Let's see how we create a CSSMathValue
and whilst doing so see what is available within the type:
const mathVal = new CSSMathSum(CSS.px(10), CSS.vw(20));
/*
Returns: CSSMathSum {
operator: "sum",
values: CSSNumericArray
0: CSSUnitValue {value: 10, unit: "px"},
1: CSSUnitValue {value: 20, unit: "vw"},
length: 2
}
*/
This creates a new CSSMathValue
with the operator type "sum". CSSMathValue
s always take CSSUnitValue
s. As you can see with what is created in the above example, CSSMathSum
actually takes a CSSNumericArray
which is an array of CSSNumericValue
s.
If we use the toString()
method on a CSSMathValue
, it returns the ready made calc()
function as a string. Handy.
console.log(mathVal.toString());
// Logs: calc(10px + 20vw)
NB As with the previous part on numeric types, there is an associated javascript file which you can find here. It is logging code to the console which you can see in the developer tools pane.
Other CSSMathValue
types that associate with the calc()
function are 'sum', 'product', 'negate' and 'invert'. Each of the mathematical operations returns it's own interface. Here's an example of each them, how to create each, set()
the value on an element and in turn get()
that value to see the interface returned:
CSSMathSum
Addition & Subtraction. Values are of type CSSNumericValue
and it takes a CSSNumericArray
, so you can put in as many values as you want.
const sumVal = new CSSMathSum(CSS.px(10), CSS.vw(20), CSS.em(1.2));
el.attributeStyleMap.set('width', sumVal);
const gotSumVal = el.attributeStyleMap.get('width');
/*
Returns: CSSMathSum {
operator: "sum",
values: CSSNumericArray
0: CSSUnitValue {value: 10, unit: "px"},
1: CSSUnitValue {value: 20, unit: "vw"},
2: CSSUnitValue {value: 1.2, unit: "em"},
length: 3
}
*/
To subtract use a negative number
const subVal = new CSSMathSum(CSS.s(2), CSS.ms(-200));
el.attributeStyleMap.set('animation-duration', subVal);
const gotSubVal = el.attributeStyleMap.get('animation-duration');
/*
Returns: CSSMathSum {
operator: "sum",
values: CSSNumericArray
0: CSSUnitValue {value: 2, unit: "s"},
1: CSSUnitValue {value: -200, unit: "ms"},
length: 2
}
*/
CSSMathProduct
Multiplication. Again here you can pass in a CSSNumericArray
.
const productVal = new CSSMathProduct(CSS.em(1.6), CSS.number(1.2));
el.attributeStyleMap.set('font-size', productVal);
const gotProductVal = el.attributeStyleMap.get('font-size');
/*
Returns: CSSMathProduct {
operator: "product",
values: CSSNumericArray
0: CSSUnitValue {value: 1.6, unit: "em"},
1: CSSUnitValue {value: 1.2, unit: "number"},
length: 2
}
*/
CSSMathNegate
Negation. Only takes one CSSNumericValue
. Note here how we have to use the aforementioned toString()
method, as the type itself does not negate the value, it just specifies it is of negated type.
const negVal = new CSSMathNegate(CSS.px(20));
// negVal.toString() returns 'calc(-20px)'
el.attributeStyleMap.set('margin-top', negVal.toString());
const gotNegVal = el.attributeStyleMap.get('margin-top');
/*
Returns: CSSMathNegate {
operator: "negate",
value: CSSUnitValue {value: 20, unit: "px"}
}
*/
The calc()
function is not the only mathematical function we have in CSS however, there is also the min()
, max()
and yet to be implemented clamp()
functions. These all have their own CSSMathValue
types. Let's take a look at creating each in turn, and what each interface contains.
CSSMathMin
Takes a CSSNumericArray
. If you are unfamiliar with the min() function you can find out more here.
const minVal = new CSSMathMin(CSS.vh(10), CSS.px(300));
/*
Returns: CSSMathMin {
operator: "min",
values: CSSNumericArray
0: CSSUnitValue {value: 10, unit: "vh"},
1: CSSUnitValue {value: 300, unit: "px"},
length: 2
}
*/
CSSMathMax
Again, takes a CSSNumericArray
. If you are unfamiliar with the max() function you can find out more here.
const maxVal = new CSSMathMax(CSS.px(50), CSS.percent(20));
/*
Returns: CSSMathMax {
operator: "max",
values: CSSNumericArray
0: CSSUnitValue {value: 50, unit: "px"},
1: CSSUnitValue {value: 20, unit: "percent"},
length: 2
}
*/
Nested calculations
In CSS you can nest any of the above functions: calc()
, min()
or max()
. Consider the following CSS: min( calc(1.2em * 1.4), 10vw)
, to recreate this using CSSMathValue
s we would also need to nest them:
const nestedVal = new CSSMathMin(
new CSSMathProduct(CSS.em(1.2), CSS.number(1.4)),
CSS.vw(10)
);
/*
Returns: CSSMathMin {
operator: "min",
values: CSSNumericArray
0: CSSMathProduct {
operator: "product"
values: CSSNumericArray
0: CSSUnitValue {value: 1.2, unit: "em"},
1: CSSUnitValue {value: 1.4, unit: "number"},
length: 2
}
1: CSSUnitValue {value: 300, unit: "px"},
length: 2
}
*/
On the surface this could seem overly complex, however it makes sense and we have to remember it's the underlying information about these CSSValueTypes
that is being exposed and we have access to. It might be a bit to take in at this point, but as support starts to advance and we gain familiarity with this API, we'll probably wonder how we ever managed without it.
There is also more. There's methods that each CSSNumericValue
is privy to, let's check them out in the next part.


You can use this page to visualize the behavior of the playlist switcher under different network conditions. It can helpful to understand how tweaks to the switching logic will affect playback.
Timeline
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That power Pulsar and help you find relevant things faster
Visualisation at it's highest extent
Dark nodes are clusters of data and small colourful nodes are your actual search results.
Different colours and colourful arcs indicate how many and how good results are.
Graphics is very responsive and dynamic, so you can easily drill down or zoom into any group of results.
Searching power behind the scenes
Mechanism to rank search results and identify
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{{gettext("""Certain statements in this whitepaper constitute forward-looking statements. Such forward-looking statements, including the intended actions and performance objectives of the Company, involve known and unknown risks, uncertainties, and other important factors that could cause the actual results of the Company to differ materially from any future results expressed or implied by such forward-looking statements. Accordingly, readers should not place undue reliance on forward-looking statements. No representation or warranty is made as to future performance or such forward-looking statements.""")}}
{{gettext("""This whitepaper was first published in September 2017, before the Origin Platform was built and deployed on the Ethereum Mainnet in October of 2018. For the most up to date information on our product development and business strategy, please refer to our website at originprotocol.com. For the latest technical information, please reference our Github account at www.github.com/OriginProtocol. To use our flagship marketplace application, please download our mobile apps (www.originprotocol.com/mobile) or visit our DApp ((www.shoporigin.com) with a web3-enabled DApp browser. To use our decentralized Shopify alternative, visit originswag.eth.link.""")}}
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On the Subject of the Dreamcipher
I'm not awake enough for this nonsense. I'm going back to bed.
- A sequence of glyphs will be displayed along the bottom half of the circular screen. Some of the glyphs in the sequence correspond to letters of the English alphabet; the other glyphs are decoys.
- To figure out which glyphs map to which letters, refer to "Recreating the Glyph Alphabet" on the next page.
- Afterwards, to obtain the decrypted word, convert each glyph on the module back to a letter in order of appearance. Treat outlined versions of glyphs the same as their filled counterparts, and ignore any glyphs that are not in the glyph alphabet.
- The decrypted word will always be a valid English word between 8 and 15 characters in length.
- While in display mode, the outer arrow buttons cycle through the glyphs quickly, the X button stops the glyphs from automatically cycling, and the O button restarts the automatic cycling.
- After decrypting the word, press the ENTER button on the top half of the module to put the module into input mode.
- While in input mode, the outer arrow buttons cycle through letters, the O button inputs the highlighted letter, and the X button deletes the last letter that was input. The top half of the circular screen will show what has currently been entered.
- To leave input mode, press the ENTER button again. If any text has been entered, it will be submitted at this time.
- If the submitted text does not match the decrypted word, the module will strike and return to display mode. However, if no text was entered, the module will return to display mode without any penalty.


About Me
Welcome to my portfolio page. I am Cam Lundy. I have a strong background in Project Management and Process Improvement. Please take a moment to take a look at some of the work I have completed.
Welcome to my portfolio page. I am Cam Lundy. I have a strong background in Project Management and Process Improvement. Please take a moment to take a look at some of the work I have completed.


