首先，我想生成一个图表，根据数据平均值与目标的接近程度进行颜色标记。（实际程序是一个试图学习权重的机器学习算法）。我想为最终图表中的每个数据点生成一个直方图，但我似乎无法独立生成这些图表。任何帮助都将不胜感激。

import matplotlib.pyplot as pltimport numpy as npdef generateData(goal):    x=[_ for _ in range(20)]    y=[10+np.random.exponential()*5 for _ in range(100)]    return x,ydef drawHistogram(data,nBins):    plt.figure(2)    plt.hist(diffs,nBins)    plt.draw()    plt.show()sweep=np.linspace(10,20,4)for goal in sweep:    for gw2 in sweep:        diffs=[]        for i in range(10):            data=generateData(goal)            diffs.append(goal-np.mean(data[1]))        #生成图表        plt.figure(1)        clr=(abs(np.mean(diffs))/goal,0,0)        plt.plot([goal], [gw2], marker="s", mew='1', ms='35', color=clr)        drawHistogram(diffs,5) ##注释掉此行以查看最终图表应有的样子plt.figure(1)plt.draw()plt.show()

回答：

这将生成17个独立的直方图和一个带有红色方块的最终图表。

import matplotlib.pyplot as pltimport numpy as npdef generateData(goal):    x=[_ for _ in range(20)]    y=[10+np.random.exponential()*5 for _ in range(100)]    return x,ydef drawHistogram(data,nBins):    plt.figure()    plt.hist(diffs,nBins)sweep=np.linspace(10,20,4)for goal in sweep:    for gw2 in sweep:        diffs=[]        for i in range(10):            data=generateData(goal)            diffs.append(goal-np.mean(data[1]))        #生成图表        plt.figure(1)        clr=(abs(np.mean(diffs))/goal,0,0)        plt.plot([goal], [gw2], marker="s", mew='1', ms='35', color=clr)        drawHistogram(diffs,5) ##注释掉此行以查看最终图表应有的样子plt.draw()plt.show()