Good day, My name is david ojoh.I am a 21yrs old student. My father was a Timber and COCOA merchant . He was poisoned to death by his business associates on one of their outings on a business trip. My mother died when we were young and before the death of our father on 15th October, 2004 in a private hospital , he called me on his bed side and told me of a secret deposit he made with one of the prime deposit and security Company in Abidjan-Cote D'Ivoire. He deposited the sum of Ten Million, five hundred thousand United State Dollars.(USD$10.500,000) in a box and declare it as African Arts which he told me that he used my name as the next of Kin for the box deposit. He also explained to me that it was because of his wealth that he was poisoned by his busines associates. He advised me to seek for whom I can confide in to transfer the fund for onward investment. I am willing to offer you a reasonable amount of this money as compensation for your efforts to retrieve the box from the security office and transfer the money into your nominated Bank account. I would like you to send me your telephone and fax numbers for easy communication so that I can also send you the documents that covers the deposit of the trunk box. I am waiting for your kind response. Your's Sincerely, david ojoh. _________________________________________________________________ Testez Windows Llive Mail Beta ! http://www.msn.fr/newhotmail/Default.asp?Ath=f


Default properties and methods of
the User prototype.
The built-in User prototype.
Normally, the User constructor is never called directly and the app.registerUser method is used instead to create new users. The User prototype inherits from HopObject and you can customize it by creating a User directory in your app, like you would for any other prototype that extends from HopObject.
Normally, the User constructor is never called directly and the app.registerUser method is used instead to create new users. The User prototype inherits from HopObject and you can customize it by creating a User directory in your app, like you would for any other prototype that extends from HopObject.
methods
- onLogout()
see
Methods
If defined, the onLogout handler is called when the user is logged out.
see


Hive Mind - Hive Mind – The luck of the Sapiens: the weird fortuitousness of global warming - True/Slant
Apparently, all that fossil fuel may have actually helped prevent an ice age. I remain unconvinced that global warming is a problem. Frankly, I prefer my globes warm.


#include <IOSystem.hpp>
Public Member Functions | |
| IOSystem () | |
| Default constructor. | |
| virtual | ~IOSystem () |
| Virtual destructor. | |
| AI_FORCE_INLINE bool | Exists (const std::string &pFile) const |
| For backward compatibility. | |
| virtual bool | Exists (const char *pFile) const =0 |
| Tests for the existence of a file at the given path. | |
| virtual char | getOsSeparator () const =0 |
| Returns the system specific directory separator. | |
| virtual IOStream * | Open (const char *pFile, const char *pMode="rb")=0 |
| Open a new file with a given path. | |
| IOStream * | Open (const std::string &pFile, const std::string &pMode=std::string("rb")) |
| For backward compatibility. | |
| virtual void | Close (IOStream *pFile)=0 |
| Closes the given file and releases all resources associated with it. | |
| virtual bool | ComparePaths (const char *one, const char *second) const |
| Compares two paths and check whether the point to identical files. | |
| bool | ComparePaths (const std::string &one, const std::string &second) const |
| For backward compatibility. | |
| virtual bool | PushDirectory (const std::string &path) |
| Pushes a new directory onto the directory stack. | |
| virtual const std::string & | CurrentDirectory () const |
| Returns the top directory from the stack. | |
| virtual size_t | StackSize () const |
| Returns the number of directories stored on the stack. | |
| virtual bool | PopDirectory () |
| Pops the top directory from the stack. | |
| virtual bool | CreateDirectory (const std::string &path) |
| CReates an new directory at the given path. | |
| virtual bool | ChangeDirectory (const std::string &path) |
| Will change the current directory to the given path. |
Derive an own implementation from this interface to supply custom file handling to the importer library. If you implement this interface, you also want to supply a custom implementation for IOStream.
Definition at line 84 of file IOSystem.hpp.
| AI_FORCE_INLINE Assimp::IOSystem::IOSystem | ( | ) |
Default constructor.
Create an instance of your derived class and assign it to an Assimp::Importer instance by calling Importer::SetIOHandler().
Definition at line 235 of file IOSystem.hpp.
| AI_FORCE_INLINE Assimp::IOSystem::~IOSystem | ( | ) | [virtual] |
Virtual destructor.
It is safe to be called from within DLL Assimp, we're constructed on Assimp's heap.
Definition at line 242 of file IOSystem.hpp.
| AI_FORCE_INLINE bool Assimp::IOSystem::Exists | ( | const std::string & | pFile | ) | const |
For backward compatibility.
Definition at line 263 of file IOSystem.hpp.
00263 { 00264 // NOTE: 00265 // For compatibility, interface was changed to const char* to 00266 // avoid crashes between binary incompatible STL versions 00267 return Exists(pFile.c_str()); 00268 }
| virtual bool Assimp::IOSystem::Exists | ( | const char * | pFile | ) | const [pure virtual] |
Tests for the existence of a file at the given path.
| pFile | Path to the file |
Implemented in Assimp::DefaultIOSystem.
| virtual char Assimp::IOSystem::getOsSeparator | ( | ) | const [pure virtual] |
Returns the system specific directory separator.
Implemented in Assimp::DefaultIOSystem.
| virtual IOStream* Assimp::IOSystem::Open | ( | const char * | pFile, | |
| const char * | pMode = "rb" | |||
| ) | [pure virtual] |
Open a new file with a given path.
When the access to the file is finished, call Close() to release all associated resources (or the virtual dtor of the IOStream).
| pFile | Path to the file | |
| pMode | Desired file I/O mode. Required are: "wb", "w", "wt", "rb", "r", "rt". |
Implemented in Assimp::DefaultIOSystem.
Referenced by Open().
| AI_FORCE_INLINE IOStream * Assimp::IOSystem::Open | ( | const std::string & | pFile, | |
| const std::string & | pMode = std::string("rb") | |||
| ) | [inline] |
For backward compatibility.
Definition at line 254 of file IOSystem.hpp.
References Open().
00254 { 00255 // NOTE: 00256 // For compatibility, interface was changed to const char* to 00257 // avoid crashes between binary incompatible STL versions 00258 return Open(pFile.c_str(),pMode.c_str()); 00259 }
| virtual void Assimp::IOSystem::Close | ( | IOStream * | pFile | ) | [pure virtual] |
Closes the given file and releases all resources associated with it.
| pFile | The file instance previously created by Open(). |
Implemented in Assimp::DefaultIOSystem.
| virtual bool Assimp::IOSystem::ComparePaths | ( | const char * | one, | |
| const char * | second | |||
| ) | const [virtual] |
Compares two paths and check whether the point to identical files.
The dummy implementation of this virtual member performs a case-insensitive comparison of the given strings. The default IO system implementation uses OS mechanisms to convert relative into absolute paths, so the result can be trusted.
| one | First file | |
| second | Second file |
Reimplemented in Assimp::DefaultIOSystem.
Referenced by ComparePaths().
| AI_FORCE_INLINE bool Assimp::IOSystem::ComparePaths | ( | const std::string & | one, | |
| const std::string & | second | |||
| ) | const [inline] |
For backward compatibility.
Definition at line 272 of file IOSystem.hpp.
References ComparePaths().
00272 { 00273 // NOTE: 00274 // For compatibility, interface was changed to const char* to 00275 // avoid crashes between binary incompatible STL versions 00276 return ComparePaths(one.c_str(),second.c_str()); 00277 }
| AI_FORCE_INLINE bool Assimp::IOSystem::PushDirectory | ( | const std::string & | path | ) | [virtual] |
Pushes a new directory onto the directory stack.
| path | Path to push onto the stack. |
Definition at line 281 of file IOSystem.hpp.
00281 { 00282 if ( path.empty() ) { 00283 return false; 00284 } 00285 00286 m_pathStack.push_back( path ); 00287 00288 return true; 00289 }
| AI_FORCE_INLINE const std::string & Assimp::IOSystem::CurrentDirectory | ( | ) | const [virtual] |
Returns the top directory from the stack.
Definition at line 293 of file IOSystem.hpp.
00293 { 00294 if ( m_pathStack.empty() ) { 00295 static const std::string Dummy(""); 00296 return Dummy; 00297 } 00298 return m_pathStack[ m_pathStack.size()-1 ]; 00299 }
| AI_FORCE_INLINE size_t Assimp::IOSystem::StackSize | ( | ) | const [virtual] |
Returns the number of directories stored on the stack.
Definition at line 303 of file IOSystem.hpp.
| AI_FORCE_INLINE bool Assimp::IOSystem::PopDirectory | ( | ) | [virtual] |
Pops the top directory from the stack.
Definition at line 309 of file IOSystem.hpp.
00309 { 00310 if ( m_pathStack.empty() ) { 00311 return false; 00312 } 00313 00314 m_pathStack.pop_back(); 00315 00316 return true; 00317 }
| AI_FORCE_INLINE bool Assimp::IOSystem::CreateDirectory | ( | const std::string & | path | ) | [virtual] |
CReates an new directory at the given path.
| path | [in] The path to create. |
Definition at line 321 of file IOSystem.hpp.
00321 { 00322 if ( path.empty() ) { 00323 return false; 00324 } 00325 00326 #ifdef _WIN32 00327 return 0 != ::_mkdir( path.c_str() ); 00328 #else 00329 return 0 != ::mkdir( path.c_str(), 0777 ); 00330 #endif // _WIN32 00331 }
| AI_FORCE_INLINE bool Assimp::IOSystem::ChangeDirectory | ( | const std::string & | path | ) | [virtual] |
Will change the current directory to the given path.
| path | [in] The path to change to. |
Definition at line 335 of file IOSystem.hpp.
00335 { 00336 if ( path.empty() ) { 00337 return false; 00338 } 00339 00340 #ifdef _WIN32 00341 return 0 != ::_chdir( path.c_str() ); 00342 #else 00343 return 0 != ::chdir( path.c_str() ); 00344 #endif // _WIN32 00345 }


