python机器学习库numpy---14、numpy实现正态分布

python机器学习库numpy---14、numpy实现正态分布

一、总结

一句话总结：

numpy实现正态分布就是 x轴模拟一些点，y轴根据正态分布的公式算出这些点的结果，然后画图即可

# Python实现正态分布
# 绘制正态分布概率密度函数
import numpy as np
import matplotlib.pyplot as plt

u = 0  # 均值μ
sig = np.sqrt(0.2)  # 标准差δ
print(sig)

x = np.linspace(u - 3 * sig, u + 3 * sig, 150)
y = np.exp(-(x - u) ** 2 / (2 * sig ** 2)) / (np.sqrt(2 * np.pi) * sig)
print(x)
print("=" * 80)
print(y)
plt.plot(x, y, "r-", linewidth=2)
plt.grid(True)
plt.show()

二、numpy实现正态分布

博客对应课程的视频位置：14、numpy实现正态分布-范仁义-读书编程笔记
https://www.fanrenyi.com/video/38/357

# Python实现正态分布
# 绘制正态分布概率密度函数
import numpy as np
import matplotlib.pyplot as plt

u = 0  # 均值μ
sig = np.sqrt(0.2)  # 标准差δ
print(sig)

x = np.linspace(u - 3 * sig, u + 3 * sig, 150)
y = np.exp(-(x - u) ** 2 / (2 * sig ** 2)) / (np.sqrt(2 * np.pi) * sig)
print(x)
print("=" * 80)
print(y)
plt.plot(x, y, "r-", linewidth=2)
plt.grid(True)
plt.show()

0.4472135954999579
[-1.34164079 -1.32363219 -1.30562358 -1.28761498 -1.26960638 -1.25159778
 -1.23358918 -1.21558058 -1.19757198 -1.17956338 -1.16155477 -1.14354617
 -1.12553757 -1.10752897 -1.08952037 -1.07151177 -1.05350317 -1.03549457
 -1.01748597 -0.99947736 -0.98146876 -0.96346016 -0.94545156 -0.92744296
 -0.90943436 -0.89142576 -0.87341716 -0.85540856 -0.83739995 -0.81939135
 -0.80138275 -0.78337415 -0.76536555 -0.74735695 -0.72934835 -0.71133975
 -0.69333114 -0.67532254 -0.65731394 -0.63930534 -0.62129674 -0.60328814
 -0.58527954 -0.56727094 -0.54926234 -0.53125373 -0.51324513 -0.49523653
 -0.47722793 -0.45921933 -0.44121073 -0.42320213 -0.40519353 -0.38718492
 -0.36917632 -0.35116772 -0.33315912 -0.31515052 -0.29714192 -0.27913332
 -0.26112472 -0.24311612 -0.22510751 -0.20709891 -0.18909031 -0.17108171
 -0.15307311 -0.13506451 -0.11705591 -0.09904731 -0.08103871 -0.0630301
 -0.0450215  -0.0270129  -0.0090043   0.0090043   0.0270129   0.0450215
  0.0630301   0.08103871  0.09904731  0.11705591  0.13506451  0.15307311
  0.17108171  0.18909031  0.20709891  0.22510751  0.24311612  0.26112472
  0.27913332  0.29714192  0.31515052  0.33315912  0.35116772  0.36917632
  0.38718492  0.40519353  0.42320213  0.44121073  0.45921933  0.47722793
  0.49523653  0.51324513  0.53125373  0.54926234  0.56727094  0.58527954
  0.60328814  0.62129674  0.63930534  0.65731394  0.67532254  0.69333114
  0.71133975  0.72934835  0.74735695  0.76536555  0.78337415  0.80138275
  0.81939135  0.83739995  0.85540856  0.87341716  0.89142576  0.90943436
  0.92744296  0.94545156  0.96346016  0.98146876  0.99947736  1.01748597
  1.03549457  1.05350317  1.07151177  1.08952037  1.10752897  1.12553757
  1.14354617  1.16155477  1.17956338  1.19757198  1.21558058  1.23358918
  1.25159778  1.26960638  1.28761498  1.30562358  1.32363219  1.34164079]
================================================================================
[0.00990991 0.01117334 0.01257742 0.01413501 0.01585976 0.01776612
 0.01986938 0.02218564 0.02473178 0.02752546 0.03058507 0.03392971
 0.03757912 0.04155362 0.04587403 0.05056158 0.05563783 0.06112453
 0.06704349 0.07341646 0.08026498 0.08761016 0.09547258 0.10387202
 0.11282733 0.12235614 0.13247473 0.1431977  0.15453784 0.16650581
 0.17910996 0.19235604 0.20624703 0.22078286 0.23596021 0.2517723
 0.26820873 0.28525524 0.30289362 0.32110154 0.33985247 0.35911557
 0.37885569 0.39903332 0.41960463 0.44052155 0.46173184 0.48317922
 0.50480361 0.52654127 0.54832514 0.57008507 0.59174821 0.61323933
 0.6344813  0.65539544 0.67590207 0.69592095 0.7153718  0.73417482
 0.75225127 0.76952396 0.78591781 0.80136042 0.81578255 0.82911869
 0.84130753 0.85229239 0.86202174 0.87044953 0.8775356  0.88324596
 0.88755311 0.8904362  0.89188126 0.89188126 0.8904362  0.88755311
 0.88324596 0.8775356  0.87044953 0.86202174 0.85229239 0.84130753
 0.82911869 0.81578255 0.80136042 0.78591781 0.76952396 0.75225127
 0.73417482 0.7153718  0.69592095 0.67590207 0.65539544 0.6344813
 0.61323933 0.59174821 0.57008507 0.54832514 0.52654127 0.50480361
 0.48317922 0.46173184 0.44052155 0.41960463 0.39903332 0.37885569
 0.35911557 0.33985247 0.32110154 0.30289362 0.28525524 0.26820873
 0.2517723  0.23596021 0.22078286 0.20624703 0.19235604 0.17910996
 0.16650581 0.15453784 0.1431977  0.13247473 0.12235614 0.11282733
 0.10387202 0.09547258 0.08761016 0.08026498 0.07341646 0.06704349
 0.06112453 0.05563783 0.05056158 0.04587403 0.04155362 0.03757912
 0.03392971 0.03058507 0.02752546 0.02473178 0.02218564 0.01986938
 0.01776612 0.01585976 0.01413501 0.01257742 0.01117334 0.00990991]

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