mlpack core class documentation

Underlying the implementations of mlpack’s machine learning algorithms are mlpack core support classes, each of which are documented on this page.

🔗 Core math utilities

mlpack provides a number of additional mathematical utility classes and functions on top of Armadillo.

🔗 Range

The Range class represents a simple mathematical range (i.e. [0, 3]), with the bounds represented as doubles.

Constructors

Accessing and modifying range properties

Working with ranges

Example:

mlpack::Range r1(5.0, 6.0); // [5, 6]
mlpack::Range r2(7.0, 8.0); // [7, 8]

mlpack::Range r3 = r1 | r2; // [5, 8]
mlpack::Range r4 = r1 & r2; // empty range

bool b1 = r1.Contains(r2); // false
bool b2 = r1.Contains(5.5); // true
bool b3 = r1.Contains(r3); // true
bool b4 = r3.Contains(r4); // false

// Create a range of `float`s and a range of `int`s.
mlpack::RangeType<float> r5(1.0f, 1.5f); // [1.0, 1.5]
mlpack::RangeType<int> r6(3, 4); // [3, 4]

Range is used by:

🔗 ColumnCovariance()

Example:

// Generate a random data matrix with 100 points in 5 dimensions.
arma::mat data(5, 100, arma::fill::randu);

// Compute the covariance matrix of the column-major matrix.
arma::mat cov = mlpack::ColumnCovariance(data);
cov.print("Covariance of random matrix:");

🔗 ColumnsToBlocks

The ColumnsToBlocks class provides a way to transform data points (e.g. columns in a matrix) into a block matrix format, primarily useful for visualization as an image.

As a simple example, given a matrix with four columns A, B, C, and D, ColumnsToBlocks can transform this matrix into the form

[[m m m m m]
 [m A m B m]
 [m m m m m]
 [m C m D m]
 [m m m m m]]

where m is a margin, and where each column may itself be reshaped into a block.

Constructors

Properties

🔗 Scaling values

ColumnsToBlocks also has the capability of linearly scaling values of the inputs to a given range.

Note: the margin element (ctb.BufValue()) is considered during the scaling process.

Transforming into block format

Examples

Reshape two 4-element vectors into one row of two blocks.

// This matrix has two columns.
arma::mat input;
input = { { -1.0000, 0.1429 },
          { -0.7143, 0.4286 },
          { -0.4286, 0.7143 },
          { -0.1429, 1.0000 } };
input.print("Input columns:");

arma::mat output;
mlpack::ColumnsToBlocks ctb(1, 2);
ctb.Transform(input, output);

// The columns of the input will be reshaped as a square which is
// surrounded by padding value -1 (this value could be changed with the
// BufValue() method):
// -1.0000  -1.0000  -1.0000  -1.0000  -1.0000  -1.0000  -1.0000
// -1.0000  -1.0000  -0.4286  -1.0000   0.1429   0.7143  -1.0000
// -1.0000  -0.7143  -0.1429  -1.0000   0.4286   1.0000  -1.0000
// -1.0000  -1.0000  -1.0000  -1.0000  -1.0000  -1.0000  -1.0000
output.print("Output using 2x2 block size:");

// Now, let's change some parameters; let's have each input column output not
// as a square, but as a 4x1 vector.
ctb.BlockWidth(1);
ctb.BlockHeight(4);
ctb.Transform(input, output);

// The output here will be similar, but each maximal input is 4x1:
// -1.0000 -1.0000 -1.0000 -1.0000 -1.0000
// -1.0000 -1.0000 -1.0000  0.1429 -1.0000
// -1.0000 -0.7143 -1.0000  0.4286 -1.0000
// -1.0000 -0.4286 -1.0000  0.7143 -1.0000
// -1.0000 -0.1429 -1.0000  1.0000 -1.0000
// -1.0000 -1.0000 -1.0000 -1.0000 -1.0000
output.print("Output using 4x1 block size:");

Load simple images and reshape into blocks.

