In [1]:

    

import os
import csv
import platform
import pandas as pd
import networkx as nx
from graph_partitioning import GraphPartitioning, utils

run_metrics = True

cols = ["WASTE", "CUT RATIO", "EDGES CUT", "TOTAL COMM VOLUME", "Qds", "CONDUCTANCE", "MAXPERM", "NMI", "FSCORE", "FSCORE RELABEL IMPROVEMENT", "LONELINESS"]

pwd = %pwd

config = {
    "DATA_FILENAME": os.path.join(pwd, "data", "predition_model_tests", "network", "rand_edge_weights", "network_1.txt"),
    #"DATA_FILENAME": os.path.join(pwd, "data", "predition_model_tests", "network", "network_1.txt"),
    "OUTPUT_DIRECTORY": os.path.join(pwd, "output"),

    # Set which algorithm is run for the PREDICTION MODEL.
    # Either: 'FENNEL' or 'SCOTCH'
    "PREDICTION_MODEL_ALGORITHM": "SCOTCH",

    # Alternativly, read input file for prediction model.
    # Set to empty to generate prediction model using algorithm value above.
    "PREDICTION_MODEL": "",

    "PARTITIONER_ALGORITHM": "SCOTCH",

    # File containing simulated arrivals. This is used in simulating nodes
    # arriving at the shelter. Nodes represented by line number; value of
    # 1 represents a node as arrived; value of 0 represents the node as not
    # arrived or needing a shelter.
    "SIMULATED_ARRIVAL_FILE": os.path.join(pwd,
                                           "data",
                                           "predition_model_tests",
                                           "dataset_1_shift_rotate",
                                           "simulated_arrival_list",
                                           "percentage_of_prediction_correct_100",
                                           "arrival_100_1.txt"
                                          ),
    
    # File containing the prediction of a node arriving. This is different to the
    # simulated arrivals, the values in this file are known before the disaster.
    "PREDICTION_LIST_FILE": os.path.join(pwd,
                                         "data",
                                         "predition_model_tests",
                                         "dataset_1_shift_rotate",
                                         "prediction_list",
                                         "prediction_1.txt"
                                        ),

    # File containing the geographic location of each node, in "x,y" format.
    "POPULATION_LOCATION_FILE": os.path.join(pwd,
                                             "data",
                                             "predition_model_tests",
                                             "coordinates",
                                             "coordinates_1.txt"
                                            ),

    # Number of shelters
    "num_partitions": 4,

    # The number of iterations when making prediction model
    "num_iterations": 1,

    # Percentage of prediction model to use before discarding
    # When set to 0, prediction model is discarded, useful for one-shot
    "prediction_model_cut_off": 1.0,

    # Alpha value used in one-shot (when restream_batches set to 1)
    "one_shot_alpha": 0.5,

    # Number of arrivals to batch before recalculating alpha and restreaming.
    # When set to 1, one-shot is used with alpha value from above
    "restream_batches": 1000,

    # When the batch size is reached: if set to True, each node is assigned
    # individually as first in first out. If set to False, the entire batch
    # is processed and empty before working on the next batch.
    "sliding_window": False,

    # Create virtual nodes based on prediction model
    "use_virtual_nodes": False,

    # Virtual nodes: edge weight
    "virtual_edge_weight": 1.0,

    # Loneliness score parameter. Used when scoring a partition by how many
    # lonely nodes exist.
    "loneliness_score_param": 1.2,

    ####
    # GRAPH MODIFICATION FUNCTIONS

    # Also enables the edge calculation function.
    "graph_modification_functions": True,

    # If set, the node weight is set to 100 if the node arrives at the shelter,
    # otherwise the node is removed from the graph.
    "alter_arrived_node_weight_to_100": False,

    # Uses generalized additive models from R to generate prediction of nodes not
    # arrived. This sets the node weight on unarrived nodes the the prediction
    # given by a GAM.
    # Needs POPULATION_LOCATION_FILE to be set.
    "alter_node_weight_to_gam_prediction": False,

    # Enables edge expansion when graph_modification_functions is set to true
    "edge_expansion_enabled": True,

    # The value of 'k' used in the GAM will be the number of nodes arrived until
    # it reaches this max value.
    "gam_k_value": 100,

    # Alter the edge weight for nodes that haven't arrived. This is a way to
    # de-emphasise the prediction model for the unknown nodes.
    "prediction_model_emphasis": 1.0,
    
    # This applies the prediction_list_file node weights onto the nodes in the graph
    # when the prediction model is being computed and then removes the weights
    # for the cutoff and batch arrival modes
    "apply_prediction_model_weights": True,

    "SCOTCH_LIB_PATH": os.path.join(pwd, "libs/scotch/macOS/libscotch.dylib")
    if 'Darwin' in platform.system()
    else "/usr/local/lib/libscotch.so",
    
    # Path to the PaToH shared library
    "PATOH_LIB_PATH": os.path.join(pwd, "libs/patoh/lib/macOS/libpatoh.dylib")
    if 'Darwin' in platform.system()
    else os.path.join(pwd, "libs/patoh/lib/linux/libpatoh.so"),
    
    "PATOH_ITERATIONS": 10,
        
    # Expansion modes: 'no_expansion', 'avg_node_weight', 'total_node_weight', 'smallest_node_weight'
    # 'largest_node_weight', 'product_node_weight'
    # add '_squared' or '_sqrt' at the end of any of the above for ^2 or sqrt(weight)
    # add '_complete' for applying the complete algorithm
    #    for hyperedge with weights: A, B, C, D
    #      new weights are computed
    #       (A*B)^2 = H0
    #       (A*C)^2 = H1, ... Hn-1
    #      then normal hyperedge expansion computed on H0...Hn-1
    # i.e. 'avg_node_weight_squared
    "PATOH_HYPEREDGE_EXPANSION_MODE": 'total_node_weight_sqrt_complete',

    # Alters how much information to print. Keep it at 1 for this notebook.
    # 0 - will print nothing, useful for batch operations.
    # 1 - prints basic information on assignments and operations.
    # 2 - prints more information as it batches arrivals.
    "verbose": 1
}

#gp = GraphPartitioning(config)

# Optional: shuffle the order of nodes arriving
# Arrival order should not be shuffled if using GAM to alter node weights
#random.shuffle(gp.arrival_order)

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

import scipy

iterations = 1000
#modes = ['product_node_weight_complete_sqrt']
modes = ['no_expansion', 'avg_node_weight_complete', 'total_node_weight_complete', 'smallest_node_weight_complete','largest_node_weight_complete']
#modes = ['no_expansion']

for mode in modes:

    metricsDataPrediction = []
    metricsDataAssign = []
    
    dataQdsOv = []
    dataCondOv = []

    config['PATOH_HYPEREDGE_EXPANSION_MODE'] = mode
    print('Mode', mode)
    for i in range(0, iterations):
        if (i % 50) == 0:
            print('Mode', mode, 'Iteration', str(i))
            
        config["DATA_FILENAME"] =  os.path.join(pwd, "data", "predition_model_tests", "network", "network_" + str(i + 1) + ".txt")
        
        gp = GraphPartitioning(config)
        gp.verbose = 0
        gp.load_network()
        gp.init_partitioner()

        m = gp.prediction_model()
        metricsDataPrediction.append(m[0])

        '''

        #write_graph_files
        #
        gp.metrics_timestamp = datetime.datetime.now().strftime('%H%M%S')
        f,_ = os.path.splitext(os.path.basename(gp.DATA_FILENAME))
        gp.metrics_filename = f + "-" + gp.metrics_timestamp


        if not os.path.exists(gp.OUTPUT_DIRECTORY):
            os.makedirs(gp.OUTPUT_DIRECTORY)
        if not os.path.exists(os.path.join(gp.OUTPUT_DIRECTORY, 'oslom')):
            os.makedirs(os.path.join(gp.OUTPUT_DIRECTORY, 'oslom'))

        
        file_oslom = os.path.join(gp.OUTPUT_DIRECTORY, 'oslom', "{}-all".format(gp.metrics_filename) + '-edges-oslom.txt')
        with open(file_oslom, "w") as outf:
            for e in gp.G.edges_iter(data=True):
                outf.write("{}\t{}\t{}\n".format(e[0], e[1], e[2]["weight"]))
        
        #file_oslom = utils.write_graph_files(gp.OUTPUT_DIRECTORY,
        #                             "{}-all".format(gp.metrics_filename),
        #                             gp.G,
        #                             quiet=True)

        community_metrics = utils.run_community_metrics(gp.OUTPUT_DIRECTORY,
                                                        "{}-all".format(gp.metrics_filename),
                                                        file_oslom)
        
        dataQdsOv.append(float(community_metrics['Qds']))
        dataCondOv.append(float(community_metrics['conductance']))
        '''


    ec = ''
    tcv = ''
    qds = ''
    conductance = ''
    maxperm = ''
    nmi = ''
    lonliness = ''
        
    qdsOv = ''
    condOv = ''

    dataEC = []
    dataTCV = [] 
    dataQDS = []
    dataCOND = []
    dataMAXPERM = []
    dataNMI = []
    dataLonliness = []

    
    for i in range(0, iterations):
        dataEC.append(metricsDataPrediction[i][2])
        dataTCV.append(metricsDataPrediction[i][3])
        dataQDS.append(metricsDataPrediction[i][4])
        dataCOND.append(metricsDataPrediction[i][5])
        dataMAXPERM.append(metricsDataPrediction[i][6])
        dataNMI.append(metricsDataPrediction[i][7])        
        dataLonliness.append(metricsDataPrediction[i][10])


