def Comparing_empirical_and_analytical_variance_b():
varslist = [] # return a list of (a list of variances)
# Analytical BLH
vars = analytical_eps_BLH(LocalHashing, 4, N)
varslist.append(vars)
# Empirical BLH
vars = empirical_eps_BLH(LocalHashing, 4, N)
varslist.append(vars)
# Analytical OLH
vars = analytical_eps_OLH(LocalHashing, 4, N)
varslist.append(vars)
# Empirical OLH
vars = empirical_eps_OLH(LocalHashing, 4, N)
varslist.append(vars)
# Draw
d = [item for item in range(2, (NUMBEROFAXIS + 1) * 2, 2)
] # 2^2, 2^4, ..., 2^18
draw.lines(
d,
varslist,
['Analytical BLH', 'Empirical BLH', 'Analytical OLH', 'Empirical OLH'],
title='Comparing empirical and analytical variance',
xlabel='Vary d(log2(x))',
ylabel='Var(log10(y))')
def Comparing_empirical_and_analytical_variance_d():
varslist = [] # return a list of (a list of variances)
# Analytical BLH
vars = analytical_BLH(LocalHashing, 2**10, N)
varslist.append(vars)
# Empirical BLH
vars = empirical_BLH(LocalHashing, 2**10, N)
varslist.append(vars)
# Analytical OLH
vars = analytical_OLH(LocalHashing, 2**10, N)
varslist.append(vars)
# Empirical OLH
vars = empirical_OLH(LocalHashing, 2**10, N)
varslist.append(vars)
# Draw
epss = [0.5 + 0.5 * item
for item in range(NUMBEROFAXIS)] # 0.5, 1.0, ..., 5.0
draw.lines(
epss,
varslist,
['Analytical BLH', 'Empirical BLH', 'Analytical OLH', 'Empirical OLH'],
title='Comparing empirical and analytical variance',
xlabel='Vary epsilon(log2(x))',
ylabel='Var(log10(y))')
def numerical_values_of_var2():
varslist = [] # return a list of (a list of variances)
d = 2**10 # 2^10
vars = analytical(SUE, d, 10000)
for i in range(len(vars)):
vars[i] = math.log(vars[i]) / math.log(10)
varslist.append(vars)
epss = [0.5 + 0.5 * item for item in range(10)] # 0.5, 1.0, ..., 5.0
draw.lines(epss, varslist, ['SUE'], ylabel='Var(log10(y))')
def numerical_values_of_var1():
varslist = [] # return a list of (a list of variances)
for d in [2, 4, 16, 128, 2048]:
vars = analytical(DE, d, 10000)
for i in range(len(vars)):
vars[i] = math.log(vars[i]) / math.log(10)
varslist.append(vars)
epss = [0.5+0.5*item for item in range(10)] # 0.5, 1.0, ..., 5.0
draw.lines(epss, varslist, ['DE(d=2)', 'DE(d=4)', 'DE(d=16)', 'DE(d=128)', 'DE(d=2048)'], ylabel='Var(log10(y))')
def numerical_values_of_var_a():
varslist = [] # return a list of (a list of variances)
for d in [2, 4, 16, 128, 2048]:
vars = analytical_DE(DirectEncoding, d, 10000)
varslist.append(vars)
# plot
epss = [0.5 + 0.5 * item for item in range(10)] # 0.5, 1.0, ..., 5.0
draw.lines(epss,
varslist,
['DE(d=2)', 'DE(d=4)', 'DE(d=16)', 'DE(d=128)', 'DE(d=2048)'],
ylabel='Var(log10(y))')
def Comparing_empirical_and_analytical_variance1():
varslist = [] # return a list of (a list of variances)
# Analytical DE
vars = analytical_eps(DE, 4, 10000)
for i in range(len(vars)): #log10
vars[i] = math.log(vars[i]) / math.log(10)
varslist.append(vars)
# Empirical DE
vars = empirical_eps(DE, 4, 10000)
# Draw
d = [item for item in range(2, 20, 2)] # 2^2, 2^4, ..., 2^18
draw.lines(d, varslist, ['Analytical DE'], xlabel='Vary epislon(log2(x))', ylabel='Var(log10(y))')
def numerical_values_of_var_b():
varslist = [] # return a list of (a list of variances)
d = 2**10 # 2^10
# DE
vars = analytical_DE(DirectEncoding, d, 10000)
varslist.append(vars)
# BLH
vars = analytical_BLH(LocalHashing, d, 10000)
varslist.append(vars)
# OLH
vars = analytical_OLH(LocalHashing, d, 10000) # g = math.exp(epsilon)+1
varslist.append(vars)
# plot
epss = [0.5 + 0.5 * item for item in range(10)] # 0.5, 1.0, ..., 5.0
draw.lines(epss, varslist, ['DE', 'BLH', 'OLH'], ylabel='Var(log10(y))')
def drawFigure3():
numbers = readIntList('./data/kosarak.dat')
numbers = numbers[0:10000] ###### too much values, so cut off
maxnum = max(numbers)
totnum = len(numbers)
print('maxnum, totnum: ', maxnum, totnum)
varslist = []
# Empirical BLH
vars = empirical_BLH(LocalHashing, d=maxnum+1, numbers=numbers)
varslist.append(vars)
# Empirical OLH
vars = empirical_OLH(LocalHashing, d=maxnum+1, numbers=numbers)
varslist.append(vars)
# Draw
epss = [0.5+0.5*item for item in range(NUMBEROFAXIS)] # 0.5, 1.0, ..., 5.0
with open('temp_drawFigure3.py', 'w') as f:
f.write(str(varslist))
draw.lines(epss, varslist, ['Empirical BLH', 'Empirical OLH'], title='Figure 3: Average squared error, varying epsilon', xlabel='Vary epsilon(x=epislon)', ylabel='y=log10(Var)')