albertlai431

#getting the hexadecimal digits
def _getDecDigit(digit):
    digits = ['0','1','2','3','4','5','6','7','8','9','a','b','c','d','e','f']
    for x in range(len(digits)):
        if digit.lower() == digits[x]:
            return(x)

#convert from hexadecimal to decimal      
def hexToDec(hexNum):
    decNum = 0

albertlai431

while True:
    data, addr = sock.recvfrom(1024)
    if 'alpha_absolute' in str(data):
        #processing data here
            
        #Gauging focus
        if time.clock() - timer > 10:
            measure = (sum(stock)/len(stock))/benchmark*100
            if measure>100:
                print("Focused : 100%")

albertlai431

stock = [] #keeping track of all the averages
benchmark = 0 #Use this to store the final focus metric

while True:
    data, addr = sock.recvfrom(1024) #receiving data from socket
    if 'alpha_absolute' in str(data): #checking for alpha
      #preprocessing data here
      
      if time.clock() - timer > 30:
         benchmark = sum(stock)/len(stock) #determining the benchmark!

albertlai431

newerOut = []
#Removing all the null values
for i in outData:
  if type(i) == int and i>0:
    newerOut += [i]

#If all the values are null, ignore the data!
if len(newerOut) == 0:
  continue

albertlai431

#Removing commas
newData = str(data).split(",")
#Removing x's
newData = newData[1].split("x")

albertlai431

UDP_IP = "172.0.0.1" #UDP IP adress
UDP_PORT = 6082 #UDP port for osc stream

sock = socket.socket(socket.AF_INET,  # Internet
                   socket.SOCK_DGRAM)  # UDP

sock.bind((UDP_IP, UDP_PORT)) #binding the name to the socket!

albertlai431

            # loss stats
            if counter % print_every == 0:
                # Get validation loss
                val_h = net.init_hidden(batch_size)
                val_losses = []
                net.eval()
                for x, y in get_batches(val_data, batch_size, seq_length):
                    # One-hot encode our data and make them Torch tensors
                    x = one_hot_encode(x, n_chars)
                    x, y = torch.from_numpy(x), torch.from_numpy(y)

albertlai431

# Generating new text
print(sample(net, 1000, prime='A', top_k=5))

albertlai431

def sample(net, size, prime='The', top_k=None):
        
    if(train_on_gpu):
        net.cuda()
    else:
        net.cpu()
    
    net.eval() # eval mode
    
    # First off, run through the prime characters

albertlai431

# Defining a method to generate the next character
def predict(net, char, h=None, top_k=None):
        ''' Given a character, predict the next character.
            Returns the predicted character and the hidden state.
        '''
        
        # tensor inputs
        x = np.array([[net.char2int[char]]])
        x = one_hot_encode(x, len(net.chars))
        inputs = torch.from_numpy(x)