Esempi in Python per ring

Esempio n. 1

File: figure-density.py Progetto: rougier/dynamic-som

    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    from network import NG,SOM,DSOM
    from distribution import uniform, normal, ring

    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)

Esempio n. 2

File: figure-double-ring.py Progetto: ecyffers/dynamic-som

if __name__ == '__main__':
    import numpy as np
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    from network import NG, SOM, DSOM
    from distribution import uniform, normal, ring

    n = 8
    epochs = 20000
    N = 10000

    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)

Esempio n. 3

File: figure-ring.py Progetto: ecyffers/dynamic-som

#           FRANCE

if __name__ == '__main__':
    import numpy as np
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    from network import NG, SOM, DSOM
    from distribution import uniform, normal, ring

    n = 8
    epochs = 20000
    N = 10000

    np.random.seed(123)
    samples = ring(n=N, radius=(0.25, 0.5))

    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))

Esempio n. 4

File: tester.py Progetto: lightbright/basic-self-organizing-map

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from network import NG,SOM,DSOM
from distribution import uniform, normal, ring

n = 8
epochs = 20000
N = 10000

np.random.seed(123)
samples = ring(n=N, radius=(0.25,0.5) )

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 = fig.add_subplot(1,3,2)
som.plot(axes)
axes = fig.add_subplot(1,3,3)
dsom.plot(axes)

Esempio n. 5

    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    from network import NG, SOM, DSOM
    from distribution import uniform, normal, ring

    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)

Esempio n. 6

File: figure-double-ring.py Progetto: rougier/dynamic-som

if __name__ == '__main__':
    import numpy as np
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    from network import NG,SOM,DSOM
    from distribution import uniform, normal, ring

    n = 8
    epochs = 20000
    N = 10000

    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)