MessageProperty -- a property storing ProtoRPC Message objects.
Basic usage:
Let's assume you have a protorpc.messages.Message subclass, like this:
from protorpc import messages
class Note(messages.Message):
text = messages.StringField(1, required=True)
when = messages.IntegerField(2)
Now suppose you'd like to store Notes in the datastore. Create a
model class to hold your notes, as follows:
from google.appengine.ext import ndb
from google.appengine.ext.ndb import msgprop
class NoteStore(ndb.Model):
note = msgprop.MessageProperty(Note)
name = ndb.StringProperty()
(The class name, 'NoteStore', and the property name, 'note', are yours
to choose.)
To store, a Note message, create a NoteStore entity and write it:
ns = NoteStore(note=my_note, name='foo')
key = ns.put()
To later retrieve the Note, read the NoteStore entity back:
ns = key.get()
my_note = ns.note
The MessageProperty class has many of the usual Property options:
- name: optional datastore name
- repeated: if True, stores a list of message values
- required: if True, the message value cannot be None
- default: optional default message value to store
- choices: optional list of allowed choices (must all be messages)
- validator: optional function to validate message values
- verbose_name: optional long name for the property
However, MessageProperty does not support the 'indexed' option.
Instead, you can specify a list of field names that will be indexed,
like this:
class MyStore(ndb.Model):
author = ndb.StringProperty()
note = msgprop.MessageProperty(Note, indexed_fields=['text', 'when'])
Now you can query for field values, like this:
stores = MyStore.query(MyStore.note.when >= 123).fetch()
Note the similarity with StructuredProperty -- in fact,
MessageProperty inherits from StructuredProperty. The main difference
is that StructuredProperty takes an Model subclass instead of a
protorpc.messages.Message subclass; and StructuredProperty doesn't
index any fields by default.
It works for nested messages (using MessageField) as well:
class Notes(messages.Message):
notes = messages.MessageField(Note, 1, repeated=True)
class MyNotesStore(ndb.Model):
author = ndb.StringProperty()
foo = msgprop.MessageProperty(Notes,
indexed_fields=['notes.text, 'notes.when'])
And given this value for indexed_fields, in this example you can also
query for subfields:
stores = MyNoteStore.query(MyNoteStore.foo.notes.when < 123).fetch()
A final option for MessageProperty is 'protocol'. This lets you
specify how the message object is serialized to the datastore. The
values are protocol names as used by protorpc.remote.Protocols class.
Supported protocol names are 'protobuf' and 'protojson'; the default
is 'protobuf'. (In the future this will use the global protocols
registry that is being added to protorpc; then any registered protocol
name will be acceptable.)
There is also an EnumProperty, which can be used to store a
messages.Enum value without wrapping it in a Message object. Example:
class Color(messages.Enum):
RED = 620
GREEN = 495
BLUE = 450
class Part(ndb.Model):
name = ndb.StringProperty()
color = msgprop.EnumProperty(Color, required=True)
p1 = Part(name='foo', color=Color.RED)
key1 = p1.put()
...
p2 = key1.get()
print p2.name, p2.color # prints "foo RED"
The EnumProperty stores the value as an integer; in fact, EnumProperty
is a subclass of IntegerProperty. This is handy to know, since it
implies that you can rename your enum values without having to modify
already-stored entities, but you cannot renumber them.
The EnumProperty supports the following standard options:
- name
- indexed
- repeated
- required
- default
- choices
- validator
- verbose_name


Fake News Buster: Dwayne ‘the rock’ Johnson is alive
by Bangalore Mirror BureauTHE CLAIM
A website using the url bbc.247updatednews, shared a post about actor Dawyne Johnson dying after a failed stunt.
SO, WHAT’S THE TRUTH?
The website is a hoax one and has a design layout similar to BBC News. It shared a video with the headline ‘Dawyne ‘The Rock’ Johnson Dies At 47 After A Terrible Stunt Attempt Failure’.


Pizza Grill
Home
varieties
Contact us
Welcome to pizza grill
order now
our varieties
We have:
Pepperoni pizza rolls
BBQ chiken pizza
spinach artichoke white pizza
stramboli
Order Here
Choose pizza size
Large=1200
medium=900
Small=500
Choose any crust
Stuffed crust
Extra cheese
Whole wheat
Choose toping
Pineapple
Ham
Pepperoni
Choose toping
large=200
medium=150
small=100
Quantity


