<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[pyswt的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/python3_i_know</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; python3_i_know]]></copyright><item><title><![CDATA[生意参谋指数之指数推理原值]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/119412867</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/119412867</guid><author>python3_i_know</author><pubDate>Thu, 05 Aug 2021 11:28:16 +0800</pubDate><description><![CDATA[生意参谋指数是通过函数将原值进行一次换算，他的正向函数是这样的：
*y = (10ln(x+1)+30)x^0.5
现在正向函数知道求反函数问题不是就解决了吗？？？

发现这个真不好求好吧，好求得话淘宝就不会花大价钱搞这个了，不能求那怎么办，只能去拟合一个函数出来咯[抠鼻.jpg]， 话不多说，代码搞起来，数据得话可以用正向函数来收集，收集完用多项式拟合一个函数出来！！！
到这我只想说大哥你太天真了（天真三连），拟合出来发现差值太多了不能用



那怎办呢，拟合结果又不能用，难道就没办法了吗？
有，当然有！]]></description><category></category></item><item><title><![CDATA[pytorch模型tensorrt加速之-pth转onnx转trt，在推理trt模型测试模型速度]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/116262690</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/116262690</guid><author>python3_i_know</author><pubDate>Thu, 29 Apr 2021 15:42:03 +0800</pubDate><description><![CDATA[首先需要安装两个必要的包tensorrt和torch2trt，tensorrt的话要在官网下载tar压缩包，建议下载tar包安装

]]></description><category></category></item><item><title><![CDATA[centos7正式服务器部署uwsgi+nginx+django项目-亲身研究[斜眼笑]]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/110818039</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/110818039</guid><author>python3_i_know</author><pubDate>Fri, 11 Dec 2020 11:04:08 +0800</pubDate><description><![CDATA[服务器是centos7的，首先从安装python3开始
安装依赖包
yum install zlib-devel bzip2-devel openssl-devel ncurses-devel sqlite-devel readline-devel tk-devel gcc* make -y

然后安装python3
wget https://www.python.org/ftp/python/3.5.2/Python-3.5.2.tar.xz

tar -Jxvf Python-3.5.2.tar.xz
]]></description><category></category></item><item><title><![CDATA[解决python pip请求超时问题]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/105584640</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/105584640</guid><author>python3_i_know</author><pubDate>Fri, 17 Apr 2020 17:20:40 +0800</pubDate><description><![CDATA[pip请求超时解决办法：
第一种，pip版本更新，输入
python -m pip install --upgrade pip

如果第一种更新完毕还是请求超时，就进行第二种：
切换源
将源切换为清华大学的源
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple

基本上这两步就ok了

...]]></description><category></category></item><item><title><![CDATA[python+sparkStreaming+kafka之大数据实时流]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/103665828</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/103665828</guid><author>python3_i_know</author><pubDate>Mon, 23 Dec 2019 16:31:20 +0800</pubDate><description><![CDATA[首先需要的是环境，我安装的是spark2…1,kafka0-10,hadoop2.7,scala2.11,因为spark是Scala语言写的，所以这个必须的安装，大数据传输存储需要用到Hadoop，HDFS，所以Hadoop需要安装，streaming接受流数据源有两种大的方式，一种是文件和套接字，直接读取文件和通过套接字传输，另一种是高级API形式，可以通过额外的实用程序类获得诸如Kafka，F...]]></description><category></category></item><item><title><![CDATA[pyspark之RDD，Data Frame，SQL Context 转换与操作]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/103593933</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/103593933</guid><author>python3_i_know</author><pubDate>Wed, 18 Dec 2019 11:12:43 +0800</pubDate><description><![CDATA[# 加载数据
Path='file:/home/swt/pythonwork/PythonProject/'
RawUserRDD = sc.textFile(Path+'data/u.user')
RawUserRDD.count()

