np.random.seed(12345)
samples = np.zeros((N, 2))
samples[:N / 2] = ring(n=N / 2, center=(.4, .4), radius=(0.3, 0.4))
samples[N / 2:] = ring(n=N / 2, center=(.6, .6), radius=(0.3, 0.4))
print 'Neural Gas'
np.random.seed(123)
ng = NG((n, n, 2))
ng.learn(samples, epochs)
print 'Self-Organizing Map'
np.random.seed(123)
som = SOM((n, n, 2))
som.learn(samples, epochs)
print 'Dynamic Self-Organizing Map'
np.random.seed(123)
dsom = DSOM((n, n, 2), elasticity=1.75)
dsom.learn(samples, epochs)
fig = plt.figure(figsize=(21, 8))
fig.patch.set_alpha(0.0)
axes = plt.subplot(1, 3, 1)
ng.plot(axes)
axes = fig.add_subplot(1, 3, 2)
som.plot(axes)
axes = fig.add_subplot(1, 3, 3)
dsom.plot(axes)
fig.savefig('double-ring.png', dpi=150)
n = 8
epochs = 20000
N = 10000
np.random.seed(123)
area_1 = np.pi*0.5**2 - np.pi*0.25**2
area_2 = np.pi*0.25**2
n1 = int(area_1*25000)
n2 = int(area_2*25000)
samples = np.zeros((n1+n2,2))
samples[:n1] = ring(n=n1, radius=(0.25,0.50))
samples[n1:] = ring(n=n2, radius=(0.00,0.25))
print 'Dynamic Self-Organizing Map 1'
np.random.seed(123)
dsom1 = DSOM((n,n,2), elasticity=1.25, init_method='fixed')
dsom1.learn(samples,epochs)
n1 = int(area_1*40000)
n2 = int(area_2*10000)
samples = np.zeros((n1+n2,2))
samples[:n1] = ring(n=n1, radius=(0.25,0.50))
samples[n1:] = ring(n=n2, radius=(0.00,0.25))
print 'Dynamic Self-Organizing Map 2'
np.random.seed(123)
dsom2 = DSOM((n,n,2), elasticity=1.25, init_method='fixed')
dsom2.learn(samples,epochs)
n1 = int(area_1*10000)
n2 = int(area_2*40000)
samples = np.zeros((n1+n2,2))
samples[:n1] = ring(n=n1, radius=(0.25,0.50))
b = np.array([v+.2, np.sin(7*(v+.2))/5 + np.random.random_sample()/3+ 0.3])
s3 = np.row_stack((s3,b))
samps = [s1,s2,s3]
n = len(samps)
epcs = [epochs//n for x in range(n)]
print 'Self-Organizing Map'
np.random.seed(12345)
som = SOM((size,size,2))
som.learn(samps,epcs)
print 'Dynamic Self-Organizing Map'
np.random.seed(12345)
dsom = DSOM((size,size,2), elasticity=2)
dsom.learn(samps,epcs)
fig = plt.figure(figsize=(16,7))
fig.patch.set_alpha(1.0)
axes = plt.subplot(1,2,1)
som.plot(axes)
axes = plt.subplot(1,2,2)
dsom.plot(axes)
fig.savefig('dynamicA.png',dpi=150)
fig.clf()
axes = plt.subplot(1,2,1)
som.plot_dist(axes)
axes = plt.subplot(1,2,2)
samples = image(filename='lena.png', shape=(p,p), n=N)
print 'Neural Gas'
np.random.seed(123)
ng = NG((n,n,p*p), init_method='fixed')
ng.learn(samples,epochs)
print 'Self-Organizing Map'
np.random.seed(123)
som = SOM((n,n,p*p), init_method='fixed')
som.learn(samples,epochs)
print 'Dynamic Self-Organizing Map'
np.random.seed(123)
dsom = DSOM((n,n,p*p), elasticity=0.5, init_method='fixed')
dsom.learn(samples,epochs)
# fig = plt.figure(figsize=(10,10))
# axes = plt.subplot(111, frameon=False)
# plot(dsom,n,p)
fig = plt.figure(figsize=(21,8))
fig.patch.set_alpha(0.0)
axes = plt.subplot(131, frameon=False)
plot(ng,n,p)
axes = plt.subplot(132, frameon=False)
plot(som,n,p)
axes = plt.subplot(133, frameon=False)
plot(dsom,n,p)
fig.savefig('image.png',dpi=150)
#np.random.seed(123)
samples = uniform(n=N)
samples[:, 0] = [0.0, 1.0]
samples[:, 1] = [0.5, 0.5]
fig = plt.figure(figsize=(15, 6))
p = 120
X, Y = np.zeros((p, )), np.zeros((p, ))
lrate = 0.1
for s in [0.5, 0.8, 0.85, 0.9, 0.95]:
for i in range(p):
elasticity = 1.0 + i * (3.0 / p)
np.random.seed(123)
dsom = DSOM((n, 1, 2), elasticity=elasticity, lrate=lrate)
#dsom.codebook[...] = [0.5,0.5]
dsom.codebook[0] = [s, 0.5]
dsom.codebook[1] = [1 - s, 0.5]
dsom.learn(samples, epochs, show_progress=False)
x1, x2 = dsom.codebook[0, 0][0], dsom.codebook[-1, 0][0]
