import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from timeit import default_timer as timer
from scipy.spatial import distance_matrix


def euclidian_distance(point_a, point_b):
    """ Calculates euclidian distance between both points

    :param point_a: numpy array
    :param point_b: numpy array
    """
    return np.linalg.norm(point_a - point_b)


def single_link_distance(cluster_a, cluster_b):
    """ Calculates single link distance between two clusters.

    :param cluster_a: numpy array of shape [n_points, n_dimensions]
    :param cluster_b: numpy array of shape [n_points, n_dimensions]
    """

    return min(euclidian_distance(point_a, point_b) for point_a in cluster_a for point_b in cluster_b)


def complete_link_distance(cluster_a, cluster_b):
    """ Calculates complete link distance between two clusters.

    :param cluster_a: numpy array of shape [n_points, n_dimensions]
    :param cluster_b: numpy array of shape [n_points, n_dimensions]
    """
    return max(euclidian_distance(point_a, point_b) for point_a in cluster_a for point_b in cluster_b)


def average_link_distance(cluster_a, cluster_b):
    """ Calculates average link distance between two clusters.

    :param cluster_a: numpy array of shape [n_points, n_dimensions]
    :param cluster_b: numpy array of shape [n_points, n_dimensions]
    """
    dist_sum = 0
    for point_a in cluster_a:
        for point_b in cluster_b:
            dist_sum += euclidian_distance(point_a, point_b)

    return sum(euclidian_distance(point_a, point_b)
               for point_a in cluster_a for point_b in cluster_b) / (len(cluster_a) * len(cluster_b))


def centroid_link_distance(cluster_a, cluster_b):
    """ Calculates centroid link distance between two clusters.

    :param cluster_a: numpy array of shape [n_points, n_dimensions]
    :param cluster_b: numpy array of shape [n_points, n_dimensions]
    """
    centroid_a = np.mean(cluster_a, axis=0)
    centroid_b = np.mean(cluster_b, axis=0)

    return euclidian_distance(centroid_a, centroid_b)


# distance measures
# -------------------------------------------------------------------------------------------------
# kMeans


def initialize_centroids(k):
    """ Initialize random points with labels.

    :param k: number of points
    :return: np array, array
    """
    centroids = []
    # create k centroids
    for i in range(k):
        # random values from 0 to 15
        centroid = np.random.random(2) * 15
        centroids.append(centroid)

    return np.array(centroids)


def get_closest_centroid(centroids, point):
    """ Finds the label of the closest centroid of a point

    :param centroids: np array of centroids
    :param point: point
    :return: index (or label)
    """
    # get closest centroid
    closest = min(centroids, key=lambda centroid: euclidian_distance(centroid, point))
    # return index of closest
    return np.where(np.all(centroids == closest, axis=1))[0][0]


def assign_points_to_centroid(points, clustering):
    """ Assigns all points to a cluster center.

    :param points: numpy array of points
    :param clustering: numpy array of cluster centers
    :return: dictionary with index of cluster center (label) as key and numpy array of assigned points as value
    """
    # initialize assignment
    assignment = {i: None for i in range(len(clustering))}
    # iterate over all points
    for point in points:
        # get index of closest centroid
        i = get_closest_centroid(clustering, point)
        if assignment[i] is None:
            # if no points assigned so far: assign point
            assignment[i] = np.atleast_2d(np.array(point))
        else:
            # else stack point to existing numpy array
            assignment[i] = np.vstack([assignment[i], point])

    return assignment


def calculate_new_centroids(clustering):
    """ Calculates new cluster centers.

    :param clustering: Dictionary with assignment of points to a cluster
    :return: centroids
    """
    centroids = []
    for label in clustering.keys():
        # if no points were assigned to cluster center then drop the cluster
        if clustering[label] is not None:
            centroids.append(np.mean(clustering[label], axis=0))

    return centroids


def k_means(points, k):
    """ Implementation of the kMeans clustering algorithm.