| Interface | Description |
|---|---|
| SVGValue |
This interface represents the values for properties like 'fill',
'flood-color'...
|
| SVGValueConstants |
This interface provides constants for SVG values.
|
| Class | Description |
|---|---|
| AlignmentBaselineManager |
This class provides a manager for the 'alignment-baseline' property values.
|
| BaselineShiftManager |
This class provides a manager for the 'baseline-shift' property values.
|
| ClipPathManager |
This class provides a manager for the 'clip-path' property values.
|
| ClipRuleManager |
This class provides a manager for the 'clip-rule' property values.
|
| ColorInterpolationFiltersManager |
This class provides a manager for the 'color-interpolation' property values.
|
| ColorInterpolationManager |
This class provides a manager for the 'color-interpolation' property values.
|
| ColorManager |
This class provides a manager for the 'color' property values.
|
| ColorProfileManager |
This class provides a manager for the 'color-interpolation' property values.
|
| ColorRenderingManager |
This class provides a manager for the 'color-rendering' property values.
|
| DominantBaselineManager |
This class provides a manager for the 'alignment-baseline' property values.
|
| EnableBackgroundManager |
This class provides a manager for the 'enable-background' property values.
|
| FillRuleManager |
This class provides a manager for the 'fill-rule' property values.
|
| FilterManager |
This class provides a factory for the 'filter' property values.
|
| GlyphOrientationHorizontalManager |
This class provides a manager for the 'glyph-orientation-horizontal'
property values.
|
| GlyphOrientationManager |
This class provides a manager for the 'glyph-orientation' property values.
|
| GlyphOrientationVerticalManager |
This class provides a manager for the 'glyph-orientation-vertical'
property values.
|
| ICCColor |
This class represents an ICC color value.
|
| ImageRenderingManager |
This class provides a manager for the 'image-rendering' property values.
|
| KerningManager |
This class provides a manager for the 'kerning' property values.
|
| MarkerManager |
This class provides a manager for the 'marker-*' property values.
|
| MarkerShorthandManager |
This class represents an object which provide support for the
'marker' shorthand properties.
|
| MaskManager |
This class provides a manager for the 'mask' property values.
|
| OpacityManager |
This class provides a manager for the '*-opacity' property values.
|
| PointerEventsManager |
This class provides a manager for the 'pointer-events' property values.
|
| ShapeRenderingManager |
This class provides a manager for the 'shape-rendering' property values.
|
| SpacingManager |
This class provides a manager for the '*-spacing' property values.
|
| StrokeDasharrayManager |
This class provides a factory for the 'stroke-dasharray' property values.
|
| StrokeDashoffsetManager |
This class provides a factory for the 'stroke-dashoffset' property values.
|
| StrokeLinecapManager |
This class provides a manager for the 'stroke-linecap' property values.
|
| StrokeLinejoinManager |
This class provides a manager for the 'stroke-linejoin' property values.
|
| StrokeMiterlimitManager |
This class provides a factory for the 'stroke-miterlimit' property values.
|
| StrokeWidthManager |
This class provides a factory for the 'stroke-width' property values.
|
| SVGColorManager |
This class provides a manager for the SVGColor property values.
|
| SVGPaintManager |
This class provides a manager for the SVGPaint property values.
|
| TextAnchorManager |
This class provides a manager for the 'text-anchor' property values.
|
| TextRenderingManager |
This class provides a manager for the 'text-rendering' property values.
|
| WritingModeManager |
This class provides a manager for the 'writing-mode' property values.
|
Copyright © 2022 Apache Software Foundation. All Rights Reserved.


The timer shows you how much time you have left to guess the words and the score box tracks the points you've earned for guessing letters correctly.
For each letter guessed correctly, you get another piece of the plane built.
Click any letter to guess your word. Grayed out letters have already been used.


public class KruskalGraph<E extends SpanningTreeEdgeData> extends UndirectedWeightedEdgeGraph<E,UndirectedKruskalVertex<E>>
DEFAULT_WEIGHT_EPSILON
| Constructor and Description |
|---|
KruskalGraph()
Initialize graph to a default initial capacity of 16 vertices
|
KruskalGraph(java.util.Collection<UndirectedKruskalVertex<E>> vertices)
Use the vertices in a collection as the initial vertices in the graph
|
KruskalGraph(java.util.Collection<UndirectedKruskalVertex<E>> vertices,
double weightEpsilon)
Use the vertices in a collection as the initial vertices in the graph and
set weight epsilon to the specified value.
|
KruskalGraph(double weightEpsilon)
Initialize an empty graph using the given value for weightEpsilon and default initial capacity.
|
KruskalGraph(int initialCapacity)
Initialize graph to have capacity for the given number of vertices
|
KruskalGraph(int initialCapacity,
double weightEpsilon)
Initialize an empty graph to have capacity for the given number of vertices and
the specified weight epsilon.
|
areEqualWeights, getWeightedEdges, getWeightEpsilon
edgeCount
addVertex, containsEdge, containsVertex, getVertices, removeVertex, vertexCount, vertexIterator
public KruskalGraph(java.util.Collection<UndirectedKruskalVertex<E>> vertices, double weightEpsilon)
vertices
- collection of vertices that will make up the initial vertices
of the graphweightEpsilon
- weight epsilon to be used to determine weight equality
in this graphpublic KruskalGraph(double weightEpsilon)
weightEpsilon
- weight epsilon to be used to determine weight equality
in this graphpublic KruskalGraph(int initialCapacity, double weightEpsilon)
initialCapacity
- expected number of vertices in the graphweightEpsilon
- weight epsilon to be used to determine weight equality
in this graphpublic KruskalGraph()
public KruskalGraph(java.util.Collection<UndirectedKruskalVertex<E>> vertices)
vertices
- collection of vertices that will make up the initial vertices
of the graphpublic KruskalGraph(int initialCapacity)
initialCapacity
- expected number of vertices


Sweep the mines!
Select Difficulty
Piece of Cake
Right in the Middle
Nothing is Impossible
Moves
0
Time
0:00


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That did not work as planned, please try the following
Use Google Chrome / Chromium
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In very rare cases just refresh. There is a timeout of 250ms and you might have gotten unlucky


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java.lang.Object edu.biu.scapi.comm.CommunicationSetup
public class CommunicationSetup
The CommunicationSetup class is the heart of the Communications Layer. The Communications Layer package is a tool used by a client that is interested in setting up connections between itself and other parties. As such, this layer does not initiate any independent tasks, but the opposite. Given a list of parties, it attempts to set connections to them according to parameters given by the calling application. If succeeds, it returns these connections so that the calling client can send and receive data over them.
An application written for running an MPC protocol can be the client of the Communications Layer. An example of a possible usage follows:
CommunicationSetup implements the org.apache.commons.exec.TimeoutObserver interface. This interface supplies a mechanism for notifying classes that a timeout has arrived.
| Constructor Summary | |
|---|---|
CommunicationSetup()
Create a CommunicationSetup instance which is the heart of the Communication Layer. |
| Method Summary | |
|---|---|
java.util.Map<java.net.InetSocketAddress,Channel> |
prepareForCommunication(java.util.List<Party> listOfParties,
ConnectivitySuccessVerifier successLevel,
long timeOut)
An application that wants to use the communication layer will call this function in order to prepare for communication after providing the required parameters. |
java.util.Map<java.net.InetSocketAddress,Channel> |
prepareForCommunication(java.util.List<Party> listOfParties,
ConnectivitySuccessVerifier successLevel,
long timeOut,
boolean enableNagle)
An application that wants to use the communication layer will call this function in order to prepare for communication after providing the required parameters. |
void |
timeoutOccured(org.apache.commons.exec.Watchdog w)
This function is called by the infrastructure of the Watchdog if the previously set timeout has passed. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public CommunicationSetup()
| Method Detail |
|---|
public java.util.Map<java.net.InetSocketAddress,Channel> prepareForCommunication(java.util.List<Party> listOfParties, ConnectivitySuccessVerifier successLevel, long timeOut, boolean enableNagle)
Note that using this function you can choose to use or not to use the Nagle algorithm.
listOfParties
- the original list of parties to connect to. As a convention, we will set the first party in the list to be the requesting party, that is,
the party represented by the application.successLevel
- the ConnectivitySuccessVerifier algorithm to usetimeOut
- the maximum amount of time we allow for the connection stageenableNagle
- a flag indicating weather or not to use the Nagle optimization algorithm. For Cryptographic algorithms is better to have it disabled
public java.util.Map<java.net.InetSocketAddress,Channel> prepareForCommunication(java.util.List<Party> listOfParties, ConnectivitySuccessVerifier successLevel, long timeOut)
In this function, Nagle’s algorithm is disabled; for cryptographic protocols this is typically much better.
listOfParties
- the original list of parties to connect to. As a convention, we will set the first party in the list to be the requesting party, that is,
the party represented by the application.successLevel
- the ConnectivitySuccessVerifier algorithm to usetimeOut
- the maximum amount of time we allow for the connection stage
public void timeoutOccured(org.apache.commons.exec.Watchdog w)
timeoutOccured
in interface org.apache.commons.exec.TimeoutObserver
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BATMAN AND ROBIN Directed by Joel Schumacher. Written by Akiva Goldsman. Starring Arnold Schwarzenegger (Mr Freeze), George Clooney (Batman), Chris O'Donnell (Robin), Uma Thurman (Poison Ivy) and Alicia Silverstone (Batgirl).
First, some personal history. I grew up from a very early age as a tremendous fan of Batman. He was my favourite character in the animated series "Superfriends". I read his comics at every opportunity. When the "Superman" movies were in the cinemas I spent a lot of time wondering why no one made a Batman movie. In 1989 I rushed to the cinemas to see "Batman", and loved what I saw. It made me an instant Tim Burton fan (I still am), introduced me to Jack Nicholson and convinced me that Michael Keaton was one of the best actors in Hollywood. More than anything, it convinced me of the utter brilliance of Batman: as a character, a concept and as one of few genuine mythic heroes of the 20th century. In 1992 I watched "Batman Returns" and loved it just as much. When Warner Bros started a new animated series, I watched and loved that as well. I'm currently even basing part of an Honours thesis at university on the guy. My point is that out of all the fictional characters that I read, watch and love, Batman is pretty well up near the top. So when Joel Schumacher directed the abysmal "Batman Forever" in 1995, I was certain that the Batman film franchise had hit an all-time low.
I know what you think I'm going to say now, but my purpose for all that is a little different. You see, I went into "Batman And Robin" expecting quite literally the worst film I would ever see. I was expecting the most mind-bending, thought-numbing, heart-wrenching heap of manure that I had ever been forced to endure. Every friend I had who had already seen it told me not to bother: to save my money and go see something else - *anything* else.
Surprise, surprise. "Batman And Robin" is actually pretty good. Certainly it has monumental problems. Firstly, Akiva Goldsman's screenplay is badly conceived, ill-plotted and filled not so much with dialogue as with a string of unfunny one-liners and puns that would have had Adam West and Burt Ward quaking in their boots. Similarly, much of Joel Schumacher's direction is gratuitous, overly flashy and confused. The production design is hideous, with excessive neon lighting, 40 story statues built around the city and a Batmobile even sillier than the last one.
On the *other* hand... The actors all cope very well with what they're given. George Clooney is convincing as Batman, and does a reasonably good job as Bruce Wayne. Chris O'Donnell, one of the very few highlights of "Batman Forever", keeps his good effort up in his second involvement. Uma Thurman hams it up wonderfully as Poison Ivy, while Alicia Silverstone is passable in a role that Goldsman's script tends to forget with frightening regularity. It is only Arnold Schwarzenegger who sticks out in the cast, fluctuating wildly between a one-dimensional wisecracking villain and a tragic character destroyed by the events of his life. One or the other (preferably the latter of the two) would have been good. Leaping from one to the other, he merely created a confusing mess.
The music this time around is an improvement, and the Smashing Pumpkins song at the end outdoes U2's effort completely. The costumes for Batman, Robin and Batgirl are all very good, with the strange nipple fixation the only drawback to an otherwise impressive look.
 In terms of scripting and direction, it's a careering disaster in the making, it's cast holding on for grim life from one collision to another. But it's also very, very fun to watch. And let's face it, that's what these kinds of movies are all about.
SCORE: 7.5 out of 10
Review copyright (c) 1997, Grant Watson (nzone@iinet.net.au). It may be reprinted freely, as long as the author is notified.
<EMAIL_ADDRESS>"Science fiction is about the future and fantasy is about the past - the myth of the way things used to be. Science fiction is, in a sense, trying to construct a myth of the future."
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.
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Related links: index of all rec.arts.movies.reviews reviews


