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Add ulm class diagram & update .clang-format

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This commit is contained in:
Ricardo Montañana Gómez
2025-03-17 13:06:15 +01:00
parent 4ca770d16b
commit be39d2dedb
3 changed files with 203 additions and 101 deletions
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8
.clang-format
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@@ -1,4 +1,10 @@
# .clang-format
---
BasedOnStyle: LLVM
AccessModifierOffset: -4
BreakBeforeBraces: Allman
ColumnLimit: 0
FixNamespaceComments: false
IndentWidth: 4
ColumnLimit: 120
NamespaceIndentation: All
TabWidth: 4

294
diagrams/BayesNet.puml
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@@ -96,6 +96,8 @@ abstract C_0002617087915615796317 #aliceblue;line:blue;line.dotted;text:blue {
{abstract} +topological_order() = 0 : std::vector<std::string>
{abstract} #trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) = 0 : void
__
#notes : std::vector<std::string>
#status : status_t
#validHyperparameters : std::vector<std::string>
}
class "bayesnet::Metrics" as C_0005895723015084986588
@@ -153,6 +155,7 @@ abstract C_0016351972983202413152 #aliceblue;line:blue;line.dotted;text:blue {
+topological_order() : std::vector<std::string>
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
__
#CLASSIFIER_NOT_FITTED : const std::string
#className : std::string
#dataset : torch::Tensor
#features : std::vector<std::string>
@@ -161,20 +164,44 @@ __
#metrics : Metrics
#model : Network
#n : unsigned int
#notes : std::vector<std::string>
#states : std::map<std::string,std::vector<int>>
#status : status_t
}
class "bayesnet::Proposal" as C_0017759964713298103839
class C_0017759964713298103839 #aliceblue;line:blue;line.dotted;text:blue {
+Proposal(torch::Tensor & pDataset, std::vector<std::string> & features_, std::string & className_) : void
+~Proposal() : void
..
#checkInput(const torch::Tensor & X, const torch::Tensor & y) : void
#fit_local_discretization(const torch::Tensor & y) : std::map<std::string,std::vector<int>>
#localDiscretizationProposal(const std::map<std::string,std::vector<int>> & states, Network & model) : std::map<std::string,std::vector<int>>
#prepareX(torch::Tensor & X) : torch::Tensor
__
#Xf : torch::Tensor
#discretizers : map<std::string,mdlp::CPPFImdlp *>
#y : torch::Tensor
}
class "bayesnet::KDB" as C_0008902920152122000044
class C_0008902920152122000044 #aliceblue;line:blue;line.dotted;text:blue {
+KDB(int k, float theta = 0.03) : void
+~KDB() = default : void
..
#add_m_edges(int idx, std::vector<int> & S, torch::Tensor & weights) : void
#buildModel(const torch::Tensor & weights) : void
+graph(const std::string & name = "KDB") const : std::vector<std::string>
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
__
}
class "bayesnet::KDBLd" as C_0002756018222998454702
class C_0002756018222998454702 #aliceblue;line:blue;line.dotted;text:blue {
+KDBLd(int k) : void
+~KDBLd() = default : void
..
+fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : KDBLd &
+graph(const std::string & name = "KDB") const : std::vector<std::string>
+predict(torch::Tensor & X) : torch::Tensor
{static} +version() : std::string
__
}
class "bayesnet::SPODE" as C_0004096182510460307610
class C_0004096182510460307610 #aliceblue;line:blue;line.dotted;text:blue {
+SPODE(int root) : void
@@ -182,6 +209,20 @@ class C_0004096182510460307610 #aliceblue;line:blue;line.dotted;text:blue {
..
#buildModel(const torch::Tensor & weights) : void
+graph(const std::string & name = "SPODE") const : std::vector<std::string>
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
__
}
class "bayesnet::SPODELd" as C_0010957245114062042836
class C_0010957245114062042836 #aliceblue;line:blue;line.dotted;text:blue {
+SPODELd(int root) : void
+~SPODELd() = default : void
..
+commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
+fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
+graph(const std::string & name = "SPODELd") const : std::vector<std::string>
+predict(torch::Tensor & X) : torch::Tensor
{static} +version() : std::string
__
}
class "bayesnet::SPnDE" as C_0016268916386101512883
@@ -200,44 +241,7 @@ class C_0014087955399074584137 #aliceblue;line:blue;line.dotted;text:blue {
..