    :param points: dataset of points
    :param k: number of cluster centers
    """
    # get initial cluster centers
    clustering_ = initialize_centroids(k)

    # empty clustering
    clustering = np.array([])

    iterations = 0

    # repeat until clustering doesn't change
    while not np.array_equal(clustering, clustering_):
        clustering = clustering_
        # update clustering
        clustering_ = calculate_new_centroids(assign_points_to_centroid(points, clustering))
        iterations += 1

    print('Final cluster centers: {}'.format(clustering))
    # during the process it is possible that there are centroid that got no assigned points
    print('Dropped {} clusters'.format(k - len(clustering)))
    print('Done after {} iterations'.format(iterations))

    return clustering


# kMeans
# -------------------------------------------------------------------------------------------------
# hierarchical clustering


def find_closest_clusters(clusters, distance_function):
    """ Finds the two clusters that have the smallest distance based on the given distance_function.

    :param clusters: List of clusters
    :param distance_function: one of the functions that are defined on the top of this python file
    :return: indices of the two clusters that have to be merged
    """
    # get first two elements as pivot
    to_merge = [0, 1]
    for i in range(len(clusters)):
        for j in range(len(clusters)):
            # make sure to not compare the same cluster to itself
            if i == j:
                continue
            if distance_function(np.array(clusters[i]), np.array(clusters[j])) < \
                    distance_function(np.array(clusters[to_merge[0]]), np.array(clusters[to_merge[1]])):
                to_merge = [i, j]

    return to_merge


def append_cluster(cluster_a, cluster_b):
    """ Appends cluster_b to cluster_a

    :param cluster_a: array of shape [n_points, n_dimensions]
    :param cluster_b: array of shape [n_points, n_dimensions]
    """
    for point in cluster_b:
        cluster_a.append(point)

    return cluster_a


def agnes(points, distance_function=single_link_distance, stop_distance=None):
    """ Implementation of the agnes clustering algorithm.

    This is the first attempt. Works but its way too slow.

    :param points: dataset of points
    :param distance_function: one of the functions that are defined on the top of this python file
    :param stop_distance: stops the tree building process if stop_distance is exceeded (simulates the tree cut)
    :return: clusters
    """
    # convert to list
    clusters = points.tolist()
    for i in range(len(clusters)):
        clusters[i] = [clusters[i]]
    while len(clusters) > 1:
        start = timer()
        to_merge = find_closest_clusters(clusters, distance_function)
        end = timer()
        print('Finding this merge took {}s'.format(end - start))
        print('Current shortest distance: {}'.format(distance_function(np.array(clusters[to_merge[0]]),
                                                                       np.array(clusters[to_merge[1]]))))
        if stop_distance is not None:
            if distance_function(np.array(clusters[to_merge[0]]), np.array(clusters[to_merge[1]])) > stop_distance:
                break
        append_cluster(clusters[to_merge[0]], clusters.pop(to_merge[1]))

    return clusters


def agnes2(data):
    """ Implementation of the agnes clustering algorithm with average link.

    This is the second attempt. A bit inspired by another solution but it works a lot better using a distance matrix,
    that gets updated after every merge.

    :param data: Dataset of points as dataframe
    :return: Dictionary with clusters
    """
    clusters = {}
    current = 0

    # iterate until one cluster left
    while len(data) != 1:
        # build distance_matrix
        dist = distance_matrix(data, data)
        # set distance of each point to itself to inf to prevent them to be found as min
        dist[dist == 0] = np.inf

        # get indices of the closest clusters
        min_dist_index = np.unravel_index(dist.argmin(), dist.shape)
        min_dist = dist[min_dist_index]

        # merge closest clusters
        closest_clusters = data.index[list(min_dist_index)]
        cluster_name = 'merge_{}'.format(current)
        # store merged clusters in clusters with current as key and a list of the merged clusters
        # and their difference as value
        clusters[cluster_name] = list(closest_clusters) + [min_dist]

        # add the new cluster to the dataset with centroid as new point for average link
        data.loc[cluster_name] = data.loc[closest_clusters].mean()
        # drop original clusters
        data = data.drop(closest_clusters)

        current += 1

    return clusters


def find_points_in_cluster(merge_key, clusters, all_clusters):
    """ Finds all data points that belong to a merge. Removes all involved merges from clusters.

