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Halide
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namespace to hold functions for imposing boundary conditions on Halide Funcs. More...
Namespaces | |
| Internal | |
Functions | |
| Func | constant_exterior (const Func &source, Tuple value, const std::vector< std::pair< Expr, Expr >> &bounds, const std::string &name="constant_exterior") |
| Impose a boundary condition such that a given expression is returned everywhere outside the boundary. More... | |
| Func | constant_exterior (const Func &source, Expr value, const std::vector< std::pair< Expr, Expr >> &bounds, const std::string &name="constant_exterior") |
| Impose a boundary condition such that a given expression is returned everywhere outside the boundary. More... | |
| template<typename T > | |
| HALIDE_NO_USER_CODE_INLINE Func | constant_exterior (const T &func_like, Tuple value) |
| Impose a boundary condition such that a given expression is returned everywhere outside the boundary. More... | |
| template<typename T > | |
| HALIDE_NO_USER_CODE_INLINE Func | constant_exterior (const T &func_like, Expr value) |
| Impose a boundary condition such that a given expression is returned everywhere outside the boundary. More... | |
| template<typename T , typename... Bounds, typename std::enable_if< Halide::Internal::all_are_convertible< Expr, Bounds... >::value >::type * = nullptr> | |
| HALIDE_NO_USER_CODE_INLINE Func | constant_exterior (const T &func_like, Tuple value, Bounds &&...bounds) |
| Impose a boundary condition such that a given expression is returned everywhere outside the boundary. More... | |
| template<typename T , typename... Bounds, typename std::enable_if< Halide::Internal::all_are_convertible< Expr, Bounds... >::value >::type * = nullptr> | |
| HALIDE_NO_USER_CODE_INLINE Func | constant_exterior (const T &func_like, Expr value, Bounds &&...bounds) |
| Impose a boundary condition such that a given expression is returned everywhere outside the boundary. More... | |
| Func | repeat_edge (const Func &source, const std::vector< std::pair< Expr, Expr >> &bounds, const std::string &name="repeat_edge") |
| Impose a boundary condition such that the nearest edge sample is returned everywhere outside the given region. More... | |
| template<typename T > | |
| HALIDE_NO_USER_CODE_INLINE Func | repeat_edge (const T &func_like) |
| Impose a boundary condition such that the nearest edge sample is returned everywhere outside the given region. More... | |
| template<typename T , typename... Bounds, typename std::enable_if< Halide::Internal::all_are_convertible< Expr, Bounds... >::value >::type * = nullptr> | |
| HALIDE_NO_USER_CODE_INLINE Func | repeat_edge (const T &func_like, Bounds &&...bounds) |
| Impose a boundary condition such that the nearest edge sample is returned everywhere outside the given region. More... | |
| Func | repeat_image (const Func &source, const std::vector< std::pair< Expr, Expr >> &bounds) |
| Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other. More... | |
| template<typename T > | |
| HALIDE_NO_USER_CODE_INLINE Func | repeat_image (const T &func_like) |
| Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other. More... | |
| template<typename T , typename... Bounds, typename std::enable_if< Halide::Internal::all_are_convertible< Expr, Bounds... >::value >::type * = nullptr> | |
| HALIDE_NO_USER_CODE_INLINE Func | repeat_image (const T &func_like, Bounds &&...bounds) |
| Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other. More... | |
| Func | mirror_image (const Func &source, const std::vector< std::pair< Expr, Expr >> &bounds) |
| Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other, but mirror them such that adjacent edges are the same. More... | |
| template<typename T > | |
| HALIDE_NO_USER_CODE_INLINE Func | mirror_image (const T &func_like) |
| Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other. More... | |
| template<typename T , typename... Bounds, typename std::enable_if< Halide::Internal::all_are_convertible< Expr, Bounds... >::value >::type * = nullptr> | |
| HALIDE_NO_USER_CODE_INLINE Func | mirror_image (const T &func_like, Bounds &&...bounds) |
| Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other. More... | |
| Func | mirror_interior (const Func &source, const std::vector< std::pair< Expr, Expr >> &bounds) |
| Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other, but mirror them such that adjacent edges are the same and then overlap the edges. More... | |
| template<typename T > | |
| HALIDE_NO_USER_CODE_INLINE Func | mirror_interior (const T &func_like) |
| Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other, but mirror them such that adjacent edges are the same and then overlap the edges. More... | |
| template<typename T , typename... Bounds, typename std::enable_if< Halide::Internal::all_are_convertible< Expr, Bounds... >::value >::type * = nullptr> | |
| HALIDE_NO_USER_CODE_INLINE Func | mirror_interior (const T &func_like, Bounds &&...bounds) |
| Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other, but mirror them such that adjacent edges are the same and then overlap the edges. More... | |
namespace to hold functions for imposing boundary conditions on Halide Funcs.
All functions in this namespace transform a source Func to a result Func where the result produces the values of the source within a given region and a different set of values outside the given region. A region is an N dimensional box specified by mins and extents.
Three areas are defined: The image is the entire set of values in the region. The edge is the set of pixels in the image but adjacent to coordinates that are not The interior is the image minus the edge (and is undefined if the extent of any region is 1 or less).
If the source Func has more dimensions than are specified, the extra ones are unmodified. Additionally, passing an undefined (default constructed) 'Expr' for the min and extent of a dimension will keep that dimension unmodified.
Numerous options for specifing the outside area are provided, including replacement with an expression, repeating the edge samples, mirroring over the edge, and repeating or mirroring the entire image.
Using these functions to express your boundary conditions is highly recommended for correctness and performance. Some of these are hard to get right. The versions here are both understood by bounds inference, and also judiciously use the 'likely' intrinsic to minimize runtime overhead.
| Func Halide::BoundaryConditions::constant_exterior | ( | const Func & | source, |
| Tuple | value, | ||
| const std::vector< std::pair< Expr, Expr >> & | bounds, | ||
| const std::string & | name = "constant_exterior" |
||
| ) |
Impose a boundary condition such that a given expression is returned everywhere outside the boundary.
Generally the expression will be a constant, though the code currently allows accessing the arguments of source.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object. Note that objects are taken by mutable ref. Pipelines capture Buffers via mutable refs, because running a pipeline might alter the Buffer metadata (e.g. device allocation state).
(This is similar to setting GL_TEXTURE_WRAP_* to GL_CLAMP_TO_BORDER and putting value in the border of the texture.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Referenced by constant_exterior(), and Halide::BoundaryConditions::Internal::func_like_to_func().
| Func Halide::BoundaryConditions::constant_exterior | ( | const Func & | source, |
| Expr | value, | ||
| const std::vector< std::pair< Expr, Expr >> & | bounds, | ||
| const std::string & | name = "constant_exterior" |
||
| ) |
Impose a boundary condition such that a given expression is returned everywhere outside the boundary.
Generally the expression will be a constant, though the code currently allows accessing the arguments of source.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object. Note that objects are taken by mutable ref. Pipelines capture Buffers via mutable refs, because running a pipeline might alter the Buffer metadata (e.g. device allocation state).
(This is similar to setting GL_TEXTURE_WRAP_* to GL_CLAMP_TO_BORDER and putting value in the border of the texture.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
|
inline |
Impose a boundary condition such that a given expression is returned everywhere outside the boundary.
Generally the expression will be a constant, though the code currently allows accessing the arguments of source.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object. Note that objects are taken by mutable ref. Pipelines capture Buffers via mutable refs, because running a pipeline might alter the Buffer metadata (e.g. device allocation state).
(This is similar to setting GL_TEXTURE_WRAP_* to GL_CLAMP_TO_BORDER and putting value in the border of the texture.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 93 of file BoundaryConditions.h.
References constant_exterior(), and Halide::BoundaryConditions::Internal::func_like_to_func().
|
inline |
Impose a boundary condition such that a given expression is returned everywhere outside the boundary.
Generally the expression will be a constant, though the code currently allows accessing the arguments of source.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object. Note that objects are taken by mutable ref. Pipelines capture Buffers via mutable refs, because running a pipeline might alter the Buffer metadata (e.g. device allocation state).
(This is similar to setting GL_TEXTURE_WRAP_* to GL_CLAMP_TO_BORDER and putting value in the border of the texture.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 102 of file BoundaryConditions.h.
References constant_exterior().
|
inline |
Impose a boundary condition such that a given expression is returned everywhere outside the boundary.
Generally the expression will be a constant, though the code currently allows accessing the arguments of source.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object. Note that objects are taken by mutable ref. Pipelines capture Buffers via mutable refs, because running a pipeline might alter the Buffer metadata (e.g. device allocation state).
(This is similar to setting GL_TEXTURE_WRAP_* to GL_CLAMP_TO_BORDER and putting value in the border of the texture.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 108 of file BoundaryConditions.h.
References Halide::Internal::collect_paired_args(), constant_exterior(), and Halide::BoundaryConditions::Internal::func_like_to_func().
|
inline |
Impose a boundary condition such that a given expression is returned everywhere outside the boundary.
Generally the expression will be a constant, though the code currently allows accessing the arguments of source.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object. Note that objects are taken by mutable ref. Pipelines capture Buffers via mutable refs, because running a pipeline might alter the Buffer metadata (e.g. device allocation state).
(This is similar to setting GL_TEXTURE_WRAP_* to GL_CLAMP_TO_BORDER and putting value in the border of the texture.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 116 of file BoundaryConditions.h.
References constant_exterior(), and repeat_edge().
| Func Halide::BoundaryConditions::repeat_edge | ( | const Func & | source, |
| const std::vector< std::pair< Expr, Expr >> & | bounds, | ||
| const std::string & | name = "repeat_edge" |
||
| ) |
Impose a boundary condition such that the nearest edge sample is returned everywhere outside the given region.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(This is similar to setting GL_TEXTURE_WRAP_* to GL_CLAMP_TO_EDGE.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Referenced by constant_exterior(), and repeat_edge().
|
inline |
Impose a boundary condition such that the nearest edge sample is returned everywhere outside the given region.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(This is similar to setting GL_TEXTURE_WRAP_* to GL_CLAMP_TO_EDGE.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 140 of file BoundaryConditions.h.
References Halide::BoundaryConditions::Internal::func_like_to_func(), and repeat_edge().
|
inline |
Impose a boundary condition such that the nearest edge sample is returned everywhere outside the given region.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(This is similar to setting GL_TEXTURE_WRAP_* to GL_CLAMP_TO_EDGE.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 152 of file BoundaryConditions.h.
References Halide::Internal::collect_paired_args(), Halide::BoundaryConditions::Internal::func_like_to_func(), repeat_edge(), and repeat_image().
| Func Halide::BoundaryConditions::repeat_image | ( | const Func & | source, |
| const std::vector< std::pair< Expr, Expr >> & | bounds | ||
| ) |
Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(This is similar to setting GL_TEXTURE_WRAP_* to GL_REPEAT.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Referenced by repeat_edge(), and repeat_image().
|
inline |
Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(This is similar to setting GL_TEXTURE_WRAP_* to GL_REPEAT.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 176 of file BoundaryConditions.h.
References Halide::BoundaryConditions::Internal::func_like_to_func(), and repeat_image().
|
inline |
Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(This is similar to setting GL_TEXTURE_WRAP_* to GL_REPEAT.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 187 of file BoundaryConditions.h.
References Halide::Internal::collect_paired_args(), Halide::BoundaryConditions::Internal::func_like_to_func(), mirror_image(), and repeat_image().
| Func Halide::BoundaryConditions::mirror_image | ( | const Func & | source, |
| const std::vector< std::pair< Expr, Expr >> & | bounds | ||
| ) |
Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other, but mirror them such that adjacent edges are the same.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(This is similar to setting GL_TEXTURE_WRAP_* to GL_MIRRORED_REPEAT.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Referenced by mirror_image(), and repeat_image().
|
inline |
Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(This is similar to setting GL_TEXTURE_WRAP_* to GL_REPEAT.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 211 of file BoundaryConditions.h.
References Halide::BoundaryConditions::Internal::func_like_to_func(), and mirror_image().
|
inline |
Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other.
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(This is similar to setting GL_TEXTURE_WRAP_* to GL_REPEAT.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 222 of file BoundaryConditions.h.
References Halide::Internal::collect_paired_args(), Halide::BoundaryConditions::Internal::func_like_to_func(), mirror_image(), and mirror_interior().
| Func Halide::BoundaryConditions::mirror_interior | ( | const Func & | source, |
| const std::vector< std::pair< Expr, Expr >> & | bounds | ||
| ) |
Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other, but mirror them such that adjacent edges are the same and then overlap the edges.
This produces an error if any extent is 1 or less. (TODO: check this.)
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(I do not believe there is a direct GL_TEXTURE_WRAP_* equivalent for this.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Referenced by mirror_image(), and mirror_interior().
|
inline |
Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other, but mirror them such that adjacent edges are the same and then overlap the edges.
This produces an error if any extent is 1 or less. (TODO: check this.)
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(I do not believe there is a direct GL_TEXTURE_WRAP_* equivalent for this.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 249 of file BoundaryConditions.h.
References Halide::BoundaryConditions::Internal::func_like_to_func(), and mirror_interior().
|
inline |
Impose a boundary condition such that the entire coordinate space is tiled with copies of the image abutted against each other, but mirror them such that adjacent edges are the same and then overlap the edges.
This produces an error if any extent is 1 or less. (TODO: check this.)
An ImageParam, Buffer<T>, or similar can be passed instead of a Func. If this is done and no bounds are given, the boundaries will be taken from the min and extent methods of the passed object.
(I do not believe there is a direct GL_TEXTURE_WRAP_* equivalent for this.)
You may pass undefined Exprs for dimensions that you do not wish to bound.
Definition at line 260 of file BoundaryConditions.h.
References Halide::Internal::collect_paired_args(), Halide::BoundaryConditions::Internal::func_like_to_func(), and mirror_interior().


