lujianfeiccie2009的专栏

# -*- coding: utf-8 -*-import socketimport structimport osdef outputCmd(cmd): r = os.popen(cmd) text = r.read() r.close() return textdef getPCName(ip): return socket.gethostbyaddr(ip)def getIpList(): ipList = [] arp_

以下是绘制指数加权平均线的 Python 代码# -*- coding: utf-8 -*-import numpy as npnp.random.seed(1337) # for reproducibilityimport matplotlib.pyplot as plt # 可视化模块#解决中文显示问题plt.rcParams['font.sans-serif'] = ['KaiTi'] # 指定默认字体plt.rcParams['axes.unicode_minus'] = False

下面是各种优化器的对比效果，包含 gd, momentum, nesterovPython 代码示例# -*- coding: utf-8 -*-import numpy as npimport matplotlib.pyplot as plt# 待解决的函数def f(x,y): return x * x + 50 * y * y# f(x,y) 的全导数def g(x): return np.array([2 * x[0], 100 * x[1]])def gd

下面是绘制的效果这里是绘制的代码# -*- coding: utf-8 -*-import numpy as npimport pandas as pdimport matplotlib.pyplot as plt# 计算x,y坐标对应的高度值def f(x,y):#the height function return x * x + y * y # 生成x,y的数据x = np.linspace(-3, 3, 7)y = np.linspace(-3, 3, 7)pr

# -*- coding: utf-8 -*-import pymongoimport timeimport threadingdef threadFunc(threadName): print("\r\n%s start" % threadName) time.sleep(5) print("\r\n%s end" % threadName) pass start = time.time()threads = []for index in ran

激活函数tanh# -*- coding: utf-8 -*-import numpy as npimport matplotlib.pyplot as pltfrom keras import backend as Kx = np.linspace(-3,3,100)y = K.tanh(x)plt.plot(x, y, 'b', label = "y = tanh (x)")plt.legend()plt.grid()plt.show()tanh 对应图像relu#

把开发者模式打开，激活 adb 调试，然后可以使用以下python代码获取安卓手机的耗电量# -*- coding: utf-8 -*-import reimport osdef getSelectDevice(): output = os.popen('adb devices') result = output.read() devices_split = result.split('\n') devices = [] for device in devi

使用 make_classification 生成分类样本# -*- coding: utf-8 -*-from sklearn.datasets.samples_generator import make_classificationimport matplotlib.pyplot as plt###生成的数据如下图所示###plt.figureX,y= make_classification(n_samples=300, # 样本个数 n

# -*- coding: utf-8 -*-import numpy as nplistdata = [[1,1,2], [1,2,2], [1,3,2], [1,4,2]]newlistdata = np.array(listdata)print("原数组:")print(newlistdata)print()print("取第一行：", newlistdata[0])print("取第二行：", newlis

# -*- coding: utf-8 -*-from keras.models import Sequential from keras.layers import Denseimport numpy as npimport randomdef generateData(): i = 1 X =[] Y = [] while i < 1000: t1 = random.randint(0,1000) t2 = rando

# -*- coding: utf-8 -*-import numpy as npnp.random.seed(1337) # for reproducibilityfrom keras.models import Sequentialfrom keras.layers import Denseimport matplotlib.pyplot as plt # 可视化模块# 创建数据# create some dataX = np.linspace(-1, 1, 200)np.ran

下面的代码可以很好地解析这个页面的html数据http://www.webmasterhome.cn/huilv/huobidaima.asp# -*- coding: utf-8 -*-"""Created on Mon Jul 13 11:20:26 2020@author: zy"""import parselimport json# 来自 http://www.webmasterhome.cn/huilv/huobidaima.aspf = open('data/curr

运行效果# -*- coding: utf-8 -*-import hashlib #导入hashlib模块import tkinterimport tkinter.messageboximport tkinter.filedialogfrom tkinter import *def hash(file_path,Bytes=1024): md5_1 = hashlib.md5()

使用python结合各种工具包对网页信息进行爬取，使用起来非常方便# -*- coding: utf-8 -*-"""Created on Mon Apr 6 15:29:40 2020@author: lujia"""# 爬取ip代理，构建ip代理池# requests# parselimport requests # pip install requestsimpor...

参考资料https://baike.baidu.com/item/%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83/829892?fr=aladdin一维正态分布若随机变量 XXX 服从一个位置参数为 μ\muμ 、尺度参数为 σ\sigmaσ 的概率分布，且其概率密度函数为f(x)=12πσexp(−(x−μ)22σ2)f(x) ={1\over \sqrt...

用pandas将数据读出来import pandas as pddata = pd.read_excel('D:\\python_workspace\\data.xlsx')用matplotlib将数据可视化处理import matplotlib.pyplot as pltplt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签da...

大家都知道，王者荣耀里的刺客赵云有被动免伤机制，但是玩的时候感受不明显，下面给大家看一个直观的数据# -*- coding: utf-8 -*-import numpy as npimport matplotlib.pyplot as plt# 在我的 notebook 里，要设置下面两行才能显示中文plt.rcParams['font.family'] = ['sans-serif']blood = 1 # 初始满血量blood_no_protect = 1 # 初始满血量damage

# -*- coding: utf-8 -*-"""Created on Fri Jun 5 17:24:46 2020"""import tensorflow as tfimport matplotlib.pyplot as pltclass Model(object): def __init__(self): # 初始化变量值为(5.0, 0.0) # 实际上，这些变量应该初始化为随机值 self.W = tf.Variable(5.0) self.