#buildModel(const torch::Tensor & weights) : void
+graph(const std::string & name = "TAN") const : std::vector<std::string>
__
}
class "bayesnet::Proposal" as C_0017759964713298103839
class C_0017759964713298103839 #aliceblue;line:blue;line.dotted;text:blue {
+Proposal(torch::Tensor & pDataset, std::vector<std::string> & features_, std::string & className_) : void
+~Proposal() : void
..
#checkInput(const torch::Tensor & X, const torch::Tensor & y) : void
#fit_local_discretization(const torch::Tensor & y) : std::map<std::string,std::vector<int>>
#localDiscretizationProposal(const std::map<std::string,std::vector<int>> & states, Network & model) : std::map<std::string,std::vector<int>>
#prepareX(torch::Tensor & X) : torch::Tensor
__
#Xf : torch::Tensor
#discretizers : map<std::string,mdlp::CPPFImdlp *>
#y : torch::Tensor
}
class "bayesnet::KDBLd" as C_0002756018222998454702
class C_0002756018222998454702 #aliceblue;line:blue;line.dotted;text:blue {
+KDBLd(int k) : void
+~KDBLd() = default : void
..
+fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : KDBLd &
+graph(const std::string & name = "KDB") const : std::vector<std::string>
+predict(torch::Tensor & X) : torch::Tensor
{static} +version() : std::string
__
}
class "bayesnet::SPODELd" as C_0010957245114062042836
class C_0010957245114062042836 #aliceblue;line:blue;line.dotted;text:blue {
+SPODELd(int root) : void
+~SPODELd() = default : void
..
+commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
+fit(torch::Tensor & X, torch::Tensor & y, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
+graph(const std::string & name = "SPODELd") const : std::vector<std::string>
+predict(torch::Tensor & X) : torch::Tensor
{static} +version() : std::string
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
__
}
class "bayesnet::TANLd" as C_0013350632773616302678
@@ -250,6 +254,64 @@ class C_0013350632773616302678 #aliceblue;line:blue;line.dotted;text:blue {
+predict(torch::Tensor & X) : torch::Tensor
__
}
class "bayesnet::XSp2de" as C_0007640742442325463418
class C_0007640742442325463418 #aliceblue;line:blue;line.dotted;text:blue {
+XSp2de(int spIndex1, int spIndex2) : void
..
#buildModel(const torch::Tensor & weights) : void
+fitx(torch::Tensor & X, torch::Tensor & y, torch::Tensor & weights_, const Smoothing_t smoothing) : void
+getClassNumStates() const : int
+getNFeatures() const : int
+getNumberOfEdges() const : int
+getNumberOfNodes() const : int
+getNumberOfStates() const : int
+graph(const std::string & title) const : std::vector<std::string>
+predict(const std::vector<int> & instance) const : int
+predict(std::vector<std::vector<int>> & test_data) : std::vector<int>
+predict(torch::Tensor & X) : torch::Tensor
+predict_proba(const std::vector<int> & instance) const : std::vector<double>
+predict_proba(std::vector<std::vector<int>> & test_data) : std::vector<std::vector<double>>
+predict_proba(torch::Tensor & X) : torch::Tensor
+score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
+score(torch::Tensor & X, torch::Tensor & y) : float
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
+to_string() const : std::string
#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
__
}
class "bayesnet::XSpode" as C_0015654113248178830206
class C_0015654113248178830206 #aliceblue;line:blue;line.dotted;text:blue {
+XSpode(int spIndex) : void
..
#buildModel(const torch::Tensor & weights) : void
+fitx(torch::Tensor & X, torch::Tensor & y, torch::Tensor & weights_, const Smoothing_t smoothing) : void
+getClassNumStates() const : int
+getNFeatures() const : int
+getNumberOfEdges() const : int
+getNumberOfNodes() const : int
+getNumberOfStates() const : int
+getStates() : std::vector<int> &
+graph(const std::string & title) const : std::vector<std::string>
+normalize(std::vector<double> & v) const : void
+predict(const std::vector<int> & instance) const : int
+predict(std::vector<std::vector<int>> & X) : std::vector<int>
+predict(torch::Tensor & X) : torch::Tensor
+predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
+predict_proba(torch::Tensor & X) : torch::Tensor
+predict_proba(const std::vector<int> & instance) const : std::vector<double>
+score(torch::Tensor & X, torch::Tensor & y) : float
+score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
+to_string() const : std::string
#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
__
}
class "bayesnet::TensorUtils" as C_0010304804115474100819
class C_0010304804115474100819 #aliceblue;line:blue;line.dotted;text:blue {
{static} +to_matrix(const torch::Tensor & X) : std::vector<std::vector<int>>
{static} +to_vector<T>(const torch::Tensor & y) : std::vector<T>
__
}
class "bayesnet::Ensemble" as C_0015881931090842884611
class C_0015881931090842884611 #aliceblue;line:blue;line.dotted;text:blue {
+Ensemble(bool predict_voting = true) : void
@@ -302,6 +364,17 @@ class C_0006288892608974306258 #aliceblue;line:blue;line.dotted;text:blue {
+setHyperparameters(const nlohmann::json & hyperparameters) : void
__
}
class "bayesnet::AODELd" as C_0003898187834670349177
class C_0003898187834670349177 #aliceblue;line:blue;line.dotted;text:blue {
+AODELd(bool predict_voting = true) : void
+~AODELd() = default : void
..
