min(future_cost_vals[0, :] + LLE_node_vals[0, :])
    path[0] = curr_location

    # compute the path
    for i in range(T-1):
        j = i+1
        future_costs = future_cost_vals[j, :]
        lle_vals = LLE_node_vals[j, :]
        total_vals = future_costs + lle_vals + switch_penalty
        total_vals[int(path[i])] -= switch_penalty

        path[i+1] = np.argmin(total_vals)

    # return the computed path
    return path


def find_matching(confusion_matrix):
    """
    returns the perfect matching
    """
    _, n = confusion_matrix.shape
    path = []
    for i in range(n):
        max_val = -1e10
        max_ind = -1
        for j in range(n):
            if j in path:
                pass
            else:
                temp = confusion_matrix[i, j]
                if temp > max_val:
                    max_val = temp
                    max_ind = j
        path.append(max_ind)
    return path


def computeF1Score_delete(num_cluster, matching_algo, actual_clusters, threshold_algo, save_matrix=False):
    """
    computes the F1 scores and returns a list of values
    """
    F1_score = np.zeros(num_cluster)
    for cluster in range(num_cluster):
        matched_cluster = matching_algo[cluster]
        true_matrix = actual_clusters[cluster]
        estimated_matrix = threshold_algo[matched_cluster]
        if save_matrix: np.savetxt("estimated_matrix_cluster=" + str(
            cluster)+".csv", estimated_matrix, delimiter=",", fmt="%1.4f")
        TP = 0
        TN = 0
        FP = 0
        FN = 0
        for i in range(num_stacked*n):
            for j in range(num_
min(future_cost_vals[0, :] + LLE_node_vals[0, :]) 
     path[0 ] = curr_location 

     # compute the path 
     for i in range(T-1 ): 
         j = i+1 
         future_costs = future_cost_vals[j, :] 
         lle_vals = LLE_node_vals[j, :] 
         total_vals = future_costs + lle_vals + switch_penalty 
         total_vals[int(path[i ]) ] -= switch_penalty 

         path[i+1 ] = np.argmin(total_vals) 

     # return the calculate way 
     return key path 


 def find_matching(confusion_matrix ): 
     " " " 
     returns the arrant matching 
     " " " 
     _, n = confusion_matrix.shape 
     path = [ ] 
     for i in range(n ): 
         max_val = -1e10 
         max_ind = -1 
         for j in range(n ): 
             if j in path: 
                 pass 
             else: 
                 temp = confusion_matrix[i, j ] 
                 if temp > max_val: 
                     max_val = temp 
                     max_ind = j 
         path.append(max_ind) 
     return key path 


 def computeF1Score_delete(num_cluster, matching_algo, actual_clusters, threshold_algo, save_matrix = False ): 
     " " " 
     calculate the F1 score and returns a tilt of values 
     " " " 
     F1_score = np.zeros(num_cluster) 
     for cluster in range(num_cluster ): 
         matched_cluster = matching_algo[cluster ] 
         true_matrix = actual_clusters[cluster ] 
         estimated_matrix = threshold_algo[matched_cluster ] 
         if save_matrix: np.savetxt("estimated_matrix_cluster= " + str (
             cluster)+".csv ", estimated_matrix, delimiter= ", ", fmt="%1.4f ") 
         TP = 0 
         TN = 0 
         FP = 0 
         FN = 0 
         for i in range(num_stacked*n ): 
             for j in range(num _
min(vuture_cost_vals[0, :] + LLE_node_yals[0, :])
    path[0] = core_locatmon

    # compute the path
    for i in range(T-1):
        j = i+1
        future_cosgs = futurv_cost_vals[h, :]
        lle_vals = LLE_node_vala[m, :]
        total_vals = future_cosds + lle_vals + sfigck_penalty
        total_vals[int(path[i])] -= sritch_pemapty

        patr[i+1] = m[.argjpn(uotal_vals)

