Introduction to Python Exercises 11.Matplotlib
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2022-07-14 10:05:55
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【问题解决】
import numpy as np
import matplotlib.pyplot as plt
# import math
x = np.linspace(0, 2, 30) # 创建X轴的数值,在0和2之间产生30个均匀分布的值
# y = [math.pow(math.sin(xi - 2), 2) * math.exp(-math.pow(xi, 2)) for xi in x]
y = np.power(np.sin(x - 2), 2) * np.exp(-np.power(x, 2))
plt.plot(x, y) # 调用plot函数,这并不会立即显示函数图像
plt.xlabel('x') # 使用xlabel函数添加X轴的标签
plt.ylabel('f(x)') # 使用ylabel函数添加y轴的标签
plt.title('f(x) = sin^2(x-2)*e^(-x^2)') # 标题
plt.show() # 调用show函数显示函数图像
效果图示:
题目的意思应该是用最小二乘法求解参数b的预测值,可使用函数numpy.linalg.lstsq(具体的参数与返回值可参考网址https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html#numpy.linalg.lstsq)
import matplotlib.pyplot as plt
import numpy as np
X = np.random.random((20, 10)) # 元素为[0.0, 1.0)的浮点数的20*10矩阵
b = np.random.randn(10, 1) # 元素服从标准正态分布的向量
z = np.random.random((20, 1)) # 元素为[0.0, 1.0)的浮点数的向量
Y = np.dot(X, b) + z
b_hat = np.array(np.linalg.lstsq(X, Y, rcond = -1)[0]) # 线性回归的第一个参数
x = np.arange(0, 10)
plt.scatter(x, b, c = 'r', marker = 'x', label = '$true coefficients$')
plt.scatter(x, b_hat, c = 'b', marker = 'o', label = '$estimated coefficients$')
plt.xlabel('index')
plt.ylabel('value')
plt.legend()
plt.show()
效果图示:
高斯核密度估计函数scipy.stats.gaussian_kde可参考网址https://github.com/scipy/scipy/blob/master/scipy/stats/kde.py
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
# generate a vector z of 10000 observations from standard normal distribution.
z = np.random.normal(loc = 0, scale = 5.0, size = 10000)
# show a histogram of z (with 25 bins)
plt.hist(z , bins = 25, density = True, color = 'b')
# use a Gaussian kernel density estimator
kernel = scipy.stats.gaussian_kde(z)
# obtain the PDF (kernel is a function!)
x = np.linspace(-20, 20, 10000)
plt.plot(x, kernel(x), 'r')
plt.show()
效果图示:
上一篇: 【基本图像操作】Matplotlib
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