#buildModel(const torch::Tensor & weights) : void
+fit(torch::Tensor & X_, torch::Tensor & y_, const std::vector<std::string> & features_, const std::string & className_, std::map<std::string,std::vector<int>> & states_, const Smoothing_t smoothing) : AODELd &
+graph(const std::string & name = "AODELd") const : std::vector<std::string>
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
__
}
abstract "bayesnet::FeatureSelect" as C_0013562609546004646591
abstract C_0013562609546004646591 #aliceblue;line:blue;line.dotted;text:blue {
+FeatureSelect(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
@@ -324,15 +397,15 @@ __
#suLabels : std::vector<double>
#weights : const torch::Tensor &
}
class "bayesnet::(anonymous_60342586)" as C_0005584545181746538542
class C_0005584545181746538542 #aliceblue;line:blue;line.dotted;text:blue {
class "bayesnet::(anonymous_60357672)" as C_0006397015156479549697
class C_0006397015156479549697 #aliceblue;line:blue;line.dotted;text:blue {
__
+CFS : std::string
+FCBF : std::string
+IWSS : std::string
}
class "bayesnet::(anonymous_60343240)" as C_0016227156982041949444
class C_0016227156982041949444 #aliceblue;line:blue;line.dotted;text:blue {
class "bayesnet::(anonymous_60358326)" as C_0013066254331852347304
class C_0013066254331852347304 #aliceblue;line:blue;line.dotted;text:blue {
__
+ASC : std::string
+DESC : std::string
@@ -343,14 +416,17 @@ class C_0009819322948617116148 #aliceblue;line:blue;line.dotted;text:blue {
+Boost(bool predict_voting = false) : void
+~Boost() = default : void
..
#add_model(std::unique_ptr<Classifier> model, double significance) : void
#buildModel(const torch::Tensor & weights) : void
#featureSelection(torch::Tensor & weights_) : std::vector<int>
#remove_last_model() : void
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
#update_weights(torch::Tensor & ytrain, torch::Tensor & ypred, torch::Tensor & weights) : std::tuple<torch::Tensor &,double,bool>
#update_weights_block(int k, torch::Tensor & ytrain, torch::Tensor & weights) : std::tuple<torch::Tensor &,double,bool>
__
#X_test : torch::Tensor
#X_train : torch::Tensor
#alpha_block : bool
#bisection : bool
#block_update : bool
#convergence : bool
@@ -364,31 +440,6 @@ __
#y_test : torch::Tensor
#y_train : torch::Tensor
}
class "bayesnet::AODELd" as C_0003898187834670349177
class C_0003898187834670349177 #aliceblue;line:blue;line.dotted;text:blue {
+AODELd(bool predict_voting = true) : void
+~AODELd() = default : void
..
#buildModel(const torch::Tensor & weights) : void
+fit(torch::Tensor & X_, torch::Tensor & y_, const std::vector<std::string> & features_, const std::string & className_, std::map<std::string,std::vector<int>> & states_, const Smoothing_t smoothing) : AODELd &
+graph(const std::string & name = "AODELd") const : std::vector<std::string>
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
__
}
class "bayesnet::(anonymous_60275628)" as C_0009086919615463763584
class C_0009086919615463763584 #aliceblue;line:blue;line.dotted;text:blue {
__
+CFS : std::string
+FCBF : std::string
+IWSS : std::string
}
class "bayesnet::(anonymous_60276282)" as C_0015251985607563196159
class C_0015251985607563196159 #aliceblue;line:blue;line.dotted;text:blue {
__
+ASC : std::string
+DESC : std::string
+RAND : std::string
}
class "bayesnet::BoostA2DE" as C_0000272055465257861326
class C_0000272055465257861326 #aliceblue;line:blue;line.dotted;text:blue {
+BoostA2DE(bool predict_voting = false) : void
@@ -398,15 +449,15 @@ class C_0000272055465257861326 #aliceblue;line:blue;line.dotted;text:blue {
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
__
}
class "bayesnet::(anonymous_60275502)" as C_0016033655851510053155
class C_0016033655851510053155 #aliceblue;line:blue;line.dotted;text:blue {
class "bayesnet::(anonymous_60425028)" as C_0000461144706913711531
class C_0000461144706913711531 #aliceblue;line:blue;line.dotted;text:blue {
__
+CFS : std::string
+FCBF : std::string
+IWSS : std::string
}
class "bayesnet::(anonymous_60276156)" as C_0000379522761622473555
class C_0000379522761622473555 #aliceblue;line:blue;line.dotted;text:blue {
class "bayesnet::(anonymous_60425682)" as C_0014849589915262463453
class C_0014849589915262463453 #aliceblue;line:blue;line.dotted;text:blue {
__
+ASC : std::string
+DESC : std::string
@@ -421,6 +472,38 @@ class C_0002867772739198819061 #aliceblue;line:blue;line.dotted;text:blue {
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
__
}
class "bayesnet::XBA2DE" as C_0008480973840710001141
class C_0008480973840710001141 #aliceblue;line:blue;line.dotted;text:blue {
+XBA2DE(bool predict_voting = false) : void
+~XBA2DE() = default : void
..
+getVersion() : std::string
+graph(const std::string & title = "XBA2DE") const : std::vector<std::string>
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
__
}
class "bayesnet::(anonymous_60414016)" as C_0008746994658440620779
class C_0008746994658440620779 #aliceblue;line:blue;line.dotted;text:blue {
__
+CFS : std::string
+FCBF : std::string
+IWSS : std::string
}
class "bayesnet::(anonymous_60414670)" as C_0008030559132212449356
class C_0008030559132212449356 #aliceblue;line:blue;line.dotted;text:blue {
__
+ASC : std::string
+DESC : std::string
+RAND : std::string
}
class "bayesnet::XBAODE" as C_0005198482342493966768
class C_0005198482342493966768 #aliceblue;line:blue;line.dotted;text:blue {
+XBAODE() : void
..
+getVersion() : std::string
#trainModel(const torch::Tensor & weights, const bayesnet::Smoothing_t smoothing) : void
__
}
class "bayesnet::CFS" as C_0000093018845530739957
class C_0000093018845530739957 #aliceblue;line:blue;line.dotted;text:blue {
+CFS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
@@ -445,43 +528,43 @@ class C_0000066148117395428429 #aliceblue;line:blue;line.dotted;text:blue {
+fit() : void
__
}
class "bayesnet::(anonymous_60730495)" as C_0004857727320042830573
class C_0004857727320042830573 #aliceblue;line:blue;line.dotted;text:blue {
class "bayesnet::(anonymous_60810808)" as C_0012002108046995621535
class C_0012002108046995621535 #aliceblue;line:blue;line.dotted;text:blue {
__
+CFS : std::string
+FCBF : std::string
+IWSS : std::string
}
class "bayesnet::(anonymous_60731150)" as C_0000076541533312623385
class C_0000076541533312623385 #aliceblue;line:blue;line.dotted;text:blue {
class "bayesnet::(anonymous_60811462)" as C_0004735044229422764240
class C_0004735044229422764240 #aliceblue;line:blue;line.dotted;text:blue {
__
+ASC : std::string
+DESC : std::string
+RAND : std::string
}
class "bayesnet::(anonymous_60653004)" as C_0001444063444142949758
class C_0001444063444142949758 #aliceblue;line:blue;line.dotted;text:blue {
class "bayesnet::(anonymous_60804220)" as C_0007082100550474633839
class C_0007082100550474633839 #aliceblue;line:blue;line.dotted;text:blue {
__
+CFS : std::string
+FCBF : std::string
+IWSS : std::string
}
