Python NN.calcRelativeError方法代码示例

# 需要导入模块: import NN [as 别名]
# 或者: from NN import calcRelativeError [as 别名]
def train(inputs, outputs, size, participants, victors, generations, threshold, cRate, mRate, printFile=False):
    '''Create and start training the NN via evolution strategy. Selection, crossover, mutation, evaluation.''' 
    global hero
    global OrigAnswers
    OrigAnswers = copy.deepcopy(outputs)
    EvaluationNN = GA.create_net(inputs, outputs)
    population = init_pop(EvaluationNN, inputs, outputs, size)
    # Test each citizen and determine initial fitness
    GA.evaluate(EvaluationNN, population, inputs, outputs)

    if printFile: f = open('ES.csv', 'w')
    gen = 0
    children = []
    # loop until a hero is found or we've reached max generations
    while gen <= generations and hero == 0:
        # Select our parents using tournament selection
        parents = GA.tournament(population, participants, victors)
        # Have our parents mate (Crossover)
        children = GA.mate(parents, cRate)
        # Have the children experience the world (Mutate)
        for child in children:
            mutate(child, mRate)
        # Test each child's fitness
        GA.evaluate(EvaluationNN, children, inputs, outputs)
        children = GA.tournament(children, participants, victors)
        population = sorted(population + children,
                            key=itemgetter(-1))[:-victors]
        if GA.heroFound(population, threshold):
            break
        else:
            print("Training: {:2.2%}".format(
                population[0][-1]), "{:2.2%}     ".format(gen / generations), end="\r")
            if printFile: f.write('%f,' % population[0][-1])
            if printFile: f.write('\n')
        gen += 1
    if printFile: f.close()
    if hero == 0:
        gen -= 1
        hero = sorted(population, key=itemgetter(-1))[0]
    EvaluationNN.SetNNWeights(hero[:-1])  # Load hero into NN, prep for usage.

    # Evaluate the hero on the inputs and outputs
    print('Generations: %d' % gen, ' ' * 20)
    print("Error Relative: {:2.5%}".format(NN.calcRelativeError(EvaluationNN, inputs, OrigAnswers)))
    print("Least Squares: %d" % NN.calcLeastSquaresError(EvaluationNN, inputs, OrigAnswers))
    print("Loss Squared: %d" % NN.calcLossSquared(EvaluationNN, inputs, OrigAnswers))
    #for x in inputs:
    #    EvaluationNN.SetStartingNodesValues(x)
    #    EvaluationNN.CalculateNNOutputs()
    #    print(x, EvaluationNN.GetNNResults(), EvaluationNN.GetNNResultsInt(), OrigAnswers[inputs.index(x)])
    print()

    return EvaluationNN

# 需要导入模块: import NN [as 别名]
# 或者: from NN import calcRelativeError [as 别名]
def evaluate(NNWorking, population, inputs, outputs):
    '''Tests each citizen in the population against a NN topology with inputs
    and outputs to generate an cumulitive fitness measurement, which should be
    minimized'''
    for citizen in population:
        citizen[-1] = 0
        NNWorking.SetNNWeights(citizen[:-1])  # Load weights into the NN
        for i in range(len(inputs)):
            NNWorking.SetStartingNodesValues(inputs[i])  # Load inputs into NN
            NNWorking.CalculateNNOutputs()  # Run the NN once
            # Calculate the fitness value and let the citizen track it
            # for j in range(len(NN.GetNNResults())):
            #    citizen[-1] += ((outputs[i][j] - NN.GetNNResults()[j]))**2
            citizen[-1] += NN.calcRelativeError(NNWorking,
                                                inputs, outputs) / len(inputs)

# 需要导入模块: import NN [as 别名]
# 或者: from NN import calcRelativeError [as 别名]
def train_test():
    global cRate, mRate, threshold, generations, size, participants, victors, inFile, algo, dataset, resultsFile
    inputs = []
    outputs = []
    evolve()
    
    resultsFile.write("DATASET: " + dataset + "\n")
    #resultsFile.write("ALGORITHM | Generations | Size | Participants | Victors | mRate | cRate | Threshold \n")
    #resultsFile.write("   " + str(algo) + "      |     " + str(generations) + "      |  " +
    #          str(size) + "  |     " + str(participants) + "       |    " + str(victors) + 
    #          "    |  " + str(mRate) + "  |  " + str(cRate) + "  |   " + str(threshold) + "     \n")