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This sonnet seems to be of Baudelairean type:
In French poetry, sonnets are traditionally composed in the French alexandrine line, which
consists of twelve syllables with a caesura in the middle. Source: Wikipedia.org
This is a metric scheme for a sonnet 61, Baudelairean type:


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public static enum FeatureState.FeatureIdEnum extends java.lang.Enum<FeatureState.FeatureIdEnum>
| Modifier and Type | Method and Description |
|---|---|
java.lang.String |
toString() |
static FeatureState.FeatureIdEnum |
valueOf(java.lang.String name)
Returns the enum constant of this type with the specified name.
|
static FeatureState.FeatureIdEnum[] |
values()
Returns an array containing the constants of this enum type, in
the order they are declared.
|
public static final FeatureState.FeatureIdEnum volumesPerPartition
public static final FeatureState.FeatureIdEnum totalNumberOfVolumes
public static final FeatureState.FeatureIdEnum storagePartitions
public static final FeatureState.FeatureIdEnum snapshot
public static final FeatureState.FeatureIdEnum volumeCopy
public static final FeatureState.FeatureIdEnum remoteMirroring
public static final FeatureState.FeatureIdEnum driveTrayExpansion
public static final FeatureState.FeatureIdEnum mixedDriveTypes
public static final FeatureState.FeatureIdEnum mgmtApplication
public static final FeatureState.FeatureIdEnum supportedDrives
public static final FeatureState.FeatureIdEnum supportedDriveTrays
public static final FeatureState.FeatureIdEnum performanceTier
public static final FeatureState.FeatureIdEnum totalNumberOfSnapshots
public static final FeatureState.FeatureIdEnum totalNumberOfVolCopies
public static final FeatureState.FeatureIdEnum goldKey
public static final FeatureState.FeatureIdEnum snapshotsPerVolume
public static final FeatureState.FeatureIdEnum totalNumberOfMirrors
public static final FeatureState.FeatureIdEnum raid6
public static final FeatureState.FeatureIdEnum stateCapture
public static final FeatureState.FeatureIdEnum sataStrLen
public static final FeatureState.FeatureIdEnum secureVolume
public static final FeatureState.FeatureIdEnum protectionInformation
public static final FeatureState.FeatureIdEnum solidStateDisk
public static final FeatureState.FeatureIdEnum driveSlotLimit
public static final FeatureState.FeatureIdEnum fdeProxyKeyManagement
public static final FeatureState.FeatureIdEnum supportedInterposer
public static final FeatureState.FeatureIdEnum vendorSupportedDrives
public static final FeatureState.FeatureIdEnum flashReadCache
public static final FeatureState.FeatureIdEnum totalNumberOfAsyncMirrorGroups
public static final FeatureState.FeatureIdEnum totalNumberOfAsyncMirrorsPerGroup
public static final FeatureState.FeatureIdEnum totalNumberOfArvmMirrorsPerArray
public static final FeatureState.FeatureIdEnum totalNumberOfPitsPerArray
public static final FeatureState.FeatureIdEnum pitGroupsPerVolume
public static final FeatureState.FeatureIdEnum totalNumberOfPitGroups
public static final FeatureState.FeatureIdEnum pitsPerPitGroup
public static final FeatureState.FeatureIdEnum memberVolsPerPitConsistencyGroup
public static final FeatureState.FeatureIdEnum totalNumberOfPitConsistencyGroups
public static final FeatureState.FeatureIdEnum totalNumberOfPitViews
public static final FeatureState.FeatureIdEnum totalNumberOfThinVolumesPerArray
public static final FeatureState.FeatureIdEnum nativeSataDriveSupport
public static final FeatureState.FeatureIdEnum solidStateDiskLimit
public static final FeatureState.FeatureIdEnum totalNumberOfRemoteMirrorsPerArray
public static final FeatureState.FeatureIdEnum asup
public static final FeatureState.FeatureIdEnum ectSelector
public static final FeatureState.FeatureIdEnum embeddedSnmpOid
public static final FeatureState.FeatureIdEnum asupOnDemand
public static final FeatureState.FeatureIdEnum dacstoreCompatId
public static final FeatureState.FeatureIdEnum samoaHicProtocol
public static final FeatureState.FeatureIdEnum targetPortLunMapping
public static final FeatureState.FeatureIdEnum hildaBaseboardProtocol
public static final FeatureState.FeatureIdEnum denali2Protocol
public static final FeatureState.FeatureIdEnum __UNDEFINED
public static FeatureState.FeatureIdEnum[] values()
for (FeatureState.FeatureIdEnum c : FeatureState.FeatureIdEnum.values()) System.out.println(c);
public static FeatureState.FeatureIdEnum valueOf(java.lang.String name)
name
- the name of the enum constant to be returned.java.lang.IllegalArgumentException
- if this enum type has no constant with the specified namejava.lang.NullPointerException
- if the argument is nullpublic java.lang.String toString()
toString
in class java.lang.Enum<FeatureState.FeatureIdEnum>


This is a Student project for SFSU CSC 648 section 2 - team 1
Be surprised by the sweeping view of San Francisco Bay.This home offers two view decks, remodeled
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FlooringFloor size: 1,980 sqftFlooring: Carpet, Hardwood, Tile
Other Interior FeaturesFireplaceRoom count: 7;


| Package | Description |
|---|---|
| net.jini.constraint |
Provides a basic implementation of
MethodConstraints and a
TrustVerifier for common constraints. |
| net.jini.jeri |
Provides the fundamental abstractions and standard implementation
classes for Jini extensible remote invocation (Jini ERI).
|
| net.jini.jeri.kerberos |
Provides implementations of
Endpoint and ServerEndpoint that use Kerberos as the underlying
network security protocol to support security related invocation
constraints for remote requests. |
| net.jini.jeri.ssl |
Provides implementations of
Endpoint and ServerEndpoint that use TLS/SSL and HTTPS (HTTP over
TLS/SSL) to support invocation constraints. |
| net.jini.security.proxytrust |
Defines a trust verifier to support verification of proxies that use
dynamically downloaded code, implementation mechanisms to support the
verification of such proxies, and an exporter for remote objects that have
such proxies.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ConstraintTrustVerifier
Trust verifier for instances of the constraint classes defined in the
net.jini.core.constraint package, and for the
BasicMethodConstraints , X500Principal and
KerberosPrincipal classes. |
| Modifier and Type | Class and Description |
|---|---|
class |
BasicJeriTrustVerifier
Trust verifier for dynamic proxies and object endpoints used in Jini
extensible remote invocation (Jini ERI).
|
| Modifier and Type | Class and Description |
|---|---|
class |
KerberosTrustVerifier
Trust verifier for verifying the Jini extensible remote
invocation (Jini ERI) endpoints of type
KerberosEndpoint ,
and principals of type KerberosPrincipal . |
| Modifier and Type | Class and Description |
|---|---|
class |
SslTrustVerifier
|
| Modifier and Type | Class and Description |
|---|---|
class |
ProxyTrustVerifier
Trust verifier for service proxies that use dynamically downloaded code.
|
| Modifier and Type | Method and Description |
|---|---|
TrustVerifier |
ServerProxyTrust.getProxyVerifier()
Returns a
TrustVerifier that can be used to verify that
a proxy can be trusted as a proxy for the service; that is, the
isTrustedObject method of the
returned verifier can be called with a candidate proxy. |
TrustVerifier |
ProxyTrust.getProxyVerifier()
Returns a
TrustVerifier which can be used to verify that
a proxy can be trusted as a proxy for the service; that is, the
isTrustedObject method of the
returned verifier can be called with a candidate proxy. |


|
retinanet-cell-detection
1
retinanet-cell-detection: RetinaNet for cell detection.
|
Functions | |
| def | default_classification_model (num_classes, num_anchors, pyramid_feature_size=256, prior_probability=0.01, classification_feature_size=256, name='classification_submodel') |
| def | default_regression_model (num_values, num_anchors, pyramid_feature_size=256, regression_feature_size=256, name='regression_submodel') |
| def | default_submodels (num_classes, num_anchors) |
| def | retinanet (inputs, backbone_layers, num_classes, num_anchors=None, create_pyramid_features=__create_pyramid_features, submodels=None, name='retinanet') |
| def | retinanet_bbox (model=None, nms=True, class_specific_filter=True, name='retinanet-bbox', anchor_params=None, kwargs) |
| def mynet.models.retinanet (copy).default_classification_model | ( | num_classes, | |
| num_anchors, | |||
pyramid_feature_size = 256 , |
|||
prior_probability = 0.01 , |
|||
classification_feature_size = 256 , |
|||
name = 'classification_submodel' |
|||
| ) |
Creates the default regression submodel. Args num_classes : Number of classes to predict a score for at each feature level. num_anchors : Number of anchors to predict classification scores for at each feature level. pyramid_feature_size : The number of filters to expect from the feature pyramid levels. classification_feature_size : The number of filters to use in the layers in the classification submodel. name : The name of the submodel. Returns A keras.models.Model that predicts classes for each anchor.
| def mynet.models.retinanet (copy).default_regression_model | ( | num_values, | |
| num_anchors, | |||
pyramid_feature_size = 256 , |
|||
regression_feature_size = 256 , |
|||
name = 'regression_submodel' |
|||
| ) |
Creates the default regression submodel. Args num_values : Number of values to regress. num_anchors : Number of anchors to regress for each feature level. pyramid_feature_size : The number of filters to expect from the feature pyramid levels. regression_feature_size : The number of filters to use in the layers in the regression submodel. name : The name of the submodel. Returns A keras.models.Model that predicts regression values for each anchor.
| def mynet.models.retinanet (copy).default_submodels | ( | num_classes, | |
| num_anchors | |||
| ) |
Create a list of default submodels used for object detection. The default submodels contains a regression submodel and a classification submodel. Args num_classes : Number of classes to use. num_anchors : Number of base anchors. Returns A list of tuple, where the first element is the name of the submodel and the second element is the submodel itself.
| def mynet.models.retinanet (copy).retinanet | ( | inputs, | |
| backbone_layers, | |||
| num_classes, | |||
num_anchors = None , |
|||
create_pyramid_features = __create_pyramid_features , |
|||
submodels = None , |
|||
name = 'retinanet' |
|||
| ) |
Construct a RetinaNet model on top of a backbone. This model is the minimum model necessary for training (with the unfortunate exception of anchors as output). Args inputs : keras.layers.Input (or list of) for the input to the model. num_classes : Number of classes to classify. num_anchors : Number of base anchors. create_pyramid_features : Functor for creating pyramid features given the features C3, C4, C5 from the backbone. submodels : Submodels to run on each feature map (default is regression and classification submodels). name : Name of the model. Returns A keras.models.Model which takes an image as input and outputs generated anchors and the result from each submodel on every pyramid level. The order of the outputs is as defined in submodels: ``` [ regression, classification, other[0], other[1], ... ] ```
| def mynet.models.retinanet (copy).retinanet_bbox | ( | model = None , |
|
nms = True , |
|||
class_specific_filter = True , |
|||
name = 'retinanet-bbox' , |
|||
anchor_params = None , |
|||
| kwargs | |||
| ) |
Construct a RetinaNet model on top of a backbone and adds convenience functions to output boxes directly. This model uses the minimum retinanet model and appends a few layers to compute boxes within the graph. These layers include applying the regression values to the anchors and performing NMS. Args model : RetinaNet model to append bbox layers to. If None, it will create a RetinaNet model using **kwargs. nms : Whether to use non-maximum suppression for the filtering step. class_specific_filter : Whether to use class specific filtering or filter for the best scoring class only. name : Name of the model. anchor_params : Struct containing anchor parameters. If None, default values are used. *kwargs : Additional kwargs to pass to the minimal retinanet model. Returns A keras.models.Model which takes an image as input and outputs the detections on the image. The order is defined as follows: ``` [ boxes, scores, labels, other[0], other[1], ... ] ```