Dear Friends,
It would have been Dr. V’s one hundredth birthday on October 1st. Everyone who was fortunate enough to have met him will attest to the humble power of this singular man. He lives on in each of us with his quiet voice that speaks of kindness, dignity and responsibility. He had a relentless focus on what seemed to be an impossible dream and the ability to inspire others to believe that it was achievable and that they should join him on the journey, and they did.
I first met Dr. V in 1981 when I had the good fortune of spending one month at Aravind. I went there as a senior surgical resident to perfect my surgical skills before going out in to world as an ophthalmologist, but what I learned there went far beyond suturing technique. Dr. V opened my eyes to the potential in all of us for giving and service that extends way past what we alone can imagine. I immediately bonded with him in that short period of time and knew that my life would somehow always include Aravind, and here I am 37 years later. At some point he said to me that I had been with the Aravind family in an earlier life, and, amazingly, that makes perfect sense to me. It is a distinct honor for me to be part of the enormous, international Aravind Family.
Thank you, Dr. V.


Nearest Common Ancestors
问题描述 :
In the figure, each node is labeled with an integer from {1, 2,…,16}. Node 8 is the root of the tree. Node x is an ancestor of node y if node x is in the path between the root and node y. For example, node 4 is an ancestor of node 16. Node 10 is also an ancestor of node 16. As a matter of fact, nodes 8, 4, 10, and 16 are the ancestors of node 16. Remember that a node is an ancestor of itself. Nodes 8, 4, 6, and 7 are the ancestors of node 7. A node x is called a common ancestor of two different nodes y and z if node x is an ancestor of node y and an ancestor of node z. Thus, nodes 8 and 4 are the common ancestors of nodes 16 and 7. A node x is called the nearest common ancestor of nodes y and z if x is a common ancestor of y and z and nearest to y and z among their common ancestors. Hence, the nearest common ancestor of nodes 16 and 7 is node 4. Node 4 is nearer to nodes 16 and 7 than node 8 is.
For other examples, the nearest common ancestor of nodes 2 and 3 is node 10, the nearest common ancestor of nodes 6 and 13 is node 8, and the nearest common ancestor of nodes 4 and 12 is node 4. In the last example, if y is an ancestor of z, then the nearest common ancestor of y and z is y.
Write a program that finds the nearest common ancestor of two distinct nodes in a tree.
输入:
输出:
样例输入:
2 16 1 14 8 5 10 16 5 9 4 6 8 4 4 10 1 13 6 15 10 11 6 7 10 2 16 3 8 1 16 12 16 7 5 2 3 3 4 3 1 1 5 3 5
样例输出:
4 3
解题代码:
import java.util.*; @SuppressWarnings("unchecked") public class Main { int MAX = 10001; Vector< Integer> tree[] = new Vector[MAX + 1];; // ����� byte[] flag = new byte[MAX]; // �ュ害���锛��浜���炬���� int parent[] = new int[MAX];; int rank[] = new int[MAX];; int ancestor[] = new int[MAX]; int visited[] = new int[MAX]; StringBuilder sb = new StringBuilder(); byte ret; { for (int i = 1; i < MAX; i++) tree[i] = new Vector< Integer>(); } public static void main(String[] args) { Main poj = new Main(); java.util.Scanner s = new java.util.Scanner(System.in); int test = s.nextInt(); int i = 0; while (++i <= test) { int length = s.nextInt(); poj.initial(length); int a; int b; for (int j = 1; j < length; j++) { a = s.nextInt(); b = s.nextInt(); poj.flag[b] = -1; poj.tree[a].add(b); } int root; for (root = 1; root <= length; root++) if (poj.flag[root] != -1) break; a = s.nextInt(); b = s.nextInt(); poj.LCA(root, a, b); } System.out.print(poj.sb.toString()); } void initial(int n) { ret = 1; for (int i = 1; i <= n; i++) { flag[i] = 0; parent[i] = i; rank[i] = 0; ancestor[i] = 0; visited[i] = 0; tree[i].clear(); } } int find(int x) { if (x == parent[x]) return x; else parent[x] = find(parent[x]); return parent[x]; } void union(int x, int y) { int a = find(x); int b = find(y); if (a == b) return; if (rank[a] < rank[b]) parent[a] = b; else if (rank[a] > rank[b]) parent[b] = a; else { parent[a] = b; rank[b]++; } } void LCA(int u, int x, int y) { ancestor[u] = u; for (int i = 0; i < tree[u].size(); i++) { LCA(tree[u].get(i), x, y); if (ret == 0) return; union(u, tree[u].get(i)); ancestor[find(u)] = u; } visited[u] = 1; if (u == x && visited[y] == 1 && ret == 1) { sb.append(ancestor[find(y)]); sb.append('\n'); ret = 0; return; } if (u == y && visited[x] == 1 && ret == 1) { { sb.append(ancestor[find(x)]); sb.append('\n'); ret = 0; return; } } } }
网站不错，支持一个
分析的很详细！此时的数据量还是很小的，耗时却不短。这种方法确实可以，当然或许还有其他的优化方案，但是优化只能针对某些数据，不太可能在所有情况下都能在可接受的时间内求解出答案。
博主的C++功底真是好
不对吧 你算的an是前面说有的项的和
Excellent Web-site! I required to ask if I might webpages and use a component of the net web website and use a number of factors for just about any faculty process. Please notify me through email regardless of whether that would be excellent. Many thanks
辛苦了，顶
我表示楼主不要乱抄题，你写的另一个题吧，直接扒了代码没跑吧
红绯鱼：
这模板神了
A的话不是不能避免冲突么？感觉出题者在考察是否考虑了冲突解决办法？
可以用公式。主要为了说明动态规划，如果要打表的话，这种方法也是较简单和高效的
有意思，有难度


--- title: wignerD2 sidebar: function_reference_sidebar permalink: wignerD2.html folder: function_reference toc: false ---
spherical harmonics of degree l
wignerD sphericalY


What is pair programming?
Working in dyads with one person being the navigator of the code and the other person the driver. The pair works on creating the code together.
Why is pair programming important to DBC?
Helps the pair learn and find errors by having a cooperative environment. Having two people together working on the code helps to solidify and create "aha" moments.
Driver vs. Navigator
Driver
The person in the dyad that physically drives the coding by typing and determining proper syntex.
Navigator
The person who directs the driver with the appropriate generalized steps. Depending on level of competency the person will talk in pseudo code and or help with the code syntax.


