Restart the live player to correct any mishaps.
Take screenshot. The image will be downloaded to your disk or opened, depending on your browser settings.
Undo last operation.
Start preview streaming. An optional 'preview' effect can be added to any position in the chain to show the intermediate image.
A video effect chain. Click here to activate and edit it. Drag to reorder or delete it.
Add a new empty video effect chain.
Duplicate the selected video effect chain.
An empty video effect chain. Drag an effect inside to add it.
A chained video effect. Drag to reorder it. Click to adjust parameters.
The pre-chain setup. This chain runs before the currently active chain. Place select_audio and map_video here. Usually this ends with a capture item.
The post-chain setup. This chain will be run after the currently active chain. Useful for location specific setup like correction by the perspective effect.
This list contains effect modules, that can be added to the chain by dragging them into it.
Drag this effect to the chain to add it.
A feedback tap. Insert this effect into the chain to make the current image available at the next frame. Use feedbackOut above this effect to retrieve the image.
A feedback output. Insert this effect into the chain to receive the image pushed by feedbackIn on the last frame. Use feedbackIn below this effect to provide an image.
A preview tap. Insert this effect into a chain and run preview to view the intermediate image.
A video source (replaces the current image)
A stack_push effect, copys the current image on the stack.
A video effect with two inputs, also consumes the topmost image on the stack. Don't forget to add push_stack somewhere above.
A stack_swap effect, exchanges the current image with the top one on the stack.
A video effect with three inputs, also consumes two images from the stack. Don't forget to add two push_stack's somewhere above.
A setup item, does not manipulate the image itself.
An audio source selector. Sets the audio device used for BEAT oscilators, waveform and spectrogram effects.
A video source mapping. Allows to replace a single video capture device number by another device. Useful to adapt changing camera setups.
Use the sliders to adjust the effect's parameters. The center area provides fine grained control, the outer large numbers. Click the numbers to edit by keyboard. Move or click outside to close.
Press buttons to add or remove oscillator or beat analyser to this parameter.
An OSC controlls this parameter.
f: Set the frequency of the value change
a: Set the amplitude, that is how much the value will change in every swing
p: Sets the phase, this can be used to move the swing in respect to another OSC
o: Sets the offset, that is the center value of the swing.
An BEAT beat analyzer controlls this parameter.
f: Set the guessed beat frequency of the audio, if any. For music, this is BPM / 60.
a: Set the amplitude, that is how much the value will change with the beat.
p: Sets the phase, this can be used to move the swing in respect to the beat.
o: Sets the offset, that is the lowest value of the swing.


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Hello from Auckland! Although the program starts on the 14th, I wanted to take the time to explore the area a bit before getting on board the ship, so I arrived on the evening of the 12th.
I flew in from LAX with a stop in Honolulu, Hawaii, and honestly the photos from then are so breathtaking I just had to share them here.
The Honolulu airport was pretty neat, it took advantage of Hawaii’s beautiful weather with an indoor/outdoor layout so I could enjoy the weather without needing to leave the airport.
Flying out of Hawaii, I caught this gem of a photo overlooking some of the coves with a rainbow coming off the end of the island
When I arrived in Auckland, it was already pretty late, so I took a bus to the hostel me and several of my shipmates stayed at for the first few nights.
The next day, a handful of my shipmates and I drove up to Cathedral Cove for a great day of swimming and hiking. (creds to Matthew for driving, I dunno how he managed driving on the left side of the road for the entire 2 hour drive)
The greenery along the way was pretty neat, so I thought it’d be cool to snap a few photos
Cathedral Cove itself was really nice, and the water was warm and pleasant to swim in. Got plenty of sun and sand in today!
We needed to board the ship the next day at 4pm, so there was plenty of time in the morning to walk around Auckland and see the city itself. The city was clean and was very walkable - despite walking around for most of the day, there were a lot of areas I didn’t get to. Here are some photos of the areas I did see though!
That’s all for now, can’t wait to board the ship tomorrow!