        # UNCOMMENT FOR BATCH ARRIVAL
        #dataECB.append(metricsDataAssign[i][2])
        #dataTCVB.append(metricsDataAssign[i][3])

        if(len(ec)):
            ec = ec + ','
        ec = ec + str(metricsDataPrediction[i][2])
        if(len(tcv)):
            tcv = tcv + ','
        tcv = tcv + str(metricsDataPrediction[i][3])

        if(len(qds)):
            qds = qds + ','
        qds = qds + str(metricsDataPrediction[i][4])

        if(len(conductance)):
            conductance = conductance + ','
        conductance = conductance + str(metricsDataPrediction[i][5])

        if(len(maxperm)):
            maxperm = maxperm + ','
        maxperm = maxperm + str(metricsDataPrediction[i][6])

        if(len(nmi)):
            nmi = nmi + ','
        nmi = nmi + str(metricsDataPrediction[i][7])

        if(len(lonliness)):
            lonliness = lonliness + ','
        lonliness = lonliness + str(dataLonliness[i])
        
        '''
        if(len(qdsOv)):
            qdsOv = qdsOv + ','
        qdsOv = qdsOv + str(dataQdsOv[i])

        if(len(condOv)):
            condOv = condOv + ','
        condOv = condOv + str(dataCondOv[i])
        '''

    ec = 'EC_PM,' + config['PATOH_HYPEREDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataEC)) + ',' + str(scipy.std(dataEC)) + ',' + ec
    tcv = 'TCV_PM,' + config['PATOH_HYPEREDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataTCV)) + ',' + str(scipy.std(dataTCV)) + ',' + tcv

    lonliness = "LONELINESS," + config['PATOH_HYPEREDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataLonliness)) + ',' + str(scipy.std(dataLonliness)) + ',' + lonliness
    
    qds = 'QDS_PM,' + config['PATOH_HYPEREDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataQDS)) + ',' + str(scipy.std(dataQDS)) + ',' + qds
    conductance = 'CONDUCTANCE_PM,' + config['PATOH_HYPEREDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataCOND)) + ',' + str(scipy.std(dataCOND)) + ',' + conductance
    maxperm = 'MAXPERM_PM,' + config['PATOH_HYPEREDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataMAXPERM)) + ',' + str(scipy.std(dataMAXPERM)) + ',' + maxperm
    nmi = 'NMI_PM,' + config['PATOH_HYPEREDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataNMI)) + ',' + str(scipy.std(dataNMI)) + ',' + nmi

    #qdsOv = 'QDS_OV,' + config['PATOH_HYPEREDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataQdsOv)) + ',' + str(scipy.std(dataQdsOv)) + qdsOv
    #condOv = 'CONDUCTANCE_OV,' + config['PATOH_HYPEREDGE_EXPANSION_MODE'] + ',' + str(scipy.mean(dataCondOv)) + ',' + str(scipy.std(dataCondOv)) + condOv

    print(ec)
    print(tcv)
    print(lonliness)
    print(qds)
    print(conductance)
    print(maxperm)
    #print(qdsOv)
    #print(condOv)


    

    




