pyrobot.brain.governor.GovernorNetwork

Package pyrobot :: Package brain :: Module governor :: Class GovernorNetwork

Class GovernorNetwork

Governor --+
           |
 Network --+
           |
          GovernorNetwork

Method Summary
	`__init__(self, bufferSize, epsilon, delta, historySize, alpha, mask, verbosity)`
	`addThreeLayers(self, i, h, o)`
	`networkStep(self, **args)`
	`setLearning(self, value)` Sets learning to value.
	`setVerbosity(self, val)`
Inherited from Governor
	`__nextitem__(self)` For use in iterable positions: >>> govnet = GovernorNetwork() >>> for item in govnet: ...
	`distancesTo(self, vector)` Computes euclidean distance from a vector to all model vectors.
	`incompatibility(self)` For each model, how different is it from each of the buffer items? Returns list of incompatibilities.
	`input(self, vector)` Wrapper around ravq.input() which returns index and mapped-to m.v.
	`loadRAVQ(self, filename)` Loads RAVQ data from a file.
	`map(self, vector)` Returns the index and vector of winning position.
	`nextItem(self)` Public interface for getting next item from RAVQ.
	`saveRAVQ(self, filename)` Saves RAVQ data to a file.
	`setBalancedMask(self)` Give each layer an equal weighting, so that all weights sum to one.
	`setMask(self, **args)` Takes a dictionary of layer names and mask weights.
	`winner(self)` Get's winning name, m.v.
Inherited from Network
	`__getitem__(self, name)` Returns the layer specified by name.
	`__len__(self)` Returns the number of layers in the network.
	`__str__(self)` Returns string representation of network.
	`activationFunction(self, x)` Determine the activation of a node based on that nodes net input.
	`ACTPRIME(self, act)` Used in compute_error.
	`add(self, layer, verbosity)` Adds a layer.
	`addLayer(self, name, size, verbosity)`
	`addPattern(self, word, vector)` Adds a pattern with key word.
	`arrayify(self)` Returns an array of node bias values and connection weights for use in a GA.
	`associate(self, inName, outName)` inName layer and outName layer will be auto-associating.
	`backprop(self)` Computes error and wed for back propagation of error.
	`ce_init(self)` Initializes error computation.
	`change_weights(self)` Changes the weights according to the error values calculated during backprop().
	`changeLayerSize(self, layername, newsize)` Changes layer size.
	`closeLog(self, layerName)` Close the layerName's log file.
	`compare(self, v1, v2)` Compares two values.
	`compute_error(self)` Computes error for all non-output layers backwards through all projections.
	`compute_wed(self)` Computes weight error derivative for all connections in self.connections starting with the last connection.
	`connect(self, fromName, toName)` Connects two layers by instantiating an instance of Connection class.
	`copyActivations(self, layer, vec, start)` Copies activations in vec to the specified layer, replacing patterns if necessary.
	`copyTargets(self, layer, vec, start)` Copies targets in vec to specified layer, replacing patterns if necessary.
	`copyVector(self, vector1, vec2, start)` Copies vec2 into vector1 being sure to replace patterns if necessary.
	`cycle(self)` Alternate to sweep().
	`delPattern(self, word)` Delete a pattern with key word.
	`diff(self, value)` Returns value to within 0.001.
	`display(self)` Displays the network to the screen.
	`getActivationsDict(self, nameList)` Returns a dictionary of layer names that map to a list of activations.
	`getActive(self, layerName)` Returns the value of the active flag for the layer specified by layerName.
	`getConnection(self, lfrom, lto)` Returns the connection instance connecting the specified (string) layer names.
	`getCorrect(self, layerName)` Returns the number of correct activation within tolerance of a layer.
	`getData(self, pos)` Returns dictionary with input and target given pos.
	`getDataCrossValidation(self, pos)`
	`getEpsilon(self)` Returns the epsilon for the Network instance.
	`getError(self, *layerNames)`
	`getLayer(self, name)` Returns the layer with the argument (string) name.
	`getLayerIndex(self, layer)` Given a reference to a layer, returns the index of that layer in self.layers.
	`getPattern(self, word)` Returns the pattern with key word.
	`getWeights(self, fromName, toName)` Gets the weights of the connection between two layers (argument strings).
	`getWord(self, pattern)` Returns the word associated with pattern.
	`initialize(self)` Initializes network by calling Connection.initialize() and Layer.initialize().
	`loadCrossValidation(self, filename)`
	`loadDataFromFile(self, filename, ocnt)` Loads data (targets/inputs) from file.
	`loadInputPatternsFromFile(self, filename, cols, everyNrows, delim, checkEven)` Loads inputs as patterns from file.
	`loadInputsFromFile(self, filename, cols, everyNrows, delim, checkEven)` Loads inputs from file.
	`loadTargetPatternssFromFile(self, filename, cols, everyNrows, delim, checkEven)` Loads targets as patterns from file.
	`loadTargetsFromFile(self, filename, cols, everyNrows, delim, checkEven)` Loads targets from file.
	`loadVectorsFromFile(self, filename, cols, everyNrows, delim, checkEven, patterned)` Load a set of vectors from a file.
	`loadWeightsFromFile(self, filename, mode)` Loads weights from a file in pickle, plain, or tlearn mode.