WITNESS LIST Subcommittee on Higher Education Committee March 15, 1999-2:00P SB 37 ON: Cobb, Sharon (Tx. Higher Educ. Coord. Board), Austin Flack, Teri (Tx. Higher Educ. Coord. Board), Austin Marburger, Darla (Senate Education Committee), Red Rock, Tx. SB 184 FOR: Otto, Brad (Self), Seguin ON: Brown, Don (Tx. Higher Educ. Coord. Board), Austin Willeford, Pamela (Tx. Higher Educ. Coord. Board), Austin SB 299 FOR: Staples, William A. (Univ. of Houston, Clear Lake), Houston SJR 16 FOR: Otto, Brad (Self), Seguin ON: Brown, Don (Tx. Higher Ed. Coord. Board), Austin Disparity Study On: Murdock, Steve (Self), College Station Redwine, Lynn (Comptroller's Office), Austin


 %}
{% endblock %}


#include "mS2C_EvoLogics.h"
#include "uwphy-clmsg.h"
Go to the source code of this file.
Implementation of the MS2C_EvoLogics class.
Definition in file mS2C_EvoLogics.cpp.


reading list
Here’s a list of books that I have read or am currently reading:
In progress
- How Not to Be Wrong: The Power of Mathematical Thinking by Jordan Ellenberg
To Read
- The Color of Law: A Forgotten History of How Our Government Segregated America by Richard Rothstein
- Gödel, Escher, Bach: An Eternal Golden Braid by Douglas Hofstadter
- Progress and Poverty: An Inquiry into the Cause of Industrial Depressions and of Increase of Want with Increase of Wealth: The Remedy by Henry George
- The Haunted Land: Facing Europe’s Ghosts After Communism by Tina Rosenberg
Have Read
- The Looming Tower: Al Qaeda and the Road to 9/11 by Lawrence Wright
Radicalism usually prospers in the gap between rising expectations and declining opportunities. This is especially true where the population is young, idle, and bored; where the art is impoverished; where entertainment—movies, theater, music—is policed or absent altogether; and where young men are set apart from the consoling and socializing presence of women.
- The Looming Tower, pg 107
- The Main Enemy: The Inside Story of the CIA’s Final Showdown with the KGB by Milton Bearden and James Risen


Simply put, emotional growth views troubling feelings like depression, anxiety and chronic stress as consequences, not symptoms. The difference is between viewing unwanted feelings as interpretations of difficult experiences, and hence as consequences, or as caused by biological defects, and hence as symptoms of those causes. This website explores this distinction and its implications for mental health. The synopsis below is a start.
 They convey messages about your outlook or perspective on life and perspective reflects the emotional issues you struggle with.
We all lose perspective from time to time: we feel we can’t let go of the past, we can’t manage the future or we lose sight of our goals. When the past, the future and the present are thus distorted we lose hope, confidence and focus respectively.
Our mental health depends on our capacity to restore perspective, to “let go,” to be more assertive and to stay focused during times of transition (which is more often than you think.)
Certain skills restore perspective. They are familiar skills you probably already possess although you might not realize how they can be used to combat depression, anxiety and chronic stress.
We all participate in the development of our emotional issues. This might make you uncomfortable but unless you confront this fact you won’t be able to take effective steps to manage your issues.
“Thinking developmentally,” as described above, outlines a process for taking charge of your emotional life. It is essentially a framework for replacing troubling feelings with productive ones and perhaps, in the process, discovering hope, confidence and peace of mind.
The framework for emotional growth provides a nonmedical understanding of mental health. It asserts that troubling feelings are windows, not symptoms. As windows, they reveal our interpretations of experience, our personal perspectives, not biological or cognitive defects. Their role is to help us identify issues, select goals and adopt strategies for change. Of course, none of this works if we don’t attend to and take responsibility for our feelings. Our troubling feelings, in short, do not just happen to us as suggested by the Medical Model. We are active participants in their formation.
A working definition of emotional growth might be stated as follows: Do you:
1. Know how to make sense of your depressive, anxious and stressful feelings?
2. Have some long term goal?
3. Have the skills you need to achieve your goals?
4. Have a back-up plan in case your original plan is blocked?
5. Know how to accept the benefits of emotional growth?
If you answer in the affirmative to these questions, even if your responses are tentative, you already know a great deal about emotional growth. You know that it doesn’t require special credentials; it is available to ordinary folks. As such, it puts our struggles into a human context. It recognizes that troubling feelings are part of life, that issues with depression, anxiety and chronic stress are consequences, and that since we don’t control the world around us the best we can do is manage the world within us. Emotional issues like depression, anxiety and chronic stress are a loss of perspective on our experiences and recovery is the process of restoring perspective.
Why does all this matter? Because in order to survive in the upheaval that is the current world ordinary, untrained people need to know that they are competent to understand their unwanted feelings. We must give up the notion that experts have special insight into our emotional life and realize that God means for us to struggle so that we can more fully appreciate His creation.


