jax.scipy package

jax.scipy.linalg

block_diag(*arrs)	Create a block diagonal matrix from provided arrays.
cho_factor(a[, lower, overwrite_a, check_finite])	Compute the Cholesky decomposition of a matrix, to use in cho_solve
cho_solve(c_and_lower, b[, overwrite_b, …])	Solve the linear equations A x = b, given the Cholesky factorization of A.
cholesky(a[, lower, overwrite_a, check_finite])	Compute the Cholesky decomposition of a matrix.
det(a[, overwrite_a, check_finite])	Compute the determinant of a matrix
eigh(a[, b, lower, eigvals_only, …])	Solve a standard or generalized eigenvalue problem for a complex
expm(A, *[, upper_triangular, max_squarings])	Compute the matrix exponential using Pade approximation.
expm_frechet(A, E, *[, method, compute_expm])	Frechet derivative of the matrix exponential of A in the direction E.
inv(a[, overwrite_a, check_finite])	Compute the inverse of a matrix.
lu(a[, permute_l, overwrite_a, check_finite])	Compute pivoted LU decomposition of a matrix.
lu_factor(a[, overwrite_a, check_finite])	Compute pivoted LU decomposition of a matrix.
lu_solve(lu_and_piv, b[, trans, …])	Solve an equation system, a x = b, given the LU factorization of a
qr(a[, overwrite_a, lwork, mode, pivoting, …])	Compute QR decomposition of a matrix.
solve(a, b[, sym_pos, lower, overwrite_a, …])	Solves the linear equation set a * x = b for the unknown x
solve_triangular(a, b[, trans, lower, …])	Solve the equation a x = b for x, assuming a is a triangular matrix.
svd(a[, full_matrices, compute_uv, …])	Singular Value Decomposition.
tril(m[, k])	Make a copy of a matrix with elements above the kth diagonal zeroed.
triu(m[, k])	Make a copy of a matrix with elements below the kth diagonal zeroed.

jax.scipy.ndimage

map_coordinates(input, coordinates, order[, …])

Map the input array to new coordinates by interpolation.

jax.scipy.optimize

minimize(fun, x0[, args, tol, options])	Minimization of scalar function of one or more variables.
OptimizeResults(x, success, …)	Object holding optimization results.

jax.scipy.signal

convolve(in1, in2[, mode, method, precision])	Convolve two N-dimensional arrays.
convolve2d(in1, in2[, mode, boundary, …])	Convolve two 2-dimensional arrays.
correlate(in1, in2[, mode, method, precision])	Cross-correlate two N-dimensional arrays.
correlate2d(in1, in2[, mode, boundary, …])	Cross-correlate two 2-dimensional arrays.

jax.scipy.sparse.linalg

bicgstab(A, b[, x0, tol, atol, maxiter, M])	Use Bi-Conjugate Gradient Stable iteration to solve Ax = b.
cg(A, b[, x0, tol, atol, maxiter, M])	Use Conjugate Gradient iteration to solve Ax = b.
gmres(A, b[, x0, tol, atol, restart, …])	GMRES solves the linear system A x = b for x, given A and b.

jax.scipy.special

betainc(a, b, x)	Incomplete beta function.
digamma(x)	The digamma function.
entr(x)	Elementwise function for computing entropy.
erf(x)	Returns the error function of complex argument.
erfc(x)	Complementary error function, 1 - erf(x).
erfinv(x)	Inverse of the error function.
expit	Expit (a.k.a.
gammainc(a, x)	Regularized lower incomplete gamma function.
gammaincc(a, x)	Regularized upper incomplete gamma function.
gammaln(x)	Logarithm of the absolute value of the gamma function.
i0(x)	Modified Bessel function of order 0.
i0e(x)	Exponentially scaled modified Bessel function of order 0.
i1(x)	Modified Bessel function of order 1.
i1e(x)	Exponentially scaled modified Bessel function of order 1.
log_ndtr	Log Normal distribution function.
logit	Logit ufunc for ndarrays.
logsumexp(a[, axis, b, keepdims, return_sign])	Compute the log of the sum of exponentials of input elements.
lpmn(m, n, z)	The associated Legendre functions (ALFs) of the first kind.
lpmn_values(m, n, z, is_normalized)	The associated Legendre functions (ALFs) of the first kind.
multigammaln(a, d)	Returns the log of multivariate gamma, also sometimes called the
ndtr(x)	Normal distribution function.
ndtri(p)	The inverse of the CDF of the Normal distribution function.
polygamma(n, x)	Polygamma functions.
xlog1py(x, y)	Compute x*log1p(y) so that the result is 0 if x = 0.
xlogy(x, y)	Compute x*log(y) so that the result is 0 if x = 0.
zeta(x[, q])	Riemann or Hurwitz zeta function.

jax.scipy.stats

jax.scipy.stats.bernoulli

logpmf(k, p[, loc])	Log of the probability mass function at k of the given RV.
pmf(k, p[, loc])	Probability mass function at k of the given RV.

jax.scipy.stats.beta

logpdf(x, a, b[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x, a, b[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.betabinom

logpmf(k, n, a, b[, loc])	Log of the probability mass function at k of the given RV.
pmf(k, n, a, b[, loc])	Probability mass function at k of the given RV.

jax.scipy.stats.cauchy

logpdf(x[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.chi2

logpdf(x, df[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x, df[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.dirichlet

logpdf(x, alpha)	Log of the Dirichlet probability density function.
pdf(x, alpha)	The Dirichlet probability density function.

jax.scipy.stats.expon

logpdf(x[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.gamma

logpdf(x, a[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x, a[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.geom

logpmf(k, p[, loc])	Log of the probability mass function at k of the given RV.
pmf(k, p[, loc])	Probability mass function at k of the given RV.

jax.scipy.stats.laplace

cdf(x[, loc, scale])	Cumulative distribution function of the given RV.
logpdf(x[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.logistic

cdf(x)	Cumulative distribution function of the given RV.
isf(x)	Inverse survival function (inverse of sf) at q of the given RV.
logpdf(x)	Log of the probability density function at x of the given RV.
pdf(x)	Probability density function at x of the given RV.
ppf(x)	Percent point function (inverse of cdf) at q of the given RV.
sf(x)	Survival function (1 - cdf) at x of the given RV.

jax.scipy.stats.multivariate_normal

logpdf(x, mean, cov[, allow_singular])	Log of the multivariate normal probability density function.
pdf(x, mean, cov)	Multivariate normal probability density function.

jax.scipy.stats.norm

cdf(x[, loc, scale])	Cumulative distribution function of the given RV.
logcdf(x[, loc, scale])	Log of the cumulative distribution function at x of the given RV.
logpdf(x[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x[, loc, scale])	Probability density function at x of the given RV.
ppf(q[, loc, scale])	Percent point function (inverse of cdf) at q of the given RV.

jax.scipy.stats.pareto

logpdf(x, b[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x, b[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.poisson

logpmf(k, mu[, loc])	Log of the probability mass function at k of the given RV.
pmf(k, mu[, loc])	Probability mass function at k of the given RV.

jax.scipy.stats.t

logpdf(x, df[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x, df[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.uniform

logpdf(x[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x[, loc, scale])	Probability density function at x of the given RV.