import numpy as np
from sklearn.cluster import KMeans
from sklearn.neural_network import MLPClassifier

f = MLPClassifier()
N = 1000
NN = 1000
g = KMeans(n_clusters=N)

def predict(steps):
    out = []
    x = series[-1][None,:]
    for _ in range(steps):
        out.append(M[f.predict(x)])
        x = out[-1]
    return np.array(out).squeeze(1)

series = np.random.randn(NN,10)
g.fit(series)
M = g.cluster_centers_ # (10,10)
i=0
while True:
    X = []
    yt = []
    for idx in np.arange(2,NN):
        yt.append(series[idx-1:idx])
        X.append(series[idx-2:idx-1])
    yt_ = np.array(yt).squeeze(1)
    X_ = np.array(X).squeeze(1)
    p = g.predict(yt_)
    f.partial_fit(X_, p, classes=range(N))
    err = yt_ - M[f.predict(X_)]
    print(i,np.sum(err**2))
    if i==100:break
    i+=1

plt.plot(series.mean(1))
plt.plot(np.concatenate([series.mean(1), predict(400).mean(1)]))
plt.show()