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))
samples[n1:] = ring(n=n2, radius=(0.00,0.25))
print 'Dynamic Self-Organizing Map 3'
np.random.seed(123)
dsom3 = DSOM((n,n,2), elasticity=1.25, init_method='fixed')
dsom3.learn(samples,epochs)
fig = plt.figure(figsize=(21,8))
fig.patch.set_alpha(0.0)
axes = fig.add_subplot(1,3,1)
dsom1.plot(axes)
axes = fig.add_subplot(1,3,2)
dsom2.plot(axes)
axes = fig.add_subplot(1,3,3)
dsom3.plot(axes)
fig.savefig('density.png',dpi=150)
# plt.show()
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)
dsom.plot_dist(axes)
fig.savefig('dynamicB.png',dpi=150)
#plt.show()
samples = np.zeros((N, 2))
#samples[:,0] = np.array([0.1, 0.1, 0.5, 0.9])
#samples[:,1] = np.array([0.1, 0.9, 0.5, 0.9])
samples[:, 0] = np.array([0.1, 0.1, 0.5, 0.9, 0.9])
samples[:, 1] = np.array([0.1, 0.9, 0.5, 0.1, 0.9])
print 'Neural Gas'
np.random.seed(2)
ng = NG((n, n, 2))
ng.learn(samples, epochs)
print 'Self-Organizing Map'
np.random.seed(2)
som = SOM((n, n, 2))
som.learn(samples, epochs)
print 'Dynamic Self-Organizing Map'
np.random.seed(2)
dsom = DSOM((n, n, 2), elasticity=1.0, lrate=0.1)
#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('false.png', dpi=150)
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))
samples[n1:] = ring(n=n2, radius=(0.00, 0.25))
print 'Dynamic Self-Organizing Map 3'
np.random.seed(123)
dsom3 = DSOM((n, n, 2), elasticity=1.25, init_method='fixed')
dsom3.learn(samples, epochs)
fig = plt.figure(figsize=(21, 8))
fig.patch.set_alpha(0.0)
axes = fig.add_subplot(1, 3, 1)
dsom1.plot(axes)
axes = fig.add_subplot(1, 3, 2)
dsom2.plot(axes)
axes = fig.add_subplot(1, 3, 3)
dsom3.plot(axes)
fig.savefig('density.png', dpi=150)
# plt.show()
