegmentPublishingConfig
| Name | Schema |
|---|---|
dataSourceId |
integer (int32) |
segmentFolderId |
integer (int32) |
traitFolderId |
integer (int32) |
SegmentSummaryResponse
| Name | Schema |
|---|---|
compatibility |
< string > array |
detailMsg |
string |
oberonRequestXml |
string |
oberonResponseXml |
string |
report |
|
supportedEngines |
< string > array |
SessionExpirationSettings
| Name | Schema |
|---|---|
containerName |
string |
events |
< string > array |
func |
enum (afterEvents, inactivity, container, beforeEvents) |
granularity |
string |
numPeriods |
integer (int32) |
Share
| Name | Schema |
|---|---|
componentId |
string |
componentType |
string |
shareId |
integer (int64) |
shareToDisplayName |
string |
shareToId |
integer (int64) |
shareToLogin |
string |
shareToType |
string |
Sort
Type : object
SubscriptionValidationResponse
| Name | Schema |
|---|---|
dimension |
string |
dimensionName |
string |
errorCode |
enum (invalid_rsid, dimension_not_classifiable, taxonomist_not_enabled_for_rsid, multiple_subscription_rsids) |
rsid |
string |
valid |
boolean |
SuiteCollectionItem
| Name | Description | Schema |
|---|---|---|
axleConfig |
||
calendarType |
||
collectionItemType |
Suite type |
enum (reportsuite, virtualreportsuite) |
currency |
string |
|
currentTimezoneOffset |
number (float) |
|
dataCurrentAsOf |
string (date-time) |
|
dataSchema |
string |
|
discoverUiEnabled |
boolean |
|
enabledSolutions |
< string > array |
|
isBlocked |
boolean |
|
isDeleted |
boolean |
|
name |
string |
|
numGroups |
integer (int32) |
|
numericRsid |
integer (int32) |
|
parentRsid |
Parent report suite id for virtual report suite |
string |
remoteLoading |
boolean |
|
rsid |
string |
|
taxonomist |
boolean |
|
timezoneZoneinfo |
Suite friendly timezone name |
string |
SummarizedUsageItem
| Name | Schema |
|---|---|
count |
integer (int32) |
itemId |
string |
mostRecentTimestamp |
string (date-time) |
relevancyScore |
number (float) |
Tag
Tag Model
| Name | Description | Schema |
|---|---|---|
components |
the list of components that have been tagged with this tag |
< TaggedComponent > array |
description |
the tag description |
string |
id |
the tag id |
integer (int32) |
name |
the tag name |
string |
TaggedComponent
| Name | Schema |
|---|---|
componentId |
string |
componentType |
string |
tags |
< Tag > array |
Timezone
| Name | Schema |
|---|---|
id |
integer (int32) |
name |
string |
TntConfig
| Name | Schema |
|---|---|
tnt |
boolean |
tntA4tDateEnabled |
boolean |
tntAllocationType |
integer (int32) |
tntClientCode |
string |
tntExpirePeriods |
integer (int32) |
tntExpireType |
integer (int32) |
TotalsReportData
| Name | Schema |
|---|---|
columns |
< Column > array |
message |
string |
reportId |
string |
totals |
< RowItem > array |
UnhashReportData
| Name | Schema |
|---|---|
firstPage |
boolean |
lastPage |
boolean |
message |
string |
number |
integer (int32) |
numberOfElements |
integer (int32) |
oberonRequestXML |
string |
oberonResponseXML |
string |
reportId |
string |
rows |
< RowItem > array |
searchAnd |
string |
searchNot |
string |
searchOr |
string |
searchPhrase |
string |
totalElements |
integer (int32) |
totalPages |
integer (int32) |
User
| Name | Schema |
|---|---|
admin |
boolean |
companyid |
integer (int32) |
email |
string |
firstName |
string |
fullName |
string |
imsUserId |
string |
lastName |
string |
login |
string |
loginId |
integer (int32) |
UserGroup
place holder description
Example
{ "Place holder example":""}
Fields
| Name | Schema |
|---|---|
allRsids |
boolean |
created |
string (date-time) |
description |
string |
groupId |
integer (int32) |
imsPlcId |
string |
imsPlcIdOrphaned |
boolean |
loginIds |
< integer (int32) > array |
modified |
string (date-time) |
name |
string |
oids |
< integer (int32) > array |
rsids |
< string > array |
tokens |
< string > array |