    dataIn = dataHandler()
    inputs = dataIn[0]
    outputs = dataIn[1]
    testInput = []
    testOutput = []
    learnrate = 0.3
    momentum = 0.5
    # Need 20% of inputs for testing
    for i in range((int(len(inputs)*0.8)+1), len(inputs)):
        x = random.choice(inputs)
        testInput.append(x)
        testOutput.append(outputs[inputs.index(x)])
        del outputs[inputs.index(x)]
        del inputs[inputs.index(x)]
    resultsFile.write("\nTest inputs: \n")
    for i in range(len(testInput)):
        resultsFile.write("%s " % testInput[i])
    resultsFile.write("\nTest expected outputs: \n")
    for i in range(len(testOutput)):
        resultsFile.write("%s " % testOutput[i])
    # Which algorithm gets chosen to run
    if algo in 'G':
        print("DOING GA TRAINING...")
        resultsFile.write("\nALGORITHM | Generations | Size | Participants | Victors | mRate | cRate | Threshold \n")
        resultsFile.write("   " + str(algo) + "      |     " + str(generations) + "      |  " + str(size) + "  |     " + str(participants) + "       |    " + str(victors) + "    |  " + str(mRate) + "  |  " + str(cRate) + "  |   " + str(threshold) + "     \n")
        testNN = GA.train(inputs, outputs, size, participants, victors, generations, threshold, cRate, mRate)
    elif algo in 'E':
        print("DOING ES TRAINING...")
        resultsFile.write("\nALGORITHM | Generations | Size | Participants | Victors | mRate | cRate | Threshold \n")
        resultsFile.write("   " + str(algo) + "      |     " + str(generations) + "      |  " + str(size) + "  |     " + str(participants) + "       |    " + str(victors) + "    |  " + str(mRate) + "  |  " + str(cRate) + "  |   " + str(threshold) + "     \n")
        testNN = ES.train(inputs, outputs, size, participants, victors, generations, threshold, cRate, mRate)
    elif algo in 'D':
        print("DOING DE TRAINING...")
        resultsFile.write("\nALGORITHM | Generations | Size | mRate | cRate | Threshold \n")
        resultsFile.write("   " + str(algo) + "      |     " + str(generations) + "      |  " +  str(size) + "    |  " + str(mRate) + "  |  " + str(cRate) + "  |   " + str(threshold) + "     \n")
        testNN = DE.train(inputs, outputs, size, generations, threshold, cRate, mRate)
    elif algo in 'B':
        print("DOING BP TRAINING...")
        resultsFile.write("\nALGORITHM | Generations | learnrate | momentum | Threshold \n")
        resultsFile.write("   " + str(algo) + "      |     " + str(generations) + "      |  " + str(learnrate) + "  |  " + str(momentum) + "  |   " + str(threshold) + "     \n")
        testNN = NN.main(inputs, [['S','S','S'], ['S','S']], ['S'], outputs, generations, learnrate, threshold, momentum)
    else:
        print("Unrecognized algorithm!")
        sys.exit()
    # Print test input/expected output - could be made prettier in a table
    # Start testing testNN
    for x in testInput:
        resultsFile.write("\nSet starting node vals\n")
        resultsFile.write("%s \n" % testNN.SetStartingNodesValues(x))
        testNN.CalculateNNOutputs()
        resultsFile.write("\nTest Input: " + str(x) + "\n")
        resultsFile.write("\nTest results: %s\n" % testNN.GetNNResults())
    resultsFile.write("\nRelative Error: {:2.2%} \n".format(NN.calcRelativeError(testNN, testInput, testOutput)))
    resultsFile.write("\nLeast Squares Error: %s \n" % NN.calcLeastSquaresError(testNN, testInput, testOutput))
    resultsFile.write("\nLoss Squared Error: %s \n" % NN.calcLossSquared(testNN, testInput, testOutput))
    resultsFile.write("\nPercent Misidentified: {:2.2%} \n".format(NN.calcPercentIncorrect(testNN, testInput, testOutput)))
    resultsFile.close()

# 需要导入模块: import NN [as 别名]
# 或者: from NN import calcRelativeError [as 别名]
def train(inputs, outputs, size, participants, victors,
          generations, threshold, cRate, mRate, printFile=False):
    '''The train method takes in a set of inputs and outputs which will be
    compared against a hardcoded NN topology. The size, participants, and
    victors are with regard to tournament selection and elitism selection
    techniques. Generations is the max number of generations allowed while
    threshold is the accuracy needed. cRate and mRate are the rate of
    crossover and rate of mutation respectively. '''
    global hero
    global OrigAnswers
    EvaluationNN = create_net(inputs, outputs)
    population = generatePopulation(EvaluationNN, inputs, outputs, size)
    # Test each citizen and determine initial fitness
    evaluate(EvaluationNN, population, inputs, outputs)
    if printFile: f = open('GA.csv', 'w')
    gen = 0
    children = []
    # loop until a hero is found or we've reached max generations
    while gen <= generations and hero == 0:
        # Select our parents using tournament selection
        parents = tournament(population, participants, victors)
        # Have our parents mate (Crossover)
        children = mate(parents, cRate)
        # Have the children experience the world (Mutate)
        for child in children:
            mutate(child, mRate)
        # Test each child's fitness
        evaluate(EvaluationNN, children, inputs, outputs)
        # We were to prolific, thus children must fight to the death via draft
        # call. Make participants len(children) to have all of them fight
        # This might not be a good idea as late generation counts result in not
        # keeping the children.
        children = tournament(children, participants, victors)
        # purging of population is determined by elitism inverted on fitness
        # level (cowardace is greater number).
        # Take number of children equal to number of tournament victors and
        # reintroduce to the population
        population = sorted(population + children,
                            key=itemgetter(-1))[:-victors]
        # Determine if a child is a hero (<threshold) and if so, return child
        if heroFound(population, threshold):
            break
        else:
            print("Training: {:2.2%}".format(
                population[0][-1]), "{:2.2%}     ".format(gen / generations), end="\r")
            if printFile: f.write('%f,' % population[0][-1])
            if printFile: f.write('\n')
        gen += 1
    # return best hero if max generations is met and hero hasn't been selected.
    if printFile: f.close()
    if hero == 0:
        gen -= 1
        hero = sorted(population, key=itemgetter(-1))[0]
    EvaluationNN.SetNNWeights(hero[:-1])  # Load hero into NN, prep for usage.