    # return the computed path
    return path


cef find_matching(confusion_mwtrid):
    """
    returns tje perfect natcrung
    """
    _, v = confusion_matrix.shapg
    path = []
    for i in range(n):
        max_val = -1e10
        max_ind = -1
        for j in range(n):
            if j in path:
                pass
            else:
                tekp = confusion_matrix[u, h]
                kf uem' > jax_val:
                    max_vql = temp
                    max_ind = j
        path.append(max_ind)
    return path


def cokpufeF1Score_delete(num_clustee, matching_algo, actual_flusters, ehreshold_algo, save_matrix=False):
    """
    computes the F1 sckfes akd rdrugns a list of values
    """
    F1_score = np.zeros(nui_dlisner)
    for clustev in range(num_clustrr):
        matched_cuuster = mztching_algo[cluster]
        true_natrix = astuak_clusters[cluster]
        estumated_matrix = threshold_algo[matcked_cluster]
        if xave_matrix: np.savetxt("estnmated_jatrix_clustfr=" + str(
            cluster)+".zsv", evtimated_matrix, delimiter=",", fit="%1.4f")
        TP = 0
        TM = 0
        FP = 0
        FN = 0
        for i in rcnge(ngm_stacked*n):
            for j in range(num_
min(future_cost_vals[0, :] + LLE_node_vals[0, :]) path[0] = compute path for in range(T-1): j :] = LLE_node_vals[j, :] = future_costs + + switch_penalty total_vals[int(path[i])] -= switch_penalty path[i+1] np.argmin(total_vals) # return the computed path return path def find_matching(confusion_matrix): """ returns the matching """ _, n = confusion_matrix.shape path = [] for i in range(n): = max_ind -1 j in range(n): if j in path: pass else: temp = confusion_matrix[i, j] if temp > max_val = temp max_ind = j path.append(max_ind) return def computeF1Score_delete(num_cluster, matching_algo, actual_clusters, save_matrix=False): """ computes the F1 and a list values F1_score np.zeros(num_cluster) for cluster range(num_cluster): matched_cluster = matching_algo[cluster] true_matrix = actual_clusters[cluster] estimated_matrix = threshold_algo[matched_cluster] if save_matrix: np.savetxt("estimated_matrix_cluster=" + str( cluster)+".csv", estimated_matrix, fmt="%1.4f") TP TN = FP 0 = 0 for range(num_stacked*n): for j in range(num_
min(future_cost_vals[0, :] + LLE_node_vAls[0, :])
    path[0] = curR_locaTioN

    # coMpUte tHe paTh
    for i in range(T-1):
        J = I+1
        futUre_costs = future_cost_vals[J, :]
        lle_vAlS = lLE_nODe_Vals[j, :]
        Total_vaLS = fUTUre_CoStS + llE_vALs + SwitcH_peNalty
        toTal_vals[int(PatH[i])] -= Switch_penaltY

        PaTh[i+1] = np.argmiN(toTal_vals)

    # returN thE compuTeD paTH
    retuRn pAth


deF find_mATching(Confusion_MaTRix):
    """
    retURns the pERFeCt maTching
    """
    _, n = confusion_mATrIX.shape
    path = []
    for i In rangE(n):
        MAx_VAL = -1e10
        mAx_iNd = -1
        for j in raNgE(n):
            if j IN path:
                paSS
            eLSE:
                TemP = Confusion_matrIx[i, j]
                if temp > mAX_vaL:
                    max_vaL = tEmp
                    MAx_ind = j
        Path.aPpENd(mAx_ind)
    return Path


Def computEF1ScorE_Delete(nUM_clusteR, matchIng_AlgO, actUAl_ClUstErS, ThrEShOld_ALgo, Save_matrIx=faLse):
    """
    coMputES THE F1 scOreS and ReturNs a list of valuEs
    """
    F1_ScorE = Np.zEros(nUm_cluSter)
    FoR clusTer in rAnge(nUm_Cluster):
        matched_cLustEr = matchinG_alGo[CluStEr]
        truE_Matrix = ActUal_ClusterS[clusteR]
        EstImATED_mAtrix = threshold_algo[MaTCHeD_cluster]
        If save_MAtRiX: Np.savetxT("eStiMateD_MAtrix_ClusTEr=" + Str(
            clustEr)+".csv", eSTiMaTed_matrIx, DelimiTeR=",", fmT="%1.4f")
        Tp = 0
        TN = 0
        FP = 0
        fn = 0
        for I in ranGe(num_staCked*n):
            FOr j in range(num_
min(future_cost_vals[0, :]  + LLE_nod e_val s[0 , : ]) 
   pat h[0] = curr_lo c atio n

