Math-GSL
view release on metacpan or search on metacpan
lib/Math/GSL/Histogram.pm view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
lib/Math/GSL/Multifit.pm view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
lib/Math/GSL/Multilarge.pm view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
lib/Math/GSL/ODEIV.pm view on Meta::CPAN
=item * C<gsl_odeiv_control_hadjust >
=item * C<gsl_odeiv_control_name >
=item * C<gsl_odeiv_control_standard_new($eps_abs, $eps_rel, $a_y, $a_dydt)> - The standard control object is a four parameter heuristic based on absolute and relative errors $eps_abs and $eps_rel, and scaling factors $a_y and $a_dydt for the system ...
=item * C<gsl_odeiv_control_y_new($eps_abs, $eps_rel)> - This function creates a new control object which will keep the local error on each step within an absolute error of $eps_abs and relative error of $eps_rel with respect to the solution y_i(t). ...
=item * C<gsl_odeiv_control_yp_new($eps_abs, $eps_rel)> - This function creates a new control object which will keep the local error on each step within an absolute error of $eps_abs and relative error of $eps_rel with respect to the derivatives of t...
=item * C<gsl_odeiv_control_scaled_new($eps_abs, $eps_rel, $a_y, $a_dydt, $scale_abs, $dim) > - This function creates a new control object which uses the same algorithm as gsl_odeiv_control_standard_new but with an absolute error which is scaled for ...
=item * C<gsl_odeiv_evolve_alloc($dim)> - This function returns a pointer to a newly allocated instance of an evolution function for a system of $dim dimensions.
=item * C<gsl_odeiv_evolve_apply($e, $c, $step, $dydt, \$t, $t1, \$h, $y)> - This function advances the system ($e, $dydt) from time $t and position $y using the stepping function $step. The new time and position are stored in $t and $y on output. Th...
=item * C<gsl_odeiv_evolve_reset($e)> - This function resets the evolution function $e. It should be used whenever the next use of $e will not be a continuation of a previous step.
=item * C<gsl_odeiv_evolve_free($e)> - This function frees all the memory associated with the evolution function $e.
=back
lib/Math/GSL/Randist.pm view on Meta::CPAN
=head2 Bernoulli
=over
=item gsl_ran_bernoulli($r, $p)
This function returns either 0 or 1, the result of a Bernoulli trial with probability $p. The probability distribution for a Bernoulli trial is, p(0) = 1 - $p and p(1) = $p. $r is a gsl_rng structure.
=item gsl_ran_bernoulli_pdf($k, $p)
This function computes the probability p($k) of obtaining $k from a Bernoulli distribution with probability parameter $p, using the formula given above.
=back
=head2 Beta
=over
=item gsl_ran_beta($r, $a, $b)
This function returns a random variate from the beta distribution. The distribution function is, p($x) dx = {Gamma($a+$b) \ Gamma($a) Gamma($b)} $x**{$a-1} (1-$x)**{$b-1} dx for 0 <= $x <= 1.$r is a gsl_rng structure.
=item gsl_ran_beta_pdf($x, $a, $b)
This function computes the probability density p($x) at $x for a beta distribution with parameters $a and $b, using the formula given above.
=back
=head2 Binomial
=over
=item gsl_ran_binomial($k, $p, $n)
This function returns a random integer from the binomial distribution, the number of successes in n independent trials with probability $p. The probability distribution for binomial variates is p($k) = {$n! \ $k! ($n-$k)! } $p**$k (1-$p)^{$n-$k} for ...
lib/Math/GSL/Randist.pm view on Meta::CPAN
=item gsl_ran_binomial_knuth($k, $p, $n)
Alternative and original implementation for gsl_ran_binomial using Knuth's algorithm.
=item gsl_ran_binomial_tpe($k, $p, $n)
Same as gsl_ran_binomial.
