42 lines
1.7 KiB
Python
42 lines
1.7 KiB
Python
import numpy as np
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import seaborn as sns
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import matplotlib.pyplot as plt
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from scipy import stats
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# 生成各种正态分布随机数
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np.random.seed(1234)
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rn1 = np.random.normal(loc = 0, scale = 1, size = 1000)
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rn2 = np.random.normal(loc = 0, scale = 2, size = 1000)
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rn3 = np.random.normal(loc = 2, scale = 3, size = 1000)
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rn4 = np.random.normal(loc = 5, scale = 3, size = 1000)
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# 绘图
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plt.style.use('ggplot')
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sns.distplot(rn1, hist = False, kde = False, fit = stats.norm,
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fit_kws = {'color':'black','label':'u=0,s=1','linestyle':'-'})
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sns.distplot(rn2, hist = False, kde = False, fit = stats.norm,
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fit_kws = {'color':'red','label':'u=0,s=2','linestyle':'--'})
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sns.distplot(rn3, hist = False, kde = False, fit = stats.norm,
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fit_kws = {'color':'blue','label':'u=2,s=3','linestyle':':'})
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sns.distplot(rn4, hist = False, kde = False, fit = stats.norm,
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fit_kws = {'color':'purple','label':'u=5,s=3','linestyle':'-.'})
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# 呈现图例
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plt.legend()
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# 呈现图形
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plt.show()
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# 生成各种指数分布随机数
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np.random.seed(1234)
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re1 = np.random.exponential(scale = 0.5, size = 1000)
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re2 = np.random.exponential(scale = 1, size = 1000)
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re3 = np.random.exponential(scale = 1.5, size = 1000)
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# 绘图
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sns.distplot(re1, hist = False, kde = False, fit = stats.expon,
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fit_kws = {'color':'black','label':'lambda=0.5','linestyle':'-'})
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sns.distplot(re2, hist = False, kde = False, fit = stats.expon,
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fit_kws = {'color':'red','label':'lambda=1','linestyle':'--'})
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sns.distplot(re3, hist = False, kde = False, fit = stats.expon,
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fit_kws = {'color':'blue','label':'lambda=1.5','linestyle':':'})
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# 呈现图例
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plt.legend()
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# 呈现图形
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plt.show() |