Paradigm Change in Operations Research:
Thirty Years of Debate
Authors
Maurice W. Kirby
Year
Notes
Tags
Relevance
Quotations
Boundary Objects
Concepts
Field of Study
Methodology
References


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| com.thoughtworks.xstream | |
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| com.thoughtworks.xstream.core.util |
| Uses of DataHolder in com.thoughtworks.xstream |
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DataHolder |
XStream.newDataHolder()
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| Methods in com.thoughtworks.xstream with parameters of type DataHolder | |
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void |
MarshallingStrategy.marshal(HierarchicalStreamWriter writer,
java.lang.Object obj,
ConverterLookup converterLookup,
Mapper mapper,
DataHolder dataHolder)
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void |
MarshallingStrategy.marshal(HierarchicalStreamWriter writer,
java.lang.Object obj,
DefaultConverterLookup converterLookup,
ClassMapper classMapper,
DataHolder dataHolder)
Deprecated. As of 1.2, use MarshallingStrategy.marshal(HierarchicalStreamWriter, Object, ConverterLookup, Mapper, DataHolder) |
void |
XStream.marshal(java.lang.Object obj,
HierarchicalStreamWriter writer,
DataHolder dataHolder)
Serialize and object to a hierarchical data structure (such as XML). |
java.lang.Object |
XStream.unmarshal(HierarchicalStreamReader reader,
java.lang.Object root,
DataHolder dataHolder)
Deserialize an object from a hierarchical data structure (such as XML). |
java.lang.Object |
MarshallingStrategy.unmarshal(java.lang.Object root,
HierarchicalStreamReader reader,
DataHolder dataHolder,
ConverterLookup converterLookup,
Mapper mapper)
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java.lang.Object |
MarshallingStrategy.unmarshal(java.lang.Object root,
HierarchicalStreamReader reader,
DataHolder dataHolder,
DefaultConverterLookup converterLookup,
ClassMapper classMapper)
Deprecated. As of 1.2, use MarshallingStrategy.unmarshal(Object, HierarchicalStreamReader, DataHolder, ConverterLookup, Mapper) |
| Uses of DataHolder in com.thoughtworks.xstream.converters |
|---|
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interface |
MarshallingContext
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interface |
UnmarshallingContext
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| Uses of DataHolder in com.thoughtworks.xstream.core |
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class |
AbstractReferenceMarshaller
Abstract base class for a TreeMarshaller, that can build refrences. |
class |
AbstractReferenceUnmarshaller
Abstract base class for a TreeUnmarshaller, that resolves refrences. |
class |
MapBackedDataHolder
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class |
ReferenceByIdMarshaller
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ReferenceByIdUnmarshaller
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class |
ReferenceByXPathMarshaller
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class |
ReferenceByXPathUnmarshaller
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class |
TreeMarshaller
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class |
TreeUnmarshaller
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void |
ReferenceByXPathMarshallingStrategy.marshal(HierarchicalStreamWriter writer,
java.lang.Object obj,
ConverterLookup converterLookup,
Mapper mapper,
DataHolder dataHolder)
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void |
TreeMarshallingStrategy.marshal(HierarchicalStreamWriter writer,
java.lang.Object obj,
ConverterLookup converterLookup,
Mapper mapper,
DataHolder dataHolder)
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void |
ReferenceByIdMarshallingStrategy.marshal(HierarchicalStreamWriter writer,
java.lang.Object obj,
ConverterLookup converterLookup,
Mapper mapper,
DataHolder dataHolder)
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void |
ReferenceByXPathMarshallingStrategy.marshal(HierarchicalStreamWriter writer,
java.lang.Object obj,
DefaultConverterLookup converterLookup,
ClassMapper classMapper,
DataHolder dataHolder)
Deprecated. As of 1.2, use ReferenceByXPathMarshallingStrategy.marshal(HierarchicalStreamWriter, Object, ConverterLookup, Mapper, DataHolder) |
void |
TreeMarshallingStrategy.marshal(HierarchicalStreamWriter writer,
java.lang.Object obj,
DefaultConverterLookup converterLookup,
ClassMapper classMapper,
DataHolder dataHolder)
Deprecated. As of 1.2, use TreeMarshallingStrategy.marshal(HierarchicalStreamWriter, Object, ConverterLookup, Mapper, DataHolder) |
void |
ReferenceByIdMarshallingStrategy.marshal(HierarchicalStreamWriter writer,
java.lang.Object obj,
DefaultConverterLookup converterLookup,
ClassMapper classMapper,
DataHolder dataHolder)
Deprecated. As of 1.2, use ReferenceByIdMarshallingStrategy.marshal(HierarchicalStreamWriter, Object, ConverterLookup, Mapper, DataHolder) |
java.lang.Object |
TreeUnmarshaller.start(DataHolder dataHolder)
|
void |
TreeMarshaller.start(java.lang.Object item,
DataHolder dataHolder)
|
java.lang.Object |
ReferenceByXPathMarshallingStrategy.unmarshal(java.lang.Object root,
HierarchicalStreamReader reader,
DataHolder dataHolder,
ConverterLookup converterLookup,
Mapper mapper)
|
java.lang.Object |
TreeMarshallingStrategy.unmarshal(java.lang.Object root,
HierarchicalStreamReader reader,
DataHolder dataHolder,
ConverterLookup converterLookup,
Mapper mapper)
|
java.lang.Object |
ReferenceByIdMarshallingStrategy.unmarshal(java.lang.Object root,
HierarchicalStreamReader reader,
DataHolder dataHolder,
ConverterLookup converterLookup,
Mapper mapper)
|
java.lang.Object |
ReferenceByXPathMarshallingStrategy.unmarshal(java.lang.Object root,
HierarchicalStreamReader reader,
DataHolder dataHolder,
DefaultConverterLookup converterLookup,
ClassMapper classMapper)
Deprecated. As of 1.2, use ReferenceByXPathMarshallingStrategy.unmarshal(Object, HierarchicalStreamReader, DataHolder, ConverterLookup, Mapper) |
java.lang.Object |
TreeMarshallingStrategy.unmarshal(java.lang.Object root,
HierarchicalStreamReader reader,
DataHolder dataHolder,
DefaultConverterLookup converterLookup,
ClassMapper classMapper)
Deprecated. As of 1.2, use TreeMarshallingStrategy.unmarshal(Object, HierarchicalStreamReader, DataHolder, ConverterLookup, Mapper) |
java.lang.Object |
ReferenceByIdMarshallingStrategy.unmarshal(java.lang.Object root,
HierarchicalStreamReader reader,
DataHolder dataHolder,
DefaultConverterLookup converterLookup,
ClassMapper classMapper)
Deprecated. As of 1.2, use ReferenceByIdMarshallingStrategy.unmarshal(Object, HierarchicalStreamReader, DataHolder, ConverterLookup, Mapper) |
| Uses of DataHolder in com.thoughtworks.xstream.core.util |
|---|
| Methods in com.thoughtworks.xstream.core.util with parameters of type DataHolder | |
|---|---|
static CustomObjectInputStream |
CustomObjectInputStream.getInstance(DataHolder whereFrom,
CustomObjectInputStream.StreamCallback callback)
|
static CustomObjectOutputStream |
CustomObjectOutputStream.getInstance(DataHolder whereFrom,
CustomObjectOutputStream.StreamCallback callback)
|
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |


{# :copyright: Copyright 2020 Sphinx Confluence Builder Contributors (AUTHORS) :license: BSD-2-Clause (LICENSE) #}
Redirecting to compatibilities.


Timothy
1
1 Dear Timothy, This is Paul writing to you. The Messiah Jesus sent me as an apostle to tell everyone that if they are united with him, God promises to make them live both now and forever.
2 Timothy, I love you like my own son. May God our Father and the Messiah Jesus our Lord act kindly and mercifully and peacefully toward you.
3 I thank God and I serve Him because I truly want to do what he wants, just as my ancestors did. I have always remembered to pray for you, Timothy, night and day.
4 I really want to see you because I remember how you cried for me. If I see you again, I will rejoice so much.
5 I remember that you truly believe in Jesus! First, your grandmother Lois and your mother Eunice trusted their lives to Jesus Christ, and I am sure that you also trust in Jesus Christ just like they did!
6 Because you trust in Jesus, I remind you to start using again the gift God gave you when I put my hands on you and prayed for you.
7 When God's Spirit came to us, he did not cause us to be afraid; instead, he caused us to have power to obey God, to love him and others, and to control ourselves.
8 So do not think that you will have shame if you tell others about our Lord Jesus. And do not think you will have shame if you are my friend, because I am in prison because I trust in Jesus. Instead, be willing to suffer hardship as you tell others about the good news.  God did not save us because of any good works we did; instead, he saved us because he planned to give us this gift! God caused the Messiah Jesus to pass this gift to us, even before the world began.
10 Now everyone can see that God can save them, because our Savior the Messiah Jesus came and destroyed death and showed everyone the Gospel, the truth that he causes people to live forever.
11 It was for this reason that God decided to send me to be an apostle, preacher, and teacher.
12 In these tasks I suffer, but I am not ashamed, because I know and have trusted Jesus Christ, and I am convinced that he is able to protect my faith in him until the final day.
13 As you trust in Jesus Christ and love him, follow the meaning of true words, which you heard from me.
14 God is relying on you, that you will preach the good message that he gave to you. Protect that message by relying on the Holy Spirit who lives in us.
15 You know that almost all the believers in Asia have turned away from me, including Phygelus and Hermogenes.
16 But I pray that the Lord will be kind to the family of Onesiphorus. Often he helped me, and he was not ashamed that I am in prison.
17 On the contrary, when he came here to Rome, he kept searching for me until he found me.
18 May the Lord be kind to Onesiphorus on the final day. You know all the ways that he helped me in Ephesus.


Hello neatFramework Chrome Ext/Apps !
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Last updated 3 mins ago
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Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.


{% extends 'layout.html' %} {% block title %} Register {% endblock %} {% block statusbar %} {% endblock %} {% block body %}
Hello! Welcome to BlockPy. If you are an instructor, you will need to register.