Email Address that messages will be sent from. Sending from a free email service such as gmail, hotmail, or yahoo is not recommended. The providers may block repeated outgoing, automated email.
Email Address that messaged will be sent to.
Hostname for your SMTP Server. Sending from a free email service such as gmail, hotmail, or yahoo is not recommended. The providers may block repeated outgoing, automated email.


The randomizer adds a few new commands you can activate by pressing multiple keys at the same time. Many of these are disabled in race seeds.
- Alt + P: Shows your progress in the seed (number of pickup collected, progress in goals and hints
- Alt + L: Reloads the current seed.
- Alt + T: Shows the textbox of the last item you collected
- Ctrl + Alt + D: Toggle Debug Menu
- Ctrl + Alt + 4: Unlock Spoilers
- Ctrl + Alt + 5: Toggle TPCheat (makes every pickup a teleporter)
- Ctrl + Alt + L: Toggles Cursor Lock
- Ctrl + Alt + C: Show current coordinates
- Ctrl + Alt + N: Toggles showing location names in spoiler filter (hold L in map)


Please review the header and line items, and make corrections where appropriate.
You can modify both the header and line items
Correct
Incorrect


John Deo
(123) 456 7890
<EMAIL_ADDRESS>India
Completed my graduation in Arts from the well known and renowned institution of India – SARDAR PATEL ARTS COLLEGE, BARODA in 2000-01, which was affiliated to M.S. University. I ranker in University exams from the same university from 1996-01.
Worked as Professor and Head of the department at Sarda Collage, Rajkot, Gujarat from 2003-2015
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Education
- B.A.,Gujarat University, Ahmedabad,India.
- M.A.,Gujarat University, Ahmedabad, India.
- P.H.D., Shaurashtra University, Rajkot
Experience
- One year experience as Jr. Professor from April-2009 to march-2010 at B. J. Arts College, Ahmedabad.
- Three year experience as Jr. Professor at V.S. Arts & Commerse Collage from April - 2008 to April - 2011.
- Lorem Ipsum is simply dummy text of the printing and typesetting industry.
- Lorem Ipsum is simply dummy text of the printing and typesetting industry.
- Lorem Ipsum is simply dummy text of the printing and typesetting industry.
- Lorem Ipsum is simply dummy text of the printing and typesetting industry.
-
-
John Doe attended a meeting with Lina Smith.
2 days ago near Alaska, USA
-
Kehn Anderson completed the task “wireframe design” within the dead line
4 days ago near Alaska, USA
-
Jacob Ryan was absent office due to sickness
4 days ago near Alaska, USA


No Internet
No internet connection was found. please check your internet connection and try again later


IIT Delhi Grievance Redressal Portal
An Initiative by IITD students to help make governance better
Ensure accountability for faster resolution of complaints.