    :param merge_key: Key of the merge in the clusters dict
    :param clusters: Dictionary with merge_key as key and merged clusters as value
    :param all_clusters: Like clusters but also includes merges with clusters larger than cut distance
    :return: list of points, updated clusters
    """
    points = []
    queue = clusters[merge_key][:-1]
    del clusters[merge_key]
    while len(queue) > 0:
        cluster = queue.pop()
        if type(cluster) == int:
            points.append(cluster)
        else:
            queue.append(all_clusters[cluster][0])
            queue.append(all_clusters[cluster][1])
            clusters.pop(cluster, None)

    return points, clusters


def assign_points_to_clusters(clusters, cut_distance, dataset):
    """ Assigns all points to clusters like in the assignment method of the kMeans algorithm.
    Simplifies handling of output of both methods.
    This is only for agnes2

    :param clusters: output of agnes()
    :param cut_distance: maximum distance for clustering
    :param dataset: np array with points
    :return: dictionary with index of cluster center (label) as key and numpy array of assigned points as value
    """
    # get clusters with distance <= cut_distance and sort descending by distance
    all_clusters = clusters
    clusters = {k: v for k, v in sorted(clusters.items(), key=lambda item: item[1][2], reverse=True)
                if v[2] <= cut_distance}

    assignment = {}
    current_label = 0

    while len(clusters) > 0:
        points, clusters = find_points_in_cluster(next(iter(clusters)), clusters, all_clusters)
        assignment[current_label] = [point for point in dataset[points]]
        current_label += 1

    return assignment

# hierarchical clustering
# -------------------------------------------------------------------------------------------------


def plot_clustering(assignment):
    """ Plots data points with assigned labels.

    :param assignment: Dictionary with assignment of points to a cluster
    """
    for cluster in assignment.values():
        # generate random color for this cluster
        color = np.random.rand(3, )
        for point in cluster:
            plt.scatter(point[0], point[1], color=color)

    plt.show()


def main():
    # get data
    file_path = 'data-clust.csv'
    df = pd.read_csv(file_path)
    dataset = df.to_numpy()

    # kMeans
    print('------------------ kMeans ------------------')
    start_clustering = timer()

    centroids = k_means(dataset, k=4)
    assignment1 = assign_points_to_centroid(dataset, centroids)

    end_clustering = timer()
    print('kMeans clustering took {}s'.format(end_clustering - start_clustering))

    print('')

