基于3D彩票Knn算法的遗漏模式挖掘

作者：白于空

关于3D彩票的介绍：略

摘要:根据遗漏模型；建立<期望遗漏，当前遗漏，平均遗漏，最大遗漏，遗漏方差>进行knn分类；挖掘模式；

直接在代码中传递表达信息；结论预测精度比期望值约高20%；

import numpy as np
import pandas as pd
data = pd.read_csv('F:/2014.csv')
data = np.array(data.value)
data = data[np.arange(0,300)]
for i in np.arange(0,len(data)):
    data[i] = data[i].split(',')
    data[i] = np.array(data[i],dtype = 'int32')
#到这里，301个数字已经完全提取完毕；开始计算遗漏值
ylz = {}
for k in np.arange(10):
    ws = ylz[k] = [] #这里是起始空列表
    for j in np.arange(0,len(data)) :
        if k in data[j]:
            ws.append(1)
        else:
            ws.append(0)
'''
#对于0-1序列，返回其中首个1到最终添加1的间隔数；
例[0,1,0]目标返回[2,4,2];总长为len+1
[0,1]返回[4,1]
'''
ylxl = {}
for s in np.arange(10):
    ylxl[s] = []
    ylz[s].append(1)
    ycc = ylz[s]
    for so in np.arange(0,len(ycc)):
        if ycc[so]==1:
            ylxl[s].append(so)
        else:
            pass
zc = {}
for oi in np.arange(10):
    tu = np.array(ylxl[oi])
    zc[oi] = [tu[1:len(tu)] - tu[0:(len(tu)-1)]]
    zc[oi].insert(0,tu[0])
zc #记录各个值在3D彩票2014年中的历史遗漏序列
#开始记录每个值得历史遗漏，最大遗漏，平均遗漏，数学期望遗漏

开始进行主函数的计算：

import numpy as np
import zcp3d2
'''
每一次的zcc和yxdl都得重新计算生成新的序列集;;50-240

'''
def sd(c):
    c = np.array(c)
    for i in np.arange(len(c)-1):
        sd = np.sum((c[i]-np.mean(c))**2)/len(c) 
    return sd
# classify using kNN  
def Classify(newInput,dataSet,labels,k):  
    numSamples = dataSet.shape[0] # shape[0] stands for the num of row  
    diff = np.tile(newInput,(numSamples,1)) - dataSet  
    squaredDiff = diff ** 2   
    squaredDist = np.sum(squaredDiff,axis = 1)   
    distance = squaredDist ** 0.5  
    sortedDistIndices = np.argsort(distance)  
    classCount = {} # define a dictionary (can be append element)  
    for i in np.arange(k):   
        voteLabel = labels[sortedDistIndices[i]]  
        classCount[voteLabel] = classCount.get(voteLabel,0) + 1  
    maxCount = 0  
    for key,value in classCount.items():  
        if value > maxCount:  
            maxCount = value  
            maxIndex = key
    return maxIndex

def qhj(knz):
    ylzc = ylz[knz]
    def train_set(p):
        ylzp = ylzc[0:p]#检测项就是ylzc[p+1]了；这里不能直接替换。
        #开始对ylzc
        x_xsj = []
        for l in np.arange(p):
            if ylzp[l]==1:
                x_xsj.append(l)
            else:
                pass
        tu = np.array(x_xsj)
        jh_xl = [tu[1:(len(tu)-1)] - tu[0:(len(tu)-2)]]
        #实现在数组中首单元插入新元素
        zcc = [tu[0]]
        zcc.extend(jh_xl)
        zcc = np.array(zcc[1])
        train_x = [np.mean(zcc),np.max(zcc),sd(zcc),np.min(zcc),zcc[(len(zcc)-1)]]
        return train_x
    train = []
    lable = []
    x = data.shape[0]
    def cesl(x):
        for ik in np.arange(50,x):
            train.append(train_set(ik))
            lable.append(ylzc[ik+1])
        #train试验集合
        #lable目标集合
        t = 1 #t取与数据集相差t内的单位,t小于70
        dataSet,labels = np.array(train),lable 
        testX = train_set(x+t)
        k = 9  
        outputLabel = Classify(testX,k)  
        return outputLabel
    return cesl(x)
jgyc = []
for u in np.arange(10):
    jgyc.append(qhj[u])

jgyc

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