Definition at line 372 of file NeodroidFBSTypes.cs.
◆ __assign()
◆ __init()
| void droid.Runtime.Messaging.FBS.FValuedVector3Point.__init |
( |
int |
_i, |
|
|
ByteBuffer |
_bb |
|
) |
| |
◆ AddPoint()
| static void droid.Runtime.Messaging.FBS.FValuedVector3Point.AddPoint |
( |
FlatBufferBuilder |
builder, |
|
|
Offset< FVector3 > |
pointOffset |
|
) |
| |
|
static |
◆ AddVal()
| static void droid.Runtime.Messaging.FBS.FValuedVector3Point.AddVal |
( |
FlatBufferBuilder |
builder, |
|
|
double |
val |
|
) |
| |
|
static |
◆ EndFValuedVector3Point()
| static Offset<FValuedVector3Point> droid.Runtime.Messaging.FBS.FValuedVector3Point.EndFValuedVector3Point |
( |
FlatBufferBuilder |
builder | ) |
|
|
static |
◆ GetRootAsFValuedVector3Point() [1/2]
◆ GetRootAsFValuedVector3Point() [2/2]
◆ StartFValuedVector3Point()
| static void droid.Runtime.Messaging.FBS.FValuedVector3Point.StartFValuedVector3Point |
( |
FlatBufferBuilder |
builder | ) |
|
|
static |
◆ ByteBuffer
| ByteBuffer droid.Runtime.Messaging.FBS.FValuedVector3Point.ByteBuffer |
|
get |
◆ Point
| FVector3 droid.Runtime.Messaging.FBS.FValuedVector3Point.Point |
|
get |
◆ Val
| double droid.Runtime.Messaging.FBS.FValuedVector3Point.Val |
|
get |
The documentation for this struct was generated from the following file:
- /home/heider/Projects/Neodroid/Unity/Examples/Assets/droid/Runtime/Messaging/FBS/NeodroidFBSTypes.cs


Betterlife Front UI
Bootstrap (currently v3.3.7) has a few easy ways to quickly get started, each one appealing to a different skill level and use case. Read through to see what suits your particular needs.
Bootstrap
Compiled and minified CSS, JavaScript, and fonts. No docs or original source files are included.
Source code
Source Less, JavaScript, and font files, along with our docs. Requires a Less compiler and some setup.
Sass
Bootstrap ported from Less to Sassfor easy inclusion in Rails, Compass, or Sass-only projects.
Bootstrap CDN
The folks over at MaxCDNgraciously provide CDN support for Bootstrap's CSS and JavaScript. Just use these Bootstrap CDNlinks.
Install with Bower
You can also install and manage Bootstrap's Less, CSS, JavaScript, and fonts using Bower:
Install with npm
You can also install Bootstrap using npm:
require('bootstrap')
will load all of Bootstrap's jQuery plugins onto the jQuery object. The
bootstrap
module itself does not export anything. You can manually load Bootstrap's jQuery plugins individually by loading the
/js/*.js
files under the package's top-level directory.
Bootstrap's
package.json
contains some additional metadata under the following keys:
-
less
- path to Bootstrap's main Lesssource file -
style
- path to Bootstrap's non-minified CSS that's been precompiled using the default settings (no customization)
Install with Composer
You can also install and manage Bootstrap's Less, CSS, JavaScript, and fonts using Composer:
Autoprefixer required for Less/Sass
Bootstrap uses Autoprefixerto deal with CSS vendor prefixes. If you're compiling Bootstrap from its Less/Sass source and not using our Gruntfile, you'll need to integrate Autoprefixer into your build process yourself. If you're using precompiled Bootstrap or using our Gruntfile, you don't need to worry about this because Autoprefixer is already integrated into our Gruntfile.
What's included
Bootstrap is downloadable in two forms, within which you'll find the following directories and files, logically grouping common resources and providing both compiled and minified variations.
jQuery required
Please note that
all JavaScript plugins require jQueryto be included, as shown in the
starter template.
Consult our
bower.json
to see which versions of jQuery are supported.
Precompiled Bootstrap
Once downloaded, unzip the compressed folder to see the structure of (the compiled) Bootstrap. You'll see something like this:
This is the most basic form of Bootstrap: precompiled files for quick drop-in usage in nearly any web project. We provide compiled CSS and JS (
bootstrap.*
), as well as compiled and minified CSS and JS (
bootstrap.min.*
). CSS
source maps(
bootstrap.*.map
) are available for use with certain browsers' developer tools. Fonts from Glyphicons are included, as is the optional Bootstrap theme.
Bootstrap source code
The Bootstrap source code download includes the precompiled CSS, JavaScript, and font assets, along with source Less, JavaScript, and documentation. More specifically, it includes the following and more:
The
less/
,
js/
, and
fonts/
are the source code for our CSS, JS, and icon fonts (respectively). The
dist/
folder includes everything listed in the precompiled download section above. The
docs/
folder includes the source code for our documentation, and
examples/
of Bootstrap usage. Beyond that, any other included file provides support for packages, license information, and development.
Compiling CSS and JavaScript
Bootstrap uses Gruntfor its build system, with convenient methods for working with the framework. It's how we compile our code, run tests, and more.
Installing Grunt
To install Grunt, you must first download and install node.js(which includes npm). npm stands for node packaged modulesand is a way to manage development dependencies through node.js.
Then, from the command line:- Install
grunt-cli
globally withnpm install -g grunt-cli
. - Navigate to the root
/bootstrap/
directory, then runnpm install
. npm will look at thepackage.json
file and automatically install the necessary local dependencies listed there.
When completed, you'll be able to run the various Grunt commands provided from the command line.
Available Grunt commands
grunt dist
(Just compile CSS and JavaScript)
Regenerates the
/dist/
directory with compiled and minified CSS and JavaScript files. As a Bootstrap user, this is normally the command you want.
grunt watch
(Watch)
Watches the Less source files and automatically recompiles them to CSS whenever you save a change.
grunt test
(Run tests)
Runs JSHintand runs the QUnittests headlessly in PhantomJS.
grunt docs
(Build & test the docs assets)
Builds and tests CSS, JavaScript, and other assets which are used when running the documentation locally via
bundle exec jekyll serve
.
grunt
(Build absolutely everything and run tests)
Compiles and minifies CSS and JavaScript, builds the documentation website, runs the HTML5 validator against the docs, regenerates the Customizer assets, and more. Requires Jekyll. Usually only necessary if you're hacking on Bootstrap itself.
Troubleshooting
Should you encounter problems with installing dependencies or running Grunt commands, first delete the
/node_modules/
directory generated by npm. Then, rerun
npm install
.
Basic template
Start with this basic HTML template, or modify these examples. We hope you'll customize our templates and examples, adapting them to suit your needs.
Copy the HTML below to begin working with a minimal Bootstrap document.
Examples
Build on the basic template above with Bootstrap's many components. We encourage you to customize and adapt Bootstrap to suit your individual project's needs.
Get the source code for every example below by
downloading the Bootstrap repository. Examples can be found in the
docs/examples/
directory.
Using the framework
Starter template
Nothing but the basics: compiled CSS and JavaScript along with a container.
Bootstrap theme
Load the optional Bootstrap theme for a visually enhanced experience.
Grids
Multiple examples of grid layouts with all four tiers, nesting, and more.
Jumbotron
Build around the jumbotron with a navbar and some basic grid columns.
Narrow jumbotron
Build a more custom page by narrowing the default container and jumbotron.
Navbars in action
Navbar
Super basic template that includes the navbar along with some additional content.
Static top navbar
Super basic template with a static top navbar along with some additional content.
Fixed navbar
Super basic template with a fixed top navbar along with some additional content.
Custom components
Cover
A one-page template for building simple and beautiful home pages.
Carousel
Customize the navbar and carousel, then add some new components.
Blog
Simple two-column blog layout with custom navigation, header, and type.
Dashboard
Basic structure for an admin dashboard with fixed sidebar and navbar.
Sign-in page
Custom form layout and design for a simple sign in form.
Justified nav
Create a custom navbar with justified links. Heads up! Not too Safari friendly.
Sticky footer
Attach a footer to the bottom of the viewport when the content is shorter than it.
Sticky footer with navbar
Attach a footer to the bottom of the viewport with a fixed navbar at the top.
Experiments
Non-responsive Bootstrap
Easily disable the responsiveness of Bootstrap per our docs.
Off-canvas
Build a toggleable off-canvas navigation menu for use with Bootstrap.
Tools
Bootlint
Bootlintis the official Bootstrap HTML lintertool. It automatically checks for several common HTML mistakes in webpages that are using Bootstrap in a fairly "vanilla" way. Vanilla Bootstrap's components/widgets require their parts of the DOM to conform to certain structures. Bootlint checks that instances of Bootstrap components have correctly-structured HTML. Consider adding Bootlint to your Bootstrap web development toolchain so that none of the common mistakes slow down your project's development.
Community
Stay up to date on the development of Bootstrap and reach out to the community with these helpful resources.
- Read and subscribe to The Official Bootstrap Blog.
- Chat with fellow Bootstrappers using IRC in the
irc.freenode.net
server, in the ##bootstrap channel. - For help using Bootstrap, ask on
StackOverflow using the tag
twitter-bootstrap-3
. - Developers should use the keyword
bootstrap
on packages which modify or add to the functionality of Bootstrap when distributing through npmor similar delivery mechanisms for maximum discoverability. - Find inspiring examples of people building with Bootstrap at the Bootstrap Expo.
You can also follow @getbootstrap on Twitterfor the latest gossip and awesome music videos.


