A relatively well-known machine learning dataset is the House Voting data, also called the Congressional Voting Records Dataset. The raw data looks like:
republican,n,y,n,y,y,y,n,n,n,y,?,y,y,y,n,y democrat,n,y,y,n,y,y,n,n,n,n,n,n,y,y,y,y democrat,n,y,n,y,y,y,n,n,n,n,n,n,?,y,y,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y . . .
There are 435 data items, corresponding to each of the 435 members of the U.S. House of Representatives in the year 1984. The first column is the member’s political party, Democrat or Republican. The next 16 values correspond to a vote on a particular bill. The possible values are ‘n’ (no), ‘y’ (yes), or ‘?’ (abstain).
The goal is to predict a person’s political party from their 16 votes.
The 16 votes/bills are related to “handicapped infants”, “water project”, and so on. The raw data and details can be found at: https://archive.ics.uci.edu/ml/datasets/congressional+voting+records.
To prepare the data, first I removed all the items that had one or more ‘?’ values, which left me with 232 items from the original 435. I encoded democrat as 0, republican as 1, and ‘n’ as 0, ‘y’ as 1. The result looks like:
0,0,1,1,0,1,1,0,0,0,0,0,0,1,1,1,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,1 . . .
I non-randomly split the data into the first 200 items for training data (86%), and the last 32 items for test data (the remaining 14%).
I implemented a 16-(10-10)-1 network architecture with tanh hidden activation, sigmoid output activation, and Xavier/Glorot weight initialization. For training, I used binary cross entropy loss with stochastic gradient descent optimization and a batch size of 10.
The trained model scored 99.50% accuracy on the training data (199 of 200 correct) and 90.62% accuracy on the test data (29 of 32 correct).
The House Voting Dataset is somewhat unusual because all the predictor values are binary/Boolean. A classical machine learning alternative to a neural network classifier is a Bernoulli naive Bayes classifier, which is a specific type of naive Bayes.
I have very little interest in politics, in part because of a disturbing amount of corruption and lack of personal integrity compared to the fields of science and mathematics. Here are just three of several members of the U.S. House of Representatives who were arrested and convicted of crimes during the administration of former politician B. Obama.
Left: Jesse Jackson Jr. (Democrat, IL) was convicted of wire and mail fraud and sentenced to two-and-one-half years in jail.
Center: Corrine Brown (Democrat, FL) was convicted on 18 felony counts of wire and tax fraud, conspiracy, lying to federal investigators, and corruption and was sentenced to five years in jail.
Right: Chaka Fattah (Democrat, PA) was convicted on 23 counts of racketeering, fraud, and corruption and was sentenced to 10 years in jail.
Demo code. Replace “lt”, “gt”, “lte”, “gte” with Boolean operator symbols.
# voting.py
# House Voting binary classification
# PyTorch 1.12.1-CPU Anaconda3-2020.02 Python 3.7.6
# Windows 10/11
import numpy as np
import torch as T
device = T.device('cpu') # apply to Tensor or Module
class VotingDataset(T.utils.data.Dataset):