A
- accessing the API, Running Proximity applications, Overview
- accuracy, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- add link tool, Adding Links to Data with Queries
- add-supervises-links query, Adding Links to Data with Queries
- AddAttribute class, Adding a New Attribute
- addAttribute(), Adding a New Attribute
- addClusterCoeffAttribute(), Clustering coefficient
- addHubsAndAuthoritiesAttributes(), Hubs and authorities
- adding (see creating)
- aggregation functions, Relational Probability Trees, Understanding relational probability trees
- aliases for shortcuts, File and directory shortcuts
- analysis (see schema analysis)
- annotations (see numeric annotations)
- API, Using Proximity, Running Proximity applications, Overview
- applications, Running Proximity applications
-
- (see also individual applications)
- apply(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- applying models, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- area under ROC curve, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- arranging
-
- queries, A First Proximity Query
- subgraph display, Exploring Containers and Subgraphs
- attribute value conditions, A First Proximity Query
- attribute value constraints, Comparing Items in a Query
- AttributeGenerator class, Code example: generate-iid-data.py
- attributes
-
- added by sampling, Code example: sample-1d-clusters.py
- case of names and values, Importing XML Data, Importing Plain Text Data
- conditioning during data generation, Generating i.i.d. data, Code example: generate-iid-data.py
- in conditions, A First Proximity Query
- in constraints, Comparing Items in a Query
- of containers, Exporting database elements to XML, Exploring Containers and Subgraphs, Working with Proximity Tables
- creating, Exploring Attributes, Adding a New Attribute
- deleting, Exploring Attributes
- dimensionality, Exploring Attributes, Using the Location Bar
- existence of, Code example: create-isStudent-attr.py
- exploring, Exploring Attributes, Using the Location Bar
- exporting, Exporting attribute values
- exporting to CSV files, Exploring Attributes
- exporting to XML, Exporting database elements to XML
- filtering by value, Exploring Attributes, Using the Location Bar, Working with Proximity Tables, Working with Proximity Tables
- finding specified values, Code Example: Finding Selected Objects, Working with Proximity Tables
- importing, Importing database elements using XML, Importing database elements using plain text
- of links, Exploring Objects and Links, Exploring Attributes, Working with Proximity Tables
- multiple values, Exploring Objects and Links, Exploring Attributes, Working with Proximity Tables
- of objects, Exploring Objects and Links, Exploring Attributes, Working with Proximity Tables
- predicting, Code example: generate-iid-data.py, The Modeling Process in Proximity, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Understanding relational probability trees, Code example: run-1d-clusters-rdn.py
- Proximity tables for, Working with Proximity Tables
- of subgraphs, Code example: sample-1d-clusters.py, Subgraphs, Subgraph attribute specification files
- text representation, attributes.data
- type information, Analyzing the Database Schema
- XML representation, Attributes, Attributes
- AttributeSource class, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Using temporal attributes in a relational probability
tree, Code example: run-1d-clusters-rdn.py
- autocorrelation
-
- generating data with, Synthetic Data Generation
- visualizing in relational dependency networks, Viewing relational dependency networks
B
- batch files (Windows), Running Proximity applications, Running Proximity applications, Proximity Shell Scripts and Batch Files
-
- db-util.bat, Running Proximity applications, Importing databases using XML, Importing databases using plain text, Code example: generate-iid-data.py, Proximity Shell Scripts and Batch Files, Updating databases using plain text
- export-text.bat, Running Proximity applications, Exporting databases using plain text, Proximity Shell Scripts and Batch Files, Updating databases using plain text
- export-xml.bat, Running Proximity applications, Proximity Shell Scripts and Batch Files
- gui.bat, Running Proximity applications, Exploring Objects and Links, Proximity Shell Scripts and Batch Files
- import-text.bat, Running Proximity applications, Proximity Shell Scripts and Batch Files, Updating databases using plain text
- import-xml.bat, Running Proximity applications, Proximity Shell Scripts and Batch Files
- query.bat, Running Proximity applications, Executing a Query from the Command Line, Proximity Shell Scripts and Batch Files
- script.bat, Running Proximity applications, Code example: helloworld.py, Proximity Shell Scripts and Batch Files
- BATs, Working with Proximity Tables
- Bayesian classification, Relational Bayesian Classifier
- binary association tables, Working with Proximity Tables
- bounded ranges for numeric annotations, Grouping Elements in a Query
- browse(), Code Example: Finding Selected Objects, Working with Proximity Tables
- browser, The Proximity User Interface
-
- (see also Proximity Database Browser)
- graphical, Visualizing Data, Visualizing Data
- NST, Working with Proximity Tables, Working with Proximity Tables
- browsing (see exploring)
- bug reports (see technical support)
C
- case matching in Proximity, A First Proximity Query
- case of attribute names and values, Importing XML Data, Importing Plain Text Data
- changing
-
- data (see updating)
- queries (see editing)
- characters
-
- converting for MonetDB, Importing XML Data, Importing Plain Text Data, Proximity XML Format, Proximity Text Data Format
- special
-
- in container names, A First Proximity Query
- in text data representation, Importing Plain Text Data, Exporting Plain Text Data, Proximity Text Data Format
- in XML data representation, Importing XML Data, Proximity XML Format
- classes
-
- importing in scripts, Importing Proximity packages
- AddAttribute, Adding a New Attribute
- AttributeGenerator, Code example: generate-iid-data.py
- AttributeSource, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Using temporal attributes in a relational probability
tree, Code example: run-1d-clusters-rdn.py
- DataTypeEnum, Attributes, Subgraphs
- FilterFactory, Working with Proximity Tables
- ItemSource, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- LoggingListener, Code example: run-1d-clusters-rdn.py
- NormalDistribution, Code example: generate-iid-data.py
- Predictions, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- Proximity class, The prox object
- RBC, Code example: run-1d-clusters-rbc.py
- RDN, Code example: run-1d-clusters-rdn.py
- RPT, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- SyntheticGraphIID, Code example: generate-iid-data.py
- TemporalAttributeSource, Using temporal attributes in a relational probability
tree
- TemporalItemSource, Using temporal attributes in a relational probability
tree
- classification models, Overview, Relational Probability Trees
- classification trees, Relational Probability Trees
- CLASSPATH, Running Proximity applications
- clear-db (db-util option), Running Proximity applications, Proximity Shell Scripts and Batch Files
- closing the MonetDB server, Running the MonetDB database server, MonetDB Server
- clustering coefficient, Social Networking Algorithms
- collective inference, Relational Dependency Networks
- column count, Exploring Attributes
- command-line editing in interpreter, Using the Proximity Python Interpreter, Proximity Python Interpreter Commands
- comparable data types, Comparing Items in a Query, Using temporal attributes in a relational probability
tree
- comparison operators, A First Proximity Query, Comparing Items in a Query
- compiling Proximity, Running Proximity applications, Proximity Shell Scripts and Batch Files
- completion in interpreter, Using the Proximity Python Interpreter
- complex conditions, A First Proximity Query, Matching Complex Subgraphs with Subqueries
-
- in filters, Working with Proximity Tables
- computeConnectedComponents(), Connected components
- conditional log likelihood, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py
- conditioning attribute values, Generating i.i.d. data, Code example: generate-iid-data.py
- conditions, A First Proximity Query
-
- attribute value, A First Proximity Query
- complex, A First Proximity Query, Matching Complex Subgraphs with Subqueries
- existence, A First Proximity Query
- in filters, Working with Proximity Tables, Working with Proximity Tables
- connected components, Social Networking Algorithms, Generating i.i.d. data
- connecting to databases, Running the MonetDB database server
- constraint tool, Comparing Items in a Query
- constraints, Comparing Items in a Query, Comparing Items in a Query, Matching Complex Subgraphs with Subqueries, Tips and Reminders
- containers, Overview
-
- attributes of, Exporting database elements to XML, Exploring Containers and Subgraphs, Working with Proximity Tables
- deleting, Exploring Containers and Subgraphs
- exploring, Exploring Containers and Subgraphs, Exploring Containers and Subgraphs
- exporting, Exporting Data to XML, Exporting database elements to XML
- importing, Importing database elements using XML, Importing XML data using noChecks, Importing database elements using plain text
- names of, A First Proximity Query
- nested, Exporting database elements to XML, Containers
- Proximity tables for, Working with Proximity Tables
- querying, Exploring Containers and Subgraphs, Querying Containers
- root, Exporting Data to XML, Exploring Containers and Subgraphs
- sampling, Sampling the Database, Code example: sample-1d-clusters.py
- text representation, containers.data
- XML representation, Containers
- converting
-
- characters during plain text export, Exporting Plain Text Data
- characters for MonetDB, Importing XML Data, Importing Plain Text Data, Proximity XML Format, Proximity Text Data Format
- tabular data to XML, Transforming Tabular Data to XML
- cornell-out-clusters query, Querying Containers
- create-isStudent-attr.py script, Code example: create-isStudent-attr.py
- creating
-
- attributes, Exploring Attributes, Adding a New Attribute
- conditions, A First Proximity Query
- constraints, Comparing Items in a Query, Comparing Items in a Query
- edges, A First Proximity Query
- links, Adding Links to Data with Queries, Adding Links to Data with Queries
- MonetDB databases, Running the MonetDB database server, Importing databases using XML, Importing databases using plain text
- numeric annotations, Grouping Elements in a Query
- queries, A First Proximity Query, A First Proximity Query
- subqueries, Matching Complex Subgraphs with Subqueries, Matching Complex Subgraphs with Subqueries
- training and test sets, Code example: sample-1d-clusters.py
- vertices, A First Proximity Query, A First Proximity Query
- CSV files of exported data, Exploring Attributes
- customizing display of object and link names, Setting Display Preferences
D
- data
-
- exploring, Overview
- exporting, Exporting Data to XML, Exporting database elements to XML, Exporting Plain Text Data, Exporting NST data, Exploring Attributes, Working with Proximity Tables, Proximity XML Format, Proximity Text Data Format
- generating, Synthetic Data Generation, Code example: generate-iid-data.py
- importing, Importing XML Data, Transforming Tabular Data to XML, Importing Plain Text Data, Importing database elements using plain text, Proximity XML Format, Proximity Text Data Format
- relational, Introduction
- representation in Proximity, Working with Proximity Tables
- shortcut names, The prox object, Working with Proximity Tables
- text representation, Proximity Text Data Format, Subgraph attribute data files
- visualizing, Visualizing Data, Visualizing Data
- XML representation, Proximity XML Format, Subgraphs
- data type, Comparing Items in a Query, Using temporal attributes in a relational probability
tree, Proximity XML Format, Attributes, Proximity Text Data Format
- database browser (see Proximity Database Browser and graphical data browser)
- databases
-
- connecting to, Running the MonetDB database server
- creating links, Adding Links to Data with Queries, Adding Links to Data with Queries
- deleting, Deleting Proximity Databases
- exporting, Exporting databases to XML, Exporting databases using plain text, Proximity XML Format, Proximity Text Data Format
- file locations, Deleting Proximity Databases, Location of database files
- importing, Importing databases using XML, Importing databases using XML, Importing databases using plain text, Importing databases using plain text, Proximity XML Format, Proximity Text Data Format
- initializing, Importing databases using XML, Importing databases using plain text, Updating databases using plain text
- new, Running the MonetDB database server, Importing databases using XML, Importing databases using plain text
- ProxWebKB, Using the Tutorial, Setting up your environment, Importing XML Data, Exploring Objects and Links
- sampling, Sampling the Database, Code example: sample-1d-clusters.py
- schema analysis, Analyzing the Database Schema
- structure, Conventional Knowledge Discovery
- tables, Working with Proximity Tables, Working with Proximity Tables
- updating, Updating MonetDB Databases
- updating via queries, Overview, Adding Links to Data with Queries, Adding Links to Data with Queries, Tips and Reminders
- WebKB, Importing databases using XML
- DataTypeEnum class, Attributes, Subgraphs
- db-util.sh/db-util.bat, Running Proximity applications, Importing databases using XML, Importing databases using plain text, Code example: generate-iid-data.py, Proximity Shell Scripts and Batch Files, Updating databases using plain text
- defineAttribute(), Attributes
- degree disparity in generated data, Synthetic Data Generation
- deleting
-
- attributes, Exploring Attributes
- containers, Exploring Containers and Subgraphs
- databases, Deleting Proximity Databases
- Query Editor elements, A First Proximity Query
- dependency networks, Relational Dependency Networks
- describe(), Working with Proximity Tables
- different-schools query, Comparing Items in a Query
- dimensionality of attributes, Exploring Attributes, Using the Location Bar
- directed edges, A First Proximity Query
- directory shortcuts, File and directory shortcuts
- disjunctive normal form, A First Proximity Query, Matching Complex Subgraphs with Subqueries
- documentation for MonetDB, Running the MonetDB database server, Installing MonetDB
- downloading Proximity and MonetDB, Obtaining Proximity
- DTDs, DTD files
-
- graph-query.dtd, DTD files, A First Proximity Query, Exploring Containers and Subgraphs, Executing a Query from the Proximity Database Browser, Tips and Reminders, DTD Files
- prox3db.dtd, DTD files, Overview, Importing databases using XML, DTD Files, Proximity XML Format
- and query validation, A First Proximity Query
- rbc2.dtd, DTD files, DTD Files
- rdn.dtd, DTD files, Viewing relational dependency networks, DTD Files
- rpt2.dtd, DTD files, Understanding and viewing relational probability trees, Code example: run-1d-clusters-rdn.py, Viewing relational dependency networks, DTD Files
E
- edge tool, A First Proximity Query
- edges
-
- automatically annotating, Grouping Elements in a Query
- in constraints, Comparing Items in a Query
- creating, A First Proximity Query