    # agnes
    print('------------------ Agnes ------------------')
    start_clustering = timer()

    clusters = agnes2(df)
    # clusters = {'merge_0': [821, 948, 0.0021927152117856216], 'merge_1': [361, 445, 0.0045650191675380754], 'merge_2': [275, 297, 0.004608709146821234], 'merge_3': [608, 633, 0.005949201627109765], 'merge_4': [815, 926, 0.006336955104780152], 'merge_5': [324, 401, 0.006550122136265109], 'merge_6': [772, 1095, 0.007469384178098505], 'merge_7': [404, 420, 0.00798410921768868], 'merge_8': [583, 686, 0.009162063086445012], 'merge_9': [569, 667, 0.009483174573949756], 'merge_10': [372, 390, 0.009684926432348182], 'merge_11': [599, 665, 0.0096897471587258], 'merge_12': [844, 1110, 0.011116766616242939], 'merge_13': [642, 646, 0.011549562762286386], 'merge_14': [840, 'merge_0', 0.011899113412352157], 'merge_15': [1099, 1109, 0.012498223873816218], 'merge_16': [874, 1013, 0.01275078428960333], 'merge_17': [510, 519, 0.012836572751322625], 'merge_18': [123, 207, 0.012837433544132474], 'merge_19': [156, 298, 0.01321277033782112], 'merge_20': [135, 239, 0.013245535096778064], 'merge_21': [200, 256, 0.01421053834307516], 'merge_22': [566, 619, 0.014719228920021863], 'merge_23': [354, 478, 0.014789337375286246], 'merge_24': [125, 272, 0.014870013449893204], 'merge_25': [219, 290, 0.014998733279845782], 'merge_26': [142, 208, 0.015091603625856603], 'merge_27': [105, 162, 0.015200029605234002], 'merge_28': [507, 524, 0.015584880493607248], 'merge_29': [312, 425, 0.01558548042249499], 'merge_30': [1396, 1514, 0.01566359154217162], 'merge_31': [1366, 1528, 0.01632904161302794], 'merge_32': [1184, 1201, 0.01634910395098128], 'merge_33': [609, 'merge_3', 0.01641411130704261], 'merge_34': [528, 571, 0.01654268720613465], 'merge_35': [210, 218, 0.01676883120554362], 'merge_36': [525, 593, 0.016982873726199207], 'merge_37': [475, 'merge_29', 0.01704211547901162], 'merge_38': [145, 203, 0.01708890283195542], 'merge_39': [442, 474, 0.017420645797444585], 'merge_40': [1288, 1313, 0.017423641984384203], 'merge_41': [365, 415, 0.017522103184262665], 'merge_42': [310, 320, 0.017558863858460918], 'merge_43': [113, 133, 0.017681289545732594], 'merge_44': [556, 688, 0.01777337615648744], 'merge_45': [725, 811, 0.018255949715091917], 'merge_46': [472, 479, 0.01827304025059855], 'merge_47': [326, 'merge_37', 0.018828776513622487], 'merge_48': [781, 1035, 0.018953619707064013], 'merge_49': [117, 247, 0.01896089924027929], 'merge_50': [754, 759, 0.019121192431436135], 'merge_51': [605, 704, 0.019209541899795637], 'merge_52': [1179, 1222, 0.019314414306418965], 'merge_53': [231, 293, 0.01950317410064315], 'merge_54': [1026, 1118, 0.01953161795653468], 'merge_55': [596, 692, 0.01960343082218141], 'merge_56': [527, 563, 0.019695243080501898], 'merge_57': [392, 481, 0.01997287160124919], 'merge_58': [582, 708, 0.02027008880098996], 'merge_59': [555, 625, 0.020372064205671406], 'merge_60': [121, 'merge_24', 0.020491981968565363], 'merge_61': [610, 636, 0.02071498491430705], 'merge_62': [440, 492, 0.020931700838678888], 'merge_63': [424, 469, 0.02112085462286119], 'merge_64': [944, 1123, 0.02112228444084643], 'merge_65': [784, 1078, 0.02123322396622904], 'merge_66': [1116, 1138, 0.02151325173003828], 'merge_67': [27, 76, 0.021928620567650978], 'merge_68': [264, 284, 0.021974462450763284], 'merge_69': [477, 'merge_5', 0.022181601046813063], 'merge_70': [301, 436, 0.022508836042763578], 'merge_71': [1347, 1380, 0.02256795294217029], 'merge_72': [463, 471, 0.022569630036841475], 'merge_73': [1164, 1205, 0.022699147561086777], 'merge_74': [1127, 1128, 0.023054554864494713], 'merge_75': [367, 'merge_1', 0.023121566772169404], 'merge_76': [584, 676, 0.02347135275181186], 'merge_77': [743, 843, 0.023480242758542918], 'merge_78': [274, 295, 0.023577118144505683], 'merge_79': [1447, 1451, 0.023620256137475527], 'merge_80': [168, 