Mode no_expansion
Mode no_expansion Iteration 0
Mode no_expansion Iteration 50
Mode no_expansion Iteration 100
Mode no_expansion Iteration 150
Mode no_expansion Iteration 200
Mode no_expansion Iteration 250
Mode no_expansion Iteration 300
Mode no_expansion Iteration 350
Mode no_expansion Iteration 400
Mode no_expansion Iteration 450
Mode no_expansion Iteration 500
Mode no_expansion Iteration 550
Mode no_expansion Iteration 600
Mode no_expansion Iteration 650
Mode no_expansion Iteration 700
Mode no_expansion Iteration 750
Mode no_expansion Iteration 800
Mode no_expansion Iteration 850
Mode no_expansion Iteration 900
Mode no_expansion Iteration 950
EC_PM,no_expansion,109.441,14.2816147196,138,113,94,105,131,97,101,86,110,112,91,114,96,118,132,83,99,110,114,125,97,98,105,128,125,110,102,125,127,121,135,114,108,119,113,103,103,112,107,92,109,118,115,93,86,101,85,114,115,103,114,101,122,116,110,156,89,119,116,93,95,95,100,105,120,107,104,113,86,86,132,102,134,103,123,109,86,96,90,111,99,101,103,84,107,92,116,127,122,111,125,115,124,102,125,96,107,115,112,109,115,131,108,108,104,116,101,128,93,123,123,99,102,104,116,121,109,110,133,100,111,101,111,123,120,90,109,96,120,110,112,99,112,81,120,137,114,100,110,154,133,115,131,100,105,105,120,103,97,121,124,117,114,100,108,102,110,109,113,80,106,105,114,149,102,109,132,124,108,89,119,94,126,92,127,104,106,105,98,124,93,92,90,107,114,128,115,110,115,108,101,116,100,112,95,105,97,104,89,115,93,104,108,114,116,93,100,98,121,99,103,88,120,103,88,109,94,128,143,129,124,95,84,109,128,93,102,104,105,104,108,120,106,111,111,123,118,130,118,118,102,105,99,114,122,98,117,116,98,100,103,127,86,103,131,108,116,107,127,111,104,93,106,94,112,143,134,99,115,125,117,106,113,114,101,106,117,97,139,98,107,113,107,99,113,110,123,95,132,102,93,122,125,82,93,114,113,108,101,98,123,110,138,107,109,103,99,101,118,108,114,105,99,94,117,115,104,108,113,113,102,107,134,79,93,120,91,121,92,112,112,98,114,125,90,116,95,112,117,109,119,129,119,111,138,113,111,110,104,119,103,118,96,123,108,95,112,100,107,111,95,110,112,108,123,120,109,110,139,125,86,104,106,135,100,94,136,84,88,104,111,135,98,116,96,112,88,100,95,92,108,117,121,122,114,82,107,143,101,106,125,98,124,103,120,103,87,132,110,135,122,92,100,142,132,92,112,110,138,94,110,93,109,110,116,96,109,118,92,84,118,95,108,132,108,101,107,122,87,152,103,129,121,102,118,113,130,115,157,106,107,113,126,101,106,96,139,114,123,120,106,115,91,110,106,94,104,109,124,113,111,105,118,137,98,122,91,113,113,102,126,109,104,105,108,98,109,82,125,128,108,119,98,98,94,93,114,89,97,76,119,106,90,98,98,94,90,110,117,110,104,131,91,117,95,122,100,105,89,108,129,91,91,113,84,103,133,138,115,123,131,117,90,111,95,119,115,88,111,92,126,118,127,120,105,104,103,110,118,138,141,84,89,123,114,134,124,114,102,102,107,107,108,129,101,106,105,97,101,103,112,143,91,118,102,125,97,90,72,86,107,107,109,106,92,127,103,106,101,127,101,128,106,107,127,99,103,105,107,112,141,118,104,105,114,111,114,85,89,107,121,108,131,123,106,121,101,68,138,123,91,114,87,121,128,89,112,129,78,130,111,95,128,129,96,112,106,115,87,107,114,101,133,93,109,135,103,114,113,102,112,94,118,107,91,115,122,115,114,113,105,123,103,94,107,124,119,106,115,121,119,107,95,95,139,110,107,106,118,112,118,113,123,108,118,121,103,128,102,108,101,90,120,114,97,140,99,124,109,128,89,110,86,109,115,94,118,102,105,118,97,111,115,104,128,110,93,116,124,109,94,137,96,118,105,103,108,114,127,132,118,120,99,116,116,114,99,119,112,80,105,127,108,117,97,83,86,140,109,101,95,121,116,109,108,114,136,116,108,92,84,95,88,118,103,99,114,118,127,110,137,87,109,129,100,117,122,121,108,101,83,122,118,122,101,105,99,113,104,96,129,122,90,107,107,117,105,118,100,97,87,117,127,100,104,123,131,108,144,102,128,125,134,146,114,96,130,117,117,107,98,110,105,96,88,96,108,120,109,102,126,111,124,99,100,86,97,120,109,106,104,95,112,101,107,97,113,108,95,99,126,116,72,120,94,101,103,93,113,108,128,97,98,81,129,93,144,96,87,97,105,100,88,127,92,110,100,85,124,100,123,134,123,116,140,81,119,95,81,97,112,99,128,95,117,104,110,106,104,109,124,100,92,98,114,98,130,128,94,110,115,113,81,117,131,96,107,129,110,105,100,96,122,126,101,109,113,105,88,137,89,110,122,109,106,109,134,125,118,111,143,93,109,113,119,122,98,96,91,92,133,107,155,125,141,79,91,108,94,115,111,88,94,110,106,122,109,141,137,118,166,97,99,110,123,119,118,114,82,71,75,98,98,118,102,93,133,77,96,102,85,124,116,100,111,120,118,108,108,85
TCV_PM,no_expansion,144.409,12.945335801,159,154,134,138,151,130,147,123,138,154,130,149,132,150,146,128,147,136,146,142,140,139,143,158,157,154,140,149,150,151,160,157,153,148,152,134,143,143,144,128,140,156,158,137,121,147,115,142,160,139,163,139,153,147,142,175,133,153,144,133,136,142,126,139,145,135,142,156,127,123,157,138,161,145,165,148,132,123,130,135,122,146,130,115,138,132,156,155,160,147,165,152,153,136,154,132,136,158,141,147,151,154,145,146,144,141,144,150,139,171,158,136,145,146,162,152,140,135,176,141,141,131,150,152,158,128,152,132,154,149,147,135,154,124,148,163,143,125,134,180,157,155,177,136,140,148,153,147,148,160,165,157,145,146,146,140,147,147,133,115,149,148,145,168,138,140,172,155,148,124,153,137,149,133,153,125,154,138,144,147,131,136,132,136,159,149,157,133,151,146,139,152,138,157,131,150,131,131,132,144,137,153,149,155,141,135,147,135,153,125,147,126,149,140,133,145,143,161,163,148,165,134,131,146,151,137,139,142,135,134,159,161,131,144,150,162,142,151,162,148,142,139,135,126,151,143,136,132,135,126,144,155,123,147,150,151,137,141,173,150,146,128,142,136,142,166,166,147,147,154,153,153,140,139,146,140,153,138,160,144,149,148,147,131,153,142,155,137,163,151,131,154,158,118,136,156,140,138,129,141,154,152,177,146,157,141,130,126,146,144,134,145,133,139,157,138,149,128,156,164,138,139,153,123,124,132,126,146,129,146,151,141,155,163,132,146,135,156,151,134,148,150,152,152,165,151,139,147,147,150,140,155,140,150,152,142,159,144,138,143,141,150,152,144,143,160,158,141,177,155,127,147,143,163,132,134,160,126,114,149,150,169,136,147,144,141,135,140,134,122,135,150,150,153,150,118,145,179,132,141,161,132,159,127,142,140,125,155,163,175,147,128,130,171,158,125,143,145,175,130,144,127,163,148,161,138,150,153,142,115,154,131,141,168,148,145,139,153,127,176,129,168,155,143,147,143,172,138,177,147,143,133,173,136,139,138,175,137,149,154,137,154,122,146,142,135,143,137,156,146,143,155,158,173,137,154,130,147,148,137,171,151,143,146,143,134,146,117,151,161,141,155,143,133,132,139,149,120,131,115,149,142,139,145,134,133,129,158,161,146,134,162,124,150,137,158,139,135,128,143,164,126,142,150,119,138,155,171,158,161,175,151,120,144,130,138,160,114,135,129,169,148,161,163,142,141,137,139,141,151,163,123,139,164,152,159,142,145,144,130,150,141,144,150,139,142,131,131,131,146,140,162,136,147,131,163,146,128,99,120,147,148,150,152,143,167,149,141,132,153,140,162,140,149,159,143,133,139,144,137,167,155,137,130,134,148,142,125,135,142,143,140,144,146,147,152,145,99,171,149,138,149,128,161,162,137,134,147,106,151,143,137,154,173,125,148,144,150,122,130,153,146,161,128,141,174,148,147,140,144,147,138,152,147,125,148,165,130,141,154,136,146,141,137,147,159,151,141,152,159,145,153,146,134,172,145,140,133,157,156,160,148,148,145,158,160,139,155,142,134,135,129,144,149,131,150,138,167,148,161,131,144,122,138,157,134,160,138,136,144,130,155,158,150,148,144,123,146,160,142,129,160,133,132,142,133,149,154,168,155,153,158,134,140,148,145,128,146,160,122,125,160,142,162,131,122,125,162,139,143,132,145,152,141,133,147,155,145,144,128,119,135,120,159,142,143,149,159,161,151,169,128,157,167,128,145,147,160,145,141,120,161,160,161,133,147,138,148,151,130,169,157,133,135,151,154,146,150,146,140,122,148,166,142,138,153,152,139,176,139,180,145,159,177,146,132,162,141,153,138,125,145,144,134,125,130,138,151,145,124,156,153,165,130,140,130,132,162,148,140,127,132,143,147,131,138,166,141,140,143,150,144,112,154,138,128,136,141,138,137,166,129,141,122,155,134,181,139,126,132,133,135,128,153,123,151,131,134,139,133,141,164,147,154,168,128,159,135,119,139,151,134,161,132,158,139,148,141,152,143,153,133,132,139,153,132,158,143,133,136,161,140,110,143,153,125,135,153,139,149,132,128,148,162,149,148,159,142,126,160,127,137,177,134,141,153,168,139,160,151,151,123,142,155,154,147,140,133,129,136,158,139,170,156,145,121,133,135,129,152,141,124,135,149,143,159,143,172,164,142,181,137,130,142,155,156,157,141,110,107,116,133,143,150,145,137,172,113,130,139,124,156,140,149,137,151,146,142,157,125