- directed vs. undirected, A First Proximity Query
- and links, Overview
- names of, A First Proximity Query
- on subquery boundaries, Tips and Reminders
- editing
-
- command line in interpreter, Using the Proximity Python Interpreter, Proximity Python Interpreter Commands
- conditions, A First Proximity Query
- edges, A First Proximity Query
- numeric annotations, Grouping Elements in a Query
- query names and descriptions, A First Proximity Query
- vertices, A First Proximity Query
- environment, Setting up your environment
- environment variables
-
- CLASSPATH, Running Proximity applications
- PATH, Running the MonetDB database server, Running Proximity applications, MonetDB Server, Installing MonetDB, Installing Proximity
- PROX_HOME, Setting up your environment, Installing Proximity
- for Windows, Setting up your environment, Installing Proximity
- evaluating models, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- exact annotation, Grouping Elements in a Query
- executing
-
- queries, Overview, A First Proximity Query, Executing a Query from the Proximity Database Browser, Executing a Query from the Command Line
- scripts, Running Proximity Scripts
- existence conditions, A First Proximity Query
- exploring
-
- attributes, Exploring Attributes, Using the Location Bar
- containers, Exploring Containers and Subgraphs, Exploring Containers and Subgraphs
- data, Overview, Working with Proximity Tables, Working with Proximity Tables
- links, Exploring Objects and Links, Using the Location Bar
- NSTs, Working with Proximity Tables, Working with Proximity Tables
- objects, Exploring Objects and Links, Using the Location Bar
- subgraphs, Exploring Containers and Subgraphs, Exploring Containers and Subgraphs
- export-text.sh/export-text.bat, Running Proximity applications, Exporting databases using plain text, Proximity Shell Scripts and Batch Files, Updating databases using plain text
- export-xml.sh/export-xml.bat, Running Proximity applications, Exporting databases to XML, Exporting database elements to XML, Proximity Shell Scripts and Batch Files
- exporting data
-
- to CSV files, Exporting attribute values, Exploring Attributes
- from NSTs, Exporting NST data, Working with Proximity Tables
- to text, Exporting Plain Text Data, Exporting databases using plain text, Updating databases using plain text, Proximity Text Data Format
- to XML, Exporting Data to XML, Exporting database elements to XML, Proximity XML Format
- exportSpec, Exporting Data to XML
- exportType, Exporting Data to XML
F
- features, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py
- file permissions, Installing Proximity
- file shortcuts, File and directory shortcuts
- FilterFactory class, Working with Proximity Tables
- filtering, Exploring Attributes, Using the Location Bar, Working with Proximity Tables
- filters
-
- complex conditions in, Working with Proximity Tables
- conditions in, Working with Proximity Tables, Working with Proximity Tables
- general form, Working with Proximity Tables
- as method parameters, Working with Proximity Tables
- operators for, Working with Proximity Tables
- find(), Code Example: Finding Selected Objects
- finding
-
- attribute values, Code Example: Finding Selected Objects
- objects, Working with Proximity Tables
G
- generate-iid-data.py script, Code example: generate-iid-data.py
- generating synthetic data, Synthetic Data Generation, Code example: generate-iid-data.py
- getAttrNST(), Working with Proximity Tables
- getAUC(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- getConditionalLogLikelihood(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py
- getDbName(), Code example: helloworld.py
- getObjects(), Working with Proximity Tables
- getStringFromUser(), Code example: create-isStudent-attr.py
- getSubgraphAttrs(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- getYesNoFromUser(), Code example: create-isStudent-attr.py, Code example: generate-iid-data.py
- getZeroOneLoss(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- Gibbs sampling, Code example: generate-iid-data.py, Relational Dependency Networks, Code example: run-1d-clusters-rdn.py
- graph-query.dtd, DTD files, A First Proximity Query, Exploring Containers and Subgraphs, Executing a Query from the Proximity Database Browser, Tips and Reminders, DTD Files
- graphical data browser, Visualizing Data, Visualizing Data
- graphing
-
- data, Visualizing Data, Visualizing Data
- relational dependency networks, Viewing relational dependency networks, Viewing relational dependency networks
- relational probability trees, Understanding and viewing relational probability trees
- subgraphs, Exploring Containers and Subgraphs
- grouping query results, Grouping Elements in a Query
- gui.sh/gui.bat, Running Proximity applications, Exploring Objects and Links, Proximity Shell Scripts and Batch Files
I
- i.i.d. (see independent and identically distributed)
- identity constraints, Comparing Items in a Query, Matching Complex Subgraphs with Subqueries
- IDs in Proximity, Visualizing Data, Working with Proximity Tables
- import-text.sh/import-text.bat, Running Proximity applications, Importing databases using plain text, Proximity Shell Scripts and Batch Files, Updating databases using plain text
- import-xml.sh/import-xml.bat, Running Proximity applications, Importing databases using XML, Importing XML data using noChecks, Proximity Shell Scripts and Batch Files
- importing
-
- classes in scripts, Importing Proximity packages
- noChecks option, Importing XML data using noChecks
- text data, Importing Plain Text Data, Importing database elements using plain text, Updating databases using plain text, Proximity Text Data Format
- XML data, Importing XML Data, Transforming Tabular Data to XML, Proximity XML Format
- independence assumption, Conventional Knowledge Discovery
- independent and identically distributed, Synthetic Data Generation
- inducing models, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- init-db (db-util option), Running Proximity applications, Importing databases using XML, Importing databases using plain text, Code example: generate-iid-data.py, Proximity Shell Scripts and Batch Files
- init-mserver.mil script, Running the MonetDB database server, Importing databases using XML, Importing databases using plain text, Exploring Objects and Links, MonetDB Server
- initializing databases, Importing databases using XML, Importing databases using plain text, Updating databases using plain text
- installation, Installing MonetDB, Installing Proximity
- instantiating models, Code example: run-1d-clusters-rbc.py
- interpreter (see Python interpreter)
- interpreting RPTs, Understanding relational probability trees
- intersect(), Working with Proximity Tables
- isProxTablesEmpty(), Code example: generate-iid-data.py
- ItemSource class, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
L
- labels (see names)
- layout
-
- of queries, A First Proximity Query
- of subgraphs, Exploring Containers and Subgraphs
- learn(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- limiting query matches, Grouping Elements in a Query
- links
-
- attributes of, Exploring Objects and Links, Exploring Attributes, Working with Proximity Tables
- creating with queries, Adding Links to Data with Queries, Adding Links to Data with Queries
- directedness, A First Proximity Query
- and edges, Overview
- exploring, Exploring Objects and Links, Using the Location Bar
- IDs for, Working with Proximity Tables
- importing, Importing database elements using XML
- Proximity tables for, Working with Proximity Tables
- setting display preferences, Setting Display Preferences
- specifications for new, Adding Links to Data with Queries
- text representation, links.data
- type information, Analyzing the Database Schema
- XML representation, Links
- Linux
-
- MonetDB installation, Installing MonetDB
- Proximity installation, Installing Proximity
- shell scripts, Running Proximity applications, Running Proximity applications, Proximity Shell Scripts and Batch Files
-
- (see also specific shell scripts)
- load(), Code example: generate-iid-data.py
- location bar
-
- in graphical data browser, Visualizing Data
- in Proximity Database Browser, Using the Location Bar, Exploring Containers and Subgraphs, Location Bar Path Syntax
- log4j package, Logging options
- logging options, Logging options
- LoggingListener class, Code example: run-1d-clusters-rdn.py
- logical operators, A First Proximity Query, Tips and Reminders, Working with Proximity Tables
M
- Mac OS X
-
- MonetDB installation, Installing MonetDB
- Proximity installation, Installing Proximity
- shell scripts, Running Proximity applications, Running Proximity applications, Proximity Shell Scripts and Batch Files
-
- (see also specific shell scripts)
- match phase in query processing, Adding Links to Data with Queries
- matching queries, Overview, A First Proximity Query, Grouping Elements in a Query
- method name completion, Using the Proximity Python Interpreter
- methods
-
- addAttribute(), Adding a New Attribute
- addClusterCoeffAttribute(), Clustering coefficient
- addHubsAndAuthoritiesAttributes(), Hubs and authorities
- apply(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- browse(), Code Example: Finding Selected Objects, Working with Proximity Tables
- computeConnectedComponents(), Connected components
- defineAttribute(), Attributes
- describe(), Working with Proximity Tables
- find(), Code Example: Finding Selected Objects
- getAttrNST(), Working with Proximity Tables
- getAUC(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- getConditionalLogLikelihood(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py
- getDbName(), Code example: helloworld.py
- getObjects(), Working with Proximity Tables
- getStringFromUser(), Code example: create-isStudent-attr.py
- getSubgraphAttrs(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- getYesNoFromUser(), Code example: create-isStudent-attr.py, Code example: generate-iid-data.py
- getZeroOneLoss(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- intersect(), Working with Proximity Tables
- isProxTablesEmpty(), Code example: generate-iid-data.py
- learn(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- load(), Code example: generate-iid-data.py
- sampleContainer(), Code example: sample-1d-clusters.py
- save(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py
- savePredictions, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- setTrueLabels(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- MIL, Running the MonetDB database server
- models
-
- applying, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- classification, Relational Bayesian Classifier, Relational Probability Trees
- evaluating, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- features in, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py
- inducing, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- instantiating, Code example: run-1d-clusters-rbc.py
- probabilistic, Overview, Relational Probability Trees
- relational Bayesian classifier, Relational Bayesian Classifier, Code example: run-1d-clusters-rbc.py
- relational dependency networks, Code example: generate-iid-data.py, Relational Dependency Networks, Viewing relational dependency networks
- relational probability trees, Code example: generate-iid-data.py, Relational Probability Trees, Understanding relational probability trees
- statistical, Relational Knowledge Discovery
- XML representation of, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- modifying
-
- data (see updating)
- queries (see editing)
- Monet Interpreter Language (see MIL)
- MonetDB
-
- character conversion for, Importing XML Data, Importing Plain Text Data, Proximity XML Format, Proximity Text Data Format
- connecting to, Running the MonetDB database server
- documentation, Running the MonetDB database server, Installing MonetDB
- installing, Installing MonetDB, Installing MonetDB
- obtaining, Obtaining Proximity
- optimized operations, Exploring Objects and Links, Analyzing the Database Schema
- server, Running the MonetDB database server, Running the MonetDB database server, Exploring Objects and Links, MonetDB Server
- versions, Running the MonetDB database server, Obtaining Proximity, Updating MonetDB Databases
- Mserver command, Running the MonetDB database server, Importing databases using XML, Importing databases using plain text, Exploring Objects and Links, MonetDB Server
- Mserver.bat (see Mserver command)
- multiple conditions, A First Proximity Query
- multiple constraints, Tips and Reminders
N
- names
-
- of containers, A First Proximity Query
- of edges, A First Proximity Query
- matching case in Proximity, A First Proximity Query
- setting display preferences, Setting Display Preferences
- of vertices, A First Proximity Query
- negated query elements, Grouping Elements in a Query
- nested containers, Exporting database elements to XML, Containers
- nested synchronized tables (see NSTs)
- noChecks import option, Importing XML Data
- NormalDistribution class, Code example: generate-iid-data.py
- NST browser, Working with Proximity Tables, Working with Proximity Tables
-
- filtering in, Working with Proximity Tables
- starting, Working with Proximity Tables
- NSTs, Working with Proximity Tables, Working with Proximity Tables
-
- exporting data from, Exporting NST data, Working with Proximity Tables
- shortcut names, The prox object, Working with Proximity Tables
- standard Proximity tables, Working with Proximity Tables
- numeric annotations, Grouping Elements in a Query, Grouping Elements in a Query, Tips and Reminders
O
- objects
-
- attributes of, Exploring Objects and Links, Exploring Attributes, Working with Proximity Tables
- exploring, Exploring Objects and Links, Using the Location Bar
- IDs for, Visualizing Data, Working with Proximity Tables
- importing, Importing database elements using XML
- Proximity tables for, Working with Proximity Tables
- setting display preferences, Setting Display Preferences
- text representation, objects.data
- type information, Analyzing the Database Schema
- and vertices, Overview
- XML representation, Objects
- OIDs, Working with Proximity Tables
- one-dimensional star query structure, Grouping Elements in a Query
- opening (see starting)
- operating systems (see specific operating system)
- operators
-
- comparison, A First Proximity Query, Comparing Items in a Query
- filter, Working with Proximity Tables
- logical, A First Proximity Query, Tips and Reminders, Working with Proximity Tables
- optional query elements, Grouping Elements in a Query
- OS X (see Mac OS X)
P
- packages, Importing Proximity packages
- PATH, Running the MonetDB database server, Running Proximity applications, MonetDB Server, Installing MonetDB, Installing Proximity
- path in location bar, Using the Location Bar, Exploring Containers and Subgraphs, Location Bar Path Syntax
- permissions, Installing Proximity
- persistence, Overview
- port number, Running Proximity applications, Importing databases using XML, Importing databases using plain text, Exploring Objects and Links
- precedence of vertex and edge annotations, Grouping Elements in a Query
- predicting attributes, Code example: generate-iid-data.py, The Modeling Process in Proximity, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Understanding relational probability trees, Code example: run-1d-clusters-rdn.py
- Predictions class, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- preferences
-
- graphical data browser, Visualizing Data
- Proximity Database Browser, Setting Display Preferences
- probabilistic models, Overview, Relational Probability Trees
- probability estimation trees, Code example: run-1d-clusters-rpt.py
- project-people query, Matching Complex Subgraphs with Subqueries
- prox object, The prox object, Code example: sample-1d-clusters.py
- prox-accessory.dirs, File and directory shortcuts
- prox3db.dtd, DTD files, Overview, Importing databases using XML, DTD Files, Proximity XML Format
- PROX_HOME, Setting up your environment, Installing Proximity
- Proximity
-
- API, Using Proximity, Running Proximity applications, Overview
- applications (see individual applications)
- client, Obtaining Proximity
- compiling, Running Proximity applications, Proximity Shell Scripts and Batch Files
- data model, A First Proximity Query, Working with Proximity Tables
- data types, Attributes, Subgraphs
- installing, Installing Proximity, Installing Proximity
- logging options, Logging options
- obtaining, Obtaining Proximity
- standard NST tables, Working with Proximity Tables
- updating, Updating MonetDB Databases
- visualizing data in, Visualizing Data
- Proximity class, The prox object
- Proximity Database Browser
-
- executing queries, Executing a Query from the Proximity Database Browser
- location bar, Using the Location Bar, Exploring Containers and Subgraphs, Location Bar Path Syntax
- new window, Exploring Containers and Subgraphs
- overview, The Proximity User Interface
- running applications, Running Proximity applications
- running scripts, Code example: helloworld.py
- setting preferences, Setting Display Preferences
- starting, Exploring Objects and Links
- updating data
-
- creating attributes, Exploring Attributes, Adding a New Attribute
- deleting attributes, Exploring Attributes
- deleting containers, Exploring Containers and Subgraphs
- proximity.jar, Running Proximity applications, Proximity Shell Scripts and Batch Files
- ProxWebKB database, Using the Tutorial, Setting up your environment, Importing XML Data, Exploring Objects and Links
- proxy nodes, Visualizing Data
- Python
-
- interpreter, Overview, Using the Proximity Python Interpreter, Working with Proximity Tables
- running scripts, Code example: helloworld.py
- scripting, Using Proximity, Running Proximity applications, Overview, Overview
Q
- QGraph, Relational Knowledge Discovery, Overview
- queries
-
- 1d-star structure, Grouping Elements in a Query
- 2d-star structure, Matching Complex Subgraphs with Subqueries
- against containers, Exploring Containers and Subgraphs, Querying Containers
- creating, A First Proximity Query, A First Proximity Query
- examples
-
- 1d-clusters, Executing a Query from the Proximity Database Browser
- add-supervises-links, Adding Links to Data with Queries
- cornell-out-clusters, Querying Containers
- different-schools, Comparing Items in a Query
- project-people, Matching Complex Subgraphs with Subqueries
- research-clusters1, A First Proximity Query
- research-clusters2, Grouping Elements in a Query, Executing a Query from the Command Line
- executing, Overview, A First Proximity Query, Executing a Query from the Proximity Database Browser, Executing a Query from the Command Line
- layout in Query Editor, A First Proximity Query
- matching, Overview, A First Proximity Query, Grouping Elements in a Query
- processing phases, Adding Links to Data with Queries
- results, Overview, Grouping Elements in a Query
-
- (see also subgraphs)
- update functionality, Overview, Adding Links to Data with Queries, Adding Links to Data with Queries, Tips and Reminders
- use in data generation, Code example: generate-iid-data.py
- validating, A First Proximity Query
- XML representation, DTD files, Overview
- Query Editor
-
- add link tool, Adding Links to Data with Queries
- automatic edge annotation, Grouping Elements in a Query
- constraint tool, Comparing Items in a Query
- deleting query elements, A First Proximity Query
- edge tool, A First Proximity Query
- keyboard shortcuts, A First Proximity Query, Query Editor Keyboard Shortcuts
- selection tool, A First Proximity Query
- starting, A First Proximity Query
- subquery tool, Matching Complex Subgraphs with Subqueries
- vertex tool, A First Proximity Query
- query.sh/query.bat, Running Proximity applications, Executing a Query from the Command Line, Proximity Shell Scripts and Batch Files
- quitting the MonetDB server, Running the MonetDB database server, MonetDB Server
R
- ranges in numeric annotations, Grouping Elements in a Query
- RBC (see relational Bayesian classifier)
- RBC class, Code example: run-1d-clusters-rbc.py
- rbc2.dtd, DTD files, DTD Files
- RDN (see relational dependency networks)
- RDN class, Code example: run-1d-clusters-rdn.py
- rdn.dtd, DTD files, Viewing relational dependency networks, DTD Files
- relational Bayesian classifier, Relational Bayesian Classifier, Code example: run-1d-clusters-rbc.py
- relational data, Introduction
- relational dependency networks, Relational Dependency Networks, Viewing relational dependency networks
-
- use in data generation, Code example: generate-iid-data.py
- visualizing, Viewing relational dependency networks, Viewing relational dependency networks
- relational probability trees, Relational Probability Trees, Understanding relational probability trees
-
- interpreting, Understanding relational probability trees
- temporal attributes in, Using temporal attributes in a relational probability
tree, Using temporal attributes in a relational probability
tree
- use in data generation, Code example: generate-iid-data.py
- visualizing, Understanding and viewing relational probability trees
- removing (see deleting)
- reports from schema analysis, Analyzing the Database Schema
- requirements for Java, Obtaining Proximity
- research-clusters1 query, A First Proximity Query
- research-clusters2 query, Grouping Elements in a Query, Executing a Query from the Command Line
- results of queries, Overview, Grouping Elements in a Query
-
- (see also subgraphs)
- ROC curve, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- root container, Exporting Data to XML, Exploring Containers and Subgraphs
- row count, Exploring Attributes
- RPT (see relational probability trees)
- RPT class, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- rpt2.dtd, DTD files, Understanding and viewing relational probability trees, Code example: run-1d-clusters-rdn.py, Viewing relational dependency networks, DTD Files
- run-1d-clusters-rbc.py script, Code example: run-1d-clusters-rbc.py
- run-1d-clusters-rdn.py script, Code example: run-1d-clusters-rdn.py
- run-1d-clusters-rpt.py script, Code example: run-1d-clusters-rpt.py
- running
-
- queries, Overview, A First Proximity Query, Executing a Query from the Proximity Database Browser, Executing a Query from the Command Line
- scripts, Running Proximity Scripts
S
- sample-1d-clusters.py script, Code example: sample-1d-clusters.py
- sampleContainer(), Code example: sample-1d-clusters.py
- sampling, Sampling the Database, Code example: sample-1d-clusters.py
- save(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py
- savePredictions(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- saving
-
- queries, A First Proximity Query
- relational Bayesian classifiers, Code example: run-1d-clusters-rbc.py
- relational probability trees, Code example: run-1d-clusters-rpt.py, Understanding and viewing relational probability trees, Code example: run-1d-clusters-rdn.py
- schema analysis reports, Analyzing the Database Schema
- schema analysis, Analyzing the Database Schema
- script.sh/script.bat, Running Proximity applications, Code example: helloworld.py, Proximity Shell Scripts and Batch Files
- scripts, Running Proximity applications, Using Scripts, Proximity Shell Scripts and Batch Files
-
- (see also shell scripts)
- accessing API via, Running Proximity applications
- create-isStudent-attr.py, Code example: create-isStudent-attr.py
- examples
-
- generate-iid-data.py, Code example: generate-iid-data.py
- helloworld.py, Code example: helloworld.py
- run-1d-clusters-rbc.py, Code example: run-1d-clusters-rbc.py
- run-1d-clusters-rdn.py, Code example: run-1d-clusters-rdn.py
- run-1d-clusters-rpt.py, Code example: run-1d-clusters-rpt.py
- sample-1d-clusters.py, Code example: sample-1d-clusters.py
- social-networking-algs.py, Code example: social-networking-algs.py
- importing classes, Importing Proximity packages
- init-mserver.mil, Running the MonetDB database server, Importing databases using XML, Importing databases using plain text, Exploring Objects and Links, MonetDB Server
- running, Running Proximity Scripts
- text2xml.pl, Transforming Tabular Data to XML
- searching for attribute values, Code Example: Finding Selected Objects
- selection tool, A First Proximity Query
- server (see MonetDB, server)
- setting display preferences, Setting Display Preferences
-
- (see also layout)
- setTrueLabels(), Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- shell scripts, Running Proximity applications, Running Proximity applications, Proximity Shell Scripts and Batch Files
-
- db-util.sh, Running Proximity applications, Importing databases using XML, Importing databases using plain text, Code example: generate-iid-data.py, Proximity Shell Scripts and Batch Files, Updating databases using plain text
- export-text.sh, Running Proximity applications, Exporting databases using plain text, Proximity Shell Scripts and Batch Files, Updating databases using plain text
- export-xml.sh, Running Proximity applications, Exporting databases to XML, Exporting database elements to XML, Proximity Shell Scripts and Batch Files
- file permissions for, Installing Proximity
- gui.sh, Running Proximity applications, Exploring Objects and Links, Proximity Shell Scripts and Batch Files
- import-text.sh, Running Proximity applications, Importing databases using plain text, Proximity Shell Scripts and Batch Files, Updating databases using plain text
- import-xml.sh, Running Proximity applications, Importing databases using XML, Importing XML data using noChecks, Proximity Shell Scripts and Batch Files
- query.sh, Running Proximity applications, Executing a Query from the Command Line, Proximity Shell Scripts and Batch Files
- script.sh, Running Proximity applications, Code example: helloworld.py, Proximity Shell Scripts and Batch Files
- shortcuts
-
- to data structures, The prox object, Working with Proximity Tables
- for file access, File and directory shortcuts
- for Query Editor, A First Proximity Query, Query Editor Keyboard Shortcuts
- social networking algorithms, Social Networking Algorithms
- social-networking-algs.py script, Code example: social-networking-algs.py
- special characters
-
- in container names, A First Proximity Query
- in text data representation, Importing Plain Text Data, Exporting Plain Text Data, Proximity Text Data Format
- in XML data representation, Importing XML Data, Proximity XML Format
- starting
-
- MonetDB server, Running the MonetDB database server, Exploring Objects and Links, MonetDB Server
- NST browser, Working with Proximity Tables
- Proximity Database Browser, Exploring Objects and Links
- Query Editor, A First Proximity Query
- statistical
-
- independence, Conventional Knowledge Discovery
- models, Relational Knowledge Discovery
- stopping the MonetDB server, Running the MonetDB database server, MonetDB Server
- subgraphs
-
- as query results, Overview, Grouping Elements in a Query
- attributes of, Code example: sample-1d-clusters.py, Subgraphs, Subgraph attribute specification files
- exploring, Exploring Containers and Subgraphs, Exploring Containers and Subgraphs
- text representation, Subgraph data files
- visualizing, Exploring Containers and Subgraphs
- XML representation, Subgraphs
- subqueries, Matching Complex Subgraphs with Subqueries, Matching Complex Subgraphs with Subqueries
- subquery tool, Matching Complex Subgraphs with Subqueries
- support (see technical support)
- synthetic data generation, Synthetic Data Generation, Code example: generate-iid-data.py
- SyntheticGraphIID class, Code example: generate-iid-data.py
T
- tables in Proximity databases, Working with Proximity Tables, Working with Proximity Tables
- tabular data, Transforming Tabular Data to XML
- technical support, Contact information, Technical Support and Documentation
- temporal attributes, Using temporal attributes in a relational probability
tree, Using temporal attributes in a relational probability
tree
- TemporalAttributeSource class, Using temporal attributes in a relational probability
tree
- TemporalItemSource class, Using temporal attributes in a relational probability
tree
- test sets
-
- creating, Code example: sample-1d-clusters.py
- use in models, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- test-db (db-util option), Running Proximity applications, Proximity Shell Scripts and Batch Files
- text data
-
- exporting, Exporting Plain Text Data, Exporting NST data
- format, Proximity Text Data Format, Subgraph attribute data files
- importing, Importing Plain Text Data, Importing database elements using plain text
- text2xml.pl, Transforming Tabular Data to XML
- thumbnails of subgraphs, Exploring Containers and Subgraphs
- training models, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- training sets
-
- creating, Code example: sample-1d-clusters.py
- use in models, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py
- two-dimensional star query structure, Matching Complex Subgraphs with Subqueries
- type of an object or link, Analyzing the Database Schema
V
- validating queries, A First Proximity Query
- vertex tool, A First Proximity Query
- vertices
-
- in constraints, Comparing Items in a Query
- creating, A First Proximity Query, A First Proximity Query
- names of, A First Proximity Query
- and objects, Overview
- on subquery boundaries, Tips and Reminders
- view-schema (db-util
option), Running Proximity applications, Proximity Shell Scripts and Batch Files
- view-stats (db-util option), Running Proximity applications, Proximity Shell Scripts and Batch Files
- visualizing
-
- data, Visualizing Data, Visualizing Data
- relational dependency networks, Viewing relational dependency networks, Viewing relational dependency networks
- relational probability trees, Understanding and viewing relational probability trees
- subgraphs, Exploring Containers and Subgraphs
W
- WebKB data set, Importing databases using XML
- wildcards in interactive Python interpreter, Code Example: Finding Selected Objects
- Windows
-
- batch files, Running Proximity applications, Running Proximity applications, Proximity Shell Scripts and Batch Files
-
- (see also specific batch files)
- environment variables, Setting up your environment, Installing Proximity
- MonetDB installation, Installing MonetDB
- Proximity installation, Installing Proximity
- wrapper file for RDN visualization, Viewing relational dependency networks
X
- XML
-
- converting tabular data to, Transforming Tabular Data to XML
- model representation, Code example: run-1d-clusters-rbc.py, Code example: run-1d-clusters-rpt.py, Code example: run-1d-clusters-rdn.py
- query representation, DTD files, Overview
- XML data
-
- exporting, Exporting Data to XML, Exporting database elements to XML
- format, Proximity XML Format, Subgraphs
- importing, Importing XML Data, Transforming Tabular Data to XML


