When I work with PyTorch I almost always use a traditional local installation. Some of my colleagues like to use the Google colab online environment because you can run a PyTorch program in a browser on a machine that has absolutely no prerequisites. This technique is also useful when I teach an introductory PyTorch training class because it doesn’t require a complicated setup process.

I hadn’t used colab in quite a long time so one cold, wet Pacific Northwest morning, after walking my dogs, I figured I’d run one of my standard PyTorch demo programs on colab.

I grabbed a Windows laptop machine that didn’t have Python or PyTorch installed. I launched a Chrome browser and logged in using my Google gmail account. I navigated to colab.research.google.com to launch a new colab project via File | New Notebook.

I wanted to use a GPU instead of the default CPU so I clicked on Runtime | Change runtime type | T4 GPU | Save.

Next, I created and uploaded a file of training data and a file of test data to the Session storage. The comma-delimited data looks like:

 1, 0.24, 1, 0, 0, 0.2950, 2
-1, 0.39, 0, 0, 1, 0.5120, 1
 1, 0.63, 0, 1, 0, 0.7580, 0
-1, 0.36, 1, 0, 0, 0.4450, 1
. . .

Each line represents a person. The fields are sex (male = -1, female = +1), age (divided by 100), State (Michigan = 100, Nebraska = 010, Oklahoma = 001), income (divided by $100,000), political leaning (0 = conservative, 1 = moderate, 2 = liberal). There are 200 training items and 40 test items.

Next, I copied the demo program into the single code cell in the project Notebook. I clicked on the Run icon in the code cell. My first attempt had a minor typo and the error was flagged with a red underline. After correcting the error, I clicked on Run, and this time the demo executed successfully. The output was:

Begin People predict politics type 

Creating People Datasets 

Creating 6-(10-10)-3 neural network 

bat_size =  10 
loss = NLLLoss()
optimizer = SGD
max_epochs = 1000 
lrn_rate = 0.010 

Starting training 
epoch =     0  |  loss =    23.1379
epoch =   200  |  loss =    19.1540
epoch =   400  |  loss =    16.1640
epoch =   600  |  loss =    12.8285
epoch =   800  |  loss =    11.5388
Training done 

Computing model accuracy
Accuracy on training data = 0.8150
Accuracy on test data = 0.7500

Predicting politics for M  30  oklahoma  $50,000: 
[[0.6905 0.3049 0.0047]]

Saving trained model state 

End People predict politics demo 

Very nice. A good way to start my day.

One of the characteristics of using colab is that it has multiple layers — browser to colab to Jupyter to Google cloud to PyTorch to Python to runtime.

As I write this blog post, Russia is out of control with its terrible invasion of Ukraine. I have worked with many engineers from Russia at the huge U.S. tech company I work for, and they’ve all been good guys. I hope that by the time you are reading this post, the conflict in Ukraine is over.

Demo program:

# people_politics_colab.py
# predict politics type from sex, age, state, income
# running on colab-GPU 

import numpy as np
import torch as T
device = T.device('cuda:0')  # apply to Tensor or Module

# -----------------------------------------------------------

class PeopleDataset(T.utils.data.Dataset):
  # sex  age    state    income   politics
  # -1,  0.27,  0, 1, 0,  0.7610,   2
  # +1,  0.19,  0, 0, 1,  0.6550,   0
  # sex: -1 = male, +1 = female
  # state: michigan, nebraska, oklahoma
  # politics: conservative, moderate, liberal

  def __init__(self, src_file):
    all_xy = np.loadtxt(src_file, usecols=range(0,7),
      delimiter=",", comments="#", dtype=np.float32)
    tmp_x = all_xy[:,0:6]   # cols [0,6) = [0,5]
    tmp_y = all_xy[:,6]     # 1-D

    self.x_data = T.tensor(tmp_x, 
      dtype=T.float32).to(device)
    self.y_data = T.tensor(tmp_y,
      dtype=T.int64).to(device)  # 1-D

  def __len__(self):
    return len(self.x_data)

  def __getitem__(self, idx):
    preds = self.x_data[idx]
    trgts = self.y_data[idx] 
    return preds, trgts  # as a Tuple

# -----------------------------------------------------------

class Net(T.nn.Module):
  def __init__(self):
    super(Net, self).__init__()
    self.hid1 = T.nn.Linear(6, 10)  # 6-(10-10)-3
    self.hid2 = T.nn.Linear(10, 10)
    self.oupt = T.nn.Linear(10, 3)

