math library for kdb+
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
cephes
conmax
coverage
debug
example
lapack
mk
nlopt
openblas
q
src
test
.gitignore
CHANGES.txt
LICENSE.txt
Makefile
README.txt
configure

README.txt

Introduction
------------

qml is a library for statistics, linear algebra, and optimization in kdb+.
It provides an interface between the q programming language and numerical
libraries such as LAPACK.


License
-------

qml is free software, distributed under a BSD-style license. It is provided in
the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the
implied warranties of MERCHANTABILITY and FITNESS FOR A PARTICULAR PURPOSE. See
LICENSE.txt for more details.

qml is linked against several other libraries. The copyrights and licenses for
these libraries are also listed in LICENSE.txt.


Installation
------------

To compile and install from source code, run

    ./configure
    make
    make test
    make install

To install a precompiled binary, copy qml.q into the same directory as q.k, and
copy qml.dll or qml.so into the same directory as q.exe or q. Then run test.q.


Usage
-----

Load with

    q)\l qml.q

All functions are in the .qml namespace. Numerical arguments are automatically
converted into floating-point. Matrixes are in the usual row-major layout (lists
of row vectors). Complex numbers are represented as pairs of their real and
imaginary parts.

    q).qml.nicdf .25 .5 .975                  / normal distribution quantiles
    -0.6744898 0 1.959964

    q).qml.mchol (1 2 1;2 5 4;1 4 6)          / Cholesky factorization
    1 2 1
    0 1 2
    0 0 1

    q).qml.poly 2 -9 16 -15                   / solve 2x^3-9x^2+16x-15=0
    2.5
    1 1.414214
    1 -1.414214

    q).qml.mlsq[(1 1;1 2;1 3;1 4);11 2 -3 -4] / fit line
    14 -5f

    q).qml.conmin[{x*y+1};{1-(x*x)+y*y};0 0]  / minimize x(y+1) s.t. x^2+y^2<=1
    -0.8660254 0.5



Constants and functions
-----------------------

  pi              pi
  e               e
  eps             smallest representable step from 1.

  sin[x]          sine
  cos[x]          cosine
  tan[x]          tangent
  asin[x]         arcsine
  acos[x]         arccosine
  atan[x]         arctangent
  atan2[x;y]      atan[x%y]
  sinh[x]         hyperbolic sine
  cosh[x]         hyperbolic cosine
  tanh[x]         hyperbolic tangent
  asinh[x]        hyperbolic arcsine
  acosh[x]        hyperbolic arccosine
  atanh[x]        hyperbolic arctangent

  exp[x]          exponential
  expm1[x]        exp[x]-1
  log[x]          logarithm
  log10[x]        base-10 logarithm
  logb[x]         extract binary exponent
  log1p[x]        log[1+x]
  pow[a;x]        exponentiation
  sqrt[x]         square root
  cbrt[x]         cube root
  hypot[x;y]      sqrt[pow[x;2]+pow[y;2]]
  floor[x]        round downward
  ceil[x]         round upward
  fabs[x]         absolute value
  fmod[x;y]       remainder of x%y

  erf[x]          error function
  erfc[x]         complementary error function
  lgamma[x]       log of absolute value of gamma function
  gamma[x]        gamma function
  beta[x;y]       beta function
  pgamma[a;x]     lower incomplete gamma function (a>0)
  pgammac[a;x]    upper incomplete gamma function (a>0)
  pgammar[a;x]    regularized lower incomplete gamma function (a>0)
  pgammarc[a;x]   regularized upper incomplete gamma function (a>0)
  ipgammarc[a;p]  inverse complementary regularized incomplete gamma function
                    (a>0,p>=.5)
  pbeta[a;b;x]    incomplete beta function (a,b>0)
  pbetar[a;b;x]   regularized incomplete beta function (a,b>0)
  ipbetar[a;b;p]  inverse regularized incomplete beta function (a,b>0)
  j0[x]           order 0 Bessel function
  j1[x]           order 1 Bessel function
  y0[x]           order 0 Bessel function of the second kind
  y1[x]           order 1 Bessel function of the second kind

