| Methods Summary | ||
|---|---|---|
protected static
|
#
createInvalidContentException( $content )
Create invalid content exception for the specified content.
|
|
protected static
|
#
traverseMatch( SDom\Dom $dom , array $selectorTokens , SDom\Node\NodeInterface $node , SDom\Node\NodeInterface $effectiveRoot )
Traverse the specified node and all of its child nodes recursively (any node type is accepted, but only element
nodes are processed) and match against the specified selector tokens.
Add all nodes that match at least one of the selector tokens to the specified Dom collection and return it.
The supplied $effectiveRoot node will be considered the root of the tree, even if there are more ancestors.
Child nodes will be treated, when matching, as if they don't have a parent node.
|
|
protected static
|
#
findFirstElement( array $nodes )
Loop over the supplied array of nodes and return the first matched element node or NULL.
|
|
protected static
|
#
findFirstInnermostElement( array $nodes , SDom\Node\Element $fallback = NULL )
Loop over the supplied array of nodes and find the first matched element node. If it has child nodes, find the
first matched element child, then recurse until the inner-most element node with no children (element nodes)
is found and return it.
If the array does not contain an element node, return NULL.
|
|
public
|
#
__construct( mixed $content = NULL )
Dom constructor.
Create new collection from the specified content.
If the content is NULL, create empty collection.
If the content is a Dom instance, copy the nodes into the new collection.
If the content is a NodeInterface, add it to the collection.
If the content is a Token, convert it to the corresponding NodeInterface (tree) and add it to the collection.
If the content is a TokenCollection, convert it to NodeInterface instances and add them to the collection.
If the content is a string, parse as HTML and treat as TokenCollection.
Strings with all whitespace produce empty collections. Strings with invalid HTML throw an exception.
|
|
public
|
||
public
|
#
getIterator( )
Return an \ArrayIterator with all nodes wrapped in a Dom instance.
Implementation of |
|
public
|
||
public
|
||
public
|
||
public
|
||
public
|
||
public
|
||
public
|
||
public
|
||
public
|
#
append( $content )
Insert content after all immediate child nodes of each Element node in the collection.
If any node derived from the content already has a parent node, a cloned copy will be used instead and it will
be assigned a new parent node. This means that, if appended to more than one Element node, references to each
appended node will only point to the very first insertion.
E.g. if the same node is appended to two or more Element nodes, its reference will point to the node with the
first Element as parent. Nodes appended to all other Element nodes will be cloned copies. The same rule applies
to child nodes of appended nodes, at any depth, as the whole sub-tree is cloned recursively.
|
|
public
|
#
prepend( $content )
Insert content before all immediate child nodes of each Element node in the collection.
If any node derived from the content already has a parent node, a cloned copy will be used instead and it will
be assigned a new parent node. This means that, if prepended to more than one Element node, references to each
prepended node will only point to the very first insertion.
E.g. if the same node is prepended to two or more Element nodes, its reference will point to the node with the
first Element as parent. Nodes prepended to all other Element nodes will be cloned copies. The same rule applies
to child nodes of prepended nodes, at any depth, as the whole sub-tree is cloned recursively.
If the supplied content resolves to a collection of nodes, they will be prepended as a group, keeping the order.
|
|
public
|
#
before( $content )
Insert content before each Element node in the collection. If an element in the collection does not have a
parent node it will be skipped / ignored.
If any node derived from the content already has a parent node, a cloned copy will be used instead and it will
be assigned a new parent node. This means that, if inserted before more than one Element node, references to
each inserted node will only point to the very first successful insertion.
E.g. if the same node is inserted before two or more Element nodes, its reference will point to the node
inserted before the first eligible Element in the collection (an Element without a parent node is not eligible
and will be ignored). Nodes inserted before all other Element nodes will be cloned copies. The same rule applies
to child nodes of inserted nodes, at any depth, as the whole sub-tree is cloned recursively.
|
|
public
|
#
after( $content )
Insert content after each Element node in the collection. If an element in the collection does not have a
parent node it will be skipped / ignored.
If any node derived from the content already has a parent node, a cloned copy will be used instead and it will
be assigned a new parent node. This means that, if inserted after more than one Element node, references to
each inserted node will only point to the very first successful insertion.
E.g. if the same node is inserted after two or more Element nodes, its reference will point to the node
inserted after the first eligible Element in the collection (an Element without a parent node is not eligible
and will be ignored). Nodes inserted after all other Element nodes will be cloned copies. The same rule applies
to child nodes of inserted nodes, at any depth, as the whole sub-tree is cloned recursively.
|
|
public
|
#
wrap( $content )
Wrap a clone of the supplied content around each node with a parent (not only element nodes) in the collection.
If the content resolves to a collection of more than one wrapping element node, use only the first one.
If the wrapping element has children use a single-element-per-level sub-tree, where the wrapping element is
the root and may contain one and only one element node, which may contain another one and only one element node
and so on.
Wrap the current collection nodes in clones of this sub-tree as immediate children of the inner-most element
node of the tree. If a node in the collection does not have a parent element, ignore it.
If the wrapping collection does not contain at least one element node, do nothing.
Return the original collection for chaining.
|
|
public
|
||
public
|
||
public
|
||
public
|
#
removeClass( string $className = NULL )
Remove a single class, multiple classes, or all classes from each Element node in the collection.
|
|
public
|
||
public
|
||
public
|
||
public
|
||
public
|
| Properties Summary | ||
|---|---|---|
protected static
Symfony\Component\CssSelector\Parser\Parser
|
$selectorParser
Singleton instance to a CSS selector parser.
|
#
NULL
|
protected static
SDom\SelectorMatcher
|
$selectorMatcher
Singleton instance to a CSS selector matcher.
|
#
NULL
|
protected static
Kevintweber\HtmlTokenizer\HtmlTokenizer
|
$tokenizer
Singleton instance to an HTML tokenizer.
|
#
NULL
|
protected
\SDom\Node\NodeInterface[]
|
$nodes
Collection of nodes.
|
#
NULL
|


Ezekiel 19:2
And say, What is thy mother? A lioness: she lay down among lions, she nourished her whelps among young lions.


TIFFCMP(1) TIFFCMP(1) NAME tiffcmp - compare two TIFF files SYNOPSIS tiffcmp [ options ] file1.tif file2.tif DESCRIPTION Tiffcmp compares the tags and data in two files created according to the Tagged Image File Format, Revision 6.0. The schemes used for compressing data in each file are immaterial when data are compared-data are compared on a scanline-by-scanline basis after decompression. Most directory tags are checked; notable exceptions are: GrayResponseCurve, ColorResponse‐ Curve, and ColorMap tags. Data will not be compared if any of the BitsPerSample, SamplesPerPixel, or ImageWidth values are not equal. By default, tiffcmp will terminate if it encounters any difference. OPTIONS -l List each byte of image data that differs between the files. -z number List specified number of image data bytes that differs between the files. -t Ignore any differences in directory tags. BUGS Tags that are not recognized by the library are not compared; they may also generate spurious diagnostics. The image data of tiled files is not compared, since the TIFFReadScan‐ line() function is used. An error will be reported for tiled files. The pixel and/or sample number reported in differences may be off in some exotic cases. SEE ALSO pal2rgb(1), tiffinfo(1), tiffcp(1), tiffmedian(1), libtiff(3TIFF) Libtiff library home page: http://www.remotesensing.org/libtiff/ libtiff November 2, 2005 TIFFCMP(1)


NEW for 2016! Release 3.0 for DS >= 1.6
(
rel. 1.0 for DS <= 1.4
|
rel. 2.0 for DS >= 1.5
|
more information on SCN
)
News...
[21.12.2015] - this release is for Design Studio 1.6, it is the active release with community support (report issue under issues on github)