    T.nn.init.xavier_uniform_(self.hid1.weight)
    T.nn.init.zeros_(self.hid1.bias)
    T.nn.init.xavier_uniform_(self.hid2.weight)
    T.nn.init.zeros_(self.hid2.bias)
    T.nn.init.xavier_uniform_(self.oupt.weight)
    T.nn.init.zeros_(self.oupt.bias)

  def forward(self, x):
    z = T.tanh(self.hid1(x))
    z = T.tanh(self.hid2(z))
    z = T.log_softmax(self.oupt(z), dim=1)  # NLLLoss() 
    return z

# -----------------------------------------------------------

def accuracy(model, ds):
  # assumes model.eval()
  # item-by-item version
  n_correct = 0; n_wrong = 0
  for i in range(len(ds)):
    X = ds[i][0].reshape(1,-1)  # make it a batch
    Y = ds[i][1].reshape(1)  # 0 1 or 2, 1D
    with T.no_grad():
      oupt = model(X)  # logits form

    big_idx = T.argmax(oupt)  # 0 or 1 or 2
    if big_idx == Y:
      n_correct += 1
    else:
      n_wrong += 1

  acc = (n_correct * 1.0) / (n_correct + n_wrong)
  return acc

# -----------------------------------------------------------

def accuracy_quick(model, dataset):
  # assumes model.eval()
  X = dataset[0:len(dataset)][0]
  # Y = T.flatten(dataset[0:len(dataset)][1])
  Y = dataset[0:len(dataset)][1]
  with T.no_grad():
    oupt = model(X)
  # (_, arg_maxs) = T.max(oupt, dim=1)
  arg_maxs = T.argmax(oupt, dim=1)  # argmax() is new
  num_correct = T.sum(Y==arg_maxs)
  acc = (num_correct * 1.0 / len(dataset))
  return acc.item()

# -----------------------------------------------------------

def confusion_matrix_multi(model, ds, n_classes):
  # n_classes must be greater than 2
  cm = np.zeros((n_classes,n_classes), dtype=np.int64)
  for i in range(len(ds)):
    X = ds[i][0].reshape(1,-1)  # make it a batch
    Y = ds[i][1].reshape(1)  # actual class 0 1 or 2, 1D
    with T.no_grad():
      oupt = model(X)  # logits form
    pred_class = T.argmax(oupt)  # 0,1,2
    cm[Y][pred_class] += 1
  return cm

# -----------------------------------------------------------

def show_confusion(cm):
  dim = len(cm)
  mx = np.max(cm)             # largest count in cm
  wid = len(str(mx)) + 1      # width to print
  fmt = "%" + str(wid) + "d"  # like "%3d"
  for i in range(dim):
    print("actual   ", end="")
    print("%3d:" % i, end="")
    for j in range(dim):
      print(fmt % cm[i][j], end="")
    print("")
  print("------------")
  print("predicted    ", end="")
  for j in range(dim):
    print(fmt % j, end="")
  print("")

# -----------------------------------------------------------  

def main():
  # 0. get started
  print("\nBegin People predict politics type ")
  T.manual_seed(1)
  np.random.seed(1)
  
  # 1. create DataLoader objects
  print("\nCreating People Datasets ")

  train_file = "./people_train.txt"     # colab tmp local
  train_ds = PeopleDataset(train_file)  # 200 rows

  test_file = "./people_test.txt"
  test_ds = PeopleDataset(test_file)    # 40 rows

  bat_size = 10
  train_ldr = T.utils.data.DataLoader(train_ds,
    batch_size=bat_size, shuffle=True)

# -----------------------------------------------------------

  # 2. create network
  print("\nCreating 6-(10-10)-3 neural network ")
  net = Net().to(device)
  net.train()

# -----------------------------------------------------------

  # 3. train model
  max_epochs = 1000
  ep_log_interval = 200
  lrn_rate = 0.01

  loss_func = T.nn.NLLLoss()  # assumes log_softmax()
  optimizer = T.optim.SGD(net.parameters(), lr=lrn_rate)

  print("\nbat_size = %3d " % bat_size)
  print("loss = " + str(loss_func))
  print("optimizer = SGD")
  print("max_epochs = %3d " % max_epochs)
  print("lrn_rate = %0.3f " % lrn_rate)

  print("\nStarting training ")
  for epoch in range(0, max_epochs):
    # T.manual_seed(epoch+1)  # checkpoint reproducibility
    epoch_loss = 0  # for one full epoch

    for (batch_idx, batch) in enumerate(train_ldr):
      X = batch[0]  # inputs
      Y = batch[1]  # correct class/label/politics

      optimizer.zero_grad()
      oupt = net(X)
      loss_val = loss_func(oupt, Y)  # a tensor
      epoch_loss += loss_val.item()  # accumulate
      loss_val.backward()
      optimizer.step()

    if epoch % ep_log_interval == 0:
      print("epoch = %5d  |  loss = %10.4f" % \
        (epoch, epoch_loss))

  print("Training done ")