Using only a minimal amount of code, you can create this Windows 8 style switch. The switch itself is created using the free tool at proto.io.
Is disabled: {{dis}}
Is selected: {{vm.isSelected}}


Gecode::Int::Limits Namespace Reference
[Integer variables]
Numerical limits for integer variables. More...
Functions | |
| bool | valid (int n) |
| Return whether n is in range. | |
| bool | valid (long long int n) |
| Return whether n is in range. | |
| void | check (int n, const char *l) |
| Check whether n is in range, otherwise throw out of limits with information l. | |
| void | check (long long int n, const char *l) |
| Check whether n is in range, otherwise throw out of limits with information l. | |
| void | positive (int n, const char *l) |
| Check whether n is in range and strictly positive, otherwise throw out of limits with information l. | |
| void | positive (long long int n, const char *l) |
| Check whether n is in range and strictly positive, otherwise throw out of limits with information l. | |
| void | nonnegative (int n, const char *l) |
| Check whether n is in range and nonnegative, otherwise throw out of limits with information l. | |
| void | nonnegative (long long int n, const char *l) |
| Check whether n is in integer range and nonnegative, otherwise throw out of limits exception with information l. | |
| bool | overflow_add (int n, int m) |
| Check whether adding n and m would overflow. | |
| bool | overflow_add (long long int n, long long int m) |
| Check whether adding n and m would overflow. | |
| bool | overflow_sub (int n, int m) |
| Check whether subtracting m from n would overflow. | |
| bool | overflow_sub (long long int n, long long int m) |
| Check whether subtracting m from n would overflow. | |
| bool | overflow_mul (int n, int m) |
| Check whether multiplying n and m would overflow. | |
| bool | overflow_mul (long long int n, long long int m) |
| Check whether multiplying n and m would overflow. | |
Variables | |
| const int | max = INT_MAX - 1 |
| Largest allowed integer value. | |
| const int | min = -max |
| Smallest allowed integer value. | |
| const int | infinity = max + 1 |
| Infinity for integers. | |
| const long long int | llmax = LLONG_MAX - 1 |
| Largest allowed long long integer value. | |
| const long long int | llmin = -llmax |
| Smallest allowed long long integer value. | |
| const long long int | llinfinity = llmax + 1 |
| Infinity for long long integers. |
Detailed Description
Numerical limits for integer variables.
The integer limits are chosen such changing the sign is always possible without overflow.
Function Documentation
| bool Gecode::Int::Limits::valid | ( | int | n | ) | [inline] |
Return whether n is in range.
Definition at line 41 of file limits.hpp.
| bool Gecode::Int::Limits::valid | ( | long long int | n | ) | [inline] |
Return whether n is in range.
Definition at line 45 of file limits.hpp.
| void Gecode::Int::Limits::check | ( | int | n, | |
| const char * | l | |||
| ) | [inline] |
Check whether n is in range, otherwise throw out of limits with information l.
Definition at line 50 of file limits.hpp.
| void Gecode::Int::Limits::check | ( | long long int | n, | |
| const char * | l | |||
| ) | [inline] |
Check whether n is in range, otherwise throw out of limits with information l.
Definition at line 55 of file limits.hpp.
| void Gecode::Int::Limits::positive | ( | int | n, | |
| const char * | l | |||
| ) | [inline] |
Check whether n is in range and strictly positive, otherwise throw out of limits with information l.
Definition at line 61 of file limits.hpp.
| void Gecode::Int::Limits::positive | ( | long long int | n, | |
| const char * | l | |||
| ) | [inline] |
Check whether n is in range and strictly positive, otherwise throw out of limits with information l.
Definition at line 66 of file limits.hpp.
| void Gecode::Int::Limits::nonnegative | ( | int | n, | |
| const char * | l | |||
| ) | [inline] |
Check whether n is in range and nonnegative, otherwise throw out of limits with information l.
Definition at line 72 of file limits.hpp.
| void Gecode::Int::Limits::nonnegative | ( | long long int | n, | |
| const char * | l | |||
| ) | [inline] |
Check whether n is in integer range and nonnegative, otherwise throw out of limits exception with information l.
Definition at line 77 of file limits.hpp.
| bool Gecode::Int::Limits::overflow_add | ( | int | n, | |
| int | m | |||
| ) | [inline] |
Check whether adding n and m would overflow.
Definition at line 83 of file limits.hpp.
| bool Gecode::Int::Limits::overflow_add | ( | long long int | n, | |
| long long int | m | |||
| ) | [inline] |
Check whether adding n and m would overflow.
Definition at line 89 of file limits.hpp.
| bool Gecode::Int::Limits::overflow_sub | ( | int | n, | |
| int | m | |||
| ) | [inline] |
Check whether subtracting m from n would overflow.
Definition at line 97 of file limits.hpp.
| bool Gecode::Int::Limits::overflow_sub | ( | long long int | n, | |
| long long int | m | |||
| ) | [inline] |
Check whether subtracting m from n would overflow.
Definition at line 103 of file limits.hpp.
| bool Gecode::Int::Limits::overflow_mul | ( | int | n, | |
| int | m | |||
| ) | [inline] |
Check whether multiplying n and m would overflow.
Definition at line 111 of file limits.hpp.
| bool Gecode::Int::Limits::overflow_mul | ( | long long int | n, | |
| long long int | m | |||
| ) | [inline] |
Check whether multiplying n and m would overflow.
Definition at line 118 of file limits.hpp.
Variable Documentation
| const int Gecode::Int::Limits::max = INT_MAX - 1 |
| const int Gecode::Int::Limits::min = -max |
| const int Gecode::Int::Limits::infinity = max + 1 |
| const long long int Gecode::Int::Limits::llmax = LLONG_MAX - 1 |
| const long long int Gecode::Int::Limits::llmin = -llmax |
| const long long int Gecode::Int::Limits::llinfinity = llmax + 1 |


Add info about your route
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Invalid distance between lines!
You should paint a route!
You can draw a route on the map!
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new InstallationManager()
Provides runtime module installation mechanisms.
- Since:
- 2.0
- Source:
Methods
checkConflict(targetPath, name) → {Promise}
Checks if there's a module installed in the given path
Parameters:
| Name | Type | Description |
|---|---|---|
targetPath |
String | Path of the module being installed |
name |
String | Name of the module being installed |
- Source:
Returns:
A promise that is resolved if there's no issues with the module being installed. Otherwise, the promise will be rejected.
- Type
- Promise
checkModuleStructure(modulePath) → {Promise}
Checks if a given module contains a valid structure by checking if it exists on the disk
Parameters:
| Name | Type | Description |
|---|---|---|
modulePath |
String | Path to the module to be inspected. |
- Source:
Returns:
A promise related to the stat operation
- Type
- Promise
cleanupUpdateBackups(name, mod) → {undefined}
Completes the update process by removing backup files, if any
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of the module being updated, for logging. |
mod |
Object | Module metadata |
- Source:
Returns:
Nothing
- Type
- undefined
install(repo) → {Promise}
Performs an installation procedure on the given repository
Parameters:
| Name | Type | Description |
|---|---|---|
repo |
String | Repository url or a username/repository format |
- Source:
Returns:
A promise related to the installation success or failure
- Type
- Promise
installDependency(depData, targetPath) → {Promise}
Provides a promise wrapper to the npmi module, allowing several dependencies to be installed at the same time.
Parameters:
| Name | Type | Description |
|---|---|---|
depData |
Object | Dependency metadata including name and version |
targetPath |
String | Destination where the dependency will be installed to |
- Source:
Returns:
A promise that will be resolved or rejected when the dependecy is installed or not.
- Type
- Promise
removeModule(path, name) → {Promise}
Removes the filesystem structure of a given module
Parameters:
| Name | Type | Description |
|---|---|---|
path |
String | Path of the module being removed |
name |
String | Name of the module being removed |
- Source:
Returns:
Promise related to the removal process
- Type
- Promise
rollback(targetPath) → {Promise}
Rollsback an installation by completely removing the module being installed
Parameters:
| Name | Type | Description |
|---|---|---|
targetPath |
String | Path to the module being installed |
- Source:
Returns:
A promise that will always will be resolved, regardless of whether the folder structure was removed or not.
- Type
- Promise
uninstall(name) → {Promise}
Removes a module by its name
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Module name being removed |
- Source:
Returns:
Promise related to the removal process
- Type
- Promise
update(name) → {Promise}
Updates a module by its name
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | The module name being updated |
- Source:
Returns:
A promise related to the update process
- Type
- Promise
updateModuleDependencies(name, mod) → {Promise}
Safely updates dependencies for a given module
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Module name being updated. Used for logging. |
mod |
Object | Metadata associated to the module being updated. |
- Source:
Returns:
A promise related to the update operation
- Type
- Promise
updateModuleRepository(name, path) → {Promise}
Updates the underlying git repository on a given path
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Module name being updated. Used for logging. |
path |
String | Module path being updated. |
- Source:
Returns:
A Promise related to the fetch-and-merge operation
- Type
- Promise
updateRollbackToRef(name, mod, gitData, skipThrow) → {Promise}
Attempts to perform a rollback operation on a module that was broken by a damaged update.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | Name of the module affected. For logging. |
mod |
Object | Metadata regarding the updated module. |
gitData |
Object | Repository metadata obtained during the checkout process |
skipThrow |
Boolean | Whether to skip the Promise rejection. Useful when you want to perform the rollback in a silent way. |
- Source:
Returns:
Promise related to the rollback process
- Type
- Promise


