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Update links in compiled bindings.

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This commit is contained in:
Ryan Curtin
2024-08-28 17:06:13 -04:00
parent b164d69848
commit 567e3482b3
5 changed files with 89 additions and 84 deletions
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doc/user/bindings/python.md
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@@ -222,7 +222,7 @@ Because the estimator computes a predictive distribution instead of a simple poi
- [Bayesian Interpolation](https://cs.uwaterloo.ca/~mannr/cs886-w10/mackay-bayesian.pdf)
- [Bayesian Linear Regression, Section 3.3](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
- [BayesianLinearRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.hpp)
- [BayesianLinearRegression C++ class documentation](../../user/methods/bayesian_linear_regression.md)
## cf()
{: #cf }
@@ -1235,7 +1235,7 @@ Then, this tree may be used to make predictions on the test set `'test_set'`, sa
- [decision_tree()](#decision_tree)
- [random_forest()](#random_forest)
- [Mining High-Speed Data Streams (pdf)](http://dm.cs.washington.edu/papers/vfdt-kdd00.pdf)
- [HoeffdingTree class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/hoeffding_trees/hoeffding_tree.hpp)
- [HoeffdingTree class documentation](../../user/methods/hoeffding_tree.md)
## kde()
{: #kde }
@@ -1607,7 +1607,7 @@ The following command uses the `'lasso_model'` to provide predicted responses fo
- [linear_regression()](#linear_regression)
- [Least angle regression (pdf)](https://mlpack.org/papers/lars.pdf)
- [LARS C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lars/lars.hpp)
- [LARS C++ class documentation](../../user/methods/lars.md)
## linear_svm()
{: #linear_svm }
@@ -1701,7 +1701,7 @@ Then, to use that model to predict classes for the dataset '`'test'`', storing t
- [random_forest()](#random_forest)
- [logistic_regression()](#logistic_regression)
- [LinearSVM on Wikipedia](https://en.wikipedia.org/wiki/Support-vector_machine)
- [LinearSVM C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/linear_svm/linear_svm.hpp)
- [LinearSVM C++ class documentation](../../user/methods/linear_svm.md)
## lmnn()
{: #lmnn }
@@ -1806,7 +1806,7 @@ Another program call making use of update interval & regularization parameter wi
- [nca()](#nca)
- [Large margin nearest neighbor on Wikipedia](https://en.wikipedia.org/wiki/Large_margin_nearest_neighbor)
- [Distance metric learning for large margin nearest neighbor classification (pdf)](http://papers.nips.cc/paper/2795-distance-metric-learning-for-large-margin-nearest-neighbor-classification.pdf)
- [LMNN C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/lmnn/lmnn.hpp)
- [LMNN C++ class documentation](../../user/methods/lmnn.md)
## local_coordinate_coding()
{: #local_coordinate_coding }
@@ -1988,7 +1988,7 @@ Then, to use that model to predict classes for the dataset '`'test'`', storing t
- [softmax_regression()](#softmax_regression)
- [random_forest()](#random_forest)
- [Logistic regression on Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression)
- [:LogisticRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/logistic_regression/logistic_regression.hpp)
- [:LogisticRegression C++ class documentation](../../user/methods/logistic_regression.md)
## lsh()
{: #lsh }
@@ -2135,7 +2135,7 @@ For example, to run mean shift clustering on the dataset `'data'` and store the
- [dbscan()](#dbscan)
- [Mean shift on Wikipedia](https://en.wikipedia.org/wiki/Mean_shift)
- [Mean Shift, Mode Seeking, and Clustering (pdf)](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1c168275c59ba382588350ee1443537f59978183)
- [mlpack::mean_shift::MeanShift C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/mean_shift/mean_shift.hpp)
- [mlpack::mean_shift::MeanShift C++ class documentation](../../user/methods/mean_shift.md)
## nbc()
{: #nbc }
@@ -2216,7 +2216,7 @@ Then, to use `'nbc_model'` to predict the classes of the dataset `'test_set'` an
- [softmax_regression()](#softmax_regression)
- [random_forest()](#random_forest)
- [Naive Bayes classifier on Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
- [NaiveBayesClassifier C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp)
- [NaiveBayesClassifier C++ class documentation](../../user/methods/naive_bayes_classifier.md)
## nca()
{: #nca }
@@ -2291,7 +2291,7 @@ By default, the SGD optimizer is used.
