| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 2 |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product1 | 2 | |
| Product2 | 2 | |
| Product3 | 2 |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 2 |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product1 | 2 | |
| Product2 | 2 | |
| Product3 | 2 |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 2 |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product1 | 2 | |
| Product2 | 2 | |
| Product3 | 2 |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 2 |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product1 | 2 | |
| Product2 | 2 | |
| Product3 | 2 |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 2 |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product1 | 2 | |
| Product2 | 2 | |
| Product3 | 2 |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Multiply-Add | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Multiply-Add | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Multiply-Add | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Multiply-Add | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Multiply-Add | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Multiply-Add | 0 | Adaptive pipelining is not supported if the block is in a feedback loop. Number of registers required: 2; Number of registers inserted: 0. |
| Block Name | Number of pipelines inserted | Notes |
|---|---|---|
| Product | 2 |
| Generated model after the transformation: | gm_sm_DynamicCompression |


Objenesis is a small Java library that serves one purpose:
Java already supports this dynamic instantiation of classes using Class.newInstance()
.
However, this only works if the class has an appropriate constructor. There are many times when a class
cannot be instantiated this way, such as when the class contains:
As a result, it is common to see restrictions in libraries stating that classes must require a default constructor. Objenesis aims to overcome these restrictions by bypassing the constructor on object instantiation.
Needing to instantiate an object without calling the constructor is a fairly specialized task, however there are certain cases when this is useful:
Objenesis uses a variety of approaches to attempt to instantiate the object, depending on the type of object, JVM version, JVM vendor and SecurityManager present. These are described in full in the detailed documentation.
For something as straight forward as instantiating an object, it can be a pain introducing yet another library dependency into your project. Objenesis is easy to embed in your existing library so your end users don't even know it's there.
The list of tested JVMs is available here. Other JVMs might be supported be haven't been tested yet. You can test your environment using the TCK and we would be glad to have feedback on any supported / unsupported JVM.
We do more and more benchmarking to make sure Objenesis has great performance but also to settled the best way to use it for multiple use-cases. Results are added here. Please, share your own results. Especially, if they differ from ours.


