<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[木右加木 就是木木]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/VDingLinlin</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; VDingLinlin]]></copyright><item><title><![CDATA[educoder 机器学习之随机森林算法]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121856089</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121856089</guid><author>VDingLinlin</author><pubDate>Fri, 10 Dec 2021 14:43:38 +0800</pubDate><description><![CDATA[第1关：Bagging
import numpy as np
from collections import Counter
from sklearn.tree import DecisionTreeClassifier
class BaggingClassifier():
    def __init__(self, n_model=10):
        '''
        初始化函数
        '''
        #分类器的数量，默认为10
        self.n_model =]]></description><category></category></item><item><title><![CDATA[educoder 决策树的构造]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121474782</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121474782</guid><author>VDingLinlin</author><pubDate>Mon, 22 Nov 2021 16:36:21 +0800</pubDate><description><![CDATA[第1关：决策树简介
"""
Parameters:
    dataSet - 训练数据集
    labels - 分类属性标签
    featLabels - 存储选择的最优特征标签
Returns:
    myTree - 决策树
"""
# 函数说明:创建决策树
def createTree(dataSet, labels, featLabels):
    ############
    #请输入你的代码

    #取分类标签(是否放贷:yes or no)
    #如果类别完全相同则停]]></description><category></category></item><item><title><![CDATA[educoder 支持向量机简介]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121474724</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121474724</guid><author>VDingLinlin</author><pubDate>Mon, 22 Nov 2021 16:34:26 +0800</pubDate><description><![CDATA[第1关：支持向量机简介
1 A 
2 A 
3 C
4 C
5 A 


]]></description><category></category></item><item><title><![CDATA[educoder SMO进行优化]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121474678</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121474678</guid><author>VDingLinlin</author><pubDate>Mon, 22 Nov 2021 16:32:53 +0800</pubDate><description><![CDATA[第1关：SMO高效优化算法
import numpy as np
import random
def calcEk(oS, k):
    """
    计算误差
    Parameters：
        oS - 数据结构
        k - 标号为k的数据
    Returns:
        Ek - 标号为k的数据误差
    """
    fXk = float(np.multiply(oS.alphas,oS.labelMat).T*(oS.X*oS.X[k,:].T) + o]]></description><category></category></item><item><title><![CDATA[educoder 基于贝叶斯决策理论的分类方法]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121474655</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121474655</guid><author>VDingLinlin</author><pubDate>Mon, 22 Nov 2021 16:31:51 +0800</pubDate><description><![CDATA[第1关：基于贝叶斯决策理论的分类方法
import numpy as np

'''
Parameters:
    无
Returns:
    postingList - 实验样本切分的词条
    classVec - 类别标签向量
'''
# 函数说明:创建实验样本
def loadDataSet():
    postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],       #切分的词条
         ]]></description><category></category></item><item><title><![CDATA[educoder Pandas初体验]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121338108</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121338108</guid><author>VDingLinlin</author><pubDate>Mon, 15 Nov 2021 16:52:59 +0800</pubDate><description><![CDATA[第1关：了解数据处理对象–Series
# -*- coding: utf-8 -*-
from pandas import Series,DataFrame
import  pandas as pd
 
def create_series():
    '''
    返回值:
    series_a: 一个Series类型数据
    series_b: 一个Series类型数据
    dict_a：  一个字典类型数据
    '''
    # 请在此添加代码 完成本关任务
    # ****]]></description><category></category></item><item><title><![CDATA[educoder 数据挖掘算法原理与实践：k-近邻]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121338034</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121338034</guid><author>VDingLinlin</author><pubDate>Mon, 15 Nov 2021 16:48:35 +0800</pubDate><description><![CDATA[第1关：knn算法概述
#encoding=utf8
import numpy as np