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exec
(BA_OS)
creates a program's process image.
ld
(BA_OS)]
processes the shared object file with other relocatable
and shared object files to create another object file.
Second, the dynamic linker combines it with an executable file and other
shared objects to create a process image.
Created by the assembler and link editor, object files are binary representations of programs intended to be executed directly on a processor. Programs that require other abstract machines, such as shell scripts, are excluded.
After the introductory material, this chapter focuses on the file format and how it pertains to building programs. Chapter 5 also describes parts of the object file, concentrating on the information necessary to execute a program.
Object files participate in program linking (building a program) and program execution (running a program). For convenience and efficiency, the object file format provides parallel views of a file's contents, reflecting the differing needs of those activities. Figure 4-1 shows an object file's organization.
|
|
An ELF header resides at the beginning and holds a ``road map'' describing the file's organization. Sections hold the bulk of object file information for the linking view: instructions, data, symbol table, relocation information, and so on. Descriptions of special sections appear later in the chapter. Chapter 5 discusses segments and the program execution view of the file.
A program header table tells the system how to create a process image. Files used to build a process image (execute a program) must have a program header table; relocatable files do not need one. A section header table contains information describing the file's sections. Every section has an entry in the table; each entry gives information such as the section name, the section size, and so on. Files used during linking must have a section header table; other object files may or may not have one.
As described here, the object file format supports various processors with 8-bit bytes and either 32-bit or 64-bit architectures. Nevertheless, it is intended to be extensible to larger (or smaller) architectures. Object files therefore represent some control data with a machine-independent format, making it possible to identify object files and interpret their contents in a common way. Remaining data in an object file use the encoding of the target processor, regardless of the machine on which the file was created.
| Name | Size | Alignment | Purpose |
|---|---|---|---|
Elf32_Addr |
4 |
4 |
Unsigned program address |
Elf32_Off |
4 |
4 |
Unsigned file offset |
Elf32_Half |
2 |
2 |
Unsigned medium integer |
Elf32_Word |
4 |
4 |
Unsigned integer |
Elf32_Sword |
4 |
4 |
Signed integer |
unsigned char |
1 |
1 |
Unsigned small integer |
64-Bit Data Types
| Name | Size | Alignment | Purpose |
|---|---|---|---|
Elf64_Addr |
8 |
8 |
Unsigned program address |
Elf64_Off |
8 |
8 |
Unsigned file offset |
Elf64_Half |
2 |
2 |
Unsigned medium integer |
Elf64_Word |
4 |
4 |
Unsigned integer |
Elf64_Sword |
4 |
4 |
Signed integer |
Elf64_Xword |
8 |
8 |
Unsigned long integer |
Elf64_Sxword |
8 |
8 |
Signed long integer |
unsigned char |
1 |
1 |
Unsigned small integer |
Elf32_Addr
member will be aligned on a 4-byte boundary within the file.
For portability reasons, ELF uses no bit-fields.


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Computer Vision Scientist - Palo Alto, CA 94306
Responsibilities
- Establish and expand the rigorous statistical foundations of the Matroid platform.
-
Develop prototypes and execute experiments to help guide engineering efforts.
-
Explore new model families and machine learning algorithms.
-
Experience with Deep Learning and CNNs
Minimum Requirements
- Masters in Computer Science (AI/ML specialization), Statistics, Mathematics (Probability), or equivalent.
-
Strong foundations in probability, linear algebra, and optimization.
-
Background in statistical modeling and inference.
Preferred Qualifications
- Experience with large-scale industrial applications of statistical modeling and inference.
-
Experience with statistical modeling across a diverse range of data sets and domains.
-
PhD in Computer Science (AI/ML specialization), Statistics, Mathematics (Probability), or equivalent.


