Interpolation

Interpolation namespace defines the unified API for various interpolation methods. Most of them also extrapolates. Methods include:

All methods are accessible from fastmath.interpolation namespace via a multimethod interpolation. Additionally each method is implemented as a regular function in the dedicated namespace. interpolation returns an interpolant function

Both examples below are equivalent:

(require '[fastmath.interpolation :as i]
         '[fastmath.interpolation.linear :as linear])
(def i1 (i/interpolation :linear [1 2 3] [4 5 -1]))
(def i2 (linear/linear [1 2 3] [4 5 -1]))
{:i1 (i1 2.5)
 :i2 (i2 2.5)}
{:i1 2.0, :i2 2.0}

List of all possible methods:

(sort (keys (methods i/interpolation)))
(:akima :b-spline :barycentric :bicubic :bilinear :cir :cubic :cubic-2d :cubic-smoothing :divided-difference :gp :kriging :linear :loess :microsphere-projection :monotone :neville :pava :polynomial :rbf :shepard :sprague :step :step-after :step-before)

The following functions and samples will be used as a target to illustrate usage of described method.

1d target

\[f(x)=\sin\left(\frac{x\cos(x+1)}{2}\right)\]

(defn target-1d [x] (m/sin (* 0.5 x (m/cos (inc x)))))
(target-1d 4.0)
0.5373775050861961

Points used in interpolation

(def xs1 [0.5 0.69 1.73 2.0 2.28 3.46 3.5 4.18 4.84 5.18 5.53 5.87 6.22 6.5])
(def ys1 (map target-1d xs1))

Monotone

(def xxss (range 0 400 5))
(def data (mapv (fn [^long v] (m/+ v (m/+ (m/* 30 (m/sin (m// v 60.0)))
                                       (r/grand (m// v -2.0) (m// v 10.0))))) xxss))
(ggplot/->file
 (ggplot/function+scatter (i/interpolation :cir xxss data) xxss data {:dot-size 2
                                                                      :dot-alpha 0.6
                                                                      :title "CIR"}))


(ggplot/->file
 (ggplot/function+scatter (i/interpolation :cir xxss data {:method :monotone})
                          xxss data {:dot-size 2
                                     :dot-alpha 0.6
                                     :title "CIR smoothed"}))

2d target

\[f(x,y)=\sin\left(\frac{x-100}{10}\cos\left(\frac{y}{20}\right)\right)+\frac{x}{100}+\left(\frac{y-100}{100}\right)^2+1\]

(defn target-2d [[x y]] (m/+ 1.0 (m/sin (* (/ (- x 100.0) 10.0) (m/cos (/ y 20.0))))
                          (m// x 100.0)
                          (m/sq (m// (m/- y 100.0) 100.0))))
(target-2d [20 20])
2.7649202623006808

Grid

Points for grid interpolation

(def xs2 [20 25 30 35 40 50 58 66 100 121 140 150 160 170 180])
(def ys2 [20 30 58 66 90  121 140 152 170     180])
(def zss (for [x xs2]
         (for [y ys2]
           (target-2d [x y]))))
(def xss (repeatedly 300 #(vector (r/drandom uniform-seed-44 20 180)
                                (r/drandom uniform-seed-44 20 180))))
(def ys3 (map target-2d xss))
(defn error-1d
  [interpolant]
  (m/sqrt (calc/integrate (fn [^double x] (m/sq (m/- (target-1d x) (interpolant x)))) 0.5 6.5)))
(error-1d (linear/linear xs1 ys1))
0.2110302144467739

For 2d case the following formula will be used:

\[error_{2d}(f,g)=\|f-g\|=\sqrt{\int_{20}^{180}\int_{20}^{180}|f(x,y)-g(x,y)|^2\,dx dy}\]

(defn error-2d
  [interpolant]
  (m/sqrt (calc/cubature (fn [xy] (m/sq (m/- (target-2d xy) (interpolant xy))))
                         [20.0 20.0]
                         [180.0 180.0])))
(error-2d (linear/bilinear xs2 ys2 zss))
102.03678750452109

1d

Linear

Linear piecewise interpolation and extrapolation. Extrapolation uses a slope from the boundaries. See more on Wikipedia

(require '[fastmath.interpolation.linear :as linear])
(def linear (linear/linear xs1 ys1))
(linear 4.0)
0.49924424111385607
(error-1d linear)
0.2110302144467739

Cubic

Natural cubic spline (second derivatives at boundary points have value \(0\)) interpolation and extrapolation. See more on Wikipedia

(require '[fastmath.interpolation.cubic :as cubic])
(def cubic (cubic/cubic xs1 ys1))
(cubic 4.0)
0.5516054931803801
(error-1d cubic)
0.0275840592896124

Akima

See more on Wikipedia

(require '[fastmath.interpolation.acm :as acm])
(def akima (acm/akima xs1 ys1))
(akima 4.0)
0.5335842087231077
(error-1d akima)
0.03487751999898592

Neville

See more on Wikipedia

(require '[fastmath.interpolation.acm :as acm])
(def neville (acm/neville xs1 ys1))
(neville 4.0)
0.5432043004304535
(error-1d neville)
0.8675392877418397

Barycentric

Rational interpolation as described in Numerical Recipes ch. 3.4. The order (default \(1\)) parameter contols number of points used to calculate weights. Higher order means better accuracy.

