.rst

jax.scipy module

`jax.scipy` module #

jax.scipy.cluster#

vq(obs, code_book[, check_finite])

Assign codes from a code book to a set of observations.

jax.scipy.fft#

`dct`(x[, type, n, axis, norm])	Computes the discrete cosine transform of the input
`dctn`(x[, type, s, axes, norm])	Computes the multidimensional discrete cosine transform of the input
`idct`(x[, type, n, axis, norm])	Computes the inverse discrete cosine transform of the input
`idctn`(x[, type, s, axes, norm])	Computes the multidimensional inverse discrete cosine transform of the input

jax.scipy.integrate#

trapezoid(y[, x, dx, axis])

Integrate along the given axis using the composite trapezoidal rule.

jax.scipy.interpolate#

RegularGridInterpolator(points, values[, ...])

Interpolate points on a regular rectangular grid.

jax.scipy.linalg#

`block_diag`(*arrs)	Create a block diagonal matrix from input arrays.
`cho_factor`(a[, lower, overwrite_a, check_finite])	Factorization for Cholesky-based linear solves
`cho_solve`(c_and_lower, b[, overwrite_b, ...])	Solve a linear system using a Cholesky factorization
`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`()	Compute eigenvalues and eigenvectors for a Hermitian matrix
`eigh_tridiagonal`(d, e, *[, eigvals_only, ...])	Solve the eigenvalue problem for a symmetric real tridiagonal matrix
`expm`(A, *[, upper_triangular, max_squarings])	Compute the matrix exponential
`expm_frechet`()	Compute the Frechet derivative of the matrix exponential.
`funm`(A, func[, disp])	Evaluate a matrix-valued function
`hessenberg`()	Compute the Hessenberg form of the matrix
`hilbert`(n)	Create a Hilbert matrix of order n.
`inv`(a[, overwrite_a, check_finite])	Return the inverse of a square matrix
`lu`()	Compute the LU decomposition
`lu_factor`(a[, overwrite_a, check_finite])	Factorization for LU-based linear solves
`lu_solve`(lu_and_piv, b[, trans, ...])	Solve a linear system using an LU factorization
`pascal`(n[, kind])	Create a Pascal matrix approximation of order n.
`polar`(a[, side, method, eps, max_iterations])	Computes the polar decomposition.
`qr`()	Compute the QR decomposition of an array
`rsf2csf`(T, Z[, check_finite])	Convert real Schur form to complex Schur form.
`schur`(a[, output])	Compute the Schur decomposition
`solve`(a, b[, lower, overwrite_a, ...])	Solve a linear system of equations.
`solve_sylvester`(A, B, C, *[, method, tol])	Solves the Sylvester equation .. math::.
`solve_triangular`(a, b[, trans, lower, ...])	Solve a triangular linear system of equations
`sqrtm`(A[, blocksize])	Compute the matrix square root
`svd`()	Compute the singular value decomposition.
`toeplitz`(c[, r])	Construct a Toeplitz matrix.

jax.scipy.ndimage#

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

Map the input array to new coordinates using interpolation.

jax.scipy.optimize#

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

jax.scipy.signal#

`fftconvolve`(in1, in2[, mode, axes])	Convolve two N-dimensional arrays using Fast Fourier Transform (FFT).
`convolve`(in1, in2[, mode, method, precision])	Convolution of two N-dimensional arrays.
`convolve2d`(in1, in2[, mode, boundary, ...])	Convolution of two 2-dimensional arrays.
`correlate`(in1, in2[, mode, method, precision])	Cross-correlation of two N-dimensional arrays.
`correlate2d`(in1, in2[, mode, boundary, ...])	Cross-correlation of two 2-dimensional arrays.
`csd`(x, y[, fs, window, nperseg, noverlap, ...])	Estimate cross power spectral density (CSD) using Welch's method.
`detrend`(data[, axis, type, bp, overwrite_data])	Remove linear or piecewise linear trends from data.
`istft`(Zxx[, fs, window, nperseg, noverlap, ...])	Perform the inverse short-time Fourier transform (ISTFT).
`stft`(x[, fs, window, nperseg, noverlap, ...])	Compute the short-time Fourier transform (STFT).
`welch`(x[, fs, window, nperseg, noverlap, ...])	Estimate power spectral density (PSD) using Welch's method.

jax.scipy.spatial.transform#

`Rotation`(quat)	Rotation in 3 dimensions.
`Slerp`(times, timedelta, rotations, rotvecs)	Spherical Linear Interpolation of Rotations.