Cryptogotchi
Account Overview
Address:
Balance:
Transaction Count:
0
Reload account
Send ETH
To:
Amount (Eth):
Send
Hatch your Cryptogotchi!
Name:
Description:
Color:
Hatch
Your Cryptogotchi


Loading...
Class Info:
Name:
Description:
ID:
Professor Access Key:
TA Access Key:
Student Access Key:
Download Options:
Download Sessions
Download Session Activity


(1) A baseball bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball.
¿How much does the ball cost? 0.05 cents.
(2) If 5 machines take 5 minutes to make 5 chairs, ¿How much would 100 machines take to make 100 chairs? 5 minutos.
(3) In a lake, there is a lirium patch. Each day, the patch duplicates in size. If it takes 48 days to cover all the lake,
¿How much will it take to cover half of the lake? 47 días.
(4) If it takes 1 minute to make 1 cut, ¿Whats the minimum time it will take to cut a wood cilinder of 30 feet in 30 equal pieces
without moving the pieces cutted? 7 minutos.


Slogan is a dynamically typed, multi-paradigm programming language with very high-level support for concurrency and parallelism. It is a practical language that is easy to learn and play well with the rest of the world.
Slogan is a dynamically and strongly typed language. This means Slogan has strict type rules that are enforced at runtime. Dynamic typing enable programming in the widest possible variety of styles. Integration of software components developed and compiled separately, becomes a trivial matter. Dynamic typing makes it easy to turn ideas into executable programs at a fast pace. Once developed and tested, a Slogan program can be compiled to efficient machine code that runs at full speed on the host platform.
Designing a clear and concise solution to a problem requires selecting the right paradigm. This may be hard or even impossible in languages that prefer one programming style over others. Slogan support many programming styles in a well-factored way. In other words, the different paradigms are not stacked together haphazardly. Instead, they are integrated in a simple model of function definition and application. Slogan treat functions as first-class entities. Just by mastering this single concept a programmer can write functional or object-based programs.
Concurrency is generally seen as difficult to get right, thanks to its inherent nondeterministic nature. Slogan's reactive variables makes it easy to write declarative concurrent programs. Slogan also support concurrency and parallelism based on message passing. More traditional concurrency with shared memory is also possible.
Slogan has all the facilities for integrating well with the outside world. There are full-featured libraries for communicating with other processes running on the same machine or on the same network. The high-level Foreign Function Interface makes it possible to call C library functions without special proxy code. The interpreter enable fast development and debugging, while the compiler and the packaging system enable software to be distributed as a stand-alone executables or as dynamically loadable libraries.
Slogan is implemented in Gambit Scheme. Gambit was chosen for its maturity, high-performance, tight integration with the C language and strong support for concurrency.


Store
Ready-to-Go Sites
Purchase sites built on Hashedit that are ready to be deployed.
Development Kits
Form Kit
Form Kit allows you to easily process and display form results within Hashedit.
$25 Coming Soon
Locations Kit
Easily manage and display a list of locations on Google Maps.
$25 Coming Soon
Commerce Kit
Quickly manage products and enable e-commerce on your site.
$25 Coming Mid 2016


Friend Finder
Survey
API Friends List
GitHub Repo
Friend Finder
Answer a few basic questions.
Then find the friend with the greatest compatibility!
Go to Survey


Hello! Glad to have you here. My name is Andy Chow and this is my portfolio! I am new to the web dev world and am excited to start on this adventure. Little by little I will add to my portfolio and be able to showcase my improvement and successes. Stay tuned.
I call Pacifica California home. It is a coastal town close to San Francisco. I currently live in Salt Lake City Utah. In April 2016 I graduated from Brigham Young University with a degree in Public Health and a minor in Business Management. I currently work for the University of Utah Hospital. I look forward to furthering my career with a skill in computer programming and web development. My goal is to work for the health world helping out in informatics.


- simple; uncomplicated; briefness;
- simple; uncomplicated; briefness;
- simple; uncomplicated; briefness;
- simple; uncomplicated; briefness;
- simple; uncomplicated; briefness;
地址：太原市小店区万立科技大厦15层
联系人：JavaJam
电话：1-888-555-55555
联系人：JavaJam
电话：1-888-555-55555


As a result, no criminal charges will be filed against the officers involved in the Aug. 3 death of De’Von Bailey in Colorado Springs, KRDO reported, citing El Paso County District Attorney Dan May.
Bailey, 19, was shot three times in the back and once in the arm.
In Colorado, district attorneys can decide to file charges, send a case to a grand jury or determine police were justified in a shooting.
“For De’Von’s family, the decision was like a kick in the gut, but of course we are not one bit surprised,” family attorney Mari Newman said. “This is the exact result that the system was designed to yield when a tainted investigation is presented by a biased prosecutor. This is precisely why we have been calling for an independent prosecutor and an independent investigation from the beginning.”
Police body camera footage shows officers talking to Bailey and another man in a neighborhood about an armed robbery that was reported nearby. One officer ordered the men to keep their hands up so that another officer could search them for weapons.
Bailey ran as he was about to be searched and was shot after he put his hands near his waistband. The footage shows him falling to the ground and the officers running up to cuff his hands behind his back.
Police said they found a gun hidden in Bailey’s shorts.
The shooting “is something neither police officers nor citizens ever want to experience,” Police Chief Vince Niski said in an open letter to the community Wednesday. “The loss of a son, a friend, a community member, is a devastating event that impacts all of us.”
He also said he “fully trusts and supports” the investigative process, noting that the El Paso County Sheriff’s Office looked into the death before the district attorney handed the case to the grand jury.
Newman, who was joined by Gov. Jared Polis and Colorado Springs Mayor John Suthers in calling for an independent investigation, said Wednesday she would pursue additional legal action.
“The criminal justice system, as we see time and time again, is skewed in favor of police,” she said.
Information from: KRDO-TV, http://www.krdo.com/
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


Dashboard
Login
Sign Up
Logout
Welcome {{name}} to the eflows4HPC Container Image Creation Service
Build requests
Available Images


