拓端tecdat|python代写用遗传算法神经网络模糊逻辑控制算法对大乐透彩票进行预测

原文链接：http://tecdat.cn/?p=3014

前言

预测是通过基于来自过去和当前状态的信息来对将要发生的事情做出声明。

每个人每天都以不同程度的成功解决预测问题。例如，需要预测天气，收获，能源消耗，外汇（外汇）货币对或股票，地震和许多其他东西的变动。...

预测分析

通过分类，深度学习能够在例如图像中的像素和人的名称之间建立相关性。你可以称之为静态预测。出于同样的原因，暴露于足够的正确数据，深度学习能够建立当前事件和未来事件之间的相关性。从某种意义上说，未来的事件就像标签一样。深度学习并不一定关心时间，或者事情尚未发生。给定时间序列，深度学习可以读取一串数字并预测下一个最可能发生的数字。

数据样例：

2011001;3;9;20;24;26;32;10
2011002;6;8;12;17;28;33;5
2011003;13;14;21;22;23;27;4
2011004;4;6;8;10;13;26;5
2011005;6;9;12;14;20;22;13
2011006;1;3;5;13;16;18;5
2011007;1;9;17;24;26;31;5
2011008;10;12;13;17;24;31;15
2011009;17;18;23;24;25;26;4
2011010;1;4;5;9;15;19;13
2011011;1;12;18;19;21;24;10
2011012;7;8;11;13;15;26;13
2011013;1;3;13;16;21;22;8
2011014;5;7;10;11;23;26;1

截屏

import random
for x in range(0,6):#NUM_OF_RED=6
    choice_num_red = random.choice( redBalls )
    print( choice_num_red )
    redBalls.remove(choice_num_red)
for y in range(0,1):#NUM_OF_BLUE=1
    choice_num_blue = random.choice( blueBalls )
    print( choice_num_blue )
#scipy test code

#matplotlib test 
print(pylab.plot(abs(b)))
#show()
#from matplotlib.mlab import normpdf
#import matplotlib.numerix as nx
#import pylab as p
#
#x = nx.arange(-4, 4, 0.01)
#y = normpdf(x, 0, 1) # unit normal
#p.plot(x,y, color='red', lw=2)
#p.show()


plt.plot(dfs_blue_balls_count_values,'x',label='Dot plot')
plt.legend()
plt.ylabel('Y-axis,number of blue balls')
plt.xlabel('X-axis,number of duplication')
plt.show()
#Jitter plot
idx_min = min(dfs_blue_balls_count_values)
idx_max = max(dfs_blue_balls_count_values)
idx_len = idx_max-idx_min
print("min:",idx_min,"max:",idx_max)
num_jitter = 0
samplers = random.sample(range(idx_min,idx_max),idx_len)
while num_jitter < 5:
    samplers += random.sample(range(idx_min,idx_max),idx_len)
    num_jitter += 1
##lots of jitter effect
print("samplers:",samplers)
#plt.plot(samplers,'ro',label='Jitter plot')
#plt.ylabel('Y-axis,number of blue balls')
#plt.xlabel('X-axis,number of duplication')
#plt.legend()
#plt.show()
#Histograms and Kernel Density Estimates:
#Scott rule,
#This rule assumes that the data follows a Gaussian distribution;

#Plotting the blue balls appear frequency histograms(x-axis:frequency,y-axis:VIPs)
##@see http://pandas.pydata.org/pandas-docs/dev/basics.html#value-counts-histogramming
num_of_bin = len(series_blue_balls_value_counts)
array_of_ball_names = series_blue_balls_value_counts.keys()
print("Blue ball names:",array_of_ball_names)
list_merged_by_ball_id = []
for x in xrange(0,num_of_bin):
    num_index = x+1.5
    list_merged_by_ball_id += [num_index]*dfs_blue_balls_count_values[x]
print("list_merged_by_ball_id:",list_merged_by_ball_id)  
##Histograms plotting
plt.hist(list_merged_by_ball_id, bins=num_of_bin)
plt.legend()
plt.xlabel('Histograms,number of appear time by blue ball number')
plt.ylabel('Histograms,counter of appear time by blue ball number')
plt.show()
###Gaussian_KDE

##CDF(The Cumulative Distribution Function
from scipy.stats import cumfreq
idx_max = max(dfs_blue_balls_count_values)
hi = idx_max
a = numpy.arange(hi) ** 2
#    for nbins in ( 2, 20, 100 ):
for nbins in dfs_blue_balls_count_values:    
    cf = cumfreq(a, nbins)  # bin values, lowerlimit, binsize, extrapoints
    w = hi / nbins
    x = numpy.linspace( w/2, hi - w/2, nbins )  # care
    # print x, cf
    plt.plot( x, cf[0], label=str(nbins) )

plt.legend()
plt.xlabel('CDF,number of appear time by blue ball number')
plt.ylabel('CDF,counter of appear time by blue ball number')
plt.show()

###Optional: Comparing Distributions with Probability Plots and QQ Plots
###Quantile plot of the server data. A quantile plot is a graph of the CDF with the x and y axes interchanged.
###Probability plot for the data set shown,a standard normal distribution:
###@see: http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.probplot.html
import scipy.stats as stats
prob_measurements = numpy.random.normal(loc = 20, scale = 5, size=num_of_bin)   
stats.probplot(prob_measurements, dist="norm", plot=plt)
plt.show()