Hello, and welcome to "The Bartletts" -- our little piece of the World Wide Web.
This site still is very much under construction, so please bear with us if not all of the links work just yet. And either check back often to see what's new, or even better subscribe to our {{ feed|lower }} and have the information presented to you in your favourite RSS reader.
Hope you enjoy your time with us!
- {{ paul }}, {{ rosie }} and {{ sophie }} Bartlett.


The toggle switches between two states, one showing "thing" and one showing "t". You can click on
the "t" to get a Kendo UI context menu.
Open the browser console to see the error message when toggling.
Note that the exception also breaks Aurelia DOM updates and there is sometimes only one "t" or one "thing"
also there should always be two of them.


|
|
gabrg2 -/- larval zebrafish |
Wildtype larval zebrafish |
|
[Top Left] Larval behaviour can be recorded effiently using a semi-automated behaviour setup. Here we record wildtype, and gabrg2 -/- larvae whilst in the dark, folled by sudden light exposure. On light stimulation, the KO larvae show uncontrolled, hypermotor phenotypes consistent with convulsive seizures.[Dynamic content: Behavioural recordings will autoplay ] [Bottom Left] Similar recordings can be performed in gabra1 -/- KOs and their wildtype siblings. Their phenotype is present in the larval stage, but much stronger in early juvenile stages - recordings from 2.5 weeks post fertilisation are shown here.[Dynamic content: Behavioural recordings will autoplay] |
[Right] Two raw-traces are shown for light sheet imaging performed at 40Hz. Recorded with visible laser wavelenghts, after a period of undisturbed darkness, the onset of the recording shows the neuronal response to light stimulation. |


"CANAANITE "a name given to the apostle Simon (Matthew 10:4; Mark 3:18). The word here does not, however, mean a descendant of Canaan, but is a translation, or rather almost a transliteration, of the Syriac word Kanenyeh (R.V. rendered “Cananaen”), which designates the Jewish sect of the Zealots. Hence he is called elsewhere (Luke 6:15) “Simon Zelotes;” i.e., Simon of the sect of the Zealots. (See SIMON.)
"CANAANITE "a name given to the apostle Simon (Matthew 10:4; Mark 3:18). The word here does not, however, mean a descendant of Canaan, but is a translation, or rather almost a transliteration, of the Syriac word Kanenyeh (R.V. rendered “Cananaen”), which designates the Jewish sect of the Zealots. Hence he is called elsewhere (Luke 6:15) “Simon Zelotes;” i.e., Simon of the sect of the Zealots. (See SIMON.)


Antivirus/Firewall
Try allowing the app through your firewall and/or deleting gmfile.dll
in your LioranBoard Receiver folder.
Voicemeeter
If you are running Voicemeeter, you need to launch it AFTER LioranBoard.
Alternatively, the default output audio device needs to be set to the Voicemeeter virtual device.
Faulty sound drivers You can try reinstalling your sound drivers.


{% extends "layout_bsa.html" %} {% block page_title %} NHS Penalty Charge Notice {% endblock %} {% block content %} BETA This is a new service – your feedback will help us to improve it. Back Challenge a Penalty Charge Notice Did you have one of these between {{checkdate}}? {% if (ticked != "D") and (gender != 'M') %} A valid maternity exemption certificate {% endif %} {% if (ticked != "E") %} A valid medical exemption certificate {% endif %} {% if (ticked != "F") %} A valid prescription prepayment certificate {% endif %} {% if (ticked != "L") %} A valid HC2 certificate for help with health costs {% endif %} {% if (ticked != "M") %} A valid NHS tax credit exemption certificate {% endif %} or No, none of these Cancel {% endblock %}
BETA This is a new service – your feedback will help us to improve it.
Challenge a Penalty Charge Notice
or
Cancel


Toggle navigation
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Session
Empty Session
Save Session
Previous Sessions
Foo
Narf
Add Graph
TimePlot
Histogram
Equaliser
{% include "graph.html" %}
{% include "controlpanel.html" %} {% include "parameters.html" %}


Convert a bounding box to a Well Known Text polygon, and a WKT to a bounding box
bbox2wkt(minx = NA, miny = NA, maxx = NA, maxy = NA, bbox = NULL) wkt2bbox(wkt)
bbox2wkt returns an object of class charactere, a Well Known Text string of the form 'POLYGON((minx miny, maxx miny, maxx maxy, minx maxy, minx miny))'
wkt2bbox returns a numeric vector of length 4, like c(minx, miny, maxx, maxy).
# Convert a bounding box to a WKT ## Pass in a vector of length 4 with all values bbox2wkt(bbox = list(c(-125.0,38.4,-121.8,40.9)))#> [1] "POLYGON((-125 38.4,-125 40.9,-121.8 40.9,-121.8 38.4,-125 38.4))"## Or pass in each value separately bbox2wkt(-125.0, 38.4, -121.8, 40.9)#> [1] "POLYGON((-125 38.4,-125 40.9,-121.8 40.9,-121.8 38.4,-125 38.4))"# Convert a WKT object to a bounding box wkt <- "POLYGON((-125 38.4,-125 40.9,-121.8 40.9,-121.8 38.4,-125 38.4))" wkt2bbox(wkt)#> min_x min_y max_x max_y #> 1 -125 38.4 -121.8 40.9identical( bbox2wkt(-125.0, 38.4, -121.8, 40.9), "POLYGON((-125 38.4,-125 40.9,-121.8 40.9,-121.8 38.4,-125 38.4))" )#> [1] TRUEidentical( c(-125.0, 38.4, -121.8, 40.9), as.numeric( wkt2bbox( "POLYGON((-125 38.4,-125 40.9,-121.8 40.9,-121.8 38.4,-125 38.4))" ) ) )#> [1] TRUE


Review Configuration
Model Framework:
Review Enpoint
Model Path
folder-plus
Output Path
folder-plus
Review
Cancel


Having trouble, you can also paste this link into your browser
I look forward to seeing your profile and extend a warm welcome to you on our platform.
Kind regards, CEO of Resources
This email was sent to at. You are receiving this email because someone (hopefully you!) used your email address to sign up for Resumes-the place for contractors, and subcontractors. If it wasn't you, safely disregard this email.


Build your own magical light installation in just a few clicks.
Drag that Window to your projector screen. Press F11 to go fullscreen


|
NewYearRain
|
Responsible for storing data for the menu scene More...
#include <MenuScene.h>
Public Member Functions | |
| MenuScene () | |
| void | SetValues (int result) |
| Sets initial data More... | |
| void | SetPositions (float windowPositionX, float windowPositionY) |
| Sets the position of elements More... | |
| bool | Input (sf::RenderWindow &renderWindow) |
| Handles events during the menu scene More... | |
| void | Update () |
| Scene menu update More... | |
| void | Draw (sf::RenderWindow &window) |
| Called in the GameRender class to render the menu scene More... | |
Responsible for storing data for the menu scene
| MenuScene::MenuScene | ( | ) |
| void MenuScene::Draw | ( | sf::RenderWindow & | window | ) |
| bool MenuScene::Input | ( | sf::RenderWindow & | renderWindow | ) |
Handles events during the menu scene
| void MenuScene::SetPositions | ( | float | windowPositionX, |
| float | windowPositionY | ||
| ) |
Sets the position of elements
| void MenuScene::SetValues | ( | int | result | ) |
Sets initial data
| void MenuScene::Update | ( | ) |
Scene menu update