Run Tests
Run automatically
I should be red
I should be green
I should be yellow
I should be black still
I should be blue
Give me a purple background


Certificate Manager - 3. Register Personal Certificate by Student
1. Register Certificate Type by Issuer
2. View Certificate Type by Issuer
3. Register Personal Certificate by Student
4. View & Confirm Personal Certificate by Issuer
5. Register Certificate Token by Student
6. View Certificate by Company using Token
Certificate Id
Certification No.
Issue Date
Valid Through Date
Certificate Type Id
Register Personal Certificate
Clear sample
Add sample1
1, "2019-01-01", "2022-01-01", "Blockchain001", "CT001" ("Blockchain Proficiency")
Add sample2
2, "2019-02-02", "20209-02-01", "AWS_CLOUD22", "CT002" ("AWS Cloud Certification")
Add sample3
3, "2019-03-03", "2099-12-31", "AWS_CA_3333", "CT003" ("AWS CA Certification")


Workflow Assets
Workflow assets consist of documents or artifacts that are packaged along with process definitions. They also include features that support the ability for end-users to own and maintain them,
with the intent being to provide a mechanism for quickly responding to evolving business conditions by allowing changes to be enacted in a controlled manner without requiring an application deployment.
Workflow assets are stored in the MDW database. Migration between environments is accomplished by Export/Import and Asset Discovery.
Types of Assets
The following are examples of the different types of workflow assets available in MDW:
- Processes, Activities
- External Event Handlers
- Dynamic Java
- Spring Artifacts
- Scripts (Groovy, JavaScript, MagicBox)
- Automated Test Cases and Expected Results
- Business Rules (Drools, Excel Decision Tables)
- Pages (HTML5, Facelets)
- Templates (Velocity, HTML, Facelets)
- Reports (BIRT)
- Web Resources (CSS, Script, Images)
- Camel Routes (Services Orchestration)
- Documentation (MS Word, HTML, Text)
Common Features
All of these assets can be managed in the same way, with the following built-in capabilities:
- Authorization Control based on LDAP Credentials
- Versioning with Comments, History, Archiving and Rollback (see Asset Persistence)
- Asset Locking to Prevent Simultaneous Modification
- Rigorous Audit Log Tracking User Changes
- Tools for Migrating between Environments
Managing Workflow Assets
Workflow assets can be managed by developers or end-users via MDW Designer, as long as they belong to the role ProcessAdmin for the target project.
To create a brand new instance of an asset, right-click on a workflow package in Process Explorer, and selecting New, and then the type of asset desired.
Assuming that you appropriate permissions, updates can be accomplished by double-clicking the asset to edit it in place, or by right clicking and selecting Import > New Version.
Either way, the version will be incremented and you'll be prompted for a comment to accompany the new version.
The style of editor and default document contents will differ according to the type of asset created, but managing the assets is the same regardless of type.
The lifecycle management aspects of workflow assets are viewed and controlled using the Tabbed Properties in Designer.
To display the Tabbed Properties, right-click on the asset to be managed and select Show Properties from the menu.
You can also use an external editor if that's more appropriate. To save a workflow asset to the file system right-click on it and select Export > Export to File.
Then after updating the asset in an external editor, import your changes by right-clicking on the asset and selecting Import > New Version (make sure you've got it locked first as described below).
MDW applications need to be able to version their assets and propagate them one environment to another (dev to test, test to prod, etc.).
Starting with MDW 5.5 asset versioning is achieved by integrating with Git (see Asset Persistence).
Importing assets from Git can be done through Designer, or through a command-line interface to support automation and continuous integration.
TODO: screenshots for Designer import from Git, and examples of command-line import scripts
Beyond environmental migrations, MDW Workflow Assets are meant to be shared and reused
across applications. For this purpose Designer provides a discovery mechanism to locate and import assets from a central repository.
The MDW base and hub packages are examples of asset collections that can be reused in this way.
To discover and import these packages, right-click on your project in Process Explorer view and select
Import > Package(s) > Discover.

A common example of this is a "Certified" flag or equivalent which indicates that a particular version of the asset should be in effect.
At design-time the developer specifies which custom attributes are needed (see "Custom" tab below), as well as which roles are allowed to set these field values.
Then, during operation, business users with the appropriate role can change the values of these fields.
Typically these values are consulted at runtime to control which version of an asset will be in force for a workflow process activity (see Drools Activities for an example of custom attributes in practice).


Thank you for visiting this webpage. This is a CE 186 Project with professor Scott Moura in 2015. We created this webpage to monitor the water usage in all of our shower stalls. You are not only able to review your shower time, but also discover interesting statistics of daily behavior! The purpose is to enhance our awareness of people's usage of water. Furthermore, we hope to save water for California by inspiring you. See how your shower behavior changes over time. Also, we will appreciate any comment or suggestion you may have. Thank you and enjoy! :)
Lead Engineer of the Smart Shower Project.
Cheif Manager of the Smart Shower Project.
Chief Engineer of the Smart Shower Project.
Web Designer of the Smart Shower Project.


Loading...
Choose the type of pattern file you want to import.
Simple warp-twined patterns and 3/1 broken twill patterns can be imported from GTT


About
What is Relief Call?
Relief Call is a nonprofit venture started by a bunch of engineers to help Chronic Kidney Disease patients. Our concentration is mainly on unprivileged patients who are under HaemoDialysis and we’re restricted to Mysore region.
How it started?
Everything begins with passion, with feelings and a story behind it. Every new beginning comes from some other beginning’s end. Relief Call is such a beginning that hit our head when a trauma called dialysis ended in one of our friend’s family.
When we realized that there are so many out there who really can’t afford a prescribed number of dialysis per week due to financial problems, a toddler called ‘Relief Call’ came into existence to help those who are in need of financial help for HaemoDialysis treatment.
Why we started Relief Call?
Kidney failure or CKD is one such disease where the treatment is really not affordable. We can break down the cost of treatment as follows,
- Dialysis Cost.
- Dialyser Cost.
- Regular Medicines.
- Blood work every month.
- Regular Checkups.
- Blood transfusions.
- Emergency can hit anytime.
Physical trauma the patient goes through and the emotional trauma the family faces is unbearable added to it one has to be financially stable as the treatment is costly and not affordable to all.
One has to ideally get dialysed thrice a week which counts up to 12 dialysis per month. Relief Call’s aim is to collect funds from the people who are willing to donate for this cause and help few unprivileged patients by providing a financial help through subsidized dialysis after a thorough background check done by our volunteers. Join our hands and support us for the cause. After all, one human can save another.


This is an introduction to the R programming language. I would like this introduction to be as accessible and useful as possible for users of all proficiency levels. Every link below will include free resources.
Before diving into the R programming language, I would highly recommend installing the RStudio development environment. It makes a lot of interactions with the R programming language and peripheral programming related tasks (version controlling, connecting to databases, code-formatting, etc.) much easier.
If you’re a beginner and you want to get started with R as fast as possible, I recommend the following guides:
Swirl : Learn R, in R - Swirl allows newcomers to start interacting with the R programming language through guided tutorials primarily aimed at beginners.
Learn X in Y Minutes : R - Learn X in Y Minutes walks readers through an example script which demonstrates many of the important R language features without getting too bogged down on any specific topic. This is a great quick reference for the syntax of R.
R Language for Programmers - If you have any experience programming but are new to R, or would simply like a quick refresher on the basic language features of R, this is a good reference.
Cookbook for R - This website (and its corresponding book) provide examples and tutorials on many of the most common tasks that programmers are accomplishing with R.
Lastly, I want to encourage people to use the help.start()
feature in R.
Open the R console, type help.start()
, and hit enter. A window will open
which allows you to view the R manuals, check out packages’ documentation,
and search for keywords on R-specific sites and resources.
Keep in mind that you can always use ?command
or help()
to look up help
without leaving your R or RStudio session.
Once you have some familiarity with R and want to understand it more rigorously, I think these resources can help with that:
An Introduction to R - This introduction to R is quite comprehensive and technical. I should point out that while other guides may discuss packages outside the base R language, this introduction focuses entirely on the base R language. Many people will recommend that it is important to understand the base R language, and then to quickly move onto using the nearly self-contained ecosystems implemented in packages like the tidyverse,
aRrgh, a newcomer’s (angry) guide to R - aRrgh takes the stance that as R is a programming language predominantly developed by statisticians and not programmers, it has some very quirky and at times outright offensive features. Despite this, R comes with tools and ideas that make it uniquely suitable for statistical data analysis and after learning its quirks many people find R to be an indespensible tool they frequently reach for. aRrgh is a guide on those quirks and “gotchas.”
The R Inferno - “If you are using R and you think you’re in hell, this is a map for you.”
R-Bloggers : Tutorials for Learning R - R-Bloggers is a popular website among R users where R tutorials and blogposts are aggregated and widely distributed. This is a great post, similar to this one of mine, delineating resources in an ordering suitable for newcomers to the R language.
Efficient R Programming - This book takes a dive into the topic of writing R code which is efficient while keeping the prerequisite knowledge of the language to a minimum.
R Packages - R Packages are an integral part of the R ecosystem, and it is considered good practice to format your work as a package where possible due to the many added perks gained from R’s package development tools.
Advanced R - Advanced R takes a closer look at the technical features of the R programming language to help users improve their R programming skills.
The R Manuals - The R Manuals from CRAN are for those who want to take a deep dive into the fundamentals of the R programming language. In particular, I recommend checking out The R Language Definition and R Internals if you really want to understand how R works.
While some may spend lifetimes understanding R’s internals and subtleties, others may have the goal of just getting things done. For achieving this, I would make different recommendations than I would to those who want to rigorously understand the language. To accomplish specific goals and finish your projects implemented in R, I would recommend the following resources:
RStudio’s Cheat Sheets - RStudio’s Cheat Sheets are great at-a-glance reference cards for many of the popular packages and features of R. They have ones for many topics, including base R, the RStudio development environment, ggplot2, a data visualization package, and many, many more.
CRAN’s Task Views - CRAN’s Task Views organize packages into topics to make them more easily discoverable.
Awesome-R - Awesome R is a curated list of R packages and tools which is great for discovering new and popular packages to help you accomplish a given task.
R for Data Science - R for Data Science, or R4DS, is a very popular book in the R community which teaches users to perform statistical analyses, visualize them, and to communicate their results effectively using the tools the R community has to offer.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction is the 2nd edition of a popular, mathematically rigorous, treatment of statistical learning with R examples. The PDF of the book is available for free, and a real gift to the community.
Text Mining with R : A Tidy Approach - Another popular topic these days is natural language processing. Text Mining with R introduces ideas on how one can use the tidy data paradigm to ease their textual analyses.
Shiny from RStudio - Shiny is a web framework for R with ever-improving documentation and articles. Shiny makes it easy for users to interact with data analyses through web-interfaces that functions through a reactive programming paradigm.
Show Me Shiny - In addition to the references available on RStudio’s website for Shiny, I also highly recommend the examples available at showmeshiny.com. These showcase many nontrivial and novel use-cases for Shiny and can often serve as inspirational material.
htmlwidgets for R - Create JavaScript based widgets to render interactive data visualizations within R.
R Graphics Cookbook - This resource appears as if it is just one chapter inside the R Cookbook already linked above, but in actuality it is a whole book on its own dedicated to the subject of producing graphics and plots with R. This book makes heavy use of ggplot2, one of the most popular graphics frameworks which employs a theory of the grammar of graphics to make writing code to produce graphics more natural and straightforward.
Rcpp, Rcpp-introduction, and the Rcpp vignettes - Rcpp is a package which provides a seamless interface between C++ and R. This is a great utility for speeding up projects in R


