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  • NSTableView not refreshing when calling method from different class

    - by Matt S.
    I have a table view that gets refreshed two different ways. One is through a button and the other is when I call my refresh method, which is the same method that I use for the button, but for some reason, when I hit the button it works, but when I call it through a different class it doesn't. Here's the code that I use to refresh the tableview: Msqv *qv = [Msqv new]; [qv refresh:self]; //refresh is an IBAction that is used by the button

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  • C# (non-abstract) class to represent paths

    - by user289770
    I'm looking for a C# class that represents a file system path. I would like to use it (instead of strings) as the data type of variables and method arguments (top reasons: type safety, concat-proof, logical comparisons). System.IO.Path provides most of the functionality I want, but it is abstract. System.IO.FileInfo, as I understand, performs IO operations to do its job. I only want a wrapper for the path string. Thanks!

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  • Use reflection to get the value of a property by name in a class instance

    - by TheMoot
    Lets say I have class Person { public Person(int age, string name) { Age = age; Name = name; } public int Age{get;set} public string Name{get;set} } and I would like to create a method that accepts a string that contains either "age" or "name" and returns an object with the value of that property. Like the following pseudo code: public object GetVal(string propName) { return <propName>.value; } How can I do this using reflection? I am coding using asp.net 3.5, c# 3.5

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  • curl problems in c++ class

    - by Danilo
    I read a few articles on c++ / curl here on stackoverflow and assembled the following. The main goal is to handle the whole request in an instance of a class -- and maybe later in a secondary thread. My problem is: "content_" seems to stay empty though its the same addr and HttpFetch.h: class HttpFetch { private: CURL *curl; static size_t handle(char * data, size_t size, size_t nmemb, void * p); size_t handle_impl(char * data, size_t size, size_t nmemb); public: std::string content_; static std::string url_; HttpFetch(std::string url); void start(); std::string data(); }; HttpFetch.cpp: HttpFetch::HttpFetch(std::string url) { curl_global_init(CURL_GLOBAL_ALL); //pretty obvious curl = curl_easy_init(); content_.append("Test"); std::cout << &content_ << "\n"; curl_easy_setopt(curl, CURLOPT_URL, &url); curl_easy_setopt(curl, CURLOPT_WRITEDATA, &content_); curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, &HttpFetch::handle); //curl_easy_setopt(curl, CURLOPT_VERBOSE, 1L); //tell curl to output its progress curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L); //std::cout << &content_ << "\n"; } void HttpFetch::start() { curl_easy_perform(curl); curl_easy_cleanup(curl); } size_t HttpFetch::handle(char * data, size_t size, size_t nmemb, void * p) { std::string *stuff = reinterpret_cast<std::string*>(p); stuff->append(data, size * nmemb); std::cout << stuff << "\n"; // has content from data in it! return size * nmemb; } main.cpp: #include "HttpFetch.h" int main(int argc, const char * argv[]) { HttpFetch call = *new HttpFetch("http://www.example.com"); call.start(); ::std::cout << call.content_ << "\n" } Thanks in advance

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  • Why doesn't this inner class compile?

    - by Vincenzo
    This is my code: #include <algorithm> class A { void f() { struct CompareMe { bool operator() (int i, int j) { return i < j; } } comp; int a[] = {1, 2, 3, 4}; int found = std::min_element(a[0], a[3], comp); } } Error message: no matching function for call to ‘min_element(int&, int&, A::f()::CompareMe&) What am I doing wrong?

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  • Python file - Want to change it to a function or a class

    - by curious2know
    Hello, I have a python program/file that I want to run repeatedly and calculate the averages of some variables over these runs. To do so, I thought it might be convenient to convert this program into a function or a class. One way I can think of is to add a def Main(): line at the top and indent every line manually within it. Is there an easier way? I am using pydev on eclipse. Thanks.

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  • [Symfony] Accessing user session from a custom routing class

    - by David
    Is there some way to acces the user object from a custom routing class? I'd like to add a parameter when generating a url, and that parameter is inside the user session, so I need to access it. The only way I found to access is using the sfContext::getInstance()-getUser(), but it's known to be inefficient. Thanks!

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  • Accessing class variable with string name?