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org.apache.xalan.lib
Class ExsltDatetime
java.lang.Object
org.apache.xalan.lib.ExsltDatetime
public class ExsltDatetime
- extends java.lang.Object
This class contains EXSLT dates and times extension functions.
It is accessed by specifying a namespace URI as follows:
xmlns:datetime="http://exslt.org/dates-and-times"
The documentation for each function has been copied from the relevant
EXSLT Implementer page.
- See Also:
- EXSLT
|
Field Summary |
(package private) static java.lang.String |
d
|
(package private) static java.lang.String |
dt
|
(package private) static java.lang.String |
EMPTY_STR
|
(package private) static java.lang.String |
gd
|
(package private) static java.lang.String |
gm
|
(package private) static java.lang.String |
gmd
|
(package private) static java.lang.String |
gy
|
(package private) static java.lang.String |
gym
|
(package private) static java.lang.String |
t
|
|
Method Summary |
static java.lang.String |
date()
See above. |
static java.lang.String |
date(java.lang.String datetimeIn)
The date:date function returns the date specified in the date/time string given
as the argument. |
static java.lang.String |
dateTime()
The date:date-time function returns the current date and time as a date/time string. |
static java.lang.String |
dayAbbreviation()
See above. |
static java.lang.String |
dayAbbreviation(java.lang.String datetimeIn)
The date:day-abbreviation function returns the abbreviation of the day
of the week of a date. |
static double |
dayInMonth()
See above. |
static double |
dayInMonth(java.lang.String datetimeIn)
The date:day-in-month function returns the day of a date as a number. |
static double |
dayInWeek()
See above. |
static double |
dayInWeek(java.lang.String datetimeIn)
The date:day-in-week function returns the day of the week given in a
date as a number. |
static double |
dayInYear()
See above. |
static double |
dayInYear(java.lang.String datetimeIn)
The date:day-in-year function returns the day of a date in a year
as a number. |
static java.lang.String |
dayName()
See above. |
static java.lang.String |
dayName(java.lang.String datetimeIn)
The date:day-name function returns the full name of the day of the week
of a date. |
static double |
dayOfWeekInMonth()
See above. |
static double |
dayOfWeekInMonth(java.lang.String datetimeIn)
The date:day-of-week-in-month function returns the day-of-the-week
in a month of a date as a number (e.g. |
static java.lang.String |
formatDate(java.lang.String dateTime,
java.lang.String pattern)
The date:format-date function formats a date/time according to a pattern. |
private static java.lang.String |
formatDigits(int q)
Represent the hours and minutes with two-digit strings. |
private static java.lang.String[] |
getEraDatetimeZone(java.lang.String in)
Returns an array with the 3 components that a datetime input string
may contain: - (for BC era), datetime, and zone. |
private static java.lang.String |
getNameOrAbbrev(java.lang.String format)
Get the full name or abbreviation for the current month or day
(no input string). |
private static java.lang.String |
getNameOrAbbrev(java.lang.String in,
java.lang.String[] formatsIn,
java.lang.String formatOut)
Get the full name or abbreviation of the month or day. |
private static double |
getNumber(java.lang.String in,
java.lang.String[] formats,
int calField)
Parse the input string and return the corresponding calendar field
number. |
private static int |
getZoneStart(java.lang.String datetime)
Get the start of zone information if the input ends
with 'Z' or +/-hh:mm. |
static double |
hourInDay()
See above. |
static double |
hourInDay(java.lang.String datetimeIn)
The date:hour-in-day function returns the hour of the day as a number. |
static boolean |
leapYear()
See above. |
static XObject |
leapYear(java.lang.String datetimeIn)
The date:leap-year function returns true if the year given in a date
is a leap year. |
static double |
minuteInHour()
See above. |
static double |
minuteInHour(java.lang.String datetimeIn)
The date:minute-in-hour function returns the minute of the hour
as a number. |
static java.lang.String |
monthAbbreviation()
See above. |
static java.lang.String |
monthAbbreviation(java.lang.String datetimeIn)
The date:month-abbreviation function returns the abbreviation of the month of
a date. |
static double |
monthInYear()
See above. |
static double |
monthInYear(java.lang.String datetimeIn)
The date:month-in-year function returns the month of a date as a number. |
static java.lang.String |
monthName()
See above. |
static java.lang.String |
monthName(java.lang.String datetimeIn)
The date:month-name function returns the full name of the month of a date. |
static double |
secondInMinute()
See above. |
static double |
secondInMinute(java.lang.String datetimeIn)
The date:second-in-minute function returns the second of the minute
as a number. |
private static java.lang.String |
strip(java.lang.String symbols,
java.lang.String pattern)
Strips occurrences of the given character from a date format pattern. |
private static java.util.Date |
testFormats(java.lang.String in,
java.lang.String[] formats)
Attempt to parse an input string with the allowed formats, returning
null if none of the formats work. |
static java.lang.String |
time()
See above. |
static java.lang.String |
time(java.lang.String timeIn)
The date:time function returns the time specified in the date/time string given
as the argument. |
static double |
weekInYear()
See above. |
static double |
weekInYear(java.lang.String datetimeIn)
The date:week-in-year function returns the week of the year as a number. |
static double |
year()
See above. |
static double |
year(java.lang.String datetimeIn)
The date:year function returns the year of a date as a number. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
dt
static final java.lang.String dt
- See Also:
- Constant Field Values
d
static final java.lang.String d
- See Also:
- Constant Field Values
gym
static final java.lang.String gym
- See Also:
- Constant Field Values
gy
static final java.lang.String gy
- See Also:
- Constant Field Values
gmd
static final java.lang.String gmd
- See Also:
- Constant Field Values
gm
static final java.lang.String gm
- See Also:
- Constant Field Values
gd
static final java.lang.String gd
- See Also:
- Constant Field Values
t
static final java.lang.String t
- See Also:
- Constant Field Values
EMPTY_STR
static final java.lang.String EMPTY_STR
- See Also:
- Constant Field Values
ExsltDatetime
public ExsltDatetime()
dateTime
public static java.lang.String dateTime()
- The date:date-time function returns the current date and time as a date/time string.
The date/time string that's returned must be a string in the format defined as the
lexical representation of xs:dateTime in
[3.2.7 dateTime] of
[XML Schema Part 2: Datatypes].
The date/time format is basically CCYY-MM-DDThh:mm:ss, although implementers should consult
[XML Schema Part 2: Datatypes] and
[ISO 8601] for details.
The date/time string format must include a time zone, either a Z to indicate Coordinated
Universal Time or a + or - followed by the difference between the difference from UTC
represented as hh:mm.
formatDigits
private static java.lang.String formatDigits(int q)
- Represent the hours and minutes with two-digit strings.
- Parameters:
q
- hrs or minutes.
- Returns:
- two-digit String representation of hrs or minutes.
date
public static java.lang.String date(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:date function returns the date specified in the date/time string given
as the argument. If no argument is given, then the current local date/time, as
returned by date:date-time is used as a default argument.
The date/time string that's returned must be a string in the format defined as the
lexical representation of xs:dateTime in
[3.2.7 dateTime] of
[XML Schema Part 2: Datatypes].
If the argument is not in either of these formats, date:date returns an empty string ('').
The date/time format is basically CCYY-MM-DDThh:mm:ss, although implementers should consult
[XML Schema Part 2: Datatypes] and
[ISO 8601] for details.
The date is returned as a string with a lexical representation as defined for xs:date in
[3.2.9 date] of [XML Schema Part 2: Datatypes]. The date format is basically CCYY-MM-DD,
although implementers should consult [XML Schema Part 2: Datatypes] and [ISO 8601] for details.
If no argument is given or the argument date/time specifies a time zone, then the date string
format must include a time zone, either a Z to indicate Coordinated Universal Time or a + or -
followed by the difference between the difference from UTC represented as hh:mm. If an argument
is specified and it does not specify a time zone, then the date string format must not include
a time zone.
- Throws:
java.text.ParseException
date
public static java.lang.String date()
- See above.
time
public static java.lang.String time(java.lang.String timeIn)
throws java.text.ParseException
- The date:time function returns the time specified in the date/time string given
as the argument. If no argument is given, then the current local date/time, as
returned by date:date-time is used as a default argument.
The date/time string that's returned must be a string in the format defined as the
lexical representation of xs:dateTime in
[3.2.7 dateTime] of
[XML Schema Part 2: Datatypes].
If the argument string is not in this format, date:time returns an empty string ('').
The date/time format is basically CCYY-MM-DDThh:mm:ss, although implementers should consult
[XML Schema Part 2: Datatypes] and
[ISO 8601] for details.
The date is returned as a string with a lexical representation as defined for xs:time in
[3.2.8 time] of [XML Schema Part 2: Datatypes].
The time format is basically hh:mm:ss, although implementers should consult [XML Schema Part 2:
Datatypes] and [ISO 8601] for details.
If no argument is given or the argument date/time specifies a time zone, then the time string
format must include a time zone, either a Z to indicate Coordinated Universal Time or a + or -
followed by the difference between the difference from UTC represented as hh:mm. If an argument
is specified and it does not specify a time zone, then the time string format must not include
a time zone.
- Throws:
java.text.ParseException
time
public static java.lang.String time()
- See above.
year
public static double year(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:year function returns the year of a date as a number. If no
argument is given, then the current local date/time, as returned by
date:date-time is used as a default argument.
The date/time string specified as the first argument must be a right-truncated
string in the format defined as the lexical representation of xs:dateTime in one
of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
xs:gYearMonth (CCYY-MM)
xs:gYear (CCYY)
If the date/time string is not in one of these formats, then NaN is returned.
- Throws:
java.text.ParseException
year
public static double year()
- See above.
monthInYear
public static double monthInYear(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:month-in-year function returns the month of a date as a number. If no argument
is given, then the current local date/time, as returned by date:date-time is used
as a default argument.
The date/time string specified as the first argument is a left or right-truncated
string in the format defined as the lexical representation of xs:dateTime in one of
the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
xs:gYearMonth (CCYY-MM)
xs:gMonth (--MM--)
xs:gMonthDay (--MM-DD)
If the date/time string is not in one of these formats, then NaN is returned.
- Throws:
java.text.ParseException
monthInYear
public static double monthInYear()
- See above.
weekInYear
public static double weekInYear(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:week-in-year function returns the week of the year as a number. If no argument
is given, then the current local date/time, as returned by date:date-time is used as the
default argument. For the purposes of numbering, counting follows ISO 8601: week 1 in a year
is the week containing the first Thursday of the year, with new weeks beginning on a Monday.
The date/time string specified as the argument is a right-truncated string in the format
defined as the lexical representation of xs:dateTime in one of the formats defined in
[XML Schema Part 2: Datatypes]. The
permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
If the date/time string is not in one of these formats, then NaN is returned.
- Throws:
java.text.ParseException
weekInYear
public static double weekInYear()
- See above.
dayInYear
public static double dayInYear(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:day-in-year function returns the day of a date in a year
as a number. If no argument is given, then the current local
date/time, as returned by date:date-time is used the default argument.
The date/time string specified as the argument is a right-truncated
string in the format defined as the lexical representation of xs:dateTime
in one of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
If the date/time string is not in one of these formats, then NaN is returned.
- Throws:
java.text.ParseException
dayInYear
public static double dayInYear()
- See above.
dayInMonth
public static double dayInMonth(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:day-in-month function returns the day of a date as a number.
If no argument is given, then the current local date/time, as returned
by date:date-time is used the default argument.
The date/time string specified as the argument is a left or right-truncated
string in the format defined as the lexical representation of xs:dateTime
in one of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
xs:gMonthDay (--MM-DD)
xs:gDay (---DD)
If the date/time string is not in one of these formats, then NaN is returned.
- Throws:
java.text.ParseException
dayInMonth
public static double dayInMonth()
- See above.
dayOfWeekInMonth
public static double dayOfWeekInMonth(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:day-of-week-in-month function returns the day-of-the-week
in a month of a date as a number (e.g. 3 for the 3rd Tuesday in May).
If no argument is given, then the current local date/time, as returned
by date:date-time is used the default argument.
The date/time string specified as the argument is a right-truncated string
in the format defined as the lexical representation of xs:dateTime in one
of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
If the date/time string is not in one of these formats, then NaN is returned.
- Throws:
java.text.ParseException
dayOfWeekInMonth
public static double dayOfWeekInMonth()
- See above.
dayInWeek
public static double dayInWeek(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:day-in-week function returns the day of the week given in a
date as a number. If no argument is given, then the current local date/time,
as returned by date:date-time is used the default argument.
The date/time string specified as the argument is a right-truncated string
in the format defined as the lexical representation of xs:dateTime in one
of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
If the date/time string is not in one of these formats, then NaN is returned.
The numbering of days of the week starts at 1 for Sunday, 2 for Monday and so on up to 7 for Saturday.
- Throws:
java.text.ParseException
dayInWeek
public static double dayInWeek()
- See above.
hourInDay
public static double hourInDay(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:hour-in-day function returns the hour of the day as a number.
If no argument is given, then the current local date/time, as returned
by date:date-time is used the default argument.
The date/time string specified as the argument is a right-truncated
string in the format defined as the lexical representation of xs:dateTime
in one of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:time (hh:mm:ss)
If the date/time string is not in one of these formats, then NaN is returned.
- Throws:
java.text.ParseException
hourInDay
public static double hourInDay()
- See above.
minuteInHour
public static double minuteInHour(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:minute-in-hour function returns the minute of the hour
as a number. If no argument is given, then the current local
date/time, as returned by date:date-time is used the default argument.
The date/time string specified as the argument is a right-truncated
string in the format defined as the lexical representation of xs:dateTime
in one of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:time (hh:mm:ss)
If the date/time string is not in one of these formats, then NaN is returned.
- Throws:
java.text.ParseException
minuteInHour
public static double minuteInHour()
- See above.
secondInMinute
public static double secondInMinute(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:second-in-minute function returns the second of the minute
as a number. If no argument is given, then the current local
date/time, as returned by date:date-time is used the default argument.
The date/time string specified as the argument is a right-truncated
string in the format defined as the lexical representation of xs:dateTime
in one of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:time (hh:mm:ss)
If the date/time string is not in one of these formats, then NaN is returned.
- Throws:
java.text.ParseException
secondInMinute
public static double secondInMinute()
- See above.
leapYear
public static XObject leapYear(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:leap-year function returns true if the year given in a date
is a leap year. If no argument is given, then the current local
date/time, as returned by date:date-time is used as a default argument.
The date/time string specified as the first argument must be a
right-truncated string in the format defined as the lexical representation
of xs:dateTime in one of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
xs:gYearMonth (CCYY-MM)
xs:gYear (CCYY)
If the date/time string is not in one of these formats, then NaN is returned.
- Throws:
java.text.ParseException
leapYear
public static boolean leapYear()
- See above.
monthName
public static java.lang.String monthName(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:month-name function returns the full name of the month of a date.
If no argument is given, then the current local date/time, as returned by
date:date-time is used the default argument.
The date/time string specified as the argument is a left or right-truncated
string in the format defined as the lexical representation of xs:dateTime in
one of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
xs:gYearMonth (CCYY-MM)
xs:gMonth (--MM--)
If the date/time string is not in one of these formats, then an empty string ('')
is returned.
The result is an English month name: one of 'January', 'February', 'March',
'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November'
or 'December'.
- Throws:
java.text.ParseException
monthName
public static java.lang.String monthName()
- See above.
monthAbbreviation
public static java.lang.String monthAbbreviation(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:month-abbreviation function returns the abbreviation of the month of
a date. If no argument is given, then the current local date/time, as returned
by date:date-time is used the default argument.
The date/time string specified as the argument is a left or right-truncated
string in the format defined as the lexical representation of xs:dateTime in
one of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
xs:gYearMonth (CCYY-MM)
xs:gMonth (--MM--)
If the date/time string is not in one of these formats, then an empty string ('')
is returned.
The result is a three-letter English month abbreviation: one of 'Jan', 'Feb', 'Mar',
'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov' or 'Dec'.
An implementation of this extension function in the EXSLT date namespace must conform
to the behaviour described in this document.
- Throws:
java.text.ParseException
monthAbbreviation
public static java.lang.String monthAbbreviation()
- See above.
dayName
public static java.lang.String dayName(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:day-name function returns the full name of the day of the week
of a date. If no argument is given, then the current local date/time,
as returned by date:date-time is used the default argument.
The date/time string specified as the argument is a left or right-truncated
string in the format defined as the lexical representation of xs:dateTime
in one of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
If the date/time string is not in one of these formats, then the empty string ('')
is returned.
The result is an English day name: one of 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
'Thursday' or 'Friday'.
An implementation of this extension function in the EXSLT date namespace must conform
to the behaviour described in this document.
- Throws:
java.text.ParseException
dayName
public static java.lang.String dayName()
- See above.
dayAbbreviation
public static java.lang.String dayAbbreviation(java.lang.String datetimeIn)
throws java.text.ParseException
- The date:day-abbreviation function returns the abbreviation of the day
of the week of a date. If no argument is given, then the current local
date/time, as returned by date:date-time is used the default argument.
The date/time string specified as the argument is a left or right-truncated
string in the format defined as the lexical representation of xs:dateTime
in one of the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
xs:dateTime (CCYY-MM-DDThh:mm:ss)
xs:date (CCYY-MM-DD)
If the date/time string is not in one of these formats, then the empty string
('') is returned.
The result is a three-letter English day abbreviation: one of 'Sun', 'Mon', 'Tue',
'Wed', 'Thu' or 'Fri'.
An implementation of this extension function in the EXSLT date namespace must conform
to the behaviour described in this document.
- Throws:
java.text.ParseException
dayAbbreviation
public static java.lang.String dayAbbreviation()
- See above.
getEraDatetimeZone
private static java.lang.String[] getEraDatetimeZone(java.lang.String in)
- Returns an array with the 3 components that a datetime input string
may contain: - (for BC era), datetime, and zone. If the zone is not
valid, return null for that component.
getZoneStart
private static int getZoneStart(java.lang.String datetime)
- Get the start of zone information if the input ends
with 'Z' or +/-hh:mm. If a zone string is not
found, return -1; if the zone string is invalid,
return -2.
testFormats
private static java.util.Date testFormats(java.lang.String in,
java.lang.String[] formats)
throws java.text.ParseException
- Attempt to parse an input string with the allowed formats, returning
null if none of the formats work.
- Throws:
java.text.ParseException
getNumber
private static double getNumber(java.lang.String in,
java.lang.String[] formats,
int calField)
throws java.text.ParseException
- Parse the input string and return the corresponding calendar field
number.
- Throws:
java.text.ParseException
getNameOrAbbrev
private static java.lang.String getNameOrAbbrev(java.lang.String in,
java.lang.String[] formatsIn,
java.lang.String formatOut)
throws java.text.ParseException
- Get the full name or abbreviation of the month or day.
- Throws:
java.text.ParseException
getNameOrAbbrev
private static java.lang.String getNameOrAbbrev(java.lang.String format)
- Get the full name or abbreviation for the current month or day
(no input string).
formatDate
public static java.lang.String formatDate(java.lang.String dateTime,
java.lang.String pattern)
- The date:format-date function formats a date/time according to a pattern.
The first argument to date:format-date specifies the date/time to be
formatted. It must be right or left-truncated date/time strings in one of
the formats defined in
[XML Schema Part 2: Datatypes].
The permitted formats are as follows:
- xs:dateTime (CCYY-MM-DDThh:mm:ss)
- xs:date (CCYY-MM-DD)
- xs:time (hh:mm:ss)
- xs:gYearMonth (CCYY-MM)
- xs:gYear (CCYY)
- xs:gMonthDay (--MM-DD)
- xs:gMonth (--MM--)
- xs:gDay (---DD)
The second argument is a string that gives the format pattern used to
format the date. The format pattern must be in the syntax specified by
the JDK 1.1 SimpleDateFormat class. The format pattern string is
interpreted as described for the JDK 1.1 SimpleDateFormat class.
If the date/time format is right-truncated (i.e. in a format other than
xs:time, or xs:dateTime) then any missing components are assumed to be as
follows: if no month is specified, it is given a month of 01; if no day
is specified, it is given a day of 01; if no time is specified, it is
given a time of 00:00:00.
If the date/time format is left-truncated (i.e. xs:time, xs:gMonthDay,
xs:gMonth or xs:gDay) and the format pattern has a token that uses a
component that is missing from the date/time format used, then that token
is replaced with an empty string ('') within the result.
The author is Helg Bredow<EMAIL_ADDRESS>strip
private static java.lang.String strip(java.lang.String symbols,
java.lang.String pattern)
- Strips occurrences of the given character from a date format pattern.
- Parameters:
symbols
- list of symbols to strip.pattern
-
- Returns:


NBMTreeClientDelegate Protocol Reference
| Conforms to | NSObject |
|---|---|
| Declared in | NBMTreeClientDelegate.h |
– client:isConnected:
required method
- (void)client:(NBMTreeClient *)client isConnected:(BOOL)connected
– client:didFailWithError:
required method
- (void)client:(NBMTreeClient *)client didFailWithError:(NSError *)error
– client:iceCandidateReceived:ofSink:tree:
required method
Event sent when a new ICE candidate is received from Kurento Media Server.
- (void)client:(NBMTreeClient *)client iceCandidateReceived:(RTCICECandidate *)candidate ofSink:(NSString *)sinkId tree:(NSString *)treeId
Parameters
client |
The current NBMTreeClient object. |
|---|---|
candidate |
The RTCICECandidate object received. |
sinkId |
Sink id to which belongs the candidate, ‘nil’ when the candidate is referred to the tree source. |
treeId |
Tree id to which belongs this candidate. |
Declared In
NBMTreeClientDelegate.h


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8. Reference¶
This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the class and function raw specifications may not be enough to give full guidelines on their uses.
List of modules
- sklearn.cluster: Clustering
- sklearn.covariance: Covariance Estimators
- sklearn.cross_validation: Cross Validation
- sklearn.datasets: Datasets
- sklearn.decomposition: Matrix Decomposition
- sklearn.ensemble: Ensemble Methods
- sklearn.feature_extraction: Feature Extraction
- sklearn.feature_selection: Feature Selection
- sklearn.gaussian_process: Gaussian Processes
- sklearn.grid_search: Grid Search
- sklearn.hmm: Hidden Markov Models
- sklearn.kernel_approximation Kernel Approximation
- sklearn.semi_supervised Semi-Supervised Learning
- sklearn.lda: Linear Discriminant Analysis
- sklearn.linear_model: Generalized Linear Models
- sklearn.manifold: Manifold Learning
- sklearn.metrics: Metrics
- sklearn.mixture: Gaussian Mixture Models
- sklearn.multiclass: Multiclass and multilabel classification
- sklearn.naive_bayes: Naive Bayes
- sklearn.neighbors: Nearest Neighbors
- sklearn.pls: Partial Least Squares
- sklearn.pipeline: Pipeline
- sklearn.preprocessing: Preprocessing and Normalization
- sklearn.qda: Quadratic Discriminant Analysis
- sklearn.svm: Support Vector Machines
- sklearn.tree: Decision Trees
- sklearn.utils: Utilities
8.1. sklearn.cluster: Clustering¶
The sklearn.cluster module gathers popular unsupervised clustering algorithms.
User guide: See the Clustering section for further details.
| cluster.AffinityPropagation([damping, ...]) | Perform Affinity Propagation Clustering of data |
| cluster.DBSCAN([eps, min_samples, metric, ...]) | Perform DBSCAN clustering from vector array or distance matrix. |
| cluster.KMeans([k, init, n_init, max_iter, ...]) | K-Means clustering |
| cluster.MiniBatchKMeans([k, init, max_iter, ...]) | Mini-Batch K-Means clustering |
| cluster.MeanShift([bandwidth, seeds, ...]) | MeanShift clustering |
| cluster.SpectralClustering([k, mode, ...]) | Apply k-means to a projection to the normalized laplacian |
| cluster.Ward([n_clusters, memory, ...]) | Ward hierarchical clustering: constructs a tree and cuts it. |
| cluster.estimate_bandwidth(X[, quantile, ...]) | Estimate the bandwith to use with MeanShift algorithm |
8.2. sklearn.covariance: Covariance Estimators¶
The sklearn.covariance module includes methods and algorithms to robustly estimate the covariance of features given a set of points. The precision matrix defined as the inverse of the covariance is also estimated. Covariance estimation is closely related to the theory of Gaussian Graphical Models.
User guide: See the Covariance estimation section for further details.
| covariance.EmpiricalCovariance([...]) | Maximum likelihood covariance estimator |
| covariance.ShrunkCovariance([...]) | Covariance estimator with shrinkage |
| covariance.LedoitWolf([store_precision, ...]) | LedoitWolf Estimator |
| covariance.OAS([store_precision, ...]) | Oracle Approximating Shrinkage Estimator |
| covariance.GraphLasso([alpha, mode, tol, ...]) | Sparse inverse covariance estimation with an l1-penalized estimator. |
| covariance.GraphLassoCV([alphas, ...]) | Sparse inverse covariance w/ cross-validated choice of the l1 penality |
| covariance.MinCovDet([store_precision, ...]) | Minimum Covariance Determinant (MCD): robust estimator of covariance |
| covariance.empirical_covariance(X[, ...]) | Computes the Maximum likelihood covariance estimator |
| covariance.ledoit_wolf(X[, assume_centered]) | Estimates the shrunk Ledoit-Wolf covariance matrix. |
| covariance.shrunk_covariance(emp_cov[, ...]) | Calculates a covariance matrix shrunk on the diagonal |
| covariance.oas(X[, assume_centered]) | Estimate covariance with the Oracle Approximating Shrinkage algorithm. |
| covariance.graph_lasso(emp_cov, alpha[, ...]) | l1-penalized covariance estimator |
8.3. sklearn.cross_validation: Cross Validation¶
The sklearn.cross_validation module includes utilities for cross- validation and performance evaluation.
User guide: See the Cross-Validation section for further details.
| cross_validation.Bootstrap(n[, ...]) | Random sampling with replacement cross-validation iterator |
| cross_validation.KFold(n, k[, indices]) | K-Folds cross validation iterator |
| cross_validation.LeaveOneLabelOut(labels[, ...]) | Leave-One-Label_Out cross-validation iterator |
| cross_validation.LeaveOneOut(n[, indices]) | Leave-One-Out cross validation iterator. |
| cross_validation.LeavePLabelOut(labels, p[, ...]) | Leave-P-Label_Out cross-validation iterator |
| cross_validation.LeavePOut(n, p[, indices]) | Leave-P-Out cross validation iterator |
| cross_validation.StratifiedKFold(y, k[, indices]) | Stratified K-Folds cross validation iterator |
| cross_validation.ShuffleSplit(n[, ...]) | Random permutation cross-validation iterator. |
| cross_validation.train_test_split(*arrays, ...) | Split arrays or matrices into random train and test subsets |
| cross_validation.cross_val_score(estimator, X) | Evaluate a score by cross-validation |
| cross_validation.permutation_test_score(...) | Evaluate the significance of a cross-validated score with permutations |
| cross_validation.check_cv(cv[, X, y, classifier]) | Input checker utility for building a CV in a user friendly way. |
8.4. sklearn.datasets: Datasets¶
The sklearn.datasets module includes utilities to load datasets, including methods to load and fetch popular reference datasets. It also features some artificial data generators.
User guide: See the Dataset loading utilities section for further details.
8.4.1. Loaders¶
| datasets.load_20newsgroups(*args, **kwargs) | DEPRECATED: Use fetch_20newsgroups instead with download_if_missing=False |
| datasets.fetch_20newsgroups([data_home, ...]) | Load the filenames of the 20 newsgroups dataset. |
| datasets.fetch_20newsgroups_vectorized([...]) | Load the 20 newsgroups dataset and transform it into tf-idf vectors. |
| datasets.load_boston() | Load and return the boston house-prices dataset (regression). |
| datasets.load_diabetes() | Load and return the diabetes dataset (regression). |
| datasets.load_digits([n_class]) | Load and return the digits dataset (classification). |
| datasets.load_files(container_path[, ...]) | Load text files with categories as subfolder names. |
| datasets.load_iris() | Load and return the iris dataset (classification). |
| datasets.load_lfw_pairs([download_if_missing]) | Alias for fetch_lfw_pairs(download_if_missing=False) |
| datasets.fetch_lfw_pairs([subset, ...]) | Loader for the Labeled Faces in the Wild (LFW) pairs dataset |
| datasets.load_lfw_people([download_if_missing]) | Alias for fetch_lfw_people(download_if_missing=False) |
| datasets.fetch_lfw_people([data_home, ...]) | Loader for the Labeled Faces in the Wild (LFW) people dataset |
| datasets.load_linnerud() | Load and return the linnerud dataset (multivariate regression). |
| datasets.fetch_olivetti_faces([data_home, ...]) | Loader for the Olivetti faces data-set from AT&T. |
| datasets.load_sample_image(image_name) | Load the numpy array of a single sample image |
| datasets.load_sample_images() | Load sample images for image manipulation. |
| datasets.load_svmlight_file(f[, n_features, ...]) | Load datasets in the svmlight / libsvm format into sparse CSR matrix |
8.4.2. Samples generator¶
| datasets.make_blobs([n_samples, n_features, ...]) | Generate isotropic Gaussian blobs for clustering. |
| datasets.make_classification([n_samples, ...]) | Generate a random n-class classification problem. |
| datasets.make_circles([n_samples, shuffle, ...]) | Make a large circle containing a smaller circle in 2di |
| datasets.make_friedman1([n_samples, ...]) | Generate the “Friedman #1” regression problem |
| datasets.make_friedman2([n_samples, noise, ...]) | Generate the “Friedman #2” regression problem |
| datasets.make_friedman3([n_samples, noise, ...]) | Generate the “Friedman #3” regression problem |
| datasets.make_low_rank_matrix([n_samples, ...]) | Generate a mostly low rank matrix with bell-shaped singular values |
| datasets.make_moons([n_samples, shuffle, ...]) | Make two interleaving half circles |
| datasets.make_multilabel_classification([...]) | Generate a random multilabel classification problem. |
| datasets.make_regression([n_samples, ...]) | Generate a random regression problem. |
| datasets.make_s_curve([n_samples, noise, ...]) | Generate an S curve dataset. |
| datasets.make_sparse_coded_signal(n_samples, ...) | Generate a signal as a sparse combination of dictionary elements. |
| datasets.make_sparse_spd_matrix([dim, ...]) | Generate a sparse symetric definite positive matrix. |
| datasets.make_sparse_uncorrelated([...]) | Generate a random regression problem with sparse uncorrelated design |
| datasets.make_spd_matrix(n_dim[, random_state]) | Generate a random symmetric, positive-definite matrix. |
| datasets.make_swiss_roll([n_samples, noise, ...]) | Generate a swiss roll dataset. |
8.5. sklearn.decomposition: Matrix Decomposition¶
The sklearn.decomposition module includes matrix decomposition algorithms, including among others PCA, NMF or ICA. Most of the algorithms of this module can be regarded as dimensionality reduction techniques.
User guide: See the Decomposing signals in components (matrix factorization problems) section for further details.
| decomposition.PCA([n_components, copy, whiten]) | Principal component analysis (PCA) |
| decomposition.ProbabilisticPCA([...]) | Additional layer on top of PCA that adds a probabilistic evaluationPrincipal component analysis (PCA) |
| decomposition.ProjectedGradientNMF([...]) | Non-Negative matrix factorization by Projected Gradient (NMF) |
| decomposition.RandomizedPCA(n_components[, ...]) | Principal component analysis (PCA) using randomized SVD |
| decomposition.KernelPCA([n_components, ...]) | Kernel Principal component analysis (KPCA) |
| decomposition.FastICA([n_components, ...]) | FastICA; a fast algorithm for Independent Component Analysis |
| decomposition.NMF([n_components, init, ...]) | Non-Negative matrix factorization by Projected Gradient (NMF) |
| decomposition.SparsePCA(n_components[, ...]) | Sparse Principal Components Analysis (SparsePCA) |
| decomposition.MiniBatchSparsePCA(n_components) | Mini-batch Sparse Principal Components Analysis |
| decomposition.SparseCoder(dictionary[, ...]) | Sparse coding |
| decomposition.DictionaryLearning(n_atoms[, ...]) | Dictionary learning |
| decomposition.MiniBatchDictionaryLearning(n_atoms) | Mini-batch dictionary learning |
| decomposition.fastica(X[, n_components, ...]) | Perform Fast Independent Component Analysis. |
| decomposition.dict_learning(X, n_atoms, alpha) | Solves a dictionary learning matrix factorization problem. |
| decomposition.dict_learning_online(X, ...[, ...]) | Solves a dictionary learning matrix factorization problem online. |
