使用np.linspace()绘制离散分布图 [英] Plot Discrete Distribution using np.linspace()

问题描述

我正在尝试使用matplotlib绘制简单的离散分布:

I'm trying to plot a simple discrete distribution using matplotlib:

• 如果-1< = x< 0，p = 0.3;
• 如果0 <= x <1，p = 0.5;
• 如果1 <= x <= 2，则p = 0.2.

如何从x = np.linspace(-1, 2)开始?

到目前为止，我尝试过的是:

What I tried so far is:

def mapDiscProb(x):
    if np.any(x < 0):
        return 0.3 + x * 0
    elif np.any(x >= 1):
        return 0.2 + x * 0
    else:
        return 0.5 + x * 0

x = np.linspace(-1, 2)
y = mapDiscProb(x4)

ax.plot(x, y, clip_on = False)

结果只是从-1到2的整条线，从0.3到elif和else都没有计算.

And the outcome is just a whole line at 0.3 from -1 to 2 as if the elif and else are not excuted.

我的预期输出是三个断开的水平线，这是离散pmf的标准.

My expected output is three disconnected horizontal lines, as is standard for a discrete pmf.

推荐答案

如果您只想为该示例执行此操作，则应该可以使用

If you just want to do it for that example, this should work

import numpy as np
from matplotlib import pyplot as plt
x=np.linspace(-1,2)
plt.plot(x[x < 0], 0.3 + x[x < 0]*0, color='blue')
plt.plot(x[(x >= 0) & (x <= 1)], 0.5 + x[(x>=0) & (x<=1)]*0, color='blue')
plt.plot(x[x > 1],0.2 + x[x > 1]*0, color='blue')
plt.show()

对于离散函数，屏蔽是我的解决方法.

masking is my way to go when it comes to discrete functions.

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