n = 2
epochs = 5000
N = 5
np.random.seed(2)
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)
n, p = 8, 16
epochs = 20000
N = 1000
np.random.seed(123)
samples = np.zeros((N, p * p))
T = np.random.uniform(low=-np.pi / 2, high=np.pi / 2, size=N)
for i in range(N):
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)
return Z
n,p = 8, 16
epochs = 20000
N = 1000
np.random.seed(123)
samples = np.zeros((N,p*p))
T = np.random.uniform(low=-np.pi/2, high=np.pi/2, size=N)
for i in range(N):
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)
from mpl_toolkits.axes_grid import make_axes_locatable
from mpl_toolkits.axes_grid import AxesGrid
from network import NG,SOM,DSOM
from distribution import uniform, normal, ring, image
n,p = 8, 8
epochs = 10000
N = 5000
np.random.seed(123)
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)
size = 8
epochs = 20000
n = 10000
np.random.seed(12345)
samples_1 = uniform(n=n, center=(0.25,0.25), scale=(0.25,0.25))
samples_2 = uniform(n=n, center=(0.75,0.75), scale=(0.25,0.25))
samples_3 = uniform(n=n, center=(0.25,0.75), scale=(0.25,0.25))
samples_4 = uniform(n=n, center=(0.75,0.25), scale=(0.25,0.25))
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)