datafit

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Installation

datafit can be installed from the official npm package repository. It is highly recommended to install the latest version, which is installed by default with the following command.

npm i datafit@1.4.5

Bugs and Requests

Is there a way we can make datafit better? Please report all bugs, issues, and new feature requests to the issues page in the official repository. For critical security issues, please send an email to [e-mail].

Contribute

Thank you for your interest in contributing to datafit! datafit is an open source software package maintained by Nicolas Ventura (@nicfv) and built by users like you! You are allowed to fork the repository as permitted by the MIT License terms. Contributions are welcome by submitting a pull request. Please follow the existing code styling if submitting a pull request. Thank you for your consideration!

Getting Started

datafit exports only 1 function, fit() which is used for curve fitting. All other exports are purely for information and defining types used within this package. fit() uses a genetic-style algorithm to fit a curve to. How it works is that it generates many sets of parameters to test, and keeps ones with smaller error than the previous iteration. Parameter sets with larger errors are discarded. Each subsequent iteration uses the sets of parameters with the least error and "mutates" them randomly.

Because of these random mutations, running the same code multiple times may yield slightly different results. See best practices for mitigation tactics.

Complexity

The folling factors affect computation time and resources:

• Number of iterations
• Number of independent/unknown function parameters
• Number of data points

Each one alone has a linear effect on performance, but combined has an incremental effect.

$$time = iterations \times ( parameters + dataset )$$

The dimensionality, or number of free x variables per data point, should not have an impact on computation time.

Best Practices

For production software that relies on a best curve fit for data, it's best to avoid critical operations using fit() for a few reasons.

1. fit() uses an algorithm that generates random mutations in a set of parameters, which could yield slightly different results, even if run on the same dataset.
2. If the number of iterations is very high (in the millions or higher), it could have a significant effect on software performance.

To circumvent some of these issues, the following is recommended.

1. Use datafit during the testing phase of application development, and use the best-fit parameters as constants in the final application.
2. If that is not possible, datafit may be helpful for determining an initial guess of curve fit constants, which can be input to fit() during production. The number of iterations could be reduced if the initial guess is reasonably close to the desired result.
3. Using datafit primarily for data visualization or rough estimation.
4. For operations that need datafit, a suggestion would be to run multiple iterations of fit() itself, and using each output as the subsequent call's input. This will converge to a result more effectively but could take longer.

And here are some general good practices.

1. Avoid overfitting your data. That means, you should never have more function unknowns than the number of data points. fit() allows this for the rare chance that it is needed. Typically, having more, accurate data, is better for curve fitting.
2. Reject outliers. fit() does not do this. Any outliers existing in the dataset will be treated like normal data points and could negatively impact the best fit.

There are also some great arguments and use cases for this function, namely...

1. Data analysis and visualization.
2. Quickly iterating on different model functions for determining which best fits the data.
3. Curve fitting for multivariate or nonlinear models.
4. The ease of use of it all! It's just one function call with as little as 2 inputs!

Examples

Here are a few quickstart examples written in JavaScript that showcase some out-of-box features of the datafit package.

Simple

This example demonstrates a simple usage of fit() to fit 3 data points to the line \(y=mx+b\). This is all you need to get started!

$$dataset=(1,-1), (2,1), (3,2)$$

Try plotting the data points and program output in a graphing calculator! Does it fit?

Instructions

1. Copy the source code
2. Paste into a new file
3. Save as Simple.mjs
4. Run this command in your terminal

   node Simple.mjs

Source

import { fit } from 'datafit';

// Define our model function: y=mx+b
function f(x, m, b) {
    return m * x + b;
}

// Define our dataset
const data = [
    { x: 1, y: -1 },
    { x: 2, y: 1 },
    { x: 3, y: 2 },
];

// Compute the best fit parameters to
// get `m` and `b`, and print result.
const summary = fit(f, data);
const m_fit = summary.params[0];
const b_fit = summary.params[1];
console.log('The best-fit line is y = ' + m_fit.toFixed(2) + 'x + ' + b_fit.toFixed(2));

Output

The best-fit line is y = 1.50x + -2.34

Single Variable

In this example written in TypeScript, we will fit a generic 2nd degree polynomial defined to a given set of \((x,y)\) points. We'll create this dataset based on a known formula for a 2nd degree polynomial, and add some random high-frequency noise. The initial guess for our constants will all be zero, given by \(a_{2} = a_{1} = a_{0} = 0\). Then, we'll determine the true value and best-fit value of \(f(3)\) using the parameter set found by the fit() function. We're able to use the best-fit curve given by our summary to extrapolate for any \(x\) value.

Here is the function used to generate the dataset.

$$f(x) = -0.2x^{2} + x + 1.3$$

Here is the model function used for curve fitting.

$$f(x, \bar{a}) = a_{2}x^{2} + a_{1}x + a_{0}$$

In the output, the line with params: contains the set of best-fit parameters in the order of the model function parameters. That means, my outputs are in the order of \(a_{0}, a_{1}, a_{2}\). How close is it to our original function? Try it for yourself and see if you obtain similar results!

The other lines in the summary tell us other information from the computation. See summary for more details.