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#

`bernoulli`(n)	Generate the first N Bernoulli numbers.
`beta`(a, b)	The beta function
`betainc`(a, b, x)	The regularized incomplete beta function.
`betaln`(a, b)	Natural log of the absolute value of the beta function
`digamma`(x)	The digamma function
`entr`(x)	The entropy function
`erf`(x)	The error function
`erfc`(x)	The complement of the error function
`erfinv`(x)	The inverse of the error function
`exp1`(x)	Exponential integral function.
`expi`	Exponential integral function.
`expit`(x)	The logistic sigmoid (expit) function
`expn`	Generalized exponential integral function.
`factorial`(n[, exact])	Factorial function
`fresnel`	The Fresnel integrals
`gamma`(x)	The gamma function.
`gammainc`(a, x)	The regularized lower incomplete gamma function.
`gammaincc`(a, x)	The regularized upper incomplete gamma function.
`gammaln`(x)	Natural log of the absolute value of the gamma function.
`gammasgn`(x)	Sign of the gamma function.
`hyp1f1`	The 1F1 hypergeometric function.
`hyp2f1`	The 2F1 hypergeometric function.
`i0`(x)	Modified bessel function of zeroth order.
`i0e`(x)	Exponentially scaled modified bessel function of zeroth order.
`i1`(x)	Modified bessel function of first order.
`i1e`(x)	Exponentially scaled modified bessel function of first order.
`kl_div`(p, q)	The Kullback-Leibler divergence.
`log_ndtr`	Log Normal distribution function.
`log_softmax`(x, /, *[, axis])	Log-Softmax function.
`logit`	The logit function
`logsumexp`()	Log-sum-exp reduction.
`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)	The natural log of the multivariate gamma function.
`ndtr`(x)	Normal distribution function.
`ndtri`(p)	The inverse of the CDF of the Normal distribution function.
`poch`	The Pochammer symbol.
`polygamma`(n, x)	The polygamma function.
`rel_entr`(p, q)	The relative entropy function.
`sici`	Sine and cosine integrals.
`softmax`(x, /, *[, axis])	Softmax function.
`spence`(x)	Spence's function, also known as the dilogarithm for real values.
`sph_harm`(m, n, theta, phi[, n_max])	Computes the spherical harmonics.
`xlog1py`	Compute x*log(1 + y), returning 0 for x=0.
`xlogy`	Compute x*log(y), returning 0 for x=0.
`zeta`	The Hurwitz zeta function.

jax.scipy.stats#

`mode`(a[, axis, nan_policy, keepdims])	Compute the mode (most common value) along an axis of an array.
`rankdata`(a[, method, axis, nan_policy])	Compute the rank of data along an array axis.
`sem`(a[, axis, ddof, nan_policy, keepdims])	Compute the standard error of the mean.

jax.scipy.stats.bernoulli#

`logpmf`(k, p[, loc])	Bernoulli log probability mass function.
`pmf`(k, p[, loc])	Bernoulli probability mass function.
`cdf`(k, p)	Bernoulli cumulative distribution function.
`ppf`(q, p)	Bernoulli percent point function.

jax.scipy.stats.beta#

`logpdf`(x, a, b[, loc, scale])	Beta log probability distribution function.
`pdf`(x, a, b[, loc, scale])	Beta probability distribution function.
`cdf`(x, a, b[, loc, scale])	Beta cumulative distribution function
`logcdf`(x, a, b[, loc, scale])	Beta log cumulative distribution function.
`sf`(x, a, b[, loc, scale])	Beta distribution survival function.
`logsf`(x, a, b[, loc, scale])	Beta distribution log survival function.

jax.scipy.stats.betabinom#

`logpmf`(k, n, a, b[, loc])	Beta-binomial log probability mass function.
`pmf`(k, n, a, b[, loc])	Beta-binomial probability mass function.

jax.scipy.stats.binom#

`logpmf`(k, n, p[, loc])	Binomial log probability mass function.
`pmf`(k, n, p[, loc])	Binomial probability mass function.

jax.scipy.stats.cauchy#

`logpdf`(x[, loc, scale])	Cauchy log probability distribution function.
`pdf`(x[, loc, scale])	Cauchy probability distribution function.
`cdf`(x[, loc, scale])	Cauchy cumulative distribution function.
`logcdf`(x[, loc, scale])	Cauchy log cumulative distribution function.
`sf`(x[, loc, scale])	Cauchy distribution log survival function.
`logsf`(x[, loc, scale])	Cauchy distribution log survival function.
`isf`(q[, loc, scale])	Cauchy distribution inverse survival function.
`ppf`(q[, loc, scale])	Cauchy distribution percent point function.