| decomposition.sparse_encode(X, dictionary[, ...]) | Sparse coding |
8.6. sklearn.ensemble: Ensemble Methods¶
The sklearn.ensemble module includes ensemble-based methods for classification and regression.
User guide: See the Ensemble methods section for further details.
| ensemble.RandomForestClassifier([...]) | A random forest classifier. |
| ensemble.RandomForestRegressor([...]) | A random forest regressor. |
| ensemble.ExtraTreesClassifier([...]) | An extra-trees classifier. |
| ensemble.ExtraTreesRegressor([n_estimators, ...]) | An extra-trees regressor. |
8.7. sklearn.feature_extraction: Feature Extraction¶
The sklearn.feature_extraction module deals with feature extraction from raw data. It currently includes methods to extract features from text and images.
User guide: See the Feature extraction section for further details.
8.7.1. From images¶
The sklearn.feature_extraction.image submodule gathers utilities to extract features from images.
| feature_extraction.image.img_to_graph(img[, ...]) | Graph of the pixel-to-pixel gradient connections |
| feature_extraction.image.grid_to_graph(n_x, n_y) | Graph of the pixel-to-pixel connections |
| feature_extraction.image.extract_patches_2d(...) | Reshape a 2D image into a collection of patches |
| feature_extraction.image.reconstruct_from_patches_2d(...) | Reconstruct the image from all of its patches. |
| feature_extraction.image.PatchExtractor(...) | Extracts patches from a collection of images |
8.7.2. From text¶
The sklearn.feature_extraction.text submodule gathers utilities to build feature vectors from text documents.
| feature_extraction.text.CountVectorizer([...]) | Convert a collection of raw documents to a matrix of token counts |
| feature_extraction.text.TfidfTransformer([...]) | Transform a count matrix to a normalized tf or tf–idf representation |
| feature_extraction.text.Vectorizer([input, ...]) | Convert a collection of raw documents to a matrix of TF-IDF features. |
8.8. sklearn.feature_selection: Feature Selection¶
The sklearn.feature_selection module implements feature selection algorithms. It currently includes univariate filter selection methods and the recursive feature elimination algorithm.
User guide: See the Feature selection section for further details.
| feature_selection.SelectPercentile(score_func) | Filter: Select the best percentile of the p_values |
| feature_selection.SelectKBest(score_func[, k]) | Filter: Select the k lowest p-values |
| feature_selection.SelectFpr(score_func[, alpha]) | Filter: Select the pvalues below alpha based on a FPR test. |
| feature_selection.SelectFdr(score_func[, alpha]) | Filter: Select the p-values for an estimated false discovery rate |
| feature_selection.SelectFwe(score_func[, alpha]) | Filter: Select the p-values corresponding to Family-wise error rate |
| feature_selection.RFE(estimator, ...[, step]) | Feature ranking with recursive feature elimination. |
| feature_selection.RFECV(estimator[, step, ...]) | Feature ranking with recursive feature elimination and cross-validated selection of the best number of features. |
| feature_selection.chi2(X, y) | Compute χ² (chi-squared) statistic for each class/feature combination. |
| feature_selection.f_classif(X, y) | Compute the Anova F-value for the provided sample |
| feature_selection.f_regression(X, y[, center]) | Univariate linear regression tests |
8.9. sklearn.gaussian_process: Gaussian Processes¶
The sklearn.gaussian_process module implements scalar Gaussian Process based predictions.
User guide: See the Gaussian Processes section for further details.
| gaussian_process.GaussianProcess([regr, ...]) | The Gaussian Process model class. |
| gaussian_process.correlation_models.absolute_exponential(...) | Absolute exponential autocorrelation model. |
| gaussian_process.correlation_models.squared_exponential(...) | Squared exponential correlation model (Radial Basis Function). |
| gaussian_process.correlation_models.generalized_exponential(...) | Generalized exponential correlation model. |
| gaussian_process.correlation_models.pure_nugget(...) | Spatial independence correlation model (pure nugget). |
| gaussian_process.correlation_models.cubic(...) | Cubic correlation model: |
| gaussian_process.correlation_models.linear(...) | Linear correlation model: |
| gaussian_process.regression_models.constant(x) | Zero order polynomial (constant, p = 1) regression model. |
| gaussian_process.regression_models.linear(x) | First order polynomial (linear, p = n+1) regression model. |
| gaussian_process.regression_models.quadratic(x) | Second order polynomial (quadratic, p = n*(n-1)/2+n+1) regression model. |
8.10. sklearn.grid_search: Grid Search¶
The sklearn.grid_search includes utilities to fine-tune the parameters of an estimator.
User guide: See the Grid Search section for further details.
| grid_search.GridSearchCV(estimator, param_grid) | Grid search on the parameters of a classifier |
| grid_search.IterGrid(param_grid) | Generators on the combination of the various parameter lists given |
8.11. sklearn.hmm: Hidden Markov Models¶
The sklearn.hmm module implements hidden Markov models.
Warning: sklearn.hmm is orphaned, undocumented and has known numerical stability issues. If nobody volunteers to write documentation and make it more stable, this module will be removed in version 0.11.
User guide: See the Hidden Markov Models section for further details.
| hmm.GaussianHMM([n_components, ...]) | Hidden Markov Model with Gaussian emissions |
| hmm.MultinomialHMM([n_components, ...]) | Hidden Markov Model with multinomial (discrete) emissions |
| hmm.GMMHMM([n_components, n_mix, startprob, ...]) | Hidden Markov Model with Gaussin mixture emissions |
8.12. sklearn.kernel_approximation Kernel Approximation¶
The sklearn.kernel_approximation module implements several approximate kernel feature maps base on Fourier transforms.
User guide: See the Kernel Approximation section for further details.
| kernel_approximation.RBFSampler([gamma, ...]) | Approximates feature map of an RBF kernel by Monte Carlo approximation |
| kernel_approximation.AdditiveChi2Sampler([...]) | Approximate feature map for additive chi² kernel. |
| kernel_approximation.SkewedChi2Sampler([...]) | Approximates feature map of the “skewed chi-squared” kernel by Monte |
8.13. sklearn.semi_supervised Semi-Supervised Learning¶
The sklearn.semi_supervised module implements semi-supervised learning algorithms.  This module includes Label Propagation.
User guide: See the Semi-Supervised section for further details.
| semi_supervised.LabelPropagation([kernel, ...]) | Label Propagation classifier |
| semi_supervised.LabelSpreading([kernel, ...]) | LabelSpreading model for semi-supervised learning |
8.14. sklearn.lda: Linear Discriminant Analysis¶
The sklearn.lda module implements Linear Discriminant Analysis (LDA).
User guide: See the Linear and Quadratic Discriminant Analysis section for further details.
| lda.LDA([n_components, priors]) | Linear Discriminant Analysis (LDA) |
8.15. sklearn.linear_model: Generalized Linear Models¶
The sklearn.linear_model module implements genelarized linear models. It includes Ridge regression, Bayesian Regression, Lasso and Elastic Net estimators computed with Least Angle Regression and coordinate descent. It also implements Stochastic Gradient Descent related algorithms.
User guide: See the Generalized Linear Models section for further details.
8.15.1. For dense data¶
| linear_model.LinearRegression([...]) | Ordinary least squares Linear Regression. |
| linear_model.Ridge([alpha, fit_intercept, ...]) | Linear least squares with l2 regularization. |
| linear_model.RidgeClassifier([alpha, ...]) | Classifier using Ridge regression. |
| linear_model.RidgeCV([alphas, ...]) | Ridge regression with built-in cross-validation. |
| linear_model.Lasso([alpha, fit_intercept, ...]) | Linear Model trained with L1 prior as regularizer (aka the Lasso) |
| linear_model.LassoCV([eps, n_alphas, ...]) | Lasso linear model with iterative fitting along a regularization path |
| linear_model.ElasticNet([alpha, rho, ...]) | Linear Model trained with L1 and L2 prior as regularizer |
| linear_model.ElasticNetCV([rho, eps, ...]) | Elastic Net model with iterative fitting along a regularization path |
| linear_model.Lars([fit_intercept, verbose, ...]) | Least Angle Regression model a.k.a. LAR |
| linear_model.LassoLars([alpha, ...]) | Lasso model fit with Least Angle Regression a.k.a. Lars |
| linear_model.LarsCV([fit_intercept, ...]) | Cross-validated Least Angle Regression model |
| linear_model.LassoLarsCV([fit_intercept, ...]) | Cross-validated Lasso, using the LARS algorithm |
| linear_model.LassoLarsIC([criterion, ...]) | Lasso model fit with Lars using BIC or AIC for model selection |
| linear_model.LogisticRegression([penalty, ...]) | Logistic Regression (aka logit, MaxEnt) classifier. |
| linear_model.OrthogonalMatchingPursuit([...]) | Orthogonal Mathching Pursuit model (OMP) |
| linear_model.Perceptron([penalty, alpha, ...]) | Perceptron |
| linear_model.SGDClassifier([loss, penalty, ...]) | Linear model fitted by minimizing a regularized empirical loss with SGD. |
| linear_model.SGDRegressor([loss, penalty, ...]) | Linear model fitted by minimizing a regularized empirical loss with SGD |
| linear_model.BayesianRidge([n_iter, tol, ...]) | Bayesian ridge regression |
| linear_model.ARDRegression([n_iter, tol, ...]) | Bayesian ARD regression. |
| linear_model.RandomizedLasso([alpha, ...]) | Randomized Lasso |
| linear_model.RandomizedLogisticRegression([...]) | Randomized Logistic Regression |
| linear_model.lasso_path(X, y[, eps, ...]) | Compute Lasso path with coordinate descent |
| linear_model.lars_path(X, y[, Xy, Gram, ...]) | Compute Least Angle Regression and Lasso path |
| linear_model.orthogonal_mp(X, y[, ...]) | Orthogonal Matching Pursuit (OMP) |
| linear_model.orthogonal_mp_gram(Gram, Xy[, ...]) | Gram Orthogonal Matching Pursuit (OMP) |
| linear_model.lasso_stability_path(X, y[, ...]) | Stabiliy path based on randomized Lasso estimates |
8.15.2. For sparse data¶
The sklearn.linear_model.sparse submodule is the sparse counterpart of the sklearn.linear_model module.
User guide: See the Generalized Linear Models section for further details.
| linear_model.sparse.Lasso([alpha, ...]) | Linear Model trained with L1 prior as regularizer |
| linear_model.sparse.ElasticNet([alpha, rho, ...]) | Linear Model trained with L1 and L2 prior as regularizer |
| linear_model.sparse.SGDClassifier(*args, ...) | |
| linear_model.sparse.SGDRegressor(*args, **kwargs) | |
| linear_model.LogisticRegression([penalty, ...]) | Logistic Regression (aka logit, MaxEnt) classifier. |
8.16. sklearn.manifold: Manifold Learning¶
The sklearn.manifold module implements data embedding techniques.
User guide: See the Manifold learning section for further details.
| manifold.LocallyLinearEmbedding([...]) | Locally Linear Embedding |
| manifold.Isomap([n_neighbors, out_dim, ...]) | Isomap Embedding |
| manifold.locally_linear_embedding(X, ...[, ...]) | Perform a Locally Linear Embedding analysis on the data. |
8.17. sklearn.metrics: Metrics¶
The sklearn.metrics module includes score functions, performance metrics and pairwise metrics and distance computations.
8.17.1. Classification metrics¶
| metrics.confusion_matrix(y_true, y_pred[, ...]) | Compute confusion matrix to evaluate the accuracy of a classification |
| metrics.roc_curve(y_true, y_score) | compute Receiver operating characteristic (ROC) |
| metrics.auc(x, y) | Compute Area Under the Curve (AUC) using the trapezoidal rule |
| metrics.precision_score(y_true, y_pred[, ...]) | Compute the precision |
| metrics.recall_score(y_true, y_pred[, ...]) | Compute the recall |
| metrics.fbeta_score(y_true, y_pred, beta[, ...]) | Compute fbeta score |
