from util import *
from sklearn.cluster import KMeans, SpectralClustering, DBSCAN
from sklearn import metrics
import numpy as np
import warnings
import os
from util.Monitor_construction import Box
from runtime_monitors import *
def k_means_cluster(data, n_clusters):
    kmeans = KMeans(n_clusters=n_clusters, init='k-means++', random_state=0, n_init="auto")
    kmeans.fit_predict(data)

    lbs = kmeans.labels_  # cluster labels
    clusters = dict()
    for lb in set(lbs):
        idx = np.where(lbs == lb)[0]
        clusters[lb] = list(zip(idx, data[idx]))
    return clusters

def spectral_cluster(data, n_clusters):
    n_neighbors = min(n_clusters, 10)
    spectral = SpectralClustering(n_clusters=n_clusters, affinity='nearest_neighbors', n_neighbors=n_neighbors, 
                                  gamma=1.0, eigen_solver="arpack", random_state=0)
    
    # catch warnings related to kneighbors_graph
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore",
            message="the number of connected components of the "
            + "connectivity matrix is [0-9]{1,2}"
            + " > 1. Completing it to avoid stopping the tree early.",
            category=UserWarning,
        )
        warnings.filterwarnings(
            "ignore",
            message="Graph is not fully connected, spectral embedding"
            + " may not work as expected.",
            category=UserWarning,
        )
        spectral = spectral.fit(data)

    lbs = spectral.labels_  # cluster labels
    clusters = dict()
    for lb in set(lbs):
        idx = np.where(lbs == lb)[0]
        clusters[lb] = list(zip(idx, data[idx]))
    return clusters

def dbscan_cluster(data, eps, min_samples):
    
    db = DBSCAN(eps=eps, min_samples=min_samples).fit(data)
    lbs = db.labels_ # cluster labels
    n_cls = len(set(lbs)) - (1 if -1 in lbs else 0) # number of clusters
    n_noise = list(lbs).count(-1)

    clusters = dict()
    for lb in set(lbs):
        idx = np.where(lbs == lb)[0]
        clusters[lb] = list(zip(idx, data[idx]))
    
    return clusters