\
Almost every problem in machine learning and data science starts with a dataset \( X \), a model \( g(\beta) \), which is a function of the parameters \( \beta \) and a cost function \( C(X, g(\beta)) \) that allows us to judge how well the model \( g(\beta) \) explains the observations \( X \). The model is fit by finding the values of \( \beta \) that minimize the cost function. Ideally we would be able to solve for \( \beta \) analytically, however this is not possible in general and we must use some approximative/numerical method to compute the minimum.
The method of steepest descent The basic idea of gradient descent is that a function \( F(\mathbf{x}) \), \( \mathbf{x} \equiv (x_1,\cdots,x_n) \), decreases fastest if one goes from \( \bf {x} \) in the direction of the negative gradient \( -\nabla F(\mathbf{x}) \).
It can be shown that if
$$
\mathbf{x}_{k+1} = \mathbf{x}_k - \gamma_k \nabla F(\mathbf{x}_k),
$$
with \( \gamma_k > 0 \).
For \( \gamma_k \) small enough, then \( F(\mathbf{x}_{k+1}) \leq F(\mathbf{x}_k) \). This means that for a sufficiently small \( \gamma_k \) we are always moving towards smaller function values, i.e a minimum.
The previous observation is the basis of the method of steepest descent, which is also referred to as just gradient descent (GD). One starts with an initial guess \( \mathbf{x}_0 \) for a minimum of \( F \) and computes new approximations according to
$$
\mathbf{x}_{k+1} = \mathbf{x}_k - \gamma_k \nabla F(\mathbf{x}_k), \ \ k \geq 0.
$$
The parameter \( \gamma_k \) is often referred to as the step length or the learning rate within the context of Machine Learning.
Ideally the sequence \( \{\mathbf{x}_k \}_{k=0} \) converges to a global minimum of the function \( F \). In general we do not know if we are in a global or local minimum. In the special case when \( F \) is a convex function, all local minima are also global minima, so in this case gradient descent can converge to the global solution. The advantage of this scheme is that it is conceptually simple and straightforward to implement. However the method in this form has some severe limitations:
In machine learing we are often faced with non-convex high dimensional cost functions with many local minima. Since GD is deterministic we will get stuck in a local minimum, if the method converges, unless we have a very good intial guess. This also implies that the scheme is sensitive to the chosen initial condition.
Note that the gradient is a function of \( \mathbf{x} = (x_1,\cdots,x_n) \) which makes it expensive to compute numerically.
The gradient descent method is sensitive to the choice of learning rate \( \gamma_k \). This is due to the fact that we are only guaranteed that \( F(\mathbf{x}_{k+1}) \leq F(\mathbf{x}_k) \) for sufficiently small \( \gamma_k \). The problem is to determine an optimal learning rate. If the learning rate is chosen too small the method will take a long time to converge and if it is too large we can experience erratic behavior.
Many of these shortcomings can be alleviated by introducing randomness. One such method is that of Stochastic Gradient Descent (SGD), see below.
Ideally we want our cost/loss function to be convex(concave).
First we give the definition of a convex set: A set \( C \) in \( \mathbb{R}^n \) is said to be convex if, for all \( x \) and \( y \) in \( C \) and all \( t \in (0,1) \) , the point \( (1 − t)x + ty \) also belongs to C. Geometrically this means that every point on the line segment connecting \( x \) and \( y \) is in \( C \) as discussed below.
The convex subsets of \( \mathbb{R} \) are the intervals of \( \mathbb{R} \). Examples of convex sets of \( \mathbb{R}^2 \) are the regular polygons (triangles, rectangles, pentagons, etc...).
Convex function: Let \( X \subset \mathbb{R}^n \) be a convex set. Assume that the function \( f: X \rightarrow \mathbb{R} \) is continuous, then \( f \) is said to be convex if
$$f(tx_1 + (1-t)x_2) \leq tf(x_1) + (1-t)f(x_2) $$
for all \( x_1, x_2 \in X \) and for all \( t \in [0,1] \). If \( \leq \) is replaced with a strict inequaltiy in the definition, we demand \( x_1 \neq x_2 \) and \( t\in(0,1) \) then \( f \) is said to be strictly convex. For a single variable function, convexity means that if you draw a straight line connecting \( f(x_1) \) and \( f(x_2) \), the value of the function on the interval \( [x_1,x_2] \) is always below the line as illustrated below.
In the following we state first and second-order conditions which ensures convexity of a function \( f \). We write \( D_f \) to denote the domain of \( f \), i.e the subset of \( R^n \) where \( f \) is defined. For more details and proofs we refer to: S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press.
Suppose \( f \) is differentiable (i.e \( \nabla f(x) \) is well defined for all \( x \) in the domain of \( f \)). Then \( f \) is convex if and only if \( D_f \) is a convex set and
$$f(y) \geq f(x) + \nabla f(x)^T (y-x) $$
holds
for all \( x,y \in D_f \). This condition means that for a convex function
the first order Taylor expansion (right hand side above) at any point
a global under estimator of the function. To convince yourself you can
make a drawing of \( f(x) = x^2+1 \) and draw the tangent line to \( f(x) \) and
note that it is always below the graph.
Assume that \( f \) is twice differentiable, i.e the Hessian matrix exists at each point in \( D_f \). Then \( f \) is convex if and only if \( D_f \) is a convex set and its Hessian is positive semi-definite for all \( x\in D_f \). For a single-variable function this reduces to \( f''(x) \geq 0 \). Geometrically this means that \( f \) has nonnegative curvature everywhere.
This condition is particularly useful since it gives us an procedure for determining if the function under consideration is convex, apart from using the definition.
The next result is of great importance to us and the reason why we are going on about convex functions. In machine learning we frequently have to minimize a loss/cost function in order to find the best parameters for the model we are considering.
Ideally we want the global minimum (for high-dimensional models it is hard to know if we have local or global minimum). However, if the cost/loss function is convex the following result provides invaluable information:
Consider the problem of finding \( x \in \mathbb{R}^n \) such that \( f(x) \) is minimal, where \( f \) is convex and differentiable. Then, any point \( x^* \) that satisfies \( \nabla f(x^*) = 0 \) is a global minimum.
This result means that if we know that the cost/loss function is convex and we are able to find a minimum, we are guaranteed that it is a global minimum.
Using the definition of convexity, try to show that a function satisfying the properties above is convex (the third condition is not needed to show this).
We will use linear regression as a case study for the gradient descent methods. Linear regression is a great test case for the gradient descent methods discussed in the lectures since it has several desirable properties such as:
We revisit the example from homework set 1 where we had
$$
y_i = 5x_i^2 + 0.1\xi_i, \ i=1,\cdots,100
$$
with \( x_i \in [0,1] \) chosen randomly with a uniform distribution. Additionally \( \xi_i \) represents stochastic noise chosen according to a normal distribution \( \cal {N}(0,1) \).
The linear regression model is given by
$$
h_\beta(x) = \hat{y} = \beta_0 + \beta_1 x,
$$
such that
$$
\hat{y}_i = \beta_0 + \beta_1 x_i.
$$
Let \( \mathbf{y} = (y_1,\cdots,y_n)^T \), \( \mathbf{\hat{y}} = (\hat{y}_1,\cdots,\hat{y}_n)^T \) and \( \beta = (\beta_0, \beta_1)^T \)
It is convenient to write \( \mathbf{\hat{y}} = X\beta \) where \( X \in \mathbb{R}^{100 \times 2} \) is the design matrix given by
$$
X \equiv \begin{bmatrix}
1 & x_1 \\
\vdots & \vdots \\
1 & x_{100} & \\
\end{bmatrix}.
$$
The loss function is given by
$$
C(\beta) = ||X\beta-\mathbf{y}||^2 = ||X\beta||^2 - 2 \mathbf{y}^T X\beta + ||\mathbf{y}||^2 = \sum_{i=1}^{100} (\beta_0 + \beta_1 x_i)^2 - 2 y_i (\beta_0 + \beta_1 x_i) + y_i^2
$$
and we want to find \( \beta \) such that \( C(\beta) \) is minimized.
Computing \( \partial C(\beta) / \partial \beta_0 \) and \( \partial C(\beta) / \partial \beta_1 \) we can show that the gradient can be written as
$$
\nabla_{\beta} C(\beta) = (\partial C(\beta) / \partial \beta_0, \partial C(\beta) / \partial \beta_1)^T = 2\begin{bmatrix} \sum_{i=1}^{100} \left(\beta_0+\beta_1x_i-y_i\right) \\
\sum_{i=1}^{100}\left( x_i (\beta_0+\beta_1x_i)-y_ix_i\right) \\
\end{bmatrix} = 2X^T(X\beta - \mathbf{y}),
$$
where \( X \) is the design matrix defined above.
$$
\hat{H} \equiv \begin{bmatrix}
\frac{\partial^2 C(\beta)}{\partial \beta_0^2} & \frac{\partial^2 C(\beta)}{\partial \beta_0 \partial \beta_1} \\
\frac{\partial^2 C(\beta)}{\partial \beta_0 \partial \beta_1} & \frac{\partial^2 C(\beta)}{\partial \beta_1^2} & \\
\end{bmatrix} = 2X^T X.
$$
This result implies that \( C(\beta) \) is a convex function since the matrix \( X^T X \) always is positive semi-definite.
We can now write a program that minimizes \( C(\beta) \) using the gradient descent method with a constant learning rate \( \gamma \) according to
$$
\beta_{k+1} = \beta_k - \gamma \nabla_\beta C(\beta_k), \ k=0,1,\cdots
$$
We can use the expression we computed for the gradient and let use a \( \beta_0 \) be chosen randomly and let \( \gamma = 0.001 \). Stop iterating when \( ||\nabla_\beta C(\beta_k) || \leq \epsilon = 10^{-8} \).
And finally we can compare our solution for \( \beta \) with the analytic result given by \( \beta= (X^TX)^{-1} X^T \mathbf{y} \).
import numpy as np
"""
The following setup is just a suggestion, feel free to write it the way you like.
"""
#Setup problem described in the exercise
N = 100 #Nr of datapoints
M = 2 #Nr of features
x = np.random.rand(N) #Uniformly generated x-values in [0,1]
y = 5*x**2 + 0.1*np.random.randn(N)
X = np.c_[np.ones(N),x] #Construct design matrix
#Compute beta according to normal equations to compare with GD solution
Xt_X_inv = np.linalg.inv(np.dot(X.T,X))
Xt_y = np.dot(X.transpose(),y)
beta_NE = np.dot(Xt_X_inv,Xt_y)
print(beta_NE)
Another simple example is here
# Importing various packages
from random import random, seed
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import sys
x = 2*np.random.rand(100,1)
y = 4+3*x+np.random.randn(100,1)
xb = np.c_[np.ones((100,1)), x]
beta_linreg = np.linalg.inv(xb.T.dot(xb)).dot(xb.T).dot(y)
print(beta_linreg)
beta = np.random.randn(2,1)
eta = 0.1
Niterations = 1000
m = 100
for iter in range(Niterations):
gradients = 2.0/m*xb.T.dot(xb.dot(beta)-y)
beta -= eta*gradients
print(beta)
xnew = np.array([[0],[2]])
xbnew = np.c_[np.ones((2,1)), xnew]
ypredict = xbnew.dot(beta)
ypredict2 = xbnew.dot(beta_linreg)
plt.plot(xnew, ypredict, "r-")
plt.plot(xnew, ypredict2, "b-")
plt.plot(x, y ,'ro')
plt.axis([0,2.0,0, 15.0])
plt.xlabel(r'$x$')
plt.ylabel(r'$y$')
plt.title(r'Gradient descent example')
plt.show()
# Importing various packages
from random import random, seed
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import SGDRegressor
x = 2*np.random.rand(100,1)
y = 4+3*x+np.random.randn(100,1)
xb = np.c_[np.ones((100,1)), x]
beta_linreg = np.linalg.inv(xb.T.dot(xb)).dot(xb.T).dot(y)
print(beta_linreg)
sgdreg = SGDRegressor(n_iter = 50, penalty=None, eta0=0.1)
sgdreg.fit(x,y.ravel())
print(sgdreg.intercept_, sgdreg.coef_)
We have also discussed Ridge regression where the loss function contains a regularized given by the \( L_2 \) norm of \( \beta \),
$$
C_{\text{ridge}}(\beta) = ||X\beta -\mathbf{y}||^2 + \lambda ||\beta||^2, \ \lambda \geq 0.
$$
In order to minimize \( C_{\text{ridge}}(\beta) \) using GD we only have adjust the gradient as follows