232, 0.023747488288238222], 'merge_81': [632, 706, 0.023770210348249207], 'merge_82': [562, 576, 0.023885403492510785], 'merge_83': [786, 873, 0.024024298116698675], 'merge_84': [1135, 1140, 0.024569690677743245], 'merge_85': [1360, 1369, 0.024671295061263976], 'merge_86': [696, 711, 0.025275017309588674], 'merge_87': [987, 1011, 0.025574616321657827], 'merge_88': [224, 'merge_27', 0.02589009318252865], 'merge_89': [1371, 1483, 0.026064420193052728], 'merge_90': [336, 470, 0.026320700978506802], 'merge_91': [534, 675, 0.02640250935043823], 'merge_92': [798, 'merge_14', 0.02647863525561736], 'merge_93': [36, 71, 0.026958238072989797], 'merge_94': [755, 914, 0.02739982664178637], 'merge_95': [586, 626, 0.02746633757893251], 'merge_96': [1133, 'merge_84', 0.027476246559528125], 'merge_97': [214, 522, 0.027624315376132903], 'merge_98': [792, 992, 0.027672905521465955], 'merge_99': [1431, 1524, 0.028019402206328436], 'merge_100': [614, 703, 0.02833326137245788], 'merge_101': [564, 'merge_100', 0.027179275652599987], 'merge_102': [884, 898, 0.028407198031485486], 'merge_103': [769, 1117, 0.028476197077559235], 'merge_104': [215, 292, 0.028639584494192145], 'merge_105': [325, 466, 0.028751132151620756], 'merge_106': [1333, 1336, 0.029108418026405725], 'merge_107': [173, 201, 0.02919964725814286], 'merge_108': [825, 1061, 0.02956578089616437], 'merge_109': [405, 461, 0.029673304500848784], 'merge_110': [540, 'merge_76', 0.029914887597984136], 'merge_111': [751, 1111, 0.029935463918236347], 'merge_112': [526, 660, 0.029963486112266395], 'merge_113': [638, 701, 0.03018879593491697], 'merge_114': [622, 663, 0.030243916743702116], 'merge_115': [343, 419, 0.030279810105086086], 'merge_116': [140, 260, 0.030445048858558556], 'merge_117': [261, 'merge_60', 0.030595181503789853], 'merge_118': [835, 1136, 0.030661298080806067], 'merge_119': [937, 1017, 0.030808273239505905], 'merge_120': [598, 662, 0.030867005037742527], 'merge_121': [1038, 1112, 0.03134738426089154], 'merge_122': [903, 979, 0.0314969792202365], 'merge_123': [331, 490, 0.03160906515542564], 'merge_124': [768, 'merge_45', 0.032052418395496454], 'merge_125': [0, 63, 0.03220426214028185], 'merge_126': [1254, 1286, 0.03220535359222147], 'merge_127': [1307, 1325, 0.03222633240069286], 'merge_128': [83, 88, 0.03235024574867991], 'merge_129': [587, 594, 0.03237678952583158], 'merge_130': [255, 509, 0.03255655080010853], 'merge_131': [791, 1113, 0.0328577859874967], 'merge_132': [22, 72, 0.03289402681339034], 'merge_133': [380, 406, 0.03293652379957636], 'merge_134': [242, 258, 0.03303029669863831], 'merge_135': [161, 184, 0.033064388698417484], 'merge_136': [749, 779, 0.033310096067108504], 'merge_137': [413, 468, 0.03365173398206987], 'merge_138': [393, 498, 0.03392213584077599], 'merge_139': [106, 221, 0.03414255116419875], 'merge_140': [795, 892, 0.0344295294768885], 'merge_141': [248, 279, 0.034454366922061275], 'merge_142': [1418, 1503, 0.034670178828497454], 'merge_143': [539, 1129, 0.03472082084283149], 'merge_144': [179, 265, 0.03479444926996161], 'merge_145': [77, 92, 0.034808119742381946], 'merge_146': [577, 'merge_82', 0.03496470398845045], 'merge_147': [1365, 1497, 0.03533331147797949], 'merge_148': [1159, 1221, 0.03534893633477534], 'merge_149': [114, 269, 0.03546198528001503], 'merge_150': [512, 518, 0.035483918892929706], 'merge_151': [240, 246, 0.0355445312249298], 'merge_152': [421, 448, 0.03557418867662337], 'merge_153': [454, 'merge_41', 0.03588502508010845], 'merge_154': [559, 649, 0.03603825189989241], 'merge_155': [635, 682, 0.03643659287035521], 'merge_156': [237, 268, 0.03652169355328402], 'merge_157': [1034, 1124, 0.03666680515125348], 'merge_158': [119, 199, 0.036801807020850556], 'merge_159': [309, 407, 0.037270375635347346], 'merge_160': [152, 266, 0.03753053290322457], 'merge_161': [770, 985, 0.03774848606235843], 'merge_162': [747, 