Created
December 23, 2020 02:59
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Median quantile finder
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| import numpy as np | |
| import torch as th | |
| import torch.nn as nn | |
| import matplotlib.pyplot as plt | |
| from torch.optim import Adam | |
| arr = np.arange(100) | |
| class QR(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.x = nn.Parameter(data=th.from_numpy(np.array([1], dtype=np.float32))) | |
| qr = QR() | |
| opt = Adam(params=qr.parameters(), lr=0.1) | |
| estimated_median, real_median, losses = [], [], [] | |
| for i in range(2000): | |
| loss = th.mean(th.abs(th.from_numpy(arr)-qr.x)) | |
| opt.zero_grad() | |
| loss.backward() | |
| opt.step() | |
| losses.append(loss.item()) | |
| estimated_median.append(qr.x.clone().detach()) | |
| real_median.append(np.median(arr)) | |
| print(f"step: {i}, loss:{loss.item()}, estimated median: {qr.x.clone().detach()}, real meadian: {np.median(arr)}") | |
| plt.plot(range(len(estimated_median)), estimated_median, label='estimated_median') | |
| plt.plot(range(len(estimated_median)), real_median, label='median') | |
| plt.plot(range(len(estimated_median)), losses, label='loss') | |
| plt.legend() | |
| plt.show() |
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