<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[钱佳佳的博客]]></title><description><![CDATA[大数据]]></description><link>https://blog.youkuaiyun.com/qq_32005671</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; qq_32005671]]></copyright><item><title><![CDATA[anaconda下载地址]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/83381821</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/83381821</guid><author>qq_32005671</author><pubDate>Thu, 25 Oct 2018 16:29:37 +0800</pubDate><description><![CDATA[Anaconda installer archive： 
地址1： https://repo.continuum.io/archive/ 
地址2：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/]]></description><category></category></item><item><title><![CDATA[MySQL的安装]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/75097406</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/75097406</guid><author>qq_32005671</author><pubDate>Fri, 14 Jul 2017 11:26:15 +0800</pubDate><description><![CDATA[一、MySQL的下载：
点击链接：https://dev.mysql.com/downloads/file/?id=470091调到如下界面，然后点击图中红色标记进行下载。

二、MySQL的安装
双击下载好的安装包，进入如图操作








接下来一直点击next直到出现下图：然后按图操作
一直next到下图为止，然后点击execute


接下来操作nex]]></description><category></category></item><item><title><![CDATA[python np.where()]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/74011993</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/74011993</guid><author>qq_32005671</author><pubDate>Fri, 30 Jun 2017 20:31:55 +0800</pubDate><description><![CDATA[numpy.where()函数是三元表达式x if condition else y的矢量化版本。假设我们有一个布尔数组和两个值数组：
x = np.array([1.1, 1.2, 1.3, 1.4, 1.5])
y = np.array([2.1, 2.2, 2.3, 2.4, 2.5])
condition = np.array([True, False, True, True, Fals]]></description><category></category></item><item><title><![CDATA[python nonzero()函数的用法]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/74011045</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/74011045</guid><author>qq_32005671</author><pubDate>Fri, 30 Jun 2017 19:04:04 +0800</pubDate><description><![CDATA[举个栗子说明一下nonzero()函数的用法：
首先构建一个简单的矩阵：
from numpy import *
a = mat([[1,1,0],[1,1,0],[1,0,3]])
print(a)输出结果如下图：

print(a.nonzero())
第一个array表示非零元素所在的行，第二个array表示非零元素所在的列，分别取对应位置的值组成非零元素的坐标
print(le]]></description><category></category></item><item><title><![CDATA[解决Segmentor: Model not loaded!]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/73770536</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/73770536</guid><author>qq_32005671</author><pubDate>Tue, 27 Jun 2017 09:46:57 +0800</pubDate><description><![CDATA[装完pyltp测试了一下发现安装成功，但是在跑第一个分词程序时却报了如下错误：
Segmentor: Model not loaded!

打开cmd窗口，输入python，进入python编程界面，输入import pyltp回车，再一次确定pyltp安装是没有问题，后来想了想既然是Model not loaded!，是不是model版本不对啊，我第一次装的是ltp3.3.0，换成最新版3]]></description><category></category></item><item><title><![CDATA[Python 解决 ：NameError: name 'reload' is not defined 问题]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/73744794</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/73744794</guid><author>qq_32005671</author><pubDate>Mon, 26 Jun 2017 21:59:43 +0800</pubDate><description><![CDATA[如果你的python版本是Python 2.X：
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
如果你的python版本是python 3.3：
import imp
imp.reload(sys)
注意： 
1. Python
 3 与 Python
 2 有很大的区别，其中Python
 3 系统默认使用的就是ut]]></description><category></category></item><item><title><![CDATA[Python Tkinter 简单登录界面]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/73410591</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/73410591</guid><author>qq_32005671</author><pubDate>Sun, 18 Jun 2017 10:25:04 +0800</pubDate><description><![CDATA[from tkinter import *

class Reg (Frame):
    def __init__(self,master):
        frame = Frame(master)
        frame.pack()
        self.lab1 = Label(frame,text = "账户:")
        self.lab1.grid(row = 0]]></description><category></category></item><item><title><![CDATA[自然语言处理一：基于朴素贝叶斯的语种检测]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/73323432</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/73323432</guid><author>qq_32005671</author><pubDate>Fri, 16 Jun 2017 11:09:16 +0800</pubDate><description><![CDATA[本文来自是对七月在线寒小阳自然语言处理课程的总结。
本文使用朴素贝叶斯完成一个语种检测的分类器，准确度经过简单的参数调优可以达到99.1%。





机器学习的算法要取得好效果，离不开数据，咱们先拉点数据（twitter数据，包含English,
 French, German, Spanish, Italian 和 Dutch 6种语言）瞅瞅。

# 读取数据
in_f =]]></description><category></category></item><item><title><![CDATA[windows下Python的Tkinter库的安装]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/72903018</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/72903018</guid><author>qq_32005671</author><pubDate>Wed, 07 Jun 2017 19:16:10 +0800</pubDate><description><![CDATA[检查有没有安装


