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  • Neural Net Optimize w/ Genetic Algorithm

    - by ServAce85
    Is a genetic algorithm the most efficient way to optimize the number of hidden nodes and the amount of training done on an artificial neural network? I am coding neural networks using the NNToolbox in Matlab. I am open to any other suggestions of optimization techniques, but I'm most familiar with GA's.

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  • whats the diference between train, validation and test set, in neural networks?

    - by Daniel
    Im using this library http://pastebin.com/raw.php?i=aMtVv4RZ to implement a learning agent. I have generated the train cases, but i dont know for sure what are the validation and test sets, the teacher says: 70% should be train cases, 10% will be test cases and the rest 20% should be validation cases. Thanks. edit i have this code, for training.. but i have no ideia when to stop training.. def train(self, train, validation, N=0.3, M=0.1): # N: learning rate # M: momentum factor accuracy = list() while(True): error = 0.0 for p in train: input, target = p self.update(input) error = error + self.backPropagate(target, N, M) print "validation" total = 0 for p in validation: input, target = p output = self.update(input) total += sum([abs(target - output) for target, output in zip(target, output)]) #calculates sum of absolute diference between target and output accuracy.append(total) print min(accuracy) print sum(accuracy[-5:])/5 #if i % 100 == 0: print 'error %-14f' % error if ? < ?: break

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  • Spiking neural networks

    - by lmsasu
    Hi all, which is the book one should start with in the domain of spiking neural networks? I know about Gerstner's "Spiking Neuron Models", published in 2002. Is there a more recent book, or maybe a more suitable one? I have a background in maths and artificial neural networks. If there are some good articles or overviews in this domain, also add them to the list. Thanks.

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  • SQL SERVER – Video – Beginning Performance Tuning with SQL Server Execution Plan

    - by pinaldave
    Traveling can be most interesting or most exhausting experience. However, traveling is always the most enlightening experience one can have. While going to long journey one has to prepare a lot of things. Pack necessary travel gears, clothes and medicines. However, the most essential part of travel is the journey to the destination. There are many variations one prefer but the ultimate goal is to have a delightful experience during the journey. Here is the video available which explains how to begin with SQL Server Execution plans. Performance Tuning is a Journey Performance tuning is just like a long journey. The goal of performance tuning is efficient and least resources consuming query execution with accurate results. Just as maps are the most essential aspect of performance tuning the same way, execution plans are essentially maps for SQL Server to reach to the resultset. The goal of the execution plan is to find the most efficient path which translates the least usage of the resources (CPU, memory, IO etc). Execution Plans are like Maps When online maps were invented (e.g. Bing, Google, Mapquests etc) initially it was not possible to customize them. They were given a single route to reach to the destination. As time evolved now it is possible to give various hints to the maps, for example ‘via public transport’, ‘walking’, ‘fastest route’, ‘shortest route’, ‘avoid highway’. There are places where we manually drag the route and make it appropriate to our needs. The same situation is with SQL Server Execution Plans, if we want to tune the queries, we need to understand the execution plans and execution plans internals. We need to understand the smallest details which relate to execution plan when we our destination is optimal queries. Understanding Execution Plans The biggest challenge with maps are figuring out the optimal path. The same way the  most common challenge with execution plans is where to start from and which precise route to take. Here is a quick list of the frequently asked questions related to execution plans: Should I read the execution plans from bottoms up or top down? Is execution plans are left to right or right to left? What is the relational between actual execution plan and estimated execution plan? When I mouse over operator I see CPU and IO but not memory, why? Sometime I ran the query multiple times and I get different execution plan, why? How to cache the query execution plan and data? I created an optimal index but the query is not using it. What should I change – query, index or provide hints? What are the tools available which helps quickly to debug performance problems? Etc… Honestly the list is quite a big and humanly impossible to write everything in the words. SQL Server Performance:  Introduction to Query Tuning My friend Vinod Kumar and I have created for the same a video learning course for beginning performance tuning. We have covered plethora of the subject in the course. Here is the quick list of the same: Execution Plan Basics Essential Indexing Techniques Query Design for Performance Performance Tuning Tools Tips and Tricks Checklist: Performance Tuning We believe we have covered a lot in this four hour course and we encourage you to go over the video course if you are interested in Beginning SQL Server Performance Tuning and Query Tuning. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology, Video Tagged: Execution Plan

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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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  • Why are two indicator-network versions being worked on?