The Security Tab provides access to user controlled security settings.
When checked, the program clears clipboard contents of any stored data (such as a password) when the application is minimized. This prevents anyone else or any other program from reading the contents of the clipboard. Note that selecting this option with the "Copy password to clipboard, and then immediately minimize" double click action in the Misc Options tab will lead to unexpected results (the password will be copied, but immediately cleared).
When checked, the program clears clipboard contents of any stored data (such as a password) when the interface is minimized or closed. This prevents anyone else or any other program from reading the contents of the clipboard.
When checked (default), items that Password Safe place on the clipboard (such as passwords) are not include in the clipboard history, in systems where this feature is supported (Windows 10 and later).
When checked, a prompt is displayed requesting user confirmation every time a password is copied from the database to the clipboard.
When the database is locked due to one of the events below, the database is saved, and then all its data is cleared from memory. However, the undo/redo log is retained so that changes can be undone (or redone) after unlocking. Note: all entry information in these commands remain encrypted in memory.
When checked, the program locks the password database when the application is minimized. When maximized again for use, a password is required to access the database.
Checking this causes the password database to lock when the workstation is locked preventing unauthorized access by users that may have access to the workstation.
Checking this option and setting a time causes Password Safe to lock the password database after the set interval has passed without user actions in the application.
By default, Password Safe copies the entry's password to the clipboard when the user selects the 'Browse to URL' action so that it is ready to paste into the required field. However, many web sites now require other information or no information (e.g., a search engine). by deselecting this option, Password Safe will no longer paste anything into the clipboard during this action.
The setting of the slider determines how much processing is required to open and save a Password Safe database. The Standard setting should provide sufficient security without delaying the opening of a database significantly on most platforms. When increasing this value, consider the slowest CPU that you will use to access the database.
Password Safe uses the original Key Stretching algorithm published by Bruce Schneier, which expands the password to contain more entropy to make it significantly harder to employ brute-force processing. The slider sets the number of iterations of the stretching, between 2048 and 222.


Login (w/ Autocomplete Disabled)
User Name
Please enter your user name.
Password
Please enter your password.
Login
Need help with your password?


Coronation Street fans confused as baby Bertie looks different from one scene to another
There is a reason why he suddenly looks different
by Charlotte RodriguesFans of Coronation Street have been left confused as baby Bertie seems to look completely different from one scene to another.
The little boy, who is the son of Daniel Osbourne and the late Sinead Osbourne, first appeared last year after he was born prematurely.
Now the tot seems to be getting a bit older fans have noticed he looks different from scene to scene.
However there is a simple explanation: Bertie is played by three baby actors.
The character is played by Ellis Bain and twins Elias and Rufus Morgan-Smith.
The three babies have played Bertie since his first appearance in January of this year.
Read More: Coronation Street viewers distracted by Ed Bailey's voice
In Bertie's short life he's experienced a lot of tragedy.
When Sinead was pregnant with him, she discovered she had cervical cancer.
However she decided not to have treatment as she wanted to put her baby's life first.
Sinead gave birth to Bertie prematurely and started treatment straight after his birth.
Read More: Coronation Street SPOILERS: Fiz and Tyrone's kids 'are taken into care'
But sadly Sinead passed away from her cancer just a couple of weeks ago as Daniel read Bertie a story.
Before her death, Sinead recorded videos for Bertie to watch when he grew up, however Daniel's laptop was stolen leaving him devastated.
Will Bertie ever get to watch the videos of his mum?
Coronation Street is on Monday, Wednesday and Friday at 7.30pm and 8.30pm.
Leave us a comment on our Facebook page @EntertainmentDailyFix and let us know what you think!


Least Square Regression Line Calculator
Input each data point on a new line with a space betwen x and y
Example:
x y
0 1
1 3
2 5
Generate Least Square Regression Line