| metrics.f1_score(y_true, y_pred[, labels, ...]) | Compute f1 score |
| metrics.precision_recall_fscore_support(...) | Compute precisions, recalls, f-measures and support for each class |
| metrics.classification_report(y_true, y_pred) | Build a text report showing the main classification metrics |
| metrics.precision_recall_curve(y_true, ...) | Compute precision-recall pairs for different probability thresholds |
| metrics.zero_one_score(y_true, y_pred) | Zero-One classification score |
| metrics.zero_one(y_true, y_pred) | Zero-One classification loss |
| metrics.hinge_loss(y_true, pred_decision[, ...]) | Cumulated hinge loss (non-regularized). |
8.17.2. Regression metrics¶
| metrics.r2_score(y_true, y_pred) | R^2 (coefficient of determination) regression score function |
| metrics.mean_squared_error(y_true, y_pred) | Mean squared error regression loss |
8.17.3. Clustering metrics¶
See the Clustering section of the user guide for further details.
The sklearn.metrics.cluster submodule contains evaluation metrics for cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the ‘quality’ of the model itself.
| metrics.adjusted_rand_score(labels_true, ...) | Rand index adjusted for chance |
| metrics.adjusted_mutual_info_score(...) | Adjusted Mutual Information between two clusterings |
| metrics.homogeneity_completeness_v_measure(...) | Compute the homogeneity and completeness and V-measure scores at once |
| metrics.homogeneity_score(labels_true, ...) | Homogeneity metric of a cluster labeling given a ground truth |
| metrics.completeness_score(labels_true, ...) | Completeness metric of a cluster labeling given a ground truth |
| metrics.v_measure_score(labels_true, labels_pred) | V-Measure cluster labeling given a ground truth |
| metrics.silhouette_score(X, labels[, ...]) | Compute the mean Silhouette Coefficient of all samples. |
8.17.4. Pairwise metrics¶
The sklearn.metrics.pairwise submodule implements utilities to evaluate pairwise distances or affinity of sets of samples.
This module contains both distance metrics and kernels. A brief summary is given on the two here.
Distance metrics are a function d(a, b) such that d(a, b) < d(a, c) if objects a and b are considered “more similar” to objects a and c. Two objects exactly alike would have a distance of zero. One of the most popular examples is Euclidean distance. To be a ‘true’ metric, it must obey the following four conditions:
1. d(a, b) >= 0, for all a and b
2. d(a, b) == 0, if and only if a = b, positive definiteness
3. d(a, b) == d(b, a), symmetry
4. d(a, c) <= d(a, b) + d(b, c), the triangle inequality
Kernels are measures of similarity, i.e. s(a, b) > s(a, c) if objects a and b are considered “more similar” to objects a and c. A kernel must also be positive semi-definite.
There are a number of ways to convert between a distance metric and a similarity measure, such as a kernel. Let D be the distance, and S be the kernel:
1. ``S = np.exp(-D * gamma)``, where one heuristic for choosing
``gamma`` is ``1 / num_features``
2. ``S = 1. / (D / np.max(D))``
| metrics.pairwise.euclidean_distances(X[, Y, ...]) | Considering the rows of X (and Y=X) as vectors, compute the |
| metrics.pairwise.manhattan_distances(X[, Y, ...]) | Compute the L1 distances between the vectors in X and Y. |
| metrics.pairwise.linear_kernel(X[, Y]) | Compute the linear kernel between X and Y. |
| metrics.pairwise.polynomial_kernel(X[, Y, ...]) | Compute the polynomial kernel between X and Y: |
| metrics.pairwise.rbf_kernel(X[, Y, gamma]) | Compute the rbf (gaussian) kernel between X and Y: |
| metrics.pairwise.distance_metrics() | Valid metrics for pairwise_distances |
| metrics.pairwise.pairwise_distances(X[, Y, ...]) | Compute the distance matrix from a vector array X and optional Y. |
| metrics.pairwise.kernel_metrics() | Valid metrics for pairwise_kernels |
| metrics.pairwise.pairwise_kernels(X[, Y, ...]) | Compute the kernel between arrays X and optional array Y. |
8.18. sklearn.mixture: Gaussian Mixture Models¶
The sklearn.mixture module implements mixture modeling algorithms.
User guide: See the Gaussian mixture models section for further details.
| mixture.GMM([n_components, covariance_type, ...]) | Gaussian Mixture Model |
| mixture.DPGMM([n_components, ...]) | Variational Inference for the Infinite Gaussian Mixture Model. |
| mixture.VBGMM([n_components, ...]) | Variational Inference for the Gaussian Mixture Model |
8.19. sklearn.multiclass: Multiclass and multilabel classification¶
8.19.1. Multiclass and multilabel classification strategies¶
- This module implements multiclass learning algorithms:
- one-vs-the-rest / one-vs-all
- one-vs-one
- error correcting output codes
The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. For example, it is possible to use these estimators to turn a binary classifier or a regressor into a multiclass classifier. It is also possible to use these estimators with multiclass estimators in the hope that their accuracy or runtime performance improves.
User guide: See the Multiclass and multilabel algorithms section for further details.
| multiclass.OneVsRestClassifier(estimator) | One-vs-the-rest (OvR) multiclass/multilabel strategy |
| multiclass.OneVsOneClassifier(estimator) | One-vs-one multiclass strategy |
| multiclass.OutputCodeClassifier(estimator[, ...]) | (Error-Correcting) Output-Code multiclass strategy |
| multiclass.fit_ovr(estimator, X, y) | Fit a one-vs-the-rest strategy. |
| multiclass.predict_ovr(estimators, ...) | Make predictions using the one-vs-the-rest strategy. |
| multiclass.fit_ovo(estimator, X, y) | Fit a one-vs-one strategy. |
| multiclass.predict_ovo(estimators, classes, X) | Make predictions using the one-vs-one strategy. |
| multiclass.fit_ecoc(estimator, X, y[, ...]) | Fit an error-correcting output-code strategy. |
| multiclass.predict_ecoc(estimators, classes, ...) | Make predictions using the error-correcting output-code strategy. |
8.20. sklearn.naive_bayes: Naive Bayes¶
The sklearn.naive_bayes module implements Naive Bayes algorithms. These are supervised learning methods based on applying Bayes’ theorem with strong (naive) feature independence assumptions.
User guide: See the Naive Bayes section for further details.
| naive_bayes.GaussianNB | Gaussian Naive Bayes (GaussianNB) |
| naive_bayes.MultinomialNB([alpha, fit_prior]) | Naive Bayes classifier for multinomial models |
| naive_bayes.BernoulliNB([alpha, binarize, ...]) | Naive Bayes classifier for multivariate Bernoulli models. |
8.21. sklearn.neighbors: Nearest Neighbors¶
The sklearn.neighbors module implements the k-nearest neighbors algorithm.
User guide: See the Nearest Neighbors section for further details.
| neighbors.NearestNeighbors([n_neighbors, ...]) | Unsupervised learner for implementing neighbor searches. |
| neighbors.KNeighborsClassifier([...]) | Classifier implementing the k-nearest neighbors vote. |
| neighbors.RadiusNeighborsClassifier([...]) | Classifier implementing a vote among neighbors within a given radius |
| neighbors.KNeighborsRegressor([n_neighbors, ...]) | Regression based on k-nearest neighbors. |
| neighbors.RadiusNeighborsRegressor([radius, ...]) | Regression based on neighbors within a fixed radius. |
| neighbors.BallTree | Ball Tree for fast nearest-neighbor searches : |
| neighbors.kneighbors_graph(X, n_neighbors[, ...]) | Computes the (weighted) graph of k-Neighbors for points in X |
| neighbors.radius_neighbors_graph(X, radius) | Computes the (weighted) graph of Neighbors for points in X |
8.22. sklearn.pls: Partial Least Squares¶
The sklearn.pls module implements Partial Least Squares (PLS).
User guide: See the Partial Least Squares section for further details.
| pls.PLSRegression([n_components, scale, ...]) | PLS regression |
| pls.PLSCanonical([n_components, scale, ...]) | PLS canonical. PLSCanonical inherits from PLS with mode=”A” and |
| pls.CCA([n_components, scale, algorithm, ...]) | CCA Canonical Correlation Analysis. CCA inherits from PLS with |
| pls.PLSSVD([n_components, scale, copy]) | Partial Least Square SVD |
8.23. sklearn.pipeline: Pipeline¶
The sklearn.pipeline module implements utilites to build a composite estimator, as a chain of transforms and estimators.
| pipeline.Pipeline(steps) | Pipeline of transforms with a final estimator |
8.24. sklearn.preprocessing: Preprocessing and Normalization¶
User guide: See the Preprocessing data section for further details.
| preprocessing.Scaler([copy, with_mean, with_std]) | Standardize features by removing the mean and scaling to unit variance |
| preprocessing.Normalizer([norm, copy]) | Normalize samples individually to unit norm |
| preprocessing.Binarizer([threshold, copy]) | Binarize data (set feature values to 0 or 1) according to a threshold |
| preprocessing.LabelBinarizer([neg_label, ...]) | Binarize labels in a one-vs-all fashion |
| preprocessing.KernelCenterer | Center a kernel matrix |
| preprocessing.scale(X[, axis, with_mean, ...]) | Standardize a dataset along any axis |
| preprocessing.normalize(X[, norm, axis, copy]) | Normalize a dataset along any axis |
| preprocessing.binarize(X[, threshold, copy]) | Boolean thresholding of array-like or scipy.sparse matrix |
8.25. sklearn.qda: Quadratic Discriminant Analysis¶
Quadratic Discriminant Analysis
User guide: See the Linear and Quadratic Discriminant Analysis section for further details.
| qda.QDA([priors]) | Quadratic Discriminant Analysis (QDA) |
8.26. sklearn.svm: Support Vector Machines¶
The sklearn.svm module includes Support Vector Machine algorithms.
User guide: See the Support Vector Machines section for further details.
8.26.1. Estimators¶
| svm.SVC([C, kernel, degree, gamma, coef0, ...]) | C-Support Vector Classification. |
| svm.LinearSVC([penalty, loss, dual, tol, C, ...]) | Linear Support Vector Classification. |
| svm.NuSVC([nu, kernel, degree, gamma, ...]) | Nu-Support Vector Classification. |
| svm.SVR([kernel, degree, gamma, coef0, tol, ...]) | epsilon-Support Vector Regression. |
| svm.NuSVR([nu, C, kernel, degree, gamma, ...]) | Nu Support Vector Regression. |
| svm.OneClassSVM([kernel, degree, gamma, ...]) | Unsupervised Outliers Detection. |
| svm.l1_min_c(X, y[, loss, fit_intercept, ...]) | Return the maximum value for C that yields a model with coefficients |
8.26.2. Low-level methods¶
| svm.libsvm.fit | Train the model using libsvm (low-level method) |
| svm.libsvm.decision_function | Predict margin (libsvm name for this is predict_values) |
| svm.libsvm.predict | Predict target values of X given a model (low-level method) |
| svm.libsvm.predict_proba | Predict probabilities svm_model stores all parameters needed to predict a given value. |
| svm.libsvm.cross_validation | Binding of the cross-validation routine (low-level routine) |
8.27. sklearn.tree: Decision Trees¶
The sklearn.tree module includes decision tree-based models for classification and regression.
User guide: See the Decision Trees section for further details.
| tree.DecisionTreeClassifier([criterion, ...]) | A decision tree classifier. |
| tree.DecisionTreeRegressor([criterion, ...]) | A tree regressor. |
| tree.ExtraTreeClassifier([criterion, ...]) | An extremely randomized tree classifier. |
| tree.ExtraTreeRegressor([criterion, ...]) | An extremely randomized tree regressor. |
| tree.export_graphviz(decision_tree[, ...]) | Export a decision tree in DOT format. |
8.28. sklearn.utils: Utilities¶
The sklearn.utils module includes various utilites.
Developer guide: See the Utilities for Developers page for further details.
| utils.check_random_state(seed) | Turn seed into a np.random.RandomState instance |
| utils.resample(*arrays, **options) | Resample arrays or sparse matrices in a consistent way |
| utils.shuffle(*arrays, **options) | Shuffle arrays or sparse matrices in a consistent way |