// Load some favicons from websites associated with mlpack.
std::vector<std::string> images;
// See the following files:
// - https://datasets.mlpack.org/images/mlpack-favicon.png
// - https://datasets.mlpack.org/images/ensmallen-favicon.png
// - https://datasets.mlpack.org/images/armadillo-favicon.png 
// - https://datasets.mlpack.org/images/bandicoot-favicon.png
images.push_back("mlpack-favicon.png");
images.push_back("ensmallen-favicon.png");
images.push_back("armadillo-favicon.png");
images.push_back("bandicoot-favicon.png");

mlpack::data::ImageInfo info;
info.Channels() = 1; // Force loading in grayscale.

arma::mat matrix;
mlpack::data::Load(images, matrix, info, true);

// Now `matrix` has 4 columns, each of which is an individual image.
// Let's save that as its own image just for visualization.
mlpack::data::ImageInfo outInfo(matrix.n_cols, matrix.n_rows, 1);
mlpack::data::Save("favicons-matrix.png", matrix, outInfo, true);

// Use ColumnsToBlocks to create a 2x2 block matrix holding each image.
mlpack::ColumnsToBlocks ctb(2, 2);
ctb.BufValue(0.0); // Use 0 for the margin value.
ctb.BufSize(2); // Use 2-pixel margins.

arma::mat blocks;
ctb.Transform(matrix, blocks);

mlpack::data::ImageInfo blockOutInfo(blocks.n_cols, blocks.n_rows, 1);
mlpack::data::Save("favicons-blocks.png", blocks, blockOutInfo, true);

The resulting images (before and after using ColumnsToBlocks) are shown below.

Before:

four favicons each as a column in a matrix, unintelligible

After:

four favicons each as a block in a larger image, much better

See also

🔗 Distribution utilities

Example:

const double d1 = mlpack::Digamma(0.25);
const double d2 = mlpack::Digamma(1.0);

const double t1 = mlpack::Trigamma(0.25);
const double t2 = mlpack::Trigamma(1.0);

std::cout << "Digamma(0.25):  " << d1 << "." << std::endl;
std::cout << "Digamma(1.0):   " << d2 << "." << std::endl;
std::cout << "Trigamma(0.25): " << t1 << "." << std::endl;
std::cout << "Trigamma(1.0):  " << t2 << "." << std::endl;

🔗 RandVector()

Example:

// Generate a random 10-dimensional vector.
arma::vec v;
v.set_size(10);
RandVector(v);
v.print("Random 10-dimensional vector: ");

std::cout << "Random 10-dimensional vector: " << std::endl;
std::cout << v.t();
std::cout << "L2-norm of vector (should be 1): " << arma::norm(v, 2) << "."
    << std::endl;

🔗 Logarithmic utilities

mlpack contains a few functions that are useful for working with logarithms, or vectors containing logarithms.

🔗 MultiplyCube2Cube()

Example usage:

// Generate two random cubes.
arma::cube x(10, 100, 5, arma::fill::randu); // 5 matrices, each 10x100.
arma::cube y(12, 100, 5, arma::fill::randu); // 5 matrices, each 12x100.

arma::cube z = mlpack::MultiplyCube2Cube(x, y, false, true);

// Output size should be 10x12x5.
std::cout << "Output size: " << z.n_rows << "x" << z.n_cols << "x" << z.n_slices
    << "." << std::endl;

🔗 MultiplyMat2Cube()

Example usage:

// Generate random inputs.
arma::mat  x(10, 100,    arma::fill::randu); // Random 10x100 matrix.
arma::cube y(12, 100, 5, arma::fill::randu); // 5 matrices, each 12x100.

arma::cube z = mlpack::MultiplyMat2Cube(x, y, false, true);

// Output size should be 10x12x5.
std::cout << "Output size: " << z.n_rows << "x" << z.n_cols << "x" << z.n_slices
    << "." << std::endl;

🔗 MultiplyCube2Mat()

Example usage:

// Generate two random cubes.
arma::cube x(12, 50, 5, arma::fill::randu); // 5 matrices, each 12x50.
arma::mat  y(12, 60,    arma::fill::randu); // Random 12x60 matrix.

arma::cube z = mlpack::MultiplyCube2Mat(x, y, true, false);

// Output size should be 50x60x5.
std::cout << "Output size: " << z.n_rows << "x" << z.n_cols << "x" << z.n_slices
    << "." << std::endl;

🔗 Quantile()

Example usage:

// 70% of points from N(0, 1) are less than q1 = 0.524.
double q1 = mlpack::Quantile(0.7);

// 90% of points from N(0, 1) are less than q2 = 1.282.
double q2 = mlpack::Quantile(0.9);

// 50% of points from N(1, 1) are less than q3 = 1.0.
double q3 = mlpack::Quantile(0.5, 1.0); // Quantile of 1.0 for N(1, 1) is 1.0.