    - by coure06
    I have a class like this $.fn.dimeBar = function(custom) { var var1 = 'test1'; var var2 = 'test2'; if(sometest){ //how can i access var1 or var2 here by using string name of variables //some thing like alert(this['var1']) -- should alert: 'test1' } }

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  • calling a cdef in a cdef class

    - by Davoud Taghawi-Nejad
    Hello, is their any way to make this work, without sacrificing the cdef in cdef caller? (no use of cpdef either) from array import * from numpy import * cdef class Agents: cdef public caller(self): print "caller" A[2].called() cdef called(self): print "called" A = [Agents() for i in range(2)] def main(): A[1].caller()

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  • Extracting array with set::class utility

    - by Sabourinov
    Is there a way to extract the array for a specified id with the set::class utiliy? I can't figure out the XPath. I.E. I would like to extract the array where the id = 1 [Document] = Array ( [0] = Array ( [id] = 1 [filename] = 1.txt ) [1] = Array ( [id] = 2 [filename] = 2.txt ) [2] = Array ( [id] = 3 [filename] = 3.txt ) )

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  • x-code cannot see my class

    - by dubbeat
    This is pretty strange. I have a class in Classes folder of my project (a .h file and a .m file). When I try to import it like so #import <myClass.h> I get an error saying "no such file or directory". It is definitely there. What could be going on?

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  • java Thread class run() method

    - by JavaUser
    Hi, Thread class has run method to implement the business logic that could be executed in parallel.But I want implement different business logics in a single run method and to run simultaneously.How to get this feature. thanks

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  • what service class to use to incapsulate method

    - by xbsxbs
    I have to write a simple method extractArticle() that returns Article object which is extracted from Message object. I have MessageService and ArticleService classes intended to handle tasks like this. What service class is more correctly to use to incapsulate extractArticle() funcionality? $article = MessageService::extractArticle(Message $message); or $article = ArticleService::extractArticleFromMessage(Message $message);

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  • Run time error in java servlet

    - by Derk
    The build of the project is succesfull, but when I go the the url I get the following error report: >HTTP Status 500 - type Exception report >message description The server encountered an internal error () that prevented it from fulfilling this request. >exception javax.servlet.ServletException: Error instantiating servlet class example.servlet.ScrapingServlet org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:102) org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:263) org.apache.coyote.http11.Http11Processor.process(Http11Processor.java:844) org.apache.coyote.http11.Http11Protocol$Http11ConnectionHandler.process(Http11Protocol.java:584) org.apache.tomcat.util.net.JIoEndpoint$Worker.run(JIoEndpoint.java:447) java.lang.Thread.run(Thread.java:619) >root cause java.lang.NoClassDefFoundError: org/apache/http/impl/client/DefaultHttpClient java.lang.Class.getDeclaredConstructors0(Native Method) java.lang.Class.privateGetDeclaredConstructors(Class.java:2389) java.lang.Class.getConstructor0(Class.java:2699) java.lang.Class.newInstance0(Class.java:326) java.lang.Class.newInstance(Class.java:308) org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:102) org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:263) org.apache.coyote.http11.Http11Processor.process(Http11Processor.java:844) org.apache.coyote.http11.Http11Protocol$Http11ConnectionHandler.process(Http11Protocol.java:584) org.apache.tomcat.util.net.JIoEndpoint$Worker.run(JIoEndpoint.java:447) java.lang.Thread.run(Thread.java:619) >root cause java.lang.ClassNotFoundException: org.apache.http.impl.client.DefaultHttpClient org.apache.catalina.loader.WebappClassLoader.loadClass(WebappClassLoader.java:1358) org.apache.catalina.loader.WebappClassLoader.loadClass(WebappClassLoader.java:1204) java.lang.ClassLoader.loadClassInternal(ClassLoader.java:319) java.lang.Class.getDeclaredConstructors0(Native Method) java.lang.Class.privateGetDeclaredConstructors(Class.java:2389) java.lang.Class.getConstructor0(Class.java:2699) java.lang.Class.newInstance0(Class.java:326) java.lang.Class.newInstance(Class.java:308) org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:102) org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:263) org.apache.coyote.http11.Http11Processor.process(Http11Processor.java:844) org.apache.coyote.http11.Http11Protocol$Http11ConnectionHandler.process(Http11Protocol.java:584) org.apache.tomcat.util.net.JIoEndpoint$Worker.run(JIoEndpoint.java:447) java.lang.Thread.run(Thread.java:619) >note The full stack trace of the root cause is available in the Apache Tomcat/6.0.14 logs. Apache Tomcat/6.0.14 And that is because I added this simple line to the code: HttpClient httpclient = new DefaultHttpClient(); What am I doing wrong?

