K-Means Clustering.
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#include <kmeans.h>
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| | KMeans (QString distance=QString("sqeuclidean"), QString start=QString("sample"), qint32 replicates=1, QString emptyact=QString("error"), bool online=true, qint32 maxit=100) |
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| bool | calculate (Eigen::MatrixXd X, qint32 kClusters, Eigen::VectorXi &idx, Eigen::MatrixXd &C, Eigen::VectorXd &sumD, Eigen::MatrixXd &D) |
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K-Means Clustering.
K-Means Clustering
Definition at line 72 of file kmeans.h.
◆ ConstSPtr
◆ SPtr
◆ KMeans()
| KMeans::KMeans |
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QString |
distance = QString("sqeuclidean"), |
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QString |
start = QString("sample"), |
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qint32 |
replicates = 1, |
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QString |
emptyact = QString("error"), |
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bool |
online = true, |
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qint32 |
maxit = 100 |
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explicit |
Constructs a KMeans algorithm object.
- Parameters
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| [in] | distance | (optional) K-Means distance measure: "sqeuclidean" (default), "cityblock" , "cosine", "correlation", "hamming". |
| [in] | start | (optional) Cluster initialization: "sample" (default), "uniform", "cluster". |
| [in] | replicates | (optional) Number of K-Means replicates, which are generated. Best is returned. |
| [in] | emptyact | (optional) What happens if a cluster wents empty: "error" (default), "drop", "singleton". |
| [in] | online | (optional) If centroids should be updated during iterations: true (default), false. |
| [in] | maxit | (optional) maximal number of iterations per replicate; 100 by default. |
Definition at line 66 of file kmeans.cpp.
◆ calculate()
| bool KMeans::calculate |
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Eigen::MatrixXd |
X, |
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qint32 |
kClusters, |
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Eigen::VectorXi & |
idx, |
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Eigen::MatrixXd & |
C, |
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Eigen::VectorXd & |
sumD, |
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Eigen::MatrixXd & |
D |
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) |
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Clusters input data X
- Parameters
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| [in] | X | Input data (rows = points; cols = p dimensional space). |
| [in] | kClusters | Number of k clusters. |
| [out] | idx | The cluster indeces to which cluster the input points belong to. |
| [out] | C | Cluster centroids k x p. |
| [out] | sumD | Summation of the distances to the centroid within one cluster. |
| [out] | D | Cluster distances to the centroid. |
Definition at line 120 of file kmeans.cpp.
The documentation for this class was generated from the following files: