Function fit

fit<T extends VariableType>(
    f: F<T>,
    data: Datum<T>[],
    params_initial?: number[],
    iterations?: number,
    maxDeviation?: number,
): Summary<T>
Minimize the sum of squared errors to fit a set of data points to a curve with a set of unknown parameters.
Type Parameters
- T extends VariableType
Parameters
- f: F<T>
  The model function for curve fitting.
- data: Datum<T>[]
  The entire dataset, as an array of points.
- params_initial: number[] = []
  The initial guess for function parameters, which defaults to an array filled with zeroes.
- iterations: number = 1e3
  The number of parameter sets to generate.
- maxDeviation: number = 1
  The relative standard parameter deviation. This is a number [0.0-1.0] and affects the standard deviation on the first iteration. Every subsequent iteration has a decayed standard deviation until the final iteration.
Returns Summary<T>
The set of parameters and error for the best fit.
Example
```
// Define model function
function f(x: number, a2: number = -0.5, a1: number = 3.9, a0: number = -1.2): number {
    return a2 * x ** 2 + a1 * x + a0;
}
// Construct a data set
const data: Datum<number>[] = [0, 2, 4].map(x => ({ x: x, y: f(x) }));
// Compute best-fit summary
const summary = fit(f, data);
```