# Entirely linear neural network learning non-linear function

I have a neural network that's trained on a sine wave. It uses a lookback of 20 to see what the last 20 predictions were and predict the next value. This network has only a single Linear layer (input size 20, output size 1) with no activation function and from just that, it is able to extrapolate a sine wave almost perfectly.

This is very confusing to me, as the $$sin$$ function is non-linear: $$sin(a+b) \neq sin(a) + sin(b)$$ so the network shouldn't be able to approximate it (very well).

The code to reproduce this is below:

import numpy as np
import matplotlib.pyplot as plt

import torch
import torch.nn as nn
import torch.optim as optim

X_train = np.arange(0,100,0.5)
y_train = np.sin(X_train)

X_test = np.arange(100,200,0.5)
y_test = np.sin(X_test)

n_features = 1

train_series = torch.from_numpy(y_train)
test_series = torch.from_numpy(y_test)

# Expects input of (batch, sequence, features)
# So shape should be (1, 179, 20) and labels (1, 1, 179)
look_back = 20

train_dataset = []
train_labels = []
for i in range(len(train_series)-look_back):
train_dataset.append(train_series[i:i+20])
train_labels.append(train_series[i+20])
train_dataset = torch.stack(train_dataset).unsqueeze(0)
train_labels = torch.stack(train_labels).unsqueeze(0).unsqueeze(2)

class Net(nn.Module):
def __init__(self, input_shape):
super(Net, self).__init__()

self.fc = nn.Linear(input_shape, 1)

def forward(self, x):
out = self.fc(x)
return out

model = Net(look_back).double()
loss_function = nn.MSELoss()

loss_curve = []
for epoch in range(300):
loss_total = 0

predictions = model(train_dataset)

loss = loss_function(predictions, train_labels)
loss_total += loss.item()
loss.backward()
optimizer.step()
loss_curve.append(loss_total)

extrapolation = []
seed_batch = test_series[:20].reshape(1, 1, 20)
current_batch = seed_batch
for i in range(180):
predicted_value = model(current_batch)
extrapolation.append(predicted_value.item())
current_batch = torch.cat((current_batch[:,:,1:], predicted_value), axis=2)

x = np.arange(110,200,0.5)
fig, ax = plt.subplots(1, 1, figsize=(15, 5))
ax.plot(X_train,y_train, lw=2, label='train data')
ax.plot(X_test,y_test, lw=3, c='y', label='test data')
ax.plot(x,extrapolation, lw=3, c='r',linestyle = ':', label='extrapolation')
ax.legend(loc="lower left")
plt.show();


And it produces the following plot: In other words, you can write the linear relationship of your sinusoid with frequency $$f$$ and phase $$\phi$$ as
$$sin (f \times n + \phi) = a_1 sin (f \times (n-1) + \phi) + a_2 sin (f \times (n-2) + \phi)$$
with real prediction coefficients $$a_1$$ and $$a_2$$ and discrete time index $$n$$.