import numpy as npimport matplotlib.pyplot as pltplt.figure(figsize=(9,6))n=5000x=np.random.randn(1,n)#返回n个随机数,具有标准正态分布y=np.random.randn(1,n)t=np.arctan2(x,y)#函数arctan2(x,y)返回给定的坐标值的反正切值plt.scatter(x,y,c=t,s=15,alpha=0.5,marker='o')#s:散...
from tensorflow.keras.optimizers import Adam import numpy as np import matplotlib.pyplot as plt (2)定义生成器模型: def build_generator(): z = Input(shape=(100,)) x = Dense(128 * 7 * 7)(z) x = LeakyReLU(alpha=0.2)(x) x = Reshape((7, 7, 128))(x) x = Conv2DTranspose(128,...
【题目】4.下列Python 程序用于研究数学函数的图像,绘制的图形如图4-8所示。程序中划线①②③处应填写的代码为()import matplotlib pyplot as plt Import numpy as numpy. linspace( start, stop, )#产生从start 到stop的等差数列,num为元素个数,默认50个x= np linspace( , 1,50)y1=x*210y-x2y2-np.(...
让我们首先导入进行预测所需的库: import numpy as np import pandas as pd from matplotlib import pyplot as plt from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM from tensorflow.keras.layers import Dense, Dropout from sklearn.preprocessing import MinMaxScaler from kera...
(1) 首先导入 Matplotlib 包中的 Pyplot 模块,并以 as 别名的形式简化引入包的名称。 from matplotlib import pyplot as plt #import matplotlib.pyplot as plt 1. 2. (2) 接下来,使用 NumPy 提供的函数 arange() 创建一组数据来绘制图像。 引入numpy 包,我们获取 -50 到 50 之间的 ndarray 对象(由于 ar...
import matplotlib.pyplot as pltimport numpy as npx=np.linspace(0,20,100)y=np.sin(x)plt.legend()pt.show()则程序划线处应填入的代码是A.plt.plot(x,"g--",label="sin(x)")B.pl ⋅plot(x,y,^ug-u/8,label=^usin(x)')C.pt.plot(x,y,"g--")D.pt ⋅plot(y,ug-u/8,label=^...
import numpy as np import scipy.stats as stats import matplotlib.pyplot as plt import matplotlib.style as style # 绘图配置 style.use('seaborn-bright') plt.rcParams['figure.figsize'] = (15, 8) plt.figure(dpi=120) # 一段时间内发生的次数 ...
xlabel(x)和 plt,ylabel(y)绘制x、y轴标签,故A正确。相关推荐 13.有如下 Python程序段。import pandas as pdimport numpy as npimport matplotlib. pyplot as pltplot_data=pd. DataFrame(np. random. randn(50,2), columns=[A',]B_1(np.random.randn(50,2)产生两列、每列50个随机数plot_data. ...
import numpy as np import sys import torch import numpy as np import torchvision.transforms as T plt.rcParams["savefig.bbox"] = 'tight' orig_img = Image.open(Path('image/000001.tif')) torch.manual_seed(0) # 设置 CPU 生成随机数的 种子 ,方便下次复现实验结果 ...
import numpy as np import pandas as pd from matplotlib import patches, pyplot as plt from scipy.spatial import ConvexHull import seaborn as sns import warnings warnings.simplefilter('ignore') sns.set_style("white") # Step 1: Prepare Data midwest = pd.read_csv("https://raw.githubusercontent...