FAQ
What is ANova Hacks?
-
A 24-hour hackathon hosted by Berkeley ANova for high school students!
-
Opportunity for kids to see their projects come to life
-
Provide a space for students to learn valuable skills in workshops like web design, college admissions, etc.
Why should my child go to ANova Hacks?
-
To collaborate with other students with similar interests to work on cool projects they won’t have the time/resources to elsewhere.
-
To learn valuable skills in computer science from qualified UC Berkeley mentors
Where will ANova Hacks be, and is the location safe?
-
Covo, San Francisco. Indoors, very comfortable
-
Located very close to Powell and Civic Center BART stations just three blocks from Market
What will be provided for my child at ANova Hacks? (ie. food, sleeping, swag, etc)
-
Dinner, breakfast, lunch, and snacks will be provided.
-
Mentors to give advice and guidance in their projects
-
Free swag from sponsors :) (including a sleeping bag!)
Who will be in charge of my child at ANova Hacks?
-
ANova mentors (all highly qualified, medical experience available, etc.)
-
Students will be monitored 24/7, and exiting Covo will be closely watched with ID systems, check-out systems
How will my child get to/from ANova Hacks?
-
Mentors will be waiting at BART stations to pick students and to drop them off.
What if my child cannot bring a laptop?
-
Partnering with Facebook to provide students with laptops to use.
-
Laptops contain top-of-the-line software that students will need at the hackathon.
Are there plans in case of an emergency? (ie. evacuation, medical, etc.)
-
Every ANova staff member will be equipped with medical information on the child
-
All staff will have the appropriate phone numbers to call in case of an emergency


Kuiper Belt
Facts
- It's a HUGE region of space beyond Neptune, a messy place, great for space racing.
- First spacecraft went there in 1983.
- First (legal!) race happened in 2037.
- The stocks surpassed "the Great Astoid Belt Race" in 2042.
Precaution
- We offer a automatic laser, capable of evoparting small-unmauverable objects for your convenience.
- You are fully responsible of every damage you caused.
- The first aid teams could be found in every 100 km, and your signal will be caught instantly.


Home
Master Section
First Section
Second Section
Sports
Hockey
Baseball
Football
Golf
Soccer
Tennis
Navigation_v1 Demo
Edit this demo on
JsFiddle


--- title: Accessibilty in Music and Sports Venues layout: default permalink: / ---
Welcome to Accessibilty in Music and Sports Venues! The purpose of this webpage is to help people with disabilities find music/sports venues that are accessible in Chicago, Illinois. Each page on this site will focus on one specific venue and give details on the accessibilty for that venue. In addition, I have included a page comparing accessibility services between the venues. I created this page so that everyone can enjoy the thirll of concerts and live sporting events. I also created this page so that the information was easily accessible for all. I wanted the information to be all in one place so that people did not have to visit multiple sites to find the information they were looking for. I hope you find this site helpful!
{{ event_venue.content }}
Source {% endfor %}For more information about the different venues and their accessibilty services feel free to contact me and I can help you get in touch with guest services for each venue.
I can be reached at eleanorhoran1203@gmail.com


Class Trie
| Constructor Attributes | Constructor Name and Description |
|---|---|
|
Trie(stem, sorting)
Trie is a kind of digital search tree.
|
| Field Attributes | Field Name and Description |
|---|---|
| <static> |
Trie.SORT_ASC
sort the trie in ascending lexical order
|
| <static> |
Trie.SORT_DESC
sort the trie in descending lexical order
|
| <static> |
Trie.SORT_NONE
sort the trie in no particular order
|
| Method Attributes | Method Name and Description |
|---|---|
|
add(word)
Add a word to the existing dictionary.
|
|
|
find(prefix)
Find a trie node that is paired with a word or prefix 's'.
|
|
|
findPrefix(prefix)
|
|
|
getChild(prefix)
Retrieve a direct child node of this dictionary with 'prefix'.
|
|
|
getPrefixCount(word)
Retrieve the prefix count of the applied argument
|
|
|
getWordCount(word)
Retrieve the word count of the applied argument
|
|
|
getWords(s)
Retrieve the Array of words that originate from this trie.
|
|
|
hasChild(prefix)
A version of getChild with a Boolean return type.
|
|
|
remove(word)
Remove a word from the dictionary.
|
|
|
sort(direction)
Resort this dictionary in lexical order, either in an ascending or
descending direction.
|
|
| <inner> <static> |
Trie.sortAsc(a, b)
Sorting helper function that can be passed to Array.sort().
|
| <inner> <static> |
Trie.sortDesc(a, b)
Sorting helper function that can be passed to Array.sort().
|
|
toString()
Overrides Object.prototype.toString to deliver a more context sensitive
String representation of a Trie.
|
|
|
update(sOld, sNew)
Update a word in the dictionary.
|
|
| <inner> <static> |
Trie.walker(word, trie, method)
NOT named after Johnny, but merely after the verb 'to walk'.
|
Class Detail
Trie(stem, sorting)
Trie is a kind of digital search tree. (See [Knuth1972] for more details
on digital search trees.)
[Fredkin1960] introduced the trie terminology, which is abbreviated from "Retrieval".
[Knuth1972] Knuth, D. E. The Art of Computer Programming Vol. 3, Sorting and Searching. Addison-Wesley. 1972.
[Fredkin1960] Fredkin, E. Trie Memory. Communication of the ACM. Vol. 3:9 (Sep 1960). pp. 490-499.
source
Author: Mike de Boer.
Author: Mike de Boer
- Parameters:
- {String} stem
- One character long representation of the trie node instance
- {Number} sorting
- Sort method. May be SORT_ASC or SORT_DESC.
- See:
- Wikipedia article The trie implementation of Dennis Byrne served as a starting point and inspiration:
- http://notdennisbyrne.blogspot.com/2008/12/javascript-trie-implementation.html
Method Detail
add(word)
Add a word to the existing dictionary. If a trie node doesn't exist
yet, it is created with that character as its stem.
Since an add already an expensive action, compared to adding nodes to
native Javascript containers like Array or Object, inserting a trie
node in lexical order is relatively cheap.
Please refer to the test suite to compare performance in your browser(s).
- Parameters:
- {String} word
- Remainder of the word that is added to the root trie
find(prefix)
Find a trie node that is paired with a word or prefix 's'. Like the
remove function, this function also uses the walker.
- Parameters:
- {String} prefix
- the word or prefix to search for in the dictionary
findPrefix(prefix)
- Parameters:
- {String} prefix
- the word or prefix to search for in the dictionary
getChild(prefix)
Retrieve a direct child node of this dictionary with 'prefix'.
- Parameters:
- {String} prefix
- s the word or prefix to search for in the children of this dictionary
getPrefixCount(word)
Retrieve the prefix count of the applied argument
- Parameters:
- {String} word
- the prefix or word-completing stem
getWordCount(word)
Retrieve the word count of the applied argument
- Parameters:
- {String} word
- the prefix or word-completing stem
getWords(s)
Retrieve the Array of words that originate from this trie.
The main use-case for this function is for implementations of the
type-ahead user experience pattern, but can be used to other ends as
well, of course.
The performance of this function still needs to be profiled against
alternatives, like pre-caching the words Array per Trie when it's
instantiated.
- Parameters:
- {String} s Optional
- prefix to prepend to a stem character to make it a word
hasChild(prefix)
A version of getChild with a Boolean return type.
- Parameters:
- {String} prefix
- s the word or prefix to search for in the children of this dictionary
remove(word)
Remove a word from the dictionary. This function uses the
walker, which is a generic implementation of a tree walker.
- Parameters:
- {String} word
- the word to remove
sort(direction)
Resort this dictionary in lexical order, either in an ascending or
descending direction.
Since it uses the native Array#sort method, sorting speed can
be considered linear O(n) to the size of the trie, i.e. the word count.
Please refer to the test suite to compare performance in your browser(s).
- Parameters:
- {Number} direction
- sorting direction. Possible values: Trie#SORT_ASC Trie#SORT_DESC
<inner> <static>
Trie.sortAsc(a, b)
Sorting helper function that can be passed to Array.sort().
The result of this helper will be that all nodes will be sorted in
ascending lexical order.
<inner> <static>
Trie.sortDesc(a, b)
Sorting helper function that can be passed to Array.sort().
The result of this helper will be that all nodes will be sorted in
descending lexical order.
toString()
Overrides Object.prototype.toString to deliver a more context sensitive
String representation of a Trie.
update(sOld, sNew)
Update a word in the dictionary. This update implementation is
implemented like a file rename action as on a filesystem: add a node
with the new name and remove the outdated, 'old' version.
- Parameters:
- {String} sOld
- the old word to be replaced by the word provided by 'sNew'
- {String} sNew
- the new word to be added to the dictionary
<inner> <static>
Trie.walker(word, trie, method)
NOT named after Johnny, but merely after the verb 'to walk'.
This function walks along a Trie top-down until it finds the node which
fully represents the term/ prefix/ word that was searched for.
It passes the parent node of the found Trie and its index to a callback
function. It passes the parent node, because otherwise Trie mutation would
become increasingly more difficult.
An earlier implementation of this function used a naive recursive algorithm,
but my friend - @ejpbruel - has shown me that you can simply rewrite any form
of tail-recursion to an inner loop.
- Parameters:
- {String} word
- the word or prefix to search for
- {Trie} trie
- the root trie node to walk through
- {Function} method
- callback function to which the results of the walker are passed


Introduction to Communication Systems
The Purpose of a communication system is to carry information from one point to another. A typical communication system consists of three main components are:
1. Source
2. Channel
3. Destination
following are some important topics:
1. Signals and Systems
2. Concepts of Energy, Power, and Bandwidth of Signals
3. Baseband and Passband Signals
4. Analog Communication Techniques: AM and FM
5. Link Budget Modeling
6. Brief overview of Digital Communication Systems
View Content