Katib API
Return generated StudyID.
| Field | Type | Label | Description |
| study_id | string |
|
Create a Study from Study Config.
Generate an unique ID and store the Study to DB.
| Field | Type | Label | Description |
| study_config | StudyConfig |
|
Return generated TrialID.
| Field | Type | Label | Description |
| trial_id | string |
|
Create a Trial from Trial Config.
Generate an unique ID and store the Trial to DB.
| Field | Type | Label | Description |
| trial | Trial |
|
| Field | Type | Label | Description |
| name | string |
|
|
| path | string |
|
Return deleted Study ID.
| Field | Type | Label | Description |
| study_id | string |
|
Delete a Study from DB by Study ID.
| Field | Type | Label | Description |
| study_id | string |
|
Parameter for EarlyStopping service. Key-value format.
| Field | Type | Label | Description |
| name | string | Name of Parameter. |
|
| value | string | Value of Parameter. |
| Field | Type | Label | Description |
| param_id | string |
|
|
| early_stopping_algorithm | string |
|
|
| early_stopping_parameters | EarlyStoppingParameter | repeated |
|
Feasible space for optimization.
Int and Double type use Max/Min.
Discrete and Categorical type use List.
| Field | Type | Label | Description |
| max | string | Max Value |
|
| min | string | Minimum Value |
|
| list | string | repeated | List of Values. |
| step | string | Step for double or int parameter |
| Field | Type | Label | Description |
| early_stopping_parameter_sets | EarlyStoppingParameterSet | repeated |
|
| Field | Type | Label | Description |
| study_id | string |
|
| Field | Type | Label | Description |
| early_stopping_parameters | EarlyStoppingParameter | repeated |
|
| Field | Type | Label | Description |
| param_id | string |
|
| Field | Type | Label | Description |
| metrics_log_sets | MetricsLogSet | repeated |
|
| Field | Type | Label | Description |
| study_id | string |
|
|
| worker_ids | string | repeated |
|
| metrics_names | string | repeated |
|
| Field | Type | Label | Description |
| model | ModelInfo |
|
| Field | Type | Label | Description |
| study_name | string |
|
|
| worker_id | string |
|
| Field | Type | Label | Description |
| models | ModelInfo | repeated |
|
| Field | Type | Label | Description |
| study_name | string |
|
| Field | Type | Label | Description |
| studies | StudyOverview | repeated |
|
| Field | Type | Label | Description |
| should_stop_worker_ids | string | repeated |
|
| Field | Type | Label | Description |
| study_id | string |
|
|
| early_stopping_algorithm | string |
|
|
| param_id | string |
|
Return a overview list of Studies.
| Field | Type | Label | Description |
| study_overviews | StudyOverview | repeated |
|
Get all Study Configs from DB.
Return a config of specified Study.
| Field | Type | Label | Description |
| study_config | StudyConfig |
|
Get a Study Config from DB by ID of Study.
| Field | Type | Label | Description |
| study_id | string |
|
| Field | Type | Label | Description |
| suggestion_parameter_sets | SuggestionParameterSet | repeated |
|
| Field | Type | Label | Description |
| study_id | string |
|
| Field | Type | Label | Description |
| suggestion_parameters | SuggestionParameter | repeated |
|
| Field | Type | Label | Description |
| param_id | string |
|
| Field | Type | Label | Description |
| trials | Trial | repeated |
|
| Field | Type | Label | Description |
| study_id | string |
|
|
| suggestion_algorithm | string |
|
|
| request_number | int32 |
|
|
| log_worker_ids | string | repeated |
|
| param_id | string |
|
Return a trial configuration by specified trial ID
| Field | Type | Label | Description |
| trial | Trial |
|
Get a trial configuration from DB by trial ID
| Field | Type | Label | Description |
| trial_id | string |
|
Return a trial list in specified Study.
| Field | Type | Label | Description |
| trials | Trial | repeated |
|
Get a Trial Configs from DB by ID of Study.
| Field | Type | Label | Description |
| study_id | string |
|
| Field | Type | Label | Description |
| worker_full_infos | WorkerFullInfo | repeated |
|
Get a full information related to specified Workers.
It includes Worker Config, HyperParameters and Metrics Logs.
| Field | Type | Label | Description |
| study_id | string |
|
|
| trial_id | string |
|
|
| worker_id | string |
|
|
| only_latest_log | bool |
|
Return a Worker list by specified condition.
| Field | Type | Label | Description |
| workers | Worker | repeated |
|
Get a configs and status of a Worker from DB by ID of Study, Trial or Worker.
| Field | Type | Label | Description |
| study_id | string |
|
|
| trial_id | string |
|
|
| worker_id | string |
|
GraphConfig contains a config of DAG
| Field | Type | Label | Description |
| num_layers | int32 | Number of layers |
|
| input_size | int32 | repeated | Dimenstions of input size |
| output_size | int32 | repeated | Dimensions of output size |
Metrics of a worker
| Field | Type | Label | Description |
| name | string | Name of metrics. |
|
| value | string | Value of metrics. Double float. |
Metrics logs of a worker
| Field | Type | Label | Description |
| name | string | Name of metrics. |
|
| values | MetricsValueTime | repeated | Log of metrics. Ordered by time series. |
Logs of metrics for a worker.
| Field | Type | Label | Description |
| worker_id | string | ID of the corresponding worker. |
|
| metrics_logs | MetricsLog | repeated | Logs of metrics. |
| worker_status | State | Status of the corresponding worker. |
Metrics of a worker with timestamp
| Field | Type | Label | Description |
| time | string | Timestamp RFC3339 format. |
|
| value | string | Value of metrics. Double float. |
| Field | Type | Label | Description |
| study_name | string |
|
|
| worker_id | string |
|
|
| parameters | Parameter | repeated |
|
| metrics | Metrics | repeated |
|
| model_path | string |
|
NasConfig contains a config of NAS job
| Field | Type | Label | Description |
| graph_config | GraphConfig | Config of DAG |
|
| operations | NasConfig.Operations | List of Operation |
| Field | Type | Label | Description |
| operation | Operation | repeated |
|
Config for operations in DAG
| Field | Type | Label | Description |
| operationType | string | Type of operation in DAG |
|
| parameter_configs | Operation.ParameterConfigs | List of ParameterConfig |
List of ParameterConfig
| Field | Type | Label | Description |
| configs | ParameterConfig | repeated |
|
Value of a Hyper parameter.
This will be created from a correcponding Config.
| Field | Type | Label | Description |
| name | string | Name of the parameter. |
|
| parameter_type | ParameterType | Type of the parameter. |
|
| value | string | Value of the parameter. |
Config for a Hyper parameter.
Katib will create each Hyper parameter from this config.
| Field | Type | Label | Description |
| name | string | Name of the parameter. |
|
| parameter_type | ParameterType | Type of the parameter. |
|
| feasible | FeasibleSpace | FeasibleSpace for the parameter. |
Return generated WorkerID.
| Field | Type | Label | Description |
| worker_id | string |
|
Create a Worker from Worker Config.
Generate an unique ID and store the Worker to DB.
| Field | Type | Label | Description |
| worker | Worker |
|
| Field | Type | Label | Description |
| study_id | string |
|
|
| metrics_log_sets | MetricsLogSet | repeated |
|
| Field | Type | Label | Description |
| model | ModelInfo |
|
|
| data_set | DataSetInfo |
|
|
| tensor_board | bool |
|
| Field | Type | Label | Description |
| study_name | string |
|
|
| owner | string |
|
|
| description | string |
|
| Field | Type | Label | Description |
| param_id | string |
|
| Field | Type | Label | Description |
| study_id | string |
|
|
| early_stopping_algorithm | string |
|
|
| param_id | string |
|
|
| early_stopping_parameters | EarlyStoppingParameter | repeated |
|
| Field | Type | Label | Description |
| param_id | string |
|
| Field | Type | Label | Description |
| study_id | string |
|
|
| suggestion_algorithm | string |
|
|
| param_id | string |
|
|
| suggestion_parameters | SuggestionParameter | repeated |
|
| Field | Type | Label | Description |
| study_id | string |
|
| Field | Type | Label | Description |
| study_id | string |
|
|
| worker_ids | string | repeated |
|
| is_complete | bool |
|
Config of a Study. Study represents a single optimization run over a feasible space.
Each Study contains a configuration describing the feasible space, as well as a set of Trials.
It is assumed that objective function f(x) does not change in the course of a Study.
| Field | Type | Label | Description |
| name | string | Name of Study. |
|
| owner | string | Owner of Study. |
|
| optimization_type | OptimizationType | Optimization type. |
|
| optimization_goal | double | Goal of optimization value. |
|
| parameter_configs | StudyConfig.ParameterConfigs | List of ParameterConfig |
|
| access_permissions | string | repeated | Access Permission |
| tags | Tag | repeated | Tag for Study |
| objective_value_name | string | Name of objective value. |
|
| metrics | string | repeated | List of metrics name. |
| jobId | string | ID of studyjob that is created from this config. |
|
| nas_config | NasConfig | Config for NAS job |
|
| job_type | string | Type of the job, NAS or HP |
List of ParameterConfig
| Field | Type | Label | Description |
| configs | ParameterConfig | repeated |
|
Overview of a study. For UI.
| Field | Type | Label | Description |
| name | string | Name of Study. |
|
| owner | string | Owner of Study. |
|
| id | string | Study ID. |
|
| description | string | Discretption of Study. |
Parameter for Suggestion service. Key-value format.
| Field | Type | Label | Description |
| name | string | Name of Parameter. |
|
| value | string | Value of Parameter. |
| Field | Type | Label | Description |
| param_id | string |
|
|
| suggestion_algorithm | string |
|
|
| suggestion_parameters | SuggestionParameter | repeated |
|
Tag for each resource.
| Field | Type | Label | Description |
| name | string | Name of tag. |
|
| value | string | Value of tag. |
A set of Hyperparameter.
In a study, multiple trials are evaluated by workers.
Suggestion service will generate next trials.
Create time will be filled in the server automatically side even user set the value
| Field | Type | Label | Description |
| trial_id | string | Trial ID. |
|
| study_id | string | Study ID. |
|
| parameter_set | Parameter | repeated | Hyperparameter set |
| objective_value | string | Objective Value |
|
| tags | Tag | repeated | Tags of Trial. |
| create_time | string | Trial create timestamp RFC3339 format. |
Update a Status of Worker.
| Field | Type | Label | Description |
| worker_id | string |
|
|
| status | State |
|
Return 1 if Suggestion Parameters is Valid, 0 if not
| Field | Type | Label | Description |
| study_config | StudyConfig |
|
|
| suggestion_algorithm | string |
|
|
| suggestion_parameters | SuggestionParameter | repeated |
|
A process of evaluation for a trial.
Types of worker supported by Katib are k8s Job, TF-Job, and Pytorch-Job.
| Field | Type | Label | Description |
| worker_id | string | Worker ID. |
|
| study_id | string | Study ID. |
|
| trial_id | string | Trial ID. |
|
| Type | string | Type of Worker |
|
| status | State | Status of Worker. |
|
| TemplatePath | string | Path for the manufest template of Worker. |
|
| tags | Tag | repeated | Tags of Worker. |
| Field | Type | Label | Description |
| Worker | Worker |
|
|
| parameter_set | Parameter | repeated |
|
| metrics_logs | MetricsLog | repeated |
|
Direction of optimization. Minimize or Maximize.
| Name | Number | Description |
| UNKNOWN_OPTIMIZATION | 0 | Undefined type and not used. |
| MINIMIZE | 1 | Minimize |
| MAXIMIZE | 2 | Maximize |
Types of value for HyperParameter.
| Name | Number | Description |
| UNKNOWN_TYPE | 0 | Undefined type and not used. |
| DOUBLE | 1 | Double float type. Use "Max/Min". |
| INT | 2 | Int type. Use "Max/Min". |
| DISCRETE | 3 | Discrete number type. Use "List" as float. |
| CATEGORICAL | 4 | Categorical type. Use "List" as string. |
Status code for worker.
This value is stored as TINYINT in MySQL.
| Name | Number | Description |
| PENDING | 0 | Pending. Created but not running. |
| RUNNING | 1 | Running. |
| COMPLETED | 2 | Completed. |
| KILLED | 3 | Killed. Not failed. |
| ERROR | 120 | Error. |
| Method Name | Request Type | Response Type | Description |
| GetShouldStopWorkers | GetShouldStopWorkersRequest | GetShouldStopWorkersReply |
Service for Main API for Katib
For each RPC service, we define mapping to HTTP REST API method.
The mapping includes the URL path, query parameters and request body.
https://cloud.google.com/service-infrastructure/docs/service-management/reference/rpc/google.api#http
| Method Name | Request Type | Response Type | Description |
| CreateStudy | CreateStudyRequest | CreateStudyReply | Create a Study from Study Config. Generate a unique ID and store the Study to DB. |
| GetStudy | GetStudyRequest | GetStudyReply | Get a Study Config from DB by ID of Study. |
| DeleteStudy | DeleteStudyRequest | DeleteStudyReply | Delete a Study from DB by Study ID. |
| GetStudyList | GetStudyListRequest | GetStudyListReply | Get all Study Configs from DB. |
| CreateTrial | CreateTrialRequest | CreateTrialReply | Create a Trial from Trial Config. Generate a unique ID and store the Trial to DB. |
| GetTrials | GetTrialsRequest | GetTrialsReply | Get a Trial Configs from DB by ID of Study. |
| GetTrial | GetTrialRequest | GetTrialReply | Get a Trial Configuration from DB by ID of Trial. |
| RegisterWorker | RegisterWorkerRequest | RegisterWorkerReply | Create a Worker from Worker Config. Generate a unique ID and store the Worker to DB. |
| GetWorkers | GetWorkersRequest | GetWorkersReply | Get a Worker Configs and Status from DB by ID of Study, Trial or Worker. |
| UpdateWorkerState | UpdateWorkerStateRequest | UpdateWorkerStateReply | Update a Status of Worker. |
| GetWorkerFullInfo | GetWorkerFullInfoRequest | GetWorkerFullInfoReply | Get full information related to specified Workers. It includes Worker Config, HyperParameters and Metrics Logs. |
| GetSuggestions | GetSuggestionsRequest | GetSuggestionsReply | Get Suggestions from a Suggestion service. |
| GetShouldStopWorkers | GetShouldStopWorkersRequest | GetShouldStopWorkersReply | |
| GetMetrics | GetMetricsRequest | GetMetricsReply | Get metrics of workers. You can get all logs of metrics since start of the worker. |
| SetSuggestionParameters | SetSuggestionParametersRequest | SetSuggestionParametersReply | Create or Update parameter set for a suggestion service. If you specify an ID of parameter set, it will update the parameter set by your request. If you don't specify an ID, it will create a new parameter set for corresponding study and suggestion service. The parameters are key-value format. |
| GetSuggestionParameters | GetSuggestionParametersRequest | GetSuggestionParametersReply | Get suggestion parameter set from DB specified. |
| GetSuggestionParameterList | GetSuggestionParameterListRequest | GetSuggestionParameterListReply | Get all suggestion parameter sets from DB. |
| SetEarlyStoppingParameters | SetEarlyStoppingParametersRequest | SetEarlyStoppingParametersReply | |
| GetEarlyStoppingParameters | GetEarlyStoppingParametersRequest | GetEarlyStoppingParametersReply | |
| GetEarlyStoppingParameterList | GetEarlyStoppingParameterListRequest | GetEarlyStoppingParameterListReply | |
| SaveStudy | SaveStudyRequest | SaveStudyReply | |
| SaveModel | SaveModelRequest | SaveModelReply | |
| ReportMetricsLogs | ReportMetricsLogsRequest | ReportMetricsLogsReply | Report a logs of metrics for workers. The logs for each worker must have timestamp and must be ordered in time series. When the log you reported are already reported before, it will be dismissed and get no error. |
| GetSavedStudies | GetSavedStudiesRequest | GetSavedStudiesReply | |
| GetSavedModels | GetSavedModelsRequest | GetSavedModelsReply | |
| ValidateSuggestionParameters | ValidateSuggestionParametersRequest | ValidateSuggestionParametersReply | Validate Suggestion Parameters from Study Job. |
| Method Name | Request Type | Response Type | Description |
| GetSuggestions | GetSuggestionsRequest | GetSuggestionsReply | |
| ValidateSuggestionParameters | ValidateSuggestionParametersRequest | ValidateSuggestionParametersReply |
| .proto Type | Notes | C++ Type | Java Type | Python Type |
| double | double | double | float | |
| float | float | float | float | |
| int32 | Uses variable-length encoding. Inefficient for encoding negative numbers – if your field is likely to have negative values, use sint32 instead. | int32 | int | int |
| int64 | Uses variable-length encoding. Inefficient for encoding negative numbers – if your field is likely to have negative values, use sint64 instead. | int64 | long | int/long |
| uint32 | Uses variable-length encoding. | uint32 | int | int/long |
| uint64 | Uses variable-length encoding. | uint64 | long | int/long |
| sint32 | Uses variable-length encoding. Signed int value. These more efficiently encode negative numbers than regular int32s. | int32 | int | int |
| sint64 | Uses variable-length encoding. Signed int value. These more efficiently encode negative numbers than regular int64s. | int64 | long | int/long |
| fixed32 | Always four bytes. More efficient than uint32 if values are often greater than 2^28. | uint32 | int | int |
| fixed64 | Always eight bytes. More efficient than uint64 if values are often greater than 2^56. | uint64 | long | int/long |
| sfixed32 | Always four bytes. | int32 | int | int |
| sfixed64 | Always eight bytes. | int64 | long | int/long |
| bool | bool | boolean | boolean | |
| string | A string must always contain UTF-8 encoded or 7-bit ASCII text. | string | String | str/unicode |
| bytes | May contain any arbitrary sequence of bytes. | string | ByteString | str |


