Python datasets.SupervisedDataSet类代码示例

def entrenarSomnolencia(red):
    #Se inicializa el dataset
    ds = SupervisedDataSet(4096,1)

    """Se crea el dataset, para ello procesamos cada una de las imagenes obteniendo los rostros,
       luego se le asignan los valores deseados del resultado la red neuronal."""

    print "Somnolencia - cara"
    for i,c in enumerate(os.listdir(os.path.dirname('/home/taberu/Imágenes/img_tesis/somnoliento/'))):
        try:
            im = cv2.imread('/home/taberu/Imágenes/img_tesis/somnoliento/'+c)
            pim = pi.procesarImagen(im)
            cara = d.deteccionFacial(pim)
            if cara == None:
                print "No hay cara"
            else:
                print i
                ds.appendLinked(cara.flatten(),10)
        except:
            pass

    trainer = BackpropTrainer(red, ds)
    print "Entrenando hasta converger"
    trainer.trainUntilConvergence()
    NetworkWriter.writeToFile(red, 'rna_somnolencia.xml')

def getErrorPercent(training_dataset, eval_dataset_list, num_hidden, num_epochs):
  num_datapoints = len(training_dataset)
  num_inputs = len(training_dataset[0][0])
  num_outputs = len(training_dataset[0][1])

  # print "Num Inputs:", num_inputs
  # print "Num Outputs:", num_outputs
  # print "Num Hidden Nodes:", num_hidden

  NN = buildNetwork(num_inputs, num_hidden, num_outputs, bias=True, hiddenclass=SigmoidLayer, outclass=SigmoidLayer)

  dataset = SupervisedDataSet(num_inputs, num_outputs)
  for datapoint in training_dataset:
    dataset.addSample(datapoint[0], datapoint[1])


  trainer = BackpropTrainer(NN, dataset=dataset, momentum=0.0, verbose=False, weightdecay=0.0)

  for epoch in range(0, num_epochs):
    #print epoch 
    trainer.train()

  errors = []
  for eval_set in eval_dataset_list:
    total_percent_errors = [0]*num_outputs
    for jj in range(0, len(eval_set)):
      nn_out = NN.activate(eval_set[jj][0])
      percent_error = computeError(eval_set[jj][1], nn_out)
      #print percent_error
      total_percent_errors = map(operator.add, percent_error, total_percent_errors)
    #print total_percent_errors
    errors.append(map(operator.div, total_percent_errors, [len(dataset)]*num_outputs))
  #print errors
  return errors

def main():
	inputs = ReadCSV('./data/input.csv')
	outputs = ReadCSV('./data/output.csv')
	
	test_set = test.keys()
	train_set = []
	for k in inputs.keys():
		if k not in test_set:
			train_set.append(k)
	print "Number of training samples", len(train_set)
	print "Number of testing samples", len(test_set)
			
	net = buildNetwork(178, 6, 5)
	ds=SupervisedDataSet(178,5)
	for id in train_set:
		ds.addSample(inputs[id],outputs[id])

	trainer = BackpropTrainer(net, ds, learningrate=0.001, momentum = 0.001)

	trainer.trainUntilConvergence(maxEpochs=1000, validationProportion = 0.5)
	
	
	for id in test_set:
		predicted = net.activate(inputs[id])
		actual = outputs[id]
		print '-----------------------------'
		print test[id]
		print '-----------------------------'
		print 'Trait\t\tPredicted\tActual\tError'
		for i in range(0,5):
			error = abs(predicted[i] - actual[i])*100/4.0
			print traits[i], '\t', predicted[i], '\t', actual[i], '\t', error,"%" 

class Brain:
	def __init__(self, hiddenNodes = 30):
		# construct neural network 
		self.myClassifierNet = buildNetwork(12, hiddenNodes, 1, bias=True, hiddenclass=TanhLayer) #parameters to buildNetwork are inputs, hidden, output
		# set up dataset
		self.myDataset = SupervisedDataSet(12, 1)
		self.myClassifierTrainer = BackpropTrainer(self.myClassifierNet, self.myDataset)

	def addSampleImageFromFile(self, imageFile, groupId):
		"adds a data sample from an image file, including needed processing"
		myImage = Image.open(imageFile)
		self.myDataset.addSample(twelveToneParallel(myImage), (groupId,))

	def train(self):
		#myClassifierTrainer.trainUntilConvergence() #this will take forever (possibly literally in the pathological case)
		for i in range(0, 15):
			self.myClassifierTrainer.train() #this may result in an inferior network, but in practice seems to work fine

	def save(self, saveFileName="recognizernet.brain"):
		saveFile = open(saveFileName, 'w')
		pickle.dump(self.myClassifierNet, saveFile)
		saveFile.close()

	def load(self, saveFileName="recognizernet.brain"):
		saveFile = open(saveFileName, 'r')
		myClassifierNet = pickle.load(saveFile)
		saveFile.close()

	def classify(self, fileName):
		myImage = Image.open(fileName)
		if self.myClassifierNet.activate(twelveToneParallel(myImage)) < 0.5:
			return 0
		else:
			return 1

def loadDataSet(ds_file):
  global X, Y
  BB = set()
  aaa = {}
  ds = SupervisedDataSet(400, 10)
  #ds = SupervisedDataSet(1024, 5)
  with open(ds_file,"rb") as f:
    lines = f.readlines()
    for line in lines:
      l = [float(a) for a in line.strip().split(',')]
      #A = [float(1.0)] + l[:-1]
      A = l[:-1]
      X.append(A)
      B = int(l[-1])
      #BB.update([B])
      #for aa,bb in enumerate(BB):
      #  aaa[bb] = aa
      #print aaa
      #Y.append(aaa[bb])
      Y.append(B)
      C = []
      for i in range(10):
        C.append(int(1) if i==B or (i==0 and B==10) else int(0))
      ds.addSample(tuple(A), tuple(C))
  return ds

def run_data():
    with open('new_data2.txt') as data_file:
        data = json.load(data_file)
    ds = SupervisedDataSet(1316, 1316)
    for i in xrange(0, 51):
        print "Adding {}th data sample".format(i),
        input = tuple(data[str(i)]['input'])
        output = tuple(data[str(i)]['output'])        
        # print len(input), len(output)
        ds.addSample( input, output)
        print ":: Done"

    print "Train"
    net = buildNetwork( 1316, 100, 1316, bias=True, )
    trainer = BackpropTrainer(net, ds)

    terrors = trainer.trainUntilConvergence(verbose = True, validationProportion = 0.33, maxEpochs = 20, continueEpochs = 10 )
    # print terrors[0][-1],terrors[1][-1]
    fo = open("results2.txt", "w")
    for input, expectedOutput in ds:
        output = net.activate(input)
        count = 0
        for q in xrange(0, 1316):
            print output[q], expectedOutput[q]
            if math.floor(output[q]) == math.floor(expectedOutput[q]):
                count+=1    
        m = float(count)/1316.00
        print "{0} ::  {1}".format(count, m)
        fo.write("{0} ::  {1}\n".format(count, m))

    def learn(self):
        # convert reinforcement dataset to NFQ supervised dataset
        supervised = SupervisedDataSet(self.module.network.indim, 1)

        for seq in self.dataset:
            lastexperience = None
            for state, action, reward in seq:
                if not lastexperience:
                    # delay each experience in sequence by one
                    lastexperience = (state, action, reward)
                    continue

                # use experience from last timestep to do Q update
                (state_, action_, reward_) = lastexperience

                Q = self.module.getValue(state_, action_[0])

                inp = r_[state_, one_to_n(action_[0], self.module.numActions)]
                tgt = Q + 0.5*(reward_ + self.gamma * max(self.module.getActionValues(state)) - Q)
                supervised.addSample(inp, tgt)

