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Functions442 in github.com/al-jshen/compute

⨍ Functions 442◇ Types & classes 42

src/linalg/array/vec.rs:191

Methodderef_mut

src/linalg/array/vec.rs:197

Functiondifference

Applies a single differencing operation to a vector. Note that the length of the vector is shortened by one.

src/timeseries/functions.rs:35

Functiondiscrete_uniform

(c: &mut Criterion)

benches/benchmark_distributions.rs:16

src/linalg/array/vec.rs:19

src/linalg/array/matrix.rs:26

src/linalg/array/vec.rs:40

(&self, other: &Vector)

src/linalg/array/vec.rs:101

(&self, other: &Matrix)

src/linalg/array/matrix.rs:595

Make an identity matrix with size `dims`.

src/linalg/array/matrix.rs:59

(&self, idx: usize)

src/linalg/array/matrix.rs:400

Methodflat_idx_replace

(&mut self, idx: usize, val: f64)

src/linalg/array/matrix.rs:405

(&self, f: &mut Formatter<'_>)

src/linalg/array/vec.rs:130

(&self, f: &mut Formatter)

src/linalg/array/matrix.rs:604

(&self, f: &mut Formatter<'_>)

src/timeseries/autoregressive.rs:74

Functionfn_resid

(params: &[Var<'a>], data: &[&[f64]])

src/optimize/sgd.rs:172

Functionfn_resid

(params: &[Var<'a>], data: &[&[f64]])

src/optimize/adam.rs:175

src/linalg/array/vec.rs:118

Methodfrom_iter

src/linalg/array/vec.rs:163

Methodfrom_par_iter

src/linalg/array/vec.rs:174

Functiongenerate

(c: &mut Criterion)

benches/benchmark_distributions.rs:4

Horizontal concatenation of matrices. Adds `other` to the right of the calling matrix.

src/linalg/array/matrix.rs:441

Repeat self horizontally. That is, make `n` total copies of self and horizontally concatenate them all together.

src/linalg/array/matrix.rs:473

(&self, i: usize)

src/linalg/array/matrix.rs:645

Methodindex_mut

(&mut self, i: usize)

src/linalg/array/matrix.rs:661

Functioninf_norm

Calculates the infinity norm of a matrix. That is, it sums the absolute values along each row, and then returns the largest of these values.

src/linalg/utils.rs:615

Methodinitial_working_response

(&self, y: &[f64])

src/predict/glms/families.rs:117

Methodinitial_working_weights

(&self, y: &[f64])

src/predict/glms/families.rs:127

Functioninrange

src/distributions/discreteuniform.rs:93

Functioninrange

src/distributions/uniform.rs:95

Functionis_design

(m: &[f64], nrows: usize)

src/linalg/utils.rs:69

Methodis_lower_triangular

Check whether the matrix is lower triangular (i.e., all the entries above the diagonal are 0).

src/linalg/array/matrix.rs:231

Methodis_positive_definite

Check whether the matrix is positive definite.

src/linalg/array/matrix.rs:105

Methodis_upper_triangular

Check whether the matrix is upper triangular (i.e., all the entries below the diagonal are 0).

src/linalg/array/matrix.rs:218

Methodlinesearch

src/optimize/lbfgs.rs:5

(&self, x: Self::PDFType)

src/distributions/normal.rs:95

(&self, x: Self::PDFType)

src/distributions/mod.rs:79

(&self, x: Self::PDFType)

src/distributions/multivariatenormal.rs:85

Functionloglikelihood

(params: &[F1], data: &[&[f64]])

src/optimize/lbfgs.rs:19

Functionlogmeanexp

Calculates the logarithm of the mean of the exponentials in a stable manner. Applies the LogMeanExp operator (like the LogSumExp, but mean instead of

src/linalg/utils.rs:555

Calculates the determinant of an LU decomposed matrix.

src/linalg/array/matrix.rs:211

examples/gp_regression/src/main.rs:8

Functionmaxprob

src/distributions/normal.rs:117

Returns the mean of the given Normal distribution.

src/distributions/normal.rs:103

Returns the mean of the given exponential distribution.

src/distributions/exponential.rs:81

Calculates the mean, which for a Gamma(a, b) distribution is given by `a / b`.

src/distributions/gamma.rs:106

Calculates the mean of the Bernoulli distribution, which is `p`.

src/distributions/bernoulli.rs:80

Calculates the mean of the Pareto distribution.

src/distributions/pareto.rs:78

Calculates the mean of the T distribution, which is 0 when the degrees of freedom is greater than 1, and undefined otherwise.

src/distributions/t.rs:65

Returns the mean of the beta distribution, which for a B(a, b) distribution is given by `a / (a + b)`.

src/distributions/beta.rs:92

src/distributions/multivariatenormal.rs:103

Calculates the mean of the given Gumbel distribution.

src/distributions/gumbel.rs:76

Calculates the mean, which for a Uniform(a, b) distribution is given by `(a + b) / 2`.

src/distributions/discreteuniform.rs:79

Calculates the mean, which is given by the rate parameter.

src/distributions/poisson.rs:74

Calculates the mean, which for a Uniform(a, b) distribution is given by `(a + b) / 2`.

src/distributions/uniform.rs:81

Calculates the mean, which is given by `np`.

src/distributions/binomial.rs:279

src/linalg/array/vec.rs:205

src/linalg/array/matrix.rs:589

Create a new Levenberg-Marquardt optimizer.