# party (0=dem, 1=rep) - 16 votes (0=no, 1=yes)
# 0,0,1,1,0,1,1,0,0,0,0,0,0,1,1,1,1
# 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,1
# . . .
def __init__(self, src_file):
all_data = np.loadtxt(src_file, usecols=range(0,17),
delimiter=",", comments="#", dtype=np.float32)
self.x_data = T.tensor(all_data[:,1:17],
dtype=T.float32).to(device)
self.y_data = T.tensor(all_data[:,0],
dtype=T.float32).to(device) # float32 label
self.y_data = self.y_data.reshape(-1,1) # 2-D required
def __len__(self):
return len(self.x_data)
def __getitem__(self, idx):
votes = self.x_data[idx,:] # idx row, all 16 cols
party = self.y_data[idx,:] # idx row, the only col
return votes, party # as a Tuple
# ---------------------------------------------------------
class Net(T.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.hid1 = T.nn.Linear(16, 10) # 16-(10-10)-1
self.hid2 = T.nn.Linear(10, 10)
self.oupt = T.nn.Linear(10, 1)
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.sigmoid(self.oupt(z)) # for BCELoss()
return z
# ---------------------------------------------------------
def metrics(model, ds, thresh=0.5):
# note: N = total number of items = TP + FP + TN + FN
# accuracy = (TP + TN) / N
# precision = TP / (TP + FP)
# recall = TP / (TP + FN)
# F1 = 2 / [(1 / precision) + (1 / recall)]
tp = 0; tn = 0; fp = 0; fn = 0
for i in range(len(ds)):
inpts = ds[i][0] # dictionary style
target = ds[i][1] # float32 [0.0] or [1.0]
target = target.int() # int 0 or 1
with T.no_grad():
p = model(inpts) # between 0.0 and 1.0
# avoid 'target == 1.0'
if target == 1 and p "gte" thresh: # TP
tp += 1
elif target == 1 and p "lt" thresh: # falsely pred neg
fn += 1
elif target == 0 and p "lt" thresh: # TN
tn += 1
elif target == 0 and p "gte" thresh: # falsely pred pos
fp += 1
N = tp + fp + tn + fn
if N != len(ds):
print("FATAL LOGIC ERROR in metrics()")
accuracy = (tp + tn) / (N * 1.0)
precision = (1.0 * tp) / (tp + fp) # tp + fp != 0
recall = (1.0 * tp) / (tp + fn) # tp + fn != 0
f1 = 2.0 / ((1.0 / precision) + (1.0 / recall))
return (accuracy, precision, recall, f1) # as a Tuple
# ---------------------------------------------------------
def main():
# 0. get started
print("\nHouse Voting Dataset using PyTorch ")
T.manual_seed(1)
np.random.seed(1)
# 1. create Dataset and DataLoader objects
print("\nCreating Voting train and test Datasets ")
train_file = ".\\Data\\votes_train.txt"
test_file = ".\\Data\\votes_test.txt"
train_ds = VotingDataset(train_file) # 200 rows
test_ds = VotingDataset(test_file) # 32 rows
bat_size = 10
train_ldr = T.utils.data.DataLoader(train_ds,
batch_size=bat_size, shuffle=True)
# 2. create neural network
print("\nCreating 16-(10-10)-1 binary NN classifier \n")
net = Net().to(device)
net.train() # set training mode
# 3. train network
lrn_rate = 0.01
loss_func = T.nn.BCELoss() # binary cross entropy