如上，没有安装TKinter
点击如下链接下载安装包：
http://wiki.python.org/moin/TkInter


windows下python3.2版本之后是自动安装tkinter的，python3.3的引入方式为：

>>> import _tkinter

>>> import tkinter

>>> tkinter._tes]]></description><category></category></item><item><title><![CDATA[机器学习案例之：甲状腺射频消融数据模型]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/72877726</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/72877726</guid><author>qq_32005671</author><pubDate>Tue, 06 Jun 2017 14:24:33 +0800</pubDate><description><![CDATA[甲状腺疾病是常见的内分泌疾病的一种，主要是由于甲状腺功能分泌不正常引起。长期的分泌不足或过剩，都会对身体产生危害，需及早治疗。射频消融的原理是利用物理热能对结节进行的原位灭火治疗，人体对于灭活坏死的组织具有吸收排除的特性，可以在一定时间内使坏死的组织消失。射频消融的核心是温度的控制，电路设计为立体电阻抗反馈式自动控制，消融温度达70°C便自动中止电流，既保证病变组织坏死，又不会损伤正常细胞。同时，]]></description><category></category></item><item><title><![CDATA[pickle的用法]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/72861867</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/72861867</guid><author>qq_32005671</author><pubDate>Sun, 04 Jun 2017 21:51:47 +0800</pubDate><description><![CDATA[import pickle

a_dict = {'da': 111, 2: [23, 1, 4], '23': {1: 2, 'd': 'dad'}}

# 保存
file = open('pickle_example.pickle', 'wb')
pickle.dump(a_dict, file)
file.close()

# 读取
file = open('pickle_example.p]]></description><category></category></item><item><title><![CDATA[sklearn模型的保存与恢复]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/72774306</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/72774306</guid><author>qq_32005671</author><pubDate>Fri, 26 May 2017 22:29:38 +0800</pubDate><description><![CDATA[举例说明：
模型的保存过程：
from sklearn.externals import joblib
from sklearn import svm
import os

# os.chdir("workspace/model_save")
X = [[0, 0], [1, 1]]
y = [0, 1]
clf = svm.SVC()
clf.fit(X, y)
joblib.dump(cl]]></description><category></category></item><item><title><![CDATA[免费数据集大全]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/69389517</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/69389517</guid><author>qq_32005671</author><pubDate>Thu, 06 Apr 2017 14:45:10 +0800</pubDate><description><![CDATA[免费数据集，分享给大家，希望能省下大家找数据的时间。
https://zhuanlan.zhihu.com/p/25138563]]></description><category></category></item><item><title><![CDATA[机器学习简易入门（三） - 聚类]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/69063753</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/69063753</guid><author>qq_32005671</author><pubDate>Tue, 04 Apr 2017 15:46:16 +0800</pubDate><description><![CDATA[摘要：本文简单叙述了如何用聚类来通过投票记录分析美国参议员的实际政治倾向

转载自：http://www.cnblogs.com/kylinlin/p/5299078.html

本文内容来源：https://www.dataquest.io/mission/60/clustering-basics






在前面的两篇文章中使用的线性回归和分类都属于有监督的机器学习（根]]></description><category></category></item><item><title><![CDATA[机器学习简易入门（二） - 分类]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/69062792</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/69062792</guid><author>qq_32005671</author><pubDate>Tue, 04 Apr 2017 14:52:50 +0800</pubDate><description><![CDATA[机器学习简易入门（二） - 分类

摘要：本文简单叙述了如何通过分类算法来评估银行发放贷款的模型

转载自：http://www.cnblogs.com/kylinlin/p/5306358.html

本文内容来源：https://www.dataquest.io/mission/57/logistic-regression

在你向银行申请信用卡或者贷款时，银行会使用根据过往的]]></description><category></category></item><item><title><![CDATA[机器学习简易入门（一） - 线性回归]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/69061849</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/69061849</guid><author>qq_32005671</author><pubDate>Tue, 04 Apr 2017 13:37:04 +0800</pubDate><description><![CDATA[摘要：本文简单叙述了如何根据标准普尔500指数使用线性回归来预测股票的走势

本文内容来源：https://www.dataquest.io/mission/58/regression-basics

标准普尔500（S&P 500）说明：http://www.investopedia.com/ask/answers/05/sp500calculation.asp

转载自：http:]]></description><category></category></item><item><title><![CDATA[svm中gamma的确定]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/62889083</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/62889083</guid><author>qq_32005671</author><pubDate>Fri, 17 Mar 2017 15:32:01 +0800</pubDate><description><![CDATA[from __future__ import print_function
from sklearn.learning_curve import  validation_curve
from sklearn.datasets import load_digits
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import n]]></description><category></category></item><item><title><![CDATA[knn算法中关于k的取值]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/62888951</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/62888951</guid><author>qq_32005671</author><pubDate>Fri, 17 Mar 2017 15:25:15 +0800</pubDate><description><![CDATA[from __future__ import print_function
from sklearn.datasets import load_iris
from sklearn.cross_validation import cross_val_score
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighbo]]></description><category></category></item><item><title><![CDATA[模型保存save]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/62887493</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/62887493</guid><author>qq_32005671</author><pubDate>Fri, 17 Mar 2017 14:24:32 +0800</pubDate><description><![CDATA[from __future__ import print_function
from sklearn import svm
from sklearn import datasets

clf = svm.SVC()
iris = datasets.load_iris()
X, y = iris.data, iris.target
clf.fit(X, y)

# method 1: pickle
]]></description><category></category></item><item><title><![CDATA[normalization数据标准化]]></title><link>https://blog.youkuaiyun.com/qq_32005671/article/details/62883684</link><guid>https://blog.youkuaiyun.com/qq_32005671/article/details/62883684</guid><author>qq_32005671</author><pubDate>Fri, 17 Mar 2017 10:59:46 +0800</pubDate><description><![CDATA[from __future__ import print_function
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
from sklearn.datasets.samples_generator import make_classification
from sk]]></description><category></category></item></channel></rss>