• f is the best-fit function we used to extrapolating from our dataset.
• params contains the set of best-fit parameters.
• error is the total residual squared error (for all data points.) This is the value that the algorithm is minimizing.
• errorAvgAbs is the average absolute error we would get from the model function with params compared to the actual dataset values. Lower numbers are better here. This only compares the best-fit with values from the dataset. By extrapolating beyond our dataset, our average error may increase. See below for an explanation.

In this example, the x values for our data points are given as [-2, -1, 0, 1, 2]. If we interpolate within this range, the model will be about as accurate as what is shown in the summary. Extrapolating very far beyond either end of this range (e.g. \(f(100)\)) will probably not be very accurate!

Mind the order of your parameters! fit() will output a parameter array that follows the same order as the model function parameters, even if it is non-intuitive! For example, if my function signature looks like f(x, a2, a1, a0), then summary.params would return an array [a2, a1, a0]. You would access a2 with a[0]!

Instructions

1. Copy the source code
2. Paste into a new file
3. Save as Single-Variable.mjs
4. Run this command in your terminal

   node Single-Variable.mjs

Source

import { fit } from 'datafit';

// Define a model function for curve fitting.
// Let's use a generic 2nd degree polynomial
// with all constants unknown. Unfortunately,
// in JavaScript we cannot define default
// parameter values in the signature, so we
// must use the `??` operator within the function.
function f(x, a0, a1, a2) {
    a0 = a0 ?? 1.3;
    a1 = a1 ?? 1.0;
    a2 = a2 ?? -0.2;
    return a2 * x ** 2 + a1 * x + a0;
}

// Define a function to add noise to the dataset
function noise(A) {
    return A * (2 * Math.random() - 1)
}

// Define the dataset from our noisy signal
const data = [-2, -1, 0, 1, 2].map(x => ({ x: x, y: f(x) + noise(0.1) }));
console.log('Dataset', data);

// Compute the best-fit set of parameters
// starting with an initial guess of [x^2 + x + 1]
// with 10,000 iterations, and each parameter
// can vary up to 50% on the first iteration
const summary = fit(f, data, [1, 1, 1], 10000, 0.5);
console.log('Summary', summary);

// Compute the actual value and
// best-fit value of f(3) to compare
const f3_act = f(3);
const f3_fit = summary.f(3);
console.log('f(3)', f3_act, f3_fit);

// Compute the relative error
const rel_error = (f3_fit - f3_act) / f3_act * 100;
console.log('Error: ' + rel_error.toFixed(2) + '%');

Output

Dataset [
  { x: -2, y: -1.5535920916306751 },
  { x: -1, y: 0.07186077016978243 },
  { x: 0, y: 1.2654434251923135 },
  { x: 1, y: 2.1690020040075093 },
  { x: 2, y: 2.5991428007783877 }
]
Summary {
  f: [Function: f],
  params: [ 1.293496767836034, 1.0404979994778107, -0.19152934802290644 ],
  error: 0.0016844015363050087,
  errorAvgAbs: 0.01835429942168869
}
f(3) 2.5 2.691226634063308
Error: 7.65%

Multi Variable

In this slightly more complicated example, we'll define a 2D planar function to fit 3-dimensional data. The main difference here is that \(\bar{x}\) is now an array, in both \(f(\bar{x})\) and the dataset, but everything else is functionally the same!

$$ax + by + cz = d$$

This general plane formula can be rewritten to solve for \(z\). \(c_{x}\), \(c_{y}\), and \(c_{z}\) are all arbirary constants that we will adjust to fit the plane to the dataset.

$$f(\bar{x}, \bar{c}) = c_{x}x + c_{y}y + c_{z}$$

In the dataset defined in our source code, the x property refers to all the inputs to the function, and the y property refers to the output. In this example, x is an array of length 2, representing our 2 independent dimensions. We can call those dimensions the x-axis and y-axis, obtained by x[0] and x[1] respectively. In mathematics, this is simply referred to as the input vector we defined as \(\bar{x}\). In this example, the y property in our dataset is actually our z-axis. It's important to remember that the x and y properties in the dataset simply refer to the inputs and output of the function, not necessarily the x-axis and y-axis.

3 points is all that is required to define the equation for a plane. In this example, our data fits to the plane defined by \(z = 2x - y + 1\).

Let's see if the algorithm can find this out just based on the dataset! Try it for yourself and see if you obtain similar results, and try plotting it in an online 3D calculator!

Instructions

1. Copy the source code
2. Paste into a new file
3. Save as Multi-Variable.mjs
4. Run this command in your terminal

   node Multi-Variable.mjs

Source

import { fit } from 'datafit';

// Define a general 3D plane function where z = f(x, y)
function f([x, y], cx, cy, cz) {
    return cx * x + cy * y + cz;
}

// These 3 points make up the plane
// z = 2x - y + 1
const data = [
    { x: [0, 0], y: 1 },
    { x: [1, 0], y: 3 },
    { x: [0, 1], y: 0 },
];

// Run the curve fitting algorithm
const summary = fit(f, data);
console.log(summary);

Output

{
  f: [Function: f],
  params: [ 2.0029307445482627, -0.9979053426343838, 0.9983861162159384 ],
  error: 0.000004569886377470723,
  errorAvgAbs: 0.0012342185621505243
}