Functions — github.com/al-jshen/compute

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Functions442 in github.com/al-jshen/compute

⨍ Functions 442◇ Types & classes 42

↓ 1 callersMethodhas_converged

(&self, loss: f64, loss_previous: f64, tolerance: f64)

src/predict/glms/glm.rs:78

↓ 1 callersMethodhas_dispersion

(&self)

src/predict/glms/families.rs:18

↓ 1 callersFunctioninterp1d_linear_unchecked

( x: &[f64], y: &[f64], tgt: &[f64], extrapolate: ExtrapolationMode, )

src/functions/interpolate.rs:42

↓ 1 callersMethodinto_iter

(self)

src/linalg/array/matrix.rs:623

↓ 1 callersMethodis_square

Check whether the matrix is square.

src/linalg/array/matrix.rs:68

↓ 1 callersFunctionis_symmetric

(m: &[f64])

src/linalg/utils.rs:82

↓ 1 callersMethodis_symmetric

Check whether the matrix is symmetric.

src/linalg/array/matrix.rs:79

↓ 1 callersFunctionlogistic

Various mathematical functions commonly used in statistics. Calculates the standard [logistic function](https://en.wikipedia.org/wiki/Logistic_functio

src/functions/statistical.rs:4

↓ 1 callersFunctionlogit

Calculates the [logit function](https://en.wikipedia.org/wiki/Logit)

src/functions/statistical.rs:9

↓ 1 callersFunctionlogsumexp

Calculates the logarithm of the sum of the exponentials in a stable manner. Applies the [LogSumExp](https://en.wikipedia.org/wiki/LogSumExp) operator

src/linalg/utils.rs:546

↓ 1 callersMethodmean

Calculates the mean of the Chi square distribution, which is the same as its degrees of freedom.

src/distributions/chi_squared.rs:71

↓ 1 callersMethodoptimize

Run the optimization algorithm, given a vector of parameters to optimize and a function which calculates the residuals.

src/optimize/sgd.rs:79

↓ 1 callersMethodpdf

Calculates the probability density function for the given T distribution at `x`.

src/distributions/t.rs:53

↓ 1 callersMethodpenalized_deviance

(&self, y: &[f64], mu: &[f64], alpha: f64, coef: &[f64])

src/predict/glms/families.rs:139

↓ 1 callersMethodpredict_one

Given some data, predict the value for a single timestep ahead.

src/timeseries/autoregressive.rs:44

↓ 1 callersFunctionsample_mult

(lambda: f64)

src/distributions/poisson.rs:87

↓ 1 callersFunctionsample_ptrs

(lam: f64)

src/distributions/poisson.rs:99

↓ 1 callersFunctionsample_var

Calculates the sample variance from an array of data points in a numerically stable manner using the Welford algorithm.

src/statistics/moments.rs:56

↓ 1 callersMethodset_alpha

(&mut self, alpha: f64)

src/distributions/gamma.rs:31

↓ 1 callersMethodset_alpha

(&mut self, alpha: f64)

src/distributions/pareto.rs:23

↓ 1 callersMethodset_alpha

(&mut self, alpha: f64)

src/distributions/beta.rs:35

↓ 1 callersMethodset_beta

(&mut self, beta: f64)

src/distributions/gamma.rs:38

↓ 1 callersMethodset_beta

(&mut self, beta: f64)

src/distributions/beta.rs:43

↓ 1 callersMethodset_beta

(&mut self, beta: f64)

src/distributions/gumbel.rs:35

↓ 1 callersMethodset_dof

(&mut self, dof: usize)

src/distributions/chi_squared.rs:24

↓ 1 callersMethodset_dof

(&mut self, dof: f64)

src/distributions/t.rs:22

↓ 1 callersMethodset_lambda

(&mut self, lambda: f64)

src/distributions/exponential.rs:28

↓ 1 callersMethodset_lambda

(&mut self, lambda: f64)

src/distributions/poisson.rs:23

↓ 1 callersMethodset_lower

(&mut self, lower: i64)