R/ml_tuning.R
, R/ml_tuning_cross_validator.R
, R/ml_tuning_train_validation_split.R
ml-tuning.Rd
Perform hyper-parameter tuning using either K-fold cross validation or train-validation split.
ml_sub_models(model) ml_validation_metrics(model) ml_cross_validator( x, estimator = NULL, estimator_param_maps = NULL, evaluator = NULL, num_folds = 3, collect_sub_models = FALSE, parallelism = 1, seed = NULL, uid = random_string("cross_validator_"), ... ) ml_train_validation_split( x, estimator = NULL, estimator_param_maps = NULL, evaluator = NULL, train_ratio = 0.75, collect_sub_models = FALSE, parallelism = 1, seed = NULL, uid = random_string("train_validation_split_"), ... )
| model | A cross validation or train-validation-split model. |
|---|---|
| x | A |
| estimator | A |
| estimator_param_maps | A named list of stages and hyper-parameter sets to tune. See details. |
| evaluator | A |
| num_folds | Number of folds for cross validation. Must be >= 2. Default: 3 |
| collect_sub_models | Whether to collect a list of sub-models trained during tuning.
If set to |
| parallelism | The number of threads to use when running parallel algorithms. Default is 1 for serial execution. |
| seed | A random seed. Set this value if you need your results to be reproducible across repeated calls. |
| uid | A character string used to uniquely identify the ML estimator. |
| ... | Optional arguments; currently unused. |
| train_ratio | Ratio between train and validation data. Must be between 0 and 1. Default: 0.75 |
The object returned depends on the class of x
.
spark_connection
: When x
is a spark_connection
, the function returns an instance of a ml_cross_validator
or ml_traing_validation_split
object.
ml_pipeline
: When x
is a ml_pipeline
, the function returns a ml_pipeline
with
the tuning estimator appended to the pipeline.
tbl_spark
: When x
is a tbl_spark
, a tuning estimator is constructed then
immediately fit with the input tbl_spark
, returning a ml_cross_validation_model
or a
ml_train_validation_split_model
object.
For cross validation, ml_sub_models()
returns a nested
list of models, where the first layer represents fold indices and the
second layer represents param maps. For train-validation split,
ml_sub_models()
returns a list of models, corresponding to the
order of the estimator param maps.
ml_validation_metrics()
returns a data frame of performance
metrics and hyperparameter combinations.
ml_cross_validator()
performs k-fold cross validation while ml_train_validation_split()
performs tuning on one pair of train and validation datasets.
if (FALSE) { sc <- spark_connect(master = "local") iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE) # Create a pipeline pipeline <- ml_pipeline(sc) %>% ft_r_formula(Species ~ .) %>% ml_random_forest_classifier() # Specify hyperparameter grid grid <- list( random_forest = list( num_trees = c(5, 10), max_depth = c(5, 10), impurity = c("entropy", "gini") ) ) # Create the cross validator object cv <- ml_cross_validator( sc, estimator = pipeline, estimator_param_maps = grid, evaluator = ml_multiclass_classification_evaluator(sc), num_folds = 3, parallelism = 4 ) # Train the models cv_model <- ml_fit(cv, iris_tbl) # Print the metrics ml_validation_metrics(cv_model) }


Gerard Arthur Way (born April 9, 1977) is an American singer, songwriter, musician, and comic
book writer. He is the lead vocalist and co-founder of My Chemical Romance from its
formation in September 2001 until its split in March 2013. His debut solo album Hesitant Alien was
released on September 30, 2014. On October 28, 2018, Way released his first major solo single in over two years,
titled "Baby You’re a Haunted House".
Way is also the co-founder of DC Comics' Young Animal imprint.
He wrote the comic mini-series The True Lives of the Fabulous Killjoys and the Eisner Award-winning
comic book series The Umbrella Academy.
Way was working as an intern for Cartoon Network in New York City during the September 11, 2001 attacks.
Seeing the effects of the attacks first-hand prompted Way to change his views on life in the following weeks.
He told Spin magazine, "I literally said to myself, 'Fuck art. I've gotta get out of the basement. I've gotta see the world.
I've gotta make a difference.'" To help deal with the emotional effects the attacks had on him, Way wrote the lyrics to the song
"Skylines and Turnstiles", which became the first song by My Chemical Romance.
Raymond Manuel Toro-Ortiz (born July 15, 1977) is an American musician. He is lead guitarist
and backing vocalist of My Chemical Romance. On January 1, 2015,
he released a new song, titled "For the Lost and Brave", on his website, mentioning Leelah Alcorn,
a transgender teen who committed suicide, in the blog post.On November 18, 2016, Toro released his
debut album, Remember the Laughter, after recording it primarily at his home since 2013.
Rodney's frontman Shawn Dillon is credited with introducing Toro to Gerard Way in the late 1990s.
Toro agreed to work with him and Pelissier in practice sessions, which were primarily held in Pelissier's attic.
After Gerard's younger brother Mikey joined the trio as bass player My Chemical Romance was formed.