LONELINESS,no_expansion,0.859394200636,0.00308889523405,0.856182730907,0.857263252071,0.857222688315,0.861082332504,0.856251994196,0.855592146161,0.860378295672,0.863955183662,0.861650228786,0.857253125941,0.859754179406,0.86001523796,0.858664213273,0.860028308455,0.860602633367,0.863053812707,0.860246288133,0.849599353288,0.860971539626,0.860685256705,0.856978957079,0.854681733818,0.855384577255,0.856945482938,0.863353286318,0.859175495511,0.854658903106,0.850919142785,0.853976402168,0.861735504607,0.856613440459,0.865331234169,0.858201817741,0.867259987011,0.857873493145,0.858617143361,0.86012302601,0.852654369176,0.856748947002,0.861279394242,0.854461079951,0.858466316867,0.858443708803,0.856898531023,0.856223752734,0.86279563522,0.862563012841,0.861617897519,0.853787217121,0.860756502561,0.85870623308,0.859567691943,0.859288339905,0.858373484316,0.861272757656,0.85968569883,0.854980929041,0.860325593575,0.858667636331,0.858996075583,0.859593500256,0.85804119328,0.857185618406,0.860493909545,0.861494971926,0.857614932665,0.858214607725,0.850748878591,0.855553557149,0.857989565938,0.851904985076,0.853633725956,0.860729340598,0.85888651882,0.857049533457,0.85663202273,0.857004262919,0.860498304605,0.856205750745,0.858948295695,0.862032357157,0.860716591271,0.855972117447,0.862041118191,0.85432572499,0.855853284852,0.861504161872,0.858169592453,0.855207973307,0.858223025096,0.862456380107,0.857292137445,0.862345838347,0.859397360198,0.861447685434,0.864868470286,0.865767248544,0.856793743392,0.856722527888,0.856878247718,0.862988703785,0.866033023086,0.858768185137,0.863518345279,0.850546021214,0.859074220272,0.862374365072,0.859521069815,0.865700490027,0.863870093648,0.86192775726,0.858563481148,0.858300422519,0.86529086002,0.858165949813,0.859875257251,0.859149780116,0.858395414453,0.855516542076,0.862067605203,0.86148977491,0.863871987746,0.86162817364,0.855594178984,0.856608404534,0.867042734781,0.857689087754,0.865163287287,0.858381271608,0.860344700889,0.862656057042,0.860401398214,0.856745634396,0.863220029637,0.862140315508,0.859301185639,0.857895586125,0.863973945344,0.866069631432,0.858759584618,0.858396030625,0.862310256793,0.863163199139,0.857921187946,0.858173593699,0.861343315862,0.858376279372,0.858338307349,0.858361813244,0.854538781713,0.862871071756,0.862854048799,0.857417402976,0.861628453283,0.854557605661,0.863731518632,0.855238632344,0.856343428615,0.862277818741,0.862972085074,0.858796898147,0.858401912903,0.861664216157,0.85696718945,0.855911276205,0.857895808147,0.856142526875,0.859861500217,0.857601605809,0.861664091297,0.863195375984,0.865114108391,0.860059582025,0.865232039762,0.857198675198,0.86479452336,0.858280039928,0.85516453658,0.86014380163,0.863125370772,0.856689077294,0.866933921826,0.857718799173,0.862024645406,0.862689233525,0.858441889752,0.857647678742,0.865803874891,0.861501160355,0.860623204704,0.858746112596,0.860722819594,0.865293979952,0.866847181182,0.861102610931,0.857948880908,0.86165022722,0.856686238392,0.85465792161,0.857607272357,0.859618358533,0.863573219508,0.853429564416,0.861521351894,0.856465497965,0.857268045921,0.860106458857,0.860740856737,0.858316406833,0.860989203785,0.859826920238,0.860873337462,0.861203670269,0.859211752839,0.86159106481,0.862699471851,0.858549549082,0.857167042992,0.86141861287,0.865497978023,0.858560493757,0.85822599081,0.854985546834,0.864662937686,0.86072527135,0.859368775415,0.861959898051,0.854966640199,0.854745497523,0.855188264091,0.859545984947,0.860132891103,0.86292386907,0.859947143906,0.867827077793,0.860107912671,0.857656829205,0.867010196985,0.858377812135,0.855822472544,0.859752419206,0.859865444897,0.85861725311,0.85879236087,0.861776883272,0.864883433333,0.861090598057,0.864711858316,0.860859259168,0.860114323828,0.857381150905,0.861240063714,0.858428756719,0.858171177323,0.85900356217,0.858516877248,0.860134806271,0.861325671163,0.855553680872,0.857372468251,0.857755282504,0.860314961225,0.860584217944,0.861786407894,0.859278175982,0.857586643314,0.856415326324,0.862377739453,0.858987237136,0.859918005773,0.857255652666,0.862594386822,0.863822867909,0.860488644117,0.852302134374,0.861249964714,0.857934965301,0.858162636585,0.860318313451,0.860549661913,0.859444243009,0.854570841661,0.856609390309,0.85539026603,0.861959395284,0.858471520678,0.859624438233,0.853456645885,0.859080201193,0.859385308114,0.856281043601,0.857308321255,0.862773459762,0.863322629783,0.863283329451,0.863558854858,0.859312012628,0.859139978274,0.861373889888,0.856315877664,0.859694323603,0.860529625518,0.855575122779,0.859387744012,0.860526696436,0.857582445508,0.859871024698,0.862655676284,0.864289767295,0.858162095456,0.862756493596,0.868223371663,0.859478308432,0.857906779051,0.86047647012,0.85534437604,0.865423037184,0.858198256251,0.862472245395,0.862489443913,0.857278073468,0.856973623695,0.860503337507,0.860634404619,0.857713993957,0.854560290894,0.861944530612,0.858111075017,0.860715118682,0.863648692896,0.862388467739,0.8558531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Mode avg_node_weight_complete
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Mode avg_node_weight_complete Iteration 900
Mode avg_node_weight_complete Iteration 950
EC_PM,avg_node_weight_complete,210.046,29.2825184026,274,178,219,246,226,176,227,193,217,262,219,219,259,222,231,205,220,203,229,204,214,207,204,243,236,241,216,218,224,208,161,269,210,181,191,229,208,177,248,185,211,206,221,146,193,217,160,164,236,131,218,225,257,190,228,207,214,192,194,226,183,220,183,142,205,168,255,223,235,190,184,188,210,214,220,224,251,193,235,204,225,216,212,228,290,196,211,195,283,157,246,168,242,191,208,209,220,149,207,216,222,226,211,282,193,181,228,202,241,232,222,201,183,191,209,232,207,251,215,217,234,178,245,219,183,234,205,170,182,179,213,221,271,182,203,226,178,204,185,253,217,235,181,193,228,209,243,168,225,190,245,211,193,235,242,223,215,243,253,172,247,189,251,268,257,211,262,225,240,186,243,200,200,179,243,215,251,205,202,197,256,185,199,189,241,218,184,146,185,216,260,229,169,242,252,256,194,147,220,220,188,237,191,236,237,221,208,179,195,265,215,221,207,230,221,211,208,231,218,198,235,271,204,194,201,202,178,234,188,235,198,230,160,202,213,194,180,167,173,208,166,180,167,193,183,210,224,268,158,160,189,212,211,228,188,156,191,178,242,210,186,254,187,217,183,232,215,175,215,231,239,213,221,235,238,230,229,165,194,170,207,226,206,229,282,192,224,197,205,204,199,219,243,189,197,165,205,184,226,154,223,225,256,197,253,249,190,201,240,225,220,206,152,218,264,203,236,188,235,264,201,224,253,210,182,208,248,196,223,199,203,203,231,222,166,198,143,193,199,255,179,231,173,217,214,297,194,215,191,233,234,179,210,186,216,160,203,250,160,226,196,210,205,264,183,278,217,227,269,223,272,177,200,233,225,185,223,232,255,226,197,234,206,191,226,215,228,176,181,185,229,226,187,187,214,180,171,228,167,245,213,208,217,163,204,220,180,203,209,241,199,254,198,191,191,164,185,173,226,174,212,176,222,238,187,217,204,221,214,203,190,196,214,196,214,191,219,195,167,215,189,212,213,195,206,228,274,210,197,216,226,208,188,170,215,181,175,203,199,213,186,193,246,205,234,230,250,156,186,191,195,230,218,220,210,213,191,219,201,149,198,201,206,208,211,218,214,212,236,180,209,218,195,183,210,225,189,185,216,179,201,187,227,209,169,168,183,217,224,260,244,208,158,239,195,233,204,154,134,166,199,221,203,191,266,175,176,234,243,231,262,174,157,219,207,219,213,204,197,209,243,200,191,209,223,181,196,163,165,188,235,164,217,229,171,179,304,249,230,169,239,250,136,201,247,173,164,207,194,248,201,199,182,212,204,199,243,260,215,142,173,184,257,228,257,289,228,209,191,254,175,210,248,215,257,262,234,203,164,252,231,203,184,203,209,222,256,243,188,169,250,257,225,203,239,200,257,149,239,177,190,189,185,145,233,155,237,224,179,240,244,164,226,254,216,175,228,228,143,250,212,157,214,189,193,223,189,221,184,227,165,265,204,185,195,213,199,207,243,241,262,208,163,231,200,223,243,186,191,241,251,211,206,241,254,184,184,221,243,277,244,212,244,263,264,216,236,249,205,222,206,204,225,180,182,179,246,261,196,202,240,194,236,214,180,208,205,218,210,184,181,205,204,171,223,222,210,202,203,218,286,215,198,207,184,190,173,273,213,210,230,204,234,224,184,233,166,214,215,183,170,242,179,180,158,179,173,254,196,208,191,219,180,167,214,190,184,195,213,212,206,191,143,186,229,232,239,255,210,189,212,199,207,241,192,191,228,250,249,179,215,182,217,231,170,268,245,238,200,199,206,254,196,242,231,229,226,224,219,202,145,198,247,191,173,194,238,248,238,227,220,186,242,231,250,237,206,175,182,179,195,187,180,179,147,243,170,192,232,197,214,196,223,184,232,253,196,223,208,256,287,209,230,195,212,179,252,161,148,265,179,243,146,249,170,233,241,132,234,196,222,199,241,189,197,191,209,212,172,164,191,226,213,237,257,215,167,158,194,208,242,208,158,262,237,179,157,233,183,218,206,190,187,217,225,172,199,205,178,202,178,221,212,221,218,210,192,223,210,266,204,145,183,205,201,212,188,220,205,262,213,162,214,189,263,195,215,238,195,261,147,210,227,207,254,223,198,267,263,233,194,182,221,283,201,229,215,233,196,221,222,135,216,158,197,178,159,232,192,173,267,205,199,208,206,197,164,214,188,191,244,191,208,237,244,276,240,279,183,256,154,172,223,199,216,172,235,233,219,212,205,201,210,249,216,209,257,185,187,237
TCV_PM,avg_node_weight_complete,240.08,25.452850528,281,218,241,246,272,211,263,246,254,262,243,247,277,254,238,245,262,218,257,219,258,246,234,268,257,253,239,249,269,240,208,279,237,214,232,261,219,196,274,234,225,245,260,204,218,245,196,193,267,166,247,261,274,221,259,245,247,220,233,256,238,256,222,174,220,210,285,265,270,229,218,246,250,241,248,251,277,226,270,209,245,250,214,263,307,227,250,212,284,196,272,218,255,224,242,252,247,202,231,254,244,259,248,292,241,211,257,237,287,244,266,235,218,223,255,261,245,264,256,229,239,208,269,252,216,262,241,221,216,210,247,271,259,212,240,257,201,228,230,284,246,277,232,240,267,252,270,213,255,236,286,236,255,277,251,228,246,257,258,202,299,220,264,294,291,252,274,241,267,211,260,222,238,217,261,242,281,239,242,230,260,210,234,223,262,257,228,173,206,256,265,250,200,256,289,287,230,183,239,239,224,257,228,264,246,253,243,210,225,279,245,259,265,248,235,246,245,281,227,217,247,291,222,220,235,217,230,259,225,263,240,254,201,240,236,236,236,195,226,229,222,225,199,215,199,247,244,277,194,199,233,237,236,256,234,218,223,213,266,247,234,284,223,246,217,254,263,196,237,257,261,235,250,210,262,263,280,182,233,206,242,240,240,226,319,241,268,226,237,246,217,256,261,221,237,215,221,217,261,213,264,259,274,239,259,280,219,232,252,238,251,235,186,252,267,240,272,204,272,293,242,249,281,224,222,225,265,228,227,241,230,240,273,263,192,234,187,218,227,268,211,262,224,256,264,299,205,263,223,246,245,215,243,221,245,198,239,266,210,269,236,250,235,281,213,285,264,275,292,251,269,206,230,262,244,206,250,249,264,246,246,257,243,225,264,253,257,210,211,204,240,251,221,211,270,215,201,267,177,268,242,226,238,195,214,254,218,231,244,269,230,260,230,232,200