下面为一个完整的示例代码: importnumpyasnpimportmatplotlib.pyplotaspltdefgaussian(x,mean,std):returnnp.exp(-((x-mean)**2)/(2*std**2))/(std*np.sqrt(2*np.pi))mean=0std=1x=np.linspace(-5,5,100)y=gaussian(x,mean,std)plt.plot(x,y)plt.fill_between(x,y,0,alpha=0.3)plt.xlabel(...
高斯分布(Gaussian Distribution)的概率密度函数(probability density function) 2018-03-27 16:01 −... amoyzhu 0 47674 python gaussian,gaussian2 2019-12-22 18:51 −import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.axisartist as axisartist from mpl_toolkits.mplot3d import ...
A Python Matplotlib, Numpy library to manage wind data, draw windrose (also known as a polar rose plot), draw probability density function and fit Weibull distribution python-windrose.github.io/windrose License Unknown, BSD-3-Clause licenses found ...
import matplotlib.pyplot as plt import numpy as np import scipy.stats as stats mu = 20 sigma = 2 x = np.linspace( mu - 5*sigma, mu + 5*sigma, 200 ) plt.plot( x, stats.norm.pdf( x, mu, sigma ) ) plt.show()6-2: 期望值 I (EXPECTATION)...
import matplotlib.pyplot as plt def plot_distribution(X, axes=None): if axes is None: fig, axes = plt.subplots(1, 2, figsize=(10, 3)) # Divide into 2 subplots x_min, x_max = X.interval(0.99) # 99% values are sampled from [0,1] ...
"""Plot the continuous PDF"""importmatplotlib.pyplotaspltimportnumpyasnpfrommatplotlib.pyplotimportMultipleLocatorx=np.linspace(50,80,100)defheight(x):return1/944.998*(x*(np.exp(-0.095*(x-64)**2)+1.1*np.exp(-0.055*(x-69.4)**2)))y=height(x)point_55=height(55)point_60=height(60)poi...
这些库不仅为初学者提供了快速入门的途径,而且为经验丰富的开发者提供了强大的工具,以高效率、高质量地完成复杂任务。例如,Matplotlib和Seaborn库在数据可视化领域内非常受欢迎,它们提供了广泛的工具和技术,可以创建高度定制化的图表和图形,帮助数据科学家和分析师在数据探索和结果展示中更有效地传达信息。
import matplotlib.pyplot as plt import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions tfb = tfp.bijectors tfpl = tfp.layers # Define base distribution normal = tfd.Normal(loc=0., scale=1.) # Sample from base distribution ...
import matplotlib.pyplot as plt aapl['Adj Close'].plot(figsize=(10,7)) plt.axhline(y=mean, color='r', linestyle='-') plt.legend() plt.grid() plt.show() The standard deviation is calculated as: Calculate the simple average of the numbers (mean) ...
devices import LocalSimulator import matplotlib.pyplot as plt %matplotlib inline import numpy as np #Bernoulli parameter p=0.8 #shift and rescale p from [0,1] to [-1,1] x=2*p-1 #convert to angle theta=np.arccos(x) state_prep_circuit = Circuit() state_prep_circuit.ry(0,theta) #...