# loss_func = T.nn.MSELoss()
optimizer = T.optim.SGD(net.parameters(),
lr=lrn_rate)
max_epochs = 500
ep_log_interval = 100
print("Loss function: " + str(loss_func))
print("Optimizer: " + str(optimizer.__class__.__name__))
print("Learn rate: " + "%0.3f" % lrn_rate)
print("Batch size: " + str(bat_size))
print("Max epochs: " + str(max_epochs))
print("\nStarting training")
for epoch in range(0, max_epochs):
epoch_loss = 0.0 # for one full epoch
for (batch_idx, batch) in enumerate(train_ldr):
X = batch[0] # [bs,8] inputs
Y = batch[1] # [bs,1] targets
oupt = net(X) # [bs,1] computeds
loss_val = loss_func(oupt, Y) # a tensor
epoch_loss += loss_val.item() # accumulate
optimizer.zero_grad() # reset all gradients
loss_val.backward() # compute new gradients
optimizer.step() # update all weights
if epoch % ep_log_interval == 0:
print("epoch = %4d loss = %8.4f" % \
(epoch, epoch_loss))
print("Done ")
# ---------------------------------------------------------
# 4. evaluate model
net.eval()
metrics_train = metrics(net, train_ds, thresh=0.5)
print("\nMetrics for train data: ")
print("accuracy = %0.4f " % metrics_train[0])
print("precision = %0.4f " % metrics_train[1])
print("recall = %0.4f " % metrics_train[2])
print("F1 = %0.4f " % metrics_train[3])
metrics_test = metrics(net, test_ds, thresh=0.5)
print("\nMetrics for test data: ")
print("accuracy = %0.4f " % metrics_test[0])
print("precision = %0.4f " % metrics_test[1])
print("recall = %0.4f " % metrics_test[2])
print("F1 = %0.4f " % metrics_test[3])
# 5. save model
print("\nSaving trained model state_dict ")
net.eval()
# path = ".\\Models\\voting_model.pt"
# T.save(net.state_dict(), path)
# 6. make a prediction
X = np.array([[0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1]])
X = T.tensor(X, dtype=T.float32).to(device)
print("\nSetting votes: ")
print(X)
net.eval()
with T.no_grad():
oupt = net(X) # a Tensor
pred_prob = oupt.item() # scalar, [0.0, 1.0]
print("Computed output: ", end="")
print("%0.4f" % pred_prob)
if pred_prob "lt" 0.5:
print("Prediction = Democrat")
else:
print("Prediction = Republican")
print("\nEnd House Voting demo ")
if __name__== "__main__":
main()
Training data:
# votes_train.txt # 1st col = democrat (0), republican (1) # next 16 cols = no vote (0) or yes (1) # 200 rows (32 in test) # 0,0,1,1,0,1,1,0,0,0,0,0,0,1,1,1,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,1 0,1,1,1,0,0,0,1,1,1,0,1,0,0,0,1,1 0,1,1,1,0,0,0,1,1,1,0,0,0,0,0,1,1 0,1,0,1,0,0,0,1,1,1,1,0,0,0,0,1,1 0,1,0,1,0,0,0,1,1,1,0,1,0,0,0,1,1 0,1,1,1,0,0,0,1,1,1,0,1,0,0,0,1,1 1,1,0,0,1,1,0,1,1,1,0,0,1,1,1,0,1 0,1,1,1,0,0,0,1,1,1,0,1,0,0,0,1,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,0 0,1,1,1,0,0,0,1,1,1,1,0,0,1,0,1,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,1 