=item gsl_ran_binomial_pdf($k, $p, $n)
This function computes the probability p($k) of obtaining $k from a binomial distribution with parameters $p and $n, using the formula given above.
=back
=head2 Exponential
=over
=item gsl_ran_exponential($r, $mu)
This function returns a random variate from the exponential distribution with mean $mu. The distribution is, p($x) dx = {1 \ $mu} exp(-$x/$mu) dx for $x >= 0. $r is a gsl_rng structure.
=item gsl_ran_exponential_pdf($x, $mu)
This function computes the probability density p($x) at $x for an exponential distribution with mean $mu, using the formula given above.
=back
=head2 Exponential Power
=over
=item gsl_ran_exppow($r, $a, $b)
This function returns a random variate from the exponential power distribution with scale parameter $a and exponent $b. The distribution is, p(x) dx = {1 / 2 $a Gamma(1+1/$b)} exp(-|$x/$a|**$b) dx for $x >= 0. For $b = 1 this reduces to the Laplace d...
=item gsl_ran_exppow_pdf($x, $a, $b)
This function computes the probability density p($x) at $x for an exponential power distribution with scale parameter $a and exponent $b, using the formula given above.
=back
=head2 Cauchy
=over
=item gsl_ran_cauchy($r, $scale)
This function returns a random variate from the Cauchy distribution with
$scale. The probability distribution for Cauchy random variates is,
p(x) dx = {1 / $scale pi (1 + (x/$$scale)**2) } dx
for x in the range -infinity to +infinity. The Cauchy distribution is also
known as the Lorentz distribution. $r is a gsl_rng structure.
=item gsl_ran_cauchy_pdf($x, $scale)
This function computes the probability density p($x) at $x for a Cauchy
distribution with $scale, using the formula given above.
=back
=head2 Chi-Squared
=over
=item gsl_ran_chisq($r, $nu)
This function returns a random variate from the chi-squared distribution with $nu degrees of freedom. The distribution function is, p(x) dx = {1 / 2 Gamma($nu/2) } (x/2)**{$nu/2 - 1} exp(-x/2) dx for $x >= 0. $r is a gsl_rng structure.
=item gsl_ran_chisq_pdf($x, $nu)
This function computes the probability density p($x) at $x for a chi-squared distribution with $nu degrees of freedom, using the formula given above.
=back
=head2 Dirichlet
=over
=item gsl_ran_dirichlet($r, $alpha)
This function returns an array of K (where K = length of $alpha array) random
lib/Math/GSL/Randist.pm view on Meta::CPAN
Z = {\prod_{i=1}^K \Gamma(\alpha_i)} / {\Gamma( \sum_{i=1}^K \alpha_i)}
The random variates are generated by sampling K values from gamma distributions
with parameters a=alpha_i, b=1, and renormalizing. See A.M. Law, W.D. Kelton,
Simulation Modeling and Analysis (1991).
=item gsl_ran_dirichlet_pdf($theta, $alpha)
This function computes the probability density p(\theta_1, ... , \theta_K) at
theta[K] for a Dirichlet distribution with parameters alpha[K], using the
formula given above. $alpha and $theta should be array references of the same size.
Theta should be normalized to sum to 1.
=item gsl_ran_dirichlet_lnpdf($theta, $alpha)
This function computes the logarithm of the probability density p(\theta_1, ...
, \theta_K) for a Dirichlet distribution with parameters alpha[K]. $alpha and
$theta should be array references of the same size.
Theta should be normalized to sum to 1.
=back
lib/Math/GSL/Randist.pm view on Meta::CPAN
=head2 F-distribution
=over
=item gsl_ran_fdist($r, $nu1, $nu2)
This function returns a random variate from the F-distribution with degrees of freedom nu1 and nu2. The distribution function is, p(x) dx = { Gamma(($nu_1 + $nu_2)/2) / Gamma($nu_1/2) Gamma($nu_2/2) } $nu_1**{$nu_1/2} $nu_2**{$nu_2/2} x**{$nu_1/2 - 1...