    - by Daniel Rodrigues
    Some months ago, on the road to Ubuntu Maverick, a new system indicator, network (with connman as a backend), started to be developed. The plan was to get it into UNE and release it with no notifcation area. Unfortunately it didn't make it into the final version. However, continued efforts are still being made to improve it, and I'm getting regular updates. From a blueprint from the last UDS, I read that the plan was to ship no notification area and only indicators. For that, it was defined that nm-applet (backend: NetworkManager) should be ported to the appindicator library. Today I discovered that those efforts are going on and a initial version is available for testing, available from Matt Trudel PPA (Natty only). So, my questions is, to whoever has the necessary info: wouldn't it be easier to join efforts and concentrate the work in just one version (probably NetworkManager backend, as that's the official plan), instead of breaking those efforts apart and hampering both testing and developing? Both indicators are being developed by Canonical engineers, and that really doesn't make much sense. So, any Canonical engineer willing to clarify this?

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  • 12.04 wired network doesn't work RTL8111/8168B

    - by laket
    its a fresh 12.04 install 64bits. wifi works fine, wired stays off with cable connected and network-manager shows as if cable is disconnected. Turning off networking lights up my network-cards leds, turning networking on shuts off the leds and no communication is possible. I already tried, turning off the network-manager (sudo service network-manager stop) and setting up my eth0 manually, as soon as I switch off the network-manager my leds light up, but after setting up manually eth0 (sudo ifconfig eth0 10.2.10.114 netmask 255.255.0.0 up) the leds turn off again. I am still dual booting with 10.04 where I have no issues at all, leaving the cable connected all time to my notebook and a switch. Here is some hardware info: lshw: *-network description: Ethernet interface product: RTL8111/8168B PCI Express Gigabit Ethernet controller vendor: Realtek Semiconductor Co., Ltd. physical id: 0 bus info: pci@0000:03:00.0 logical name: eth0 version: 03 serial: c8:0a:a9:d7:05:97 size: 10Mbit/s capacity: 1Gbit/s width: 64 bits clock: 33MHz capabilities: pm msi pciexpress msix vpd bus_master cap_list rom ethernet physical tp mii 10bt 10bt-fd 100bt 100bt-fd 1000bt 1000bt-fd autonegotiation configuration: autonegotiation=on broadcast=yes driver=r8169 driverversion=2.3LK-NAPI duplex=half firmware=rtl_nic/rtl8168d-2.fw latency=0 link=no multicast=yes port=MII speed=10Mbit/s resources: irq:42 ioport:2000(size=256) memory:f0004000-f0004fff memory:f0000000-f0003fff memory:f0010000-f001ffff lspci: 02:00.0 Network controller: Atheros Communications Inc. AR9285 Wireless Network Adapter (PCI-Express) (rev 01) 03:00.0 Ethernet controller: Realtek Semiconductor Co., Ltd. RTL8111/8168B PCI Express Gigabit Ethernet controller (rev 03) ifconfig eth0: eth0 Link encap:Ethernet HWaddr c8:0a:a9:d7:05:97 inet addr:10.2.10.114 Bcast:10.2.255.255 Mask:255.255.0.0 UP BROADCAST MULTICAST MTU:1500 Metric:1 RX packets:0 errors:0 dropped:0 overruns:0 frame:0 TX packets:0 errors:0 dropped:0 overruns:0 carrier:0 collisions:0 txqueuelen:1000 RX bytes:0 (0.0 B) TX bytes:0 (0.0 B) Interrupt:42 Base address:0xc000 cat /etc/network/interfaces: (already tried here with and w/o eth0) auto lo eth0 iface lo inet loopback cat /etc/NetworkManager/NetworkManager.conf [main] plugins=ifupdown,keyfile dns=dnsmasq [ifupdown] managed=false Any help is welcome ;) Laket

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  • Help with Neuroph neural network

    - by user359708
    For my graduate research I am creating a neural network that trains to recognize images. I am going much more complex than just taking a grid of RGB values, downsampling, and and sending them to the input of the network, like many examples do. I actually use over 100 independently trained neural networks that detect features, such as lines, shading patterns, etc. Much more like the human eye, and it works really well so far! The problem is I have quite a bit of training data. I show it over 100 examples of what a car looks like. Then 100 examples of what a person looks like. Then over 100 of what a dog looks like, etc. This is quite a bit of training data! Currently I am running at about one week to train the network. This is kind of killing my progress, as I need to adjust and retrain. I am using Neuroph, as the low-level neural network API. I am running a dual-quadcore machine(16 cores with hyperthreading), so this should be fast. My processor percent is at only 5%. Are there any tricks on Neuroph performance? Or Java peroformance in general? Suggestions? I am a cognitive psych doctoral student, and I am decent as a programmer, but do not know a great deal about performance programming.

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  • How do I ban a wifi network in Network Manager?