,184,226,209,246,195,248,217,259,264,223,263,247,226,246,223,217,226,234,242,261,232,227,236,202,253,223,268,252,234,227,248,303,222,248,238,259,226,228,212,262,227,225,224,237,241,213,224,278,248,234,236,265,199,225,220,232,274,276,261,238,228,227,245,227,183,240,241,248,239,243,235,263,233,255,227,237,260,235,219,230,254,231,230,243,213,227,210,243,251,216,197,234,248,270,252,226,228,200,254,227,252,244,178,172,207,241,237,225,241,278,204,224,274,260,263,288,201,204,254,221,262,243,221,228,240,285,247,231,256,263,212,216,204,215,218,254,186,245,253,211,226,283,282,252,186,238,280,184,244,254,213,199,223,208,264,233,214,217,248,231,238,261,282,225,184,208,228,292,260,290,293,255,252,227,261,223,238,278,255,267,263,251,230,198,280,260,222,231,227,230,245,270,262,204,206,269,260,232,224,257,225,286,191,273,221,234,241,218,199,261,207,257,252,205,276,282,199,248,261,235,216,258,263,186,265,257,198,256,217,227,247,240,251,218,250,199,258,226,226,222,242,252,228,265,279,301,250,211,243,219,231,289,220,234,268,275,248,241,263,258,219,219,243,271,276,295,257,248,283,302,273,268,274,239,246,256,253,243,228,218,221,278,280,236,251,261,221,268,235,223,243,257,243,246,229,202,245,239,217,257,245,235,244,244,245,276,251,220,214,230,220,198,303,270,245,260,235,259,251,228,237,199,252,246,197,201,255,233,232,193,213,216,268,226,247,235,239,207,213,241,228,225,229,264,246,219,206,185,223,264,247,285,292,238,230,250,232,238,258,200,226,260,266,274,213,242,227,236,273,200,270,258,268,243,223,262,277,242,266,258,242,254,244,264,228,181,217,267,236,218,231,270,269,269,253,280,216,251,261,284,252,233,211,200,211,220,220,220,204,188,277,212,218,253,229,246,237,245,227,247,290,218,251,251,285,287,222,256,215,249,228,289,197,184,278,203,261,179,280,195,254,264,175,250,234,273,209,284,226,229,225,242,236,219,199,227,257,253,244,281,245,201,191,225,227,270,247,208,275,251,206,206,261,214,259,248,236,222,241,243,207,239,236,205,234,224,256,264,253,237,231,222,222,235,277,252,199,212,241,239,243,208,255,211,287,256,200,235,216,299,235,252,259,202,266,184,222,260,229,285,244,242,282,289,250,231,222,262,306,229,268,244,243,234,265,246,189,248,195,218,199,199,265,223,211,272,233,224,233,244,224,196,247,233,237,271,213,229,276,272,290,267,294,202,256,190,209,249,240,242,210,255,252,220,249,238,222,236,256,241,239,265,207,215,251
LONELINESS,avg_node_weight_complete,0.854197119417,0.00339693899678,0.849124962931,0.854470843728,0.851034963757,0.855217128406,0.851072390994,0.849824094859,0.851600114536,0.858232997796,0.856522556403,0.847813835299,0.851773551966,0.853774190941,0.851655101254,0.855318601328,0.854630544487,0.857042329686,0.852759307316,0.843211201545,0.854284291436,0.856629735986,0.8501457599,0.847477184696,0.850751705682,0.850548757988,0.856958753532,0.854003927774,0.848583595981,0.847519204067,0.84606064249,0.857417209266,0.855444896361,0.857921723967,0.85211021703,0.862566067607,0.854039951332,0.851303288623,0.855409313687,0.849927626777,0.849753327175,0.854488629702,0.850930457727,0.852507612724,0.85314809736,0.855240509749,0.852194431117,0.854686968287,0.859071331609,0.857629519862,0.849109145254,0.85858275496,0.852884930232,0.852752307062,0.853443331287,0.854407193327,0.854305477369,0.855782376605,0.847393687201,0.856673247564,0.85448757259,0.851735288335,0.854781206105,0.850702627441,0.852030943396,0.857356816195,0.856938696626,0.852820763136,0.851893577387,0.845019383787,0.849906604079,0.852902985656,0.847673634487,0.847045194833,0.855449516789,0.852505394673,0.852990568452,0.851887596508,0.850255319033,0.854946874689,0.849398669271,0.853827696417,0.854621361907,0.855634905691,0.852273457074,0.853295544529,0.845893375097,0.85110963702,0.856995345794,0.855430419884,0.850326804916,0.856146296452,0.854782013609,0.851806868738,0.857079139315,0.854490150457,0.857355155577,0.85818300567,0.860590792944,0.855190499577,0.851284573615,0.852206178309,0.857229509268,0.862342114735,0.853366812702,0.855845774965,0.84508468688,0.854877712887,0.856627219984,0.853822164025,0.855817808212,0.860188536578,0.856635247152,0.853183583298,0.85431042518,0.8622047065,0.851989796351,0.856167151406,0.852741054558,0.852263119813,0.851259981635,0.856133112767,0.855401869981,0.86172351243,0.854915237588,0.849947825724,0.852607790278,0.85962829646,0.8536061026,0.859610176409,0.853694519789,0.857562276181,0.856046314649,0.853548710695,0.851491422855,0.857963021675,0.856107337927,0.854853293017,0.853771223266,0.85814014954,0.862880010141,0.854366301056,0.851728176861,0.856464924978,0.861805135585,0.854910147196,0.850520916368,0.854067033386,0.851532789527,0.854632376213,0.8535581644,0.84887107555,0.853234020789,0.858147718134,0.850156099069,0.855917457184,0.848998656906,0.859013698277,0.849611105459,0.849592230694,0.852216582456,0.859793173927,0.852581587429,0.853821036109,0.8554974539,0.849911432147,0.846093436713,0.852294763754,0.848948619046,0.854637599264,0.852133717124,0.856878358601,0.857235680914,0.859116644185,0.854388764969,0.859356308848,0.851657673688,0.858565792455,0.851989339211,0.849959045702,0.855301628849,0.857806966416,0.848450681448,0.864561690859,0.852474284186,0.855556076248,0.857456362773,0.853676348501,0.854019200287,0.864230777199,0.85910750453,0.853377635346,0.852279538381,0.855733886802,0.862606521703,0.86091045927,0.853271958806,0.850866983871,0.857514969991,0.855540262248,0.848097989454,0.85287339044,0.85317129781,0.855402519752,0.849704036205,0.855522788485,0.850174910618,0.852345828391,0.856533567448,0.857823039231,0.852665752321,0.853735099548,0.852611444812,0.853426599027,0.855930524861,0.852884980915,0.856390844389,0.85706772805,0.851636895184,0.850745833395,0.857560152613,0.861231881992,0.85441646484,0.851874384949,0.85047446694,0.859945612976,0.855913792171,0.854378244196,0.858048850662,0.846698915664,0.849583179075,0.849785116397,0.855898914846,0.856349313075,0.859250732928,0.855381942442,0.862223664562,0.855992965876,0.850684631533,0.86411984417,0.854072544892,0.852304077965,0.853661118085,0.854879201274,0.855013690146,0.854998611826,0.858289415931,0.858721863011,0.855114033932,0.858066677525,0.857296606427,0.857335315164,0.853646392716,0.857953525441,0.851701648849,0.850740380907,0.858403985262,0.853321298492,0.856297844754,0.858711703821,0.848983762827,0.851721520271,0.85200411304,0.851808444899,0.856687892374,0.856304237827,0.856631966574,0.853910594172,0.850790100739,0.859066491298,0.854223394238,0.854741897864,0.852576516952,0.859022908692,0.857890263148,0.858035402312,0.845701432209,0.855378785737,0.850945129772,0.854514511771,0.856458469771,0.856545979822,0.853425497805,0.850147624136,0.85145684005,0.85136096452,0.852778771839,0.854226365739,0.852387366295,0.84790491143,0.854165398488,0.853972129355,0.849924329723,0.851532722636,0.858154478913,0.858468925312,0.85809035494,0.858774763134,0.856354138117,0.85399916334,0.855388333635,0.853508527036,0.853691267053,0.853338608233,0.850687646128,0.853699884114,0.854786243919,0.850379072881,0.854779801928,0.859378705295,0.859510657275,0.852107847022,0.857704122885,0.862370215043,0.855952010948,0.85194472258,0.854655294821,0.848806659025,0.857607228116,0.853081765644,0.854771592534,0.855953601748,0.849701354775,0.850535904645,0.853291101322,0.855819330278,0.854244746876,0.849907817141,0.855834911254,0.855752290124,0.855533212457,0.859721777687,0.857311589479,0.848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Mode total_node_weight_complete
Mode total_node_weight_complete Iteration 0
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
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Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
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MAXPERM_PM,total_node_weight_complete,0.01466428255,0.0759284923389,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.403285455,0.0,0.0,0.0,0.400073668,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.405273891,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.390433395,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.414788495,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.390029361,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.40699364200000004,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.43062934799999997,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.409954099,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.41551684699999997,0.0,0.0,0.0,0.409949676,0.0,0.0,0.0,0.423562792,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.414440067,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.42824054300000003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.44074362199999995,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.39068953700000003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.406109612,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.41458256699999996,0.0,0.0,0.0,0.0,0.0,0.40456989800000004,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4110564360000001,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.411896115,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.41193174699999996,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.396934428,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3883107249999999,0.0,0.0,0.0,0.40030667,0.39229548,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.403835119,0.0,0.0,0.40468697400000003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.423944279,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.412617757,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.36748280299999997,0.0,0.0,0.0,0.0,0.423828205,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.41259667199999994,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.39281842800000005,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.40900533399999994,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.400868863,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