Created by Chen Xi
Objective-C is a strict superset of C, which means that it’s possible to seamlessly combine both languages in the same source file.
// This is an inline comment
/* This is a block comment.
It can span multiple lines. */
// main.m
#import <Foundation/Foundation.h>
int main(int argc, const char * argv[]) {
@autoreleasepool {
double odometer = 9200.8;
int odometerAsInteger = (int)odometer;
NSLog(@"You've driven %.1f miles", odometer); // 9200.8
NSLog(@"You've driven %d miles", odometerAsInteger); // 9200
}
return 0;
}
double const pi = 3.14159;
pi = 42001.0; // Compiler error
id
The id type is the generic type for all Objective-C objects. Like a void pointer, it can store a reference to any type of object.
id mysteryObject = @"An NSString object";
NSLog(@"%@", [mysteryObject description]);
mysteryObject = @{@"model": @"Ford", @"year": @1967};
NSLog(@"%@", [mysteryObject description]);
Class
Objective-C classes are represented as objects themselves, using a special data type called Class. This lets you, for example, dynamically check an object’s type at runtime.
Class targetClass = [NSString class];
id mysteryObject = @"An NSString object";
if ([mysteryObject isKindOfClass:targetClass]) {
NSLog(@"Yup! That's an instance of the target class");
}
SEL
The SEL data type is used to store selectors, which are Objective-C’s internal representation of a method name.
SEL someMethod = @selector(sayHello);
standard alloc/init pattern
NSNumber *twentySeven = [[NSNumber alloc] initWithInt:27];
factory methods provided by Foundation Framework
NSNumber *twentySeven = [NSNumber numberWithInt:27];
int modelYear = 1990;
if (modelYear < 1967) {
NSLog(@"That car is an antique!!!");
} else if (modelYear <= 1991) {
NSLog(@"That car is a classic!");
} else if (modelYear == 2013) {
NSLog(@"That's a brand new car!");
} else {
NSLog(@"There's nothing special about that car.");
}
// Switch statements (only work with integral types)
switch (modelYear) {
case 1987:
NSLog(@"Your car is from 1987.");
break;
case 1988:
NSLog(@"Your car is from 1988.");
break;
case 1989:
case 1990:
NSLog(@"Your car is from 1989 or 1990.");
break;
default:
NSLog(@"I have no idea when your car was made.");
break;
}
// For-in loops ("Fast-enumeration," specific to Objective-C)
NSArray *models = @[@"Ford", @"Honda", @"Nissan", @"Porsche"];
for (id model in models) {
NSLog(@"%@", model);
}
Objective-C is similar to C++ in that it abstracts a class’s interface from its implementation. An interface declares the public properties and methods of a class, and the corresponding implementation defines the code that actually makes these properties and methods work.
// Car.h
#import <Foundation/Foundation.h>
@interface Car : NSObject {
// Protected instance variables
}
@property (copy) NSString *model;
- (void)drive;
@end
// Car.m
#import "Car.h"
@implementation Car {
// Private instance variables
double _odometer;
}
@synthesize model = _model; // Optional for Xcode 4.4+
- (void)drive {
NSLog(@"Driving a<EMAIL_ADDRESS>Vrooooom!", self.model);
}
@end
// main.m
#import <Foundation/Foundation.h>
#import "Car.h"
int main(int argc, const char * argv[]) {
@autoreleasepool {
Car *toyota = [[Car alloc] init];
[toyota setModel:@"Toyota Corolla"];
NSLog(@"Created a %@", [toyota model]);
toyota.model = @"Toyota Camry";
NSLog(@"Changed the car to a %@", toyota.model);
[toyota drive];
}
return 0;
}
// Car.h
+ (void)setDefaultModel:(NSString *)aModel;
// Car.m
#import "Car.h"
static NSString *_defaultModel;
@implementation Car {
...
+ (void)setDefaultModel:(NSString *)aModel {
_defaultModel = [aModel copy];
}
@end
// main.m
[Car setDefaultModel:@"Nissan Versa"];
// Car.h
- (id)initWithModel:(NSString *)aModel;
// Car.m
- (id)initWithModel:(NSString *)aModel {
self = [super init];
if (self) {
// Any custom setup work goes here
_model = [aModel copy];
_odometer = 0;
}
return self;
}
- (id)init {
// Forward to the "designated" initialization method
return [self initWithModel:_defaultModel];
}
// Car.m
+ (void)initialize {
if (self == [Car class]) {
// Makes sure this isn't executed more than once
_defaultModel = @"Nissan Versa";
}
}
// main.m
#import <Foundation/Foundation.h>
#import "Car.h"
int main(int argc, const char * argv[]) {
@autoreleasepool {
// Instantiating objects
Car *nissan = [[Car alloc] init];
NSLog(@"Created a %@", [nissan model]);
Car *chevy = [[Car alloc] initWithModel:@"Chevy Corvette"];
NSLog(@"Created a %@, too.", chevy.model);
}
return 0;
}
// Car.h
#import <Foundation/Foundation.h>
@interface Car : NSObject
@property BOOL running;
@end
// Car.m
#import "Car.h"
@implementation Car
@synthesize running = _running; // Optional for Xcode 4.4+
@end
- (BOOL)running {
return _running;
}
- (void)setRunning:(BOOL)newValue {
_running = newValue;
}
// main.m
#import <Foundation/Foundation.h>
#import "Car.h"
int main(int argc, const char * argv[]) {
@autoreleasepool {
Car *honda = [[Car alloc] init];
honda.running = YES; // [honda setRunning:YES]
NSLog(@"%d", honda.running); // [honda running]
}
return 0;
}
honda.running code actually calls setRunning: when you assign a value to it and the running method when you read a value from it.
If you don’t like @property’s default naming conventions, you can change the getter/setter method names with the getter= and setter= attributes.
@property (getter=isRunning) BOOL running;
Car *honda = [[Car alloc] init];
honda.running = YES; // [honda setRunning:YES]
NSLog(@"%d", honda.running); // [honda isRunning]
NSLog(@"%d", [honda running]); // Error: method no longer exists
The readonly attribute is an easy way to make a property read-only. It omits the setter method and prevents assignment via dot-notation, but the getter is unaffected.
#import <Foundation/Foundation.h>
@interface Car : NSObject
@property (getter=isRunning, readonly) BOOL running;
- (void)startEngine;
- (void)stopEngine;
@end
// Car.m
#import "Car.h"
@implementation Car
- (void)startEngine {
_running = YES;
}
- (void)stopEngine {
_running = NO;
}
@end
Car *honda = [[Car alloc] init];
[honda startEngine];
NSLog(@"Running: %d", honda.running);
honda.running = NO; // Error: read-only property
@property (nonatomic) NSString *model;
// Accessors
- (BOOL)isRunning;
- (void)setRunning:(BOOL)running;
// Action methods
- (void)driveForDistance:(double)theDistance;
- (void)driveFromOrigin:(id)theOrigin toDestination:(id)theDestination;
// Constructor methods
- (id)initWithModel:(NSString *)aModel;
- (id)initWithModel:(NSString *)aModel mileage:(double)theMileage;
// Comparison methods
- (BOOL)isEqualToCar:(Car *)anotherCar;
[porsche initWithModel:@"Porsche"];
[porsche initWithModel:@"Porsche" mileage:42000.0];
[[Car alloc] init];
Selectors are Objective-C’s internal representation of a method name. They let you treat a method as an independent entity, enabling you to separate an action from the object that needs to perform it.
// main.m
#import <Foundation/Foundation.h>
#import "Car.h"
int main(int argc, const char * argv[]) {
@autoreleasepool {
Car *porsche = [[Car alloc] init];
porsche.model = @"Porsche 911 Carrera";
SEL stepOne = NSSelectorFromString(@"startEngine");
SEL stepTwo = @selector(driveForDistance:);
SEL stepThree = @selector(turnByAngle:quickly:);
// This is the same as:
// [porsche startEngine];
[porsche performSelector:stepOne];
// This is the same as:
// [porsche driveForDistance:[NSNumber numberWithDouble:5.7]];
[porsche performSelector:stepTwo
withObject:[NSNumber numberWithDouble:5.7]];
if ([porsche respondsToSelector:stepThree]) {
// This is the same as:
// [porsche turnByAngle:[NSNumber numberWithDouble:90.0]
// quickly:[NSNumber numberWithBool:YES]];
[porsche performSelector:stepThree
withObject:[NSNumber numberWithDouble:90.0]
withObject:[NSNumber numberWithBool:YES]];
}
NSLog(@"Step one: %@", NSStringFromSelector(stepOne));
}
return 0;
}


'Sign him' - Chelsea fans make Aston Villa transfer demand after spotting this
Latest Aston Villa news | Chelsea fans are calling on John Terry to return to Stamford Bridge after the AVFC assistant's sublime goal in training
by Shane Ireland, https://www.facebook.com/ShaneIrelandJournalistChelsea fans want to see John Terry return to the club after the Aston Villa assistant scored a sublime volley on training.
Terry still enjoys legendary status at Stamford Bridge having left Chelsea at the end of his contract to initially join Villa as a player in 2017.
He made over 700 appearances for the club in a 22-year stint which began as a 14-year-old, winning the Premier League and FA Cup on five occasions and three League Cup as well as captaining them to Champions League and Europa League glory.
Terry hung up his boots last year and returned to Villa last October as assistant head coach to Dean Smith, who delivered promotion back to the top flight in May.
Aston Villa latest
This is your round up of all things Villa; the one-stop shop that will keep you updated on the latest goings on at the club and beyond.
Transfers, injuries, match-days and managers, we’ve got you covered.
We’ll be bringing the very latest on the Villains throughout the week and around the clock.
Make sure you keep yourself updated with our handy daily catch up...
Must reads...
- Villa fans make Chelsea transfer demand
- 'Worried' fans react to major announcement
- Former Villan reveals Newcastle bombshell
- Morelos saga takes new twist
For all your latest AVFC news, opinion, analysis and transfer gossip, click here
You can follow our Villa reporter Ashley Preece on Twitter, and his email address is<EMAIL_ADDRESS>Terry - known for his deadly poaching instinct at set-pieces - showed he still has it in training this week.
The 38-year-old volleyed in from distance when the ball sat up just right for him with the goalkeeper standing no chance.
Predictably, Bodymoor Heath cameras caught the strike from all angles and Chelsea fans were quick to spot the clip and asked Terry to return to Stamford Bridge, reports the Daily Star.
One supporter said: “You’ve still got it, JT. When is [Frank] Lampard signing you?”
Another commented: “Sign him up again.”
“The comeback is on,” a third said.
And another added: “Frank, sign him.”
Under Terry's pal Lampard, Chelsea are third in the Premier League with 26 points from 12 games having won their last six on the bounce.


Adding Text ¶
To add text, click the note icon on the right end of the controls. A text window will popup. The intent was to provide two types of annotation: notes and titles. The icon in the upper left toggles between and to switch between text sizes, as shown in Figures 47A and 47B. The icon increases the text size to generate a title. The icon decreases the text size back to a font more suitable for notes. The text window can be closed and resized using the and icons, respectively. The icons are only visible when the mouse is in the window. Figure 48 provides an example showing both types of annotation. Notes in combination with bookmarks can be used to draw the attention of project members to discoveries or generate explanatory visualizations that can be self-contained.