Especially since My Chemical Romance's third album The Black Parade, Toro has been compared to Queen guitarist Brian May.
He has long cited May as a key influence on his guitar style. Toro was the leading force behind My Chemical Romance's
#SINGItForJapan project, which was dedicated to supporting those affected by the 2011 Tōhoku earthquake and tsunami
in Japan. He arranged much of the instrumentation of the band's original track "Sing" to create a sound that
emulated traditional Japanese music.
Frank Anthony Iero, Jr. (born October 31, 1981) is an American musician. He is the rhythm guitarist and
backup vocalist of My Chemical Romance and the post-hardcore band Leathermouth. He has a solo punk rock
project titled Frank Iero and the Future Violents (formerly frnkiero andthe cellabration and Frank Iero and the Patience).
He released his debut solo album titled Stomachaches on August 26, 2014.
Frank started playing in local bands on the New Jersey punk scene when aged 11. Before joining My Chemical Romance
he served as frontman for the punk band Pencey Prep. The band released an album, Heartbreak in Stereo,
on the independent Eyeball Records before disbanding.
Whilst playing with Pencey Prep, he became friends with
Gerard Way and the other My Chemical Romance members, became a fan of their original demo and helped them get
their first shows. After his band broke up, Frank played in several bands, including I Am A Graveyard, Hybrid,
Sector 12, and American Nightmare before being offered the slot of rhythm guitar in My Chemical Romance. Iero
joined My Chemical Romance in 2002 and was featured on two tracks, "Honey, This Mirror Isn't Big Enough
for the Two of Us" and "Early Sunsets Over Monroeville", on their debut album I Brought You My Bullets, You Brought Me Your Love.
Michael James Way (born September 10, 1980) is an American musician and actor. He is best known as the bassist of My Chemical Romance.
He is also the multi-instrumentalist and backing vocalist of rock duo Electric Century. They announced their debut album For the Night
to Control on February 24, 2016, and was released on March 9, exclusively with that week's edition of Kerrang! Magazine.
Mikey co-wrote Collapser with Shaun Simon due to begin release July 2019 on DC Comics.
Way joined American rock band My Chemical Romance in 2001 as their bassist and suggested the band's name after
he read the book Ecstasy: Three Tales of Chemical Romance. When Way joined the band, he had to improve
his bass skills in less than a week.
In November 2015, Way was featured in Good Charlotte's music video for their first single since their hiatus, "Makeshift Love".
He also contributed to Andy Black's debut solo album The Shadow Side, a pseudonym project from Black Veil Brides frontman
Andy Biersack. Way recorded bass for American pop punk band Waterparks on their Cluster EP, and later joined them on tour
with Good Charlotte, including performances at Reading and Leeds festivals.


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AWS::IoTAnalytics::Dataset
The AWS::IoTAnalytics::Dataset resource stores data retrieved from a data store by applying a
queryAction
(an SQL query) or a containerAction
(executing a containerized application).
The data set can be populated manually by calling CreateDatasetContent
or automatically according
to a trigger
you specify. For more information, see
How to Use AWS IoT Analytics in the AWS IoT Analytics User Guide.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "Type" : "AWS::IoTAnalytics::Dataset", "Properties" : { "Actions" :
[ Action, ... ]
, "ContentDeliveryRules" :[ DatasetContentDeliveryRule, ... ]
, "DatasetName" :String
, "LateDataRules" :[ LateDataRule, ... ]
, "RetentionPeriod" :RetentionPeriod
, "Tags" :[ Tag, ... ]
, "Triggers" :[ Trigger, ... ]
, "VersioningConfiguration" :VersioningConfiguration
} }
YAML
Type: AWS::IoTAnalytics::Dataset Properties: Actions:
- Action
ContentDeliveryRules:- DatasetContentDeliveryRule
DatasetName:String
LateDataRules:- LateDataRule
RetentionPeriod:RetentionPeriod
Tags:- Tag
Triggers:- Trigger
VersioningConfiguration:VersioningConfiguration
Properties
Actions
-
The
DatasetAction
objects that automatically create the dataset contents.Required: Yes
Type: List of Action
Maximum:
1
Update requires: No interruption
ContentDeliveryRules
-
When dataset contents are created they are delivered to destinations specified here.
Required: No
Type: List of DatasetContentDeliveryRule
Maximum:
20
Update requires: No interruption
DatasetName
-
The name of the dataset.