# 查看RDD数据
RawUserRDD.take(5)

['1|24|M|technician|85711',
 '2|53|F|other|94043',...]]></description><category></category></item><item><title><![CDATA[pyspark之LogisticRegression算法]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/103577464</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/103577464</guid><author>python3_i_know</author><pubDate>Tue, 17 Dec 2019 11:48:55 +0800</pubDate><description><![CDATA[import sys
import time
import pandas as pd
import matplotlib.pyplot as plt
from pyspark import SparkConf,SparkContext
from pyspark.mllib.classification import LogisticRegressionWithSGD
from pyspark.ml...]]></description><category></category></item><item><title><![CDATA[pyspark之二分类决策树算法]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/103577125</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/103577125</guid><author>python3_i_know</author><pubDate>Tue, 17 Dec 2019 11:29:17 +0800</pubDate><description><![CDATA[# 首先还是设定数据目录，取决于你的pyspark是什么方式启动的
global Path
if sc.master[0:5] == 'local':
    Path = 'file:/home/swt/pythonwork/PythonProject/'
else:
    Path = "hdfs://localhost:9000/user/swt/"

import numpy as np...]]></description><category></category></item><item><title><![CDATA[pyspark运行ALS推荐算法]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/103559978</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/103559978</guid><author>python3_i_know</author><pubDate>Mon, 16 Dec 2019 12:30:40 +0800</pubDate><description><![CDATA[首先创建读取路径，这个取决于你的pyspark是以什么方式启动的
global Path
if sc.master[0:5] == 'local':
    Path='file:/home/swt/pythonwork/PythonProject/'
else:
    Path="hdfs://ubuntu:9000/user/swt/"

我是本地启动
sc.master
'local[*]...]]></description><category></category></item><item><title><![CDATA[pyspark RDD基本操作]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/103531130</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/103531130</guid><author>python3_i_know</author><pubDate>Fri, 13 Dec 2019 18:11:42 +0800</pubDate><description><![CDATA[RDD转换运算
# 创建intRDD
intRDD = sc.parallelize([3,1,2,5,5,6])
intRDD.collect()

[3, 1, 2, 5, 5, 6]

# 创建stringRDD
stringRDD = sc.parallelize(['apple','pen','banana'])
stringRDD.collect()

['apple', 'pen',...]]></description><category></category></item><item><title><![CDATA[keras Embedding层报InvalidArgumentError: indices[23,18] = -1 is not in [0, 480)类似错误解决办法]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/103419194</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/103419194</guid><author>python3_i_know</author><pubDate>Fri, 06 Dec 2019 11:26:18 +0800</pubDate><description><![CDATA[对于码农来说，最害怕的就是各种bug。关于InvalidArgumentError: indices[23,18] = -1 is not in [0, 480)这个类型的错误，我之前也遇到了，然后在网上找了半天也没有解决，最后想了想看了看数据，词向量里面有负值(&lt;0的值)，我想和这个有没有关系，然后就把数据标准化了一下把负值变成了正值，运行竟然可以了，好神奇啊~（咳咳），想想也是，词向量里...]]></description><category></category></item><item><title><![CDATA[基于LSTM网络的文本生成]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/103305595</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/103305595</guid><author>python3_i_know</author><pubDate>Fri, 29 Nov 2019 10:49:20 +0800</pubDate><description><![CDATA[import tensorflow as tf
import numpy as np
import os

#使用cpu进行运算，注释掉使用gpu运算
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"


# 读取文件，所有文本读成一个字符串，总长度为600893
pat...]]></description><category></category></item><item><title><![CDATA[Linux安装kafka，flink并运行word count.py测试demo]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/103183404</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/103183404</guid><author>python3_i_know</author><pubDate>Thu, 21 Nov 2019 15:55:25 +0800</pubDate><description><![CDATA[Linux上安装kafka与flink非常简单（前提需要Java环境），首先到官网下载压缩包，kafka官网选择适合自己的tgz包，下载完可以放到usr/local/下（我的放到这个目录下，可以根据自己的情况），
之后进行解压缩
tar -xzvf 压缩包.tgz

然后进入解压目录启动zookeeper,(因为现在安装kafka无需安装zookeeper，kafka自带)
bin/zookeep...]]></description><category></category></item><item><title><![CDATA[centos7成功安装python3环境selenium+chrome+chrome driver]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/102860791</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/102860791</guid><author>python3_i_know</author><pubDate>Fri, 01 Nov 2019 17:01:43 +0800</pubDate><description><![CDATA[首先安装chrome浏览器，首先可以先建一个文件加用来存放chrome和chrome driver文件。比如我的就在/usr/local/chrome
wget https://dl.google.com/linux/direct/google-chrome-stable_current_x86_64.rpm --no-check-certificate