Everybody wants to have a website. A lot less people realize how easy it is to get started. Lucky you, I’m going to show you exactly how you can get started with a live website in less than 10 minutes - for free!
We’re going to build our site using GitHub Pages. GitHub harnesses the power of Jekyll to turn markdown documents into lovely websites.
Create a New Repository on GitHub
If you don’t have an account sign up for free.
Once you have an account, go back to the GitHub landing page, click on the (+) dropdown and select New repository.
Add a repository title, optional description, and make sure you check the box next to ‘Initialize this repository with a README’.
Generate Your Site
After your repository is created, go to the Settings tab and scroll down to the GitHub Pages section.
 We want to build our site from our repository’s master
branch.
This will prompt GitHub to start building your site. At the top of the GitHub Pages section you’ll see the url GitHub will publish your site to.
If you preview the site now, it will just be a prettified-version of your README. This is the essence of the Jekyll blog. But we want our site to have a bit more pizzaz, so we’re going to pick a theme.
Pick A Theme
Click on Choose a theme button under the Theme Chooser section.
Here, we can select any theme for our site. Most built-in Jekyll themes in GitHub Pages offer us a simple UI boost for our README, so choose whichever one you like the best. For my basic site I’m picking the Minimal theme.
Now we can check out our site (from that link in the Settings page). Here’s what mine looks like.
Update Site Content
The Minimal theme is just that - minimal. All you have to do to update your site content is update your README.md
file.
I’m just going to add a couple of my own posts to my ‘blog’.
When you’re done updating, click on the Commit changes button to save your updates. Your changes should be updated almost immediately. Here’s what mine look like.
And that’s pretty much it! You have a live site and blank slate for your updates!
What’s Next?
You just published a site in less than 10 minutes! But we just scratched the surface of the possibilities of Jekyll and GitHub Pages. Here’s some resources to get you on to your next site.
Jekyll Docs
Themes
I like themes from CloudCannon which are all available on their GitHub profile.
If you like my theme, check it out here. Fair warning - this theme is deprecated.


|
MPS Toolkit
|
Defines the primaries of a pre-defined or custom colorspace i.e. Rec709 or Rec2020. More...
#include <ColorSpaces.hpp>
Public Member Functions | |
| colorPrimaries () | |
| Default constructor. More... | |
| colorPrimaries (ColorSpaces colorSpace) | |
| Construct the object using a predefined colorspace. More... | |
| void | selectPrimary (ColorSpaces colorspace) |
| Construct the object using a predefined colorspace. More... | |
| void | printPrimaries () |
| Prints the primaries to std out. More... | |
| float | getChromaPoint (ChromaPoint chromaPoint) |
| Construct the object using a predefined colorspace. More... | |
Data Fields | |
| std::vector< float > | Primaries |
| Stores the RGB primaires within the object as a std::vector. More... | |
| std::vector< float > | WhitePoint |
| Stores the white point within the object as a std::vector. More... | |
Defines the primaries of a pre-defined or custom colorspace i.e. Rec709 or Rec2020.
Provides easy access and storage to predefined and custom primary sets Methods and members allow for easy modification of selected primarie sets. Data stored in this class should be in CIE xy as outlined in the CIE 1931 Color Space
| MPS::colorPrimaries::colorPrimaries | ( | ) |
Default constructor.
| MPS::colorPrimaries::colorPrimaries | ( | MPS::ColorSpaces | colorSpace | ) |
Construct the object using a predefined colorspace.
[In] ColorSpace enum
| float MPS::colorPrimaries::getChromaPoint | ( | ChromaPoint | chromaPoint | ) |
Construct the object using a predefined colorspace.
[In] ChromaPoint enum
[Return] Returns the specified color corrdinate
| void MPS::colorPrimaries::printPrimaries | ( | ) |
Prints the primaries to std out.
| void MPS::colorPrimaries::selectPrimary | ( | MPS::ColorSpaces | colorspace | ) |
Construct the object using a predefined colorspace.
[In] ColorSpace enum
| std::vector<float> MPS::colorPrimaries::Primaries |
Stores the RGB primaires within the object as a std::vector.
Primaries are store in Red x, Red y, Green x, Green y, Blue x, Blue y
| std::vector<float> MPS::colorPrimaries::WhitePoint |
Stores the white point within the object as a std::vector.


