How to be a Big data Master step 1

Python初学者之路

Never too late to learn...... 

I guss never too late to be a cool buty smart girl......

I am A master student i CityU Macau, major in interational tourism and hospitality(English section),

while it is my secend year(also the last year)

I did my betchler program in China mainland with double degrees, The Internet of Thigs & Eglish......lucky me,IoT is the hottest major in my generation,even now...

here I start

 

Knowing about some kownledge about java and scala.....I choose python....hh

get the softwere of python 3.* and pycharm....set the computer env....(4 my windows system)

and write my first "Hello Word!"in Python..then learn char...,defination grammar...loop grammar...interaction of customers...detiles will not to be maintioned..

practise the code just like that:

#Author:Mini
#!/usr/bin/env python
#-*-utf-8-*-
#import getpass
print("hello world")
name=input("name:")
name2=name
name="baby"
'''hello world'''
print("hello",name,name2)
info="""name%s
age %s"""% (name,name2)
print(info)
print(type(name2))
age=int(input("age:"))
info2="""
name={a}
job={b}
""".format(a="a1", b="b1")
info3="""
name={0}
job={1}""".format("aa","bb")
print(info,info2,info3)
password1=input("passwprd:")
if name2==password1 and name2!= name:
print(password1)
else:print("")

boyage=int(56)
count =0
while count<3 :
gussage=int(input("gussage:"))
if gussage==boyage:
print(gussage)
break
elif gussage>=boyage:
print("smaller!")
else:print("biger")
count=count+1
else:print("try 2 many times!fuck off!")

for i in range(0,10,2):
print(i)

for c in range(3):
print("like u +"+c)

else:print("love u")


#Author:Mini
#!/usr/bin/env python
for i in range(0,10,2):
print(i)
if i<3:
print("like u+",i)
else:
continue
print("love u")

 



                
MATLAB代码实现了一个基于多种智能优化算法优化RBF神经网络的回归预测模型,其核心是通过智能优化算法自动寻找最优的RBF扩展参数(spread),以提升预测精度。 1.主要功能 多算法优化RBF网络:使用多种智能优化算法优化RBF神经网络的核心参数spread。 回归预测:对输入特征进行回归预测,适用于连续值输出问题。 性能对比:对比不同优化算法在训练集和测试集上的预测性能,绘制适应度曲线、预测对比图、误差指标柱状图等。 2.算法步骤 数据准备:导入数据,随机打乱,划分训练集和测试集(默认7:3)。 数据归一化:使用mapminmax将输入和输出归一化到[0,1]区间。 标准RBF建模:使用固定spread=100建立基准RBF模型。 智能优化循环: 调用优化算法(从指定文件夹中读取算法文件)优化spread参数。 使用优化后的spread重新训练RBF网络。 评估预测结果,保存性能指标。 结果可视化: 绘制适应度曲线、训练集/测试集预测对比图。 绘制误差指标(MAE、RMSE、MAPE、MBE)柱状图。 十种智能优化算法分别是: GWO:灰狼算法 HBA:蜜獾算法 IAO:改进天鹰优化算法,改进①:Tent混沌映射种群初始化,改进②:自适应权重 MFO:飞蛾扑火算法 MPA:海洋捕食者算法 NGO:北方苍鹰算法 OOA:鱼鹰优化算法 RTH:红尾鹰算法 WOA:鲸鱼算法 ZOA:斑马算法
To create a PAL decoder block in GNU Radio Companion (GRC), you can follow these steps: 1. Open GNU Radio Companion and create a new flow graph. 2. Drag a "QT GUI Frequency Sink" block onto the flow graph canvas and connect it to the output of your PAL decoder block. This will allow you to visualize the output of your decoder. 3. Drag a "Feedforward AGC" block onto the flow graph canvas and connect it to the output of your PAL decoder block. This block will adjust the gain of the signal to a constant level. 4. Drag a "Low Pass Filter" block onto the flow graph canvas and connect it to the output of the AGC block. This block will remove high-frequency noise from the signal. 5. Drag a "Throttle" block onto the flow graph canvas and connect it to the output of the low pass filter block. This block limits the rate at which the signal is processed to prevent overloading the CPU. 6. Drag a "QT GUI Time Sink" block onto the flow graph canvas and connect it to the output of the throttle block. This block will allow you to visualize the output of the decoder in the time domain. 7. Finally, add a "PAL Decoder" block to the flow graph. You can find this block in the "Digital" section of the block library. Connect the input of the decoder block to your PAL signal source and the output to the input of the AGC block. 8. Configure the PAL Decoder block according to your signal's characteristics, such as the frequency offset and the type of PAL signal being used. 9. Save the flow graph and run it to see the output of your PAL decoder block. Note that the specific steps may vary depending on your signal source and the requirements of your PAL decoder.
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