src/optimize/lm.rs:71

Create a new SGD optimizer. stepsize: step size momentum: momentum factor nesterov: whether to use Nesterov momentum

src/optimize/sgd.rs:51

Create a new Adam optimizer. stepsize: step size beta1: exponential decay rate for first moment beta2: exponential decay rate for second moment epsilo

src/optimize/adam.rs:53

src/linalg/array/vec.rs:23

Make a new matrix with the given number of rows and columns.

src/linalg/array/matrix.rs:253

Create a new Normal distribution with mean `mu` and standard deviation `sigma`. # Errors Panics if `sigma < 0`.

src/distributions/normal.rs:20

Create a new Exponential distribution with rate parameter `lambda`. # Errors Panics if `lambda <= 0`.

src/distributions/exponential.rs:19

Create a new Chi square distribution with # Errors Panics if degrees of freedom is not positive.

src/distributions/chi_squared.rs:17

Create a new Gamma distribution with shape `alpha` and rate `beta`. # Errors Panics if `alpha <= 0` or `beta <= 0`.

src/distributions/gamma.rs:20

Create a new Bernoulli distribution with probability `p`. # Errors Panics if p is not in [0, 1].

src/distributions/bernoulli.rs:16

Create a new Pareto distribution with shape `alpha` and minimum value `minval`. # Errors Panics if `alpha <= 0` or `minval <= 0`.

src/distributions/pareto.rs:17

Create a new t distribution with # Errors Panics if degrees of freedom is not positive.

src/distributions/t.rs:18

Create a new Beta distribution with parameters `alpha` and `beta`. # Errors Panics if `alpha <= 0` or `beta <= 0`.

src/distributions/beta.rs:24

(mean: V, covariance_matrix: M)

src/distributions/multivariatenormal.rs:19

Create a new Gumbel distribution with location `mu` and scale `beta`. # Errors Panics if `beta <= 0`.

src/distributions/gumbel.rs:21

Create a new discrete uniform distribution with lower bound `lower` and upper bound `upper` (inclusive on both ends). # Errors Panics if `lower > upp

src/distributions/discreteuniform.rs:17

Create a new Poisson distribution with rate parameter `lambda`. # Errors Panics if `lambda <= 0.0`.

src/distributions/poisson.rs:17

Create a new Uniform distribution with lower bound `lower` and upper bound `upper`. # Errors Panics if `lower > upper`.

src/distributions/uniform.rs:18

Create a new Binomial distribution with parameters `n` and `p`. # Remarks `n` must be a non-negative integer, and `p` must be in [0, 1].

src/distributions/binomial.rs:17

Create a new polynomial regressor with degree `deg` (e.g., deg = 1 is a linear model).

src/predict/polynomial.rs:14

Create a new general linear model with the given exponential family. `alpha` sets the L2 (ridge regression) regularization strength, and `tolerance` s

src/predict/glms/glm.rs:29

(formula: &'a str, data: HashMap<&'b str, Vec<f64>>)

src/predict/glms/formula.rs:10

(var: f64, length_scale: f64)

src/predict/gps/kernels.rs:23

src/timeseries/autoregressive.rs:17

src/linalg/array/vec.rs:62

Make a matrix filled with ones.

src/linalg/array/matrix.rs:40

src/optimize/lbfgs.rs:9

( &self, f: F, parameters: &[f64], data: &[&[f64]], maxsteps: usize,

src/optimize/lm.rs:83

src/predict/glms/formula.rs:14

Calculates the probability density function of the given Normal distribution at `x`.

src/distributions/normal.rs:91

Calculates the [probability density function](https://en.wikipedia.org/wiki/Probability_density_function) for the given Exponential distribution at `x

src/distributions/exponential.rs:70

Calculates the probability density function for the given Chi square distribution at `x`. # Remarks If `dof = 1` then x should be positive. Otherwise

src/distributions/chi_squared.rs:58

Calculates the probability density function for the given Gamma function at `x`. # Remarks x should be positive.

src/distributions/gamma.rs:93

Calculates the probability density function for the given Pareto function at `x`. # Remarks This returns 0 if `x < minval`

src/distributions/pareto.rs:67

Calculates the probability density function for the given Beta function at `x`. # Remarks Returns 0. if x is not in `[0, 1]`

src/distributions/beta.rs:80

(&self, x: Self::PDFType)

src/distributions/multivariatenormal.rs:68

Calculates the probability density function for the given Gumbel function at `x`.

src/distributions/gumbel.rs:67

Calculates the [probability density function](https://en.wikipedia.org/wiki/Probability_density_function) for the given Uniform distribution at `x`.

src/distributions/uniform.rs:69

Calculates the [probability mass function](https://en.wikipedia.org/wiki/Probability_mass_function) for the given Bernoulli distribution at `x`.

src/distributions/bernoulli.rs:66

Calculates the [probability mass function](https://en.wikipedia.org/wiki/Probability_mass_function) for the given discrete uniform distribution at `x`

src/distributions/discreteuniform.rs:67

Calculates the [probability mass function](https://en.wikipedia.org/wiki/Probability_mass_function) for the given Poisson distribution at `k`.

src/distributions/poisson.rs:62

Calculates the [probability mass function](https://en.wikipedia.org/wiki/Probability_mass_function) for the given Binomial distribution at `k`.

src/distributions/binomial.rs:269

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