这个命令是安装最新的版本的chrome浏...]]></description><category></category></item><item><title><![CDATA[Tensorflow之CNN网络分类mnist数据集]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/102563660</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/102563660</guid><author>python3_i_know</author><pubDate>Tue, 15 Oct 2019 11:48:01 +0800</pubDate><description><![CDATA[本文是用专为图像而生的CNN网络学习mnist数据集并测试准确度，全部代码如下
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('data/',one_hot=True)

首先下载mnist数据集到指定的路径下
...]]></description><category></category></item><item><title><![CDATA[Tensorflow之基本操作]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/102460636</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/102460636</guid><author>python3_i_know</author><pubDate>Wed, 09 Oct 2019 15:15:06 +0800</pubDate><description><![CDATA[Tensor flow是现在最流行的深度学习框架之一，要想使用它，就得按照七要求使用Tensor flow自己的生态环境，那就从最基础的开始学起
#Variable用法
import tensorflow as tf

w  = tf.Variable([[0.5,1.0]])
x = tf.Variable([[2.0],[1.0]])
y = tf.matmul(w,x)
y

&lt;tf....]]></description><category></category></item><item><title><![CDATA[手写简单神经网络]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/99823643</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/99823643</guid><author>python3_i_know</author><pubDate>Tue, 20 Aug 2019 15:30:50 +0800</pubDate><description><![CDATA[最进在学神经网络，所以就得先从基础得做起，手写神经网络（斜眼笑），那就~开始吧！！
import numpy as np

#sigmoid激活函数
def sigmoid(x,deriv=False):
    if deriv == True:
        return x*(1-x)
    else:
        return 1/(1+np.exp(-x))

在这里先写一个si...]]></description><category></category></item><item><title><![CDATA[keras深度学习框架进行文本分类]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/93895193</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/93895193</guid><author>python3_i_know</author><pubDate>Thu, 27 Jun 2019 19:07:39 +0800</pubDate><description><![CDATA[近期接手的一个项目就是深度学习文本分类，我选用的是keras深度学习框架，下面把处理过程给大家分享一下，首先是要加载数据，我的数据是在文档里面，首先读取csv文件选取字段。
def laod_csv():
    print('正在加载数据...')
    data = pd.read_csv(r'data\3_9.csv',encoding='gbk')
    target_list = [...]]></description><category></category></item><item><title><![CDATA[利用eda函数对文本数据进行增强]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/93894778</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/93894778</guid><author>python3_i_know</author><pubDate>Thu, 27 Jun 2019 18:11:28 +0800</pubDate><description><![CDATA[近期接手一个项目，时关于深度学习文本分类，我用的是keras深度学习框架LSTM模型，学习前首先得对数据进行处理，由于文本类别比较多有96类，并且有部分类别数据量比较少，这将会影响算法的拟合，我首先想到的是过采样的方法，但是过采样对文本数据无用，所以得先进行词向量的转换，转换完之后我发现准确率还是上不去，后来看了一下word2vec原理，是按照输入的文本数据进行分词统计，在计算词向量，所以这样利用...]]></description><category></category></item><item><title><![CDATA[特征工程常用的函数及使用方法]]></title><link>https://blog.youkuaiyun.com/python3_i_know/article/details/90168111</link><guid>https://blog.youkuaiyun.com/python3_i_know/article/details/90168111</guid><author>python3_i_know</author><pubDate>Mon, 13 May 2019 10:23:53 +0800</pubDate><description><![CDATA[近期参加了天池的数据可视化比赛，我在队伍中担当的是特征工程和后端，虽然但最后后端部署到服务器上的时候我不会，但是，这次比赛也让我学到了许多东西，下面我来总结一下我做特征工程常用的方法
第一个最简单的读写文件，pandas库里面有许多读写文件的函数，支持的文件类型也有很多，常见的csv，excel，sql等，
第二个，最常用的分组函数groupby，许多功能都要基于这个函数，而且后面也可跟许多函数
...]]></description><category></category></item></channel></rss>