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  • If Html File Has No Ending "/tr" Tag OR "/td" Tag Then HTML Agility Pack Does Not Read That Informat

    - by Harikrishna
    I am using HTML Agility Pack to parse html content. I am using parsing to extract table information. It works. But if there is no ending "/tr" tag or "/td" tag then it does not parse that information perfectly.(in which there is no ending tr tag or td tag.) Like <TABLE border=0><TBODY><TR height=20><TD class=xl27boL noWrap width="7%">01890345&nbsp;</TD> <TD class=xl27boL noWrap width="4%">1416</TD> <TD class=xl27boL noWrap width="7%">kjlkjkls&nbsp;</TD><TD class=xl27boL noWrap width="4%">14:01:57&nbsp;</TD> <TD class=xl27boL noWrap align=left width="15%">Football</TD><TD class=xl27boL noWrap align=right width="5%">&nbsp;</TD> <TD class=xl27boL noWrap align=right width="5%">50&nbsp;</TD> <TD class=xl27boL noWrap align=right width="5%">4997.2500</TD><TD class=xl27boL noWrap align=right width="7%">249862.50&nbsp;</TD><TD class=xl27boL noWrap align=right width="5%">&nbsp;</TD><TD class=xl27boL noWrap align=right width="5%">&nbsp;</TD><TD class=xl27boRLnoWrap align=right width="8%">249612.64&nbsp;</TD><TD class=xl27boL noWrap align=right width="5%">4997.2500</TD><TD class=xl27boL noWrap align=right width="7%">249862.50&nbsp;</TD><TD class=xl27boL noWrap align=right width="5%">249.86</TD><TD class=xl27boL noWrap align=right width="5%">4992.2528</TD><TD class=xl27boL noWrap align=right width="5%">&nbsp;</TD><TD class=xl27boL noWrap align=right width="5%">&nbsp;</TD> <TD class=xl27boRL noWrap align=right width="8%">249612.64&nbsp;</TD> </table> So for that what should I do ?

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  • Delphi: EInvalidOp in neural network class (TD-lambda)