# -----------------------------------------------------------

  # 4. evaluate model accuracy
  print("\nComputing model accuracy")
  net.eval()
  acc_train = accuracy(net, train_ds)  # item-by-item
  print("Accuracy on training data = %0.4f" % acc_train)
  acc_test = accuracy(net, test_ds) 
  print("Accuracy on test data = %0.4f" % acc_test)

  # 5. make a prediction
  print("\nPredicting politics for M  30  oklahoma  $50,000: ")
  X = np.array([[-1, 0.30,  0,0,1,  0.5000]], dtype=np.float32)
  X = T.tensor(X, dtype=T.float32).to(device) 

  with T.no_grad():
    logits = net(X)  # do not sum to 1.0
  probs = T.exp(logits)  # sum to 1.0
  probs = probs.cpu().numpy()  # numpy vector prints better
  np.set_printoptions(precision=4, suppress=True)
  print(probs)

  # 6. save model (state_dict approach)
  # print("\nSaving trained model state ")
  # fn = "./people_model.pt"
  # T.save(net.state_dict(), fn)

  print("\nEnd People predict politics demo ")

if __name__ == "__main__":
  main()

Training data:

# people_train.txt
# sex (M=-1, F=1)  age  state (michigan, 
# nebraska, oklahoma) income
# politics (consrvative, moderate, liberal)
#
 1, 0.24, 1, 0, 0, 0.2950, 2
-1, 0.39, 0, 0, 1, 0.5120, 1
 1, 0.63, 0, 1, 0, 0.7580, 0
-1, 0.36, 1, 0, 0, 0.4450, 1
 1, 0.27, 0, 1, 0, 0.2860, 2
 1, 0.50, 0, 1, 0, 0.5650, 1
 1, 0.50, 0, 0, 1, 0.5500, 1
-1, 0.19, 0, 0, 1, 0.3270, 0
 1, 0.22, 0, 1, 0, 0.2770, 1
-1, 0.39, 0, 0, 1, 0.4710, 2
 1, 0.34, 1, 0, 0, 0.3940, 1
-1, 0.22, 1, 0, 0, 0.3350, 0
 1, 0.35, 0, 0, 1, 0.3520, 2
-1, 0.33, 0, 1, 0, 0.4640, 1
 1, 0.45, 0, 1, 0, 0.5410, 1
 1, 0.42, 0, 1, 0, 0.5070, 1
-1, 0.33, 0, 1, 0, 0.4680, 1
 1, 0.25, 0, 0, 1, 0.3000, 1
-1, 0.31, 0, 1, 0, 0.4640, 0
 1, 0.27, 1, 0, 0, 0.3250, 2
 1, 0.48, 1, 0, 0, 0.5400, 1
-1, 0.64, 0, 1, 0, 0.7130, 2
 1, 0.61, 0, 1, 0, 0.7240, 0
 1, 0.54, 0, 0, 1, 0.6100, 0
 1, 0.29, 1, 0, 0, 0.3630, 0
 1, 0.50, 0, 0, 1, 0.5500, 1
 1, 0.55, 0, 0, 1, 0.6250, 0
 1, 0.40, 1, 0, 0, 0.5240, 0
 1, 0.22, 1, 0, 0, 0.2360, 2
 1, 0.68, 0, 1, 0, 0.7840, 0
-1, 0.60, 1, 0, 0, 0.7170, 2
-1, 0.34, 0, 0, 1, 0.4650, 1
-1, 0.25, 0, 0, 1, 0.3710, 0
-1, 0.31, 0, 1, 0, 0.4890, 1
 1, 0.43, 0, 0, 1, 0.4800, 1
 1, 0.58, 0, 1, 0, 0.6540, 2
-1, 0.55, 0, 1, 0, 0.6070, 2
-1, 0.43, 0, 1, 0, 0.5110, 1
-1, 0.43, 0, 0, 1, 0.5320, 1
-1, 0.21, 1, 0, 0, 0.3720, 0