Package org.bitcoinj.core
Enum TransactionConfidence.ConfidenceType
- java.lang.Object
-
- java.lang.Enum<TransactionConfidence.ConfidenceType>
-
- org.bitcoinj.core.TransactionConfidence.ConfidenceType
-
- All Implemented Interfaces:
java.io.Serializable
,java.lang.Comparable<TransactionConfidence.ConfidenceType>
- Enclosing class:
- TransactionConfidence
public static enum TransactionConfidence.ConfidenceType extends java.lang.Enum<TransactionConfidence.ConfidenceType>
Describes the state of the transaction in general terms. Properties can be read to learn specifics.
-
-
Enum Constant Summary
Enum Constants Enum Constant Description BUILDING
If BUILDING, then the transaction is included in the best chain and your confidence in it is increasing.DEAD
If DEAD, then it means the transaction won't confirm unless there is another re-org, because some other transaction is spending one of its inputs.IN_CONFLICT
If IN_CONFLICT, then it means there is another transaction (or several other transactions) spending one (or several) of its inputs but nor this transaction nor the other/s transaction/s are included in the best chain.PENDING
If PENDING, then the transaction is unconfirmed and should be included shortly, as long as it is being announced and is considered valid by the network.UNKNOWN
If a transaction hasn't been broadcast yet, or there's no record of it, its confidence is UNKNOWN.
-
Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description int
getValue()
static TransactionConfidence.ConfidenceType
valueOf(java.lang.String name)
Returns the enum constant of this type with the specified name.static TransactionConfidence.ConfidenceType[]
values()
Returns an array containing the constants of this enum type, in the order they are declared.
-
-
-
Enum Constant Detail
-
BUILDING
public static final TransactionConfidence.ConfidenceType BUILDING
If BUILDING, then the transaction is included in the best chain and your confidence in it is increasing.
-
PENDING
public static final TransactionConfidence.ConfidenceType PENDING
If PENDING, then the transaction is unconfirmed and should be included shortly, as long as it is being announced and is considered valid by the network. A pending transaction will be announced if the containing wallet has been attached to a livePeerGroup
usingPeerGroup.addWallet(Wallet)
. You can estimate how likely the transaction is to be included by connecting to a bunch of nodes then measuring how many announce it, usingTransactionConfidence.numBroadcastPeers()
. Or if you saw it from a trusted peer, you can assume it's valid and will get mined sooner or later as well.
-
DEAD
public static final TransactionConfidence.ConfidenceType DEAD
If DEAD, then it means the transaction won't confirm unless there is another re-org, because some other transaction is spending one of its inputs. Such transactions should be alerted to the user so they can take action, eg, suspending shipment of goods if they are a merchant. It can also mean that a coinbase transaction has been made dead from it being moved onto a side chain.
-
IN_CONFLICT
public static final TransactionConfidence.ConfidenceType IN_CONFLICT
If IN_CONFLICT, then it means there is another transaction (or several other transactions) spending one (or several) of its inputs but nor this transaction nor the other/s transaction/s are included in the best chain. The other/s transaction/s should be IN_CONFLICT too. IN_CONFLICT can be thought as an intermediary state between a) PENDING and BUILDING or b) PENDING and DEAD. Another common name for this situation is "double spend".
-
UNKNOWN
public static final TransactionConfidence.ConfidenceType UNKNOWN
If a transaction hasn't been broadcast yet, or there's no record of it, its confidence is UNKNOWN.
-
-
Method Detail
-
values
public static TransactionConfidence.ConfidenceType[] values()
Returns an array containing the constants of this enum type, in the order they are declared. This method may be used to iterate over the constants as follows:for (TransactionConfidence.ConfidenceType c : TransactionConfidence.ConfidenceType.values()) System.out.println(c);
- Returns:
- an array containing the constants of this enum type, in the order they are declared
-
valueOf
public static TransactionConfidence.ConfidenceType valueOf(java.lang.String name)
Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)- Parameters:
name
- the name of the enum constant to be returned.- Returns:
- the enum constant with the specified name
- Throws:
java.lang.IllegalArgumentException
- if this enum type has no constant with the specified namejava.lang.NullPointerException
- if the argument is null
-
getValue
public int getValue()
-
-


