Python编程作业【第十一周】（Matplotlib homework）

Matplotlib Exercise

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Exercise 11.1: Plotting a function

import numpy as np
import matplotlib.pyplot as plt
import math
x = np.linspace(-1, 3, 500)
y = np.power(np.sin(x - 2), 2) * np.power(np.e, -1.0 * (np.power(x, 2)))

plt.plot(x, y, 'b-', label = '$\sin^2(x - 2)e^{-x^2}$')
plt.xlim((0, 2))
plt.xlabel('x')
plt.ylabel('y')
plt.title('$\sin^2(x - 2)e^{-x^2}$')
plt.legend()
plt.show()

Exercise 11.2: Data

import numpy as np
import matplotlib.pyplot as plt
import math
X = np.random.randint(10, 20, (20, 10))
b = np.random.random(10)
z = np.random.random(20)
y = np.dot(X, b) + z
b_hat = np.linalg.lstsq(X, y, rcond=None)[0]
x = list(range(1, 11))
true_b = plt.scatter(x, b, c='r', marker='x', label='true coefficients')
estimated_b = plt.scatter(x, b_hat, c='b', marker='o', label='estimated coefficients')
plt.legend()
plt.xlabel('index of b')
plt.show()

Exercise 11.3: Histogram and density estimation

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import math

x = np.random.normal(loc = 1, scale = 2, size = 1000)
x = sorted(x)
print(x)
plt.hist(x, bins=50, density=True)
kernel = stats.gaussian_kde(x)
plt.plot(x, kernel.pdf(x))
plt.show()