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 an ordinary or generalized eigenvalue problem for a complex |
expm(A, *[, upper_triangular]) |
Compute the matrix exponential using Pade approximation. |
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 k-th diagonal zeroed. |
triu(m[, k]) |
Make a copy of a matrix with elements below the k-th diagonal zeroed. |
jax.scipy.ndimage¶
map_coordinates(input, coordinates, order[, …]) |
Map the input array to new coordinates by interpolation. |
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 erf. |
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. |
i0e(x) |
Exponentially scaled modified Bessel function of order 0. |
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. |
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. |
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. |
jax.scipy.stats¶
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.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.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.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. |