=item gsl_ran_fdist_pdf($x, $nu1, $nu2)
This function computes the probability density p(x) at x for an F-distribution with nu1 and nu2 degrees of freedom, using the formula given above.
=back
=head2 Uniform/Flat distribution
=over
=item gsl_ran_flat($r, $a, $b)
This function returns a random variate from the flat (uniform) distribution from a to b. The distribution is, p(x) dx = {1 / ($b-$a)} dx if $a <= x < $b and 0 otherwise. $r is a gsl_rng structure.
=item gsl_ran_flat_pdf($x, $a, $b)
This function computes the probability density p($x) at $x for a uniform distribution from $a to $b, using the formula given above.
=back
=head2 Gamma
=over
=item gsl_ran_gamma($r, $shape, $scale)
This function returns a random variate from the gamma distribution. The distribution function is,
p(x) dx = {1 \over \Gamma($shape) $scale^$shape} x^{$shape-1} e^{-x/$scale} dx
for x > 0.
Uses Marsaglia-Tsang method. Can also be called as gsl_ran_gamma_mt.
=item gsl_ran_gamma_pdf($x, $shape, $scale)
This function computes the probability density p($x) at $x for a gamma distribution with parameters $shape and $scale, using the formula given above.
=item gsl_ran_gamma($r, $shape, $scale)
Same as gsl_ran_gamma.
=item gsl_ran_gamma_knuth($r, $shape, $scale)
Alternative implementation for gsl_ran_gamma, using algorithm in Knuth volume 2.
=for comment
lib/Math/GSL/Randist.pm view on Meta::CPAN
=item gsl_ran_gaussian_ratio_method($r, $sigma)
This function computes a Gaussian random variate using the alternative Kinderman-Monahan-Leva ratio method.
=item gsl_ran_gaussian_ziggurat($r, $sigma)
This function computes a Gaussian random variate using the alternative Marsaglia-Tsang ziggurat ratio method. The Ziggurat algorithm is the fastest available algorithm in most cases. $r is a gsl_rng structure.
=item gsl_ran_gaussian_pdf($x, $sigma)
This function computes the probability density p($x) at $x for a Gaussian distribution with standard deviation sigma, using the formula given above.
=item gsl_ran_ugaussian($r)
=item gsl_ran_ugaussian_ratio_method($r)
=item gsl_ran_ugaussian_pdf($x)
This function computes results for the unit Gaussian distribution. It is equivalent to the gaussian functions above with a standard deviation of one, sigma = 1.
=item gsl_ran_bivariate_gaussian($r, $sigma_x, $sigma_y, $rho)
This function generates a pair of correlated Gaussian variates, with mean zero, correlation coefficient rho and standard deviations $sigma_x and $sigma_y in the x and y directions. The first value returned is x and the second y. The probability distr...
=item gsl_ran_bivariate_gaussian_pdf($x, $y, $sigma_x, $sigma_y, $rho)
This function computes the probability density p($x,$y) at ($x,$y) for a bivariate Gaussian distribution with standard deviations $sigma_x, $sigma_y and correlation coefficient $rho, using the formula given above.
=back
=head2 Gaussian Tail
=over
=item gsl_ran_gaussian_tail($r, $a, $sigma)
This function provides random variates from the upper tail of a Gaussian distribution with standard deviation sigma. The values returned are larger than the lower limit a, which must be positive. The probability distribution for Gaussian tail random ...
=item gsl_ran_gaussian_tail_pdf($x, $a, $gaussian)
This function computes the probability density p($x) at $x for a Gaussian tail distribution with standard deviation sigma and lower limit $a, using the formula given above.
=item gsl_ran_ugaussian_tail($r, $a)
This functions compute results for the tail of a unit Gaussian distribution. It is equivalent to the functions above with a standard deviation of one, $sigma = 1. $r is a gsl_rng structure.