Mode smallest_node_weight_complete
Mode smallest_node_weight_complete Iteration 0
Mode smallest_node_weight_complete Iteration 50
Mode smallest_node_weight_complete Iteration 100
Mode smallest_node_weight_complete Iteration 150
Mode smallest_node_weight_complete Iteration 200
Mode smallest_node_weight_complete Iteration 250
Mode smallest_node_weight_complete Iteration 300
Mode smallest_node_weight_complete Iteration 350
Mode smallest_node_weight_complete Iteration 400
Mode smallest_node_weight_complete Iteration 450
Mode smallest_node_weight_complete Iteration 500
Mode smallest_node_weight_complete Iteration 550
Mode smallest_node_weight_complete Iteration 600
Mode smallest_node_weight_complete Iteration 650
Mode smallest_node_weight_complete Iteration 700
Mode smallest_node_weight_complete Iteration 750
Mode smallest_node_weight_complete Iteration 800
Mode smallest_node_weight_complete Iteration 850
Mode smallest_node_weight_complete Iteration 900
Mode smallest_node_weight_complete Iteration 950
EC_PM,smallest_node_weight_complete,375.836,43.8353408108,201,355,392,393,335,319,285,379,337,383,376,318,436,371,271,374,333,347,395,311,430,350,376,432,444,374,378,396,324,371,391,448,379,372,401,362,402,358,388,414,378,417,350,299,317,425,293,374,384,404,364,421,375,360,408,350,297,337,316,316,366,329,334,301,378,268,415,367,438,347,394,391,347,353,426,419,349,335,396,338,369,410,392,350,387,327,405,388,461,359,361,363,311,352,430,390,369,274,317,375,423,397,450,379,376,322,349,349,411,387,436,442,401,341,347,408,367,326,385,316,332,342,427,376,375,406,374,322,348,345,456,334,348,260,381,427,385,395,330,380,453,438,454,351,329,393,372,308,386,392,464,382,412,336,321,366,335,376,395,353,397,431,433,421,432,359,425,364,421,361,351,434,400,426,387,379,376,293,386,378,406,386,386,318,367,445,344,387,399,350,340,359,315,436,291,369,425,323,347,373,340,387,340,472,390,341,331,353,341,444,353,308,360,341,376,334,421,468,342,348,419,384,390,359,368,314,418,372,316,380,332,346,385,365,433,440,301,457,353,380,374,436,342,445,368,393,373,370,317,333,478,412,415,409,323,360,395,344,391,395,383,400,349,372,413,404,364,424,390,298,399,381,405,373,410,373,331,322,308,375,346,399,330,349,528,411,349,356,322,403,256,393,331,413,371,395,323,348,376,344,425,338,357,345,480,370,385,294,416,431,400,418,320,391,399,364,487,318,391,344,315,471,341,410,337,351,389,405,374,356,305,309,460,439,367,303,354,401,384,384,298,391,377,335,412,432,441,440,304,365,320,347,357,290,351,333,372,364,437,364,354,362,406,370,388,390,377,395,448,398,358,344,434,412,304,336,385,332,320,344,372,340,375,402,363,343,372,328,336,363,459,351,431,412,315,296,314,369,366,379,400,423,401,302,340,415,393,418,360,386,384,416,388,405,420,326,431,384,392,302,435,371,311,413,332,433,432,421,490,344,333,330,410,380,385,368,325,279,351,393,333,335,440,397,412,424,408,389,387,292,473,293,386,324,380,320,347,391,348,408,390,390,375,355,374,388,497,348,391,370,386,316,419,331,392,303,328,385,400,339,367,337,470,356,357,413,392,341,369,361,344,382,373,348,407,386,387,359,310,392,401,341,407,365,373,381,344,429,369,389,301,371,369,373,374,432,327,388,301,310,368,297,416,365,366,360,347,371,341,451,406,457,350,353,397,313,313,292,359,322,444,379,400,394,442,426,330,449,326,325,441,384,280,363,430,393,412,368,408,338,410,402,375,375,312,319,396,406,324,415,295,396,428,343,288,378,452,363,364,336,322,404,360,461,360,423,393,384,314,404,417,278,453,345,414,359,317,322,322,457,447,345,396,351,369,338,309,452,386,384,442,322,409,289,334,399,439,331,417,317,450,415,331,416,323,361,344,437,262,359,368,306,354,481,397,416,417,430,390,414,420,310,430,357,381,423,343,343,273,389,350,452,470,479,314,414,457,362,300,362,355,381,328,306,338,372,363,306,416,323,397,338,406,438,396,430,349,394,431,354,405,402,448,383,404,390,433,412,396,434,359,341,347,318,380,416,317,446,347,445,403,394,354,406,376,390,478,386,363,333,380,460,408,360,357,419,384,402,356,370,389,404,414,403,412,322,403,377,391,425,397,336,386,384,383,363,325,357,415,401,361,466,381,376,367,374,319,415,347,399,347,399,411,328,381,423,371,439,361,366,351,344,356,353,264,395,417,435,359,379,432,400,362,407,341,355,406,366,396,385,370,390,497,383,331,450,368,353,411,361,436,384,414,350,340,373,373,380,311,315,336,437,464,341,327,447,373,446,441,403,395,394,301,319,452,391,337,431,448,332,389,416,409,430,342,339,353,386,364,362,330,306,416,324,374,439,364,382,431,454,425,306,409,343,287,407,366,370,319,365,300,328,403,413,460,413,398,350,306,401,400,427,413,359,273,417,409,389,415,407,406,384,362,406,352,393,393,386,412,452,392,400,341,335,414,382,395,405,320,393,375,365,413,386,406,354,375,389,383,415,290,368,409,328,373,327,407,367,378,395,279,317,365,390,380,349,303,401,377,388,399,321,328,466,364,421,478,425,429,384,331,409,369,325,374,406,429,418,425,437,336,367,366,336,411,439,406,323,353,363,441,396,403,402,392,378,351,354,343,397,427,347,341,395,419,303,407,461,357,380,395,359,316,413,447,351,271,381,384,368,296,349,439,349,385,347,349,372,348,439,327,406,399,411,334,377,351,364,356,338
TCV_PM,smallest_node_weight_complete,356.667,34.9579191457,208,350,377,355,332,304,275,372,327,369,351,329,415,362,272,354,337,331,384,296,414,336,348,404,398,360,356,364,336,367,383,409,375,346,384,356,340,302,378,381,347,379,369,310,326,392,286,348,359,358,334,379,354,350,367,358,317,319,302,330,377,315,312,280,342,281,390,356,388,342,359,373,354,357,388,414,352,320,372,312,350,397,338,325,373,332,352,350,420,355,350,357,293,337,378,379,351,299,319,370,376,382,425,370,385,326,327,344,392,376,430,409,368,322,350,387,329,330,373,313,323,324,390,374,346,379,368,330,332,332,431,344,362,257,352,410,360,391,304,373,428,409,428,331,321,381,353,284,354,403,417,363,402,344,309,335,322,353,377,342,369,385,412,369,402,341,396,332,386,349,354,403,379,375,398,362,384,270,370,373,378,349,345,333,363,394,348,347,346,336,336,368,295,415,298,355,380,304,338,353,320,373,307,442,372,343,352,347,316,390,348,308,364,349,351,338,409,426,330,318,376,362,346,335,356,295,394,352,320,345,333,349,371,354,401,378,302,378,374,366,366,391,339,365,344,392,365,354,308,320,428,382,391,397,332,355,375,331,382,365,368,407,330,347,355,395,356,389,333,305,351,366,365,318,391,377,364,332,319,398,328,357,325,315,494,394,352,348,325,374,264,369,334,371,342,379,299,352,354,335,388,341,342,353,430,367,363,300,353,413,365,377,304,376,374,355,447,318,359,360,304,434,342,357,308,315,365,392,327,356,299,320,409,382,368,280,357,366,341,365,301,381,382,330,402,377,387,415,310,366,312,328,354,308,349,319,347,345,403,355,343,361,372,358,382,377,339,370,428,385,359,317,393,390,299,327,371,319,310,327,357,328,355,381,360,356,387,302,348,351,405,342,374,373,322,289,325,357,299,363,390,358,407,315,311,387,359,371,342,399,337,379,363,386,403,307,382,382,377,283,377,344,312,412,338,407,436,381,421,317,331,307,371,363,381,350,305,287,317,368,317,336,402,367,373,373,377,359,383,301,453,288,348,306,360,313,347,375,302,393,358,380,371,378,352,358,404,336,377,357,349,326,421,331,357,320,323,377,347,331,349,333,430,341,371,377,365,325,349,347,350,362,385,309,392,363,379,352,307,361,383,340,376,371,371,372,332,425,346,355,294,336,339,351,344,411,325,351,304,305,360,277,393,370,364,356,352,359,331,398,390,401,321,341,350,312,318,292,314,320,428,374,380,387,389,387,306,383,306,313,384,350,298,360,415,384,366,337,392,288,345,362,357,351,315,315,355,400,301,386,283,398,370,337,293,378,434,342,308,320,328,349,346,419,355,409,371,348,315,375,394,288,443,354,400,347,308,311,324,418,412,333,347,352,350,352,311,403,357,370,410,332,354,281,298,371,386,297,394,301,411,376,335,369,317,358,306,414,245,362,372,312,367,423,354,393,388,388,337,382,414,323,397,351,396,374,328,326,287,356,324,400,439,453,308,400,431,346,308,357,344,358,346,303,317,350,331,323,370,307,371,360,390,394,383,395,322,361,397,315,377,399,397,352,407,352,401,402,370,398,350,348,327,342,388,383,321,380,341,404,368,372,336,384,342,370,432,366,344,318,363,412,376,350,364,349,330,403,365,339,355,391,367,358,386,309,359,371,370,376,391,317,376,385,348,340,291,329,409,378,350,400,362,375,329,350,313,379,325,360,356,393,378,337,366,384,365,402,345,339,341,353,337,367,277,367,419,398,343,374,399,343,349,383,328,345,393,370,384,358,362,368,372,343,325,427,369,365,373,335,432,380,391,304,320,343,341,367,302,289,334,409,425,349,306,393,354,365,395,394,342,357,283,304,416,343,356,374,384,309,367,385,380,355,305,325,340,367,344,332,304,315,356,307,350,415,325,375,417,445,365,300,383,328,289,389,366,355,315,336,307,340,353,372,391,365,372,347,305,369,395,365,406,335,267,384,350,369,371,386,342,367,356,399,354,377,341,355,382,401,361,389,325,333,388,361,355,374,317,373,391,349,392,360,390,341,375,363,362,356,283,371,400,324,352,311,368,361,356,393,290,330,332,355,378,291,322,367,340,399,389,315,323,423,376,409,389,384,370,376,330,369,359,300,344,370,382,384,411,398,322,320,359,329,362,420,407,332,338,382,408,370,386,382,363,345,328,337,337,373,384,368,339,374,383,291,382,414,341,374,358,330,301,395,425,340,292,361,341,340,289,322,395,362,383,336,350,357,340,400,346,379,357,410,332,363,329,329,321,328