// 10% of points from N(1, 0.1) are less than q4 = 0.871.
double q4 = mlpack::Quantile(0.1, 1.0, 0.1);

std::cout << "Quantile(0.7): " << q1 << "." << std::endl;
std::cout << "Quantile(0.9): " << q2 << "." << std::endl;
std::cout << "Quantile(0.5, 1.0): " << q3 << "." << std::endl;
std::cout << "Quantile(0.1, 1.0, 0.1): " << q4 << "." << std::endl;

🔗 RNG and random number utilities

On top of the random number generation support that Armadillo provides via randu(), randn(), and randi(), mlpack provides a few additional thread-safe random number generation functions for generating random scalar values.

Examples:

mlpack::RandomSeed(123); // Set a specific random seed.

const double r1 = mlpack::Random();             // In the range [0, 1).
const double r2 = mlpack::Random(3, 4);         // In the range [3, 4).
const double r3 = mlpack::RandBernoulli(0.25);  // P(1) = 0.25.
const int    r4 = mlpack::RandInt(10);          // In the range [0, 10).
const int    r5 = mlpack::RandInt(5, 10);       // In the range [5, 10).
const double r6 = mlpack::RandNormal();         // r6 ~ N(0, 1).
const double r7 = mlpack::RandNormal(2.0, 3.0); // r7 ~ N(2, 3).

std::cout << "Random():            " << r1 << "." << std::endl;
std::cout << "Random(3, 4):        " << r2 << "." << std::endl;
std::cout << "RandBernoulli(0.25): " << r3 << "." << std::endl;
std::cout << "RandInt(10):         " << r4 << "." << std::endl;
std::cout << "RandInt(5, 10):      " << r5 << "." << std::endl;
std::cout << "RandNormal():        " << r6 << "." << std::endl;
std::cout << "RandNormal(2, 3):    " << r7 << "." << std::endl;

🔗 RandomBasis()

The RandomBasis() function generates a random d-dimensional orthogonal basis.

Example:

arma::mat basis;

// Generate a 10-dimensional random basis.
mlpack::RandomBasis(basis, 10);

// Each two vectors are orthogonal.
std::cout << "Dot product of basis vectors 2 and 4: "
    << arma::dot(basis.col(2), basis.col(4))
    << " (should be zero or very close!)." << std::endl;

🔗 ShuffleData()

Shuffle a column-major dataset and associated labels/responses, optionally with weights. This preserves the connection of each data point to its label (and optionally its weight).

Note: when inputData is a cube (e.g. arma::cube or similar), the columns of the cube will be shuffled.

Example usage:

// See https://datasets.mlpack.org/iris.csv.
arma::mat dataset;
mlpack::data::Load("iris.csv", dataset, true);
// See https://datasets.mlpack.org/iris.labels.csv.
arma::Row<size_t> labels;
mlpack::data::Load("iris.labels.csv", labels, true);

// Now shuffle the points in the iris dataset.
arma::mat shuffledDataset;
arma::Row<size_t> shuffledLabels;
mlpack::ShuffleData(dataset, labels, shuffledDataset, shuffledLabels);

std::cout << "Before shuffling, the first point was: " << std::endl;
std::cout << "  " << dataset.col(0).t();
std::cout << "with label " << labels[0] << "." << std::endl;
std::cout << std::endl;
std::cout << "After shuffling, the first point is: " << std::endl;
std::cout << "  " << shuffledDataset.col(0).t();
std::cout << "with label " << shuffledLabels[0] << "." << std::endl;

// Generate random weights, then shuffle those also.
arma::rowvec weights(dataset.n_cols, arma::fill::randu);
arma::rowvec shuffledWeights;
mlpack::ShuffleData(dataset, labels, weights, shuffledDataset, shuffledLabels,
    shuffledWeights);

std::cout << std::endl << std::endl;
std::cout << "Before shuffling with weights, the first point was: "
    << std::endl;
std::cout << "  " << dataset.col(0).t();
std::cout << "with label " << labels[0] << " and weight " << weights[0] << "."
    << std::endl;
std::cout << std::endl;
std::cout << "After shuffling with weights, the first point is: " << std::endl;
std::cout << "  " << shuffledDataset.col(0).t();
std::cout << "with label " << shuffledLabels[0] << " and weight "
    << shuffledWeights[0] << "." << std::endl;

🔗 Distributions

mlpack has support for a number of different distributions, each supporting the same API. These can be used with, for instance, the HMM class.