Take the Pledge
Home
Get the Facts
Share Your Story
Act Now
Become an Individual Endorser of Born Perfect NC
I Am A: (Choose 1 or More)
Faith Leader
Mental Health Professional
Child Welfare Advocate
Survivor of 'Conversion Therapy'
Loved One of a 'Conversion Therapy' Survivor
Other Supporter of LGBTQ Youth


Click
here
to go back to the home page.
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draftkings.com
and
fanduel.com
,respectively.
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---
layout: quickstart
title: Quickstart Guides
---
These quickstarts are designed to get you started with the Clarify API quickly. With the exception of Human Transcripts, any of these quickstarts should take you 5-10 minutes to fully implement.
Media Search
The Media Search Quickstart demonstates how to get started. The code samples walk you through getting a helper library, loading your first audio file, searching for a few keywords, and exploring the results.
Multi-language Support
The Multi-Language Quickstart demonstrates how to get started with multiple languages. The included code walks you through getting a helper library, loading a French media file, searching for a French terms, and exploring the results.
Human Transcripts
The Human Transcript Quickstart demonstrates how to take any English language audio and get a transcript. The code samples walk you through getting a helper library, loading your audio file, requesting the transcript, and receiving the results.
Keywords Insight
The Keyword Insight Quickstart demonstrates how to quickly extract the most frequently said words from any audio or video file. The code samples walk you through getting set up, loading your audio/video, requesting keywords, and receiving the results.
Topics Insight
The Topics Insight Quickstart demonstrates how to quickly extract the important topics from any audio or video file. The code samples walk you through getting set up, loading your audio/video, requesting topics, and receiving the results.


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Like everything else in Python, functions are objects. Since they are objects, they can be supplied as arguments to other functions.
We can test this concept by creating an example function that simply runs other functions:
def function_runner(f):
""" (function) -> NoneType
Call f.
"""
f()
|
We can test this concept by having it call one of two other functions:
def function_1():
print("function_1 was called.")
def function_2():
print("function_2 was called.")
|
If we call
function_runner
passing it the name of
function_1
,
function_runner(function_1)
|
we get the results:
If we call it using
function_2
function_runner(function_2)
|
we see the following:


POINTS2D description of functions operating on points A point is defined by its two cartesian coordinate, put into a row vector of 2 elements: P = [x y]; Several points are stores in a matrix with two columns, one for the x-coordinate, one for the y-coordinate. PTS = [x1 y1 ; x2 y2 ; x3 y3]; Example P = [5 6]; See also: centroid, polarPoint angle2Points, angle3Points, angleSort distancePoints, minDistancePoints pointOnLine transformPoint, clipPoints, drawPoint ------ Author: David Legland e-mail<EMAIL_ADDRESS>Created: 2008-10-13, using Matlab <IP_ADDRESS>7 (R2007a) Copyright 2008 INRA - BIA PV Nantes - MIAJ Jouy-en-Josas. Licensed under the terms of the LGPL, see the file "license.txt"


- For those who want to think their phone has a macOS problem. Or not. It looks cool as well.
-
I don't know for sure right now if this works on SnowBoard, or any other theme application, so install at your own risk!
If someone wants to test them, go ahead! Just please report it to me on Twitter.
Depends on:
Preview
In this version
- Just made it less resource intensive by lowering the size of the image, whilst keeping the quality.


Preston Market Hall reveals new canopy design to protect traders and shoppers from freezing temperatures
by Matthew CalderbankA new canopy design has been unveiled at Preston Market Hall to help protect traders and shoppers from the venue's freezing winter temperatures.
Preston City Council has temporarily installed a prototype section of the proposed canopy, which is designed to improve conditions in the Market Hall when it is completed next year.
Made from steel frames and filled with clear plastic panels, the new canopy is designed to reduce draught which led to uncomfortable conditions for traders and shoppers last winter.
The Council said the new structure will sit above the internal walkways between stalls, and will preserve the traditional appearance of the underside of the original 1875 canopy.
The prototype is situated in the north west corner of the Market Hall, close to Brew + Bake cafe and Pickles of Preston.
The Council has not provided an estimated completion date, but said timescales for the project are currently being planned with tenants.
But the Council said work on the new canopy is expected to begin soon.
Traders have complained of freezing temperatures in the multi-million pound facility since it reopened in February 2018.
The draughty glass building, which cost the Council £3million, has even been nicknamed "The Ice Cube” among stall holders.
Last year, the Council installed two sets of automatic sliding doors on the breezy western side of the market – opposite the top of Orchard Street – to block off the draughts.
Funding for the new canopy was agreed by Preston City Council on August 22, as part of a major capital investment scheme to improve conditions in the Market Hall.
Sam Livesey, chairman of the Traders Association at Preston Markets said: "This prototype is a good first step in the Council’s solution to the current conditions in the Market Hall.
"The traders will be working with the council to decide an installation plan that will cause the least disruption, while installing the new canopies as quickly as possible."
READ MORE: Traders feeling the chill inside Preston's new market hall
READ MORE: Bid to beat big freeze in Preston's 'ice cube' market hall
Neil Fairhurst, deputy chief executive at Preston City Council added: "This is another step forward for the improvements agreed at Council for the Market Hall.
"Preston Markets is full of excellent products and we need Prestonians to support these local businesses and enjoy what Preston Markets has to offer especially over the upcoming festive period."
.


Learn Network Structure¶
-
finmarketpy.network_analysis.learn_network_structure.
learn_network_structure
(ts_returns_data, names, alphas=4, cv=5, mode='cd', assume_centered=False, n_components=2, n_neighbors=5, eigen_solver='dense', method='standard', neighbors_algorithm='auto', random_state=None, n_jobs=None, standardise=False)¶ - ts_returns_dataarray-like of shape [n_samples, n_instruments]
time series matrix of returns
- namesarray-like of shape [n_samples, 1]
Individual names of the financial instrument
- alphasint or positive float, optional
Number of points on the grids to be used
- cvint, optional
Number of folds for cross-validation splitting strategy
- modestr, optional
Solver to use to compute the graph
- assume_centeredbool, optional
Centre the data if False.
- n_componentsint
Number of components for the manifold
- n_neighbors: int
Number of neighbours to consider for each point
- eigen_solverstr
Algorithm to compute eigenvalues
- methodstr
Algorithm to use for local linear embedding
- neighbors_algorithmstr
Algorithm to use for nearest neighbours search
- random_stateint, RandomState instance or None, optional
If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator. If None, the random number generator is the RandomState instance used by np.random. Used when eigen_solver == ‘arpack’
- n_jobsint or None, optional
number of parallel jobs to run
- standardisebool
standardise data if True
Returns : sklearn.covariance.graph_lasso_.GraphicalLassoCV
sklearn.manifold.locally_linear.LocallyLinearEmbedding
array-like of shape [n_components, n_instruments] Transformed embedding vectors
array-like of shape [n_instruments, 1] numeric identifier of each cluster


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{% extends "clan/update/free.html" %}
{% load i18n %}
{% block promo_base_content %}
With free plan, you can update the data no more frequently than once every three days. To update the data you need to visit this page and click the "Refresh data".
When you switch to base plan, update will occur automatically every day.
The cost of servicing the base plan is ${{base_mode_usd}} per {{base_mode_days}} days per clan.
{% endblock %}
{% block promo_premium_content %}
Premium plan includes all the features of the base plan, plus some additional detailed information.
The cost of servicing the premium mode is ${{prem_mode_usd}} per {{prem_mode_days}} days per clan.
{% endblock %}


FALLEN RATING: 7 / 10 --> Good movie
For more reviews and movie trivia, visit http://www.joblo.com/
PLOT: Prize cop is haunted by the soul of a murderer whom he helped capture. Even after the execution of said killer, the spirit of the man returns in others, and is passed around different bodies by the slightest touch.
CRITIQUE: A fascinating and creepy premise, as well as an authentic performance by Washington, and an inventive stylish look, wrestle this film from its "just another serial killer" roots, and qualify it as an interesting, occasionally suspenseful supernatural thriller. Some aspects of the film didn't work for me, like Washington's home life, the way the killer kept leaving cryptic clues which Washington always ended up finding and solving (how original!), and the final, final scene, which seemed tacked on for obvious reasons. That aside, the film kept me interested with its original tale of the Azazel spirit switch, a little action, and some suspense. But I will admit that I felt like they could've done so much more with the subject matter at hand. Other than Washington, the cast were throwaways like Gandolfini in a funny-looking fu-manchu mustache, Donald Sutherland playing yet another authoritative gruffy ol' coot, and John Goodman stapling himself into his mainstay role of the main cop's buddy partner. I also enjoyed the resolution that Washington deduced by the film's slick conclusion. All in all, a decent serial killer thriller that doesn't come close to the ultimate in the genre, SEVEN (9.5/10), but does come up with an intriguing premise, a solid performance from Washington, and just enough suspense to keep you on your toes for its two hour runtime. Just remember..."Time is on your side". :)
Little known facts about the movie and its stars: Screenwriter Nicholas Kazan is the son of infamous director Elia Kazan.
Review Date: April 5, 1999 Director: Gregory Hoblit Writer: Nicholas Kazan Producers: Charles Roven and Dawn Steel Actors: Denzel Washington as John Hobbes Embeth Davidtz as Gretta Milano John Goodman as Jonesy Genre: Thriller Year of Release: 1998
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Tony Arnold,
Kevin Ballard,
Yann Bizeul,
Ethan Bold,
Eric Casadei,
Nelumbo Chan,
Jackie Chappell,
James Cox,
Wincent Colaiuta,
Nathan Day,
Dan Dickinson,
Brian Donovan,
Alex Eiser,
Steve Gehrman,
Nik Gervae,
Kathy Ho,
Nicholas Jitkoff,
Uli Kusterer,
Brent Lew,
Merlin Mann,
Cédric Menard,
Chris Messina,
Allan Odgaard,
David Remahl,
Jonathan Terleski,
Collin Westlake
Hosting and Web Support generously provided by
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Rapper Lil Reese gives update on health and shares new single following shooting incident
The rapper was shot in the neck last week
Rapper Lil Reese has given fans an update on his health following a shooting incident which left him in a “critical condition.”
As first reported by ABC News Chicago, the rapper, real name Tavares Taylor, was shot in the neck last week (November 11) when his vehicle stopped at an intersection in the County Club Hills, Illinois.
Witnesses reported seeing a male suspect exit a separate vehicle and then shoot Taylor with “a small rifle” before fleeing, according to Consequence of Sound.
Now, Lil Reese has reassured fans after sharing a more positive update on his health, together with some new music that he recorded prior to the shooting. You can listen to his new single, ‘Come Outside’, below.
Posting on Instagram, the rapper shared a photo of his recent injury with the caption “Lifes a gamble I got hella luck.”
Later on, he also tweeted his thanks to fans for sending messages of support: “I appreciate everybody keeping me in they prayers tho I can’t lie thank y’all no matter what you do out here always talk to god.”
Following the incident last week, reports emerged that suggested Lil Reese was already at the hospital when police arrived on the scene.
He was reportedly taken to the Advocate South Suburban Hospital immediately following the incident by an unidentified man. He was later transferred to Christ Medical Center in Oak Lawn.
When police arrived on the scene, officers reportedly found Taylor’s vehicle abandoned with blood stains on the driver’s seat and by the door. The police investigation is still ongoing.
NME has reached out to representatives of Lil Reese for comment.