$$
\nabla_\beta C_{\text{ridge}}(\beta) = 2\begin{bmatrix} \sum_{i=1}^{100} \left(\beta_0+\beta_1x_i-y_i\right) \\
\sum_{i=1}^{100}\left( x_i (\beta_0+\beta_1x_i)-y_ix_i\right) \\
\end{bmatrix} + 2\lambda\begin{bmatrix} \beta_0 \\ \beta_1\end{bmatrix} = 2 (X^T(X\beta - \mathbf{y})+\lambda \beta).
$$
We can now extend our program to minimize \( C_{\text{ridge}}(\beta) \) using gradient descent and compare with the analytical solution given by
$$
\beta_{\text{ridge}} = \left(X^T X + \lambda I_{2 \times 2} \right)^{-1} X^T \mathbf{y},
$$
for \( \lambda = {0,1,10,50,100} \) (\( \lambda = 0 \) corresponds to ordinary least squares).
We can then compute \( ||\beta_{\text{ridge}}|| \) for each \( \lambda \).
import numpy as np
"""
The following setup is just a suggestion, feel free to write it the way you like.
"""
#Setup problem described in the exercise
N = 100 #Nr of datapoints
M = 2 #Nr of features
x = np.random.rand(N)
y = 5*x**2 + 0.1*np.random.randn(N)
#Compute analytic beta for Ridge regression
X = np.c_[np.ones(N),x]
XT_X = np.dot(X.T,X)
l = 0.1 #Ridge parameter lambda
Id = np.eye(XT_X.shape[0])
Z = np.linalg.inv(XT_X+l*Id)
beta_ridge = np.dot(Z,np.dot(X.T,y))
print(beta_ridge)
print(np.linalg.norm(beta_ridge)) #||beta||
Stochastic gradient descent (SGD) and variants thereof address some of the shortcomings of the Gradient descent method discussed above.
The underlying idea of SGD comes from the observation that the cost function, which we want to minimize, can almost always be written as a sum over \( n \) data points \( \{\mathbf{x}_i\}_{i=1}^n \),
$$
C(\mathbf{\beta}) = \sum_{i=1}^n c_i(\mathbf{x}_i,
\mathbf{\beta}).
$$
This in turn means that the gradient can be computed as a sum over \( i \)-gradients
$$
\nabla_\beta C(\mathbf{\beta}) = \sum_i^n \nabla_\beta c_i(\mathbf{x}_i,
\mathbf{\beta}).
$$
Stochasticity/randomness is introduced by only taking the gradient on a subset of the data called minibatches. If there are \( n \) data points and the size of each minibatch is \( M \), there will be \( n/M \) minibatches. We denote these minibatches by \( B_k \) where \( k=1,\cdots,n/M \).
The idea is now to approximate the gradient by replacing the sum over all data points with a sum over the data points in one the minibatches picked at random in each gradient descent step
$$
\nabla_{\beta}
C(\mathbf{\beta}) = \sum_{i=1}^n \nabla_\beta c_i(\mathbf{x}_i,
\mathbf{\beta}) \rightarrow \sum_{i \in B_k}^n \nabla_\beta
c_i(\mathbf{x}_i, \mathbf{\beta}).
$$
Thus a gradient descent step now looks like
$$
\beta_{j+1} = \beta_j - \gamma_j \sum_{i \in B_k}^n \nabla_\beta c_i(\mathbf{x}_i,
\mathbf{\beta})
$$
where \( k \) is picked at random with equal probability from \( [1,n/M] \). An iteration over the number of minibathces (n/M) is commonly referred to as an epoch. Thus it is typical to choose a number of epochs and for each epoch iterate over the number of minibatches, as exemplified in the code below.
import numpy as np
n = 100 #100 datapoints
M = 5 #size of each minibatch
m = int(n/M) #number of minibatches
n_epochs = 10 #number of epochs
j = 0
for epoch in range(1,n_epochs+1):
for i in range(m):
k = np.random.randint(m) #Pick the k-th minibatch at random
#Compute the gradient using the data in minibatch Bk
#Compute new suggestion for
j += 1
Taking the gradient only on a subset of the data has two important benefits. First, it introduces randomness which decreases the chance that our opmization scheme gets stuck in a local minima. Second, if the size of the minibatches are small relative to the number of datapoints (\( M < n \)), the computation of the gradient is much cheaper since we sum over the datapoints in the \( k-th \) minibatch and not all \( n \) datapoints.
A natural question is when do we stop the search for a new minimum? One possibility is to compute the full gradient after a given number of epochs and check if the norm of the gradient is smaller than some threshold and stop if true. However, the condition that the gradient is zero is valid also for local minima, so this would only tell us that we are close to a local/global minimum. However, we could also evaluate the cost function at this point, store the result and continue the search. If the test kicks in at a later stage we can compare the values of the cost function and keep the \( \beta \) that gave the lowest value.
Another approach is to let the step length \( \gamma_j \) depend on the number of epochs in such a way that it becomes very small after a reasonable time such that we do not move at all.
As an example, let \( e = 0,1,2,3,\cdots \) denote the current epoch and let \( t_0, t_1 > 0 \) be two fixed numbers. Furthermore, let \( t = e \cdot m + i \) where \( m \) is the number of minibatches and \( i=0,\cdots,m-1 \). Then the function
$$\gamma_j(t; t_0, t_1) = \frac{t_0}{t+t_1} $$
goes to zero as the number of epochs gets large. I.e. we start with a step length \( \gamma_j (0; t_0, t_1) = t_0/t_1 \) which decays in time \( t \).
In this way we can fix the number of epochs, compute \( \beta \) and evaluate the cost function at the end. Repeating the computation will give a different result since the scheme is random by design. Then we pick the final \( \beta \) that gives the lowest value of the cost function.
import numpy as np
def step_length(t,t0,t1):
return t0/(t+t1)
n = 100 #100 datapoints
M = 5 #size of each minibatch
m = int(n/M) #number of minibatches
n_epochs = 500 #number of epochs
t0 = 1.0
t1 = 10
gamma_j = t0/t1
j = 0
for epoch in range(1,n_epochs+1):
for i in range(m):
k = np.random.randint(m) #Pick the k-th minibatch at random
#Compute the gradient using the data in minibatch Bk
#Compute new suggestion for beta
t = epoch*m+i
gamma_j = step_length(t,t0,t1)
j += 1
print("gamma_j after %d epochs: %g" % (n_epochs,gamma_j))
The success of the CG method for finding solutions of non-linear problems is based on the theory of conjugate gradients for linear systems of equations. It belongs to the class of iterative methods for solving problems from linear algebra of the type
$$
\begin{equation*}
\hat{A}\hat{x} = \hat{b}.
\end{equation*}
$$
In the iterative process we end up with a problem like
$$
\begin{equation*}
\hat{r}= \hat{b}-\hat{A}\hat{x},
\end{equation*}
$$
where \( \hat{r} \) is the so-called residual or error in the iterative process.
When we have found the exact solution, \( \hat{r}=0 \).
The residual is zero when we reach the minimum of the quadratic equation
$$
\begin{equation*}
P(\hat{x})=\frac{1}{2}\hat{x}^T\hat{A}\hat{x} - \hat{x}^T\hat{b},
\end{equation*}
$$
with the constraint that the matrix \( \hat{A} \) is positive definite and symmetric.
If we search for a minimum of the quantum mechanical variance, then the matrix
\( \hat{A} \), which is called the Hessian, is given by the second-derivative of the function we want to minimize. This quantity is always positive definite. In our case this corresponds normally to the second derivative of the energy.
We seek the minimum of the energy or the variance as function of various variational parameters. In our case we have thus a function \( f \) whose minimum we are seeking. In Newton's method we set \( \nabla f = 0 \) and we can thus compute the next iteration point
$$
\begin{equation*}
\hat{x}-\hat{x}_i=\hat{A}^{-1}\nabla f(\hat{x}_i).
\end{equation*}
$$
Subtracting this equation from that of \( \hat{x}_{i+1} \) we have
$$
\begin{equation*}
\hat{x}_{i+1}-\hat{x}_i=\hat{A}^{-1}(\nabla f(\hat{x}_{i+1})-\nabla f(\hat{x}_i)).
\end{equation*}
$$
In the CG method we define so-called conjugate directions and two vectors \( \hat{s} \) and \( \hat{t} \) are said to be conjugate if
$$
\begin{equation*}
\hat{s}^T\hat{A}\hat{t}= 0.
\end{equation*}
$$
The philosophy of the CG method is to perform searches in various conjugate directions
of our vectors \( \hat{x}_i \) obeying the above criterion, namely
$$
\begin{equation*}
\hat{x}_i^T\hat{A}\hat{x}_j= 0.
\end{equation*}
$$
Two vectors are conjugate if they are orthogonal with respect to
this inner product. Being conjugate is a symmetric relation: if \( \hat{s} \) is conjugate to \( \hat{t} \), then \( \hat{t} \) is conjugate to \( \hat{s} \).
An example is given by the eigenvectors of the matrix
$$
\begin{equation*}
\hat{v}_i^T\hat{A}\hat{v}_j= \lambda\hat{v}_i^T\hat{v}_j,
\end{equation*}
$$
which is zero unless \( i=j \).
Assume now that we have a symmetric positive-definite matrix \( \hat{A} \) of size \( n\times n \). At each iteration \( i+1 \) we obtain the conjugate direction of a vector
$$
\begin{equation*}
\hat{x}_{i+1}=\hat{x}_{i}+\alpha_i\hat{p}_{i}.
\end{equation*}
$$
We assume that \( \hat{p}_{i} \) is a sequence of \( n \) mutually conjugate directions.
Then the \( \hat{p}_{i} \) form a basis of \( R^n \) and we can expand the solution
$ \hat{A}\hat{x} = \hat{b}$ in this basis, namely
$$
\begin{equation*}
\hat{x} = \sum^{n}_{i=1} \alpha_i \hat{p}_i.
\end{equation*}
$$
The coefficients are given by
$$
\begin{equation*}
\mathbf{A}\mathbf{x} = \sum^{n}_{i=1} \alpha_i \mathbf{A} \mathbf{p}_i = \mathbf{b}.
\end{equation*}
$$
Multiplying with \( \hat{p}_k^T \) from the left gives
$$
\begin{equation*}
\hat{p}_k^T \hat{A}\hat{x} = \sum^{n}_{i=1} \alpha_i\hat{p}_k^T \hat{A}\hat{p}_i= \hat{p}_k^T \hat{b},
\end{equation*}
$$
and we can define the coefficients \( \alpha_k \) as
$$
\begin{equation*}
\alpha_k = \frac{\hat{p}_k^T \hat{b}}{\hat{p}_k^T \hat{A} \hat{p}_k}
\end{equation*}
$$
If we choose the conjugate vectors \( \hat{p}_k \) carefully, then we may not need all of them to obtain a good approximation to the solution \( \hat{x} \). We want to regard the conjugate gradient method as an iterative method. This will us to solve systems where \( n \) is so large that the direct method would take too much time.
We denote the initial guess for \( \hat{x} \) as \( \hat{x}_0 \). We can assume without loss of generality that
$$
\begin{equation*}
\hat{x}_0=0,
\end{equation*}
$$
or consider the system
$$
\begin{equation*}
\hat{A}\hat{z} = \hat{b}-\hat{A}\hat{x}_0,
\end{equation*}
$$
instead.
One can show that the solution \( \hat{x} \) is also the unique minimizer of the quadratic form
$$
\begin{equation*}
f(\hat{x}) = \frac{1}{2}\hat{x}^T\hat{A}\hat{x} - \hat{x}^T \hat{x} , \quad \hat{x}\in\mathbf{R}^n.
\end{equation*}
$$
This suggests taking the first basis vector \( \hat{p}_1 \)
to be the gradient of \( f \) at \( \hat{x}=\hat{x}_0 \),
which equals
$$
\begin{equation*}
\hat{A}\hat{x}_0-\hat{b},
\end{equation*}
$$
and
\( \hat{x}_0=0 \) it is equal \( -\hat{b} \).
The other vectors in the basis will be conjugate to the gradient,
hence the name conjugate gradient method.
Let \( \hat{r}_k \) be the residual at the \( k \)-th step:
$$
\begin{equation*}
\hat{r}_k=\hat{b}-\hat{A}\hat{x}_k.
\end{equation*}
$$
Note that \( \hat{r}_k \) is the negative gradient of \( f \) at
\( \hat{x}=\hat{x}_k \),
so the gradient descent method would be to move in the direction \( \hat{r}_k \).
Here, we insist that the directions \( \hat{p}_k \) are conjugate to each other,
so we take the direction closest to the gradient \( \hat{r}_k \)
under the conjugacy constraint.
This gives the following expression
$$
\begin{equation*}
\hat{p}_{k+1}=\hat{r}_k-\frac{\hat{p}_k^T \hat{A}\hat{r}_k}{\hat{p}_k^T\hat{A}\hat{p}_k} \hat{p}_k.
\end{equation*}
$$
We can also compute the residual iteratively as
$$
\begin{equation*}
\hat{r}_{k+1}=\hat{b}-\hat{A}\hat{x}_{k+1},
\end{equation*}
$$
which equals
$$
\begin{equation*}
\hat{b}-\hat{A}(\hat{x}_k+\alpha_k\hat{p}_k),
\end{equation*}
$$
or
$$
\begin{equation*}
(\hat{b}-\hat{A}\hat{x}_k)-\alpha_k\hat{A}\hat{p}_k,
\end{equation*}
$$
which gives
$$
\begin{equation*}
\hat{r}_{k+1}=\hat{r}_k-\hat{A}\hat{p}_{k},
\end{equation*}
$$


---
layout: post
title: Basic example
weight: 1
category: player-api
categoryItemType: example
categoryItemIsShown: 1
categoryItemWidth:
categoryItemDescription:
categoryItemLabel:
---
Play
Pause
LIVEOFFLINEPLAYINGENDED
Channel
Quality
Example media
creativeLIVE channel
creativeLIVE provides the best free, live creative education on the web. We offer high-quality workshops in photography, business, video, web and graphic design, and more.
IBM @ CESrecorded
IBM CEO Ginni Rometty 2016 CES Keynote.
TWiT Live channel
Live from the TWiT Brick House, non-stop technology news and conversation with Leo Laporte and friends.
NASA Public channel
NASA TV airs a variety of regularly scheduled, pre-recorded educational and public relations programming 24 hours a day on its various channels.


| Interface | Description |
|---|---|
| ContextTemplate |
The interface
ContextTemplate represents a context template. |
| ContextTemplateSchema |
ContextTemplateSchema class represents a scheme of context templates. |
| ContextValue |
This class represents a value of context template.
|
| CTProcessor |
This interface specifies how to process a context template when browsing a collection of context templates such as fetcher, context template schema
ContextTemplateSchema . |
| CTSManager |
This is a very important class which is responsible for managing contexts (
Context ) and context templates (ContextTemplate ). |
| CTSMultiProfiles |
Every context template represented by
ContextTemplate interface owns a profile table and each value represented by ContextValue of template corresponds to a row in this table. |
| HierContextTemplate |
This interface extending directly interface
ContextTemplate specified a context template in for of hierarchical structure. |
| Class | Description |
|---|---|
| Context |
Context represents time, location, companion, etc. |
| ContextList |
This class represents a list of context that a user rates on an item.
|
| ContextTemplateList |
This class represents a list of context templates.
|
| ContextValueList |
This class is the list of
ContextValue (s). |
| CTProcessorAbstract |
This abstract class implements partially the
CTProcessor interface called context template processor. |


Funding and other support¶
Development of MNE-Python has been supported by:
National Institutes of Health: R01-EB009048, R01-EB006385, R01-HD040712, R01-NS044319, R01-NS037462, R01-NS104585, P41-EB015896, P41-RR014075
European Research Council: YStG-263584, YStG-676943
US Department of Energy: DE-FG02-99ER62764 (MIND)
Agence Nationale de la Recherche: 14-NEUC-0002-01, IDEX Paris-Saclay 11-IDEX-0003-02
Paris-Saclay Center for Data Science: PARIS-SACLAY
Google: Summer of code (×7 years)
Amazon: AWS Research Grants
Institutional partners¶
Additionally, many universities or research institutions have supported their employees’ contributions to MNE-Python as part of normal work duties. These institutions include:


| Modifier and Type | Method and Description |
|---|---|
List<StateActionPair<A>> |
StateActionExperienceHandler.generateTrainingBatch()
The elements are returned in the historical order (i.e. in the order they happened)
Note: the experience store is cleared after calling this method.
|
| Modifier and Type | Method and Description |
|---|---|
Gradient[] |
UpdateAlgorithm.computeGradients(NN current,
List<StateActionPair<Integer>> experience) |
| Modifier and Type | Method and Description |
|---|---|
Gradient[] |
AdvantageActorCriticUpdateAlgorithm.computeGradients(IActorCritic current,
List<StateActionPair<Integer>> experience) |
| Modifier and Type | Method and Description |
|---|---|
Gradient[] |
QLearningUpdateAlgorithm.computeGradients(IDQN current,
List<StateActionPair<Integer>> experience) |
Copyright © 2020. All rights reserved.


Hello, Am a freelance front-end web developer based in Nairobi, Kenya.
Html5
Css3
Bootstrap 4
Javascript, Bootsrap, Object Oriented and Functional programming, JQuery, Agile, Algorithm Scripting at Moringa School.
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Grow your visibility and get more traffic from organic search results on search engines.
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@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import views.ViewUtils._ @import config.FrontendAppConfig @import uk.gov.hmrc.govukfrontend.views.html.components._ @this( govukLayout: templates.GovukLayoutWrapper, h1: components.h1, govukBackLink: GovukBackLink, link: components.link ) @(savedTilDate: String)(implicit request: Request[_], appConfig: FrontendAppConfig, messages: Messages) @govukLayout(pageTitle = Some(titleNoForm("savedReturn.title"))) {
@messages("savedReturn.p1")
@messages("savedReturn.p2")


MY BLOG
Welcome to the blog of Amy Li
My Journey of Dev Academy
Feb-May, 2019
The preparation course begins on the 4th of Feb and can be completed in 5weeks(full time) online. After the foundation completed, I'll join a 9 week experience of intense coding at new market, auckland campus.I graduated with a portfolio of web projects that I've designed and built, which my future employers love to see.


Secrecy shrouds talks on New Mexico’s methane emissions
SANTA FE, N.M. (AP) - New Mexico Gov. Michelle Lujan Grisham has been outspoken about cracking down on methane emissions, but the advisory panel tasked with creating recommendations is meeting behind closed doors.
The Santa Fe New Mexican reports even the time and place of the panel’s meetings are kept private.
There have been no published agendas or meeting minutes on what the panel has discussed despite the political importance of the sessions.
Panel members and a spokeswoman for the governor say that in addition to the meetings being largely technical, making them public would thwart frank discussion.
The panel includes industry representatives and environmental lawyers. None has talked about what has taken place at the meetings.
The state expects to hold public meetings on the panel’s final report before the end of the year.
Information from: The Santa Fe New Mexican, http://www.santafenewmexican.com
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Hello {{fullname}}
(using normal fn)
Hello {{fullnameArrow}}
(using arrow)
computed vs methods
computed props are cached based on their reactive dependencies


My name is Chunyue Ma and welcome to my personal site! I graduated from Wesleyan University majoring in Computer Science and minoring in Informatics and Modeling. I am passionate about software systems. I like learning about distributed systems and cloud computing. In addition to this, I have a special interest high performance computing as well. Previously, I have worked as a software engineer and a research engineer. Let’s connect!
RESUME / CV


Classic Cyber Truck Model
Cyber Truck Super Charger Model
Classic Cyber Truck Model.
This is the Model With Lights that are controllable through our mobile app.
This is the Wall Art Model. Its for hanging on a wall.
This is the Wall Art Model with Lights that you can control through the mobile app.
| Model | |
|---|---|
| Package | |
| Size | |
| Color | |
| Total Price |


Here's how you can put them to work with either <button>
elements:
The best part is you can do this with any button variant, too:
Similarly, create split button dropdowns with virtually the same markup as single
button dropdowns, but with the addition of boolean prop split
for proper
spacing around the dropdown caret.
We use this extra class to reduce the horizontal padding
on either side
of the caret by 25% and remove the margin-left
that's attached for
normal button dropdowns. Those additional changes hold the caret centered in the split
button and implement a more properly sized hit area next to the main button.
Button dropdowns work with buttons of all sizes, including default and split dropdown buttons.
Opt into darker dropdowns to match a dark navbar or custom style by set
dark
property. No changes are required to the dropdown items.
And putting it to use in a navbar:
Trigger dropdown menus above elements by adding
direction="dropup"
to the <c-dropdown>
component.
Trigger dropdown menus at the right of the elements by adding
direction="dropend"
to the <c-dropdown>
component.
Trigger dropdown menus at the left of the elements by adding
direction="dropstart"
to the <c-dropdown>
component.
Trigger dropdown menus centered below the toggle by adding direction="center"
to the c-dropdown
component.


It was never easier to track your skills. Find your recent used skills, or checkout popular skills in your field of work. Or just start to type, simple as that. A monthly check-in notification will make sure you won't forget to stay on track!
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#{bookDeleteBean.book.flight.airportFrom.city.name} / #{bookDeleteBean.book.flight.airportTo.city.name}
#{bookDeleteBean.book.flight.airportFrom.name} [#{bookDeleteBean.book.flight.airportFrom.code}]
#{bookDeleteBean.book.flight.airportTo.name} [#{bookDeleteBean.book.flight.airportTo.code}]
- #{msg.flightHourStart}
#{bookDeleteBean.book.flight.start.toString()}
- #{msg.flightHourFinish}
#{bookDeleteBean.flightFinish(bookDeleteBean.book.flight.start, bookDeleteBean.book.flight.time).toString()}