@* * Copyright 2017 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.epaye.html._ @(xref: String)(implicit userProfile: uk.gov.hmrc.epaye.connectors.UserProfile, authContext: uk.gov.hmrc.play.frontend.auth.AuthContext, request: Request[_], lang: Lang, messages: Messages) @main_template(title = Messages("epaye.landp.pay.charges.bank.title")) {
@Messages("epaye.landp.pay.charges.bank.overseas.message")
@Messages("epaye.landp.pay.charges.bank.overseas.address.message")


Pundit says LFC should buy Ibrahimovic as Origi isn’t good enough
by Jordan ChamberlainFormer Scotland international turned pundit Craig Burley reckons Liverpool would be missing a trick by not offering fee agent Zlatan Ibrahimovic a short-term contract until the end of the season.
The Swede, undeniably one of the best players of his generation, is a free agent having left LA Galaxy recently – and Jurgen Klopp recently joked that he would snap the forward up if he hadn’t have previously played for Manchester United [Star].
Burley though has slammed Divock Origi, our current first-choice backup to Roberto Firmino and suggested Ibra would do a better job.
“We laughed when we talked about if Jurgen Klopp had been asked about him,” Burley explained on ESPN FC . “But this is something I genuinely believe can work short-term.
“I know he doesn’t fit their style, with the pressing style and all that.
“But they have nobody else, literally nobody else, other than Roberto Firmino [in a central attacking role].
“Please don’t talk to me about Divock Origi, you’re talking about Liverpool trying to get over the line in the Premier League for the first time in 20-odd years. Sometimes you need something different.
“We’re talking about a guy who may only be coming off the bench now and again for 20 minutes or half an hour, and just bringing something different, that bit of quality.
“Nobody is asking him to run around or close teams down in the first half of a game.
“But I think he can really offer something to a team like that for six months towards the end of the season. I don’t suggest it’ll happen, but it would work.”
Ibrahimovic is 38-years-old, and as Burley suggests, is not set out for the high intensity football Jurgen Klopp demands from all eleven Reds on the field.
Origi has done well this term already and suits the role of impact sub, as his many important late goals prove.
Ibra and his enormous ego could potentially damage our excellent squad harmony – especially considering the multiple outrageous statements he makes about how he’s better than his team-mates – something that wouldn’t fit well with Klopp’s humble group.
It’s a fantasy idea, and not something we should or will seriously consider.


Full Stack Developer
Designer and developer based in central Virginia
Growing up in central Virginia, I was able to experience many different professions. I've been a substitute teacher, fast-food worker, insurance sales agent, waitress, and am currently a kennel technician at a doggie daycare. Due to quarantine, I spent many months trying to figure out what I wanted out of life. Because I graduated from high school with my Associate's degree, this left me forced to choose a major my first semester in college. I was lucky enough to attend Virginia Tech, but quickly realized I was wasting thousands of dollars by not fully knowing what I wanted to study while I was there.
My mom was the one that introduced me to the tech world. I had been out of school for a couple years working dead-end jobs when she mentioned web development as a new profession idea. Being as I had never coded before, I looked up what would make a good software developer. After looking up those qualities, I realized this may be something I could enjoy! Boy, was I right! Since starting this bootcamp last November, I have absolutley fallen in love with conceptualizing, designing, and developing different software applications. Watching my creativity come to life gives me reassurance that I'm exactly where I need to be.
Seeking full-time software engineering employment
Coming into this bootcamp, I was nervous. Taking on a full set of skills that I really had no experience in (other than a few courses in high school) was worrisome. Looking back now, I'm so proud of myself for how far I've come! I've fully immersed myself in the many different technologies, frameworks, and database management systems that the software community has to offer. Now, my skills include building full-stack web applications using HTML, CSS, JavaScript, jQuery, incorporating different API's, using Node, Express, MySQL, Handlebars, Sequelize, React, and so much more. Learning all of these different technologies at such a fast pace within the last six months has me ready to continue learning and sharpen my skills even more!
Some other skills I accquire is my love to work with other people. I find myself to be one of the best when it comes to communcation, patience, and solving problems within a team. Understanding other people, their belief systems, their mental and personal characteristics is something I pride myself in. I'm easily adaptable to all kinds of situations and find myself to be completely solution focused. I manage my time very well and will always surround myself with a positive work environment.
My desired role within a company is to write and style the front-end components that meet the requirements of our mocks and fulfill our user stories. I would love the chance to meet you and your hiring team to get to know each other better and the qualities I can add to your company.


MiniMiser is a simple personal finance management and tracking program. Here is the welcome text.....


Governance is basically addressing questions of: who (accountability), when (timing), how (processes), what (outputs), why (rationale) etc.
The school is governed by the administrative units that were depicted under the organisation structure website page. These units should not be seen as stand-alone silos that work independently but rather specialised units that work together to achieve a common goal. The units are supposed to address both internal and external demands and expectations of the organisation.
For effective management of the school, polices, guidelines, regulations and code of conduct have been developed and disseminated so that all stakeholders are aware of what is expected of them, either individually or collectively. These policies affect the following:
-
| The Institution/school |
-
There is a board of directors with a chart that spells out on how many meetings in a year , how the meetings proceedings are to be conducted, voting , retiring/removal of a director, special committees to be set up to address key issues, duties and functions of CEO.
-
External audit and internal controls & checks: our financial statements are audited by an external auditor at the end of each financial year, internally we continuously assess our academic, financial & administrative systems with the aim of enhancing them so that we become more efficient and effective in service delivery.
-
Code of conduct/ethic: always striving to operate using our core values as a guide in what activity or venture we are involved in; to not only be seen but are actually practicing ‘good corporate' citizen attributes/values in the society we operate in.
-
Conflict of interest: to disclose affront and early enough whenever the School or any board member has an interest in a venture to be carried out.
-
Corporate Social Responsibility (CSR): CSR has been dealt with in a separate website page.
|
-
| Student Discipline |
-
We have developed school rules that are shared between the student , parent/guardian, teachers.
-
We have strategies in place that promote positive student behaviour and respect to others and school property.
-
We have developed practices that recognise , reward and reinforce student achievements.
-
We have developed strategies and practices that manage inappropriate behaviour.
|
-
| Principal/Head teacher roles |
-
A handbook has been developed that details the roles of both the head teacher and teachers, which covers wide topics on: student discipline, curriculum, extra-curriculum activities, code of ethics, responsibilities, dressing & grooming etc.
|
-
| Reliable |
-
Oversees that curriculum is implemented according to ministry of education guidelines, carries out random on site visits to classrooms to note how curriculum is being covered, protects the image of the school by ensuring code of conduct & ethic is strictly adhered to, ensures there is a safe, secure learning environment for children include all school staff.
|