X[i], Y[i] = elasticity, 1 - np.sqrt((x2 - x1)**2)
print s, elasticity, Y[i]
plt.plot(X, Y, lw=2)
plt.legend((r'$x_0 = 1-y_0 = 0.50$', r'$x_0 = 1-y_0 = 0.20$',
r'$x_0 = 1-y_0 = 0.15$', r'$x_0 = 1-y_0 = 0.10$',
r'$x_0 = 1-y_0 = 0.05$'), 'upper right')
plt.xlabel('Elasticity', fontsize=16)
plt.ylabel('Error', fontsize=16)
plt.title('Error as a function of initial conditions and elasticity',
fontsize=20)
plt.show()
epochs = 1000
N = 2
#np.random.seed(123)
samples = uniform(n=N)
samples[:,0] = [0.0,1.0]
samples[:,1] = [0.5,0.5]
fig = plt.figure(figsize=(12,9))
p = 200
X,Y = np.zeros((p,)), np.zeros((p,))
for n in [2,4,6,8,10]:
for i in range(p):
elasticity = 0.5 +i*2.0/p
np.random.seed(123)
dsom = DSOM((n,1,2), elasticity=elasticity)
dsom.codebook[...] = [0.5,0.5]
dsom.learn(samples,epochs,show_progress=False)
x1,x2 = dsom.codebook[0,0][0], dsom.codebook[-1,0][0]
X[i],Y[i] = elasticity, 1-np.sqrt((x2-x1)**2)
print n, elasticity, Y[i]
plt.plot(X,Y,lw=2)
plt.legend(('n=2', 'n=4', 'n=6', 'n=8', 'n=10'), 'upper left')
plt.xlabel('Elasticity', fontsize=20)
plt.ylabel('Distortion', fontsize=20)
plt.show()
samples[i] = gaussian(shape=(p,p), sigma=(.5,2), theta=T[i]).flatten()
print 'Neural Gas'
np.random.seed(123)
ng = NG((n,n,p*p), init_method='fixed')
ng.learn(samples,epochs, noise=0.1)
print 'Self-Organizing Map'
np.random.seed(123)
som = SOM((n,n,p*p), init_method='fixed')
som.learn(samples,epochs, noise=0.1)
print 'Dynamic Self-Organizing Map'
np.random.seed(123)
dsom = DSOM((n,n,p*p), elasticity=1.5, init_method='fixed')
dsom.learn(samples,epochs, noise=0.1)
fig = plt.figure(figsize=(21,8))
fig.patch.set_alpha(0.0)
axes = plt.subplot(131, frameon=False)
plot(ng,n,p)
axes = plt.subplot(132, frameon=False)
plot(som,n,p)
axes = plt.subplot(133, frameon=False)
plot(dsom,n,p)
fig.savefig('gaussian-filters.png',dpi=150)
#plt.show()
n = 8
epochs = 20000
N = 10000
np.random.seed(123)
area_1 = np.pi * 0.5**2 - np.pi * 0.25**2
area_2 = np.pi * 0.25**2
n1 = int(area_1 * 25000)
n2 = int(area_2 * 25000)
samples = np.zeros((n1 + n2, 2))
samples[:n1] = ring(n=n1, radius=(0.25, 0.50))
samples[n1:] = ring(n=n2, radius=(0.00, 0.25))
print 'Dynamic Self-Organizing Map 1'
np.random.seed(123)
dsom1 = DSOM((n, n, 2), elasticity=1.25, init_method='fixed')
dsom1.learn(samples, epochs)
n1 = int(area_1 * 40000)
n2 = int(area_2 * 10000)
samples = np.zeros((n1 + n2, 2))
samples[:n1] = ring(n=n1, radius=(0.25, 0.50))
samples[n1:] = ring(n=n2, radius=(0.00, 0.25))
print 'Dynamic Self-Organizing Map 2'
np.random.seed(123)
dsom2 = DSOM((n, n, 2), elasticity=1.25, init_method='fixed')
dsom2.learn(samples, epochs)
n1 = int(area_1 * 10000)
n2 = int(area_2 * 40000)
samples = np.zeros((n1 + n2, 2))
samples[:n1] = ring(n=n1, radius=(0.25, 0.50))
print 'Neural gas'
np.random.seed(12345)
ng = NG((size,size,2))
ng.learn([samples_1, samples_2, samples_3, samples_4],
[2*epochs//8, 2*epochs//8, 2*epochs//8, 2*epochs//8])
print 'Self-Organizing Map'
np.random.seed(12345)
som = SOM((size,size,2))
som.learn([samples_1, samples_2, samples_3, samples_4],
[2*epochs//8, 2*epochs//8, 2*epochs//8, 2*epochs//8])
print 'Dynamic Self-Organizing Map'
np.random.seed(12345)
dsom = DSOM((size,size,2), elasticity=2.5)
dsom.learn([samples_1, samples_2, samples_3, samples_4],
[2*epochs//8, 2*epochs//8, 2*epochs//8, 2*epochs//8])
fig = plt.figure(figsize=(21,8))
fig.patch.set_alpha(0.0)
axes = plt.subplot(1,3,1)
ng.plot(axes)
axes = plt.subplot(1,3,2)
som.plot(axes)
axes = plt.subplot(1,3,3)
dsom.plot(axes)
fig.savefig('dynamic.png',dpi=150)
#plt.show()