🔗 DiscreteDistribution

DiscreteDistribution represents a multidimensional categorical distribution (or generalized Bernoulli distribution) where integer-valued vectors (e.g. [0, 3, 4]) are associated with specific probabilities in each dimension.

Example: a 3-dimensional DiscreteDistribution will have a specific probability value associated with each integer value in each dimension. So, for the vector [0, 3, 4], P(0) in dimension 0 could be, e.g., 0.3, P(3) in dimension 1 could be, e.g., 0.4, and P(4) in dimension 2 could be, e.g., 0.6. Then, P([0, 3, 4]) would be 0.3 * 0.4 * 0.6 = 0.072.

Constructors

Access and modify properties of distribution

Compute probabilities of points

Sample from the distribution

Fit the distribution to observations

Example usage:

// Create a single-dimension Bernoulli distribution: P([0]) = 0.3, P([1]) = 0.7.
mlpack::DiscreteDistribution bernoulli(2);
bernoulli.Probabilities(0)[0] = 0.3;
bernoulli.Probabilities(0)[1] = 0.7;

const double p1 = bernoulli.Probability(arma::vec("0")); // p1 = 0.3.
const double p2 = bernoulli.Probability(arma::vec("1")); // p2 = 0.7.

// Create a 3-dimensional discrete distribution by specifying the probabilities
// manually.
arma::vec probDim0 = arma::vec("0.1 0.3 0.5 0.1"); // 4 possible values.
arma::vec probDim1 = arma::vec("0.7 0.3");         // 2 possible values.
arma::vec probDim2 = arma::vec("0.4 0.4 0.2");     // 3 possible values.
std::vector<arma::vec> probs { probDim0, probDim1, probDim2 };
mlpack::DiscreteDistribution d(probs);

arma::vec obs("2 0 1");
const double p3 = d.Probability(obs); // p3 = 0.5 * 0.7 * 0.4 = 0.14.

// Estimate a 10-dimensional discrete distribution.
// Each dimension takes values between 0 and 9.
arma::mat observations = arma::randi<arma::mat>(10, 1000,
    arma::distr_param(0, 9));

// Create a distribution with 10 observations in each of the 10 dimensions.
mlpack::DiscreteDistribution d2(
    arma::Col<size_t>("10 10 10 10 10 10 10 10 10 10"));
d2.Train(observations);

// Compute the probabilities of each point.
arma::vec probabilities;
d2.Probability(observations, probabilities);
std::cout << "Average probability: " << arma::mean(probabilities) << "."
    << std::endl;

🔗 GaussianDistribution

GaussianDistribution is a standard multivariate Gaussian distribution with parameterized mean and covariance.

Constructors

Access and modify properties of distribution

Compute probabilities of points

Sample from the distribution

Fit the distribution to observations

Example usage:

// Create a Gaussian distribution in 3 dimensions with zero mean and unit
// covariance.
mlpack::GaussianDistribution g(3);

// Compute the probability of the point [0, 0.5, 0.25].
const double p = g.Probability(arma::vec("0 0.5 0.25"));

// Modify the mean in dimension 0.
g.Mean()[0] = 0.5;

// Set a random covariance.
arma::mat newCov(3, 3, arma::fill::randu);
newCov *= newCov.t(); // Ensure covariance is positive semidefinite.
g.Covariance(std::move(newCov)); // Set new covariance.

// Compute the probability of the same point [0, 0.5, 0.25].
const double p2 = g.Probability(arma::vec("0 0.5 0.25"));

// Create a Gaussian distribution that is estimated from random samples in 50
// dimensions.
arma::mat samples(50, 10000, arma::fill::randn); // Normally distributed.

mlpack::GaussianDistribution g2(50);
g2.Train(samples);

// Compute the probability of all of the samples.
arma::vec probabilities;
g2.Probability(samples, probabilities);

std::cout << "Average probability is: " << arma::mean(probabilities) << "."
    << std::endl;

🔗 Metrics

mlpack includes a number of distance metrics for its distance-based techniques. These all implement the same API, providing one Evaluate() method, and can be used with a variety of different techniques, including:

Supported metrics:

🔗 LMetric

The LMetric template class implements a generalized L-metric (L1-metric, L2-metric, etc.). The class has two template parameters:

LMetric<Power, TakeRoot>

Several convenient typedefs are available:

The static Evaluate() method can be used to compute the distance between two vectors.