For the alpha release, we made the decision to go with four different wrappers. Their documented API can be found here:
The flows diagram and introduction to the api is useful here too
Go to Java (Javadoc) Go to Python 2.7 doc Go to .net / C# doc Go to Node.js doc| Parameter | Description |
| setup | Sets up the wrapper to be able to start communicating with the underlying SDK. |
| addService | Adds a service of type WWService to the producer, used if the device you are operating on is a producer, if added to a device you intend as a consumer this will give that device producer functionality. |
| removeService | This removes the service from the producer |
| initConsumer | This initiates the device as a consumer, which enables it to find services, negotiate prices, make payments and receive services. |
| initProducer | This initiates the device as a producer / or initialises the devices producer capability. |
| getDevice | This is able to provide back details of the the current device that the SDK is running on, and it credentials / information. |
| startServiceBroadcast | This enables the producer device to start broadcasting itself via UDP broadcast over the network to notify devices it is available to be consumed. |
| stopServiceBroadcast | This method stops the SDK from broadcasting the current service messages that it is broadcasting. |
| deviceDiscovery | This enables the consumer device to discovery other devices (producers) on the network that are UDP broadcasting. |
| requestServices | Get a list of services that are available from the broadcasting device. |
| getServicePrices | This is used by the consumer to get the list of prices associated with a particular serviceId |
| selectService | Selection of a service is performed by the consumer, providing details of the service, the amount and at what price point it wants to purchase the service. |
| makePayment | This allows the consumer to request a payment be made at the producer device, by providing the total price response object as the request. The producer will then make the payment (or attempt to) and send back a Payment Response detailing whether it was successful or not. |
| beginServiceDelivery | This begins the service delivery, and is requested by the consumer, and will proceed as long as the correct information is provided to the producer. If the correct credentials are passed through, then the producer will start releasing the service known as a 'trusted trigger' |
| endServiceDelivery | This ends the service delivery, a request initiated by the consumer. |


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A hireling generator designed with Swords & Wizardry Complete in mind, but also suitable for other Old School RPGs
Number to generate:
Stat generation:
Equipment generation:
Hit-Dice options:
Max Level:
Click on a character name to see a printable character sheet. Click on the sheet to return to this view.
Name
Str
Dex
Con
Int
Wis
Cha
HP
Class
Lvl
Possible Classes
This site is not affiliated with Matthew J. Finch or Mythmere Games™. Swords & Wizardry, S&W, and Mythmere Games are the trademarks of Matthew J. Finch.


Email Settings
Email Server Setup (Required to send Email)
Enter the "From:" Email Address:
SMTP Server (IP Address or DNS Name):
Auto Detect SMTP Server
SMTP Port:
(1 ~ 65535)
How to find SMTP Server and port
Email Send Authentication
Authentication Type:
No Authentication
*SMTP AUTH (AUTO)
How to determine Authentication Type
SMTP Login Name:
SMTP Password:
Re-enter SMTP Password:
SMTP Server Connection Status:
Ready
Refresh Status


|
QuantLib: a free/open-source library for quantitative finance
Fully annotated sources - version 1.30
|
#include <ql/cashflows/conundrumpricer.hpp>
Public Member Functions | |
| MarketQuotedOptionPricer (Rate forwardValue, Date expiryDate, const Period &swapTenor, const ext::shared_ptr< SwaptionVolatilityStructure > &volatilityStructure) | |
| Real | operator() (Real strike, Option::Type optionType, Real deflator) const override |
| Public Member Functions inherited from VanillaOptionPricer | |
| virtual | ~VanillaOptionPricer ()=default |
| virtual Real | operator() (Real strike, Option::Type optionType, Real deflator) const =0 |
Private Attributes | |
| Rate | forwardValue_ |
| Date | expiryDate_ |
| Period | swapTenor_ |
| ext::shared_ptr< SwaptionVolatilityStructure > | volatilityStructure_ |
| ext::shared_ptr< SmileSection > | smile_ |
Definition at line 45 of file conundrumpricer.hpp.
| MarketQuotedOptionPricer | ( | Rate | forwardValue, |
| Date | expiryDate, | ||
| const Period & | swapTenor, | ||
| const ext::shared_ptr< SwaptionVolatilityStructure > & | volatilityStructure | ||
| ) |
|
overridevirtual |
Implements VanillaOptionPricer.
Definition at line 61 of file conundrumpricer.cpp.
|
private |
Definition at line 57 of file conundrumpricer.hpp.
|
private |
Definition at line 58 of file conundrumpricer.hpp.
|
private |
Definition at line 59 of file conundrumpricer.hpp.
|
private |
Definition at line 60 of file conundrumpricer.hpp.
|
private |
Definition at line 61 of file conundrumpricer.hpp.