Loan Prediction system
Education
Graduate
Not Graduate
No. of Dependents
0
1
2
3+
Property Area
Rural
Semiurban
Urban
Self-Employed
Yes
No
Loan Amount Term (in years)
One
Three
Five
Seven
Fifteen
TwentyFiventy
TwentyFive
Thirty
Forty
Gender
Male
Female
Married
Yes
No
Credit_History
Yes
No
Applicant Income
Co-Applicant Income
Loan Amount (in thousands)
Prediction: Will loan get approved ?
{{ prediction_text }}


| I l@ve RuBoard |
12.11 The msvcrt Module(Windows/DOS only) The msvcrt module gives you access to a number of functions in the Microsoft Visual C/C++ Runtime Library (MSVCRT). Example 12-13 demonstrates the getch function reading a single keypress from the console. Example 12-13. Using the msvcrt Module to Get Key PressesFile: msvcrt-example-1.py import msvcrt print "press 'escape' to quit..." while 1: char = msvcrt.getch() if char == chr(27): break print char, if char == chr(13): print press 'escape' to quit... h e l l o The kbhit function returns true if a key has been pressed (which means that getch won't block), as shown in Example 12-14. Example 12-14.  Example 12-15. Using the msvcrt Module for File LockingFile: msvcrt-example-3.py import msvcrt import os LK_UNLCK = 0 # unlock the file region LK_LOCK = 1 # lock the file region LK_NBLCK = 2 # non-blocking lock LK_RLCK = 3 # lock for writing LK_NBRLCK = 4 # non-blocking lock for writing FILE = "counter.txt" if not os.path.exists(FILE): file = open(FILE, "w") file.write("0") file.close() for i in range(20): file = open(FILE, "r+") # look from current position (0) to end of file msvcrt.locking(file.fileno(), LK_LOCK, os.path.getsize(FILE)) counter = int(file.readline()) + 1 file.seek(0) file.write(str(counter)) file.close() # unlocks the file print os.getpid(), "=>", counter time.sleep(0.1) 208 => 21 208 => 22 208 => 23 208 => 24 208 => 25 208 => 26 |
| I l@ve RuBoard |


{% overextends "account/settings.html" %} {% load fax_tags %} {% block post_password %} {% include "froide_fax/_signature_form.html" with form=request.user|get_signature_form next=request.get_full_path %} {% endblock %}


Goodreads Review
March 24, 2017 10:30am
Killing Lincoln: The Shocking Assassination That Changed America Forever is a book by Bill O'Reilly and Martin Dugard concerning the 1865 assassination of U.S. President Abraham Lincoln. The book was released on September 27, 2011.
The anchor of The O'Reilly Factor recounts one of the most dramatic stories in American history—how one gunshot changed the country forever.  President Abraham Lincoln's generous terms for Robert E. Lee's surrender are devised to fulfill Lincoln's dream of healing a divided nation, with the former Confederates allowed to reintegrate into American society. But one man and his band of murderous accomplices, perhaps reaching into the highest ranks of the U.S. government, are not appeased.


Following up on THE vs. Emacs. Though I'm a little skeptical of Raskin and THE in general, I'm actually extremely receptive to his ideas. (I haven't read The Humane Interface, but I suppose I should.)
My skepticism is because Raskin seems to be really full of himself (and everything I read by or about him seems to reinforce this impression). That annoys me. When I read through the Apple Folklore site, I couldn't help but notice his absence from much of the project's history. (Though some articles speak of him quite fondly.) But this all probably isn't fair, I'm sure he is a very smart guy.
Anyway, one big idea for Raskin seems to be LEAP, which shows up even in the early history of the Macintosh. LEAP is the idea of having a special meta key, that acts similar to a shift key. When you want to type in a command, you hit the LEAP key and start typing the command (all commands are named) -- when you release the LEAP key the command is run. It's THE's answer to Emacs' M-x. The standard line is that it's really great, but without the right keyboard it doesn't work well -- the right keyboard would I think have a key below the space, so you can hit LEAP with your thumb and keep typing.
 Like, ruins-a-good-idea stupid, because it's just too hard to type like that. The benefit of LEAP is based on this theory of minimalizing keystrokes -- specifically counting each key when it is pressed and release -- but even that doesn't add up, in part because it doesn't acknowledge that chords (more than one key at a time) are harder than single keys, and extended chords (holding one key while typing several others) are much harder. With a simple chord you can arrange your hands properly for that combination -- e.g., right pinky hitting shift, left hand hitting T -- but you can't do that for an extended chord.
And what do you buy? The only thing you buy is one keystroke. Counting up and down strokes make no difference! Every key you hit you'll eventually release. Using a modal selection -- one where you hit a key and enter a new mode, type your command, then signal that the command is done -- takes only one more keystroke, the keystroke that says you're done (e.g., enter). I think it's mostly modal paranoia, the idea that any mode is bad, which is just silly in my mind.
Also LEAP is Raskin's one clear invention, the one thing that he owns (literally, since he has a patent). So it just has to go in there.
The other related theory in THE is that there's no keyboard shortcuts, you type every command out. (Actually, I believe there is one shortcut, equivalent to C-s in Emacs, which is important because you use incremental search for all navigation instead of arrow keys! I like incremental search a lot, but not that much!) I'll let you judge the wisdom of that on your own.
Oh well. I wouldn't care so much if I didn't otherwise really like the ideas. I'll try to write a positive article next.
What I think is really lost in all of the recent noice being made about Raskin's LEAP thing is that with utilities like QuickSilver _we already have this? on the Mac. My "LEAP" key is command-space. I hit that key combo, type in a command (with incremental search on the results and a learning algorithm) and hit return to run the command. I can even make advanced command selections using arrow keys and tab to further refine or enhance the action I have selected. I can be in the middle of one app, and with a few keystrokes append a note into a text file and pop back without evern needing to lift my hands from the keyboard.
# Jim
There is some distinction in that THE commands are much more granular than Quicksilver commands -- basically every command that is available, things like "save" and "next page". Of course part of the idea of THE is that there's no fundamental distinction between these small commands and other larger commands.
# Ian Bicking
$2 million for another crappy text editor? 99.9% of the world does not like emacs or vi. Apps like this and Chandler and bittorrent (bad interface vs. java Azureus client with a nice interface) are just going to hurt Python's reputation.# anonymous
LEAP is not about counting the key strokes but about modes. I am not sure the LEAP key can leap us from all the modes, and for sure it will be very awkward to type when keeping to press it, but the argument against modes is quite strong.
Talking about other his ideas - I like the Zoom Interface very much, but I am sure they need to think up a way to add links to it. The way we connect things one to another does not allways look good as a planar graph.
"for sure it will be very awkward to type when keeping to press it, but the argument against modes is quite strong"
Well, no, it's not, actually, though the dogma certainly is -- statements of the above form are paradigmatic of dogmatism. Paraphrasing, "sure we put you through ergonomic hell, but we have a good theoretical basis for doing so".
There are good ways to do modes and bad ways to do modes, and LEAP is the very worst way -- it has two modes, the "LEAP key is up" mode and the "LEAP key is down" mode; denying that these are modes is pure sophistry, since they affect the meaning of keystrokes just like any of form of mode specification. But "LEAP key is down" requires the user to enter a different personal physical mode -- one that is in fact very limiting and demanding. This makes LEAP an ergonomic disaster.
emacs does modes right. modes are attached to buffers -- different logical spaces. There's never any question what mode you're in, since you have both a different conceptual/logical space -- you're operating on some specific buffer -- and an explicit message on the screen indicating what mode (actually modes, a major mode and 0 or more minor modes) are in effect in that buffer. There are many different possible modes, which is a very good thing in terms of functionality and usability. Raskin would perhaps put several dozen different "hold this key down while ..." keys on the keyboard, or more likely simply not provide the functionality while providing a dogmatic claim as to why that's a good thing. Meanwhile, emacs users get work done, pleasurably.
A special case of emacs moding, which isn't referred to as such, is the minibuffer. In order to search or to enter a command or to find a file or to do almost anything where the entered text has semantic meaning to emacs, you type an appropriate control key or sequence which enters you into the minibuffer, with an explicit message at the front of the minibuffer indicating what sort of information you are entering. The key sequence that puts you in the minibuffer of course is putting you into a mode, but that mode is conceptually isolated by transferring the cursor to a special place that is clearly marked. It so happens that, since the minibuffer is a buffer, you can switch from the minibuffer to any other buffer -- the buffer switching key isn't moded -- and then you're in the mode of the buffer you have switched to, where you are free to continue typing, but most likely you have switched to that buffer to select text that you wish to enter into the minibuffer -- text to search for, text that's a command invocation or part of one, text that's a file to open, etc. Try doing that with your finger holding down the LEAP key, hah hah -- so much for modelessness. emacs in fact gives you modelessness in the way it counts -- by being able to switch from mode to mode at will, without destroying the moded space. This is exactly equivalent to non-modal popups, which allow you to continue to look at and even perform operations in the windows they cover. We all know how badly modal windows suck -- we shouldn't be trying to recreate that experience in an editor.
The emacs mode model works, and it works very well, which is a very strong refutation of the a priori claim that there are strong arguments against it.
# jqb
LEAP is actually C-s.  That's why it's called LEAP. It lets you LEAP to another part of the document. I don't remember how commands other than LEAP work in THE.
The rationale for it is not just the saving-half-a-keystroke thing, it's also that it reduces the problems of modes; since you have to hold down the LEAP key to use it, you can't possibly forget that you're in search mode rather than edit mode.
But I agree totally about the crack-smoking-ness of it physically. My carpal tunnels would explode before the end of the first week.


