def test_sampling_bias2(self):
from spikecount.shannon import entropy2, bootstrap
from numpy import pi, e, log2
from numpy import array, polyfit
print
print "Sampling bias 2d"
x = generate_gaussian(sigma = 5, N=100000)
y = generate_gaussian(sigma = 5, N=100000)
print entropy2(x,y), entropy2(x,y)-log2(x.std())-log2(y.std()), log2(2*pi*e)
scompare = bootstrap(entropy2, x, y, debug=False)['predict']
print scompare, scompare - log2(x.std()) - log2(y.std()), log2(2*pi*e)
def test_entropy2(self):
from spikecount.shannon import entropy2
from numpy import pi, e, log2
print
print "Test 2d entropy."
x = generate_gaussian(sigma = 2)
y = generate_gaussian(sigma = 2)
print entropy2(x,y), entropy2(x,y)-log2(x.std())-log2(y.std()), log2(2*pi*e)
x = generate_gaussian(sigma = 10)
y = generate_gaussian(sigma = 10)
print entropy2(x,y), entropy2(x,y)-log2(x.std())-log2(y.std()), log2(2*pi*e)
x = generate_gaussian(sigma = 20)
y = generate_gaussian(sigma = 20)
print entropy2(x,y), entropy2(x,y)-log2(x.std())-log2(y.std()), log2(2*pi*e)
def test_mix_samples(self): from spikecount.shannon import mix_samples, entropy2, entropy1, log2, bootstrap from numpy import hstack print "\nTest mixing samples." c = 0.3 x1, y1 = generate_correlated_gaussian(sigma=5, N=100000, c = c) x2, y2 = generate_correlated_gaussian(sigma=10, N=100000, c = c) print "Well mixed samples" z1 = mix_samples([x1,x2]) z2 = mix_samples([y1,y2]) print entropy1(z1) + entropy1(z2) - entropy2(z1, z2), -log2(1-c**2)/2 print "Datasets pasted side by side" z1 = hstack((x1, x2)) z2 = hstack((y1, y2)) print entropy1(z1) + entropy1(z2) - entropy2(z1, z2), -log2(1-c**2)/2 print "Now testing shifting and mixing samples" x1, y1 = generate_correlated_gaussian(sigma=4, N=100000, c = c) x2, y2 = generate_correlated_gaussian(sigma=4, N=100000, c = c) z1 = mix_samples((x1,x2+1000)) z2 = mix_samples((y1,y2+1000)) print bootstrap(entropy1, z1)['predict'] + bootstrap(entropy1, z2)['predict'] - bootstrap(entropy2, z1, z2)['predict'], -log2(1-c**2)/2 print bootstrap(entropy1, x2)['predict'] + bootstrap(entropy1, y2)['predict'] - bootstrap(entropy2, x2, y2)['predict'], -log2(1-c**2)/2