Recipe 19.3. Generating the Fibonacci SequenceCredit: Tom Good, Leandro Mariano Lopez ProblemYou want an unbounded generator that yields, one item at a time, the entire (infinite) sequence of Fibonacci numbers. SolutionGenerators are particularly suitable for implementing infinite sequences, given their intrinsically "lazy evaluation" semantics: def fib( ): ''' Unbounded generator for Fibonacci numbers ''' x, y = 0, 1 while True: yield x x, y = y, x + y if _ _name_ _ == "_ _main_ _": import itertools print list(itertools.islice(fib( ), 10)) # outputs: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34] DiscussionGenerators make it quite easy to work with unbounded (infinite) sequences. In this recipe, we show a generator that produces all of the (infinitely many) Fibonacci numbers one after the "other". (If you want the variant in which the sequence starts with 1, 1, 2, . . . , rather than the one, implemented here, which starts with 0, 1, 1, . . . , just interchange the two statements in the loop's body.) It's worth reflecting on why a generator is so perfectly suitable for implementing an unbounded sequence and letting you work with it. Syntactically, a generator is "just" a function containing the keyword yield. When you call a generator, however, the function body does not yet execute. Rather, calling the generator gives you a special anonymous iterator object that wraps the function's body, the function's local variables (including arguments, which, for any function, are local variables that happen to be initialized by the caller), and the current point of execution, which is initially the start of the function. When you call this anonymous iterator object's next method, the function body executes up to the next yield statement. yield's argument is returned as the result of the iterator's next method, and the function is "frozen", with its execution state intact. When you call next again on the same iterator object, the function "thaws" and continues from where it left off, again up to the next yield statement. If the function body "falls off the end", or executes a return, the iterator object raises StopIteration to indicate the end of the sequence. But, of course, if the sequence that the generator is producing is not bounded, the iterator never raises StopIteration. That's okay, as long as you don't rely on such an exception as the only way to terminate a loop. In this recipe, for example, the anonymous iterator object is passed as an argument to itertools.islice: as shown in Recipe 19.2, islice is the most typical way in which an unbounded iterator is made finite (truncated at an externally imposed boundary). The main point to retain is that it's all right to have infinite sequences represented by generators, since generators are computed lazily (in other words, each item gets computed just in time, when required), as long as some control structure ensures that only a finite number of items are required from the generator. The answer to our curiosity as to why generators are so excellently suitable for this use is in the anonymous iterator object which a generator returns when we call it: that anonymous iterator wraps some code (the generator's function body) and some state (the function's local variables, and, crucially, the point at which the function's execution is to resume) in just the way that's most convenient for the computation of most sequences, be they bounded or unbounded. Leonardo Pisano (meaning "from Pisa"), most often called Leonardo Bigollo (the traveler or "the good for nothing") during his lifetime in the 12th and 13th centuries, and occasionally Leonardo Fibonacci (for his connection to the Bonacci family), must look down with considerable pride from his place in the mathematicians' Empyreon. Although his most notable contributions were the introduction of decimal notation (arabic numerals) in the West, and the codification of the rules for double-entry bookkeeping, these monumental achievements are not usually connected to his name. The one that is, howeverfrom the third problem in his Liber Abaci, which he originally expressed in terms of a rabbit-raising farmstill provides interesting applications for the distant successors of the abacus, modern computers. See AlsoRecipe 19.2, shows how to make bounded iterators from unbounded (or "potentially unbounded") ones. |