 1, 0.55, 0, 0, 1, 0.6460, 0
 1, 0.64, 0, 1, 0, 0.7480, 0
-1, 0.41, 1, 0, 0, 0.5880, 1
 1, 0.64, 0, 0, 1, 0.7270, 0
-1, 0.56, 0, 0, 1, 0.6660, 2
 1, 0.31, 0, 0, 1, 0.3600, 1
-1, 0.65, 0, 0, 1, 0.7010, 2
 1, 0.55, 0, 0, 1, 0.6430, 0
-1, 0.25, 1, 0, 0, 0.4030, 0
 1, 0.46, 0, 0, 1, 0.5100, 1
-1, 0.36, 1, 0, 0, 0.5350, 0
 1, 0.52, 0, 1, 0, 0.5810, 1
 1, 0.61, 0, 0, 1, 0.6790, 0
 1, 0.57, 0, 0, 1, 0.6570, 0
-1, 0.46, 0, 1, 0, 0.5260, 1
-1, 0.62, 1, 0, 0, 0.6680, 2
 1, 0.55, 0, 0, 1, 0.6270, 0
-1, 0.22, 0, 0, 1, 0.2770, 1
-1, 0.50, 1, 0, 0, 0.6290, 0
-1, 0.32, 0, 1, 0, 0.4180, 1
-1, 0.21, 0, 0, 1, 0.3560, 0
 1, 0.44, 0, 1, 0, 0.5200, 1
 1, 0.46, 0, 1, 0, 0.5170, 1
 1, 0.62, 0, 1, 0, 0.6970, 0
 1, 0.57, 0, 1, 0, 0.6640, 0
-1, 0.67, 0, 0, 1, 0.7580, 2
 1, 0.29, 1, 0, 0, 0.3430, 2
 1, 0.53, 1, 0, 0, 0.6010, 0
-1, 0.44, 1, 0, 0, 0.5480, 1
 1, 0.46, 0, 1, 0, 0.5230, 1
-1, 0.20, 0, 1, 0, 0.3010, 1
-1, 0.38, 1, 0, 0, 0.5350, 1
 1, 0.50, 0, 1, 0, 0.5860, 1
 1, 0.33, 0, 1, 0, 0.4250, 1
-1, 0.33, 0, 1, 0, 0.3930, 1
 1, 0.26, 0, 1, 0, 0.4040, 0
 1, 0.58, 1, 0, 0, 0.7070, 0
 1, 0.43, 0, 0, 1, 0.4800, 1
-1, 0.46, 1, 0, 0, 0.6440, 0
 1, 0.60, 1, 0, 0, 0.7170, 0
-1, 0.42, 1, 0, 0, 0.4890, 1
-1, 0.56, 0, 0, 1, 0.5640, 2
-1, 0.62, 0, 1, 0, 0.6630, 2
-1, 0.50, 1, 0, 0, 0.6480, 1
 1, 0.47, 0, 0, 1, 0.5200, 1
-1, 0.67, 0, 1, 0, 0.8040, 2
-1, 0.40, 0, 0, 1, 0.5040, 1
 1, 0.42, 0, 1, 0, 0.4840, 1
 1, 0.64, 1, 0, 0, 0.7200, 0
-1, 0.47, 1, 0, 0, 0.5870, 2
 1, 0.45, 0, 1, 0, 0.5280, 1
-1, 0.25, 0, 0, 1, 0.4090, 0
 1, 0.38, 1, 0, 0, 0.4840, 0
 1, 0.55, 0, 0, 1, 0.6000, 1
-1, 0.44, 1, 0, 0, 0.6060, 1
 1, 0.33, 1, 0, 0, 0.4100, 1
 1, 0.34, 0, 0, 1, 0.3900, 1
 1, 0.27, 0, 1, 0, 0.3370, 2
 1, 0.32, 0, 1, 0, 0.4070, 1
 1, 0.42, 0, 0, 1, 0.4700, 1
-1, 0.24, 0, 0, 1, 0.4030, 0
 1, 0.42, 0, 1, 0, 0.5030, 1
 1, 0.25, 0, 0, 1, 0.2800, 2
 1, 0.51, 0, 1, 0, 0.5800, 1
-1, 0.55, 0, 1, 0, 0.6350, 2
 1, 0.44, 1, 0, 0, 0.4780, 2
-1, 0.18, 1, 0, 0, 0.3980, 0
-1, 0.67, 0, 1, 0, 0.7160, 2
 1, 0.45, 0, 0, 1, 0.5000, 1
 1, 0.48, 1, 0, 0, 0.5580, 1
-1, 0.25, 0, 1, 0, 0.3900, 1
-1, 0.67, 1, 0, 0, 0.7830, 1
 1, 0.37, 0, 0, 1, 0.4200, 1
-1, 0.32, 1, 0, 0, 0.4270, 1
 1, 0.48, 1, 0, 0, 0.5700, 1
-1, 0.66, 0, 0, 1, 0.7500, 2
 1, 0.61, 1, 0, 0, 0.7000, 0
-1, 0.58, 0, 0, 1, 0.6890, 1
 1, 0.19, 1, 0, 0, 0.2400, 2
 1, 0.38, 0, 0, 1, 0.4300, 1