=item gsl_ran_ugaussian_tail_pdf($x, $a)
This functions compute results for the tail of a unit Gaussian distribution. It is equivalent to the functions above with a standard deviation of one, $sigma = 1.
=back
lib/Math/GSL/Randist.pm view on Meta::CPAN
=head2 Landau
=over
=item gsl_ran_landau($r)
This function returns a random variate from the Landau distribution. The probability distribution for Landau random variates is defined analytically by the complex integral, p(x) = (1/(2 \pi i)) \int_{c-i\infty}^{c+i\infty} ds exp(s log(s) + x s) For...
=item gsl_ran_landau_pdf($x)
This function computes the probability density p($x) at $x for the Landau distribution using an approximation to the formula given above.
=back
=head2 Geometric
=over
=item gsl_ran_geometric($r, $p)
This function returns a random integer from the geometric distribution, the number of independent trials with probability $p until the first success. The probability distribution for geometric variates is, p(k) = p (1-$p)^(k-1) for k >= 1. Note that...
=item gsl_ran_geometric_pdf($k, $p)
This function computes the probability p($k) of obtaining $k from a geometric distribution with probability parameter p, using the formula given above.
=back
=head2 Hypergeometric
=over
=item gsl_ran_hypergeometric($r, $n1, $n2, $t)
This function returns a random integer from the hypergeometric distribution. The probability distribution for hypergeometric random variates is, p(k) = C(n_1, k) C(n_2, t - k) / C(n_1 + n_2, t) where C(a,b) = a!/(b!(a-b)!) and t <= n_1 + n_2. The do...
=item gsl_ran_hypergeometric_pdf($k, $n1, $n2, $t)
This function computes the probability p(k) of obtaining k from a hypergeometric distribution with parameters $n1, $n2 $t, using the formula given above.
=back
=head2 Gumbel
=over
=item gsl_ran_gumbel1($r, $a, $b)
This function returns a random variate from the Type-1 Gumbel distribution. The Type-1 Gumbel distribution function is, p(x) dx = a b \exp(-(b \exp(-ax) + ax)) dx for -\infty < x < \infty. $r is a gsl_rng structure.
=item gsl_ran_gumbel1_pdf($x, $a, $b)
This function computes the probability density p($x) at $x for a Type-1 Gumbel distribution with parameters $a and $b, using the formula given above.
=item gsl_ran_gumbel2($r, $a, $b)
This function returns a random variate from the Type-2 Gumbel distribution. The Type-2 Gumbel distribution function is, p(x) dx = a b x^{-a-1} \exp(-b x^{-a}) dx for 0 < x < \infty. $r is a gsl_rng structure.
=item gsl_ran_gumbel2_pdf($x, $a, $b)
This function computes the probability density p($x) at $x for a Type-2 Gumbel distribution with parameters $a and $b, using the formula given above.
=back
=head2 Logistic
=over
=item gsl_ran_logistic($r, $a)
This function returns a random variate from the logistic distribution. The distribution function is, p(x) dx = { \exp(-x/a) \over a (1 + \exp(-x/a))^2 } dx for -\infty < x < +\infty. $r is a gsl_rng structure.
=item gsl_ran_logistic_pdf($x, $a)
This function computes the probability density p($x) at $x for a logistic distribution with scale parameter $a, using the formula given above.
=back
=head2 Lognormal
=over
=item gsl_ran_lognormal($r, $zeta, $sigma)
This function returns a random variate from the lognormal distribution. The distribution function is, p(x) dx = {1 \over x \sqrt{2 \pi \sigma^2} } \exp(-(\ln(x) - \zeta)^2/2 \sigma^2) dx for x > 0. $r is a gsl_rng structure.
=item gsl_ran_lognormal_pdf($x, $zeta, $sigma)
This function computes the probability density p($x) at $x for a lognormal distribution with parameters $zeta and $sigma, using the formula given above.