    - by user89818
    I have the following draft for a neural network class. This neural network should learn with TD-lambda. It is started by calling the getRating() function. But unfortunately, there is an EInvalidOp (invalid floading point operation) error after about 1000 iterations in the following lines: neuronsHidden[j] := neuronsHidden[j]+neuronsInput[t][i]*weightsInput[i][j]; // input -> hidden weightsHidden[j][k] := weightsHidden[j][k]+LEARNING_RATE_HIDDEN*tdError[k]*eligibilityTraceOutput[j][k]; // adjust hidden->output weights according to TD-lambda Why is this error? I can't find the mistake in my code :( Can you help me? Thank you very much in advance! unit uNeuronalesNetz; interface uses Windows, Messages, SysUtils, Variants, Classes, Graphics, Controls, Forms, Dialogs, ExtCtrls, StdCtrls, Grids, Menus, Math; const NEURONS_INPUT = 43; // number of neurons in the input layer NEURONS_HIDDEN = 60; // number of neurons in the hidden layer NEURONS_OUTPUT = 1; // number of neurons in the output layer NEURONS_TOTAL = NEURONS_INPUT+NEURONS_HIDDEN+NEURONS_OUTPUT; // total number of neurons in the network MAX_TIMESTEPS = 42; // maximum number of timesteps possible (after 42 moves: board is full) LEARNING_RATE_INPUT = 0.25; // in ideal case: decrease gradually in course of training LEARNING_RATE_HIDDEN = 0.15; // in ideal case: decrease gradually in course of training GAMMA = 0.9; LAMBDA = 0.7; // decay parameter for eligibility traces type TFeatureVector = Array[1..43] of SmallInt; // definition of the array type TFeatureVector TArtificialNeuralNetwork = class // definition of the class TArtificialNeuralNetwork private // GENERAL SETTINGS START learningMode: Boolean; // does the network learn and change its weights? // GENERAL SETTINGS END // NETWORK CONFIGURATION START neuronsInput: Array[1..MAX_TIMESTEPS] of Array[1..NEURONS_INPUT] of Extended; // array of all input neurons (their values) for every timestep neuronsHidden: Array[1..NEURONS_HIDDEN] of Extended; // array of all hidden neurons (their values) neuronsOutput: Array[1..NEURONS_OUTPUT] of Extended; // array of output neurons (their values) weightsInput: Array[1..NEURONS_INPUT] of Array[1..NEURONS_HIDDEN] of Extended; // array of weights: input->hidden weightsHidden: Array[1..NEURONS_HIDDEN] of Array[1..NEURONS_OUTPUT] of Extended; // array of weights: hidden->output // NETWORK CONFIGURATION END // LEARNING SETTINGS START outputBefore: Array[1..NEURONS_OUTPUT] of Extended; // the network's output value in the last timestep (the one before) eligibilityTraceHidden: Array[1..NEURONS_INPUT] of Array[1..NEURONS_HIDDEN] of Array[1..NEURONS_OUTPUT] of Extended; // array of eligibility traces: hidden layer eligibilityTraceOutput: Array[1..NEURONS_TOTAL] of Array[1..NEURONS_TOTAL] of Extended; // array of eligibility traces: output layer reward: Array[1..MAX_TIMESTEPS] of Array[1..NEURONS_OUTPUT] of Extended; // the reward value for all output neurons in every timestep tdError: Array[1..NEURONS_OUTPUT] of Extended; // the network's error value for every single output neuron t: Byte; // current timestep cyclesTrained: Integer; // number of cycles trained so far (learning rates could be decreased accordingly) last50errors: Array[1..50] of Extended; // LEARNING SETTINGS END public constructor Create; // create the network object and do the initialization procedure UpdateEligibilityTraces; // update the eligibility traces for the hidden and output layer procedure tdLearning; // learning algorithm: adjust the network's weights procedure ForwardPropagation; // propagate the input values through the network to the output layer function getRating(state: TFeatureVector; explorative: Boolean): Extended; // get the rating for a given state (feature vector) function HyperbolicTangent(x: Extended): Extended; // calculate the hyperbolic tangent [-1;1] procedure StartNewCycle; // start a new cycle with everything set to default except for the weights procedure setLearningMode(activated: Boolean=TRUE); // switch the learning mode on/off procedure setInputs(state: TFeatureVector); // transfer the given feature vector to the input layer (set input neurons' values) procedure setReward(currentReward: SmallInt); // set the reward for the current timestep (with learning then or without) procedure nextTimeStep; // increase timestep t function getCyclesTrained(): Integer; // get the number of cycles trained so far procedure Visualize(imgHidden: Pointer); // visualize the neural network's hidden layer end; implementation procedure