888, 0.037758184543220843], 'merge_163': [837, 993, 0.03803427533159991], 'merge_164': [460, 488, 0.03831918579510843], 'merge_165': [358, 'merge_70', 0.03856128660975847], 'merge_166': [139, 262, 0.03859362253015539], 'merge_167': [853, 925, 0.038643201213150305], 'merge_168': [98, 'merge_67', 0.03891690249750186], 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0.403902624343495], 'merge_1387': ['merge_1025', 'merge_1154', 0.4063938992913764], 'merge_1388': ['merge_1258', 'merge_1336', 0.4085328005562072], 'merge_1389': [1102, 'merge_1367', 0.41282631729809555], 'merge_1390': ['merge_1019', 'merge_1243', 0.41341893730673374], 'merge_1391': ['merge_703', 'merge_747', 0.4141063939753296], 'merge_1392': ['merge_1327', 'merge_1378', 0.4163467744510894], 'merge_1393': ['merge_988', 'merge_1247', 0.4186336483664803], 'merge_1394': [1413, 'merge_1149', 0.4213683769874044], 'merge_1395': ['merge_1048', 'merge_1240', 0.4225260141995883], 'merge_1396': ['merge_1230', 'merge_1256', 0.4234449451409882], 'merge_1397': ['merge_1200', 'merge_1396', 0.4178872297984442], 'merge_1398': ['merge_1257', 'merge_1267', 0.4239986123256808], 'merge_1399': ['merge_1070', 'merge_1174', 0.4247759114931889], 'merge_1400': ['merge_1242', 'merge_1399', 0.39729882921241544], 'merge_1401': ['merge_1165', 'merge_1246', 0.42483011705047996], 'merge_1402': ['merge_1340', 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'merge_1418': [679, 'merge_1306', 0.4651543796033602], 'merge_1419': ['merge_1192', 'merge_1310', 0.46655933807033256], 'merge_1420': ['merge_1254', 'merge_1381', 0.4700911106488132], 'merge_1421': ['merge_1224', 'merge_1225', 0.4702837589495195], 'merge_1422': ['merge_1368', 'merge_1405', 0.47035527314820225], 'merge_1423': ['merge_1270', 'merge_1316', 0.47094389712170914], 'merge_1424': ['merge_1208', 'merge_1333', 0.4724125528127704], 'merge_1425': ['merge_1285', 'merge_1321', 0.47502354518299683], 'merge_1426': ['merge_1278', 'merge_1411', 0.47922119763818916], 'merge_1427': ['merge_1185', 'merge_1297', 0.48745799193687045], 'merge_1428': ['merge_1133', 'merge_1427', 0.48982732889780245], 'merge_1429': ['merge_1395', 'merge_1425', 0.4914255160041113], 'merge_1430': [1279, 'merge_1328', 0.4942721334752506], 'merge_1431': ['merge_1339', 'merge_1366', 0.501767942429297], 'merge_1432': ['merge_1143', 'merge_1282', 0.5067595944843591], 'merge_1433': ['merge_1308', 'merge_1365', 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0.5742024418976714], 'merge_1450': ['merge_1346', 'merge_1348', 0.574369573256145], 'merge_1451': ['merge_1341', 'merge_1408', 0.5846077727996775], 'merge_1452': ['merge_1144', 'merge_1382', 0.5855576646414189], 'merge_1453': ['merge_1374', 'merge_1390', 0.592019009533415], 'merge_1454': ['merge_1418', 'merge_1446', 0.5956729830089915], 'merge_1455': ['merge_1287', 'merge_1416', 0.5965669572665835], 'merge_1456': ['merge_1288', 'merge_1344', 0.6004232858784002], 'merge_1457': ['merge_1274', 'merge_1453', 0.6053763297766381], 'merge_1458': [1084, 'merge_1389', 0.6066700119461376], 'merge_1459': ['merge_1380', 'merge_1451', 0.6071822500112034], 'merge_1460': ['merge_1334', 'merge_1401', 0.6088635624881662], 'merge_1461': ['merge_1387', 'merge_1423', 0.6132159064380239], 'merge_1462': ['merge_1304', 'merge_1455', 0.617876300345885], 'merge_1463': ['merge_1283', 'merge_1361', 0.6237558345385477], 'merge_1464': ['merge_1299', 'merge_1397', 0.6256700445221353], 'merge_1465': ['merge_1402', 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'merge_1430', 0.7167701254577756], 'merge_1482': ['merge_1377', 'merge_1438', 0.7246988039618422], 'merge_1483': ['merge_1424', 'merge_1457', 0.7330811318496122], 'merge_1484': ['merge_1337', 'merge_1437', 0.7352445258480353], 'merge_1485': ['merge_1420', 'merge_1484', 0.6976100011462215], 'merge_1486': ['merge_1433', 'merge_1477', 0.7459963385134211], 