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foehnix.
get_demodata
(which)[source]¶Returns a demo data.
The foehnix package comes with two sets of meteorological observations: one for Tyrol, Austria, and one for Southern California, USA. Both data sets come with observations from two stations, one valley station (or target station) and one station further upstream of the main foehn wind direction (crest station) used to filter the data (see foehnix_filter). For Tyrol, observations for station Ellbögen (valley) and station Sattelberg (crest) are included, the Californian data set consists of the crest station ‘Lucky Five Ranch’ and the valley station ‘Viejas Casino and Resort’.
| Parameters: |
|
|---|---|
| Returns: |
|


The agenda component
This page explains how to integrate the agenda component into your own web page. Some JavaScript knowledge is required.
Before you start
To integrate koliseo-agenda with your web page you first need to:
- Create the r4p and select the talks to be part of your agenda.
- Create the agenda and integrate it into your web page.
- Optionally, create an Application and copy the Client ID into your component configuration (see an example). This task is required to enable the write operations on the agenda (add bookmark and submit feedback).
Once this is done, you are ready to integrate the JavaScript component.
Using the component
koliseo-agenda is a JavaScript component that you can integrate into your web page. You can import the component from npm:
# npm i --save koliseo-agenda
To use the component, invoke its render()
method:

Responsive design
The agenda is a responsive component. In devices with small screens like smartphones the agenda will be displayed as a list instead of a table. You can customize this behavior using CSS.
Embed videos and slides
Speakers and event organizers can add slides and youtube recordings to their talks at any time (read more). You may choose to hide these videos on your website using CSS.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 1
Dietary
Preheat the oven to 200C/400F/Gas 6.
Place the blinis on a baking tray and bake for eight minutes in the oven.
To make the hollandaise, place the egg yolks, lemon juice and vinegar in a food processor. Season with salt and freshly ground black pepper and blend together.
Very slowly add the melted butter while blending.
Slowly add the cream then the hot water in the same way.
To make the poached eggs, simmer the water and vinegar in a saucepan. Swirl the water to create a whirlpool, then gently add the eggs and poach for three minutes. Remove with a slotted spoon.
Serve two piles of blinis with a poached egg on each and some of the hollandaise spooned over. Garnish with chopped radish.


Before Change
policy_loss = self.calc_policy_loss(batch, advs) // from actor
val_loss = self.calc_val_loss(batch, v_targets) // from critic
loss = policy_loss + val_loss
self.net.training_step(loss=loss, lr_clock=self.body.env.clock)
// reset
self.to_train = 0
self.body.entropies = []
self.body.log_probs = []
After Change
Trains the network when the actor and critic share parameters
loss = self.policy_loss_coef * policy_loss + self.val_loss_coef * val_loss
"""
clock = self.body.env.clock
if self.to_train == 1:
batch = self.sample()
with torch.no_grad():
advs, v_targets = self.calc_advs_v_targets(batch)
policy_loss = self.calc_policy_loss(batch, advs) // from actor
val_loss = self.calc_val_loss(batch, v_targets) // from critic
loss = policy_loss + val_loss
self.net.training_step(loss=loss, lr_clock=clock)
// reset
self.to_train = 0
self.body.entropies = []
self.body.log_probs = []


Fix Democracy First
{% capture panel_body %}
Fight corruption in politics
Help us build a national grassroots network of activists to elect champions of democracy!
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{% endcapture %} {% include panels/panel_empty.html %}
{% capture jumbotron_signup_js %} {% endcapture %} {% capture footer_scripts %}{{footer_scripts}}{{jumbotron_signup_js}}{% endcapture %}


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My Five Favorite Desserts
Number
Desserts
1
Cannoli
2
Cheesecake
3
Chocolate Cake
4
Cherry Pie
5
Milk Bar Crack Pie