Spagety is a Single Page App (SPA) using GET requests. It uses Requirejs and Knockout to work.
Spagety creates custom Knockout bindings that allow a user to specify the resources needed for a specific module. The resources could be html, javascript, css, or any combination of those three. These bindings form a tree hierarchy with each other that help to resolve URL paths at run time, and to allow lazy-loading of content.
By using Spagety, it makes it easy to develop AMD modules for your javascript, as well as any accompanying html or css. Developing isolated modules allows for easier development and testability. By decoupling modules from the rigid hierarchy of url paths, this allows for modules to remain independant, only knowing about their immediate dependencies. Spagety resolves paths at run time, depending on the custom bindings provided. Spagety also allows for lazy-loading content, so that only the content a user wants to see will be loaded.
How it works
By adding the page Knockout binding within a modules html, will indicate that it has a sub page that would like to be lazily-loaded.
- id: specifies the crumb it would like to be identified by within the html.
- html: specifies an external html file to be loaded, and then placed inside this div tag
- css: specifies an external css file that will be scoped to this element
- viewModel: specifies the javascript module to be loaded, and bound as a viewModel


|
LibDriver FM24CLXX
1.0.0
FM24CLXX full function driver
|
fm24clxx example driver modules More...
Functions | |
| uint8_t | fm24clxx_basic_init (fm24clxx_t type, fm24clxx_address_t address) |
| basic example init More... | |
| uint8_t | fm24clxx_basic_deinit (void) |
| basic example deinit More... | |
| uint8_t | fm24clxx_basic_read (uint16_t address, uint8_t *buf, uint16_t len) |
| basic example read More... | |
| uint8_t | fm24clxx_basic_write (uint16_t address, uint8_t *buf, uint16_t len) |
| basic example write More... | |
fm24clxx example driver modules
| uint8_t fm24clxx_basic_deinit | ( | void | ) |
basic example deinit
Definition at line 148 of file driver_fm24clxx_basic.c.
| uint8_t fm24clxx_basic_init | ( | fm24clxx_t | type, |
| fm24clxx_address_t | address | ||
| ) |
basic example init
| [in] | type | is the chip type |
| [in] | address | is the chip address pin |
Definition at line 50 of file driver_fm24clxx_basic.c.
| uint8_t fm24clxx_basic_read | ( | uint16_t | address, |
| uint8_t * | buf, | ||
| uint16_t | len | ||
| ) |
basic example read
| [in] | address | is the register address |
| [out] | *buf | points to a data buffer |
| [in] | len | is the buffer length |
Definition at line 105 of file driver_fm24clxx_basic.c.
| uint8_t fm24clxx_basic_write | ( | uint16_t | address, |
| uint8_t * | buf, | ||
| uint16_t | len | ||
| ) |
basic example write
| [in] | address | is the register address |
| [in] | *buf | points to a data buffer |
| [in] | len | is the buffer length |
Definition at line 128 of file driver_fm24clxx_basic.c.


This interface is implemented by all DOMElement that supports DOM event model.
More...
Inherited by DotNetBrowser.DOM.DOMNode.
|
| void | AddEventListener (DOMEventType eventType, DOMEventHandler handler, bool useCapture) |
| | This method allows the registration of event listeners on the event target. If an DOMEventHandler is added to an DOMEventTarget while it is processing an event, it will not be triggered by the current actions but may be triggered during a later stage of event flow, such as the bubbling phase.
If multiple identical DOMEventHandlers are registered on the same DOMEventTarget with the same parameters the duplicate instances are discarded. They do not cause the DOMEventHandler to be called twice and since they are discarded they do not need to be removed with the RemoveEventListener method. More...
|
| |
| void | AddEventListener (DOMEvent domEvent, DOMEventHandler handler, bool useCapture) |
| | This method allows the registration of event listeners on the event target. If an DOMEventHandler is added to an DOMEventTarget while it is processing an event, it will not be triggered by the current actions but may be triggered during a later stage of event flow, such as the bubbling phase.
If multiple identical DOMEventHandlers are registered on the same DOMEventTarget with the same parameters the duplicate instances are discarded. They do not cause the DOMEventHandler to be called twice and since they are discarded they do not need to be removed with the RemoveEventListener method. More...
|
| |
| void | RemoveEventListener (DOMEventType eventType, DOMEventHandler handler, bool useCapture) |
| | This method allows the removal of event listeners from the event target. If an DOMEventHandler is removed from an DOMEventTarget while it is processing an event, it will not be triggered by the current actions. DOMEventHandlers can never be invoked after being removed.
Calling RemoveEventListener with arguments which do not identify any currently registered DOMEventHandler on the DOMEventTarget has no effect. More...
|
| |
| void | RemoveEventListener (DOMEvent domEvent, DOMEventHandler handler, bool useCapture) |
| | This method allows the removal of event listeners from the event target. If an DOMEventHandler is removed from an DOMEventTarget while it is processing an event, it will not be triggered by the current actions. DOMEventHandlers can never be invoked after being removed.
Calling RemoveEventListener with arguments which do not identify any currently registered DOMEventHandler on the DOMEventTarget has no effect. More...
|
| |
| List< DOMEventHandler > | GetEventListeners (DOMEventType eventType) |
| | Returns a list of registered DOMEventHandler's for specific eventType. More...
|
| |
| List< DOMEventHandler > | GetEventListeners (DOMEvent domEvent) |
| | Returns a list of registered DOMEventHandler's for specific custom event. More...
|
| |
| bool | DispatchEvent (DOMEvent domEvent) |
| | Sends an event to the current element. More...
|
| |
This interface is implemented by all DOMElement that supports DOM event model.
This method allows the registration of event listeners on the event target. If an DOMEventHandler is added to an DOMEventTarget while it is processing an event, it will not be triggered by the current actions but may be triggered during a later stage of event flow, such as the bubbling phase.
If multiple identical DOMEventHandlers are registered on the same DOMEventTarget with the same parameters the duplicate instances are discarded. They do not cause the DOMEventHandler to be called twice and since they are discarded they do not need to be removed with the RemoveEventListener method.
- Parameters
-
| eventType | the event type for which the user is registering. |
| handler | the handler parameter takes an interface implemented by the user which contains the methods to be called when the event occurs. |
| useCapture | if true, useCapture indicates that the user wishes to initiate capture. After initiating capture, all events of the specified type will be dispatched to the registered DOMEventHandler before being dispatched to any DOMEventTarget beneath them in the tree. Events which are bubbling upward through the tree will not trigger an DOMEventHandler designated to use capture. |
| void DotNetBrowser.DOM.Events.DOMEventTarget.AddEventListener |
( |
DOMEvent |
domEvent, |
|
|
DOMEventHandler |
handler, |
|
|
bool |
useCapture |
|
) |
| |
This method allows the registration of event listeners on the event target. If an DOMEventHandler is added to an DOMEventTarget while it is processing an event, it will not be triggered by the current actions but may be triggered during a later stage of event flow, such as the bubbling phase.
If multiple identical DOMEventHandlers are registered on the same DOMEventTarget with the same parameters the duplicate instances are discarded. They do not cause the DOMEventHandler to be called twice and since they are discarded they do not need to be removed with the RemoveEventListener method.
- Parameters
-
| domEvent | the event for which the user is registering. |
| handler | the handler parameter takes an interface implemented by the user which contains the methods to be called when the event occurs. |
| useCapture | if true, useCapture indicates that the user wishes to initiate capture. After initiating capture, all events of the specified type will be dispatched to the registered DOMEventHandler before being dispatched to any DOMEventTarget beneath them in the tree. Events which are bubbling upward through the tree will not trigger an DOMEventHandler designated to use capture. |
| bool DotNetBrowser.DOM.Events.DOMEventTarget.DispatchEvent |
( |
DOMEvent |
domEvent | ) |
|
Sends an event to the current element.
- Parameters
-
| domEvent | specifies the event of the DOMEventHandler. |
- Returns
- false if event is cancelable and at least one of the event handlers which handled this event called cancelable action. Otherwise it returns true.
Returns a list of registered DOMEventHandler's for specific eventType.
- Parameters
-
| eventType | specifies the event type of the DOMEventHandler. |
- Returns
- a list of registered DOMEventHandler's for specific eventType.
Returns a list of registered DOMEventHandler's for specific custom event.
- Parameters
-
| domEvent | the event of the DOMEventHandler. |
- Returns
- a list of registered DOMEventHandler's for specific eventType.
This method allows the removal of event listeners from the event target. If an DOMEventHandler is removed from an DOMEventTarget while it is processing an event, it will not be triggered by the current actions. DOMEventHandlers can never be invoked after being removed.
Calling RemoveEventListener with arguments which do not identify any currently registered DOMEventHandler on the DOMEventTarget has no effect.
- Parameters
-
| eventType | specifies the event type of the DOMEventHandler being removed. |
| handler | the DOMEventHandler parameter indicates the DOMEventHandler to be removed. |
| useCapture | specifies whether the DOMEventHandler being removed was registered as a capturing listener or not. If a listener was registered twice, one with capture and one without, each must be removed separately. Removal of a capturing listener does not affect a non-capturing version of the same listener, and vice versa. |
| void DotNetBrowser.DOM.Events.DOMEventTarget.RemoveEventListener |
( |
DOMEvent |
domEvent, |
|
|
DOMEventHandler |
handler, |
|
|
bool |
useCapture |
|
) |
| |
This method allows the removal of event listeners from the event target. If an DOMEventHandler is removed from an DOMEventTarget while it is processing an event, it will not be triggered by the current actions. DOMEventHandlers can never be invoked after being removed.
Calling RemoveEventListener with arguments which do not identify any currently registered DOMEventHandler on the DOMEventTarget has no effect.
- Parameters
-
| domEvent | the event of the DOMEventHandler being removed. |
| handler | the DOMEventHandler parameter indicates the DOMEventHandler to be removed. |
| useCapture | specifies whether the DOMEventHandler being removed was registered as a capturing listener or not. If a listener was registered twice, one with capture and one without, each must be removed separately. Removal of a capturing listener does not affect a non-capturing version of the same listener, and vice versa. |


| 023:028 Khan | : |
And when you have embarked on the ship, you and whoever is with you, then say: "All the praises and thanks be to Allah, Who has saved us from the people who are Zalimun (i.e. oppressors, wrong-doers, polytheists, those who join others in worship with Allah, etc.). |
| 023:028 Maulana | : |
Then when thou art firmly seated, thou and those with thee, in the ark, say: Praise be to Allah, Who delivered us from the unjust people! |
| 023:028 Pickthal | : |
And when thou art on board the ship, thou and whoso is with thee, then say: Praise be to Allah Who hath saved us from the wrongdoing folk! |
| 023:028 Rashad | : |
"Once you are settled, together with those who are with you, on the watercraft, you shall say, 'Praise GOD for saving us from the evil people.' |
| 023:028 Sarwar | : |
When all of you settle in the Ark, say, "Only God Who has saved us from the unjust people deserves all praise". |
| 023:028 Shakir | : |
And when you are firmly seated, you and those with you, in the ark, say: All praise is due to Allah who delivered us from the unjust people: |
| 023:028 Sherali | : |
'And when thou art settled in the Ark - thou and those that are with thee - say, 'All praise belongs to ALLAH who has saved us from the unjust people.' |
| 023:028 Yusufali | : |
And when thou hast embarked on the Ark - thou and those with thee,- say: "Praise be to Allah, Who has saved us from the people who do wrong." |
Concepts/Themes Covered:
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Home
Education
Experience
Contact Me
My Coding Experience
My Projects
Project #1
This is one of my first projects I ever created.