  ncdf[x]         CDF of normal distribution
  nicdf[p]        its inverse
  c2cdf[k;x]      CDF of chi-squared distribution (k>=1)
  c2icdf[k;p]     its inverse
  stcdf[k;x]      CDF of Student's t-distribution (natural k)
  sticdf[k;p]     its inverse
  fcdf[d1;d2;x]   CDF of F-distribution (d1,d2>=1,x>=0)
  ficdf[d1;d2;p]  its inverse
  gcdf[k;th;x]    CDF of gamma distribution
  gicdf[k;th;p]   its inverse
  bncdf[k;n;p]    CDF of binomial distribution
  bnicdf[k;n;x]   its inverse for p (k<n)
  pscdf[k;lambda] CDF of Poisson distribution
  psicdf[k;p]     its inverse for lambda
  smcdf[n;e]      CDF for one-sided Kolmogorov-Smirnov test
  smicdf[n;e]     its inverse
  kcdf[x]         CDF for Kolmogorov distribution
  kicdf[p]        its inverse (p>=1e-8)

  diag[diag]      make diagonal matrix
  mdim[matrix]    number of (rows; columns)
  mdiag[matrix]   extract main diagonal
  mdet[matrix]    determinant
  mrank[matrix]   rank
  minv[matrix]    inverse
  mpinv[matrix]   pseudoinverse
  dot[a;b]        dot product
  mm[A;B]         multiply
  mmx[opt;A;B]    mm[] with options
                   `lflip: flip A
                   `rflip: flip B
  ms[A;B]         solve B=A mm X, A is triangular
  mev[matrix]     (eigenvalues; eigenvectors) sorted by decreasing modulus
  mchol[matrix]   Cholesky factorization upper matrix
  mqr[matrix]     QR factorization: (Q; R)
  mqrp[matrix]    QR factorization with column pivoting:
                    (Q; R; P), matrix@\:P=Q mm R
  mlup[matrix]    LUP factorization with row pivoting:
                    (L; U; P), matrix[P]=L mm U
  msvd[matrix]    singular value decomposition: (U; Sigma; V)
  mkron[A;B]      Kronecker product

  poly[coef]      roots of a polynomial (highest-degree coefficient first)

  mls[A;B]        solve B=A mm X
  mlsx[opt;A;B]   mls[] with options
                   `equi: equilibrate the system (default: don't)
                   `flip: flip A, and flip B and X unless B is a vector
  mlsq[A;B]       solve min ||B-A mm X||
  mlsqx[opt;A;B]  mlsq[] with options
                   `svd:  use SVD algorithm      (default: QR or LQ)
                   `flip: flip A, and flip B and X unless B is a vector

  root[f;(x0;x1)]         find root on interval (f(x0)f(x1)<0)
  rootx[opt;f;(x0;x1)]    root[] with options (as dictionary or mixed list)
                           `iter:  max iterations         (default: 100)
                           `tol:   numerical tolerance    (default: ~1e-8)
                           `full:  full output            (default: only x)
                           `quiet: return null on failure (default: signal)
  solve[eqs;x0]           solve nonlinear equations (given as functions)
  solvex[opt;eqs;x0]      solve[] with options
                           `iter:  max iterations         (default: 1000)
                           `tol:   numerical tolerance    (default: ~1e-8)
                           `full:  full output            (default: only x)
                           `quiet: return null on failure (default: signal)
                           `steps: RK steps per iteration (default: 1)
                           `rk:    use RK steps only      (default: RK, SLP)
                           `slp:   use SLP steps only     (default: RK, SLP)
  line[f;base;x0]         line search for minimum from base
  linex[opt;f;base;x0]    line[] with same options as rootx[]
  min[f;x0]               find unconstrained minimum
  min[(f;df);x0]          min[] with analytic gradient function
  minx[opt;f;x0]          min[] with same options as solvex[], plus
                           `nm:    use Nelder–Mead method (default: CONMAX)
                           `sbplx: use Subplex method     (default: CONMAX)
  conmin[f;cons;x0]       find constrained minimum (functions cons>=0)
  conmin[(f;df);flip(cons;dcons);x0] conmin[] with analytic gradient functions
  conminx[opt;f;cons;x0]  conmin[] with same options as solvex[], plus
                           `lincon: assume linear cons    (default: nonlinear)
                           `cobyla: use COBYLA method     (default: CONMAX)