def uniform_example(batch_size=100, reg=10, filename='uniform_example1'):
m_list = ((np.array(list(range(1, 100))) / 50.0 - 1)).tolist()
loss = []
for theta in m_list:
x = np.zeros((batch_size, 2))
y = np.zeros((batch_size, 2))
x[:, 1] = np.random.uniform(0, 1, batch_size)
y[:, 1] = np.random.uniform(0, 1, batch_size)
y[:, 0] = theta
x = Variable(torch.from_numpy(x).float())
y = Variable(torch.from_numpy(y).float())
M = dmat(x, y)
loss.append(sink(M, reg=reg).data.numpy())
plt.plot(m_list, loss)
plt.xlabel('Theta')
plt.ylabel('Sinkhorn Distance')
plt.title('Uniform Example')
fig_name = 'plots/uniform_example/' + filename + '.png'
plt.savefig(fig_name)
plt.show()
df = pd.DataFrame({'theta': m_list, 'sink_dist': loss})
data_name = 'data/uniform_example/' + filename + '.csv'
df.to_csv(data_name)
def gaussian_example(batch_size=100,
reg=10,
dim=10,
filename='gaussian_example1'):
m_list = range(21)
loss = []
for mu in m_list:
x = np.random.normal(0, 1, (batch_size, dim))
y = np.random.normal(mu, 1, (batch_size, dim))
x = Variable(torch.from_numpy(x).float())
y = Variable(torch.from_numpy(y).float())
M = dmat(x, y)
loss.append(sink(M, reg=reg).data.numpy())
plt.plot(m_list, loss)
plt.xlabel('Mu')
plt.ylabel('Sinkhorn Distance')
plt.title('Gaussian Example (Dim = ' + str(dim) + ')')
fig_name = 'plots/gaussian_example/' + filename + '.png'
plt.savefig(fig_name)
plt.show()
df = pd.DataFrame({'mu': m_list, 'sink_dist': loss})
data_name = 'data/gaussian_example/' + filename + '.csv'
df.to_csv(data_name)