src/distributions/discreteuniform.rs:23

↓ 1 callersMethodset_lower

(&mut self, lower: f64)

src/distributions/uniform.rs:24

↓ 1 callersMethodset_minval

(&mut self, minval: f64)

src/distributions/pareto.rs:30

↓ 1 callersMethodset_mu

(&mut self, mu: f64)

src/distributions/normal.rs:26

↓ 1 callersMethodset_mu

(&mut self, mu: f64)

src/distributions/gumbel.rs:31

↓ 1 callersMethodset_n

(&mut self, n: u64)

src/distributions/binomial.rs:23

↓ 1 callersMethodset_p

(&mut self, p: f64)

src/distributions/bernoulli.rs:22

↓ 1 callersMethodset_p

(&mut self, p: f64)

src/distributions/binomial.rs:27

↓ 1 callersMethodset_sigma

(&mut self, sigma: f64)

src/distributions/normal.rs:30

↓ 1 callersMethodset_upper

(&mut self, upper: i64)

src/distributions/discreteuniform.rs:30

↓ 1 callersMethodset_upper

(&mut self, upper: f64)

src/distributions/uniform.rs:31

↓ 1 callersFunctionsoftmax

Calculates the softmax (the normalized exponential) function, which is a generalization of the logistic function to multiple dimensions. Takes in a v

src/functions/statistical.rs:43

↓ 1 callersFunctionsolve_sys

Solves a system of linear scalar equations. `a` must represent a square matrix.

src/linalg/utils.rs:222

↓ 1 callersFunctionstd

Calculates the standard deviation of an array of data points. This is the square root of the variance.

src/statistics/moments.rs:63

↓ 1 callersMethodt_mut

Transpose the matrix in-place.

src/linalg/array/matrix.rs:418

↓ 1 callersFunctiontoeplitz

Given a vector of length n, creates n stacked duplicates, resulting in a square [Toeplitz matrix](https://en.wikipedia.org/wiki/Toeplitz_matrix). This

src/linalg/utils.rs:488

↓ 1 callersFunctionvandermonde

Given some length m data x, create an nth order [Vandermonde matrix](https://en.wikipedia.org/wiki/Vandermonde_matrix).

src/linalg/utils.rs:473

↓ 1 callersMethodvariance

(&self, mu: &[f64])

src/predict/glms/families.rs:29

↓ 1 callersFunctionwelford_mean

Calculates the mean of an array of data points using the Welford algorithm.

src/statistics/moments.rs:42

↓ 1 callersFunctionwelford_update

An implementation of Welford's online algorithm, which is used for calculating statistics in a recurrent and stable manner. See https://en.wikipedia.o

src/statistics/moments.rs:10

Methodaic

Calculates the [Akaike information criterion](https://en.wikipedia.org/wiki/Akaike_information_criterion) for the model.

src/predict/glms/glm.rs:256

Methodapply_along_col

Apply a closure to every element in a row.

src/linalg/array/matrix.rs:346

Methodapply_along_row

Apply a closure to every element in a row. The closure should take a value and return the value to replace it with.

src/linalg/array/matrix.rs:338

Functionargmax

Returns the index of the largest element in the array.

src/statistics/order.rs:25

Functionargmin

Returns the index of the smallest element in the array.

src/statistics/order.rs:14

Methodbic

Calculates the [Bayesian information criterion](https://en.wikipedia.org/wiki/Bayesian_information_criterion) for the model.

src/predict/glms/glm.rs:263

Functionbinom_coeff_alt

An alternative method for computing binomial coefficients. There is no significant difference between the compute time using the `binom_coeff` method

src/functions/combinatorial.rs:26

Functionbinomial

(c: &mut Criterion)

benches/benchmark_distributions.rs:40

Functionboxcox

Calculates the one-parameter Box-Cox transformation with some power parameter `lambda`.