- [lmnn()](#lmnn)
- [Neighbourhood components analysis on Wikipedia](https://en.wikipedia.org/wiki/Neighbourhood_components_analysis)
- [Neighbourhood components analysis (pdf)](http://papers.nips.cc/paper/2566-neighbourhood-components-analysis.pdf)
- [NCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/nca/nca.hpp)
- [NCA C++ class documentation](../../user/methods/nca.md)
## knn()
{: #knn }
@@ -2524,7 +2524,8 @@ For example, to run NMF on the input matrix `'V'` using the 'multdist' update ru
- [cf()](#cf)
- [Non-negative matrix factorization on Wikipedia](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization)
- [Algorithms for non-negative matrix factorization (pdf)](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf)
- [AMF C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/amf/amf.hpp)
- [NMF C++ class documentation](../../user/methods/nmf.md)
- [AMF C++ class documentation](../../user/methods/amf.md)
## pca()
{: #pca }
@@ -2586,7 +2587,7 @@ For example, to reduce the dimensionality of the matrix `'data'` to 5 dimensions
### See also
- [Principal component analysis on Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis)
- [PCA C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/pca/pca.hpp)
- [PCA C++ class documentation](../../user/methods/pca.md)
## perceptron()
{: #perceptron }
@@ -2660,7 +2661,7 @@ Note that all of the options may be specified at once: predictions may be calcul
- [adaboost()](#adaboost)
- [Perceptron on Wikipedia](https://en.wikipedia.org/wiki/Perceptron)
- [Perceptron C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/perceptron/perceptron.hpp)
- [Perceptron C++ class documentation](../../user/methods/perceptron.md)
## preprocess_split()
{: #preprocess_split }
@@ -3092,7 +3093,7 @@ For example, to perform ICA on the matrix `'X'` with 40 replicates, saving the i
- [Independent component analysis on Wikipedia](https://en.wikipedia.org/wiki/Independent_component_analysis)
- [ICA using spacings estimates of entropy (pdf)](https://www.jmlr.org/papers/volume4/learned-miller03a/learned-miller03a.pdf)
- [Radical C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/radical/radical.hpp)
- [Radical C++ class documentation](../../user/methods/radical.md)
## random_forest()
{: #random_forest }
@@ -3183,7 +3184,7 @@ Then, to use that model to classify points in `'test_set'` and print the test er
- [softmax_regression()](#softmax_regression)
- [Random forest on Wikipedia](https://en.wikipedia.org/wiki/Random_forest)
- [Random forests (pdf)](https://www.eecis.udel.edu/~shatkay/Course/papers/BreimanRandomForests2001.pdf)
- [RandomForest C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/random_forest/random_forest.hpp)
- [RandomForest C++ class documentation](../../user/methods/random_forest.md)
## krann()
{: #krann }
@@ -3347,7 +3348,7 @@ Then, to use `'sr_model'` to classify the test points in `'test_points'`, saving
- [logistic_regression()](#logistic_regression)
- [random_forest()](#random_forest)
- [Multinomial logistic regression (softmax regression) on Wikipedia](https://en.wikipedia.org/wiki/Multinomial_logistic_regression)
- [SoftmaxRegression C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/softmax_regression/softmax_regression.hpp)
- [SoftmaxRegression C++ class documentation](../../user/methods/softmax_regression.md)
## sparse_coding()
{: #sparse_coding }
@@ -3436,7 +3437,7 @@ Then, this model could be used to encode a new matrix, `'otherdata'`, and save t
- [Sparse dictionary learning on Wikipedia](https://en.wikipedia.org/wiki/Sparse_dictionary_learning)
- [Efficient sparse coding algorithms (pdf)](http://papers.nips.cc/paper/2979-efficient-sparse-coding-algorithms.pdf)
- [Regularization and variable selection via the elastic net](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=46217f372a75dddc2254fdbc6b9418ba3554e453)
- [SparseCoding C++ class documentation](https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/sparse_coding/sparse_coding.hpp)
- [SparseCoding C++ class documentation](../../user/methods/sparse_coding.md)
## class Adaboost
{: #adaboost }