                # update last experience with current one
                lastexperience = (state, action, reward)

        # train module with backprop/rprop on dataset
        trainer = RPropMinusTrainer(self.module.network, dataset=supervised, batchlearning=True, verbose=False)
        trainer.trainUntilConvergence(maxEpochs=self.maxEpochs)

class NeuralKinect():
    def __init__(self):
        # Softmax layer -> great for classification networks
        #self.neuralNet = buildNetwork(60, 60, 5, outclass=SoftmaxLayer)
        #self.neuralNet = buildNetwork(60, 60, 5, hiddenclass=TanhLayer)
        #self.neuralNet = buildNetwork(60, 60, 5, bias=True)
        self.neuralNet = buildNetwork(60, 60, 5)
        self.dataSet = SupervisedDataSet(60, 5)

    def trainBackProp(self):
        trainer = BackpropTrainer(self.neuralNet, self.dataSet)
        start = time.time()
        trainer.trainEpochs(EPOCHS)
        end = time.time()
        print("Training time -> " + repr(end-start))
        print(repr(trainer.train()))

    def loadDataSet(self):
        points = []
        for csvFile in glob.iglob("TrainData/*.csv"):
            with open(csvFile, 'rt') as letterSet:
                reader = csv.reader(letterSet)
                header = str(reader.next())
                letter = header[2:3]
                targetStr = header[4:9]
                print("Processing Dataset for letter -> " + letter)
                target = []
                for digit in targetStr:
                    target.append(digit)
                rows = 1
                for row in reader:              
                    for col in row:
                        points.append(col)
                    if rows % 20 == 0:
                        self.dataSet.addSample(points, target)
                        points = []
                    rows += 1
                    
    def processResults(self, output):
        result = ""
        for digit in output:
            if digit > 0.5:
                result += "1"
            else:
                result += "0"
        print("Network result -> " + chr(64+int(result,2)))
                    
    def testNetwork(self):
        points = []
        for csvFile in glob.iglob("TestData/*.csv"):
            with open(csvFile, 'rt') as testPose:
                reader = csv.reader(testPose)
                rows = 1
                for row in reader:
                    for col in row:
                        points.append(col)
                    if rows % 20 == 0:
                        self.processResults(self.neuralNet.activate(points))
                        points = []
                    rows += 1

def neural_network(data, target, network):
    DS = SupervisedDataSet(len(data[0]), 1)
    nn = buildNetwork(len(data[0]), 7, 1, bias = True)
    kf = KFold(len(target), 10, shuffle = True);
    RMSE_NN = []
    for train_index, test_index in kf:
        data_train, data_test = data[train_index], data[test_index]
        target_train, target_test = target[train_index], target[test_index]
        for d,t in zip(data_train, target_train):
            DS.addSample(d, t)
        bpTrain = BackpropTrainer(nn,DS, verbose = True)
        #bpTrain.train()
        bpTrain.trainUntilConvergence(maxEpochs = 10)
        p = []
        for d_test in data_test:
            p.append(nn.activate(d_test))
        
        rmse_nn = sqrt(np.mean((p - target_test)**2))
        RMSE_NN.append(rmse_nn)
        DS.clear()
    time = range(1,11)
    plt.figure()
    plt.plot(time, RMSE_NN)
    plt.xlabel('cross-validation time')
    plt.ylabel('RMSE')
    plt.show()
    print(np.mean(RMSE_NN))

def get_dataset_for_pybrain_regression(X,y):
	ds = SupervisedDataSet(250,1)
	tuples_X = [tuple(map(float,tuple(x))) for x in X.values]
	tuples_y = [tuple(map(float,(y,))) for y in y.values]
	for X,y in zip(tuples_X,tuples_y):
		ds.addSample(X,y)
	return ds

    def learn(self,dataset):
        """
            This function trains network

            Input:
            dataset     - Dataset to train network

            Returns:
            Nothing
        """
        from pybrain.supervised.trainers import BackpropTrainer
        from pybrain.datasets import SupervisedDataSet
        from neuraltrainer import NeuralTrainer

        if self._net == None: raise NeuralBrainException("Brain is not configured!")
        if dataset == {}: raise NeuralBrainException("Dataset for learning is empty.")