Required: No
Type: String
Minimum:
1
Maximum:
128
Pattern:
(^(?!_{2}))(^[a-zA-Z0-9_]+$)
Update requires: Replacement
LateDataRules
-
A list of data rules that send notifications to CloudWatch, when data arrives late. To specify
lateDataRules
, the dataset must use a DeltaTimer filter.Required: No
Type: List of LateDataRule
Maximum:
1
Update requires: No interruption
RetentionPeriod
-
Optional. How long, in days, message data is kept for the dataset.
Required: No
Type: RetentionPeriod
Update requires: No interruption
Tags
-
Metadata which can be used to manage the data set.
For more information, see Tag.
Required: No
Type: List of Tag
Maximum:
50
Update requires: No interruption
Triggers
-
The
DatasetTrigger
objects that specify when the dataset is automatically updated.Required: No
Type: List of Trigger
Maximum:
5
Update requires: No interruption
VersioningConfiguration
-
Optional. How many versions of dataset contents are kept. If not specified or set to null, only the latest version plus the latest succeeded version (if they are different) are kept for the time period specified by the
retentionPeriod
parameter. For more information, see Keeping Multiple Versions of AWS IoT Analytics datasets in the AWS IoT Analytics User Guide.Required: No
Type: VersioningConfiguration
Update requires: No interruption
Examples
Simple SQL Dataset
The following example creates a simple SQL dataset.
JSON
{ "Description": "Create a simple SQL Dataset", "Resources": { "Dataset": { "Type": "AWS::IoTAnalytics::Dataset", "Properties": { "DatasetName": "SimpleSQLDataset", "Actions": [ { "ActionName": "SqlAction", "QueryAction": { "SqlQuery": "select * from Datastore" } } ], "Triggers": [ { "Schedule": { "ScheduleExpression": "cron(0 12 * * ? *)" } } ] } } } }
YAML
--- Description: "Create a simple SQL Dataset" Resources: Dataset: Type: "AWS::IoTAnalytics::Dataset" Properties: DatasetName: "SimpleSQLDataset" Actions: - ActionName: "SqlAction" QueryAction: SqlQuery: "select * from Datastore" Triggers: - Schedule: ScheduleExpression: "cron(0 12 * * ? *)"
Complex SQL Dataset
The following example creates a complex SQL dataset.
JSON
{ "Description": "Create a complex SQL Dataset", "Resources": { "Dataset": { "Type": "AWS::IoTAnalytics::Dataset", "Properties": { "DatasetName": "ComplexSQLDataset", "Actions": [ { "ActionName": "SqlAction", "QueryAction": { "SqlQuery": "select * from Datastore", "Filters": [ { "DeltaTime": { "OffsetSeconds": 1, "TimeExpression": "timestamp" } } ] } } ], "Triggers": [ { "Schedule": { "ScheduleExpression": "cron(0 12 * * ? *)" } } ], "RetentionPeriod": { "Unlimited": false, "NumberOfDays": 10 }, "Tags": [ { "Key": "keyname1", "Value": "value1" }, { "Key": "keyname2", "Value": "value2" } ] } } } }
YAML
--- Description: "Create a complex SQL Dataset" Resources: Dataset: Type: "AWS::IoTAnalytics::Dataset" Properties: DatasetName: "ComplexSQLDataset" Actions: - ActionName: "SqlAction" QueryAction: SqlQuery: "select * from Datastore" Filters: - DeltaTime: OffsetSeconds: 1 TimeExpression: "timestamp" Triggers: - Schedule: ScheduleExpression: "cron(0 12 * * ? *)" RetentionPeriod: Unlimited: false NumberOfDays: 10 Tags: - Key: "keyname1" Value: "value1" - Key: "keyname2" Value: "value2"
Simple Container Dataset
The following example creates a simple Container dataset.
JSON
{ "Description": "Create a simple container Dataset", "Resources": { "ContainerDataset": { "Type": "AWS::IoTAnalytics::Dataset", "Properties": { "DatasetName": "SimpleContainerDataset", "Actions": [ { "ActionName": "ContainerAction", "ContainerAction": { "Image": "<your_Account_Id>.dkr.ecr.us-east-1.amazonaws.com/sampleimage", "ExecutionRoleArn": "arn:aws:iam::<your_Account_Id>:role/ExecutionRole", "ResourceConfiguration": { "ComputeType": "ACU_1", "VolumeSizeInGB": 10 }, "Variables": [ { "VariableName": "Variable1", "StringValue": "StringValue" } ] } } ], "Triggers": [ { "Schedule": { "ScheduleExpression": "cron(0 12 * * ? *)" } } ] } } } }
YAML
--- Description: "Create a simple container Dataset" Resources: ContainerDataset: Type: "AWS::IoTAnalytics::Dataset" Properties: DatasetName: "SimpleContainerDataset" Actions: - ActionName: ContainerAction ContainerAction: Image: "<your_Account_Id>.dkr.ecr.us-east-1.amazonaws.com/sampleimage" ExecutionRoleArn: "arn:aws:iam::<your_Account_Id>:role/ExecutionRole" ResourceConfiguration: ComputeType: "ACU_1" VolumeSizeInGB: 10 Variables: - VariableName: "Variable1" StringValue: StringValue Triggers: - Schedule: ScheduleExpression: "cron(0 12 * * ? *)"
Complex Container Dataset
The following example creates a complex Container dataset.