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Professional Rugby player
Extreamly Athletic
Great Cook


| Description | Show the interactive Guideline. |
| Keyword | useInteractiveGuideline |
| Type | boolean |
| Default | true |
"useInteractiveGuideline": true
| Description | Duration of the animation in milliseconds. |
| Keyword | transitionDuration |
| Type | number |
| Default | 350 |
"transitionDuration": 350
| Description | Show the legend, allowing users to turn on/off line series. |
| Keyword | showLegend |
| Type | boolean |
| Default | true |
"showLegend": true
| Description | Adjust chart margins to give the x-axis some breathing room. |
| Keyword | marginLeft |
| Type | boolean |
| Default | true |
"marginLeft": true


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.apache.commons.collections.map.Flat3Map
public class Flat3Map
A Map
implementation that stores data in simple fields until
the size is greater than 3.
This map is designed for performance and can outstrip HashMap. It also has good garbage collection characteristics.
The performance gains on puts are due to not needing to create a Map Entry object. This is a large saving not only in performance but in garbage collection.
Whilst in flat mode this map is also easy for the garbage collector to dispatch. This is because it contains no complex objects or arrays which slow the progress.
Do not use Flat3Map
if the size is likely to grow beyond 3.
Note that Flat3Map is not synchronized and is not thread-safe.
If you wish to use this map from multiple threads concurrently, you must use
appropriate synchronization. The simplest approach is to wrap this map
using Collections.synchronizedMap(Map)
. This class may throw
exceptions when accessed by concurrent threads without synchronization.
| Nested Class Summary |
|---|
| Nested classes/interfaces inherited from interface java.util.Map |
|---|
java.util.Map.Entry |
| Constructor Summary | |
|---|---|
Flat3Map()
Constructor. |
|
Flat3Map(java.util.Map map)
Constructor copying elements from another map. |
| Method Summary | |
|---|---|
void |
clear()
Clears the map, resetting the size to zero and nullifying references to avoid garbage collection issues. |
java.lang.Object |
clone()
Clones the map without cloning the keys or values. |
boolean |
containsKey(java.lang.Object key)
Checks whether the map contains the specified key. |
boolean |
containsValue(java.lang.Object value)
Checks whether the map contains the specified value. |
protected AbstractHashedMap |
createDelegateMap()
Create an instance of the map used for storage when in delegation mode. |
java.util.Set |
entrySet()
Gets the entrySet view of the map. |
boolean |
equals(java.lang.Object obj)
Compares this map with another. |
java.lang.Object |
get(java.lang.Object key)
Gets the value mapped to the key specified. |
int |
hashCode()
Gets the standard Map hashCode. |
boolean |
isEmpty()
Checks whether the map is currently empty. |
java.util.Set |
keySet()
Gets the keySet view of the map. |
MapIterator |
mapIterator()
Gets an iterator over the map. |
java.lang.Object |
put(java.lang.Object key,
java.lang.Object value)
Puts a key-value mapping into this map. |
void |
putAll(java.util.Map map)
Puts all the values from the specified map into this map. |
java.lang.Object |
remove(java.lang.Object key)
Removes the specified mapping from this map. |
int |
size()
Gets the size of the map. |
java.lang.String |
toString()
Gets the map as a String. |
java.util.Collection |
values()
Gets the values view of the map. |
| Methods inherited from class java.lang.Object |
|---|
finalize, getClass, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public Flat3Map()
public Flat3Map(java.util.Map map)
map
- the map to copy
java.lang.NullPointerException
- if the map is null| Method Detail |
|---|
public java.lang.Object get(java.lang.Object key)
get
in interface java.util.Map
key
- the key
public int size()
size
in interface java.util.Map
public boolean isEmpty()
isEmpty
in interface java.util.Map
public boolean containsKey(java.lang.Object key)
containsKey
in interface java.util.Map
key
- the key to search for
public boolean containsValue(java.lang.Object value)
containsValue
in interface java.util.Map
value
- the value to search for
public java.lang.Object put(java.lang.Object key, java.lang.Object value)
put
in interface java.util.Map
key
- the key to addvalue
- the value to add
public void putAll(java.util.Map map)
putAll
in interface java.util.Map
map
- the map to add
java.lang.NullPointerException
- if the map is nullprotected AbstractHashedMap createDelegateMap()
This can be overridden by subclasses to provide a different map implementation. Not every AbstractHashedMap is suitable, identity and reference based maps would be poor choices.
public java.lang.Object remove(java.lang.Object key)
remove
in interface java.util.Map
key
- the mapping to remove
public void clear()
clear
in interface java.util.Map
public MapIterator mapIterator()
A MapIterator returns the keys in the map. It also provides convenient methods to get the key and value, and set the value. It avoids the need to create an entrySet/keySet/values object. It also avoids creating the Map Entry object.
mapIterator
in interface IterableMap
public java.util.Set entrySet()
mapIterator()
.
entrySet
in interface java.util.Map
public java.util.Set keySet()
mapIterator()
.
keySet
in interface java.util.Map
public java.util.Collection values()
mapIterator()
.
values
in interface java.util.Map
public java.lang.Object clone()
clone
in class java.lang.Object
public boolean equals(java.lang.Object obj)
equals
in interface java.util.Map
equals
in class java.lang.Object
obj
- the object to compare to
public int hashCode()
hashCode
in interface java.util.Map
hashCode
in class java.lang.Object
public java.lang.String toString()
toString
in class java.lang.Object
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
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Our story from founder Andrew Burrill.
Rainbows n' stuff.
Chateau de Beau was born out of a University of Virginia class project to create a unique and couture Bow Tie for the annual Foxfield Races in Charlottesville, Virginia. Originally starting out selling a few dozen every week to friends, has escalated to an unbelievable amount of orders in the last year. Chateau de Beau is proud to always be American made using only the highest quality fabrics and innovative thinking practices. Our purpose is to enable our customers to stand out with our unique styles and patterns. We find great joy in helping to cultivate individual confidence and identity through our artistry.