'merge_1487': ['merge_1440', 'merge_1468', 0.7485996911831106], 'merge_1488': [1021, 'merge_1463', 0.7642882589709364], 'merge_1489': ['merge_1376', 'merge_1488', 0.7037530366237095], 'merge_1490': ['merge_1392', 'merge_1426', 0.7842911913645817], 'merge_1491': [229, 'merge_1441', 0.8036516693646159], 'merge_1492': [1536, 'merge_89', 0.8050926149207925], 'merge_1493': ['merge_1386', 'merge_1393', 0.8375292941527986], 'merge_1494': ['merge_1462', 'merge_1491', 0.8384797082575208], 'merge_1495': ['merge_1454', 'merge_1474', 0.8399724803752331], 'merge_1496': ['merge_1394', 'merge_1448', 0.8404390132046653], 'merge_1497': ['merge_1354', 'merge_1415', 0.8431818551501182], 'merge_1498': [664, 'merge_1400', 0.8435239584684683], 'merge_1499': ['merge_1450', 'merge_1459', 0.8650068516093203], 'merge_1500': ['merge_1434', 'merge_1496', 0.869389120310345], 'merge_1501': ['merge_1444', 'merge_1461', 0.882529134564123], 'merge_1502': ['merge_1486', 'merge_1493', 0.921627508431935], 'merge_1503': ['merge_1445', 'merge_1501', 0.9294502434594806], 'merge_1504': ['merge_1479', 'merge_1487', 0.9304702490800436], 'merge_1505': [860, 'merge_1359', 0.9333061751384496], 'merge_1506': ['merge_1435', 'merge_1473', 0.9649511467674086], 'merge_1507': ['merge_1443', 'merge_1470', 0.9692159462101345], 'merge_1508': ['merge_1428', 'merge_1481', 1.0082226885579417], 'merge_1509': ['merge_1480', 'merge_1489', 1.039615749308948], 'merge_1510': ['merge_1499', 'merge_1506', 1.0410666521242804], 'merge_1511': ['merge_1436', 'merge_1447', 1.049463424773684], 'merge_1512': ['merge_1490', 'merge_1494', 1.0708704202543], 'merge_1513': ['merge_1507', 'merge_1508', 1.0815161295332412], 'merge_1514': ['merge_1465', 'merge_1467', 1.0960564558097148], 'merge_1515': ['merge_1502', 'merge_1511', 1.1051747977156545], 'merge_1516': ['merge_1412', 'merge_1472', 1.1061374830130366], 'merge_1517': ['merge_1442', 'merge_1516', 1.0582530137240622], 'merge_1518': ['merge_1429', 'merge_1485', 1.147330478279042], 'merge_1519': ['merge_1456', 'merge_1482', 1.2820444361152028], 'merge_1520': ['merge_1509', 'merge_1519', 1.211553500970808], 'merge_1521': ['merge_1505', 'merge_1514', 1.3603476875123444], 'merge_1522': [1419, 'merge_1476', 1.4512349940230052], 'merge_1523': ['merge_1495', 'merge_1498', 1.4903967495274735], 'merge_1524': ['merge_1475', 'merge_1478', 1.492448614587955], 'merge_1525': ['merge_1510', 'merge_1517', 1.5751176350378198], 'merge_1526': ['merge_1500', 'merge_1525', 1.5879448966202616], 'merge_1527': [1420, 'merge_1197', 1.6016021542583536], 'merge_1528': ['merge_1458', 'merge_1503', 1.691289056066595], 'merge_1529': ['merge_1492', 'merge_1526', 1.7052398032671081], 'merge_1530': ['merge_1504', 'merge_1520', 1.8042585225516767], 'merge_1531': ['merge_1497', 'merge_1512', 1.8477718091143653], 'merge_1532': ['merge_1483', 'merge_1518', 2.1177079357613424], 'merge_1533': ['merge_1521', 'merge_1530', 2.1979795572821312], 'merge_1534': ['merge_1513', 'merge_1524', 2.2211691704799006], 'merge_1535': ['merge_1532', 'merge_1534', 2.346531584933017], 'merge_1536': ['merge_1527', 'merge_1529', 2.429986628596809], 'merge_1537': ['merge_1528', 'merge_1533', 2.4372095521149393], 'merge_1538': ['merge_1522', 'merge_1536', 2.842148020931613], 'merge_1539': ['merge_1515', 'merge_1531', 3.2339193045390746], 'merge_1540': ['merge_1523', 'merge_1537', 3.331345122678388], 'merge_1541': ['merge_1535', 'merge_1538', 5.520462569298805], 'merge_1542': ['merge_1539', 'merge_1540', 6.388675561198944], 'merge_1543': ['merge_1541', 'merge_1542', 7.87483994617152]}
    assignment2 = assign_points_to_clusters(clusters, 2, dataset)

    end_clustering = timer()
    print('Agnes clustering took {}s'.format(end_clustering - start_clustering))

    # plotting
    # plot_clustering(assignment1)
    plot_clustering(assignment2)


if __name__ == '__main__':
    main()
