CountChu

#
# Initially infer Hoeffding's Inequality.
#

from scipy import signal
import numpy as np
import math
import pandas as pd
import matplotlib.pyplot as plt

CountChu

#
# Draw the Hoeffding's Inequality with the epsilons = 0.3 and the probability.
#

from scipy import signal
import numpy as np
import math

def hoeffding (x, epsilon):
    y = 2.0 * math.exp (-2*epsilon*epsilon*x)

CountChu

#
# Draw the Hoeffding's Inequality with different epsilons, 0.1, 0.3, 0.5, and 1.
#

from scipy import signal
import numpy as np
import math

def hoeffding (x, epsilon):
	y = 2.0 * math.exp (-2*epsilon*epsilon*x)

CountChu

#
# Use random numbers to emulates the Sampling with Replacement.
#
# We can specify the number of balls in the bin with 40% red balls and 60% green balls.
# We select 10 balls from the bin and calculate the probability of the red 
# balls in the samples. 
#

import random

CountChu

#
# Peak balls from a bin (Sampling wihtout Replacement)
#

import math
import matplotlib.pyplot as plt
import numpy as np

#
# n Choice r

CountChu

import sys
import os
from cx_Freeze import setup, Executable

Exe1 = Executable (
        script=r"MyApp\MyApp.py",
        base=None,
        appendScriptToExe = True,
        appendScriptToLibrary = False,        
        )

CountChu

def __init__ (self):
    
    wx.Frame.__init__ (self, None, -1, self.Title)

    if "<TABLE>":

        self.Hs = wx.BoxSizer (wx.VERTICAL)

        if "<TR>":
            self.St = wx.StaticText(self, label="Your name :", pos=(20,60))

CountChu

python ChgEnv.py %1
call Temp.bat