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Fix compilation issues and make sure examples work.

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This commit is contained in:
Ryan Curtin
2023-11-15 11:23:30 -05:00
parent bcd51d2f1e
commit 652aabea19
2 changed files with 38 additions and 28 deletions
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doc/user/methods/adaboost.md
+25 -15
View File
@@ -22,7 +22,7 @@ arma::Row<size_t> labels =
arma::randi<arma::Row<size_t>>(1000, arma::distr_param(0, 4));
arma::mat testDataset(10, 500, arma::fill::randu); // 500 test points.
AdaBoost<> ab; // Step 1: create model.
AdaBoost ab; // Step 1: create model.
ab.Train(dataset, labels, 5); // Step 2: train model.
arma::Row<size_t> predictions;
ab.Classify(testDataset, predictions); // Step 3: classify points.
@@ -231,14 +231,14 @@ arma::mat dataset;
data::Load("iris.csv", dataset, true);
// See https://datasets.mlpack.org/iris.labels.csv.
arma::Row<size_t> labels;
data::Load("iris.labels.csv", dataset, true);
data::Load("iris.labels.csv", labels, true);
// Create a weak learner with the desired hyperparameters.
Perceptron<> p;
p.MaxIterations() = 500; // We'll use a custom maximum number of iterations.
AdaBoost<> ab;
ab.Train(dataset, labels, 3, p);
AdaBoost ab;
// Train with a custom number of perceptron iterations, and custom AdaBoost
// parameters.
ab.Train(dataset, labels, 3, 75 /* maximum number of weak learners */,
1e-6 /* tolerance for AdaBoost convergence */,
100 /* maximum number of perceptron iterations */);
// Now predict the label of a point and the probabilities of each class.
size_t prediction;
@@ -261,9 +261,9 @@ arma::mat dataset;
data::Load("iris.csv", dataset, true);
// See https://datasets.mlpack.org/iris.labels.csv.
arma::Row<size_t> labels;
data::Load("iris_labels.csv", dataset, true);
data::Load("iris.labels.csv", dataset, true);
AdaBoost<> ab;
AdaBoost ab;
ab.MaxIterations() = 50; // Use at most 50 weak learners.
ab.Tolerance() = 1e-4; // Set a custom tolerance for convergence.
@@ -280,7 +280,7 @@ Load an AdaBoost model and print some information about it.
```c++
// Load a saved model named "adaboost_model" from `adaboost_model.bin`.
AdaBoost<> ab;
AdaBoost ab;
data::Load("adaboost_model.bin", "adaboost_model", ab, true);
std::cout << "Details about the model in `adaboost_model.bin`:" << std::endl;
@@ -400,8 +400,16 @@ arma::Row<size_t> labels =
arma::randi<arma::Row<size_t>>(1000, arma::distr_param(0, 4));
// Train in the constructor.
// Note that we specify decision stumps as the weak learner type.
AdaBoost<ID3DecisionStump> ab(dataset, labels, 5);
// Note that we specify decision stumps as the weak learner type, and pass
// hyperparameters for the decision stump (these could be omitted). See the
// DecisionTree documentation for more details on the ID3DecisionStump-specific
// hyperparameters.
AdaBoost<ID3DecisionStump> ab(dataset, labels, 5,
25 /* maximum number of decision stumps */,
1e-6 /* tolerance for convergence of AdaBoost */,
/** Hyperparameters specific to ID3DecisionStump: **/
10 /* minimum number of points in each leaf of the decision stump */,
1e-5 /* minimum gain for splitting the root node of the decision stump */);
// Create test data (500 points).
arma::mat testDataset(10, 500, arma::fill::randu);
@@ -427,8 +435,10 @@ arma::Row<size_t> labels =
arma::randi<arma::Row<size_t>>(1000, arma::distr_param(0, 4));
// Train in the constructor, using floating-point data.
// (TODO: do we have to explicitly write MatType?)
