cubicSpline

cubicSpline(x, y, bc_type="not-a-knot")

x is a numeric vector containing values of the independent variable. The length of x must be no smaller than 3. Its values must be real and in strictly increasing order.

y is a numeric vector containing values of the dependent variable. The length of y must match the length of x.

bc_type is of STRING type, which can be a scalar, pair, or a vector of length no greater than 2. It specifies the boundary condition type.

• If bc_type is a string or a vector of length 1, the specified condition will be applied at both ends of a spline.

• If bc_type is a pair or a vector of length 2, the first and the second value will be applied at the curve start and end respectively.

Its value can be:

• "not-a-knot" (default): The first and second segment at a curve end are the same polynomial.

• "clamped": The first derivative at curves ends are zero.

• "natural": The second derivative at curve ends are zero.

Cubic spline data interpolator.

Return value: A dictionary withthe following keys:

• c: Coefficients of the polynomials on each segment.
• x: Breakpoints. The input x.
• predict: A prediction function of the model, which returns the cubic spline interpolation result at point X. It can be called using model.predict(X) or predict(model, X), where
  • model: A dictionary indicating the output of cubicSpline.
  • X: A numeric vector indicating the X-coordinate of the point to be queried.
• modelName: A string indicating the model name, which is “cubicSpline”.

n = 10
x = 0..(n-1)
y = sin(x)
model = cubicSpline(x, y, bc_type="not-a-knot")
model

// output
x->[0,1,2,3,4,5,6,7,8,9]
predict->cubicSplinePredict
modelName->cubicSpline
c->[-0.0418500756165063,-0.2612720445455365,1.1445931049699394,0.0,-0.0418500756165067,-0.3868222713950554,0.4964987890293473,0.8414709848078965,0.1468910600890447,-0.5123724982445756,...]

Related Function: cubicSplinePredict