def topK(i,k,x,y):
    '''
    input:
        i(int):第i个样本
        k(int):最近邻样本个数
        x(ndarray):数据特征
        y(ndarray):数据标签
    output:
        topK(list):样本i的最近k个样本标签
    '''
    #*********Begin********]]></description><category></category></item><item><title><![CDATA[educoder Numpy初体验]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121337896</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121337896</guid><author>VDingLinlin</author><pubDate>Mon, 15 Nov 2021 16:45:16 +0800</pubDate><description><![CDATA[第1关：Numpy创建数组
# 引入numpy库
import numpy as np
# 定义cnmda函数
def cnmda(m,n):
    '''
    创建numpy数组
    参数：
           m:第一维的长度
           n: 第二维的长度
    返回值:
        ret: 一个numpy数组
    '''
    
    ret = 0
    
    # 请在此添加创建多维数组的代码并赋值给ret
    #********** Begin *]]></description><category></category></item><item><title><![CDATA[educoder 数据挖掘算法原理与实践：线性回归（房价预测）]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121337833</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121337833</guid><author>VDingLinlin</author><pubDate>Mon, 15 Nov 2021 16:42:42 +0800</pubDate><description><![CDATA[第1关：线性回归算法思想
1 BC

第2关：动手实现线性回归
#encoding=utf8
import numpy as np

#mse
def mse_score(y_predict,y_test):
    #********* Begin *********#
    m = len(y_predict)
    a = y_test - y_predict
    mse = np.dot(a,a)/m
    #********* End *********#
    return mse
]]></description><category></category></item><item><title><![CDATA[educoder 数据挖掘算法原理与实践：决策树]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121337778</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121337778</guid><author>VDingLinlin</author><pubDate>Mon, 15 Nov 2021 16:40:57 +0800</pubDate><description><![CDATA[第1关：决策树算法思想
1 AB
2 B

第2关：决策树算法原理
import numpy as np


def calcInfoGain(feature, label, index):
    '''
    计算信息增益
    :param feature:测试用例中字典里的feature，类型为ndarray
    :param label:测试用例中字典里的label，类型为ndarray
    :param index:测试用例中字典里的index，即feature部分特征列的索引。该索]]></description><category></category></item><item><title><![CDATA[educoder 数据挖掘算法原理与实践：k-均值]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121337699</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121337699</guid><author>VDingLinlin</author><pubDate>Mon, 15 Nov 2021 16:38:47 +0800</pubDate><description><![CDATA[第1关：什么是质心
#encoding=utf8
import numpy as np

#计算样本间距离
def distance(x, y, p=2):
    '''
    input:x(ndarray):第一个样本的坐标
          y(ndarray):第二个样本的坐标
          p(int):等于1时为曼哈顿距离，等于2时为欧氏距离
    output:distance(float):x到y的距离      
    '''
    #********* Begin **]]></description><category></category></item><item><title><![CDATA[educoder 机器学习 --- 绪论]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212405</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212405</guid><author>VDingLinlin</author><pubDate>Mon, 08 Nov 2021 17:34:05 +0800</pubDate><description><![CDATA[第1关：什么是机器学习
1.BC

第2关：机器学习的常见术语
1.B

第3关：机器学习的主要任务
1.B
2.AD
3.B


]]></description><category></category></item><item><title><![CDATA[educoder 机器学习 --- 模型评估、选择与验证]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212294</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212294</guid><author>VDingLinlin</author><pubDate>Mon, 08 Nov 2021 17:30:29 +0800</pubDate><description><![CDATA[第1关：为什么要有训练集与测试集
１、Ｄ
２、Ａ

第2关：欠拟合与过拟合
１、Ｂ
２、ＡＢＤ

第3关：偏差与方差
１、Ｂ

第4关：验证集与交叉验证
１、Ｄ
２、ＢＣＤ

第5关：衡量回归的性能指标
１、ＡＢ

第6关：准确度的陷阱与混淆矩阵
import numpy as np

def confusion_matrix(y_true, y_predict):
    '''
    构建二分类的混淆矩阵，并将其返回
    :param y_true: 真实类别，类型为ndarray
    :pa]]></description><category></category></item><item><title><![CDATA[educoder python入门和Numpy函数库基础]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212237</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212237</guid><author>VDingLinlin</author><pubDate>Mon, 08 Nov 2021 17:25:46 +0800</pubDate><description><![CDATA[第1关：Numpy函数库实践
##########
from numpy import *
arr = random.rand(4,4)
print(arr)
randmat = mat(arr)
invrandmat = randmat.I
print(invrandmat)
result = randmat*invrandmat
print(result)
##########