Every time a dataSource (either a dataSet or a detailSet) notify a region about changes inside its data or state, it send along a context object. All the data stored inside this object can be referred from a template.
The context object also contains some additional properties, metadata that reflect the dataSource current state:
All these properties can be used inside a region's template. Information about state can be especially useful for displaying different content depending on the datasource state.
Custom dataSets may add their own metadata or return a customized context.
| Context properties | ||
|---|---|---|
| recordCount | {number} | The amount of records currently stored inside the dataSet (not available for detailSet) |
| state | {string} | Current dataSource state, expressed as a string |
| isStateLoading | {boolean} | True if the dataSource is currently loading data |
| isStateError | {boolean} | True if the dataSource encountered an error during an XHR request |
| isStateReady | {boolean} | True if the dataSource is ready to be used |
This sample region demonstrates the properties available inside a dataSet's context.

Context properties

recordCount: {{recordCount}}

state: {{state}}

isStateLoading: {{isStateLoading}}

isStateError: {{isStateError}}

isStateReady: {{isStateReady}}

{{#each entities}}

{{firstName}} {{lastName}}

{{/each}}


I am a PhD Candidate at The University of Wisconsin—Madison , where I am studing structural geology. I've been here since the fall of 2014, when I was but a young strapping 23 year old without a plan. But now, as the years have passed, I see more clearly through the looking glass--the winter's fog is clearing.
I'm interested in long walks on the beach, pina coladas, brisk fall days, etc.