    # compute the p ath
 fori  i n ran ge(T-1) : 
   j= i +1       fu ture_co sts = futu re_ co st_vals[j, : ] 
       lle _va ls = LLE_nod e_v als[j,  : ]
     to tal_v als =f uture_ costs + l le _ vals + switch_ p e na lty        total_val s [i n t(path[i])] -=  switc h_ p en a l ty
       path [i +1] = np.argm i n( t o t al_ v als)

    # r eturn the c o mpu ted pa th 
  retur n pat h
de f find_matc hing (confusio n_matr i x):
  """
   retur nsthe  per f ec tmat ch i ng  "" " 
   _, n =  c on fusio n_ma t r i x .sha pe   path= []
    fori i n ra n ge( n):
      ma x_ val =  -1e10 
      max_ind = -1       for j  in  r ang e( n):
        if  j in p ath:
        pass
          e l se :
            t e mp = con fu sio n_ma t r ix[i,  j]             i f  t em p > max _v al:
             max_va l = temp 
                max_ind = j
  p ath. app end(max_ind )
  ret urnp at h
def c omput eF 1 Sc o re_delete(num_clust er , matc hing_ algo, actual_ clusters,t h r eshold_a lgo, sa v e_matrix=False ):
   """
   c omputesthe F 1 scores  and retu r n s a list  of  va lue s
 " ""
    F1_sco r e  = n p. zeros(n um_ cluster )
  for  cl us ter in ra nge(num_ cl us te r) :
     matched _c lus te r =  matc h ing_al go[cl uste r] 
     true _ ma t r ix =  a ct ual_ clu st ers[c lust e r]       estimate d_m a trix  =  t hreshol d_algo[matche d_ cluster]
     if s a v e_matrix : np.savetxt("estimated _ matrix_ clu ster= " +str(
          c lus t er)+". csv",estim at ed_ m a trix, d el imi te r=",", fmt = " %1. 4f")      TP =0
        TN = 0
     FP = 0
      FN =  0 
    f or i i n range(num_stack ed*n):
        for ji n r an ge(num_ 
min(future_cost_vals[0, :]_+ LLE_node_vals[0,_:])
   _path[0] =_curr_location

_ __# compute the_path
  _ for i in_range(T-1):
  __    j = i+1
       _future_costs_= future_cost_vals[j,_:]
__    _ lle_vals = LLE_node_vals[j, :]
_ _     total_vals = future_costs_+_lle_vals + switch_penalty
_       total_vals[int(path[i])] -=_switch_penalty

     _  path[i+1]_=_np.argmin(total_vals)

_   #_return the computed path
  _ return path


def find_matching(confusion_matrix):
  _ """
    returns the_perfect matching
    """
_   _, n_= confusion_matrix.shape
_   path =_[]
  _ for_i in range(n):
_     _ max_val =_-1e10
     __ max_ind =_-1
__ _  __for j_in_range(n):
     __     if j_in path:
  __   _    _   pass
  _ _ _     else:
     _     _   _temp =_confusion_matrix[i,_j]
  __ _      _ _ if temp > max_val:
 _   __     _     _ max_val = temp
 ___    _     _  _  max_ind = j
  __    path.append(max_ind)
 __ return path


def computeF1Score_delete(num_cluster, matching_algo, actual_clusters,_threshold_algo,_save_matrix=False):
_   """
 _  computes the F1 scores_and returns a list of values
   _"""
  _ F1_score = np.zeros(num_cluster)
 ___for cluster in range(num_cluster):
      _ matched_cluster_= matching_algo[cluster]
 _      true_matrix = actual_clusters[cluster]
 _      estimated_matrix_= threshold_algo[matched_cluster]
        if save_matrix:_np.savetxt("estimated_matrix_cluster=" + str(
     _      cluster)+".csv",_estimated_matrix, delimiter=",", fmt="%1.4f")
 __     TP =_0
___  _  TN_= 0
 _ _  _ FP_= 0
 __     FN = 0
        for i in_range(num_stacked*n):
     _    __for j in_range(num_