http://edgeATX.github.io/slides
— Brian Moeskau (@bmoeskau)
— Joe McCullough (@joe_query)
Thanks to our sponsor:
Next-generation customer service software
Thanks to our sponsor:
Includes most of our slide decks from the past 2 years:
Last version check — July 2014 (last month):
| 36 | 31 | 22 | 7 | 11 |
As of August 26, 2014:
| 36 37 |
31 | 22 23 |
7 | 11 |
http://www.w3.org/2014/07/mobile-web-app-state/
Summarizes the various technologies developed in W3C that increase the capabilities of Web applications, and how they apply more specifically to the mobile context.
Summary of specs by technology category:
http://www.w3.org/2014/07/mobile-web-app-state/
Summary of specs by W3C status:
| Emerging work | First Public Working Draft | Last Call for Feedback | Candidate Recommendation |
|
|
|
|
The latest information on how to use the technology that runs the web — HTML, CSS, JavaScript and more.
A crowdsourced, work-in-progress (early) resource for documenting web standards
Currently pushing for JavaScript-focused contributions
http://www.futureinsights.com/home/the-state-of-the-componentised-web.html
Excellent overview of web components
Current W3C status update
Custom component example implemented in several frameworks to compare syntax
http://developer.telerik.com/featured/web-components-ready-production/
Focused on custom elements portion of Web Components
Real-world implementation of a clock face component with example code
Discussion of practical application, polyfills, potential issues
Swift is a new object-oriented programming language for iOS and OS X development. Swift is modern, powerful, and easy to use.
Introduced at WWDC in June, still in development
JS-like syntax, compiled, modern features, compatible with Objective-C
http://realm.io/news/swift-for-javascript-developers/
Great overview of Swift for JS developers (screencast + code examples)
// JavaScript
function Car(model){
this.model = model;
}
Car.prototype.drive = function() {
return 'Driving my ' + this.model;
}
// Swift
class Car {
var model = ""
func drive() -> String {
return "Driving my " + model
}
}
https://github.com/facebook/flux
An application architecture for React utilizing a unidirectional data flow
New Backbone-inspired, modular JS framework
"Tiny modules" philosophy (a la Node), minimal DOM manipulation
Mobile-optimized, offline-first
http://a2labs.github.io/barekit/
A Bare Minimum Responsive Framework
List of free JavaScript/Node (and related) books
/
#


{% extends 'wopr/layout.html' %} {%block content %}
Page not found. You can go back to the previous page, or return home.


[gecode-users] Python wrappers
Guido Tack
tack at ps.uni-sb.de
Thu Oct 29 21:10:44 CET 2009
Hi Ellis,
Ellis Breen wrote:
> I'm currently looking for a free-as-in-beer constraint programming
> library with bindings in both C/C++ and Python. It seems Logilab
> started a Python wrapper for Gecode but never completed it. Are
> there any rival implementations? Would it be non-trivial to use SWIG
> for this?
we're currently implementing an interface to the Numberjack Python
library (http://4c110.ucc.ie/numberjack/home). It's work in progress,
but we expect enough progress for it to be usable soon. The interface
is generated using SWIG.
> Apparently Cassowary also supports Python but I can't seem to find
> the bindings.
>
> Also, has anyone attempted to integrate the Minizinc to Flatzinc
> compiler into a project containing Gecode, to allow interpretation
> of Minizinc scripts without using an intermediary program?
That's not so easy, because mzn2fzn is written in Mercury, and
currently only available as a stand-alone program. It's should be
possible to wrap the Mercury code in a C library, but that hasn't been
done yet.
Cheers,
Guido
More information about the gecode-users
mailing list


I love coding since it makes a difference in people's lives. Currently, I am working as a Senior Full-Stack Developer at BYJUS. I have also started working in the field of Machine Learning and Deep Learning. I completed my B.Tech in Information Technology from Galgotia College of Engineering and Technology, Greater Noida.
View CV Blogs KaggleI'm a Young ML Developer working for 4 years on Web Technology, Full-Stack Development and over a year on Machine Learning applications
I Love working with technologies and solving problems. Espacially Machine Learning and Web
Persuing my dream I had build a social media Collegebuzzer for my campus. Collegebuzzer is a app were club can post there recent events and anoucements with photos for campus students.
Further in my journey I build real work applications using Machine Leanring. One of the example is Text Detection
Machine Learning. MERN . C++ . Full-Stack Development
Previous companies and my tasks
Help Students by build Machine Leanring Solutions
Worked on various web technologies and devOps.
Worked on MERN
I'm a quick Learner and Perpetual Developer
Leanred a lot about Machine Learing, Deep Learning, Neural Networks, Structuring Machine Learning Projects, Convolutional Neural Networks and Sequence Models
I'm a middler from Galgotias College of Engineering and Technology with Information Technology branch. I have a passion for coding, developing new things and learning technologies.
I Joined Konfinity for Web Development Course and Build a Teacher Student Interactive System for Colleges.
Detect text in your surrounding. #no_api #no_server
Build a very lightweight mobile and web-based Text Detector using Pytorch and TensorFlow js
Started with YOLO small model (less than 5MB) and fine-tuned it on a synthetically created dataset for detecting text and creating a bounding box around it
Then build a custom Text Recognition model (RCNN architecture using CTC loss function) to do OCR.
Both models are very light (less than 5 MB) and all the processing is on the client side. So, No Server is Needed. Must try here
App to tell type of bear.
A web based live code editor. Open https://huggingface.co/spaces/kaizen97/bear-classifier
Try uploading a bear image. Two examples are added or you can take few from internet. This app has 5 classes Panda, Grizzly, Teddy, Black and Polar. App is build by finetuning a Resnet Model
Build to make learning easy. Build in React
Often we only learn to code but never know how it works in reality. In this project we tried to visualise algorithms which are moslty taught in basic CS courses. We cover all sorting algorithms and two of main path finding Algorithms in one App. Open https://reactsorting.netlify.app/
This App works seamlessly on all kinds of devices either it be Mobile, Tablet or Desktop.
Everything you need to get connected to your college
Build a mobile and web based social media application. App was used by 4000 students on campus. Open http://collegebuzzer.com/#/
Clubs and Students are able to post an detailed description of events with photos seen by each and every student in campus. Students have a profile to maintain just like facebook with updated skills and achievements.
Only for lazy Developers
A web based live code editor. Open https://shubham-shinde.github.io/codelive/
This is a Code Editor for CSS and HTML were programmer will have live representation of output. This app is very helpful for those who makes aniamtions using HTML & CSS.


