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README.md

ch.ethz.idsc.tensor Build Status

Library for tensor computations in Java, version 0.8.1

The tensor library was developed with the following objectives in mind

  • support for exact precision using integer fractions
  • support for calculation with physical units
  • suitable for use in safety-critical real-time systems
  • API and string expressions inspired by Mathematica

Diverse projects rely on the tensor library:

usecase_amodeus

Mobility-on-Demand

usecase_swisstrolley

SwissTrolley plus

usecase_motionplan

Motion Planning

usecase_gokart

Autonomous Gokart

Features

  • multi-dimensional arrays: scalars, vectors, matrices, n-linear forms, Lie-algebra ad-tensor, ...
  • unstructured, nested tensors, for instance {{1+2*I[A], -3/4}, {{5.678}, 9[kg*s^-1], 2[m^3]}}
  • scalars are real-, or complex numbers, from finite fields, or quantities with physical units
  • values are encoded as exact integer fractions, in double precision, and as java.math.BigDecimal
  • probability distributions for random variate generation: Binomial-, Poisson-, Exponential-distribution, etc.
  • matrix functions LinearSolve, SingularValueDecomposition, QRDecomposition, etc.
  • parametric functions LinearInterpolation, BSplineFunction, etc.
  • window functions: Gaussian, Hamming, Hann, Blackman, etc.
  • spectral analysis: Fourier, SpectrogramArray, etc.
  • import from and export to Mathematica, CSV, and image files

Gallery

gammademo

Gamma function

inversetrigdemo2

Trigonometry

mandelbulbdemo

Nylander's formula

newtondemo

Newton's method

Examples

Exact Precision

Solving systems of linear equations

Tensor matrix = Tensors.matrixInt(new int[][] { { 2, -3, 2 }, { 4, 9, -3 }, { -1, 3, 2 } });
System.out.println(Pretty.of(Inverse.of(matrix)));

[
 [   9/37    4/37   -3/37 ]
 [ -5/111    2/37  14/111 ]
 [   7/37   -1/37   10/37 ]
]

Linear programming

Tensor x = LinearProgramming.maxLessEquals( //
    Tensors.vector(1, 1), // rewards
    Tensors.fromString("{{4, -1}, {2, 1}, {-5, 2}}"), // matrix
    Tensors.vector(8, 7, 2)); // rhs
System.out.println(x);

{4/3, 13/3}

Null-space

Tensor matrix = Tensors.fromString("{{-1/3, 0, I}}");
System.out.println(Pretty.of(NullSpace.of(matrix)));

[
 [    1     0  -I/3 ]
 [    0     1     0 ]
]

Statistics

Distribution distribution = HypergeometricDistribution.of(10, 50, 100);
System.out.println(RandomVariate.of(distribution, 20));
PDF pdf = PDF.of(distribution);
System.out.println("P(X=3)=" + pdf.at(RealScalar.of(3)));

{6, 5, 1, 4, 3, 4, 7, 5, 7, 4, 6, 3, 5, 4, 5, 4, 6, 2, 6, 7}
P(X=3)=84000/742729

Physical Quantities

The tensor library implements Quantity, i.e. numbers with physical units. Several algorithms are verified to work with scalars of type Quantity.

Tensor matrix = Tensors.fromString( //
  "{{60[m^2], 30[m*rad], 20[kg*m]}, {30[m*rad], 20[rad^2], 15[kg*rad]}, {20[kg*m], 15[kg*rad], 12[kg^2]}}");
CholeskyDecomposition cd = CholeskyDecomposition.of(matrix);
System.out.println(cd.diagonal());
System.out.println(Pretty.of(cd.getL()));
System.out.println(cd.det().divide(Quantity.of(20, "m^2*rad")));

{60[m^2], 5[rad^2], 1/3[kg^2]}
[
 [             1              0              0 ]
 [ 1/2[m^-1*rad]              1              0 ]
 [  1/3[kg*m^-1]   1[kg*rad^-1]              1 ]
]
5[kg^2*rad]

The units of a quantity are chosen by the application layer. For instance, Quantity.of(3, "Apples") is valid syntax.

The tensor library contains the resource /unit/si.properties that encodes the SI unit system in the familiar strings such as m, kg, s, but the use of this convention is optional. The example below makes use of these provided definitions

Scalar mass = Quantity.of(300, "g"); // in gram
Scalar a = Quantity.of(981, "cm*s^-2"); // in centi-meters per seconds square
Scalar force = mass.multiply(a);
System.out.println(force);
Scalar force_N = UnitConvert.SI().to(Unit.of("N")).apply(force);
System.out.println(force_N);

294300[cm*g*s^-2]
2943/1000[N]

The arithmetic for the scalar type Quantity was developed in collaboration with the project SwissTrolley+.

Geometry

convexhullimage

Graham's scan

spherefitimage

Sphere fit

bipartitionimage

Hungarian algorithm

spatialmedianimage

Weiszfeld’s method

Miscellaneous

Tensors of rank 3

Tensor ad = LieAlgebras.so3();
Tensor x = Tensors.vector(7, 2, -4);
Tensor y = Tensors.vector(-3, 5, 2);
System.out.println(ad);
System.out.println(ad.dot(x).dot(y)); // coincides with cross product of x and y

{{{0, 0, 0}, {0, 0, -1}, {0, 1, 0}}, {{0, 0, 1}, {0, 0, 0}, {-1, 0, 0}}, {{0, -1, 0}, {1, 0, 0}, {0, 0, 0}}}
{24, -2, 41}

Functions for complex numbers

System.out.println(Sqrt.of(RationalScalar.of(-9, 16)));

3/4*I

Several functions support evaluation to higher than machine precision for type DecimalScalar.

System.out.println(Exp.of(DecimalScalar.of(10)));
System.out.println(Sqrt.of(DecimalScalar.of(2)));

220255.6579480671651695790064528423`34
1.414213562373095048801688724209698`34

The number after the prime indicates the precision of the decimal. The string representation is compatible with Mathematica.

Indices for the set and get functions start from zero like in C/Java:

Tensor matrix = Array.zeros(3, 4);
matrix.set(Tensors.vector(9, 8, 4, 5), 2);
matrix.set(Tensors.vector(6, 7, 8), Tensor.ALL, 1);
System.out.println(Pretty.of(matrix));
System.out.println(matrix.get(Tensor.ALL, 3)); // extraction of the 4th column

[
 [ 0  6  0  0 ]
 [ 0  7  0  0 ]
 [ 9  8  4  5 ]
]
{0, 0, 5}

Visualization

Image functions: ArrayPlot, Spectrogram

Tensor data = Cos.of(Subdivide.of(0, 100, 2000).map(Series.of(Tensors.vector(0, 5, 1))));
Tensor image = Spectrogram.of(data, ColorDataGradients.VISIBLESPECTRUM);
Export.of(HomeDirectory.file("spectrogram.png"), ImageResize.nearest(image, 4));

gives the image

spectrogram

Predefined color gradients

colordatagradients

Predefined color lists

colordatalists

Integration

Specify repository and dependency of the tensor library in the pom.xml file of your maven project:

<repositories>
  <repository>
    <id>tensor-mvn-repo</id>
    <url>https://raw.github.com/idsc-frazzoli/tensor/mvn-repo/</url>
    <snapshots>
      <enabled>true</enabled>
      <updatePolicy>always</updatePolicy>
    </snapshots>
  </repository>
</repositories>

<dependencies>
  <dependency>
    <groupId>ch.ethz.idsc</groupId>
    <artifactId>tensor</artifactId>
    <version>0.8.1</version>
  </dependency>
</dependencies>

The source code is attached to every release.


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