jax.scipy.stats.chi2#

`logpdf`(x, df[, loc, scale])	Chi-square log probability distribution function.
`pdf`(x, df[, loc, scale])	Chi-square probability distribution function.
`cdf`(x, df[, loc, scale])	Chi-square cumulative distribution function.
`logcdf`(x, df[, loc, scale])	Chi-square log cumulative distribution function.
`sf`(x, df[, loc, scale])	Chi-square survival function.
`logsf`(x, df[, loc, scale])	Chi-square log survival function.

jax.scipy.stats.dirichlet#

`logpdf`(x, alpha)	Dirichlet log probability distribution function.
`pdf`(x, alpha)	Dirichlet probability distribution function.

jax.scipy.stats.expon#

`logpdf`(x[, loc, scale])	Exponential log probability distribution function.
`pdf`(x[, loc, scale])	Exponential probability distribution function.
`logcdf`(x[, loc, scale])	Exponential log cumulative density function.
`cdf`(x[, loc, scale])	Exponential cumulative density function.
`logsf`(x[, loc, scale])	Exponential log survival function.
`sf`(x[, loc, scale])	Exponential survival function.
`ppf`(q[, loc, scale])	Exponential survival function.

jax.scipy.stats.gamma#

`logpdf`(x, a[, loc, scale])	Gamma log probability distribution function.
`pdf`(x, a[, loc, scale])	Gamma probability distribution function.
`cdf`(x, a[, loc, scale])	Gamma cumulative distribution function.
`logcdf`(x, a[, loc, scale])	Gamma log cumulative distribution function.
`sf`(x, a[, loc, scale])	Gamma survival function.
`logsf`(x, a[, loc, scale])	Gamma log survival function.

jax.scipy.stats.gumbel_l#

`logpdf`(x[, loc, scale])	Gumbel Distribution (Left Skewed) log probability distribution function.
`pdf`(x[, loc, scale])	Gumbel Distribution (Left Skewed) probability distribution function.
`cdf`(x[, loc, scale])	Gumbel Distribution (Left Skewed) cumulative density function.
`logcdf`(x[, loc, scale])	Gumbel Distribution (Left Skewed) log cumulative density function.
`sf`(x[, loc, scale])	Gumbel Distribution (Left Skewed) survival function.
`logsf`(x[, loc, scale])	Gumbel Distribution (Left Skewed) log survival function.
`ppf`(p[, loc, scale])	Gumbel Distribution (Left Skewed) percent point function (inverse of CDF)

jax.scipy.stats.gumbel_r#

`logpdf`(x[, loc, scale])	Gumbel Distribution (Right Skewed) log probability distribution function.
`pdf`(x[, loc, scale])	Gumbel Distribution (Right Skewed) probability distribution function.
`cdf`(x[, loc, scale])	Gumbel Distribution (Right Skewed) cumulative density function.
`logcdf`(x[, loc, scale])	Gumbel Distribution (Right Skewed) log cumulative density function.
`sf`(x[, loc, scale])	Gumbel Distribution (Right Skewed) survival function.
`logsf`(x[, loc, scale])	Gumbel Distribution (Right Skewed) log survival function.
`ppf`(p[, loc, scale])	Gumbel Distribution (Right Skewed) percent point function.

jax.scipy.stats.gennorm#

`cdf`(x, beta)	Generalized normal cumulative distribution function.
`logpdf`(x, beta)	Generalized normal log probability distribution function.
`pdf`(x, beta)	Generalized normal probability distribution function.

jax.scipy.stats.geom#

`logpmf`(k, p[, loc])	Geometric log probability mass function.
`pmf`(k, p[, loc])	Geometric probability mass function.

jax.scipy.stats.laplace#

`cdf`(x[, loc, scale])	Laplace cumulative distribution function.
`logpdf`(x[, loc, scale])	Laplace log probability distribution function.
`pdf`(x[, loc, scale])	Laplace probability distribution function.

jax.scipy.stats.logistic#

`cdf`(x[, loc, scale])	Logistic cumulative distribution function.
`isf`(x[, loc, scale])	Logistic distribution inverse survival function.
`logpdf`(x[, loc, scale])	Logistic log probability distribution function.
`pdf`(x[, loc, scale])	Logistic probability distribution function.
`ppf`(x[, loc, scale])	Logistic distribution percent point function.
`sf`(x[, loc, scale])	Logistic distribution survival function.