LONELINESS,smallest_node_weight_complete,0.846653448452,0.00364402036519,0.853568138915,0.845785664185,0.842117469127,0.847554027584,0.845588743754,0.843224003366,0.851432280687,0.850850120496,0.8504620337,0.844716383356,0.844089240999,0.848328724739,0.841671707343,0.84695696599,0.852698720817,0.848722426963,0.847869348437,0.837529707767,0.848118104639,0.852282864886,0.837676185669,0.842547136512,0.843962538059,0.842221751422,0.848279729724,0.845974283495,0.841455513875,0.838373689777,0.841119264315,0.847194051625,0.844782676022,0.851454619788,0.844659854949,0.855892010638,0.846383670526,0.844422182712,0.846796808957,0.843870669947,0.843317056415,0.843342551079,0.845372507319,0.845197414246,0.844422038014,0.847419905377,0.844717561217,0.850082266237,0.851551626308,0.847434985423,0.842278071685,0.848910369036,0.848773241766,0.844510242744,0.847208211858,0.845221623192,0.846833546878,0.847905851948,0.843751044627,0.848639019112,0.847726065068,0.848950149569,0.845142781193,0.845418599634,0.846844410481,0.850988019002,0.850974750929,0.846674257561,0.844850657987,0.840066581084,0.839457062081,0.842998036407,0.839420808943,0.838259815479,0.849664323679,0.841025065548,0.844407454216,0.84200530614,0.84442741589,0.848450680081,0.843012159653,0.846234180895,0.849268966712,0.84637016974,0.841313309076,0.847362934567,0.842339412583,0.845312445251,0.848934166994,0.845301114856,0.838067394519,0.84627979973,0.850775304223,0.845915989534,0.853287497071,0.849652410373,0.849130016212,0.84996961942,0.853448172817,0.84674670636,0.844690609377,0.84232973984,0.846550606161,0.854962493266,0.84273947396,0.850314354638,0.835590083636,0.848976755867,0.852113355151,0.844983555275,0.849439773046,0.853721748973,0.846244084965,0.841658499823,0.842888240039,0.855154783132,0.844211050432,0.848105506398,0.846253781842,0.847101668036,0.845107194138,0.849921358857,0.852408833578,0.853955038808,0.846880587505,0.844581314631,0.845262962516,0.854151904777,0.843715148226,0.851893745284,0.845956748005,0.850539194138,0.844208483415,0.847935930808,0.846274773977,0.854057677671,0.848075979214,0.845453221537,0.843209341549,0.848532611499,0.856639539046,0.848091550421,0.843555001114,0.847819117636,0.847186236845,0.847561719364,0.849209560221,0.846019399022,0.844811928649,0.849236997229,0.843292316868,0.840267854081,0.844266557012,0.851492446816,0.840460650967,0.851347237293,0.843403952146,0.852236687006,0.845216896533,0.843252208504,0.8460801699,0.851079054398,0.845751342199,0.844543461127,0.846140034993,0.841892757929,0.83773584668,0.845878800411,0.842762604784,0.847315832809,0.84284958793,0.848845040232,0.851870285043,0.850511117402,0.846605935886,0.850640418062,0.842440602895,0.852251015606,0.843717309529,0.847849731666,0.845029853254,0.850572027491,0.840061498885,0.853922713828,0.846221480024,0.847916461028,0.852053893883,0.842354437363,0.847658176046,0.85406799726,0.847442795129,0.847979272954,0.847508480522,0.846539303845,0.855258268742,0.848959740021,0.848383933957,0.844777283937,0.846954893972,0.848621530795,0.843114317254,0.8452296939,0.846757909649,0.850230980889,0.842700946375,0.846686959973,0.844045972953,0.84465743985,0.848355765428,0.849809982852,0.848705162422,0.845663333806,0.84633674912,0.849512168568,0.847959087081,0.84625880261,0.847950129181,0.851928144995,0.842598966318,0.839130138347,0.852544785163,0.856263634649,0.845935604738,0.846898728654,0.841404211199,0.852267874359,0.849222897471,0.847984042945,0.846823640826,0.840891482931,0.845514892366,0.843762598226,0.849206895653,0.848727564859,0.849542791467,0.847852137255,0.851552612272,0.84763706463,0.846212343828,0.854177937618,0.848078148633,0.841857134742,0.845389510776,0.847055104472,0.847830130042,0.844762660108,0.850155100573,0.850479737718,0.847243057949,0.852205632476,0.849947298375,0.848241406787,0.841752275028,0.847650471513,0.843749457539,0.843924538807,0.853204676129,0.845047717117,0.847675747593,0.849288923524,0.842653219988,0.840998232566,0.845167717261,0.844649587533,0.848872307177,0.849451970708,0.847428697402,0.843494880231,0.844584682892,0.845757740341,0.847804443975,0.850056857146,0.847149332637,0.847065785513,0.850926240225,0.85217604992,0.836866747342,0.846084141075,0.844919831669,0.845759066686,0.850618616807,0.844981604988,0.847960000573,0.840269721199,0.845859737475,0.844895769075,0.842735228829,0.845560442339,0.845310405481,0.838971668652,0.848174755294,0.845784750127,0.846195376916,0.845610950343,0.85434133811,0.846520912445,0.852704952291,0.849280333206,0.85070261756,0.845617207935,0.847523795725,0.844746564819,0.84752747393,0.847137758561,0.846005990683,0.844989042392,0.845313912159,0.846086723625,0.845580786252,0.854791709263,0.850927618105,0.842671966267,0.849623101682,0.855333857841,0.84707528588,0.842202262486,0.846999218591,0.842936064566,0.84646829466,0.845915775482,0.84730258853,0.851527588473,0.848143469716,0.840175506859,0.850035494888,0.847452839147,0.847083169301,0.843602104303,0.848812114317,0.844190584036,0.848916318071,0.850103955239,0.8522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Mode largest_node_weight_complete
Mode largest_node_weight_complete Iteration 0
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Mode largest_node_weight_complete Iteration 50
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
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Mode largest_node_weight_complete Iteration 100
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Mode largest_node_weight_complete Iteration 150
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Mode largest_node_weight_complete Iteration 200
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Mode largest_node_weight_complete Iteration 250
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Mode largest_node_weight_complete Iteration 300
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Mode largest_node_weight_complete Iteration 350
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Mode largest_node_weight_complete Iteration 400
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Mode largest_node_weight_complete Iteration 450
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Mode largest_node_weight_complete Iteration 500
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Mode largest_node_weight_complete Iteration 550
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Mode largest_node_weight_complete Iteration 600
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Mode largest_node_weight_complete Iteration 650
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Mode largest_node_weight_complete Iteration 700
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Mode largest_node_weight_complete Iteration 750
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Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Mode largest_node_weight_complete Iteration 850
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Mode largest_node_weight_complete Iteration 900
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Mode largest_node_weight_complete Iteration 950
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
Error Allocating Memory for PaToH
EC_PM,largest_node_weight_complete,156.996,57.5327209855,261,164,170,169,175,133,145,175,216,0,181,188,161,202,166,209,184,172,204,153,137,177,142,203,0,212,158,191,177,182,178,186,166,164,132,176,170,136,203,160,0,181,185,153,170,143,175,154,176,110,164,168,205,153,195,192,0,169,159,220,162,157,138,145,0,162,220,217,173,147,178,147,0,197,157,141,245,151,208,177,210,176,173,199,217,132,0,153,181,163,0,184,169,97,172,185,202,197,212,161,165,151,162,182,165,144,159,167,205,187,188,168,135,172,170,212,166,174,0,172,193,169,166,198,216,192,171,159,158,143,209,208,186,148,153,175,122,180,104,233,178,173,170,178,171,190,199,143,185,129,195,173,168,200,204,169,181,190,0,146,196,172,199,235,211,201,174,136,191,0,195,136,156,142,0,134,210,171,160,161,189,0,159,154,210,171,174,142,151,199,209,173,148,189,213,170,0,146,150,193,158,201,0,229,147,221,188,178,159,175,161,188,168,226,0,158,210,0,197,155,196,243,163,142,175,170,140,169,149,174,176,173,186,204,216,0,158,132,198,161,172,147,133,146,157,158,175,177,146,127,147,145,0,177,149,0,145,159,191,187,185,201,193,177,145,201,175,148,170,162,196,187,185,155,195,181,187,141,0,165,189,178,145,176,191,175,202,192,183,203,193,192,197,0,148,132,176,152,212,156,172,165,196,188,174,178,156,181,131,202,0,197,152,175,226,145,168,133,189,214,167,181,235,199,151,107,182,186,162,171,139,191,205,0,178,194,149,155,161,187,146,202,144,194,204,189,133,181,188,183,140,126,158,178,175,0,177,171,155,174,192,191,134,213,170,209,0,178,232,183,205,149,0,186,201,130,193,186,201,188,145,228,142,186,168,171,152,156,193,172,176,147,180,210,173,141,175,209,150,0,0,148,213,177,179,177,142,188,176,184,209,201,103,186,207,144,0,171,0,128,171,163,211,204,172,219,170,186,178,159,0,131,171,169,217,165,177,0,0,202,170,187,163,149,177,201,237,181,192,0,179,159,163,180,154,162,177,148,130,172,151,179,169,170,165,140,202,130,174,146,0,158,199,204,181,172,0,168,169,150,174,177,200,170,184,0,166,153,205,161,0,182,155,0,125,169,0,177,203,160,168,145,175,213,169,156,166,194,175,156,210,172,136,184,152,126,160,0,138,121,207,188,152,154,212,177,173,233,204,194,215,144,0,175,181,193,178,185,184,148,175,204,162,207,192,166,178,169,141,189,201,0,192,167,162,179,192,190,164,134,202,166,157,159,178,188,144,172,147,169,189,178,0,190,0,179,218,196,128,0,149,145,176,176,0,195,170,184,191,199,197,191,243,201,200,162,185,166,167,172,201,157,153,153,196,0,197,155,118,136,159,181,139,166,181,177,178,121,192,0,141,138,168,143,244,217,177,173,176,200,198,153,183,210,168,162,205,168,168,0,178,151,0,173,175,199,165,150,180,171,159,198,183,199,143,203,169,152,189,186,191,173,145,176,130,165,157,158,162,176,199,156,214,174,224,190,167,176,149,175,205,160,147,222,232,202,196,200,230,186,176,161,0,157,0,0,163,0,177,186,194,0,162,174,145,156,152,0,152,174,0,173,181,151,178,0,194,0,200,171,200,213,0,168,154,174,143,200,213,223,191,204,167,194,154,0,165,191,180,129,0,190,175,148,0,151,132,0,167,203,141,200,169,150,150,176,125,166,162,185,0,158,0,171,223,0,238,178,0,155,200,137,151,166,143,202,197,165,192,117,150,183,160,197,153,222,200,0,0,158,182,242,152,0,173,158,193,172,205,194,0,147,159,187,0,179,208,180,175,164,152,147,201,196,208,133,166,178,0,177,147,176,156,165,130,218,208,178,172,165,177,201,114,0,190,209,183,182,165,225,196,142,184,152,159,181,189,138,142,0,0,205,162,193,135,189,209,133,164,145,193,0,200,172,180,235,242,154,152,116,180,192,150,235,205,0,0,151,190,125,196,217,131,223,0,173,182,167,141,216,169,202,152,182,201,158,218,193,164,206,149,162,165,162,183,0,175,139,146,193,185,120,159,164,144,176,197,206,146,190,152,0,159,143,0,182,160,151,144,192,0,157,176,209,0,166,162,200,202,193,0,184,168,188,169,168,169,0,114,168,0,133,180,193,193,154,145,163,0,151,204,0,139,183,170,165,153,160,147,174,0,0,137,192,0,159,223,193,181,192,146,0,144,0,142,152,205,174,140,0,191,165,185,179,221,145,198,134,0,211