Rohan Weigel
Data Source: http://nces.ed.gov/programs/digest/d12/tables/dt12_460.asp
Scores per Hour of Instruction for 4th Graders


In recent years, a range of derivative technologies arrived which exploit (flexible) multibody simulation paradigms:
This web page visualizes the function \( u(t) = Ae^{-bt}\sin (w t), \hbox{ for } t\in [0,T] \).
The first two steps which rely on external software are briefly discussed in the following section. The next section are then focussed on describing the reduced order modeling scheme.
In this work it is assumed that the original model contains only flexible bodies. In general, the differential algebraic equations describing the motion of the system can be written as: In these equations the inertial forces are generally a nonlinear function of the generalized multibody coordinates \(q\) and their velocities \(\dot{q}\). The same is true for the damping forces \(F_{damp}\) and internal elastic forces in the bodies \(F_{int}\). $$ \sum_{i=0}^n i^2 = \frac{(n^2+n)(2n+1)}{6} $$ with \(n \in \RR\).


{% extends 'webapp/base.html' %} {% block title %}Registration Form{% endblock %} {% block body %}
Please correct the error{{ form.errors|pluralize }} below.
{{ error }}
Username can contain any letters or numbers, without spaces
Please provide your E-mail
Password should be at least 4 characters
Already have an account? Click here to login. {% endblock %}


It Already Is Enough.
Thank you for being who you are too.
by Isabel MarIt already is enough. It is simply a thought that makes you think it needs to be more than what it already is.
You already are enough. It is simply a thought that makes you think you need to be more than who you already are.
Simply be here. So you can see it too. Everything that is already here. Everything that has always been here for you too.
Simply be here. So you can hear it too. The Stillness and the Silence. The Peace and the Calm too.
Simply be here. So you can feel it too. Feel your own presence. The Presence of You.
It already is enough.  It is simply a thought that makes you think it needs to be anything other what it already is too.
Sometimes, we can get so caught up in our own heads about thinking it is needs to be something other than what it already is.
How much brighter can the Sun and Moon be? How much lighter can the sunrise and sunset be? How much mightier can your presence and understanding be?
Yet we think it needs to be more. Because we learned to think we need to be more too.
We don’t. You don’t. And I don’t.
Because you are enough. You have always been enough. Simply being you.
Your mind simply got conditioned to think you need to be more than you already are.
Which is how you learned to think you need to keep doing more, wanting more, having more.
And why there is no end to your thoughts. Why your mind never stops.
Because there is no more Understanding of Enough. What it means to be enough, have enough, do enough.
And this is how you break the cycle. From the Understanding. The Understanding of Who You Are.
And you are enough. You have always been enough. Which is why there is only one you.
Because no one else can be who you are. No one else can do what only you can do. Be you.
Begin from this Understanding. So your mind can receive this Understanding too.
Because when your mind understands, it is settled. So you no longer need to think about it. Because you understand too.
How you already are enough. How it already is enough too.
My name is Isabel Mar and I am your gentle guide back to you. Who you are.
Sign up here for your Daily Purejoojoo.
Daily reminders of everything Life already is and everything you already are too.
— Published on November 25, 2019


Stocks¶
Common stock typically represents ownership in a company. Three types are provided to model stocks, Stock, LongStock and ShortStock. The Stock type can be used to represent both long and short positions, simply by providing positive or negative values in the basis element. LongStock and ShortStock types are provided as a convenience and methods dispatched on them assumes long and short positions, respectively.
| Stock type | Description |
|---|---|
| Stock | common stock with either long or short posiition permittead |
| LongStock | common stock with only long positions allowed |
| ShortStock | common stock with only short positions allowed |
All the stock types hold the same fields. To create an object, only the ticker and basis elements needs to be provided. Default values will be filled in for tick (.01), currency (USD) and multiplier (1.0).
| Type field | Description | Type |
|---|---|---|
| ticker | trading symbol | Ticker |
| basis | position price | Float64 |
| currency | national currency | Currency |
| tick | increments traded in | Float64 |
| multiplier | degree to adjust position based on price | Flot64 |
| id | a unique non-exchange identification symbol | FinancialID |
An example in REPL, creating an object representing Apple Computer, Inc.:
julia> Apple = Stock(Ticker("AAPL"), 100.00)
ticker: AAPL
Other field information is accessible with standard . notation:
julia> Apple.currency
USD


7 March 2003
Disease Outbreak Reported
As of 6 March, the Ministry of Health of Burkina Faso has reported a total of 3 691 cases, including 542 deaths in the country since the outbreak began in January 2003 (see previous report). Of the 53 districts in Burkina Faso, 5 districts have reached the epidemic threshold: Batie, Bogande, Manga, Po and Zabre and 8 districts are in the alert phase: Boulsa, Bousse, Dande, Leo, Nanoro, Sapone, Secteur 30 de Ouagadougou, and Zorgho.
A mass vaccination campaign in Manga and Bogande is continuing. A team from the Ministry of Health and WHO have carried out an assessment mission to those districts having reached the epidemic threshold and have developed action plans to contain the outbreaks in these areas. Laboratory tests from samples in these districts confirm Neisseria meningitidis serogroups A and W135.


Before the page is loaded BuildHeader.js ensures that a user is logged in, if not the browser is
redirected to the login page.
This page is used for uploading new images.
For multiple file uploading the plugin Uploadify (http://www.uploadify.com/) is used.
Before the page is finished loading a call is sent out to the server to get the groups that the user
belongs to. This call ensures the user is logged in again and returns a list of groups. This is
then used to populated the group dropdown for selecting a group.
After the user has a selected an image (or images) and populated the desired fields they can hit
upload. This will send one request per image to the server.
On upload, the inputs are initially validated and processed client side before being sent to the
server. The server then ensures that the user is logged in once more before committing the
new image.
Created with the Personal Edition of HelpNDoc: Free EPub producer


PonyFest Charity Auction
PonyFest Online! 5.0’s charity auction will benefit Doctors Without Borders.
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Math Sprinter Game
10 Questions
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25 Questions
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3
Your Time


Virtual DOM
Virtual DOM simplifies the way to manage DOM mutations, just describe how your component should look at any point in time and Virtual DOM reconciliation algorithm will make all necessary DOM operations to update component representation in a most efficient way.
The basic Virtual DOM API that kivi provides is enough for most use cases. But when it isn't enough, kivi has many advanced tools that can reduce Virtual DOM diffing overhead, and it will be quite hard to beat its performance without reimplementing similar algorithms.
Creating Virtual DOM Nodes
Kivi has several simple functions to create virtual nodes:
// Creates a virtual node representing a HTML Element.
function createVElement(tagName: string): VNode;
// Creates a virtual node representing a SVG Element.
function createVSvgElement(tagName: string): VNode;
// Creates a virtual node representing a Text Node.
function createVText(content: string): VNode;
Setting VNode properties
Virtual node instance has many different methods to set properties, all property methods support method chaining. For example:
const e = createVElement("a")
.attrs({
"href": "localhost",
"title": "Link to localhost",
})
.child("Link to localhost");
List of basic properties:
VNode.attrs(attrs: {[key: string]: any}): VNode;
VNode.props(props: {[key: string]: any}): VNode;
VNode.style(style: string): VNode;
VNode.className(className: string): VNode;
VNode.children(children: VNode[] | null): VNode;
VNode.child(child: VNode | string | null): VNode;
Using keys to preserve identity
To prevent from losing internal state of DOM elements and components, we should track nodes by some property. Virtual
DOM reconciliation algorithm is using key
property to find virtual nodes representing the same DOM node from
the previous render.
To enable tracking by keys, children property should be assigned with VNode.trackByKeyChildren(children)
method. Each
child should have a key
property and it should be unique among its siblings. For example:
const container = createVElement("div")
.trackByKeyChildren([
createVElement("span").key(1).children("1"),
createVElement("span").key(2).children("2"),
createVElement("span").key(3).children("3"),
]);
To learn more about kivi children reconciliation algorithm, read the comment in the source code, it explains how kivi is able to find minimum number of DOM operations. This algorithm is highly optimized and runs really fast in most real world use cases, see uibench benchmark to find out how kivi handles different use cases.