        data = SupervisedDataSet(self._input,self._output)
        for input,output in dataset.items():
            input = self._normalize(input,self._input)
            output = self._normalize(output,self._output)
            data.addSample(input,output)
            data.addSample(input,output)# For better learning 2x

        trainer = NeuralTrainer(self._net, data)
        trainer.simpleTrain()

def get_train_samples(input_num,output_num):
    '''
    从new_samples文件夹中读图，根据输入数和输出数制作样本，每一原始样本加入随机噪音生成100个样本
    '''
    print 'getsample start.'
    sam_path='./new_samples'
    samples = SupervisedDataSet(input_num,output_num)
    nlist = os.listdir(sam_path)
    t=int(np.sqrt(input_num))
    for n in nlist:
        file = os.path.join(sam_path,n)
        im = Image.open(file)
        im = im.convert('L')
        im = im.resize((t,t),Image.BILINEAR)
        buf = np.array(im).reshape(input_num,1)
        buf = buf<200
        buf = tuple(buf)
        buf1=int(n.split('.')[0])
        buf2=range(output_num)
        for i in range(len(buf2)):
            buf2[i] = 0
        buf2[buf1]=1
        buf2 = tuple(buf2)
        samples.addSample(buf,buf2)
        for i in range(100):
            buf3 = list(buf)
            for j in range(len(buf)/20):
                buf3[np.random.randint(len(buf))] = bool(np.random.randint(2))
            samples.addSample(tuple(buf3),buf2)
    return samples 

def convertDataNeuralNetwork(x, y):
	data = SupervisedDataSet(x.shape[1], 1)
	for xIns, yIns in zip(x, y):
    	data.addSample(xIns, yIns)    
	return data

def NN(xTrain, yTrain, xTest, yTest):
	trainData = convertDataNeuralNetwork(xTrain, yTrain)
	testData = convertDataNeuralNetwork(xTest, yTest)
	fnn = FeedForwardNetwork()
	inLayer = SigmoidLayer(trainData.indim)
	hiddenLayer = SigmoidLayer(5)
	outLayer = LinearLayer(trainData.outdim)
	fnn.addInputModule(inLayer)
	fnn.addModule(hiddenLayer)
	fnn.addOutputModule(outLayer)
	in_to_hidden = FullConnection(inLayer, hiddenLayer)
	hidden_to_out = FullConnection(hiddenLayer, outLayer)
	fnn.addConnection(in_to_hidden)
	fnn.addConnection(hidden_to_out)
	fnn.sortModules()
	trainer = BackpropTrainer(fnn, dataset = trainData, momentum = 0.1, verbose = True, weightdecay = 0.01)

	for i in xrange(10):
	    trainer.trainEpochs(500)
	    
	rmse = percentError(trainer.testOnClassData(dataset = testData), yTest)
	return rmse/100

def main():
	rmse = NN(xTrain, yTrain, xTest, yTest)
	print rmse

if __name__=="__main__":
	main()

    def run_try(self, rand_chance=0, rand_count=0, rand_count_ref=0, render=False):
        ds = SupervisedDataSet(env_size, 1)
        observation = env.reset()

        random_indexes = []

        while len(random_indexes) < rand_count:
            random_index = math.floor(random() * rand_count_ref)
            if random_index not in random_indexes:
                random_indexes.append(random_index)

        for t in range(max_frames):
            if render:
                env.render()
            # print(observation)

            action = 0 if net.activate(observation)[0] < 0 else 1

            if t in random_indexes or random() < rand_chance:
                action = (action + 1) % 1

            ds.addSample(observation, (action,))
            observation, reward, done, info = env.step(action)

            if done:
                print("Episode finished after {} timesteps".format(t + 1))
                break

        if t == max_frames - 1:
            print("Passed!!")
            self.run_try(render=True)

        return t, ds

class dataset:
    # Initialize the dataset with input and label size
    def __init__(self, inputsize, labelsize):
        self.inputsize = inputsize
        self.labelsize = labelsize
        self.DS = SupervisedDataSet(self.inputsize, self.labelsize)
    
    # Adds data to existing training dataset
    def addTrainingData(self,inputdata, labeldata):
        try:
            if inputdata.size == self.inputsize and labeldata.size == self.labelsize:
                self.DS.appendLinked(inputdata, labeldata)
                return 1
        except AttributeError:
            print "Input error."
            return 0
    
    def getTrainingDataset(self):
        return self.DS
    
    def generateDataSet(self):
        for line in fileinput.input(['data/inputdata3.txt']):
            x = line.split(':')
#            print ft.feature.getImageFeatureVector(x[0]),np.array([int(x[1])])
            self.addTrainingData(ft.feature.getImageFeatureVector(x[0]),np.array([int(x[1])]))
        return 1