Class: Aws::EC2::Types::SpotFleetRequestConfigData
| Inherits: |
Struct
|
| Defined in: |
(unknown) |
Overview
Describes the configuration of a Spot fleet request.
Returned by:
- RequestSpotFleetRequest#spot_fleet_request_config
- Aws::EC2::Types::SpotFleetRequestConfig#spot_fleet_request_config
Note: When passing SpotFleetRequestConfigData
as input to a Client method, you can use a vanilla Hash
:
{
client_token: "String",
spot_price: "String", # required
target_capacity: 1, # required
valid_from: Time.now,
valid_until: Time.now,
terminate_instances_with_expiration: false,
iam_fleet_role: "String", # required
launch_specifications: [ # required
{
image_id: "String",
key_name: "String",
security_groups: [
{
group_name: "String",
group_id: "String",
},
],
user_data: "String",
addressing_type: "String",
instance_type: "t1.micro", # accepts t1.micro, t2.nano, t2.micro, t2.small, t2.medium, t2.large, m1.small, m1.medium, m1.large, m1.xlarge, m3.medium, m3.large, m3.xlarge, m3.2xlarge, m4.large, m4.xlarge, m4.2xlarge, m4.4xlarge, m4.10xlarge, m2.xlarge, m2.2xlarge, m2.4xlarge, cr1.8xlarge, r3.large, r3.xlarge, r3.2xlarge, r3.4xlarge, r3.8xlarge, x1.4xlarge, x1.8xlarge, x1.16xlarge, x1.32xlarge, i2.xlarge, i2.2xlarge, i2.4xlarge, i2.8xlarge, hi1.4xlarge, hs1.8xlarge, c1.medium, c1.xlarge, c3.large, c3.xlarge, c3.2xlarge, c3.4xlarge, c3.8xlarge, c4.large, c4.xlarge, c4.2xlarge, c4.4xlarge, c4.8xlarge, cc1.4xlarge, cc2.8xlarge, g2.2xlarge, g2.8xlarge, cg1.4xlarge, d2.xlarge, d2.2xlarge, d2.4xlarge, d2.8xlarge
placement: {
availability_zone: "String",
group_name: "String",
},
kernel_id: "String",
ramdisk_id: "String",
block_device_mappings: [
{
virtual_name: "String",
device_name: "String",
ebs: {
snapshot_id: "String",
volume_size: 1,
delete_on_termination: false,
volume_type: "standard", # accepts standard, io1, gp2, sc1, st1
iops: 1,
encrypted: false,
},
no_device: "String",
},
],
monitoring: {
enabled: false,
},
subnet_id: "String",
network_interfaces: [
{
network_interface_id: "String",
device_index: 1,
subnet_id: "String",
description: "String",
private_ip_address: "String",
groups: ["String"],
delete_on_termination: false,
private_ip_addresses: [
{
private_ip_address: "String", # required
primary: false,
},
],
secondary_private_ip_address_count: 1,
associate_public_ip_address: false,
},
],
iam_instance_profile: {
arn: "String",
name: "String",
},
ebs_optimized: false,
weighted_capacity: 1.0,
spot_price: "String",
},
],
excess_capacity_termination_policy: "noTermination", # accepts noTermination, default
allocation_strategy: "lowestPrice", # accepts lowestPrice, diversified
fulfilled_capacity: 1.0,
type: "request", # accepts request, maintain
}
Instance Attribute Summary collapse
-
#allocation_strategy ⇒ String
Indicates how to allocate the target capacity across the Spot pools specified by the Spot fleet request.
-
#client_token ⇒ String
A unique, case-sensitive identifier you provide to ensure idempotency of your listings.
-
#excess_capacity_termination_policy ⇒ String
Indicates whether running Spot instances should be terminated if the target capacity of the Spot fleet request is decreased below the current size of the Spot fleet.
Possible values:
-
#fulfilled_capacity ⇒ Float
The number of units fulfilled by this request compared to the set target capacity.
-
#iam_fleet_role ⇒ String
Grants the Spot fleet permission to terminate Spot instances on your behalf when you cancel its Spot fleet request using CancelSpotFleetRequests or when the Spot fleet request expires, if you set
terminateInstancesWithExpiration
. -
#launch_specifications ⇒ Array<Types::SpotFleetLaunchSpecification>
Information about the launch specifications for the Spot fleet request.
-
#spot_price ⇒ String
The bid price per unit hour.
-
#target_capacity ⇒ Integer
The number of units to request.
-
#terminate_instances_with_expiration ⇒ Boolean
Indicates whether running Spot instances should be terminated when the Spot fleet request expires.
-
#type ⇒ String
The type of request.
-
#valid_from ⇒ Time
The start date and time of the request, in UTC format (for example, YYYY-MM-DDTHH:MM:SSZ).
-
#valid_until ⇒ Time
The end date and time of the request, in UTC format (for example, YYYY-MM-DDTHH:MM:SSZ).
Instance Attribute Details
#allocation_strategy ⇒ String
Indicates how to allocate the target capacity across the Spot pools
specified by the Spot fleet request. The default is lowestPrice
.
Possible values:
- lowestPrice
- diversified
#client_token ⇒ String
A unique, case-sensitive identifier you provide to ensure idempotency of your listings. This helps avoid duplicate listings. For more information, see Ensuring Idempotency.
#excess_capacity_termination_policy ⇒ String
Indicates whether running Spot instances should be terminated if the target capacity of the Spot fleet request is decreased below the current size of the Spot fleet.
Possible values:
- noTermination
- default
#fulfilled_capacity ⇒ Float
The number of units fulfilled by this request compared to the set target capacity.
#iam_fleet_role ⇒ String
Grants the Spot fleet permission to terminate Spot instances on your
behalf when you cancel its Spot fleet request using
CancelSpotFleetRequests or when the Spot fleet request expires,
if you set terminateInstancesWithExpiration
.
#launch_specifications ⇒ Array<Types::SpotFleetLaunchSpecification>
Information about the launch specifications for the Spot fleet request.
#spot_price ⇒ String
The bid price per unit hour.
#target_capacity ⇒ Integer
The number of units to request. You can choose to set the target capacity in terms of instances or a performance characteristic that is important to your application workload, such as vCPUs, memory, or I/O.
#terminate_instances_with_expiration ⇒ Boolean
Indicates whether running Spot instances should be terminated when the Spot fleet request expires.
#type ⇒ String
The type of request. Indicates whether the fleet will only request
the
target capacity or also attempt to maintain
it. When you request
a
certain target capacity, the fleet will only place the required bids. It
will not attempt to replenish Spot instances if capacity is diminished,
nor will it submit bids in alternative Spot pools if capacity is not
available. When you want to maintain
a certain target capacity, fleet
will place the required bids to meet this target capacity. It will also
automatically replenish any interrupted instances. Default: maintain
.
Possible values:
- request
- maintain
#valid_from ⇒ Time
The start date and time of the request, in UTC format (for example, YYYY-MM-DDTHH:MM:SSZ). The default is to start fulfilling the request immediately.
#valid_until ⇒ Time
The end date and time of the request, in UTC format (for example, YYYY-MM-DDTHH:MM:SSZ). At this point, no new Spot instance requests are placed or enabled to fulfill the request.


TABLE OF INTERACTIONS WITH
FLUVOXAMINE
See also hyponatremia inducing medications
See also: selective serotonin reuptake inhibitors (SSRIs)
See also: medications that lower the epileptic threshold
See also: medications at the origin of a serotonin syndrome
From the French ANSM drug interactions document of
September 2016, pp. 100-101
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
AGOMELATINE RxNorm: 306718 ATC: N06AX22 |
Increase of the concentrations of agomelatine, with risk of increase of the undesirable effects. |
CONTRAINDICATION |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
CARBAMAZEPINE RxNorm: 2002 ATC: N03AF01 |
Increase of the plasma concentrations of carbamazepine with signs of overdose |
Precaution for use Clinical monitoring, testing of the plasma concentrations of carbamazepine and possible reduction of the dosage of the carbamazepine during the treatment with the serotoninergic antidepressant and after it is stopped. |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
CLOZAPINE RxNorm: 2626 ATC: N05AH02 |
Increase of the plasma concentrations of clozapine with signs of overdose |
Precaution for use Increased clinical monitoring; if needed, adjustment of the dosage of the clozapine during the treatment with the fluvoxamine and after it is stopped. |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
DULOXETINE RxNorm: 72625 ATC: N06AX21 |
Risk of increase of the undesirable effects of the duloxetine due to decrease of its hepatic metabolism by the fluvoxamine |
CONTRAINDICATION |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
LIDOCAINE RxNorm: 6387 ATC: C01BB01 C05AD01 D04AB01 N01BB02 R02AD02 S01HA07 S02DA01 |
Increase of the plasma concentrations of the lidocaine with possibilities of undesirable neurological and cardiac effects (decrease of the hepatic clearance of the lidocaine) |
Precaution for use Clinical monitoring, EKG, and possibly testing of the plasma concentrations of the lidocaine during and after the administration of the two substances together. Adjustment, if needed, of the dosage of the lidocaine. |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
METHADONE RxNorm: 6813 ATC: N02AC52 N07BC02 |
Increase of the plasma concentrations of methodone with overdose and increased risk of lengthening of the QT interval and of ventricular arrhythmias, especially of torsades de pointes |
Precaution for use Strengthen clinical monitoring and EKG; if needed, adjustment of the dosage of the methadone during the treatment with the fluvoxamine and after it is stopped. |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
MEXILETINE RxNorm: 6926 ATC: C01BB02 |
Risk of increase of the undesirable effects of the mexiletine, due to inhibition of its metabolism by the fluvoxamine |
Precaution for use Clinical monitoring and EKG. Adjustment of the dosage of the mexiletine during the treatment with the fluvoxamine and after it is stopped. |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
OLANZAPINE RxNorm: 61381 ATC: N05AH03 |
Increase of the concentrations of the olanzapine, with risk of increase of the undesirable effects, due to decrease of its hepatic metabolism by the fluvoxamine |
Precaution for use Clinical monitoring and possible reduction of the dosage of the olanzapine during the treatment with fluvoxamine. |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
PHENYTOIN (AND, BY EXTRAPOLATION, FOSPHENYTOIN) CLASS CODE: N03AB |
Increase of the plasma concentrations of phenytoin with signs of overdose, due to inhibition of the hepatic metabolism of the phenytoin. |
Precaution for use Clinical monitoring and possibly testing of the plasma concentrations of phenytoin. If needed, adjustment of the dosage of the phenytoin during the treatment with the fluvoxamine and after it is stopped. |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
PIRFENIDONE RxNorm: 88221 ATC: L04AX05 |
Increase of the plasma concentrations of pirfenidone with signs of overdose |
CONTRAINDICATION |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
PROPRANOLOL RxNorm: 8787 ATC: C07AA05 |
Increase of the plasma concentrations of propranolol due to inhibition of its hepatic metabolism, with increase of the activity and of the undesirable effects, for example: significant bradycardia |
Precaution for use Increased clinical monitoring, and, if needed, adjustment of the dosage of the propranolol during the treatment with the fluvoxamine and after it is stopped. |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
ROPINIROLE RxNorm: 72302 ATC: N04BC04 |
Increase of the concentrations of ropinirole, with risk of overdose, due to decrease of its hepatic metabolism by the fluvoxamine |
Precaution for use Clinical monitoring and reduction of the dosage of the ropinirole during the treatment with fluvoxamine and after it is stopped. |
FLUVOXAMINE RxNorm: 42355 ATC: N06AB08 |
THEOPHYLLINE (AND, BY EXTRAPOLATION, AMINOPHYLLINE) CLASS CODE: R03DA |
Increase of the theophyllinemia with signs of overdose (decrease of the hepatic metabolism of the theophylline) |
Precaution for use Clinical monitoring and possibly monitoring of the theophyllinemia; if needed, adjustment of the dosage of the theophylline during the treatment with fluvoxamine and after it is stopped. |


{% extends 'base.html' %} {% block title %} {% if tree %} {{ tree.label }} details {% else %} Tree Methods {% endif %} {% endblock %} {% block container %} {% import 'macros/pagination.html' as macro %}
| ID | {% endif %}Name | based on |
|---|---|---|
| {{ tm.id }} | {% endif %}{{ tm.description }} | {{ tm.gf_method.method }} |
Tree for gene family {{ tree.family.name }}. View expression as heatmap: raw | row-normalized
{% if tree.phyloxml %}(The heatmaps are normalized by row. Light yellow cells indicate no/low expression, dark blue cells show the maximum expression. White cells lacking a border are missing values.)