0,1,1,1,0,0,0,1,1,1,0,0,0,0,0,1,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,0 1,1,1,0,1,1,1,0,0,0,0,0,0,1,1,0,1 1,0,1,0,1,1,1,0,0,0,1,0,1,1,1,0,0 0,1,0,1,0,0,0,1,1,1,1,1,0,1,0,1,1 0,1,0,1,0,0,0,1,1,1,0,0,0,0,0,0,1 0,1,0,1,0,0,0,1,1,1,0,0,0,0,0,1,1 0,1,1,1,0,0,0,1,1,1,0,0,0,0,0,0,1 0,1,1,1,0,0,0,1,1,0,0,0,0,0,1,0,1 0,1,1,1,0,0,0,1,1,1,0,1,0,0,0,1,1 1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,0 1,0,1,0,1,1,1,0,0,0,1,1,1,1,1,0,0 1,0,1,0,1,1,1,0,0,0,1,1,1,1,1,0,1 1,0,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1 1,0,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,0 0,1,1,1,0,0,0,1,1,1,0,1,0,0,0,0,1 1,1,1,0,1,1,1,1,0,0,0,0,1,1,1,0,1 1,0,1,0,1,1,1,1,0,0,0,1,1,1,1,0,1 1,0,1,0,1,1,1,0,0,0,1,0,1,1,1,0,0 0,1,1,1,0,0,0,1,1,1,0,0,0,0,0,1,1 1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,0,1 1,1,0,1,1,1,0,1,0,1,1,0,0,1,1,0,1 0,1,0,1,0,0,1,1,1,1,1,1,0,0,1,1,1 0,0,1,1,1,1,1,0,0,0,1,1,0,1,1,0,0 0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1 0,1,1,1,0,1,1,0,0,0,1,1,0,1,1,0,1 1,0,0,0,1,1,0,0,0,0,1,0,1,1,1,0,0 1,0,0,0,1,1,1,0,0,0,1,0,1,1,1,0,1 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,0 0,0,0,1,0,1,1,0,0,0,1,1,1,1,1,0,1 1,0,0,0,1,1,1,0,0,0,1,0,1,1,1,0,0 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,0 0,0,1,1,0,1,1,1,0,1,1,1,0,1,1,0,1 0,1,0,1,0,0,0,1,1,1,1,0,0,0,0,1,1 0,1,0,1,0,0,0,1,1,1,1,1,0,0,0,1,1 0,1,0,1,0,0,0,1,0,1,1,1,0,0,0,1,1 0,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,1 0,1,0,0,0,1,1,1,0,0,1,1,0,0,1,0,1 0,1,1,1,0,0,1,1,1,1,1,0,0,0,0,0,1 0,1,0,0,0,1,1,0,0,0,0,1,1,0,1,0,1 0,1,0,1,0,1,1,1,0,0,0,1,0,0,1,0,1 0,1,1,1,0,0,0,0,1,1,0,1,0,0,0,1,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,1 0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,1,1 0,1,0,1,0,0,0,1,1,1,0,1,0,0,0,1,1 1,1,1,1,1,1,0,1,0,0,0,0,1,1,1,0,1 0,0,1,1,0,0,0,0,1,1,1,1,0,0,0,1,1 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,0 1,0,0,0,1,1,1,0,0,0,1,0,1,0,1,0,1 0,0,0,1,0,0,1,0,1,1,1,0,0,0,1,0,1 1,0,0,0,1,1,1,0,0,0,1,0,1,1,1,0,1 1,0,0,0,1,1,1,0,0,0,1,0,1,1,1,0,0 1,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,1 0,0,0,1,0,0,1,1,1,1,1,0,0,0,1,0,1 0,1,0,1,0,0,1,1,1,1,0,0,0,0,0,1,1 1,0,0,0,1,0,0,1,1,1,1,0,0,1,1,0,1 1,0,0,0,1,1,1,1,1,1,1,0,1,1,1,0,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,1 0,0,0,0,0,0,0,1,1,1,1,0,1,1,1,1,1 1,0,1,0,1,1,1,0,0,0,1,1,1,1,1,0,1 0,0,0,1,0,0,0,1,1,1,1,0,0,1,0,1,1 1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1 0,0,1,1,0,0,1,0,1,1,1,1,0,1,0,1,1 0,0,0,1,0,0,1,1,1,1,1,1,0,1,1,0,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,0 1,1,1,0,1,1,1,1,0,0,0,0,1,1,1,0,0 1,0,1,0,1,1,1,0,0,0,1,0,1,1,1,0,0 0,0,1,0,0,1,1,0,0,0,0,0,1,1,1,1,1 0,0,0,0,0,1,1,1,0,0,0,0,1,1,1,0,1 0,0,1,1,0,1,1,1,0,0,0,1,1,1,1,0,1 1,0,1,0,1,1,1,1,0,0,0,0,1,1,1,0,1 1,1,0,1,1,1,1,1,1,0,1,0,1,0,1,1,1 1,1,0,1,1,1,1,1,1,0,1,1,1,0,1,1,1 