AdaBoost<> ab(dataset, labels, 5);
// The weak learner type is now a floating-point Perceptron.
typedef Perceptron<SimpleWeightUpdate, ZeroInitialization, arma::fmat>
PerceptronType;
AdaBoost<PerceptronType, arma::fmat> ab(dataset, labels, 5);
// Create test data (500 points).
arma::fmat testDataset(10, 500, arma::fill::randu);
doc/user/methods/perceptron.md
+13 -13
View File
@@ -24,10 +24,10 @@ arma::Row<size_t> labels =
arma::randi<arma::Row<size_t>>(1000, arma::distr_param(0, 4));
arma::mat testDataset(10, 500, arma::fill::randu); // 500 test points.
Perceptron<> p; // Step 1: create model.
p.Train(dataset, labels, 5); // Step 2: train model.
Perceptron p; // Step 1: create model.
p.Train(dataset, labels, 5); // Step 2: train model.
arma::Row<size_t> predictions;
tree.Classify(testDataset, predictions); // Step 3: classify points.
p.Classify(testDataset, predictions); // Step 3: classify points.
// Print some information about the test predictions.
std::cout << arma::accu(predictions == 1) << " test points classified as class "
@@ -211,27 +211,27 @@ arma::mat dataset;
data::Load("iris.csv", dataset, true);
// See https://datasets.mlpack.org/iris.labels.csv.
arma::Row<size_t> labels;
data::Load("iris.labels.csv", dataset, true);
data::Load("iris.labels.csv", labels, true);
// Create a Perceptron object.
Perceptron<> p;
Perceptron p;
// Set the maximum number of iterations to 100. (This can also be done in the
// constructor.)
p.MaxIterations() = 100;
// Train the model for up to 100 iterations.
p.Train(data, labels, 3);
p.Train(dataset, labels, 3);
// Now, compute and print accuracy on the training set.
arma::Row<size_t> predictions;
p.Classify(data, predictions);
p.Classify(dataset, predictions);
std::cout << "Training set accuracy after 100 iterations: "
<< (100.0 * double(arma::accu(labels == predictions)) / labels.n_elem)
<< "\%." << std::endl;
// Train for another 250 iterations and compute training set accuracy again.
p.Train(data, labels, 3, 250);
p.Classify(data, predictions);
p.Train(dataset, labels, 3, 250);
p.Classify(dataset, predictions);
std::cout << "Training set accuracy after 350 iterations: "
<< (100.0 * double(arma::accu(labels == predictions)) / labels.n_elem)
<< "\%." << std::endl;
@@ -242,7 +242,7 @@ std::cout << "Training set accuracy after 350 iterations: "
Load a saved perceptron from disk and print information about it.
```c++
Perceptron<> p;
Perceptron p;
// This call assumes a perceptron called "p" has already been saved to
// `perceptron.bin` with `data::Save()`.
data::Load("perceptron.bin", "p", p, true);
@@ -382,7 +382,7 @@ Perceptron<SimpleWeightUpdate, RandomPerceptronInitialization> p(dataset,
// Create test data (500 points).
arma::mat testDataset(10, 500, arma::fill::randu);
arma::Row<size_t> predictions;
tree.Classify(testDataset, predictions);
p.Classify(testDataset, predictions);
// Now `predictions` holds predictions for the test dataset.
// Print some information about the test predictions.
@@ -404,13 +404,13 @@ arma::Row<size_t> labels =
arma::randi<arma::Row<size_t>>(1000, arma::distr_param(0, 4));
// Train in the constructor.
Perceptron<> p(dataset, labels, 5);
Perceptron p(dataset, labels, 5);
// Create test data (500 points).
arma::sp_fmat testDataset;
testDataset.sprandu(100, 500, 0.01);
arma::Row<size_t> predictions;
tree.Classify(testDataset, predictions);
p.Classify(testDataset, predictions);
// Now `predictions` holds predictions for the test dataset.
// Print some information about the test predictions.