=back
=head2 Logarithmic
=over
=item gsl_ran_logarithmic($r, $p)
This function returns a random integer from the logarithmic distribution. The probability distribution for logarithmic random variates is, p(k) = {-1 \over \log(1-p)} {(p^k \over k)} for k >= 1. $r is a gsl_rng structure.
=item gsl_ran_logarithmic_pdf($k, $p)
This function computes the probability p($k) of obtaining $k from a logarithmic distribution with probability parameter $p, using the formula given above.
=back
=head2 Multinomial
=over
=item gsl_ran_multinomial($r, $P, $N)
This function computes and returns a random sample n[] from the multinomial
lib/Math/GSL/Randist.pm view on Meta::CPAN
=head2 Negative Binomial
=over
=item gsl_ran_negative_binomial($r, $p, $n)
This function returns a random integer from the negative binomial distribution, the number of failures occurring before n successes in independent trials with probability p of success. The probability distribution for negative binomial variates is, p...
=item gsl_ran_negative_binomial_pdf($k, $p, $n)
This function computes the probability p($k) of obtaining $k from a negative binomial distribution with parameters $p and $n, using the formula given above.
=back
=head2 Pascal
=over
=item gsl_ran_pascal($r, $p, $n)
This function returns a random integer from the Pascal distribution. The Pascal distribution is simply a negative binomial distribution with an integer value of $n. p($k) = {($n + $k - 1)! \ $k! ($n - 1)! } $p**$n (1-$p)**$k for $k >= 0. $r is gsl_rn...
=item gsl_ran_pascal_pdf($k, $p, $n)
This function computes the probability p($k) of obtaining $k from a Pascal distribution with parameters $p and $n, using the formula given above.
=back
=head2 Pareto
=over
=item gsl_ran_pareto($r, $a, $b)
This function returns a random variate from the Pareto distribution of order $a. The distribution function is p($x) dx = ($a/$b) / ($x/$b)^{$a+1} dx for $x >= $b. $r is a gsl_rng structure
=item gsl_ran_pareto_pdf($x, $a, $b)
This function computes the probability density p(x) at x for a Pareto distribution with exponent a and scale b, using the formula given above.
=back
=head2 Poisson
=over
=item gsl_ran_poisson($r, $lambda)
This function returns a random integer from the Poisson distribution with mean
$lambda. $r is a gsl_rng structure. The probability distribution for Poisson
variates is,
p(k) = {$lambda**$k \ $k!} exp(-$lambda)
for $k >= 0. $r is a gsl_rng structure.
=item gsl_ran_poisson_pdf($k, $lambda)
This function computes the probability p($k) of obtaining $k from a Poisson
distribution with mean $lambda, using the formula given above.
=for Comment
gsl_ran_poisson_array removed b/c it's an undoc'd function which merely returns array of poisson draws
=back
=head2 Rayleigh
=over
=item gsl_ran_rayleigh($r, $sigma)
This function returns a random variate from the Rayleigh distribution with scale parameter sigma. The distribution is, p(x) dx = {x \over \sigma^2} \exp(- x^2/(2 \sigma^2)) dx for x > 0. $r is a gsl_rng structure
=item gsl_ran_rayleigh_pdf($x, $sigma)
This function computes the probability density p($x) at $x for a Rayleigh distribution with scale parameter sigma, using the formula given above.
=item gsl_ran_rayleigh_tail($r, $a, $sigma)
This function returns a random variate from the tail of the Rayleigh distribution with scale parameter $sigma and a lower limit of $a. The distribution is, p(x) dx = {x \over \sigma^2} \exp ((a^2 - x^2) /(2 \sigma^2)) dx for x > a. $r is a gsl_rng st...
=item gsl_ran_rayleigh_tail_pdf($x, $a, $sigma)
This function computes the probability density p($x) at $x for a Rayleigh tail distribution with scale parameter $sigma and lower limit $a, using the formula given above.