0,1,0,1,0,0,0,1,1,1,1,1,0,0,1,0,1 0,0,0,0,0,1,1,0,0,0,1,1,1,1,1,0,1 0,0,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1 1,0,0,1,1,0,0,1,1,1,1,0,0,0,1,1,1 0,1,0,1,0,0,0,1,1,1,1,0,0,0,0,1,1 0,0,0,1,0,0,0,1,1,1,1,1,0,0,0,1,1 0,0,0,1,0,0,0,1,1,1,1,1,0,0,0,1,1 0,0,1,1,0,0,0,1,1,1,1,1,0,0,0,1,1 0,1,0,1,0,0,0,1,1,1,0,0,0,0,0,1,1 0,0,0,0,0,0,1,1,1,1,0,1,0,0,1,1,1 0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,1,1 0,0,0,1,0,0,0,1,1,1,0,0,0,0,1,1,1 0,1,1,1,0,0,0,1,1,1,0,0,0,0,0,1,1 0,1,0,1,0,0,1,1,1,1,1,1,0,0,0,1,1 0,1,0,1,0,0,0,1,1,1,1,0,0,0,0,1,1 1,0,0,1,1,1,1,1,0,0,0,0,1,1,1,0,1 0,0,0,1,0,0,1,1,1,1,1,0,1,0,0,0,1 1,0,0,0,1,1,1,0,0,0,1,0,1,0,1,0,1 0,1,1,1,0,0,0,1,1,1,1,1,0,0,0,0,1 0,0,0,1,0,0,1,1,1,1,0,0,0,0,0,1,1 1,0,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1 0,0,0,1,0,0,0,1,1,1,0,1,0,0,0,1,1 0,0,1,1,0,0,1,0,1,1,0,1,0,1,0,1,1 1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1 1,0,1,0,1,1,1,0,0,0,1,0,1,1,1,0,0 0,0,1,1,0,0,0,0,1,1,0,1,0,0,1,1,1 1,0,0,0,1,1,0,0,0,0,0,0,1,1,1,0,1 0,0,0,1,0,0,1,1,1,1,0,1,0,0,1,1,1 1,0,1,1,1,1,1,1,0,1,1,0,1,1,1,0,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,1 1,0,1,0,1,1,1,0,0,1,1,0,1,1,1,0,1 1,0,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1 1,0,0,0,1,1,1,0,0,0,1,0,1,0,1,0,1 0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,1,1 0,1,0,1,0,0,1,1,1,0,0,0,1,1,0,0,1 1,0,0,0,1,1,1,1,0,0,1,0,0,0,1,1,1 0,1,0,1,0,0,0,1,1,1,1,1,0,0,1,1,1 0,1,0,1,0,0,0,0,1,1,1,0,0,0,0,1,1 0,1,0,1,0,0,0,1,1,1,1,1,0,0,0,1,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,0 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,0 0,1,1,1,0,0,1,1,1,1,0,0,0,0,0,1,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,1 0,1,0,1,0,0,0,1,1,1,1,0,0,0,0,0,1 0,1,0,1,0,0,0,1,1,1,1,0,0,0,1,1,1 1,0,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0 0,0,0,1,0,0,0,1,1,1,0,1,0,0,0,1,1 0,1,0,1,0,0,0,1,1,1,0,0,0,0,0,0,1 0,1,0,1,0,0,0,1,1,1,1,0,0,0,0,0,1 0,1,0,1,0,0,0,1,1,1,1,0,0,0,0,0,1 1,0,0,0,1,1,1,0,0,0,1,0,1,0,1,0,1 1,1,0,0,0,0,0,1,1,1,1,0,0,0,1,0,1 0,1,0,1,0,0,0,1,1,1,0,0,0,0,0,0,1 0,1,1,1,0,0,0,1,1,1,0,0,0,0,0,1,1 1,1,0,0,1,1,0,1,0,0,1,0,0,0,1,1,1 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,1,0 1,0,0,1,1,1,1,1,1,0,1,0,0,0,1,0,1 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,1 1,0,0,0,1,1,1,0,0,0,1,0,1,1,1,0,0 0,0,0,1,0,0,0,1,1,1,1,0,0,0,1,0,1 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,1 0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,1,1 0,1,1,1,0,1,1,0,1,0,1,1,0,1,1,1,1 0,1,0,1,0,0,1,1,1,0,1,1,0,1,1,1,1 0,1,1,1,0,0,1,1,1,1,1,1,0,1,1,1,1 1,0,0,1,1,1,1,0,0,0,1,0,1,1,1,1,1 0,0,1,0,0,0,0,1,1,1,1,1,0,0,0,1,1 0,0,1,1,0,0,1,1,1,1,1,0,0,1,1,1,1 1,0,0,0,1,1,0,1,1,1,1,0,1,1,1,0,1 1,0,0,0,1,1,1,1,0,0,1,0,1,1,1,0,1 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,0 1,0,0,0,1,1,1,0,0,0,1,0,1,1,1,0,0 