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Ensemble Methods¶
Ensemble methods provide multi-label classification compatible ensemble methods, where a number of estimators (or classifiers) are used to gather a number of predictions, and then obtain votes by majority vote or averaging. This is expected to achieve better results, as the diversity of classifiers (optimally) works as an error correction to the other classifiers.
-
class
skml.ensemble.
EnsembleClassifierChain
(estimator, number_of_chains=10, threshold=0.5, max_features=1.0)[source]¶ Methods
fit
(X, y)Fit underlying estimators. get_params
([deep])Get parameters for this estimator. predict
(X)Predicts the labels for the given instances. score
(X, y[, sample_weight])Returns the mean accuracy on the given test data and labels. set_params
(**params)Set the parameters of this estimator. -
__init__
(estimator, number_of_chains=10, threshold=0.5, max_features=1.0)[source]¶ Ensemble of classifier chains (ECC) trains an ensemble of bagged classifier chains. Each chain is trained on a randomly sampled subset of the training data (with replacement, also known as bagging).
Parameters: estimator : scikit-learn compatible classifier instance.
Will be copied (with all hyperparameters) before use, hence will be left untouched.
number_of_chains : number, default = 10
Number of chains the ensemble shall train
threshold : number in [0,1], default = 0.5
Decision threshold to assign a label or not. Has to be between 0 and 1.
max_features : number in [0, 1], default = 1.0
Fractions of features to use at once.
Returns: ensemble classifier chain instance
-


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
inputs_real = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], name='inputs_real')
inputs_z = tf.placeholder(tf.float32, [None, z_dim], name='inputs_z')
learning_rate = tf.placeholder(tf.float32, None, name='learning_rate')
return inputs_real, inputs_z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).
def discriminator(images, reuse=False, alpha=0.1):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
with tf.variable_scope('discriminator', reuse=reuse):
# Layer 1; convloution kernal = 5, subsample stride = 2, 32x32x3 => 16x16x32
conv1 = tf.layers.conv2d(images, 32, 5, strides=2, activation=None, padding='same', use_bias=False)
conv1 = tf.layers.batch_normalization(conv1, training=True)
conv1 = tf.maximum(alpha*conv1, conv1) # Leaky RELU
# Layer 2; convloution 16x16x32 => 8x8x64
conv2 = tf.layers.conv2d(conv1, 64, 5, strides=2, activation=None, padding='same', use_bias=False)
conv2 = tf.layers.batch_normalization(conv2, training=True)
conv2 = tf.maximum(alpha*conv2, conv2) # Leaky RELU
# Layer 3; convloution 8x8x64 => 4x4x128
conv3 = tf.layers.conv2d(conv2, 128, 5, strides=2, activation=None, padding='same', use_bias=False)
conv3 = tf.layers.batch_normalization(conv3, training=True)
conv3 = tf.maximum(alpha*conv3, conv3) # Leaky RELU
# flatten the convolution to feed into the fully connected layers
size = conv3.get_shape().as_list()
feature_size = size[1]*size[2]*size[3] # get the number of features to fead into the NN
flatten = tf.reshape(conv3, (-1, feature_size))
# Layer 4; Fully connect, 32x32x128 = 2048 => 512
den1 = tf.layers.dense(flatten, feature_size//4, use_bias=False)
den1 = tf.layers.batch_normalization(den1, training=True)
den1 = tf.maximum(alpha*den1, den1)
# Layer 5; Fully connect, 512 => 128
den2 = tf.layers.dense(den1, feature_size//16, use_bias=False)
den2 = tf.layers.batch_normalization(den2, training=True)
den2 = tf.maximum(alpha*den2, den2)
# Layer 6; Fully connect, 128 => 1
logits = tf.layers.dense(den2, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
with tf.variable_scope("generator", reuse=not is_train):
alpha=0.2
"""
# Layer 1; fully connected (z => 8192) => convolution (4 x 4 x 512)
dens2 = tf.layers.dense(z, 7*7*512)
conv1 = tf.reshape(dens2, (-1, 7, 7, 512)) # reshape to the first convolution layer
conv1 = tf.layers.batch_normalization(conv1, training=is_train)
conv1 = tf.maximum(alpha * conv1, conv1)
# Layer 2; convolution (8 x 8 x 512) => (16 x 16 x 256)
conv3 = tf.layers.conv2d_transpose(conv1, 128, 5, strides=2, padding='same', use_bias=False)
conv3 = tf.layers.batch_normalization(conv3, training=is_train)
conv3 = tf.maximum(alpha * conv3, conv3)
#res2 = tf.image.resize_images(conv3, [16,16])
# Layer 3; convolution (28 x 28 x 128) => (28 x 28 x out_channel_dim)
logits = tf.layers.conv2d_transpose(conv3, out_channel_dim, 5, strides=2, padding='same')
"""
# Dense Layer 1; Fully connect, z => 1024
#den1 = tf.layers.dense(z, 1024, use_bias=False)
#den1 = tf.layers.batch_normalization(den1, training=True)
#den1 = tf.maximum(alpha*den1, den1)
# Layer 1; fully connected (z => 8192) => convolution (4 x 4 x 512)
dens2 = tf.layers.dense(z, 4*4*512)
conv1 = tf.reshape(dens2, (-1, 4, 4, 512)) # reshape to the first convolution layer
conv1 = tf.layers.batch_normalization(conv1, training=is_train)
conv1 = tf.maximum(alpha * conv1, conv1)
# Layer 2; convolution (4 x 4 x 512) => (8 x 8 x 256)
conv2 = tf.layers.conv2d_transpose(conv1, 256, 3, strides=2, padding='same', use_bias=False)
conv2 = tf.layers.batch_normalization(conv2, training=is_train)
conv2 = tf.maximum(alpha * conv2, conv2)
# Layer 3; convolution (8 x 8 x 256) => (16 x 16 x 128)
conv3 = tf.layers.conv2d_transpose(conv2, 128, 5, strides=2, padding='same', use_bias=False)
conv3 = tf.layers.batch_normalization(conv3, training=is_train)
conv3 = tf.maximum(alpha * conv3, conv3)
# Layer 4; convolution (16 x 16 x 128) => (32 x 32 x out_channel_dim)
conv4 = tf.layers.conv2d_transpose(conv3, out_channel_dim, 5, strides=2, padding='same')
logits = tf.image.resize_images(conv4, [28,28])
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real, alpha=0.1)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=0.1)
smooth = 0.1
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_model_real) * (1 - smooth)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
g_vars = [v for v in tf.trainable_variables() if v.name.startswith('generator')]
d_vars = [v for v in tf.trainable_variables() if v.name.startswith('discriminator')]
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1)\
.minimize(d_loss, var_list=d_vars)
# https://blog.metaflow.fr/tensorflow-mutating-variables-and-control-flow-2181dd238e62
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
g_updates = [opt for opt in update_ops if opt.name.startswith('generator')]
with tf.control_dependencies(g_updates):
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1)\
.minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
tf.reset_default_graph()
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
inputs_real, inputs_z, learn_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
d_loss, g_loss = model_loss(inputs_real, inputs_z, data_shape[3])
d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
steps = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# create tensorboard session at location log_path and save the graph there
log_path = '/tmp/tensorboard/data/mnist'
writer = tf.summary.FileWriter(log_path, graph=sess.graph)
for epoch_i in range(epoch_count):
for image in get_batches(batch_size):
# TODO: Train Model
steps += 1
image = image*2
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(100, z_dim))
# Run optimizers
_ = sess.run(d_opt, feed_dict={inputs_real: image, inputs_z: batch_z, learn_rate: learning_rate})
_ = sess.run(g_opt, feed_dict={inputs_z: batch_z, learn_rate: learning_rate})
#_ = sess.run(g_opt, feed_dict={inputs_z: batch_z, learn_rate: learning_rate})
if steps % 20 == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({inputs_z: batch_z, inputs_real: image})
train_loss_g = g_loss.eval({inputs_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
if steps % 200 == 0:
show_generator_output(sess, 25, inputs_z, data_shape[3], data_image_mode)
# display rge output of the trained network
show_generator_output(sess, 25, inputs_z, data_shape[3], data_image_mode)
print("\nFinished")
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.1
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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