Note: The vectors given to Evaluate() can have any type so long as the type implements the Armadillo API (e.g. arma::fvec, arma::sp_fvec, etc.).

Example usage:

// Create two vectors: [0, 1.0, 5.0] and [1.0, 3.0, 5.0].
arma::vec a("0.0 1.0 5.0");
arma::vec b("1.0 3.0 5.0");

const double d1 = mlpack::ManhattanDistance::Evaluate(a, b);        // d1 = 3.0
const double d2 = mlpack::EuclideanDistance::Evaluate(a, b);        // d2 = 2.24
const double d3 = mlpack::SquaredEuclideanDistance::Evaluate(a, b); // d3 = 5.0
const double d4 = mlpack::ChebyshevDistance::Evaluate(a, b);        // d4 = 2.0
const double d5 = mlpack::LMetric<4>::Evaluate(a, b);               // d5 = 2.03
const double d6 = mlpack::LMetric<3, false>::Evaluate(a, b);        // d6 = 9.0

std::cout << "Manhattan distance:         " << d1 << "." << std::endl;
std::cout << "Euclidean distance:         " << d2 << "." << std::endl;
std::cout << "Squared Euclidean distance: " << d3 << "." << std::endl;
std::cout << "Chebyshev distance:         " << d4 << "." << std::endl;
std::cout << "L4-distance:                " << d5 << "." << std::endl;
std::cout << "Cubed L3-distance:          " << d6 << "." << std::endl;

// Compute the distance between two random 10-dimensional vectors in a matrix.
arma::mat m(10, 100, arma::fill::randu);

const double d7 = mlpack::EuclideanDistance::Evaluate(m.col(0), m.col(7));

std::cout << std::endl;
std::cout << "Distance between two random vectors: " << d7 << "." << std::endl;
std::cout << std::endl;

// Compute the distance between two 32-bit precision `float` vectors.
arma::fvec fa("0.0 1.0 5.0");
arma::fvec fb("1.0 3.0 5.0");

const double d8 = mlpack::EuclideanDistance::Evaluate(fa, fb); // d8 = 2.236

std::cout << "Euclidean distance (fvec): " << d8 << "." << std::endl;

🔗 Kernels

mlpack includes a number of Mercer kernels for its kernel-based techniques. These all implement the same API, providing one Evaluate() method, and can be used with a variety of different techniques, including:

Supported kernels:

🔗 GaussianKernel

The GaussianKernel class implements the standard Gaussian kernel (also called the radial basis function kernel or RBF kernel).

The Gaussian kernel is defined as: k(x1, x2) = exp(-|| x1 - x2 ||^2 / (2 * bw^2)) where bw is the bandwidth parameter of the kernel.

Constructors and properties

Kernel evaluation

Other utilities

Example usage:

// Create a Gaussian kernel with default bandwidth.
mlpack::GaussianKernel g;

// Create a Gaussian kernel with bandwidth 5.0.
mlpack::GaussianKernel g2(5.0);

// Evaluate the kernel value between two 3-dimensional points.
arma::vec x1("0.5 1.0 1.5");
arma::vec x2("1.5 1.0 0.5");
const double k1 = g.Evaluate(x1, x2);
const double k2 = g2.Evaluate(x1, x2);
std::cout << "Kernel values: " << k1 << " (bw=1.0), " << k2 << " (bw=5.0)."
    << std::endl;

// Evaluate the kernel value when the distance between two points is already
// computed.
const double distance = 1.5;
const double k3 = g.Evaluate(distance);

// Change the bandwidth of the kernel to 2.5.
g.Bandwidth(2.5);
const double k4 = g.Evaluate(x1, x2);
std::cout << "Kernel value with bw=2.5: " << k4 << "." << std::endl;

// Evaluate the kernel value between x1 and all points in a random matrix.
arma::mat r(3, 100, arma::fill::randu);
arma::vec kernelValues(100);
for (size_t i = 0; i < r.n_cols; ++i)
  kernelValues[i] = g.Evaluate(x1, r.col(i));
std::cout << "Average kernel value for random points: "
    << arma::mean(kernelValues) << "." << std::endl;

// Compute the kernel value between two 32-bit floating-point vectors.
arma::fvec fx1("0.5 1.0 1.5");
arma::fvec fx2("1.5 1.0 0.5");
const double k5 = g.Evaluate(fx1, fx2);
const double k6 = g2.Evaluate(fx1, fx2);