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,0 1,0,0,0,1,1,1,0,0,0,1,0,1,1,1,0,0 0,1,0,0,0,0,1,1,1,1,1,0,0,0,1,1,1 1,0,0,0,1,1,1,0,0,0,1,0,1,1,1,1,0 0,0,0,1,0,0,1,1,1,1,1,0,0,1,0,0,1 0,1,1,1,0,0,0,1,1,1,1,0,0,0,0,1,1 1,0,1,1,1,1,1,0,0,0,1,0,1,1,1,0,1 1,0,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1 0,0,0,0,0,0,1,0,1,1,0,1,1,1,1,1,0 0,1,0,0,0,0,0,1,1,1,1,0,0,0,0,1,1 0,0,1,1,0,0,1,0,1,1,1,0,0,1,1,0,1 0,1,1,1,0,0,0,1,1,1,1,0,0,1,0,0,1 1,0,1,0,1,1,1,0,0,0,0,1,1,1,1,0,0 0,1,1,0,1,0,0,1,1,1,0,1,0,0,1,0,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,1 0,1,1,1,0,0,0,1,1,1,0,1,0,0,0,0,1 1,0,1,0,1,1,1,0,0,0,1,0,1,1,1,0,0 0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,1,1 0,1,0,1,0,0,0,1,1,1,0,0,0,0,0,1,1 0,1,0,1,0,0,0,1,1,1,1,0,0,0,1,1,1 1,1,0,0,1,1,1,0,0,0,0,1,1,1,1,0,0 1,0,0,0,1,1,1,0,0,0,1,1,1,0,1,0,1 1,0,0,0,1,1,0,1,0,1,1,0,0,0,1,0,1 0,0,0,1,0,0,0,1,1,1,1,1,0,0,0,1,1 1,0,0,0,1,1,1,1,0,0,1,0,1,0,1,1,1 1,0,0,0,1,1,1,0,0,0,1,0,1,1,1,0,1 1,1,0,0,1,1,1,0,0,0,1,0,1,1,1,0,0 1,0,1,1,1,1,1,1,1,1,0,0,1,1,1,0,1 0,0,1,0,0,0,1,1,0,1,0,1,0,0,0,1,1 1,0,0,1,1,1,1,1,1,1,1,0,1,1,1,1,1 1,0,0,1,1,1,1,1,0,0,1,1,1,1,1,0,1 1,1,0,1,1,0,0,0,1,1,1,0,0,0,1,1,1 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,0 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,0 0,1,0,1,0,0,1,1,1,1,1,0,0,1,0,0,1 0,1,1,1,0,0,1,1,1,1,1,1,1,1,0,0,1 1,1,1,0,1,1,1,0,0,0,1,1,0,1,0,0,0 1,1,1,0,1,1,1,0,0,0,0,1,0,1,1,0,1 0,0,1,0,0,1,1,0,0,0,1,1,0,1,1,0,0 0,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,1 1,0,1,0,1,1,1,0,0,0,0,0,0,1,1,0,1 1,0,1,0,1,1,1,0,0,0,0,0,1,1,1,0,1
Test data:
0,0,1,0,1,1,1,0,0,0,0,1,1,0,1,0,0 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,1 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,1 0,1,1,1,0,1,1,0,1,1,1,1,0,0,0,0,1 0,1,1,1,1,1,1,0,0,0,0,1,1,1,1,0,1 0,1,1,0,0,1,1,0,0,0,0,1,1,1,1,1,0 0,1,1,0,0,0,0,0,1,1,0,1,0,0,0,1,0 1,1,1,0,1,1,1,0,0,0,0,1,1,1,1,0,1 0,1,1,1,0,1,1,0,1,0,0,1,0,1,0,1,1 0,0,1,1,0,1,1,0,1,0,0,0,0,0,0,0,1 1,0,1,0,1,1,1,0,0,0,1,1,1,1,1,0,0 1,1,1,0,1,1,1,0,0,0,1,0,1,1,1,0,1 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,1 0,1,0,1,0,1,1,0,0,1,1,0,0,1,1,0,1 0,0,0,0,1,1,1,0,0,0,0,1,1,1,1,0,0 1,0,0,0,1,1,1,0,0,0,0,0,1,1,1,0,0 1,0,0,0,1,1,1,0,0,0,0,1,1,1,1,0,1 0,1,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1 1,0,0,0,1,1,1,0,0,0,1,0,1,1,1,0,1 0,1,1,1,0,0,0,1,1,1,0,0,0,0,0,0,1 1,1,1,0,1,1,1,0,0,0,1,0,0,1,1,0,1 0,1,1,1,0,0,0,1,1,1,1,1,0,1,0,0,1 0,1,1,1,0,0,0,1,1,0,1,0,0,0,0,0,1 0,1,1,1,0,0,0,1,1,1,0,0,0,0,0,0,1 1,1,1,1,1,1,1,1,1,0,1,0,0,1,1,0,1 0,0,1,1,0,1,1,1,1,0,0,1,0,1,0,1,1 0,0,0,1,0,0,1,1,1,1,0,1,0,0,0,1,1 0,0,1,1,0,0,1,1,1,1,0,1,0,0,1,1,1 0,1,0,1,0,0,0,1,1,1,1,0,0,0,0,1,1 1,0,0,0,1,1,1,1,1,0,1,0,1,1,1,0,1 1,0,0,1,1,1,1,0,0,1,1,0,1,1,1,0,1 0,0,0,1,0,0,0,1,1,1,1,0,0,0,0,0,1
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