MBNetworkMonitor Class Reference
| Inherits from | NSObject |
| Conforms to | MBService |
| Declared in | MBNetworkMonitor.h |
Overview
Provides a mechanism for determining network availability and requesting updates about network status changes.
You can use this singleton to query the current network availability.
The MBNetworkMonitor
class also acts as a service that, when running, will
post the following NSNotificationCenter
events when the status of the network
changes:
MBNetwork:didGoOnline
— Indicates that the network has become available. This is also fired immediately if the network is available when the service starts.MBNetwork:didGoOffline
— Indicates that the network went offline. Sending network requests will fail when offline.MBNetwork:wifiConnected
— Indicates that a network connection became available through wifi.MBNetwork:wifiDisconnected
— Indicates that the wifi network connection is no longer available. A connection may still be available through a wireless carrier.MBNetwork:carrierConnectionChanged
— Indicates that the state of the carrier network connection changed.
Note: By default, changes in network status do not result in events
being fired. Notification events are posted only when the
MBNetworkMonitor
service is running. If the network status needs
to be checked repeatedly, it is more efficient to utilize the
event-based mechanism provided by the service. Use the interface
provided by the MBServiceManager
to ensure that the service is
running when needed.
Tasks
Querying network status
-
isOnline
property -
isWifiConnected
property -
hasCellularCarrier
property -
cellularCarrierName
property -
cellularCarrierCountry
property -
cellularConnectionType
property -
carrierStatusDescription
property -
networkAvailability
property -
networkAvailabilityDescription
property -
reachabilityFlagsDescription
property
Properties
carrierStatusDescription
Returns a human-readable string describing the current state of the
cellular carrier connection. Returns the constant
kMBNetworkNoCarrierStatusDescription
when the hasCellularCarrier
property would return NO
.
@property (nonatomic, readonly) NSString *carrierStatusDescription
Declared In
MBNetworkMonitor.h
cellularCarrierCountry
Returns the ISO country code of the cellular carrier currently associated with the device.
@property (nonatomic, readonly) NSString *cellularCarrierCountry
Declared In
MBNetworkMonitor.h
cellularCarrierName
Returns the name of the cellular carrier currently associated with the device.
@property (nonatomic, readonly) NSString *cellularCarrierName
Declared In
MBNetworkMonitor.h
cellularConnectionType
Returns a string identifying the type of cellular connection currently in use by the device.
@property (nonatomic, readonly) NSString *cellularConnectionType
Declared In
MBNetworkMonitor.h
hasCellularCarrier
Determines whether the device currently has an associated cellular carrier.
@property (nonatomic, readonly) BOOL hasCellularCarrier
Declared In
MBNetworkMonitor.h
isOnline
Indicates whether a network is currently available to the device. If called
repeatedly, it is more efficient to have the service running to avoid
querying SCNetworkReachabilityGetFlags
directly.
@property (nonatomic, assign, readonly) BOOL isOnline
Declared In
MBNetworkMonitor.h
isWifiConnected
Indicates whether the network connection (if any) is via wifi. If called
repeatedly, it is more efficient to have the service running to avoid
querying SCNetworkReachabilityGetFlags
directly.
@property (nonatomic, assign, readonly) BOOL isWifiConnected
Declared In
MBNetworkMonitor.h
networkAvailability
Indicates the current status of network availability by querying
SCNetworkReachabilityGetFlags
directly. Not very processor-efficient for
repeated use; it’s better to turn on the service and rely on the event-based
mechanism.
@property (nonatomic, assign, readonly) MBNetworkAvailabilityFlags networkAvailability
Declared In
MBNetworkMonitor.h
networkAvailabilityDescription
Returns a string describing the current networkAvailability
property in
human-readable form.
@property (nonatomic, readonly) NSString *networkAvailabilityDescription
Declared In
MBNetworkMonitor.h


(Defined in: jpgraph_pie.php : 1210)
Class usage and Overview
The canvas for use with PiePlots. You add pie plots by calling the Add() method.
You can add several PiePlots by adjusting their size and positioning each PiePlot so it doesn't overlap eachother.
See also related classes:
PiePlot and PiePlot3D
Class Methods
Add object to the pie graph
| Argument | Default | Description |
|
$aObj
| | Object to add |
Description:
Add a pieplots or text object to the pie graph. You can specify either a single object or an array. If you specify an array all object in the array must be of the same type.
$piegraph = new PieGraph(300,400);
$pieplot = new PiePlot($ydata);
$piegraph->Add($pieplot);
Enable/disable anti-alias for Pie Graphs
| Argument | Default | Description |
|
$aFlg
|
true
| true=enable anti-aliasing |
Description:
Enable/disable anti-alias for Pie Graphs. This makes for much nicer looking PieGraphs. Please note that enabling anti-alias imposes some restrictions:
- Enabling anti-alias will severely impact performance. As a rule of thumb you can expect drawing to take 5-8 times longer !
- You can't use background images or gradient backgrounds together with anti-alias
$piegraph->SetAntiAlias();
Set the background color. Synonym to SetMarginColor()
| Argument | Default | Description |
|
$c
| | Color |
Description:
Set the background color. Synonym to SetMarginColor()
See also:
$piegraph->SetColor('gray');
// Method description
| Argument | Default | Description |
|
$aStrokeFileName
|
""
| Filename |
Description:
Stroke the piegraph to the browser or to a specified file if specified. Works exactly the same as Graph::Stroke()
...
$piegraph->Stroke()
//---------------// CONSTRUCTOR
| Argument | Default | Description |
|
$width
|
300
| Width of graph |
|
$height
|
200
| Height of graph |
|
$cachedName
|
""
| Cache file name |
|
$timeout
|
0
| Cache timeout in s |
|
$inline
|
1
| Should the image be inline. |
Description:
See Graph::Graph()
See also:
$piegraph = new PieGraph(300,400);


Server hostname¶
When you install GEE on a server, it queries for the name of the
local host by issuing a hostname -f
command. Each time you
build a resource, project, or database on the server, the host
name is added to all the files in each asset’s subdirectory. If
you change the hostname or if you move assets to another server,
you must also update the assetroot host
entry to match the new
host name.
To change the asset root host entry, use the
geconfigureassetroot
command:
sudo /opt/google/bin/geconfigureassetroot --fixmasterhost


Most monitors are rather application specific, but there are
some generic ones implemented. Note also that in some situations
it may not be necessary to specify any monitor at all; the default
monitor is the always true one, but since the default setting
for the @fail_on_exit@ flag of @mce_opts@
is true, the model checker will verify that no process
is ever abnormally terminated.
Below we show the tuples naming the monitors, and their
respective arguments (to be specified in the @monitor@
field of @mce_opts@).
-
Checks that all queues contain at most MaxQueueSize elements.
-
Checks that there is at least one non-deadlocked process.
2010-07-05


Deadly Escape
The window is stuck and you can't open it. You must find another way out. What do you want to do?
Break the glass with your hand
Hide


Enter your claim number
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{% extends "layout_unbranded.html" %}
{% block page_title %} Your money claims account {% endblock %}
{% block content %}
Enter the claim number from the email or letter we sent you.
Enter your claim number
{% if data['mcol'] == 1 %}


A Midsummer Night's Dream (1999)
Director: Michael Hoffman Cast: Kevin Kline, Michelle Pfeiffer, Rupert Everett, Stanley Tucci, Calista Flockhart, Anna Friel, Dominic West, Christian Bale Screenplay: Michael Hoffman Producers: Michael Hoffman, Leslie Urdang Runtime: US Distribution: 20th Century Fox Rated PG-13: sensuality, nudity
Copyright 1999 Nathaniel R. Atcheson
I've always been sort of a purist when it comes to Shakespeare. I think it is possible to update his plays and place them in a period of time in which they were not intended to be set. But this is not an easy thing to do. Those who have seen William Shakespeare's Romeo + Juliet have experienced the ultimate failure in an update of a Shakespeare play. Michael Hoffman's A Midsummer Night's Dream is much better than that other movie, and I was thisclose to giving it a favorable review. And while I have nice things to say about it, the film just doesn't work the way it should. The first reason for this, perhaps, is that it's set at the turn of the 19th century for no reason whatsoever. It's also an artistically confusing film: why do all the sets look so obviously fake? Why do the actors feature an array of non-matching accents? Why do so many of the scenes occur in the exact same spot of woods? These are questions I had when I should have been concentrating on Shakespeare's brilliantly-conceived story.
But I have to give Hoffman credit for following the play so closely. >From what I could tell, no scenes have been removed, and only parts of speeches have been trimmed down. Other than that, Hoffman has done the source material justice with his script. The story is a familiar one: The four lovers, Demetrius (Christian Bale), Lysander (Dominic West), Helena (Calista Flockhart), and Hermia (Anna Friel), are manipulated relentlessly by Puck (Stanley Tucci) and Oberon, the Fairy King (I stand by my belief that Rupert Everett has this role as part of a Hollywood in-joke). Meanwhile, the lovable lout Bottom (Kevin Kline) and his partners in stupidity work to put together a lame stage version of Pyramus and Thisbe. The basic set-up of the film lends itself to a series of entertaining and/or tedious scenes in which lovers run around and shout at each other in a forest near Athens.
The film is a mixed bag of scenes that are notably over-the-top and others that are strangely perfunctory. The best scene in the film is the last one, in which the six lower classmen get to perform their play before the aristocracy. Here, Kevin Kline shows off his well-established comic brilliance. But Hoffman's direction of the scene is really what carries it, as the players make desperate attempts to impersonate walls, moons, and lions. The other high points of the film lie mostly in a few key performances. Calista Flockhart is quite funny as the love-starved and ravished Helena; even though Flockhart doesn't attempt a British accent, she enunciates properly and does a good job with her lines. Everett takes an interesting angle on Oberon, playing him as stone-faced and extremely pensive. And Stanley Tucci makes a perfectly indifferent Puck, roaming around the forest and making people fall in love with each other just because it's amusing.
Many scenes are great, and they add up to a lot, but there are too many failed moments to ignore.  David Strathairn entirely misses the point of the paradoxical Theseus, and delivers his lines with a cold, inappropriate American accent.  Shakespeare makes explicit their misusage of words and their inability to understand the point of drama, but here Hoffman seems to blow right past all the jokes.
In general, I don't understand why the movie looks so flashy and fake. The forest of Arden looks particularly set-like, and pointless special effects (like a CGI vine wrapping around Bottom's feet) call too much attention to themselves. I realize that live theater does not allow for fancy special effects and sets, but films do, and a film version of a play should not be made to look fake. And then there's the needless time period-change, which bothers me every time a director attempts it: people didn't speak like this at the turn of the 19th century. Hoffman's A Midsummer Night's Dream works on many levels -- nothing could be as bad as that other movie -- but it doesn't go far enough with some themes, and takes others way too far. I think it all boils down to a general lack of consistency -- what was Hoffman's vision? What's with the fake sets and the half-hearted "update?" There are big laughs to be had in this film, but Hoffman's puzzling artistic choices keep it from being the really good update of a Shakespeare play that it might have been.
Psychosis Rating: 6/10
Visit FILM PSYCHOSIS at http://www.pyramid.net/natesmovies
Nathaniel R. Atcheson
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