first commit

dev-0/out.tsv

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main.py

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+import torch 
+import torch.nn as nn
+import numpy as np
+import pandas as pd
+from torch.autograd import Variable
+
+train = 'train/train.tsv'
+in_file = 'test-A/in.tsv'
+out_file = 'test-A/out.tsv'
+learning_rate = 0.00001
+
+
+class linearRegression(nn.Module):
+   def __init__(self, input_dim, output_dim):
+       super(linearRegression, self).__init__()
+       self.linear = nn.Linear(input_dim, output_dim)
+   def forward(self, x):
+       out = self.linear(x)
+       return out
+
+
+inputDim = 1        
+outputDim = 1       
+model = linearRegression(inputDim, outputDim)
+	
+data = pd.read_csv('train/train.tsv',sep='\t',header=None)
+
+y_values = data[0].tolist()
+x_values = data[8].tolist()
+        
+x_train = np.array(x_values, dtype=np.float32)
+x_train = x_train.reshape(-1, 1)
+y_train = np.array(y_values, dtype=np.float32)
+y_train = y_train.reshape(-1, 1)
+	
+if torch.cuda.is_available():
+   model.cuda()
+
+criterion = nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
+	
+
+
+for epoch in range(1000):
+    epoch += 1
+
+    if torch.cuda.is_available():
+        inputs = Variable(torch.from_numpy(x_train).cuda())
+        labels = Variable(torch.from_numpy(y_train).cuda())
+    else:
+        inputs = Variable(torch.from_numpy(x_train))
+        labels = Variable(torch.from_numpy(y_train))
+
+optimizer.zero_grad()
+
+outputs = model(inputs)
+    
+loss = criterion(outputs, labels)
+    
+loss.backward()
+
+optimizer.step()
+
+
+result = pd.read_csv('test-A/in.tsv',sep='\t',header=None)
+end_x = result[7].tolist()
+end_x = np.array(end_x, dtype=np.float32)
+end_x = end_x.reshape(-1, 1)
+
+predictions = model(torch.from_numpy(end_x).requires_grad_()).data.numpy()
+
+
+output = open('test-A/out.tsv', 'w')
+for p in predictions:
+   output.write(str(p[0])+'\n')

test-A/out.tsv

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