Python gmmの例

コード例 #1

ファイル: gauss1.py プロジェクト: pfrank7/non-parametric-clustering

    n1, n2 = np.random.multinomial(total_points, [0.5, 0.5])
    X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
    return X, z


r = []
for _ in range(num_experiments):
    for dim in dimensions:
        X, z = generate_data(dim)
        G = eclust.kernel_matrix(X, lambda x, y: np.linalg.norm(x - y))

        zh = wrapper.kmeans(k, X)
        a = metric.accuracy(z, zh)
        r.append(['k-means', dim, a])

        zh = wrapper.gmm(k, X)
        a = metric.accuracy(z, zh)
        r.append(['gmm', dim, a])

        zh = wrapper.spectral_clustering(k, X, G)
        a = metric.accuracy(z, zh)
        r.append(['spectral clustering', dim, a])

        zh = wrapper.kernel_kmeans(k, X, G)
        a = metric.accuracy(z, zh)
        r.append(['kernel k-means', dim, a])

        zh = wrapper.kernel_kgroups(k, X, G)
        a = metric.accuracy(z, zh)
        r.append(['kernel k-groups', dim, a])

コード例 #2

ファイル: wine.py プロジェクト: pfrank7/non-parametric-clustering

sigma2 = sum([np.linalg.norm(x-y)**2 
                for x in data for y in data])/(len(data)**2)
sigma = np.sqrt(sigma2)

rho_exp = lambda x, y: 2-2*np.exp(-np.linalg.norm(x-y)/(2*sigma))
rho_gauss = lambda x, y: 2-2*np.exp(-np.linalg.norm(x-y)**2/(2*(sigma)**2))

G = eclust.kernel_matrix(data, rho)
#G = eclust.kernel_matrix(data, rho_gauss)
#G = eclust.kernel_matrix(data, rho_exp)

k = 3

r = []
r.append(wrapper.kmeans(k, data, run_times=5))
r.append(wrapper.gmm(k, data, run_times=5))
r.append(wrapper.spectral_clustering(k, data, G, run_times=5))
r.append(wrapper.spectral(k, data, G, run_times=5))
r.append(wrapper.kernel_kmeans(k, data, G, run_times=5, ini='random'))
#r.append(wrapper.kernel_kmeans(k, data, G, run_times=5, ini='k-means++'))
#r.append(wrapper.kernel_kmeans(k, data, G, run_times=5, ini='spectral'))
r.append(wrapper.kernel_kgroups(k,data,G,run_times=5, ini='random'))
#r.append(wrapper.kernel_kgroups(k,data,G,run_times=5, ini='k-means++'))
#r.append(wrapper.kernel_kgroups(k,data,G,run_times=5, ini='spectral'))

t = PrettyTable(['Algorithm', 'Accuracy', 'A-Rand'])
algos = ['kmeans', 'GMM', 'spectral clustering', 'spectral', 
         'kernel k-means', 'kernel k-groups']

for algo, zh in zip(algos, r):
    t.add_row([algo, 

コード例 #3

ファイル: gauss1.py プロジェクト: neurodata/non-parametric-clustering

    s2 = np.eye(D)
    n1, n2 = np.random.multinomial(total_points, [0.5, 0.5])
    X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
    return X, z

r = []
for _ in range(num_experiments):
    for dim in dimensions:
        X, z = generate_data(dim)
        G = eclust.kernel_matrix(X, lambda x, y: np.linalg.norm(x-y))
        
        zh = wrapper.kmeans(k, X)
        a = metric.accuracy(z, zh)
        r.append(['k-means', dim, a])
        
        zh = wrapper.gmm(k, X)
        a = metric.accuracy(z, zh)
        r.append(['gmm', dim, a])
        
        zh = wrapper.spectral_clustering(k, X, G)
        a = metric.accuracy(z, zh)
        r.append(['spectral clustering', dim, a])
        
        zh = wrapper.kernel_kmeans(k, X, G)
        a = metric.accuracy(z, zh)
        r.append(['kernel k-means', dim, a])
        
        
        zh = wrapper.kernel_kgroups(k, X, G)
        a = metric.accuracy(z, zh)
        r.append(['kernel k-groups', dim, a])

コード例 #4

X, z = data.univariate_normal([m1, m2], [s1, s2], [n1, n2])
#X, z = data.univariate_lognormal([m1, m2], [s1, s2], [n1, n2])
Y = np.array([[x] for x in X])
bw = 0.5  # bandwidth
num_points = 1500  # number points for linspace
low = -6
high = 6
#low = -2
#high = 20

### clustering
t = PrettyTable(['Method', 'Accuracy'])
G = eclust.kernel_matrix(Y, lambda x, y: np.linalg.norm(x - y))
zh_kmeans = wrapper.kmeans(k, Y)
t.add_row(['k-means', metric.accuracy(z, zh_kmeans)])
zh_gmm = wrapper.gmm(k, Y)
t.add_row(['gmm', metric.accuracy(z, zh_gmm)])
zh_kgroups = wrapper.kernel_kgroups(k, Y, G)
t.add_row(['kernel k-groups', metric.accuracy(z, zh_kgroups)])
print t