=back
=head2 Student-t
=over
=item gsl_ran_tdist($r, $nu)
This function returns a random variate from the t-distribution. The distribution function is, p(x) dx = {\Gamma((\nu + 1)/2) \over \sqrt{\pi \nu} \Gamma(\nu/2)} (1 + x^2/\nu)^{-(\nu + 1)/2} dx for -\infty < x < +\infty.
=item gsl_ran_tdist_pdf($x, $nu)
This function computes the probability density p($x) at $x for a t-distribution with nu degrees of freedom, using the formula given above.
=back
=head2 Laplace
=over
=item gsl_ran_laplace($r, $a)
This function returns a random variate from the Laplace distribution with width $a. The distribution is, p(x) dx = {1 \over 2 a} \exp(-|x/a|) dx for -\infty < x < \infty.
=item gsl_ran_laplace_pdf($x, $a)
This function computes the probability density p($x) at $x for a Laplace distribution with width $a, using the formula given above.
=back
=head2 Levy
=over
=item gsl_ran_levy($r, $c, $alpha)
This function returns a random variate from the Levy symmetric stable distribution with scale $c and exponent $alpha. The symmetric stable probability distribution is defined by a fourier transform, p(x) = {1 \over 2 \pi} \int_{-\infty}^{+\infty} dt ...
lib/Math/GSL/Randist.pm view on Meta::CPAN
$scale and $exponent (aka scale). The distribution function is
p(x) dx = {$exponent \over $scale^$exponent} x^{$exponent-1}
\exp(-(x/$scale)^$exponent) dx
for x >= 0. $r is a gsl_rng structure
=item gsl_ran_weibull_pdf($x, $scale, $exponent)
This function computes the probability density p($x) at $x for a Weibull
distribution with $scale and $exponent, using the formula given above.
=back
=head2 Spherical Vector
=over
=item gsl_ran_dir_2d($r)
This function returns two values. The first is $x and the second is $y of a
lib/Math/GSL/Statistics.pm view on Meta::CPAN
=item * C<gsl_stats_minmax($data, $stride, $n)> - This function finds both the minimum and maximum values in $data, which is an array reference, in a single pass and returns them in this order.
=item * C<gsl_stats_max_index($data, $stride, $n)> - This function returns the index of the maximum value in $data array reference, a dataset of length $n with stride $stride. The maximum value is defined as the value of the element x_i which satisfi...
=item * C<gsl_stats_min_index($data, $stride, $n)> - This function returns the index of the minimum value in $data array reference, a dataset of length $n with stride $stride. The minimum value is defined as the value of the element x_i which satisfi...
=item * C<gsl_stats_minmax_index($data, $stride, $n)> - This function returns the indexes of the minimum and maximum values in $data, an array reference in a single pass. The value are returned in this order.
=item * C<gsl_stats_median_from_sorted_data($sorted_data, $stride, $n)> - This function returns the median value of $sorted_data (which is an array reference), a dataset of length $n with stride $stride. The elements of the array must be in ascending...
=item * C<gsl_stats_quantile_from_sorted_data($sorted_data, $stride, $n, $f)> - This function returns a quantile value of $sorted_data, a double-precision array reference of length $n with stride $stride. The elements of the array must be in ascendin...
=back
The following function are simply variants for int and char of the last functions:
=over 4
=item * C<gsl_stats_int_mean >
=item * C<gsl_stats_int_variance >
pm/Math/GSL/Histogram.pm.1.15 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Histogram.pm.1.16 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Histogram.pm.2.0 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Histogram.pm.2.1 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Histogram.pm.2.2 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Histogram.pm.2.2.1 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Histogram.pm.2.3 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Histogram.pm.2.4 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Histogram.pm.2.5 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Histogram.pm.2.6 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Histogram.pm.2.7 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Histogram.pm.2.8 view on Meta::CPAN
The values of the ranges are formatted using range_format and the value of the bins are formatted using bin_format. Each line contains the lower and upper limit of the range of the bins and the value of the bin itself. Since the upper limit of one bi...