--- layout: interior-page title: 'Community Garden' parent: 'Green Team' ---
The Rutherford Community Garden is located at West Erie Ave between Grand and Jackson.
Garden rentals will be open April 1 on a first come first served basis for $30 via RecDesk. Review the 2022 Garden Layout and pay ($30) through the Recreation department’s RecDesk on or after April 1.
The Borough of Rutherford is known for beautiful trees, welcoming parks, and a strong community with a tradition of volunteerism. The “Borough of Trees” first established the community garden along Erie Ave in 2010, with official grand opening in April 2011.
Through the hard work of the Green Team and scores of volunteers representing many clubs and organizations, The Rutherford Community Garden continues to be a beautiful and engaging place for all residents. The garden is a model of sustainability and recycling. The original funding for the garden came from Sustainable Jersey as a $25,000 grant from Walmart. Many of the beds and pathways were created using recycled stone from the borough’s sidewalks. Other paths incorporate wood chips from street trees that had reached the end of their lives and are now protecting young trees in our tree nursery. Water barrels capture rainwater and help with irrigation, thus reducing the need for tap water. When necessary, water is delivered via water-saving drip irrigation. With fifteen planting areas, two learning areas (one boasting a green roof), a community shed, senior and handicap-accessible beds, a compost area and a tree nursery, the garden has transformed an empty lot into a community asset.
The mission for the garden is to provide the young, and the young at heart, a place to observe and appreciate nature. Given our fast-paced world, we also worked to make the garden low maintenance and user friendly. It is a beautiful and welcoming place created by residents for the residents.
Any garden is much better off with a compost bin, allowing for the recycling of organic material and nutrients. Pretty much any plant-based organic wasted can be composted, with the exception of very hard or woody materials. When left to compost, these materials will be broken down by bacteria into more basic components, which can then be re-used as fertilizer in next year’s planting. Many factors go into maintaining a compost heap, such as ensuring a proper balance of materials, regular aeration, and temperature control, all of which affect the quality of the compost created.
A very important part of any garden is water management. One of the most effective ways to take advantage of rainwater at our garden is the green roof. Situated atop the pergola in the center of the garden, the green roof consists of a roof which is planted with water-tolerant plants. The roof is able to use up much of the rainwater which would otherwise go to waste. These plants also filter out contaminants which would otherwise reach the water table.
The runoff from this roof leads into our rainwater collection system. This water, already filtered by the roots of green roof, is collected in two large barrels to the side of the covered area. The water is available for a variety of uses, though is mainly used to water plants.
A quintessential part of any community garden, planting beds are used to grow and contain a variety of plants, be it vegetables, herbs, or just ornamentals. Each planting bed is rented out to a community member or group for the entirety of the planting season. Each box is built sustainably, in order to both protect the plants inside and the garden as a whole. All materials, such as the stain, wood, and even nails, were chosen to ensure that they caused the minimal amount of environmental disruption possible.
At the community garden, we hold sustainability as one of our highest priorities. Oftentimes, rain will pick up harmful contaminants which will be drawn into our nearest river, the Passaic. One of the best ways to alleviate the amount of contamination coming from runoff is to install a rain garden. A rain garden, installed in the lowest part of an area affected by runoff, contains plants which help to absorb and filter excess rainwater runoff. The soil itself will help to contain excess water, and the roots of the plants will filter chemicals out of the water. At the Rutherford Community Garden, our rain garden is in the southwestern corner.
Sign up for our mailing list to learn about Garden events as well as general Green Team events, such as:


| Play in Source Monitor | |
| Ingest through Premiere DOM (user sandbox) | |
| Drag Me (into the production panel, source monitor or timeline) |
Adobe Anywhere Example Panel
| Play in Source Monitor | |
| Ingest through Premiere DOM (user sandbox) | |
| Drag Me (into the production panel, source monitor or timeline) |


興趣使然的網頁設計學生,喜歡樂器貝斯，嘗試遊戲實況和剪輯，大愛日本歌手"美波"
I'm interesting to web design,like play bass,try to gaming live,love japaness vocal "minami"
Photo shape作品
Don't work for McDonald's,you will regret.
Bass,fuck you edge
Overtime work,overtime work and overtime work
桃園市永豐國中 graduation to Yung-Feng High School
桃園市永豐高中graduation to Yung-Feng High School
世新資傳studying for Shih Hsin University
| 技能Skill |
|---|
| 貝斯彈奏 Bass play |
| 鋼琴彈奏 Piano play |
| 樂團仔 Band man |
| 影片剪輯 Film editing |
| 遊戲實況 Gaming |
| 程式設計 Programming |
| 簡易日語 Japanese |
| 我會跳Hardcore dance |
| 美波Minami |
| 米津玄師Yonezu Kenshi |
| 凜冽時雨Ling tosite sigure |
| ヨルシカYorushika |
| ずっと真夜中でいいのにZutomaya |
| EVE |
| 神啊、我已經察覺到了 |
| XJapan |
| Lisa |
| Aimer |
| majiko |
| 愛繆Aimyon |
| 上野優華Yuuka Ueno |
| 椎名林檎Sheena Ringo |
| 生物股長Ikimono gakari |
| Goose house |
| 和樂器樂團 |
| Back number |
| Sayuri |
| 花たんHanatan |
| Asian kung fu generation |
| Nano |
| CHiCO with HoneyWorks |
| Spyair |
| ZAQ |
| ChouCho |
| Egoist |
| Kalafina |
| Claris |
| Cliff Edge |
| Supercell |
平時喜歡玩玩遊戲，彈彈貝斯，看看動畫，睡覺
有時也會釣魚，但更喜歡釣蝦，因為蝦子好吃
平時喜歡聽日文歌，最喜歡的歌手有米津玄師和美波
Play gaming,play bass,watch animate and sleep.
Sometime I fishing,but I more like shrimp fishing.Because shrimp delicious.
I like sound Japanese song,my favorite vocal are Yonezu Kenshi and Minami.