NOTE: We generated a new gene set for this species using up-to-date software. See parasite.wormbase.org for the original gene set.
From [1]:
"Heterorhabditis bacteriophora is an entomopathogenic nematode (EPN) mutually associated with the enteric bacterium, Photorhabdus luminescens, used globally for the biological control of insects. Much of the previous research concerning H. bacteriophora has dealt with applied aspects related to biological control. However, H. bacteriophora is an excellent model to investigate fundamental processes such as parasitism and mutualism in addition to its comparative value to Caenorhabditis elegans."
See below for more information.
The genome of H. bacteriophora M31a was sequenced, assembled and published by Xiaodong Bai and colleagues [2]. Some small, contaminant contigs have since been removed from the assembly.
Flo McLean (Blaxter lab) repredicted genes using BRAKER [3], which utilises RNA-seq information. This new gene set appears to be a significant improvement on the original set. See parasite.wormbase.org for the original gene set.


def functionName(): # This is the function that is being made
print('line 1')
print('line 2')
print('line 3')
functionName() # Run the function
functionName() # Run the function again
line 1 line 2 line 3 line 1 line 2 line 3
Functions are not very useful this way because they run exactly the same each time.
To make them useful, there is a way to pass a value to them so that they can be different each time. The order of values is used to set the values.
Notice that the value 'blue' got set into the color variable similar to the equal sign.
def addClothesToDrawer(color, clothes): # The 1st value is color, 2nd value is clothes
print('Add ' + color + ' ' + clothes)
addClothesToDrawer('blue', 'socks') # The 1st value sets color, 2nd value sets clothes
addClothesToDrawer('red', 'shirt')
Add blue socks Add red shirt
Names for functions are important. Names can be very descriptive like, "ThisAddsTwoNumbersAndPrintsTheResult", but this can be hard to read in a program if every name is really long especially when functions get more complex.
def add(num1, num2):
val = num1 - num2 # THIS IS NOT AN ADD!
print(val)
add(5, 2) # This really subtracts, so it is very confusing.
3
Notice that the "return" word can send a value back to the place where the function was run or "called from".
def add(num1, num2):
val = num1 + num2
return(val)
def subtract(num1, num2):
val = num1 - num2
return(val)
print(add(5, 3)) # Run or "call" the "add" function, then print what was returned.
print(subtract(5,3))
8 2
Notice in the following code that the "val" variable is different in every function. There are different storage locations inside functions. Also notice that the returned value is named "val" inside the function, and gets set into (like the equal sign) "returnedValue" outside of the function.
def change(val):
val = 5
print('In change function:', val)
return val
val = 3
returnedValue = change(val)
print('Outside of change function:', val)
print('Outside of change function returned value:', returnedValue)
In change function: 5 Outside of change function: 3 Outside of change function returned value: 5
Lists and dictionaries are different and are shared between functions.
def change(values):
values[0] = 5
print('In change function:', values)
return values
values = [0] * 1 # Make a list with a size of 1 and filled with 0.
values[0] = 3 # The value of 3 is changed in the change function.
returnedValues = change(values)
print('Outside of change function:', values)
print('Outside of change function returned values:', returnedValues)
In change function: [5] Outside of change function: [5] Outside of change function returned values: [5]
Functions can be nested. For example, a rectangle is made from 4 lines. Each line is made from many points on the line. See the fields/graphics/6-drawRectangle.py example.
So there are many advantages of functions.


Newton's Cupcake
Newton’s Cupcake
Isaac Newton published a famous thought experiment in A Treatise of the System of the World. He imagined what would happen if one were to shoot a cannonball from the top of a very high mountain (so high up that air resistance could be ignored). If the canonball were launched with a relatively small velocity, it would strike the ground at some distance from the mountain. However, if the velocity of the canonball were much larger, it might “miss” the ground entirely and go into orbit around the planet. This thought experiment was the key to linking the force of gravity to orbital motion.
It is easy to determine the speed at which circular orbital motion will occur. Let \(M\) be the mass of the planet, \(m\) be the mass of the cannonball, and \(r\) be the radius of the orbit. We know that the force of gravity provides the centripetal force for a body in uniform circular motion around the planet.
\[\begin{equation} m\frac{v^2}{r} = \frac{GMm}{r^2} \end{equation}\] \[\begin{equation} v^2 = \frac{GM}{r} \end{equation}\] \[\begin{equation} v = \sqrt{\frac{GM}{r}} \end{equation}\]After learning about the HTML5 canvas tag in a workshop at the AAPT Summer Meeting, I decided to make a little simulation in order to visualize this thought experiment. The planet below has the same radius as the Earth; you can adjust the mass of the planet, the initial velocity of the projectile, and the angle of launch of the projectile. Sadly, we are out of cannonballs, but we do have a brave cupcake volunteer.
| Initial Cupcake Speed | 7500 m/s | |
| Planet Mass | 6.0e24 kg | |
| Angle of Fire | 0 degrees |


Coordinate information
Enter your coordinates in 0:000:00 format or 1234:5678:1234:5678 format
Welcome to the Computer Oriented Flight For Euclidean Explorers
This is your estimated route to ... draw your location and select some destination first! beep
Galaxy map


Class: Aws::Lightsail::Types::AccountLevelBpaSync
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-lightsail/lib/aws-sdk-lightsail/types.rb |
Overview
Describes the synchronization status of the Amazon Simple Storage
Service (Amazon ::Aws::S3
) account-level block public access (BPA) feature
for your ::Aws::Lightsail
buckets.
The account-level BPA feature of Amazon S3 provides centralized
controls to limit public access to all Amazon S3 buckets in an
account. BPA can make all Amazon S3 buckets in an Amazon Web Services
account private regardless of the individual bucket and object
permissions that are configured. ::Aws::Lightsail
buckets take into account
the Amazon S3 account-level BPA configuration when allowing or denying
public access. To do this, ::Aws::Lightsail
periodically fetches the
account-level BPA configuration from Amazon S3. When the account-level
BPA status is InSync
, the Amazon S3 account-level BPA configuration
is synchronized and it applies to your ::Aws::Lightsail
buckets. For more
information about Amazon Simple Storage Service account-level BPA and
how it affects ::Aws::Lightsail
buckets, see Block public access for buckets
in Amazon Lightsail in the Amazon ::Aws::Lightsail
Developer Guide.
Constant Summary
Instance Attribute Summary
-
#bpa_impacts_lightsail ⇒ Boolean
rw
A Boolean value that indicates whether account-level block public access is affecting your
::Aws::Lightsail
buckets. -
#last_synced_at ⇒ Time
rw
The timestamp of when the account-level BPA configuration was last synchronized.
-
#message ⇒ String
rw
A message that provides a reason for a
Failed
orDefaulted
synchronization status. -
#status ⇒ String
rw
The status of the account-level BPA synchronization.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#bpa_impacts_lightsail ⇒ Boolean
(rw)
A Boolean value that indicates whether account-level block public
access is affecting your ::Aws::Lightsail
buckets.
# File 'gems/aws-sdk-lightsail/lib/aws-sdk-lightsail/types.rb', line 307
class AccountLevelBpaSync < Struct.new( :status, :last_synced_at, :, :bpa_impacts_lightsail) SENSITIVE = [] include Aws::Structure end
#last_synced_at ⇒ Time
(rw)
The timestamp of when the account-level BPA configuration was last synchronized. This value is null when the account-level BPA configuration has not been synchronized.
# File 'gems/aws-sdk-lightsail/lib/aws-sdk-lightsail/types.rb', line 307
class AccountLevelBpaSync < Struct.new( :status, :last_synced_at, :, :bpa_impacts_lightsail) SENSITIVE = [] include Aws::Structure end
#message ⇒ String
(rw)
A message that provides a reason for a Failed
or Defaulted
synchronization status.
The following messages are possible:
SYNC_ON_HOLD
- The synchronization has not yet happened. This status message occurs immediately after you create your first Lightsail bucket. This status message should change after the first synchronization happens, approximately 1 hour after the first bucket is created.DEFAULTED_FOR_SLR_MISSING
- The synchronization failed because the required service-linked role is missing from your Amazon Web Services account. The account-level BPA configuration for your Lightsail buckets is defaulted to active until the synchronization can occur. This means that all your buckets are private and not publicly accessible. For more information about how to create the required service-linked role to allow synchronization, see Using Service-Linked Roles for Amazon Lightsail in the Amazon Lightsail Developer Guide.DEFAULTED_FOR_SLR_MISSING_ON_HOLD
- The synchronization failed because the required service-linked role is missing from your Amazon Web Services account. Account-level BPA is not yet configured for your Lightsail buckets. Therefore, only the bucket access permissions and individual object access permissions apply to your Lightsail buckets. For more information about how to create the required service-linked role to allow synchronization, see Using Service-Linked Roles for Amazon Lightsail in the Amazon Lightsail Developer Guide.Unknown
- The reason that synchronization failed is unknown. Contact Amazon Web Services Support for more information.
# File 'gems/aws-sdk-lightsail/lib/aws-sdk-lightsail/types.rb', line 307
class AccountLevelBpaSync < Struct.new( :status, :last_synced_at, :, :bpa_impacts_lightsail) SENSITIVE = [] include Aws::Structure end
#status ⇒ String
(rw)
The status of the account-level BPA synchronization.
The following statuses are possible:
InSync
- Account-level BPA is synchronized. The Amazon::Aws::S3
account-level BPA configuration applies to your Lightsail buckets.NeverSynced
- Synchronization has not yet happened. The Amazon S3 account-level BPA configuration does not apply to your Lightsail buckets.Failed
- Synchronization failed. The Amazon::Aws::S3
account-level BPA configuration does not apply to your Lightsail buckets.Defaulted
- Synchronization failed and account-level BPA for your Lightsail buckets is defaulted to active.
Failed
or Defaulted
. The message
parameter provides more information
for those statuses.
# File 'gems/aws-sdk-lightsail/lib/aws-sdk-lightsail/types.rb', line 307
class AccountLevelBpaSync < Struct.new( :status, :last_synced_at, :, :bpa_impacts_lightsail) SENSITIVE = [] include Aws::Structure end


Dear Sir/Madam
Thank you very much for your generous donation to my works. Your donation supports me to contribute to science and art as much as possible. Serving science and art is my biggest honor and happiness. Your name is in my sponsors list with full of thanks.
Best regards,
Mr. Loc Nguyen,
Independent Scholar,
Homepage: www.locnguyen.net,
Email<EMAIL_ADDRESS>Tel: 84-975250362