JSON
{ "Description": "Create a complex container Dataset", "Resources": { "TriggeringDataset": { "Type": "AWS::IoTAnalytics::Dataset", "Properties": { "DatasetName": "TriggeringDataset", "Actions": [ { "ActionName": "SqlAction", "QueryAction": { "SqlQuery": "select * from Datastore" } } ] } }, "ContainerDataset": { "Type": "AWS::IoTAnalytics::Dataset", "DependsOn": "TriggeringDataset", "Properties": { "DatasetName": "ComplexContainerDataset", "Actions": [ { "ActionName": "ContainerAction", "ContainerAction": { "Image": "<your_Account_Id>.dkr.ecr.us-east-1.amazonaws.com/sampleimage", "ExecutionRoleArn": "arn:aws:iam::<your_Account_Id>:role/ExecutionRole", "ResourceConfiguration": { "ComputeType": "ACU_1", "VolumeSizeInGB": 10 }, "Variables": [ { "VariableName": "Variable1", "StringValue": "StringValue" }, { "VariableName": "Variable2", "DoubleValue": 1 }, { "VariableName": "Variable3", "DatasetContentVersionValue": { "DatasetName": "BasicDataset" } }, { "VariableName": "Variable4", "OutputFileUriValue": { "FileName": "fileName" } } ] } } ], "Triggers": [ { "TriggeringDataset": { "DatasetName": "TriggeringDataset" } } ] } } } }
YAML
--- Description: "Create a complex container Dataset" Resources: TriggeringDataset: Type: "AWS::IoTAnalytics::Dataset" Properties: DatasetName: "TriggeringDataset" Actions: - ActionName: "SqlAction" QueryAction: SqlQuery: "select * from Datastore" ContainerDataset: Type: "AWS::IoTAnalytics::Dataset" DependsOn: TriggeringDataset Properties: DatasetName: "ComplexContainerDataset" Actions: - ActionName: "ContainerAction" ContainerAction: Image: "<your_Account_Id>.dkr.ecr.us-east-1.amazonaws.com/sampleimage" ExecutionRoleArn: "arn:aws:iam::<your_Account_Id>:role/ExecutionRole" ResourceConfiguration: ComputeType: "ACU_1" VolumeSizeInGB: 10 Variables: - VariableName: "Variable1" StringValue: "StringValue" - VariableName: "Variable2" DoubleValue: 1 - VariableName: "Variable3" DatasetContentVersionValue: DatasetName: "BasicDataset" - VariableName: "Variable4" OutputFileUriValue: FileName: "fileName" Triggers: - TriggeringDataset: DatasetName: "TriggeringDataset"
See also
-
How to Use AWS IoT Analytics in the AWS IoT Analytics User Guide
-
CreateDataset in the AWS IoT Analytics API Reference


File: ../model/addon/index.ts
/** In EmberData a `Model` is a class defining the attributes and relationships of a specific resource `type` (model name). In this sense it represents a static "schema". Data for individual resources fetched from your API is presented to the UI via instances of the `Model`s you define. An instantiated `Model` is refered to as a `record`. When we refer to the `ModelClass` we are referring to the class definition and the static schema methods present on it. When we refer to a `record` we refer to a specific class instance presenting the resource data for a given `type` and `id`. ### Defining a Model ```app/models/person.js import Model, { attr, belongsTo, hasMany } from '@ember-data/model'; export default Model.extend({ name: attr(), dog: belongsTo('pet', { inverse: 'owners', async: false }), friends: hasMany('person', { inverse: 'friends', async: true }), }); ``` ### modelName convention By convention, the name of a given model (its `type`) matches the name of the file in the `app/models` folder and should be lowercase, singular and dasherized. @module @ember-data/model @main @ember-data/model @class Model @public */ export { Model as default, attr, belongsTo, hasMany } from './-private';