|
AllEqual()
|
Check if all values in a vector are the same |
|
ClusterProfiler.GSEA()
|
Perform GSEA analysis by cluster profiler |
|
ClusterProfiler.OverRepresentationTest.Compare()
|
Perform over-representation test across multiple group by cluster profiler |
|
ClusterProfiler.OverRepresentationTest()
|
Perform over-representation test by cluster profiler |
|
DESeq2_differential()
|
Differential analysis by DESEQ2 |
|
MA_plot()
|
M-A plot |
|
MannWhitneyU_WilcoxonRankSumTest_differential()
|
Calculate p value by MannWhitneyU (WilcoxonRankSum) Test manually |
|
SNF_Similairity_Hist()
|
Title |
|
build.coxregression.model()
|
Title |
|
build.logistic.model()
|
Build a logistic model |
|
confidence_interval()
|
confidence interval |
|
confusion_matrix()
|
Get confusion matrix |
|
convertDfToNumeric()
|
Convert data frame to numeric |
|
cross.validation()
|
Title |
|
decisionCurveAnalysis()
|
Decision curve analysis |
|
download.geo.dataset()
|
Download dataset from GEO by accession ID and platform |
|
draw.expression.dotplot()
|
Draw expression by dotplot
Need to be update |
|
draw.mutation.type()
|
Count each event type and draw pie chart |
|
draw.mutation.type.barplot()
|
Draw bar plot |
|
draw.target.coverage.plot()
|
Draw target coverage plot |
|
drawScatter()
|
Draw scatter plot |
|
draw_confusion_matrix()
|
Confusion matrix visulization |
|
ensembl_EntrezID()
|
Title |
|
export2ppt()
|
Export figure to PPT
https://github.com/tomwenseleers/export |
|
feature.selection.4givenMLAlgorithm()
|
Variable Importance from a specific Machine Learning Algorithms |
|
feature.selection.RFE()
|
Recursive feature elimnation |
|
feature.selection.boruta()
|
Feature selection by Boruta |
|
fpkm2tpm()
|
Convert FPKM data fram to TPM |
|
generate.sequencial.color()
|
Generate sequencial color |
|
get.AUC()
|
Title |
|
get.YoudenIndex()
|
Get Youden index value |
|
get.ggsci.color()
|
Get colors by ggsci |
|
get.mostDistint.color.palette()
|
Generate a most distinctive color palatte |
|
get.palette.color()
|
Use ggpubr function to get all kinds of color palette |
|
get.sample.project.infomap()
|
Get sample barcode and TCGA project map information |
|
getGeneRelatedTranscripts.Gff()
|
Retrieve all transcripts related to target genes from gtf/gff file |
|
getOneRoundCVRes()
|
Title |
|
getPromoterRegions()
|
Get promoters by GenomicFeatures package |
|
getTranscriptLength()
|
Obtain transcript length from gff file or GRanges object |
|
getTranscriptProteinSequence()
|
Retrieve protein sequence by biomaRt |
|
get_full_mapping_table()
|
Retrieve full ID mapping table |
|
get_performance()
|
Title Get model performace, sensitivity, sepcificity and others |
|
heatmap.with.lgfold.riskpro()
|
Plot heatmap with log 2 fold change and risk probability information |
|
hello()
|
Hello, World! |
|
hyperGeoTest()
|
Title |
|
id_mapping()
|
Title |
|
lasso.cv.select.feature()
|
Perform multiple round lasso to select stable feature |
|
lasso.select.feature()
|
Feature selection by lasso |
|
lasso_best_lamda()
|
Get best lamda by perform multiple round lasso-sv
https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html#log
https://stackoverflow.com/questions/62170965/looping-cv-glmnet-and-get-the-best-coefficients |
|
limma.differential.cv.selection()
|
Perform multiple rounds differential analysis to select stable feature |
|
limma_differential()
|
Differential analysis by LIMMA |
|
load.rsem.matrix()
|
Load quantification result from RSEM by tximport |
|
load.salmon.matrix()
|
Read Salmon output |
|
loadGff()
|
Convert gff file to data frame |
|
loadMethylationArraryData()
|
Title |
|
log2dfQantileNormalization()
|
Title |
|
logit2prob()
|
Convert logti to probability |
|
loo.cv()
|
Leave one out cross validation |
|
loo.cv.cox()
|
Leave one out cross validation for cox regression |
|
multi_roc_with_ci()
|
Plot multiple ROCs in one figure |
|
multiplePlotPredictedPro()
|
plot predictive probability distribution |
|
multivariate_cox()
|
Title |
|
multivariate_or()
|
Variate logistic analysis
Row: sample, Column: gene expression |
|
normalize_miRNACount_toCPM()
|
Normalize miRNA counts to CPM |
|
oneway.anova()
|
Kruskal Wallis Test One Way Anova by Ranks or F test |
|
plotBarWithErr()
|
Plot bar with mean and standard error |
|
plotClevelandDot()
|
Draw Celventland dot plot |
|
plotJitterBoxplot()
|
Boxplot with jitter |
|
plotPCA()
|
PCA plot
Please note: scale will be performed autmaticly |
|
plotPie()
|
Count each event type and draw pie chart |
|
plotSilhouette()
|
Title |
|
plotUpset()
|
Upset plot |
|
plotVenn()
|
Plot venn diagram by ggvenn |
|
plotVennLegacy()
|
Plot venn diagram |
|
plot_miRCorrelation()
|
Plot miR's correlation |
|
plot_waterfall()
|
Waterfall plot |
|
read_mirdeep2_result()
|
Load miRDeep2 expression matrix |
|
refSeq_Ensembl()
|
Convert RefSeq ID to Ensembl ID by biomaRt |
|
remove.verylong.isoform.gff()
|
Remove isforms with extremly long interval (isoform.End - isoform.Start) |
|
riskCalibrationPlot()
|
Calibration plot |
|
roc_with_ci()
|
plot ROC with CI. Stratified bootstrap 2000 times |
|
run_SNF()
|
Title Similarity network fusion |
|
scaleDF()
|
Scale a data.frame by column or row |
|
show_hcluster()
|
Perform hclustering analysis |
|
splitDataByGroup()
|
split data frame by group |
|
splitSampleByGroup()
|
Split sample by group into traning and validation set |
|
suppa.event.pie()
|
Count each event type and draw pie chart |
|
suppa.get.final.table()
|
Combine dpis, psi and tpm file together
I modified the code from https://github.com/comprna/SUPPA/blob/master/scripts/Volcano_MA_plot.R |
|
tanimoto()
|
Title |
|
tcgabiolinks.get.RNA.expression.log2tpm()
|
Download RAN expression by TCGAbiolinks |
|
tcgabiolinks.get.junction.coverage()
|
Download junction data by TCGAbiolinks. Pls note, V2 tag and Illumina HiSeq platform were selected |
|
ttest_differential()
|
Calculate p value by t.test manually |
|
unique_gene_expression()
|
Title Remove redundant gene expression data, and select the maximum one |
|
univariate_cox()
|
Title |
|
univariate_cox_sthda()
|
Title |
|
univariate_or()
|
Variate logistic analysis
Row: sample, Column: gene expression
score E.g.: Gene or miRNA expression, or risk score |
|
volcano_plot()
|
Volcano Plot |
|
writeGff()
|
Write to gff file |


{% extends "base.html" %} {% block title %}
Medieval catalogues > HENRY DE KIRKESTEDE > Catalogus, c. 1360
{% endif %}9 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


Standard Application Case Standard Application Case Standard Application Case, Standard Application Case


This is the first Bruce Sterling talk that I encountered. Now I’m an inveterate Sterlingite, but at the time, 2006, I had only barely crossed intellectual paths with Sterling. I downloaded it with some long dead, precambrian cousin of google reader that lived in the swamps and estuaries of windows computers and survived by allowing the user to view RSS feeds on his desktop. Then pushed it to my equally antediluvian, single purpose device, an Archos mp3 player. I listened to this talk many times before the Archos died off. I thought this audio was lost to the bit bucket of history, until one day… archive.org. These people are the new Library of Alexandria, with podcasts instead of papyrus.
The full mp3 is available here.
Here are some excerpts:
Commons based peer production, as an industrial method, is getting it’s legs under it. This is something I complained about for years. I used to complain that GNU had the wrong name, because the recursive name for GNU is GNU’s Not Unix. And I describe that as rather childish, because you should not name yourself in opposition to something else, you should have your own name. It’s like, if GNU’s not Unix, what is it?
Well, it’s commons based peer production.
Flickr is not a copy of anything else. It is not a hippie knockoff of a commercial product. Wikipedia is not like anything else. A wiki is like nothing known to mankind. Collaborative web-filters are very spooky things; they are without historical precedent. Websites that throw their API’s open and turn themselves into platforms rather than sites… it is a little hard to explain the significance of that to everyday people who are not techies and programmers, but that is a major development. The net community is no longer hanging on the coat-tails of Gates. That monopolistic chokehold, that did so much to reduce innovation, and to introduce global criminality to hapless Windows users.
Only in the United States do dying phone companies lobby the government as if they were indian casinos.
As you may or may not know, I am spending a lot of my time in Europe, after spending a year in California. I get to see America from the outside now. I get to see America as 94% of the planet sees America. And I look at wireless spreading in London and the spread of broadband in Korea. I’ve got broadband in Serbia where the phone companies are literally run by criminals in exile. And my broadband in Serbia costs $20 a month. And it works.
It is decadent, it is sclerotic. It looks like the Soviet Union.
…they are turning the USA into a banana republic with rockets.
Not just politically backward, technically backward.
The twenty first centry is definitely fertilizing my cyberpunk excentricities.
People ask me “have you moved to Belgrade? Is this permanent?” No, it’s not permament. Just that, some of my shoes are there, and a closet… I live out of my laptop now. That’s how I live. And so do increasing numbers of my collegues.
The twenty first centry is definitely fertilizing my cyberpunk excentricities.
It’s a world of diaspora and globalization. Gypsies and jetsetters. Refugees and tech pioneers. And the differences are that thin.
I no longer need to be a resident of any particular city. I don’t make any money in any foreign state. Nothing enters or leaves Belgrade except for ones and zeroes. That’s all. I never stay there long enough to become permanent. I don’t even DO permanent. National borders? They’re like speed bumps.
This talk is well composed. It is shot through with diamond bullets of poetic insight. Sterling’s own voice and clearly communicated emotion turns this from a discursion on the quirks of Eastern Europe’s up and coming intelligentsia into a full-blown harangue on the nature of the world as we find it right now in 2013, from the view of someone looking ahead in 2006.