    - by Chris Conway
    My wifi connection drops sometimes and, for some reason, Network Manager attempts to connect to my neighbor's network, which requires a password that I don't know. The network in question is not listed in the "Edit Connections..." dialog and I can find no reference to it in any configuration file, but still the password dialog pops up every time my main connection drops. Is there a way to blacklist a wireless network so that the Network Manager will never attempt to connect to it? Or, equivalently, how can I remove the configuration data that causes the Network Manager to attempt to connect to this particular network?

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  • Adding network share as the system account breaks Win7 backup to network

    - by ChrisBenn
    (On Win7 Ultimate x64 SP1) So I've been using Win7 backup to \192.168.0.100\Backup\main-desktop\ for awhile without issue. Yesterday I tried to setup crashplan to synchronize my dropbox folder and a network share. I then found out that crashplan, as it runs under the system account, can't see my user mapped drives. So I created a startup script net use O: \192.168.0.100\Documents /USER:192.168.0.100\username password and set it to run, on startup, after the network interface is up, as the system account. (the username & password are the same for the net use script above, the locally logged in user, and the explicit username/password entered in windows backup). I woke up this morning to find error flags from the windows backup and get "Network location cannot be used" (0x800704B3). If I disable the startup task & reboot then windows backup works fine. I'm not sure why having a mapped drive for another user is killing windows backup (same server, different folder). I can work around the issue by using another program to synchronize the two folders, but I'm completely in the dark as to why this happens (and it's 100% repeatable). Uninstalling the crashplan client doesn't change anything - it's the net use run under the system account that breaks win7 backup (to a network location).

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  • Which network performance management software do you use?

    - by Jamie Keeling
    Hello, I am looking at the various options available for network performance management software, some of the solutions I've found so far are: Proprietary: HP - ProCurve Universal: SolarWinds - Orion Open Source: OpenNMS I am trying to discover the benefits of each package over the other and reasons as to why you would go for one (Such as size of the network, overall cost etc..). I'm curious as to which ones other people use and why? Each customer has their own needs and requirements and it would be great to hear some of yours. Thank you for your time.

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  • OCR with Neural network: data extraction

    - by Sebastian Hoitz
    I'm using the AForge library framework and its neural network. At the moment when I train my network I create lots of images (one image per letter per font) at a big size (30 pt), cut out the actual letter, scale this down to a smaller size (10x10 px) and then save it to my harddisk. I can then go and read all those images, creating my double[] arrays with data. At the moment I do this on a pixel basis. So once I have successfully trained my network I test the network and let it run on a sample image with the alphabet at different sizes (uppercase and lowercase). But the result is not really promising. I trained the network so that RunEpoch had an error of about 1.5 (so almost no error), but there are still some letters left that do not get identified correctly in my test image. Now my question is: Is this caused because I have a faulty learning method (pixelbased vs. the suggested use of receptors in this article: http://www.codeproject.com/KB/cs/neural_network_ocr.aspx - are there other methods I can use to extract the data for the network?) or can this happen because my segmentation-algorithm to extract the letters from the image to look at is bad? Does anyone have ideas on how to improve it?

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  • Neural Network Output Grouping 0.5?

    - by Mike
    I tried to write a Neural Network system, but even running through simple AND/OR/NOR type problems, the outputs seem to group around 0.5 (for a bias of -1) and 0.7 (for a bias of 1). It doesn't look exactly "wrong"... The 1,1 in the AND pattern does seem higher than the rest and the 0,0 in the OR looks lower, but they are still all grouped so it's debatable. I was wondering a) if there's some obvious mistake I've made or b) if there's any advice for debugging Neural Nets... seeing as you can't always track back exactly where an answer came from... Thanks! Mike

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  • Neural network for aproximation function for board game

    - by Pax0r
    I am trying to make a neural network for aproximation of some unkown function (for my neural network course). The problem is that this function has very many variables but many of them are not important (for example in [f(x,y,z) = x+y] z is not important). How could I design (and learn) network for this kind of problem? To be more specific the function is an evaluation function for some board game with unkown rules and I need to somehow learn this rules by experience of the agent. After each move the score is given to the agent so actually it needs to find how to get max score. I tried to pass the neighborhood of the agent to the network but there are too many variables which are not important for the score and agent is finding very local solutions.