X_plot = np.linspace(low, high, num_points)[:, np.newaxis]

### kernel density estimation
x1_true = X[np.where(z == 0)][:, np.newaxis]
x2_true = X[np.where(z == 1)][:, np.newaxis]

fig = plt.figure()
ax = fig.add_subplot(111)

kde1 = KernelDensity(kernel='gaussian', bandwidth=bw).fit(x1_true)

コード例 #5

ファイル: dermatology.py プロジェクト: neurodata/non-parametric-clustering

# delete missing entries
delete_missing = np.where(data=='?')[0]
data = np.delete(data, delete_missing, axis=0)
data = np.array(data, dtype=float)
z = np.delete(z, delete_missing, axis=0)

# normalize data
data = (data - data.mean(axis=0))/data.std(axis=0)

G = eclust.kernel_matrix(data, rho)
#G = energy.eclust.kernel_matrix(data, rho_gauss)
#G = energy.eclust.kernel_matrix(data, rho_exp)

r = []
r.append(wrapper.kmeans(6, data, run_times=10))
r.append(wrapper.gmm(6, data, run_times=10))
r.append(wrapper.spectral_clustering(6, data, G, run_times=10))

r.append(wrapper.spectral(6, data, G, run_times=10))

#r.append(wrapper.kernel_kmeans(6, data, G, run_times=10, ini='random'))
r.append(wrapper.kernel_kmeans(6, data, G, run_times=10, ini='k-means++'))
#r.append(wrapper.kernel_kmeans(6, data, G, run_times=10, ini='spectral'))

#r.append(wrapper.kernel_kgroups(6,data,G,run_times=10, ini='random'))
r.append(wrapper.kernel_kgroups(6,data,G,run_times=10, ini='k-means++'))
#r.append(wrapper.kernel_kgroups(6,data,G,run_times=10, ini='spectral'))

t = PrettyTable(['Algorithm', 'Accuracy', 'A-Rand'])
algos = ['kmeans', 'GMM', 'spectral clustering', 'spectral', 
         'kernel k-means', 'kernel k-groups']

コード例 #6

ファイル: kernel_density.py プロジェクト: neurodata/non-parametric-clustering

n = 2000
n1, n2 = np.random.multinomial(n, [0.5, 0.5])
m1 = 0
s1 = 1.5
m2 = 1.5
s2 = 0.3
#X, z = data.univariate_normal([m1, m2], [s1, s2], [n1, n2])
X, z = data.univariate_lognormal([m1, m2], [s1, s2], [n1, n2])
Y = np.array([[x] for x in X])

### clustering
t = PrettyTable(['Method', 'Accuracy'])
G = eclust.kernel_matrix(Y, lambda x, y: np.linalg.norm(x-y))
zh_kmeans = wrapper.kmeans(k, Y)
t.add_row(['k-means', metric.accuracy(z, zh_kmeans)])
zh_gmm = wrapper.gmm(k, Y)
t.add_row(['gmm', metric.accuracy(z, zh_gmm)])
zh_kgroups = wrapper.kernel_kgroups(k, Y, G)
t.add_row(['kernel k-groups', metric.accuracy(z, zh_kgroups)])
print t

### estimated classes
x1_true = X[np.where(z==0)]
x2_true = X[np.where(z==1)]

x1_kmeans = X[np.where(zh_kmeans==0)]
x2_kmeans = X[np.where(zh_kmeans==1)]

x1_gmm = X[np.where(zh_gmm==0)]
x2_gmm = X[np.where(zh_gmm==1)]

コード例 #7

# delete missing entries
delete_missing = np.where(data == '?')[0]
data = np.delete(data, delete_missing, axis=0)
data = np.array(data, dtype=float)
z = np.delete(z, delete_missing, axis=0)

# normalize data
data = (data - data.mean(axis=0)) / data.std(axis=0)

G = eclust.kernel_matrix(data, rho)
#G = energy.eclust.kernel_matrix(data, rho_gauss)
#G = energy.eclust.kernel_matrix(data, rho_exp)

r = []
r.append(wrapper.kmeans(6, data, run_times=10))
r.append(wrapper.gmm(6, data, run_times=10))
r.append(wrapper.spectral_clustering(6, data, G, run_times=10))

r.append(wrapper.spectral(6, data, G, run_times=10))

#r.append(wrapper.kernel_kmeans(6, data, G, run_times=10, ini='random'))
r.append(wrapper.kernel_kmeans(6, data, G, run_times=10, ini='k-means++'))
#r.append(wrapper.kernel_kmeans(6, data, G, run_times=10, ini='spectral'))

#r.append(wrapper.kernel_kgroups(6,data,G,run_times=10, ini='random'))
r.append(wrapper.kernel_kgroups(6, data, G, run_times=10, ini='k-means++'))
#r.append(wrapper.kernel_kgroups(6,data,G,run_times=10, ini='spectral'))

t = PrettyTable(['Algorithm', 'Accuracy', 'A-Rand'])
algos = [
    'kmeans', 'GMM', 'spectral clustering', 'spectral', 'kernel k-means',