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#train the network
dp_model <- mx.model.FeedForward.create(symbol = out
                                         ,X = train.x
                                         ,y = train.y
                                         ,ctx = mx.cpu()
                                         ,num.round = 100
                                         ,eval.metric = mx.metric.accuracy
                                         ,array.batch.size = 50
                                         ,learning.rate = 0.005)

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#predict on test
pred_dp <- predict(dp_model,test.x)
str(pred_dp) #contains 2 rows and 16281 columns

#transpose the pred matrix
pred.val <- max.col(t(pred_dp))-1

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function expression:
    token = next_token()
    if token is one of (-, +, 0-9, .):
        putback the token
        val = number()
    elif token is (:
        op = operator()
        exp1 = expression()
        exp2 = expression()
        c = next_token()

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from buffer import UngetableInput

token_stream = UngetableInput()

def expression():
    val = None
    ch = token_stream.getc()
    while ch == ' ':
        ch = token_stream.getc()
    if ch in {'-', '+', '.'} or ch.isdigit():

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class UngetableInput:
    def __init__(self, initialBuffer=''):
        self.buffer = initialBuffer

    def getc(self):
        if not self.buffer:
            self.buffer = input()
        c = self.buffer[0]
        self.buffer = self.buffer[1:]
        return c

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import RPi.GPIO as GPIO

GPIO.setmode(GPIO.BCM)
GPIO.setup(23, GPIO.IN, pull_up_down = GPIO.PUD_DOWN)
GPIO.setup(24, GPIO.IN, pull_up_down = GPIO.PUD_UP)

while True:
    if(GPIO.input(23) ==1):
        print(“Button 1 pressed”)
    if(GPIO.input(24) == 0):

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sudo add-apt-repository ppa:fkrull/deadsnakes
sudo apt-get update
sudo apt-get install python3.5

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class BufferedInput:
    def __init__(self):
        # the buffer holds the content that has be entered by the user but has not been read by into a variable yet
        self.buffer = ''

    def getc(self):
        self.fill_buffer()

        byte = self.buffer[0]
        self.buffer = self.buffer[1:]

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from sklearn import datasets, svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
iris = datasets.load_iris()
X = iris.data[:, :2]
y = iris.target
#Split the data into test and train
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)  

# Linear Kernel

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library(e1071)
library(caTools)
data(iris)
iris$spl=sample.split(iris,SplitRatio=0.7)
train=subset(iris, iris$spl==TRUE)
  test=subset(iris, iris$spl==FALSE)
x=train[,-5]
y=train[,5]
svm_model <- svm(Species ~ ., data=train)
table(predict(svm_model, test[,-5]), test[,5])