TArtificialNeuralNetwork.UpdateEligibilityTraces; var i, j, k: Integer; begin // how worthy is a weight to be adjusted? for j := 1 to NEURONS_HIDDEN do begin for k := 1 to NEURONS_OUTPUT do begin eligibilityTraceOutput[j][k] := LAMBDA*eligibilityTraceOutput[j][k]+(neuronsOutput[k]*(1-neuronsOutput[k]))*neuronsHidden[j]; for i := 1 to NEURONS_INPUT do begin eligibilityTraceHidden[i][j][k] := LAMBDA*eligibilityTraceHidden[i][j][k]+(neuronsOutput[k]*(1-neuronsOutput[k]))*weightsHidden[j][k]*neuronsHidden[j]*(1-neuronsHidden[j])*neuronsInput[t][i]; end; end; end; end; procedure TArtificialNeuralNetwork.setReward; VAR i: Integer; begin for i := 1 to NEURONS_OUTPUT do begin // +1 = player A wins // 0 = draw // -1 = player B wins reward[t][i] := currentReward; end; end; procedure TArtificialNeuralNetwork.tdLearning; var i, j, k: Integer; begin if learningMode then begin for k := 1 to NEURONS_OUTPUT do begin if reward[t][k] = 0 then begin tdError[k] := GAMMA*neuronsOutput[k]-outputBefore[k]; // network's error value when reward is 0 end else begin tdError[k] := reward[t][k]-outputBefore[k]; // network's error value in the final state (reward received) end; for j := 1 to NEURONS_HIDDEN do begin weightsHidden[j][k] := weightsHidden[j][k]+LEARNING_RATE_HIDDEN*tdError[k]*eligibilityTraceOutput[j][k]; // adjust hidden->output weights according to TD-lambda for i := 1 to NEURONS_INPUT do begin weightsInput[i][j] := weightsInput[i][j]+LEARNING_RATE_INPUT*tdError[k]*eligibilityTraceHidden[i][j][k]; // adjust input->hidden weights according to TD-lambda end; end; end; end; end; procedure TArtificialNeuralNetwork.ForwardPropagation; var i, j, k: Integer; begin for j := 1 to NEURONS_HIDDEN do begin neuronsHidden[j] := 0; for i := 1 to NEURONS_INPUT do begin neuronsHidden[j] := neuronsHidden[j]+neuronsInput[t][i]*weightsInput[i][j]; // input -> hidden end; neuronsHidden[j] := HyperbolicTangent(neuronsHidden[j]); // activation of hidden neuron j end; for k := 1 to NEURONS_OUTPUT do begin neuronsOutput[k] := 0; for j := 1 to NEURONS_HIDDEN do begin neuronsOutput[k] := neuronsOutput[k]+neuronsHidden[j]*weightsHidden[j][k]; // hidden -> output end; neuronsOutput[k] := HyperbolicTangent(neuronsOutput[k]); // activation of output neuron k end; end; procedure TArtificialNeuralNetwork.setLearningMode; begin learningMode := activated; end; constructor TArtificialNeuralNetwork.Create; var i, j, k: Integer; begin inherited Create; Randomize; // initialize random numbers generator learningMode := TRUE; cyclesTrained := -2; // only set to -2 because it will be increased twice in the beginning StartNewCycle; for j := 1 to NEURONS_HIDDEN do begin for k := 1 to NEURONS_OUTPUT do begin weightsHidden[j][k] := abs(Random-0.5); // initialize weights: 0 <= random < 0.5 end; for i := 1 to NEURONS_INPUT do begin weightsInput[i][j] := abs(Random-0.5); // initialize weights: 0 <= random < 0.5 end; end; for i := 1 to 50 do begin last50errors[i] := 0; end; end; procedure TArtificialNeuralNetwork.nextTimeStep; begin t := t+1; end; procedure TArtificialNeuralNetwork.StartNewCycle; var i, j, k, m: Integer; begin t := 1; // start in timestep 1 cyclesTrained := cyclesTrained+1; // increase the number of cycles trained so far for j := 1 to NEURONS_HIDDEN do begin neuronsHidden[j] := 0; for k := 1 to NEURONS_OUTPUT do begin eligibilityTraceOutput[j][k] := 0; outputBefore[k] := 0; neuronsOutput[k] := 0; for m := 1 to MAX_TIMESTEPS do begin reward[m][k] := 0; end; end; for i := 1 to NEURONS_INPUT do begin for k := 1 to NEURONS_OUTPUT do begin eligibilityTraceHidden[i][j][k] := 0; end; end; end; end; function TArtificialNeuralNetwork.getCyclesTrained; begin result := cyclesTrained; end; procedure TArtificialNeuralNetwork.setInputs; var k: Integer; begin for k := 1 to NEURONS_INPUT do begin neuronsInput[t][k] := state[k]; end; end; function TArtificialNeuralNetwork.getRating; begin setInputs(state); ForwardPropagation; result := neuronsOutput[1]; if not explorative then begin tdLearning; // adjust the weights according to TD-lambda ForwardPropagation; // calculate the network's output again outputBefore[1] := neuronsOutput[1]; // set outputBefore which will then be used in the next timestep UpdateEligibilityTraces; // update the eligibility traces for the next timestep nextTimeStep; // go to the next timestep end; end; function TArtificialNeuralNetwork.HyperbolicTangent; begin if x > 5500 then // prevent overflow result := 1 else result := (Exp(2*x)-1)/(Exp(2*x)+1); end; end.

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