    # Evaluate the hero on the inputs and outputs
    print('Generations: %d' % gen, ' ' * 20)
    print("Error Relative: {:2.5%}".format(NN.calcRelativeError(EvaluationNN, inputs, OrigAnswers)))
    print("Least Squares: %d" % NN.calcLeastSquaresError(EvaluationNN, inputs, OrigAnswers))
    print("Loss Squared: %d" % NN.calcLossSquared(EvaluationNN, inputs, OrigAnswers))
    #for x in inputs:
    #    EvaluationNN.SetStartingNodesValues(x)
    #    EvaluationNN.CalculateNNOutputs()
    #    print(x, EvaluationNN.GetNNResults(), EvaluationNN.GetNNResultsInt(), OrigAnswers[inputs.index(x)])
    print()

    return EvaluationNN

# 需要导入模块: import NN [as 别名]
# 或者: from NN import calcRelativeError [as 别名]
def train(inputs, outputs, size, generations, threshold, cRate, mRate, printFile=False):
    """The train method creates a neural netwrok from the sets of 
    inputs and outputs. A population vector of size, is initialized 
    with ranodm weight vectors associated with the weights between 
    nodes in the neural network and will be the values being trained.
    Generations is the max number of generations allowed while 
    threshold is the accuracy needed. cRate and mRate are the 
    crossover and mutation rates respectively."""
    global hero
    global OrigAnswers

    OrigAnswers = copy.deepcopy(outputs)
    # set up NN
    EvaluationNN = GA.create_net(inputs, outputs)

    # initialize population of size as random weights of NN
    population = GA.generatePopulation(EvaluationNN, inputs, outputs, size)

    if printFile:
        f = open("DE.csv", "w")
    gen = 0
    trialV = []
    offspringV = []

    # evaluate the entire population
    GA.evaluate(EvaluationNN, population, inputs, outputs)

    # loop until a hero is found or we've reached max generations
    while gen <= generations and hero == 0:
        for i in range(size):
            # mutate with DE/x/1/bin
            trialV = mutate(population, i, mRate)
            # perform binomial crossover
            offspringV = crossover(population[i], trialV, cRate)
            # evaluation of offspring
            GA.evaluate(EvaluationNN, [offspringV], inputs, outputs)
            # selection of better vector
            if population[i][-1] > offspringV[-1]:
                population[i] = offspringV
        population = sorted(population, key=itemgetter(-1))
        # check for hero in population
        if GA.heroFound(population, threshold):
            break
        else:
            print("Training: {:2.2%}".format(population[0][-1]), "{:2.2%}     ".format(gen / generations), end="\r")
            if printFile:
                f.write("%f," % population[0][-1])
            if printFile:
                f.write("\n")
        gen += 1
    # return best hero if max generations is met and hero hasn't been selected.
    # hero = sorted(population, key=itemgetter(-1))[0]  # default to best in
    # population if no hero steps forward
    if printFile:
        f.close()
    if hero == 0:
        gen -= 1
        hero = sorted(population, key=itemgetter(-1))[0]
    EvaluationNN.SetNNWeights(hero[:-1])  # Load hero into NN, prep for usage.

    # Evaluate the hero on the inputs and outputs
    print("Generations: %d" % gen, " " * 20)
    print("Error Relative: {:2.5%}".format(NN.calcRelativeError(EvaluationNN, inputs, OrigAnswers)))
    print("Least Squares: %d" % NN.calcLeastSquaresError(EvaluationNN, inputs, OrigAnswers))
    print("Loss Squared: %d" % NN.calcLossSquared(EvaluationNN, inputs, OrigAnswers))
    # for x in inputs:
    #    EvaluationNN.SetStartingNodesValues(x)
    #    EvaluationNN.CalculateNNOutputs()
    #    print(x, EvaluationNN.GetNNResults(), EvaluationNN.GetNNResultsInt(), OrigAnswers[inputs.index(x)])
    print()

    return EvaluationNN