(require '[fastmath.interpolation.barycentric :as barycentric])
(defn barycentric
  ([] (barycentric/barycentric xs1 ys1))
  ([order] (barycentric/barycentric xs1 ys1 {:order order})))
((barycentric) 4.0)
0.5492673111356233
order error barrycentric(4.0) error at 4.0
0 0.5193270391333753 0.6329368698778738 0.0955593647916777
1 0.03176373180495161 0.5492673111356233 0.011889806049427243
2 0.05019164899125852 0.5160607443493412 0.021316760736854845
3 0.028650229888319802 0.5232915410624766 0.014085964023719533
4 0.00351102181650211 0.5349629695697342 0.0024145355164618687
5 0.009022181871044352 0.5387189359388596 0.0013414308526634722

B-spline

(require '[fastmath.interpolation.ssj :as ssj])
(defn b-spline
  ([] (ssj/b-spline xs1 ys1))
  ([degree] (b-spline degree nil))
  ([degree hp1] (ssj/b-spline xs1 ys1 {:degree degree :hp1 hp1})))
((b-spline) 4.0)
0.1610170071559863

Divided difference

(require '[fastmath.interpolation.acm :as acm])
(def divided-difference (acm/divided-difference xs1 ys1))
(divided-difference 4.0)
0.5432043004304531
(error-1d divided-difference)
0.8675392877418397

Polynomial

(require '[fastmath.interpolation.ssj :as ssj])
(def polynomial (ssj/polynomial xs1 ys1))
(polynomial 4.0)
0.5432043380309324
(error-1d polynomial)
0.8675392846805364

Monotone

(require '[fastmath.interpolation.monotone :as monotone])
(def monotone (monotone/monotone xs1 ys1))
(monotone 4.0)
0.6588206176299103
(error-1d monotone)
0.1517488499630331

Step

(require '[fastmath.interpolation.step :as step])
(defn step
  ([] (step/step xs1 ys1))
  ([point] (step/step xs1 ys1 {:point point})))
(def step-before (step/step-before xs1 ys1))
(def step-after (step/step-after xs1 ys1))
method error value at 4.0
step-before 0.849159325039357 0.8087819747808206
step-after 0.7429959099336633 -0.3605827968499356
step 0.4328611328633974 0.8087819747808206
step (point=0.55) 0.42287483285733546 0.8087819747808206
step (point=0.25) 0.5962341092433667 0.8087819747808206
step (point=0.75) 0.49446591280052093 -0.3605827968499356

Loess

(require '[fastmath.interpolation.acm :as acm])
(defn loess
  ([] (acm/loess xs1 ys1))
  ([bandwidth] (acm/loess xs1 ys1 {:bandwidth bandwidth})))
ERR 
Oct 17, 2025 2:18:19 PM clojure.tools.logging$eval39757$fn__39760 invoke
INFO: [:clojisr.v1.session/gc-clean-r-objects {:objects-before 20590, :objects-after 7028}]

Cubic smoothing

(require '[fastmath.interpolation.ssj :as ssj])
(defn cubic-smoothing
  ([] (ssj/cubic-smoothing xs1 ys1))
  ([rho] (ssj/cubic-smoothing xs1 ys1 {:rho rho})))

2d grid

Bilinear

(require '[fastmath.interpolation.linear :as linear])
(def bilinear (linear/bilinear xs2 ys2 zss))
(error-2d bilinear)
102.03678750452109

Bicubic

(require '[fastmath.interpolation.acm :as acm])
(def bicubic (acm/bicubic xs2 ys2 zss))
(error-2d bicubic)
103.97025992767536

Cubic 2d

(require '[fastmath.interpolation.cubic :as cubic])
(def cubic-2d (cubic/cubic-2d xs2 ys2 zss))
(error-2d cubic-2d)
103.23387133898065

Multivariate and kernel based

Microsphere projection

(require '[fastmath.interpolation.acm :as acm])
 
(error-1d (acm/microsphere-projection xs1 ys1))
0.2600888857710009
(error-2d (acm/microsphere-projection xss ys3))
52.72571398573958

Shepard

(require '[fastmath.interpolation.shepard :as shepard])

Radial Basis Function

(require '[fastmath.interpolation.rbf :as rbf])
(defn chart-f [f title] (-> (ggplot/function f {:x [-5 5] :title title})
                         (ggplot/->image)))

Polynomial term

(defn polynomial-terms-1d [^double x]
  [1.0 x (m/sq x)])
(defn polynomial-terms-2d [[^double x ^double y]]
  [1.0 x y (m/* x y) (m/sq x) (m/sq y)])
 