]]></description><category></category></item><item><title><![CDATA[educoder机器学习 --- 线性回归]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212193</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212193</guid><author>VDingLinlin</author><pubDate>Mon, 08 Nov 2021 17:24:38 +0800</pubDate><description><![CDATA[第1关：简单线性回归与多元线性回归
１、ＢＣ
２、ＡＢＣ
３、Ａ

第2关：线性回归的正规方程解
#encoding=utf8 
import numpy as np
def mse_score(y_predict,y_test):
    '''
    input:y_predict(ndarray):预测值
          y_test(ndarray):真实值
    ouput:mse(float):mse损失函数值
    '''
    #********* Begin *********]]></description><category></category></item><item><title><![CDATA[educoder机器学习 --- 神经网络]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212059</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212059</guid><author>VDingLinlin</author><pubDate>Mon, 08 Nov 2021 17:20:14 +0800</pubDate><description><![CDATA[第1关：神经网络基本概念
１、Ｃ

第2关：激活函数
#encoding=utf8

def relu(x):
    '''
    x:负无穷到正无穷的实数
    '''
    #********* Begin *********#
    if x &lt;= 0:
        return 0
    else:
        return x

    #********* End *********#


第3关：反向传播算法
#encoding=utf8
import os
impo]]></description><category></category></item><item><title><![CDATA[educoder决策树的构造]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212009</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121212009</guid><author>VDingLinlin</author><pubDate>Mon, 08 Nov 2021 17:16:09 +0800</pubDate><description><![CDATA[第1关：决策树简介
"""
Parameters:
    dataSet - 训练数据集
    labels - 分类属性标签
    featLabels - 存储选择的最优特征标签
Returns:
    myTree - 决策树
"""
# 函数说明:创建决策树
def createTree(dataSet, labels, featLabels):
    ############
    #请输入你的代码

    #取分类标签(是否放贷:yes or no)
    #如果类别完全相同则停]]></description><category></category></item><item><title><![CDATA[educoder机器学习 --- 多分类学习]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121211861</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121211861</guid><author>VDingLinlin</author><pubDate>Mon, 08 Nov 2021 17:12:49 +0800</pubDate><description><![CDATA[第1关：OvO多分类策略
import numpy as np

# 逻辑回归
class tiny_logistic_regression(object):
    def __init__(self):
        #W
        self.coef_ = None
        #b
        self.intercept_ = None
        #所有的W和b
        self._theta = None
        #01到标签的映射
        self]]></description><category></category></item><item><title><![CDATA[educoder 机器学习 --- 线性判别分析]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121211793</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121211793</guid><author>VDingLinlin</author><pubDate>Mon, 08 Nov 2021 17:09:26 +0800</pubDate><description><![CDATA[第1关：线性判别分析 - 随机生成数的降维
# encoding=utf8
import numpy as np
from numpy.linalg import inv


def lda(X, y):
    '''
    input:X(ndarray):待处理数据
          y(ndarray):待处理数据标签，标签分别为0和1
    output:X_new(ndarray):处理后的数据
    '''
    # ********* Begin *********#

    #]]></description><category></category></item><item><title><![CDATA[educoder 机器学习 --- 逻辑回归]]></title><link>https://blog.youkuaiyun.com/VDingLinlin/article/details/121211626</link><guid>https://blog.youkuaiyun.com/VDingLinlin/article/details/121211626</guid><author>VDingLinlin</author><pubDate>Mon, 08 Nov 2021 17:07:17 +0800</pubDate><description><![CDATA[第1关：逻辑回归核心思想
#encoding=utf8
import numpy as np

def sigmoid(t):
    '''
    完成sigmoid函数计算
    :param t: 负无穷到正无穷的实数
    :return: 转换后的概率值
    :可以考虑使用np.exp()函数
    '''
    #********** Begin **********#
    sig = 1/(1+np.exp(-t))
    return sig
    #*********]]></description><category></category></item></channel></rss>