env.step(action) if done: print("info", info) break plt.figure(figsize=(15, 6)) plt.cla...env.step(action) if done: print("info", info) break plt.figure(figsize=(15, 6)) plt.cla
plt.ion()# 打开交互模式 plt.rcParams["font.sans-serif"] = "SimHei"# 消除中文乱码 for iter in range(n_iter): plt.cla
200 == 0: predict = model(torch.autograd.Variable(x_train)) predict = predict.data.numpy() plt.cla
in range(100,n+1,10): for k in range(i+1): Psn[k] = comb(i,k) * (p**k) * ((1-p)**(i-k)) plt.cla
def plot(loss_list, predictions_series, batchX, batchY): plt.subplot(2, 3, 1) plt.cla() plt.plot...0] < 0.5 else 0) for out in one_hot_output_series]) plt.subplot(2, 3, batch_series_idx + 2) plt.cla...total_loss) def plot(loss_list, predictions_series, batchX, batchY): plt.subplot(2, 3, 1) plt.cla...0] < 0.5 else 0) for out in one_hot_output_series]) plt.subplot(2, 3, batch_series_idx + 2) plt.cla
def plot(loss_list, predictions_series, batchX, batchY): plt.subplot(2, 3, 1) plt.cla() plt.plot...else 0) for out in one_hot_output_series]) plt.subplot(2, 3, batch_series_idx + 2) plt.cla...minimize(total_loss)def plot(loss_list, predictions_series, batchX, batchY): plt.subplot(2, 3, 1) plt.cla...else 0) for out in one_hot_output_series]) plt.subplot(2, 3, batch_series_idx + 2) plt.cla
read_sum_exec_runtime(vcpu_thread1, keywords)) ydata2.append(read_sum_exec_runtime(vcpu_thread2, keywords)) plt.cla
pd.read_csv('data.csv') x = data['x_value'] y1 = data['total_1'] y2 = data['total_2'] plt.cla
cell_size) plt.xlabel('timestep:' + str(t)) 让图片动起来(同样注意缩进),plt.pause()函数让当前的图像维持pause_time长的时间,随后plt.cla...plt.pause(pause_time) plt.cla()
continue # 选择一个数 board[row][col] = num plt.cla...backtrack(board, index+1) # 取消选择的数 board[row][col] = 0 plt.cla...continue # 选择一个数 board[row][col] = num plt.cla...backtrack(board, index+1) # 取消选择的数 board[row][col] = 0 plt.cla...else: # backtrack(board,index+1) index = index + 1 plt.cla
% display_step == 0: # plt.figure(num=3) plt.ion() plt.cla
) plt.grid() plt.savefig(os.path.join(input_dir, "loss.pdf"), bbox_inches='tight', pad_inches=0) plt.cla
的参数值以及loss,方便观察其变化情况 wi, bi, loss_i, _ = sess.run([w, b, loss, op]) # 绘图部分 plt.cla
: items.append(theta) items.append(theta) j = 0 for theta in items: plt.cla
图表紧凑型(旁边不留那么多空白) """ # plt.savefig("test.png", dpi=200, bbox_inches="tight") # 清空画布 plt.cla
= [] fig = plt.figure() # 保存动图用 # camera = Camera(fig) for i in range(600): plt.cla...trajectory_x.append(vehicle.x) trajectory_y.append(vehicle.y) # 显示动图 plt.cla
l:r+1] = reversed(new_path[l:r+1]) return new_path #路线变化图 def show(path,city_position,dis): plt.cla
plt.subplots_adjust(hspace=0.5, wspace=0.5) # 调整垂直和水平间距 plt.subplot(3, 1, 1) plt.cla...target_ind2, 1]) + 3) plt.legend() plt.grid(True) plt.subplot(3, 1, 2) plt.cla...trajectory_y2[-1]) + 0.5) plt.legend() plt.grid(True) plt.subplot(3, 1, 3) plt.cla
prediction, y) optimizer.zero_grad() loss.backward() optimizer.step() # 绘制中间结果 plt.cla
fig.canvas.manager.set_window_title(filename) plt.tight_layout() plt.box(False) for window in windows: plt.cla...magnitudes in windows: bar_width = (frequencies[-1] / frequencies.size) * (1 - bar_gap) plt.cla
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