(error-2d (rbf/rbf xss ys3 (kernel/rbf :gaussian {:shape 0.1})))
365.96788014805696
(error-2d (rbf/rbf xss ys3 (kernel/rbf :matern-c2 {:shape 0.15})))
316.10296877225454
(error-2d (rbf/rbf xss ys3 (kernel/rbf :gaussians-laguerre-22 {:shape 0.07})))
376.435234072405
(error-2d (rbf/rbf xss ys3 (kernel/rbf :thin-plate)))
49.070725801843054

Smoothing

Kriging

Variograms

(require '[fastmath.kernel.variogram :as variogram]
         '[fastmath.interpolation.kriging :as kriging])
(defn svar-image [f emp title]
  (let [x (map :h emp)
        y (map :gamma emp)]
    (-> (ggplot/function+scatter f x y {:title title :ylim [0 nil]})
        (ggplot/->image))))
(def empirical-matheron-1d (variogram/empirical xs1 ys1))
(def empirical-matheron (variogram/empirical xss ys3 {:size 20}))
empirical-matheron
[{:n 116, :h 3.3604788552002978, :gamma 0.05610650116225876} {:n 385, :h 7.841219583707931, :gamma 0.18995728933979483} {:n 606, :h 12.72578480586009, :gamma 0.399844655716175} {:n 856, :h 17.611233442491304, :gamma 0.4194793467635297} {:n 1019, :h 22.694649851980536, :gamma 0.4993167205111838} {:n 1173, :h 27.74503808347392, :gamma 0.5232922610403302} {:n 1322, :h 32.696030602647625, :gamma 0.5223125852571721} {:n 1527, :h 37.65818295299809, :gamma 0.5426056881417575} {:n 1594, :h 42.65227807229347, :gamma 0.5227361157844413} {:n 1803, :h 47.67443166086058, :gamma 0.5243286183079909} {:n 1784, :h 52.675449304556054, :gamma 0.5272068497879318} {:n 1922, :h 57.731016242599935, :gamma 0.5285755419763869} {:n 1909, :h 62.70585327047225, :gamma 0.5652895296636227} {:n 1966, :h 67.77992830214916, :gamma 0.5265816327093601} {:n 1977, :h 72.71580210020514, :gamma 0.49759061919470493}]
(def empirical-cressie (variogram/empirical xss ys3 {:estimator :cressie :size 20}))
(def empirical-highly-robust (variogram/empirical xss ys3 {:estimator :highly-robust :size 20
                                                         :remove-outliers? true}))
empirical-highly-robust
[{:n 116, :h 3.3604788552002978, :gamma 0.02913016400873318} {:n 385, :h 7.841219583707931, :gamma 0.14056514492191244} {:n 606, :h 12.72578480586009, :gamma 0.42633847448010354} {:n 856, :h 17.611233442491304, :gamma 0.44483727296978925} {:n 1019, :h 22.694649851980536, :gamma 0.5434941912574424} {:n 1173, :h 27.74503808347392, :gamma 0.571757191071463} {:n 1322, :h 32.696030602647625, :gamma 0.5504904163482222} {:n 1527, :h 37.65818295299809, :gamma 0.5905951431086924} {:n 1594, :h 42.65227807229347, :gamma 0.5577607587471852} {:n 1803, :h 47.67443166086058, :gamma 0.555261258787647} {:n 1784, :h 52.675449304556054, :gamma 0.561444986664588} {:n 1922, :h 57.731016242599935, :gamma 0.5631892305752904} {:n 1909, :h 62.70585327047225, :gamma 0.6128688264262536} {:n 1966, :h 67.77992830214916, :gamma 0.5591266023267967} {:n 1977, :h 72.71580210020514, :gamma 0.5142798048326319}]
(def empirical-quantile (variogram/empirical xss ys3 {:estimator :quantile :size 50
                                                    :quantile 0.92}))
(def empirical-M-robust (variogram/empirical xss ys3 {:estimator :m-robust :size 50}))

Semi-variograms

(def variogram-linear  (variogram/fit empirical-quantile :linear))
(def variogram-gaussian (variogram/fit empirical-highly-robust :gaussian))
(def variogram-pentaspherical (variogram/fit empirical-highly-robust :pentaspherical))
(def variogram-rbf-wendland-2-3 (variogram/fit empirical-highly-robust (kernel/rbf :wendland {:s 2 :k 3})))
(def variogram-superspherical-1d (variogram/fit empirical-matheron-1d :tplstable {:order 1.9 :defaults {:beta 14.0}}))
(((variogram/->superspherical 1.0) {:nugget 0.1 :psill 0.5 :range 1.0}) 0.4)
0.384
 
(def kriging-linear (kriging/kriging xss ys3 variogram-linear))
(def kriging-gaussian (kriging/kriging xss ys3 variogram-gaussian))
(def kriging-pentaspherical (kriging/kriging xss ys3 variogram-pentaspherical))
(def kriging-rbf-wendland-2-3 (kriging/kriging xss ys3 variogram-rbf-wendland-2-3))
(error-2d kriging-linear)
56.63634949359084
(def vl (variogram/linear {:nugget 0.03 :sill 0.5 :range 14.0}))

Smoothing

Gaussian processes

source: clay/interpolation.clj