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  • Getting Xbox Live via a wired network with my laptop that has internet access wirelessly

    - by Alex Franco
    I'm running the latest version (as of yesterday anyways) of Ubuntu Desktop 64bit, but installed on my laptop if it makes a difference. I had Windows 7 preinstalled when i bought it and it worked fine with the wireless from my house and bridging the connection with a LAN to my xbox for Live. Now with Ubuntu I tried the same setup, but I'm unfamiliar with Ubuntu so I didn't get far. Best I got so far is wireless internet on my laptop and a wired connection to the xbox that continually connects and disconnects. Heres my network settings. if theres fields not included its because theyre empty on mine or theyre my MAC address or network password Wireless Network 1 settings: Connect Automatically: Checked. Available to all Users: Checked Wireless: SSID: Franco's Mode: Infrastructure MTU: Automatic IPv4 Settings: Method: Automatic (DHCP) IPv6 Settings: Method: Automatic Wired Network 1: Connect Automatically: Checked Available to all Users: Checked Wired: MTU: Automatic IPv4 Settings: Method: Automatic (DHCP) IPv6 Settings: Method: Automatic Any help would be greatly appreciated. EDIT: 6:26pm It seems to be staying connected now. Doing the Network test on my xbox it pickups the network, but cannot detect any PC. Restarting the Xbox, however, leaves my computer unable to connect bringing up the Wire Network disconnected 'blip' every minute or so again. Before I had restarted the Xbox it said "Connected 100 MB/s". Now it only says "connecting". I did have my computer and xbox on in this Wired Network Disconnected blip cycle for a long period of time so it may have finally connected, just without the ability to detect my laptop. I left for 2 hours or so in the middle of typing up the original question. I finished posting this when i got back and then tried to mess with it a bit again, in case youre wondering why i didnt include this before... I've said too much. Forgive my long-winded fingers :p

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  • Tuning GlassFish for Production

    - by arungupta
    The GlassFish distribution is optimized for developers and need simple deployment and server configuration changes to provide the performance typically required for production usage. The formal Performance Tuning Guide provides an explanation of capacity planning and tuning tips for application, GlassFish, JVM, and the operating system. The GlassFish Server Control (only with the commercial edition) also comes with Performance Tuner that optimizes the runtime for optimal throughput and scalability. And then there are multiple blogs that provide more insights as well: • Optimizing GlassFish for Production (Diego Silva, Mar 2012) • GlassFish Production Tuning (Vegard Skjefstad, Nov 2011) • GlassFish in Production (Sunny Saxena, Jul 2011) • Putting GlassFish v3 in Production: Essential Surviving Guide (JeanFrancois, Nov 2009) • A GlassFish Tuning Primer (Scott Oaks, Dec 2007) What is your favorite source for GlassFish Performance Tuning ?

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  • Updated copy of the OBIEE Tuning whitepaper

    - by inowodwo
    The Product Assurance team have released an updated copy of the OBIEE Tuning Whitepaper. You can find it on the PA blog https://blogs.oracle.com/pa/entry/test or via Support note OBIEE 11g Infrastructure Performance Tuning Guide (Doc ID 1333049.1) https://support.us.oracle.com/oip/faces/secure/km/DocumentDisplay.jspx?id=1333049.1&recomm=Y This new revised document contains following useful tuning items: 1.    New improved HTTP Server caching algorithm. 2.    Oracle iPlanet Web Server tuning parameters. 3.    New tuning parameters settings / values for OPIS/OBIS components.

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  • Network printing -- setting up and printing over network using Mac OS X

    - by crippledlambda
    I've connected a USB printer to a Mac OS X (10.4) machine -- this computer is connected to a network with a registered IP address -- and enabled sharing through the System Preferences setting (under "Shared" and "Print & Fax". Is this printer shared through the Bonjour or CUPS protocol (does it matter that I know)? How do I sent a print job to this computer (also from a 10.4 OS X machine)? It does not show up in the list of printers in the Default Network when I go to "add printer"; do I need to do anything else (on either machine)? Thanks much!

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  • need for tcp fine-tuning on heavily used proxy server

    - by Vijay Gharge
    Hi all, I am using squid like Internet proxy server on RHEL 4 update 6 & 8 with quite heavy load i.e. 8k established connections during peak hour. Without depending much on application provider's expertise I want to achieve maximum o/p from linux. W.r.t. that I have certain questions as following: How to find out if there is scope for further tcp fine-tuning (without exhausting available resources) as the benchmark values given by vendor looks poor! Is there any parameter value that is available from OS / network stack that will show me the results. If at all there is scope, how shall I identify & configure OS tcp stack parameters i.e. using sysctl or any specific parameter Post tuning how shall I clearly measure performance enhancement / degradation ?

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  • Would you expect this error ?

    - by GrumpyOldDBA
    Now I know why, but what I'm thinking is that if I create an error should I get valid data returned? To explain, I was browsing through the dmvs for queries which might benefit from tuning and I identified a query with two clustered index scans ( table scans ). I don't know all the schema off by heart and I was looking for a select by a LoginID column. I assumed this would be numeric and promptly entered an integer value to examine the query plan, yeah I should have looked at the table definition...(read more)

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