	`logLayer(self, layerName, fileName)` Sets the layerName's log feature.
	`logMsg(self, layerName, message)` Logs a message with layerName log.
	`mapInput(self, layerName, offset)` Adds layerName and offset to inputMap.
	`mapInputs(self, nameOffsetPairs)`
	`mapTarget(self, layerName, offset)` Adds layerName and offset to targetMap.
	`mapTargets(self, nameOffsetPairs)`
	`path(self, startLayer, endLayer)` Used in error checking with verifyArchitecture() and in prop_from().
	`patternVector(self, vector)` Replaces vector with patterns.
	`postStep(self)`
	`postSweep(self)`
	`preStep(self)`
	`preSweep(self)`
	`Print(self, msg)`
	`prompt(self)`
	`prop_from(self, startLayers)` Start propagation from the layers in the list startLayers.
	`propagate(self, **args)` Propagates activation through the network.
	`propagateFrom(self, startLayer, **args)` Propagates activation through the network.
	`putActivations(self, dict)` Puts a dict of name: activations into their respective layers.
	`randomizeOrder(self)` Randomizes self.loadOrder, the order in which inputs set with self.setInputs() are presented.
	`replacePatterns(self, vector)` Replaces patterned inputs or targets with activation vectors.
	`reportEpoch(self, epoch, tssErr, totalCorrect, totalCount, rmsErr)`
	`reportFinal(self, epoch, tssErr, totalCorrect, totalCount, rmsErr)`
	`reportPattern(self)`
	`reportStart(self)`
	`reset(self)` Resets seed values.
	`resetFlags(self)` Resets layer flags for activation and target.
	`RMSError(self)` Returns Root Mean Squared Error for all output layers in this network.
	`saveDataToFile(self, filename)` Saves data (targets/inputs) to file.
	`saveInputsToFile(self, filename)` Saves inputs to file.
	`saveNetworkForCrossValidation(self, filename, mode)`
	`saveNetworkToFile(self, filename, makeWrapper)` Saves network to file using pickle.
	`saveTargetsToFile(self, filename)` Saves targets to file.
	`saveWeightsToFile(self, filename, mode)` Saves weights to file in pickle, plain, or tlearn mode.
	`setActive(self, layerName, value)` Sets a layer to active.
	`setAutoCrossValidation(self, value)`
	`setAutoSaveWeightsFile(self, filename)`
	`setBatch(self, value)` Sets self.batch to value.
	`setEpsilon(self, value)` Sets epsilon value for the network.
	`setInputs(self, inputs)` Sets self.input to inputs.
	`setInputsAndTargets(self, data1, data2)` Network.setInputsAndTargets() Sets the corpus of data for training.
	`setInteractive(self, value)` Sets interactive to value.
	`setLayerVerification(self, value)`
	`setMaxRandom(self, value)` Sets the maxRandom Layer attribute for each layer to value.Specifies the global range for randomly initialized values, [-max, max].
	`setMomentum(self, value)` Sets self.momentum to value.
	`setOrderedInputs(self, value)` Sets self.orderedInputs to value.
	`setOutputs(self, outputs)` For compatiblity.
	`setPattern(self, word, vector)` Sets a pattern with key word.
	`setPatterned(self, value)` Sets the network to use patterns for inputs and targets.
	`setPatterns(self, patterns)` Sets patterns to the dictionary argument.
	`setReportRate(self, value)` Sets self.reportRate to value.
	`setResetEpoch(self, value)` Sets self.resetEpoch to value.
	`setResetLimit(self, value)` Sets self.resetLimit to value.
	`setSeed(self, value)` Sets the seed to value.
	`setSigmoid_prime_offset(self, value)` Sets self.sigmoid_prime_offset to value.
	`setStopPercent(self, value)` Sets self.stopPercent to value.
	`setSweepReportRate(self, value)` Sets self.sweepReportRate to value.
	`setTargets(self, targets)` Sets the targets.
	`setTolerance(self, value)` Sets tolerance to value.
	`setup(self)`
	`setUseCrossValidationToStop(self, value)` Sets flag so that self.stopPercent is compared to cross validation percent rather than the regular training data percentage correct.
	`setWeight(self, fromName, fromPos, toName, toPos, value)` Sets the weight of the connection between two layers (argument strings).
	`shareWeights(self, network, listOfLayerNamePairs)` Share weights with another network.
	`step(self, **args)` Network.step() Does a single step.
	`sweep(self)` Runs through entire dataset.
	`sweepCrossValidation(self)` sweepCrossValidation() will go through each of the crossvalidation input/targets.
	`toString(self)` Returns the network layers as a string.
	`train(self, cont)` Trains the network on the dataset till a stopping condition is met.
	`TSSError(self, layerName)` Returns Total Sum Squared error for the specified layer's pattern.
	`unArrayify(self, gene)` Copies gene bias values and weights to network bias values and weights.
	`verifyArchitecture(self)` Check for orphaned layers or connections.
	`verifyArguments(self, arg)` Verifies that arguments to setInputs and setTargets are appropriately formatted.
	`verifyInputs(self)` Used in propagate() to verify that the network input activations have been set.
	`verifyTargets(self)` Used in backprop() to verify that the network targets have been set.

Method Details

setLearning(self, value)

Sets learning to value. Determines if the network learns,
ie. changes connection weights.

Overrides:: pyrobot.brain.conx.Network.setLearning (inherited documentation)

Generated by Epydoc 2.1 on Mon Jul 25 01:39:28 2005

http://epydoc.sf.net