!pip install matplotlib import matplotlib print(matplotlib.__version__) #查看版本 import matplotlib.pyplot as plt # 在图中从0 0 到 6 250 画一条直线 默认绘制直线 import numpy as np xpoints = np.array([0,6]) # 注意是两个x坐标值 ypoints = np.array([0,250]) plt.plot(xpoints , y...
使用DQN模型,代码绘制了在测试数据上获得的利润。x轴表示测试数据的指数,y轴表示DQN模型预测的相对利润增益。通过将训练利润相加并除以导入数据中的最后收盘价来计算相对收益。该图的标签为“rl (test)”。使用Matplotlib库的“show”函数...
1.plot绘制线型图 plot是python中最基本的绘制二维线性折线图的函数 基本使用方式:plt.plot(x,y,s) 代码实现: import matplotlib.pyplot as plt import numpy as np import pandas as pd plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False #plt.plot(x,y,s)...
Matplotlib的' plt '模块用于生成绘图。 plt.figure(figsize=(23,8)) plt.plot(data.index,((data['Close']-data['Close'][0])/data['Close'][-1]), label='buy and hold') plt.plot(train.index, ([0] + train_profits)/data['Close'][-1], label='rl (train)') plt.plot(test.index, ...
_frames, axis=-1) self._frames = None return self._out def __array__(self, dtype=None): out = self._force() if dtype is not None: out = out.astype(dtype) return out def __len__(self): return len(self._force()) def __getitem__(self, i): return self._force()[..., ...
figure['layout']['xaxis2'].update(title='epoch') figure['layout'].update(height=400, width=900, showlegend=False) iplot(figure) plot_loss_reward”使用Plotly库的“make_subplots”函数创建一个带有两个子图的图形。在训练周期内,该图显示了损失值和奖励值的趋势,提供了对DQN模型性能的洞察。
model_selection import train_test_split %matplotlib inline # Treatment effect function def exp_te(x): return np.exp(2*x[0]) # DGP constants np.random.seed(123) n = 2000 n_w = 30 support_size = 5 n_x = 1 # Outcome support support_Y = np.random.choice(np.arange(n_w), size=...
(),dtype=np.int32)b_reward=np.array(batch[:, 2].tolist(),dtype=np.int32)b_obs=np.array(batch[:, 3].tolist(),dtype=np.float32).reshape(batch_size, -1)b_done=np.array(batch[:, 4].tolist(),dtype=np.bool)q=Q(b_pobs)maxq=np.max(Q_ast(b_obs).data,axis=1)target=...
具体到因果推断的例子上,我们只关心Treatment T 对 outcome Y的影响,因此我们可以首先使用X回归T,得到一个T的残差(实际T - 预测T),然后使用X回归Y,得到一个Y的残差(实际Y - 预测Y),最后使用T的残差回归Y的残差,估计的参数即我们想要的ATE。 (Y - (Y \sim X)) \sim (T - (T \sim X)) ...
Applying the parallel axis theorem, we account for the rotational dynamics about the pendulum ends. The effects of any dissipative forces such as air resistance or friction are neglected therefore, the total mechanical energy is conserved. Mathematical formulation For this conservative system, the equat...