-1, 0.27, 1, 0, 0, 0.3640, 1
 1, 0.42, 1, 0, 0, 0.4800, 1
 1, 0.60, 1, 0, 0, 0.7130, 0
-1, 0.27, 0, 0, 1, 0.3480, 0
 1, 0.29, 0, 1, 0, 0.3710, 0
-1, 0.43, 1, 0, 0, 0.5670, 1
 1, 0.48, 1, 0, 0, 0.5670, 1
 1, 0.27, 0, 0, 1, 0.2940, 2
-1, 0.44, 1, 0, 0, 0.5520, 0
 1, 0.23, 0, 1, 0, 0.2630, 2
-1, 0.36, 0, 1, 0, 0.5300, 2
 1, 0.64, 0, 0, 1, 0.7250, 0
 1, 0.29, 0, 0, 1, 0.3000, 2
-1, 0.33, 1, 0, 0, 0.4930, 1
-1, 0.66, 0, 1, 0, 0.7500, 2
-1, 0.21, 0, 0, 1, 0.3430, 0
 1, 0.27, 1, 0, 0, 0.3270, 2
 1, 0.29, 1, 0, 0, 0.3180, 2
-1, 0.31, 1, 0, 0, 0.4860, 1
 1, 0.36, 0, 0, 1, 0.4100, 1
 1, 0.49, 0, 1, 0, 0.5570, 1
-1, 0.28, 1, 0, 0, 0.3840, 0
-1, 0.43, 0, 0, 1, 0.5660, 1
-1, 0.46, 0, 1, 0, 0.5880, 1
 1, 0.57, 1, 0, 0, 0.6980, 0
-1, 0.52, 0, 0, 1, 0.5940, 1
-1, 0.31, 0, 0, 1, 0.4350, 1
-1, 0.55, 1, 0, 0, 0.6200, 2
 1, 0.50, 1, 0, 0, 0.5640, 1
 1, 0.48, 0, 1, 0, 0.5590, 1
-1, 0.22, 0, 0, 1, 0.3450, 0
 1, 0.59, 0, 0, 1, 0.6670, 0
 1, 0.34, 1, 0, 0, 0.4280, 2
-1, 0.64, 1, 0, 0, 0.7720, 2
 1, 0.29, 0, 0, 1, 0.3350, 2
-1, 0.34, 0, 1, 0, 0.4320, 1
-1, 0.61, 1, 0, 0, 0.7500, 2
 1, 0.64, 0, 0, 1, 0.7110, 0
-1, 0.29, 1, 0, 0, 0.4130, 0
 1, 0.63, 0, 1, 0, 0.7060, 0
-1, 0.29, 0, 1, 0, 0.4000, 0
-1, 0.51, 1, 0, 0, 0.6270, 1
-1, 0.24, 0, 0, 1, 0.3770, 0
 1, 0.48, 0, 1, 0, 0.5750, 1
 1, 0.18, 1, 0, 0, 0.2740, 0
 1, 0.18, 1, 0, 0, 0.2030, 2
 1, 0.33, 0, 1, 0, 0.3820, 2
-1, 0.20, 0, 0, 1, 0.3480, 0
 1, 0.29, 0, 0, 1, 0.3300, 2
-1, 0.44, 0, 0, 1, 0.6300, 0
-1, 0.65, 0, 0, 1, 0.8180, 0
-1, 0.56, 1, 0, 0, 0.6370, 2
-1, 0.52, 0, 0, 1, 0.5840, 1
-1, 0.29, 0, 1, 0, 0.4860, 0
-1, 0.47, 0, 1, 0, 0.5890, 1
 1, 0.68, 1, 0, 0, 0.7260, 2
 1, 0.31, 0, 0, 1, 0.3600, 1
 1, 0.61, 0, 1, 0, 0.6250, 2
 1, 0.19, 0, 1, 0, 0.2150, 2
 1, 0.38, 0, 0, 1, 0.4300, 1
-1, 0.26, 1, 0, 0, 0.4230, 0
 1, 0.61, 0, 1, 0, 0.6740, 0
 1, 0.40, 1, 0, 0, 0.4650, 1
-1, 0.49, 1, 0, 0, 0.6520, 1
 1, 0.56, 1, 0, 0, 0.6750, 0
-1, 0.48, 0, 1, 0, 0.6600, 1
 1, 0.52, 1, 0, 0, 0.5630, 2
-1, 0.18, 1, 0, 0, 0.2980, 0
-1, 0.56, 0, 0, 1, 0.5930, 2
-1, 0.52, 0, 1, 0, 0.6440, 1
-1, 0.18, 0, 1, 0, 0.2860, 1
-1, 0.58, 1, 0, 0, 0.6620, 2
-1, 0.39, 0, 1, 0, 0.5510, 1
-1, 0.46, 1, 0, 0, 0.6290, 1
-1, 0.40, 0, 1, 0, 0.4620, 1
-1, 0.60, 1, 0, 0, 0.7270, 2
 1, 0.36, 0, 1, 0, 0.4070, 2
 1, 0.44, 1, 0, 0, 0.5230, 1
 1, 0.28, 1, 0, 0, 0.3130, 2
 1, 0.54, 0, 0, 1, 0.6260, 0