jax.scipy.stats.multinomial#

`logpmf`(x, n, p)	Multinomial log probability mass function.
`pmf`(x, n, p)	Multinomial probability mass function.

jax.scipy.stats.multivariate_normal#

`logpdf`(x, mean, cov[, allow_singular])	Multivariate normal log probability distribution function.
`pdf`(x, mean, cov)	Multivariate normal probability distribution function.

jax.scipy.stats.nbinom#

`logpmf`(k, n, p[, loc])	Negative-binomial log probability mass function.
`pmf`(k, n, p[, loc])	Negative-binomial probability mass function.

jax.scipy.stats.norm#

`logpdf`(x[, loc, scale])	Normal log probability distribution function.
`pdf`(x[, loc, scale])	Normal probability distribution function.
`cdf`(x[, loc, scale])	Normal cumulative distribution function.
`logcdf`(x[, loc, scale])	Normal log cumulative distribution function.
`ppf`(q[, loc, scale])	Normal distribution percent point function.
`sf`(x[, loc, scale])	Normal distribution survival function.
`logsf`(x[, loc, scale])	Normal distribution log survival function.
`isf`(q[, loc, scale])	Normal distribution inverse survival function.

jax.scipy.stats.pareto#

`logcdf`(x, b[, loc, scale])	Pareto log cumulative distribution function.
`logpdf`(x, b[, loc, scale])	Pareto log probability distribution function.
`logsf`(x, b[, loc, scale])	Pareto log survival function.
`cdf`(x, b[, loc, scale])	Pareto cumulative distribution function.
`pdf`(x, b[, loc, scale])	Pareto probability distribution function.
`ppf`(q, b[, loc, scale])	Pareto percent point function (inverse CDF).
`sf`(x, b[, loc, scale])	Pareto survival function.

jax.scipy.stats.poisson#

`logpmf`(k, mu[, loc])	Poisson log probability mass function.
`pmf`(k, mu[, loc])	Poisson probability mass function.
`cdf`(k, mu[, loc])	Poisson cumulative distribution function.

jax.scipy.stats.t#

`logpdf`(x, df[, loc, scale])	Student's T log probability distribution function.
`pdf`(x, df[, loc, scale])	Student's T probability distribution function.

jax.scipy.stats.truncnorm#

`cdf`(x, a, b[, loc, scale])	Truncated normal cumulative distribution function.
`logcdf`(x, a, b[, loc, scale])	Truncated normal log cumulative distribution function.
`logpdf`(x, a, b[, loc, scale])	Truncated normal log probability distribution function.
`logsf`(x, a, b[, loc, scale])	Truncated normal distribution log survival function.
`pdf`(x, a, b[, loc, scale])	Truncated normal probability distribution function.
`sf`(x, a, b[, loc, scale])	Truncated normal distribution log survival function.

jax.scipy.stats.uniform#

`logpdf`(x[, loc, scale])	Uniform log probability distribution function.
`pdf`(x[, loc, scale])	Uniform probability distribution function.
`cdf`(x[, loc, scale])	Uniform cumulative distribution function.
`ppf`(q[, loc, scale])	Uniform distribution percent point function.

jax.scipy.stats.gaussian_kde#

`gaussian_kde`(dataset[, bw_method, weights])	Gaussian Kernel Density Estimator
`gaussian_kde.evaluate`(points)	Evaluate the Gaussian KDE on the given points.
`gaussian_kde.integrate_gaussian`(mean, cov)	Integrate the distribution weighted by a Gaussian.
`gaussian_kde.integrate_box_1d`(low, high)	Integrate the distribution over the given limits.
`gaussian_kde.integrate_kde`(other)	Integrate the product of two Gaussian KDE distributions.
`gaussian_kde.resample`(key[, shape])	Randomly sample a dataset from the estimated pdf
`gaussian_kde.pdf`(x)	Probability density function
`gaussian_kde.logpdf`(x)	Log probability density function

jax.scipy.stats.vonmises#

`logpdf`(x, kappa)	von Mises log probability distribution function.
`pdf`(x, kappa)	von Mises probability distribution function.

jax.scipy.stats.wrapcauchy#

`logpdf`(x, c)	Wrapped Cauchy log probability distribution function.
`pdf`(x, c)	Wrapped Cauchy probability distribution function.

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