def online_k_means(k,b,t,X_in):
random_number = 11232015
random_num = np.random.randint(X_in.shape[0], size =300 )
rng = np.random.RandomState(random_number)
permutation1 = rng.permutation(len(random_num))
random_num = random_num[permutation1]
x_input = X_in[random_num]
c,l = mykmeansplusplus(x_input,k,t)
v = np.zeros((k))
for i in range(t):
random_num = np.random.randint(X_in.shape[0], size = b)
rng = np.random.RandomState(random_number)
permutation1 = rng.permutation(len(random_num))
random_num = random_num[permutation1]
M = X_in[random_num]
Y = cdist(M,c,metric='euclidean', p=2, V=None, VI=None, w=None)
clust_index = np.argmin(Y,axis = 1)
for i in range(M.shape[0]):
c_in = clust_index[i]
v[c_in] += 1
ita = 1 / v[c_in]
c[c_in] = np.add(np.multiply((1 - ita),c[c_in]),np.multiply(ita,M[i]))
Y_l = cdist(X_in,c,metric='euclidean', p=2, V=None, VI=None, w=None)
l = np.argmin(Y_l,axis = 1)
return c,l
def online_k_means(k, b, t, X_in):
random_number = 11232015
random_num = np.random.randint(X_in.shape[0], size=300)
rng = np.random.RandomState(random_number)
permutation1 = rng.permutation(len(random_num))
random_num = random_num[permutation1]
x_input = X_in[random_num]
c, l = mykmeansplusplus(x_input, k, t)
v = np.zeros((k))
for i in range(t):
random_num = np.random.randint(X_in.shape[0], size=b)
rng = np.random.RandomState(random_number)
permutation1 = rng.permutation(len(random_num))
random_num = random_num[permutation1]
M = X_in[random_num]
Y = cdist(M, c, metric='euclidean', p=2, V=None, VI=None, w=None)
clust_index = np.argmin(Y, axis=1)
for i in range(M.shape[0]):
c_in = clust_index[i]
v[c_in] += 1
ita = 1 / v[c_in]
c[c_in] = np.add(np.multiply((1 - ita), c[c_in]),
np.multiply(ita, M[i]))
Y_l = cdist(X_in, c, metric='euclidean', p=2, V=None, VI=None, w=None)
l = np.argmin(Y_l, axis=1)
return c, l
def my_kmeans_new(k,t,x,b):
print x.shape[0]
if x.shape[0] > 1000:
return online_k_means(k,b,t,x)
else:
return mykmeansplusplus(x,k,t)
def my_kmeans_new(k, t, x, b):
print x.shape[0]
if x.shape[0] > 1000:
return online_k_means(k, b, t, x)
else:
return mykmeansplusplus(x, k, t)
