View source on GitHub
|
TensorFlow Probability math functions.
Modules
hypergeometric module: Implements hypergeometric functions in TensorFlow.
ode module: TensorFlow Probability ODE solvers.
psd_kernels module: Positive-semidefinite kernels package.
Classes
class MinimizeTraceableQuantities: Namedtuple of quantities that may be traced from tfp.math.minimize.
Functions
atan_difference(...): Difference of arctan(x) and arctan(y).
batch_interp_rectilinear_nd_grid(...): Multi-linear interpolation on a rectilinear grid.
batch_interp_regular_1d_grid(...): Linear 1-D interpolation on a regular (constant spacing) grid.
batch_interp_regular_nd_grid(...): Multi-linear interpolation on a regular (constant spacing) grid.
bessel_iv_ratio(...): Computes I_{v} (z) / I_{v - 1} (z) in a numerically stable way.
bessel_ive(...): Computes exponentially scaled modified Bessel function of the first kind.
bessel_kve(...): Computes exponentially scaled modified Bessel function of the 2nd kind.
betainc(...): Computes the regularized incomplete beta function element-wise.
betaincinv(...): Computes the inverse of tfp.math.betainc with respect to x.
bracket_root(...): Finds bounds that bracket a root of the objective function.
cholesky_concat(...): Concatenates chol @ chol.T with additional rows and columns.
cholesky_update(...): Returns cholesky of chol @ chol.T + multiplier * u @ u.T.
clip_by_value_preserve_gradient(...): Clips values to a specified min and max while leaving gradient unaltered.
custom_gradient(...): Embeds a custom gradient into a Tensor.
dawsn(...): Computes Dawson's integral element-wise.
diag_jacobian(...): Computes diagonal of the Jacobian matrix of ys=fn(xs) wrt xs.
erfcinv(...): Computes the inverse of tf.math.erfc of z element-wise.
erfcx(...): Computes the scaled complementary error function exp(x**) * erfc(x).
fill_triangular(...): Creates a (batch of) triangular matrix from a vector of inputs.
fill_triangular_inverse(...): Creates a vector from a (batch of) triangular matrix.
find_root_chandrupatla(...): Finds root(s) of a scalar function using Chandrupatla's method.
find_root_secant(...): Finds root(s) of a function of single variable using the secant method.
gram_schmidt(...): Implementation of the modified Gram-Schmidt orthonormalization algorithm.
hpsd_logdet(...): Computes log|det(matrix)|, where matrix is a HPSD matrix.
hpsd_quadratic_form_solve(...): Computes rhs^T matrix^-1 rhs, where matrix is HPSD.
hpsd_quadratic_form_solvevec(...): Computes rhs^T matrix^-1 rhs, where matrix is HPSD.
hpsd_solve(...): Computes matrix^-1 rhs, where matrix is HPSD.
hpsd_solvevec(...): Computes matrix^-1 rhs, where matrix is HPSD.
igammacinv(...): Computes the inverse to tf.math.igammac with respect to p.
igammainv(...): Computes the inverse to tf.math.igamma with respect to p.
interp_regular_1d_grid(...): Linear 1-D interpolation on a regular (constant spacing) grid.
lambertw(...): Computes Lambert W of z element-wise.
lambertw_winitzki_approx(...): Computes Winitzki approximation to Lambert W function at z >= -1/exp(1).
lbeta(...): Returns log(Beta(x, y)).
log1mexp(...): Compute log(1 - exp(-|x|)) elementwise in a numerically stable way.
log1psquare(...): Numerically stable calculation of log(1 + x**2) for small or large |x|.
log_add_exp(...): Computes log(exp(x) + exp(y)) in a numerically stable way.
log_bessel_ive(...): Computes log(tfp.math.bessel_ive(v, z)).
log_bessel_kve(...): Computes log(tfp.math.bessel_kve(v, z)).
log_combinations(...): Log multinomial coefficient.
log_cosh(...): Compute log(cosh(x)) in a numerically stable way.
log_cumsum_exp(...): Computes log(cumsum(exp(x))).
log_gamma_correction(...): Returns the error of the Stirling approximation to lgamma(x) for x >= 8.
log_gamma_difference(...): Returns lgamma(y) - lgamma(x + y), accurately.
log_sub_exp(...): Compute log(exp(max(x, y)) - exp(min(x, y))) in a numerically stable way.
logerfc(...): Computes the logarithm of tf.math.erfc of x element-wise.
logerfcx(...): Computes the logarithm of tfp.math.erfcx of x element-wise.
low_rank_cholesky(...): Computes a low-rank approximation to the Cholesky decomposition.
lu_matrix_inverse(...): Computes a matrix inverse given the matrix's LU decomposition.
lu_reconstruct(...): The inverse LU decomposition, X == lu_reconstruct(*tf.linalg.lu(X)).
lu_solve(...): Solves systems of linear eqns A X = RHS, given LU factorizations.
minimize(...): Minimize a loss function using a provided optimizer.
minimize_stateless(...): Minimize a loss expressed as a pure function of its parameters.
owens_t(...): Computes Owen's T function of h and a element-wise.
pivoted_cholesky(...): Computes the (partial) pivoted cholesky decomposition of matrix.
reduce_kahan_sum(...): Reduces the input tensor along the given axis using Kahan summation.
reduce_log_harmonic_mean_exp(...): Computes log(1 / mean(1 / exp(input_tensor))).
reduce_logmeanexp(...): Computes log(mean(exp(input_tensor))).
reduce_weighted_logsumexp(...): Computes log(abs(sum(weight * exp(elements across tensor dimensions)))).
round_exponential_bump_function(...): Function supported on [-1, 1], smooth on the real line, with a round top.
scan_associative(...): Perform a scan with an associative binary operation, in parallel.
secant_root(...): Finds root(s) of a function of single variable using the secant method.
smootherstep(...): Computes a sigmoid-like interpolation function on the unit-interval.
soft_sorting_matrix(...): Computes a matrix representing a continuous relaxation of sorting.
soft_threshold(...): Soft Thresholding operator.
softplus_inverse(...): Computes the inverse softplus, i.e., x = softplus_inverse(softplus(x)).
sparse_or_dense_matmul(...): Returns (batched) matmul of a SparseTensor (or Tensor) with a Tensor.
sparse_or_dense_matvecmul(...): Returns (batched) matmul of a (sparse) matrix with a column vector.
sqrt1pm1(...): Compute sqrt(x + 1) - 1 elementwise in a numerically stable way.
trapz(...): Integrate y(x) on the specified axis using the trapezoidal rule.
value_and_gradient(...): Computes f(*args) and its gradients wrt to *args.