TCV_PM,largest_node_weight_complete,192.448,67.925306742,261,208,201,194,227,181,190,214,269,0,228,221,198,241,192,238,245,192,242,184,192,210,177,240,0,234,194,221,228,232,220,229,217,211,182,208,217,175,250,182,0,223,222,205,212,187,202,194,223,159,210,212,240,186,244,222,0,212,209,252,216,204,171,188,0,198,251,255,212,188,217,191,0,235,197,181,265,195,250,190,216,217,209,239,248,168,0,199,219,211,0,226,211,135,212,225,241,239,249,214,219,195,207,223,211,197,196,209,244,216,224,209,189,219,215,248,211,215,0,225,206,203,213,237,231,207,209,206,209,197,236,263,217,178,200,225,165,212,158,268,222,211,214,222,214,230,243,188,227,185,237,208,218,241,218,201,224,220,0,186,246,206,221,272,253,241,203,176,223,0,229,179,197,183,0,168,251,195,196,206,220,0,207,204,249,223,224,190,187,245,239,231,189,235,267,227,0,192,201,223,203,239,0,261,172,253,240,210,192,207,201,232,214,241,0,211,249,0,224,192,218,273,201,180,218,203,192,213,189,201,218,225,213,235,250,0,206,166,232,203,213,200,174,180,197,213,197,222,187,167,195,198,0,227,189,0,179,205,229,232,217,252,237,215,196,244,228,195,213,198,230,227,213,181,236,222,247,188,0,204,223,205,193,195,227,218,246,235,231,245,218,225,225,0,190,181,193,206,244,209,207,220,241,241,203,223,202,206,173,223,0,218,174,208,264,195,219,169,241,263,203,220,263,219,191,156,232,212,203,216,176,230,241,0,222,232,197,192,209,240,190,232,186,242,231,215,170,234,211,225,185,175,209,219,209,0,223,209,206,215,230,238,191,252,202,247,0,220,260,235,232,180,0,219,232,182,232,224,205,205,188,256,187,225,222,209,200,208,236,201,210,180,203,230,231,179,216,254,168,0,0,176,244,206,216,220,192,199,229,222,224,230,148,226,243,191,0,215,0,173,211,205,248,233,235,258,218,225,209,186,0,158,216,227,270,203,219,0,0,246,203,229,203,196,193,227,266,200,240,0,223,199,222,214,213,206,230,178,177,213,184,223,207,223,195,181,222,174,211,184,0,204,253,252,212,221,0,221,194,182,223,222,240,214,233,0,211,185,236,212,0,232,202,0,167,231,0,227,222,207,206,182,223,262,222,184,218,245,231,183,228,207,171,230,187,184,210,0,190,163,245,215,198,200,246,206,205,262,227,239,266,181,0,215,213,232,217,213,205,201,219,253,210,253,236,223,226,201,198,207,232,0,233,219,218,225,236,235,211,170,223,210,212,195,230,218,185,204,176,218,213,210,0,250,0,199,244,232,163,0,201,192,218,230,0,244,203,215,231,232,221,230,279,254,214,202,221,215,199,211,233,179,198,191,231,0,242,201,172,176,194,211,163,198,211,222,221,160,244,0,189,188,217,189,265,256,208,227,212,238,242,198,217,250,202,208,248,199,201,0,228,196,0,200,222,235,207,194,209,210,199,226,221,240,182,219,230,170,212,226,237,219,198,213,178,204,211,214,206,208,233,210,251,205,265,236,202,225,198,218,267,200,179,260,268,251,237,244,256,231,209,213,0,210,0,0,200,0,215,227,235,0,192,203,182,204,202,0,185,201,0,228,217,192,221,0,237,0,236,222,211,255,0,193,212,220,181,246,254,249,228,226,211,215,197,0,199,230,217,170,0,237,236,208,0,196,182,0,205,244,188,222,205,196,193,225,169,199,212,230,0,177,0,214,259,0,289,235,0,199,217,183,191,207,177,255,231,194,233,167,187,222,199,240,188,233,240,0,0,192,233,246,196,0,200,199,229,203,238,223,0,188,213,216,0,231,220,205,232,202,218,186,245,238,251,174,207,228,0,216,193,217,206,203,181,271,232,223,203,206,200,215,161,0,205,234,227,224,217,255,227,188,225,180,200,229,242,178,178,0,0,232,182,235,187,212,230,175,210,190,235,0,246,202,230,249,273,195,188,173,208,227,188,254,234,0,0,189,226,179,230,260,180,243,0,195,224,212,184,236,224,241,192,220,223,188,242,235,203,219,196,207,225,205,219,0,217,167,182,232,235,171,182,212,192,206,210,224,188,230,202,0,208,177,0,222,206,189,174,202,0,191,243,227,0,206,216,227,227,228,0,225,220,229,207,209,209,0,153,219,0,177,229,219,233,193,197,212,0,188,250,0,175,217,213,212,189,218,195,210,0,0,180,241,0,200,254,233,198,222,185,0,169,0,187,199,252,199,195,0,226,200,220,234,241,198,234,182,0,244
LONELINESS,largest_node_weight_complete,0.770277912364,0.256778874765,0.85138173245,0.855431165115,0.853924379795,0.858205330286,0.854609407784,0.853075059552,0.856026781186,0.858718510483,0.85652600389,0.0,0.854906701834,0.855759929799,0.854682733565,0.856975980045,0.857751994129,0.855369915889,0.854960464539,0.845380398222,0.854706502139,0.858998582308,0.853775899025,0.849978164468,0.852984073458,0.853873870746,0.0,0.854002395378,0.851834261865,0.849465252562,0.848787558078,0.859017047496,0.854856767077,0.861690562415,0.854196191356,0.864036863441,0.856425337136,0.853933584185,0.855236552482,0.850286324824,0.851744291301,0.857749753071,0.0,0.854631615097,0.854803736031,0.853469608606,0.851206609058,0.860733332218,0.859872763113,0.857376592037,0.850461167372,0.85941419125,0.856463845272,0.855062001635,0.85600075953,0.857229268513,0.855588659781,0.858322794746,0.0,0.856970078125,0.85619058718,0.854048930247,0.855811386586,0.854197372564,0.854226503324,0.858737384464,0.0,0.854370002693,0.852448303341,0.844455821345,0.851294930505,0.85467873811,0.847986637322,0.851351427919,0.0,0.851816387872,0.854164895763,0.854982927892,0.849187545684,0.856355678209,0.850823215902,0.855461527176,0.855755955133,0.856337727483,0.850056783885,0.855543606729,0.849798975513,0.854493330358,0.0,0.855448348365,0.853008026264,0.855031614193,0.0,0.853539014584,0.85906961678,0.85890583441,0.858812625558,0.859353097463,0.861084767873,0.853616067546,0.851073914144,0.853963487055,0.859707939779,0.865011546892,0.856578789732,0.859110068156,0.848476871529,0.85658753178,0.859305454507,0.855605217783,0.86012666975,0.862722394094,0.858912722314,0.854611975536,0.85522837067,0.862697910904,0.8545151811,0.85701806635,0.855043838824,0.854580279756,0.0,0.857558629666,0.856790156917,0.86165771532,0.858279228982,0.85030912951,0.85278799249,0.862627388901,0.855028551047,0.861185366147,0.854511660501,0.8567366995,0.856082401174,0.85543012991,0.855140338087,0.859918708596,0.859410140219,0.855540369971,0.856369526265,0.859671061693,0.865629910749,0.855194073709,0.855557266517,0.861620133264,0.861246415782,0.85478390938,0.854294039376,0.855788946181,0.853602960263,0.856563220531,0.855485945031,0.852916135931,0.856661256886,0.859304301979,0.851693014142,0.856911384727,0.850419351934,0.861121047321,0.852676220357,0.851762798594,0.0,0.860091149251,0.856282033673,0.856258198639,0.858260412631,0.851581906079,0.848762050026,0.854070711956,0.852739591429,0.857402873805,0.854170537367,0.0,0.858958998587,0.862706637051,0.856976108443,0.862352658001,0.0,0.862894297982,0.853881927601,0.851903565016,0.857103949962,0.860106448229,0.85185844491,0.0,0.854055575291,0.858210737718,0.857734122671,0.854133875582,0.854697445443,0.862570534436,0.86024425532,0.854693785945,0.854530702185,0.856710939092,0.86234983845,0.863199706622,0.85324607115,0.854240889716,0.0,0.854816244672,0.850816548505,0.853535089733,0.855183314507,0.85791068004,0.0,0.856007117261,0.854728107741,0.851396293214,0.857391776375,0.857060917354,0.855377854405,0.85591871373,0.856859933044,0.856152062581,0.857235812819,0.853907543043,0.0,0.86083783455,0.852019688363,0.0,0.858374433507,0.864370309826,0.856210073733,0.852337277988,0.852501323378,0.862227619596,0.858009713141,0.855952666065,0.860191793893,0.849852278647,0.853810200136,0.851290465679,0.85751421277,0.858268923418,0.857619109601,0.855713895524,0.863637407547,0.0,0.854254446958,0.866174333835,0.855730582305,0.854084254868,0.85464973352,0.85780211297,0.85680420419,0.857445987632,0.860019230527,0.859995280862,0.856217281556,0.861294010748,0.857524770367,0.858628430131,0.855968415425,0.85868591144,0.0,0.853354259942,0.859720621051,0.0,0.858934236129,0.858266596118,0.851910174066,0.852860084098,0.854157924557,0.855124480977,0.854756916424,0.857632678856,0.858449550105,0.85523289245,0.853313239232,0.858801822367,0.855561810965,0.85762597878,0.853416201017,0.860384714139,0.860921194403,0.859018446003,0.84695760206,0.857423035648,0.853758070679,0.853999291153,0.0,0.856809375166,0.855733475439,0.851797663924,0.854219784321,0.853267352378,0.85729308261,0.855403771285,0.854077534847,0.848840390322,0.854272085583,0.852381156967,0.85227800273,0.854791365554,0.860966589188,0.0,0.860720460139,0.861747867442,0.857735193764,0.854981939394,0.856536828394,0.854317583013,0.856958259942,0.856548742718,0.85287062409,0.854919059979,0.858409567854,0.854496396687,0.856608538603,0.860129910145,0.862610095448,0.852412067407,0.0,0.864403278539,0.855926380422,0.854845140491,0.854380590673,0.852102767612,0.861877236349,0.856178371716,0.857663584649,0.857919066374,0.853807322204,0.852893842175,0.854974508598,0.855055133583,0.854651995164,0.853628301006,0.858449574152,0.854978127743,0.855980014783,0.860060112688,0.861022573911,0.850526628136,0.85156441389,0.0,0.850386290055,0.858291930617,0.856375033541,0.86024290507,0.854959980865,0.855032120635,0.857806099984,0.848747564276,0.85769546277,0.853561043227,0.856682743852,0.858293459234,0.861045936581,0.86078171671,0.856004716973,0.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