=item C<gsl_histogram_fscanf($stream, $h)> - This function reads formatted data from the stream $stream, which has been opened by the gsl_fopen function from the Math::GSL module, into the histogram $h. The data is assumed to be in the three-column f...
=item C<gsl_histogram_pdf_alloc($n)> - This function allocates memory for a probability distribution with $n bins and returns a pointer to a newly initialized gsl_histogram_pdf struct. If insufficient memory is available a null pointer is returned an...
=item C<gsl_histogram_pdf_init($p, $h)> - This function initializes the probability distribution $p with the contents of the histogram $h. If any of the bins of $h are negative then the error handler is invoked with an error code of $GSL_EDOM because...
=item C<gsl_histogram_pdf_free($p)> - This function frees the probability distribution function $p and all of the memory associated with it.
=item C<gsl_histogram_pdf_sample($p, $r)> - This function uses $r, a uniform random number between zero and one, to compute a single random sample from the probability distribution $p. The algorithm used to compute the sample s is given by the fo...
=back
=head1 EXAMPLES
The following example shows how to create a histogram with logarithmic bins with ranges [1,10), [10,100) and [100,1000).
$h = gsl_histogram_alloc (3);
# bin[0] covers the range 1 <= x < 10
pm/Math/GSL/Multifit.pm.2.1 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
pm/Math/GSL/Multifit.pm.2.2 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
pm/Math/GSL/Multifit.pm.2.2.1 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
pm/Math/GSL/Multifit.pm.2.3 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
pm/Math/GSL/Multifit.pm.2.4 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
pm/Math/GSL/Multifit.pm.2.5 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
pm/Math/GSL/Multifit.pm.2.6 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
pm/Math/GSL/Multifit.pm.2.7 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
pm/Math/GSL/Multifit.pm.2.8 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
pm/Math/GSL/Multilarge.pm.2.1 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
pm/Math/GSL/Multilarge.pm.2.2 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
pm/Math/GSL/Multilarge.pm.2.2.1 view on Meta::CPAN
=item C<gsl_multifit_linear_svd($X, $y, $tol, $c, $cov, $work)> - This function computes the best-fit parameters c of the model y = X c for the observations vector $y and the matrix of predictor variables $X. The variance-covariance matrix of the mod...
=item C<gsl_multifit_wlinear($X, $w, $y, $c, $cov, $work> - This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The covariance matrix ...
=item C<gsl_multifit_wlinear_svd($X, $w, $y, $tol, $rank, $c, $cov, $work) > This function computes the best-fit parameters vector $c of the weighted model y = X c for the observations y with weights $w and the matrix of predictor variables $X. The c...
=item C<gsl_multifit_linear_est($x, $c, $cov)> - This function uses the best-fit multilinear regression coefficients vector $c and their covariance matrix $cov to compute the fitted function value $y and its standard deviation $y_err for the model y ...
=item C<gsl_multifit_linear_residuals($X, $y, $c, $r)> - This function computes the vector of residuals r = y - X c for the observations vector $y, coefficients vector $c and matrix of predictor variables $X. $r is also a vector.
=item C<gsl_multifit_gradient($J, $f, $g)> - This function computes the gradient $g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix $J and the function values $f, using the formula $g = $J^T $f. $g and $f are vectors.
=item C<gsl_multifit_test_gradient($g, $epsabas)> - This function tests the residual gradient vector $g against the absolute error bound $epsabs. Mathematically, the gradient should be exactly zero at the minimum. The test returns $GSL_SUCCESS if the...
=item C<gsl_multifit_test_delta($dx, $x, $epsabs, $epsrel)> - This function tests for the convergence of the sequence by comparing the last step vector $dx with the absolute error $epsabs and relative error $epsrel to the current position x. The test...
=back
The following functions are not yet implemented. Patches Welcome!
=over
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