class "bayesnet::(anonymous_60653658)" as C_0007139277546931322856
class C_0007139277546931322856 #aliceblue;line:blue;line.dotted;text:blue {
class "bayesnet::(anonymous_60804874)" as C_0003669430095936529648
class C_0003669430095936529648 #aliceblue;line:blue;line.dotted;text:blue {
__
+ASC : std::string
+DESC : std::string
+RAND : std::string
}
class "bayesnet::(anonymous_60731375)" as C_0010493853592456211189
class C_0010493853592456211189 #aliceblue;line:blue;line.dotted;text:blue {
class "bayesnet::(anonymous_60809706)" as C_0012336951062058157227
class C_0012336951062058157227 #aliceblue;line:blue;line.dotted;text:blue {
__
+CFS : std::string
+FCBF : std::string
+IWSS : std::string
}
class "bayesnet::(anonymous_60732030)" as C_0007011438637915849564
class C_0007011438637915849564 #aliceblue;line:blue;line.dotted;text:blue {
class "bayesnet::(anonymous_60810360)" as C_0002435892998884329673
class C_0002435892998884329673 #aliceblue;line:blue;line.dotted;text:blue {
__
+ASC : std::string
+DESC : std::string
@@ -513,37 +596,43 @@ C_0010428199432536647474 --> C_0010428199432536647474 : -children
C_0009493661199123436603 ..> C_0013393078277439680282
C_0009493661199123436603 o-- C_0010428199432536647474 : -nodes
C_0002617087915615796317 ..> C_0013393078277439680282
C_0002617087915615796317 ..> C_0005907365846270811004
C_0002617087915615796317 o-- C_0005907365846270811004 : #status
C_0016351972983202413152 ..> C_0013393078277439680282
C_0016351972983202413152 ..> C_0005907365846270811004
C_0016351972983202413152 o-- C_0009493661199123436603 : #model
C_0016351972983202413152 o-- C_0005895723015084986588 : #metrics
C_0016351972983202413152 o-- C_0005907365846270811004 : #status
C_0002617087915615796317 <|-- C_0016351972983202413152
C_0017759964713298103839 ..> C_0009493661199123436603
C_0016351972983202413152 <|-- C_0008902920152122000044
C_0016351972983202413152 <|-- C_0004096182510460307610
C_0016351972983202413152 <|-- C_0016268916386101512883
C_0016351972983202413152 <|-- C_0014087955399074584137
C_0017759964713298103839 ..> C_0009493661199123436603
C_0002756018222998454702 ..> C_0013393078277439680282
C_0008902920152122000044 <|-- C_0002756018222998454702
C_0017759964713298103839 <|-- C_0002756018222998454702
C_0016351972983202413152 <|-- C_0004096182510460307610
C_0010957245114062042836 ..> C_0013393078277439680282
C_0004096182510460307610 <|-- C_0010957245114062042836
C_0017759964713298103839 <|-- C_0010957245114062042836
C_0016351972983202413152 <|-- C_0016268916386101512883
C_0016351972983202413152 <|-- C_0014087955399074584137
C_0013350632773616302678 ..> C_0013393078277439680282
C_0014087955399074584137 <|-- C_0013350632773616302678
C_0017759964713298103839 <|-- C_0013350632773616302678
C_0007640742442325463418 ..> C_0013393078277439680282
C_0016351972983202413152 <|-- C_0007640742442325463418
C_0015654113248178830206 ..> C_0013393078277439680282
C_0016351972983202413152 <|-- C_0015654113248178830206
C_0015881931090842884611 ..> C_0013393078277439680282
C_0015881931090842884611 o-- C_0016351972983202413152 : #models
C_0016351972983202413152 <|-- C_0015881931090842884611
@@ -552,22 +641,29 @@ C_0015881931090842884611 <|-- C_0001410789567057647859
C_0015881931090842884611 <|-- C_0006288892608974306258
C_0005895723015084986588 <|-- C_0013562609546004646591
C_0009819322948617116148 --> C_0013562609546004646591 : #featureSelector
C_0015881931090842884611 <|-- C_0009819322948617116148
C_0003898187834670349177 ..> C_0013393078277439680282
C_0015881931090842884611 <|-- C_0003898187834670349177
C_0017759964713298103839 <|-- C_0003898187834670349177
C_0005895723015084986588 <|-- C_0013562609546004646591
C_0009819322948617116148 ..> C_0016351972983202413152
C_0009819322948617116148 --> C_0013562609546004646591 : #featureSelector
C_0015881931090842884611 <|-- C_0009819322948617116148
C_0000272055465257861326 ..> C_0013393078277439680282
C_0009819322948617116148 <|-- C_0000272055465257861326
C_0002867772739198819061 ..> C_0013393078277439680282
C_0009819322948617116148 <|-- C_0002867772739198819061
C_0008480973840710001141 ..> C_0013393078277439680282
C_0009819322948617116148 <|-- C_0008480973840710001141
C_0005198482342493966768 ..> C_0013393078277439680282
C_0009819322948617116148 <|-- C_0005198482342493966768
C_0013562609546004646591 <|-- C_0000093018845530739957
C_0013562609546004646591 <|-- C_0001157456122733975432

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