src/functions/statistical.rs:17

Functionboxcox_shifted

Calculates the two-parameter Box-Cox transformation with some power parameter `lambda` and some shift parameter `alpha`.

src/functions/statistical.rs:28

Functioncalc_broadcast_shape

(m1: &Matrix, m2: &Matrix)

src/linalg/array/broadcast.rs:12

Methodcdf

TODO: make `cdf` a method of the `Continuous` trait.

src/distributions/normal.rs:38

Methodclose_to

(&self, other: &Vector, tol: f64)

src/linalg/array/vec.rs:75

Methodclose_to

Check whether a matrix is close to another matrix within some tolerance.

src/linalg/array/matrix.rs:97

Functioncovariance

Calculates the covariance between two vectors x and y. This is a two-pass algorithm which centers the data before computing the covariance, which impr

src/statistics/covariance.rs:6

Functioncriterion_binomial

(c: &mut Criterion)

benches/benchmark_functions.rs:16

Functioncriterion_dot

(c: &mut Criterion)

benches/benchmark_linalg.rs:7

Functioncriterion_hist_bins

(c: &mut Criterion)

benches/benchmark_statistics.rs:14

Functioncriterion_invert

(c: &mut Criterion)

benches/benchmark_linalg.rs:171

Functioncriterion_logsumexp

(c: &mut Criterion)

benches/benchmark_linalg.rs:21

Functioncriterion_ludecomp

(c: &mut Criterion)

benches/benchmark_linalg.rs:152

Functioncriterion_matmul

(c: &mut Criterion)

benches/benchmark_linalg.rs:180

Functioncriterion_matmul_blocked

(c: &mut Criterion)

benches/benchmark_linalg.rs:200

Functioncriterion_matrix_sum

(c: &mut Criterion)

benches/benchmark_linalg.rs:146

Functioncriterion_mean

(c: &mut Criterion)

benches/benchmark_functions.rs:8

Functioncriterion_mean

(c: &mut Criterion)

benches/benchmark_statistics.rs:6

Functioncriterion_norm

(c: &mut Criterion)

benches/benchmark_linalg.rs:16

Functioncriterion_solve

(c: &mut Criterion)

benches/benchmark_linalg.rs:159

Functioncriterion_tsfuncs

(c: &mut Criterion)

benches/benchmark_timeseries.rs:4

Functioncriterion_vops

(c: &mut Criterion)

benches/benchmark_linalg.rs:81

Functioncriterion_vops_assign

(c: &mut Criterion)

benches/benchmark_linalg.rs:26

Functioncriterion_xtx

(c: &mut Criterion)

benches/benchmark_linalg.rs:215

Methoddata

Return a reference to the underlying Vector holding the data.

src/linalg/array/matrix.rs:426

Methoddata_mut

Return a mutable reference to the underlying Vector holding the data.

src/linalg/array/matrix.rs:431

Methoddefault

()

src/optimize/lm.rs:59

Methoddefault

()

src/optimize/sgd.rs:66

Methoddefault

Uses the defaults recommended by Kingma and Ba 2014

src/optimize/adam.rs:76

Methoddefault

()

src/linalg/array/vec.rs:95

Methoddefault

()

src/linalg/array/matrix.rs:638

Methoddefault

()

src/distributions/normal.rs:44

Methoddefault

()

src/distributions/exponential.rs:38

Methoddefault

()

src/distributions/chi_squared.rs:32

Methoddefault

()

src/distributions/gamma.rs:48

Methoddefault

()

src/distributions/bernoulli.rs:32

Methoddefault

()

src/distributions/pareto.rs:40

Methoddefault

()

src/distributions/t.rs:30

Methoddefault

()

src/distributions/beta.rs:54

Methoddefault

()

src/distributions/gumbel.rs:45

Methoddefault

()

src/distributions/discreteuniform.rs:40

Methoddefault

()

src/distributions/poisson.rs:33

Methoddefault

()

src/distributions/uniform.rs:41

Methoddefault

()

src/distributions/binomial.rs:37

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