Test data:

# people_test.txt
#
-1, 0.51, 1, 0, 0, 0.6120, 1
-1, 0.32, 0, 1, 0, 0.4610, 1
 1, 0.55, 1, 0, 0, 0.6270, 0
 1, 0.25, 0, 0, 1, 0.2620, 2
 1, 0.33, 0, 0, 1, 0.3730, 2
-1, 0.29, 0, 1, 0, 0.4620, 0
 1, 0.65, 1, 0, 0, 0.7270, 0
-1, 0.43, 0, 1, 0, 0.5140, 1
-1, 0.54, 0, 1, 0, 0.6480, 2
 1, 0.61, 0, 1, 0, 0.7270, 0
 1, 0.52, 0, 1, 0, 0.6360, 0
 1, 0.30, 0, 1, 0, 0.3350, 2
 1, 0.29, 1, 0, 0, 0.3140, 2
-1, 0.47, 0, 0, 1, 0.5940, 1
 1, 0.39, 0, 1, 0, 0.4780, 1
 1, 0.47, 0, 0, 1, 0.5200, 1
-1, 0.49, 1, 0, 0, 0.5860, 1
-1, 0.63, 0, 0, 1, 0.6740, 2
-1, 0.30, 1, 0, 0, 0.3920, 0
-1, 0.61, 0, 0, 1, 0.6960, 2
-1, 0.47, 0, 0, 1, 0.5870, 1
 1, 0.30, 0, 0, 1, 0.3450, 2
-1, 0.51, 0, 0, 1, 0.5800, 1
-1, 0.24, 1, 0, 0, 0.3880, 1
-1, 0.49, 1, 0, 0, 0.6450, 1
 1, 0.66, 0, 0, 1, 0.7450, 0
-1, 0.65, 1, 0, 0, 0.7690, 0
-1, 0.46, 0, 1, 0, 0.5800, 0
-1, 0.45, 0, 0, 1, 0.5180, 1
-1, 0.47, 1, 0, 0, 0.6360, 0
-1, 0.29, 1, 0, 0, 0.4480, 0
-1, 0.57, 0, 0, 1, 0.6930, 2
-1, 0.20, 1, 0, 0, 0.2870, 2
-1, 0.35, 1, 0, 0, 0.4340, 1
-1, 0.61, 0, 0, 1, 0.6700, 2
-1, 0.31, 0, 0, 1, 0.3730, 1
 1, 0.18, 1, 0, 0, 0.2080, 2
 1, 0.26, 0, 0, 1, 0.2920, 2
-1, 0.28, 1, 0, 0, 0.3640, 2
-1, 0.59, 0, 0, 1, 0.6940, 2