Documentation¶
learning_curves.learning_curves module¶
-
class
learning_curves.learning_curves.
LearningCurve
(predictors=[], scoring=<function r2_score>, name=None)[source]¶ Bases:
object
-
best_predictor
(predictors='all', prefer_conv_delta=0.002, **kwargs)[source]¶ Find the Predictor having the best fit of a learning curve.
- Parameters
predictors (list(Predictor), "all") – A list of Predictors to consider.
prefer_conv_delta (float) – If the difference of the two best Predictor fit scores is lower than prefer_conv_delta, then if the converging Predict will be prefered (if any).
kwargs (dict) – Parameters that will be forwarded to internal functions.
- Returns
The Predicto having the best fit of the learning curve.
- Return type
-
static
compare
(lcs, fit=True, figsize=(12, 6), colors=None, what='both', fig=None, **kwargs)[source]¶ Stack learning curves on a single plot (max 10).
- Parameters
lcs (list(LearningCurve)) – List of LearningCurves to stack.
fit (bool) – If True, calls
LearningCurve.fit_all()
on all the learning curve objects.figsize (tuple) – Dimensions of the figure
colors (cycle, list) – cycle of the learning curves colors. A cycler an be created as follows: cycle = cycle(‘color’, [“color1”, “color2”, …])
what ("train", "valid", "both") – curves to show
fig (Matplotlib.figure) – The resulting figure
kwargs (dict) – Dictionary of values that will be passed to each
LearningCurve.plot()
method
- Returns
The resulting figure
- Return type
fig (Matplotlib.figure)
-
static
compare_time
(lcs, what='both', figsize=(12, 6), colors=None, **kwargs)[source]¶ Stack times of the computing of the learning curves on a single plot.
- Parameters
lcs (list(LearningCurve)) – List of LearningCurves to stack (max 10).
what (str) – Value in [“both”, “fit”, “score”]. Select the curve to show.
figsize (tuple) – Dimensions of the figure.
colors (cycle, list) – cycle of the learning curves colors. A cycler can be created as follows: cycle = cycle(‘color’, [“color1”, “color2”, …])
kwargs (dict) – Dictionary of values that will be passed to each
LearningCurve.plot_time()
method
- Returns
The resulting figure ax
- Return type
ax (Matplotlib.axes)
-
eval_fitted_curve
(validation, **kwargs)[source]¶ Split the data points in two sets then fit predictors in the first set and evaluate them using RMSE on the second set. See
eval_fitted_curve_cust()
- Parameters
validation (float, int) – Percentage or number of samples of the validation set (the highest training sizes will be used for validation)
kwargs (dict) – Parameters passed to
eval_fitted_curve_cust()
- Returns
The Root Mean Squared Error of the validation set against the fitted curve of the Predictor
- Return type
fit_score (float)
-
eval_fitted_curve_cust
(train_sizes_fit, test_scores_mean_fit, test_scores_std_fit, train_sizes_val, test_scores_mean_val, predictor='best', fit=True, metric=<function mean_bias_error>)[source]¶ Compute the error of a fitted curve on a validation set.
- Parameters
train_sizes_fit (array) – List of train sizes used for the fitting of the curve
test_scores_mean_fit (array) – Means computed by the estimator for the train sizes.
test_scores_std_fit (array) – Standard deviations computed by the estimator for the train sizes.
train_sizes_val (array) – List of train sizes used for vscoring of the fitting of the curve (the computation of the RMSE).
test_scores_mean_val (array) – Values computed by the estimator for the validation train sizes.
predictor (Predictor, "best") – Predictor to consider
fit (bool) – If True, perform a fit of the Predictors using the test_scores_mean_fit data points.
metric (function) – Function to use for the evaluation of the fit of the validation points.
- Returns
The score of the extrapolation using the validation set
- Return type
fit_score (float)
-
eval_train_sizes
()[source]¶ Compute the difference of scale between the first and last gradients of accuracies of the train_sizes.
If this number is lower than 2, then it indicates that the provided training set sizes don’t cover a wide enough range of the accuracies values to fit a curve. In that case, you should look at the generated plot to determine if you need more points close to the minimum or the maximum training set size.
- Returns
The difference of scale between the first and last gradients of accuracies of the train_sizes
- Return type
tain_size_score (float)
Example
get_train_sizes_grads([ 2, 8, …, 2599, 2824]) > 2.7156
-
fit
(P, x, y, **kwargs)[source]¶ Fit a curve with a predictor, compute and save the score of the fit.
- Parameters
x (list) – 1D array (list) representing the training sizes
y (list) – 1D array (list) representing the test scores
kwargs (dict) – Parameters that will be forwarded to Scipy curve_fit.
- Returns
The Predictor with the updated params and score. Score will be None if a ValueError exception occures while computing the score.
- Return type
-
fit_all
(**kwargs)[source]¶ Fit a curve with all the predictors using the recorder data and retrieve score if y_pred is finite.
- Parameters
kwargs (dict) – Parameters that will be forwarded to internal functions.
- Returns
an array of predictors with the updated params and score.
- Return type
list
-
fit_all_cust
(x, y, predictors, **kwargs)[source]¶ Fit a curve with all the predictors and retrieve score if y_pred is finite.
- Parameters
x (list) – 1D array (list) representing the training sizes
y (list) – 1D array (list) representing the test scores
- Returns
an array of predictors with the updated params and score.
- Return type
list
-
get_label
(label)[source]¶ Prefix the label with the name of the LearningCurve instance.
- Parameters
label (str) – label to prefix
- Returns
label prefixed with name, if any.
- Return type
label (str)
-
get_lc
(estimator, X, Y, train_kwargs={}, **kwargs)[source]¶ Compute and plot the learning curve. See
train()
andplot()
functions for parameters.- Parameters
estimator (Object) – Must implement a fit(X,Y) and predict(Y) method.
X (array) – Features to use for prediction
Y (array) – Values to be predicted
train_kwargs (dict) – See
train()
parameterskwargs (dict) – See:meth:plot parameters.
-
get_predictor
(pred)[source]¶ Get a
learning_curves.predictor
from the list of the Predictors.
-
plot
(predictor=None, figsize=(12, 6), fig=None, **kwargs)[source]¶ Plot the training and test learning curves of the recorder data, with optionally fitted functions and saturation. See
plot_cust()
:- Parameters
predictor (str, list(str), Predictor, list(Predictor)) – The predictor(s) to use for plotting the fitted curve. Can also be “all” and “best”.
figsize (2uple) – Size of the figure (only taken in account if ax is None)
ax (Matplotlib.axes) – A figure on which the learning curve will be drawn. If None, a new one is created.
kwargs (dict) – Parameters that will be forwarded to internal functions.
- Returns
A Matplotlib figure of the result.
- Raises
RuntimeError – If the recorder is empty.
-
plot_cust
(train_sizes, train_scores_mean, train_scores_std, test_scores_mean, test_scores_std, predictor=None, what='both', xlim=None, ylim=None, figsize=(12, 6), title=None, saturation=None, target=None, validation=0, close=True, uncertainty=False, fig=None, alpha=1, alpha_fit=1, std=True, **kwargs)[source]¶ Plot any training and test learning curves, with optionally fitted functions and saturation.
- Parameters
train_sizes (list) – Training sizes (x values).
train_scores_std (list) – Train score standard deviations.
test_scores_mean (list) – Test score means(y values).
test_scores_std (list) – Train score means.
predictor (str, list(str), Predictor, list(Predictor)) – The predictor(s) to use for plotting the fitted curve. Can be “all” or “best”.
what ("train", "valid", "both") – learning curves to show
xlim (2uple) – Limits of the x axis of the plot.
ylim (2uple) – Limits of the y axis of the plot.
figsize (2uple) – Size of the figure
title (str) – Title of the figure
saturation (str, list(str), Predictor, list(Predictor)) – Predictor(s) to consider for displaying the saturation on the plot. Can be “all” or “best”.
target (int) – Training size to reach. The training size axis will be extended and the fitted curve extrapolated until reaching this value.
validation (float) – Percentage or number of data points to keep for validation of the curve fitting (they will not be used during the fitting but displayed afterwards)
close (bool) – If True, close the figure before returning it. This is usefull if a lot of plots are being created because Matplotlib won’t close them, potentially leading to warnings. If False, the plot will not be closed. This can be desired when working on Jupyter notebooks, so that the plot will be rendered in the output of the cell.
uncertainty (bool) – If True, plot the standard deviation of the best fitted curve for the validation data points.
fig (Matplotlib.figure) – A figure which the learning curve will be drawn. If None, a new one is created.
alpha (float) – Controls transparency of the learning curve
alpha_fit (float) – Controls transparency of the fitted line
std (bool) – Whether to plot standard deviations of points or not.
kwargs (dict) – Parameters that will be forwarded to internal functions.
- Returns
fig (Matplotlib.figure)
-
plot_fitted_curve
(ax, P, x, scores=True, best=False, best_ls='-.', alpha=1, **kwargs)[source]¶ Add to figure ax a fitted curve.
- Parameters
ax (Matplotlib.axes) – Figure used to print the curve.
P (Predictor) – Predictor to use for the computing of the curve.
x (array) – 1D array (list) representing the training sizes.
scores (bool) – Print the score of each curve fit in the legend if True.
best (bool) – use a higher zorder to make the curve more visible if True.
best_ls (Matplotlib line-style) – line-style of the curve whose Predictor is used for computing saturation accuracy.
alpha (float) – Controls the transparency of the fitted curve
kwargs (dict) – Parameters that will be forwarded to internal functions.
- Returns
The updated figure.
- Return type
Matplotlib axes
-
plot_time
(fig=None, what='both', figsize=(12, 6))[source]¶ Plot training sizes against fit/score computing times.
- Parameters
fig (Matplotlib.figure) – A figure on which the curves will be drawn. If None, a new one will be created.
what (str) – Value in [“both”, “fit”, “score”]. Select the curve to show.
figsize (2-uple) – Dimensions of the figure (ignored if ax is not None).
- Returns
A Matplotlib figure of the result.
- Return type
fig (Matplotlib.figure)
-
save
(path=None)[source]¶ Save the LearningCurve object as a pickle object in disk, or as a string.
It uses the dill library to save the instance because the object contains lambda functions, that can not be pickled otherwise.
- Parameters
path (str) – Path where to save the object. If None, the string representing the object is returned
-
threshold
(P='best', **kwargs)[source]¶ See
threshold_cust()
function. This function callsthreshold_cust()
with the recorder data.- Parameters
P (Predictor, string) – Predictor to use.
kwargs (dict) – Parameters that will be forwarded to internal functions.
- Returns
(x_thresh, y_thresh, sat_val, threshold). If P is diverging, the saturation value will be 1.
- Return type
Tuple
- Raises
RuntimeError – If the recorder is empty.
-
threshold_cust
(P, x, threshold=0.99, max_scaling=1, resolution=10000.0, strategies={'max_scaling': 1, 'threshold': -0.01}, **kwargs)[source]¶ Find the training set size providing the highest accuracy up to a predefined threshold.
P(x) = y and for x -> inf, y -> saturation value. This method approximates x_thresh such as P(x_thresh) = threshold * saturation value.
- Parameters
P (str, Predictor) – The predictor to use for the calculation of the saturation value.
x (array) – Training set sizes
threshold (float) – In [0.0, 1.0]. Percentage of the saturation value to use for the calculus of the best training set size.
max_scaling (float) – Order of magnitude added to the order of magnitude of the maximum train set size. Generally, a value of 1-2 is enough.
resolution (float) – Only considered for diverging Predictors without inverse function. The higher it is, the more accurate the value of the training set size will be.
strategies (dict) – A dictionary of the values to add / substract to the other parameters in case a saturation value can not be found. If an RecursionError raises, (None, None, sat_val, threshold) will be returned.
kwargs (dict) – Parameters that will be forwarded to internal functions.
- Returns
(x_thresh, y_thresh, saturation_arrucacy, threshold)
- Return type
Tuple
-
threshold_cust_approx
(P, x, threshold, max_scaling, resolution, strategies, **kwargs)[source]¶ Find the training set size providing the highest accuracy up to a predefined threshold for a Predictor having no inverse function. See
threshold_cust()
.
-
threshold_cust_inv
(P, x, threshold, **kwargs)[source]¶ Find the training set size providing the highest accuracy up to a desired threshold for a Predictor having an inverse function. See
threshold_cust()
.
-
train
(estimator, X, Y, train_sizes=None, test_size=0.2, n_splits=5, verbose=1, n_jobs=-1, n_samples=20, **kwargs)[source]¶ Compute the learning curve of an estimator over a dataset.
- Parameters
estimator (Object) – Must implement a fit(X,Y) and predict(Y) method.
X (array) – Features to use for prediction
Y (array) – Values to be predicted
train_sizes (list) – See sklearn learning_curve function documentation. If None, np.geomspace will be used with 20 values
n_split (int) – Number of random cross validation calculated for each train size
verbose (int) – The higher, the more verbose.
n_jobs (int) – See sklearn learning_curve function documentation.
n_samples (int) – if train_sizes is None, n_samples is the number of samples of to use for the learning curve.
kwargs (dict) – See sklearn learning_curve function parameters. Invalid parameters raise errors.
- Returns
The resulting object can then be passed to
plot()
function.- Return type
Dict
-
learning_curves.predictor module¶
-
class
learning_curves.predictor.
Predictor
(name, func, guess, inv=None, diverging=False, bounds=None)[source]¶ Bases:
object
Object representing a function to fit a learning curve (See
learning_curves.LearningCurve
).-
get_saturation
()[source]¶ Compute the saturation accuracy of the Predictor.
The saturation accuracy is the best accuracy you will get from the model without changing any other parameter than the training set size. If the Predictor is diverging, this value should be disregarded, being meaningless.
- Returns
- saturation accuracy of the Predictor.
This value is 1 if the Predictor is diverging without inverse function. This valus is the first parameter of the Predictor if it is converging. This value is calculated if the Predictor is diverging with inverse function.
- Return type
float
-
learning_curves.tools module¶
-
learning_curves.tools.
get_absolute_value
(validation, len_vector)[source]¶ Get the absolute value of the length of a vector.
-
learning_curves.tools.
get_scale
(val, floor=True)[source]¶ Returns the scale of a value.
- Parameters
floor (bool) – if True, apply np.floor to the result
Examples
get_scale(1.5e-15) > -15 get_scale(1.5e-15, False) > -14.823908740944319
-
learning_curves.tools.
get_unique_list
(predictors)[source]¶ Return a list of unique predictors. Two Predictors are equal if they have the same name.
-
learning_curves.tools.
is_strictly_increasing
(L)[source]¶ Returns True if the list contains strictly increasing values.
Examples
is_strictly_increasing([0,1,2,3,4,5]) > True is_strictly_increasing([0,1,2,2,4,5]) > False is_strictly_increasing([0,1,2,1,4,5]) > False
-
learning_curves.tools.
load
(path='./lc_data.pkl')[source]¶ Load a
learning_curves.LearningCurve
object from disk.
-
learning_curves.tools.
mean_bias_error
(y_trues, y_preds)[source]¶ Computes the Mean Bias Error of two vectors.


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http://eng.wealthfront.com/2012/03/22/scrolling-z-axis-with-css-3d-transforms
Time Travel at your Fingertips! Paradigm


How does the UK compare to other countries in full-fibre broadband coverage?
Labour has unveiled plans to bring free, full-fibre broadband to every home and business by 2030.
Figures suggest the UK is one of the worst places in Europe for access to full-fibre broadband, as Labour announced plans to provide the technology to every home and business in the UK by 2030.
The party has announced it would bring parts of BT into public ownership under plans which it said will result in a massive upgrade in the UK’s internet infrastructure.
While the UK is among the best-performing EU countries for broadband coverage generally, it lags behind most other EU nations when it comes to full-fibre coverage.
This is significantly lower than many other parts of the EU – where only Belgium, Cyprus and Greece have lower levels of full-fibre coverage than the UK, according to figures from the European Commission from 2018 which were published earlier this year.
The figures indicate that those countries are joined by the UK and Germany as the only EU nations with full-fibre coverage under 10%.
In contrast, the figures show that full-fibre coverage in Latvia stands at just under 90%, while Spain, Portugal and Sweden have coverage of 70% or higher.
Further afield, countries such as Japan and South Korea both have full-fibre coverage of over 95%.


Role in Marjaavaan was powerful: Rakul Preet Singh
She reveals that while narrating the story, the director Milap Zaveri, compared her part to some of the greatest characters.
by Lipika VarmaPost the success of De De Pyaar De, Rakul Preet Singh is excited about her latest release Marjaavaan, opposite Siddharth Malhotra. Talking about the movie, where she’s playing a tawaif (courtesan), Rakul says, “My character’s name on-screen is Arzoo. Everyone is in love with Arzoo but she is crazy about Raghu (Siddharth).”
She reveals that while narrating the story, the director Milap Zaveri, compared her part to some of the greatest characters, which was enough for her to agree.
“He said this is a powerful and special role and can be compared to Rekhaji from Muqaddar Ka Sikandar and Tabu from Jeet and Chandramkukhi from Devdas,” she says. Further, the actress talks about her character. “The exciting factor is that she narrates shayari, and has a typical adaa. I have not seen such a role after the ’90s, it was contradicting to De De.. and is not conventional.” Marjaavaan also stars Tara Sutaria and Riteish Deshmukh.