Curve Fitting

These functions support constructing a curve.

Polynomial Curve Fitting

The polyfit function is a general purpose curve fitter used to model the non-linear relationship between two random variables.

The polyfit function is passed x- and y-axes and fits a smooth curve to the data. If only a single array is provided it is treated as the y-axis and a sequence is generated for the x-axis.

The polyfit function also has a parameter the specifies the degree of the polynomial. The higher the degree the more curves that can be modeled.

The example below uses the polyfit function to fit a curve to an array using a 3 degree polynomial. The fitted curve is then subtracted from the original curve. The output shows the error between the fitted curve and the original curve, known as the residuals. The output also includes the sum-of-squares of the residuals which provides a measure of how large the error is.

let(echo="residuals, sumSqError",
    y=array(0, 1, 2, 3, 4, 5.7, 6, 7, 6, 5, 5, 3, 2, 1, 0),
    curve=polyfit(y, 3),
    residuals=ebeSubtract(y, curve),
    sumSqError=sumSq(residuals))

When this expression is sent to the /stream handler it responds with:

{
  "result-set": {
    "docs": [
      {
        "residuals": [
          0.5886274509803899,
          -0.0746078431372561,
          -0.49492135315664765,
          -0.6689571213100631,
          -0.5933591898297781,
          0.4352283990519288,
          0.32016160310277897,
          1.1647963800904968,
          0.272488687782805,
          -0.3534055160525744,
          0.2904697263520779,
          -0.7925296272355089,
          -0.5990476190476182,
          -0.12572829131652274,
          0.6307843137254909
        ],
        "sumSqError": 4.7294282482223595
      },
      {
        "EOF": true,
        "RESPONSE_TIME": 0
      }
    ]
  }
}

In the next example the curve is fit using a 5 degree polynomial. Notice that the curve is fit closer, shown by the smaller residuals and lower value for the sum-of-squares of the residuals. This is because the higher polynomial produced a closer fit.

let(echo="residuals, sumSqError",
    y=array(0, 1, 2, 3, 4, 5.7, 6, 7, 6, 5, 5, 3, 2, 1, 0),
    curve=polyfit(y, 5),
    residuals=ebeSubtract(y, curve),
    sumSqError=sumSq(residuals))

When this expression is sent to the /stream handler it responds with:

{
  "result-set": {
    "docs": [
      {
        "residuals": [
          -0.12337461300309674,
          0.22708978328173413,
          0.12266015718028167,
          -0.16502738747320755,
          -0.41142804563857105,
          0.2603044014808713,
          -0.12128970101106162,
          0.6234168308471704,
          -0.1754692675745293,
          -0.5379689969473249,
          0.4651616185671843,
          -0.288175756132409,
          0.027970945463215102,
          0.18699690402476687,
          -0.09086687306501587
        ],
        "sumSqError": 1.413089480179252
      },
      {
        "EOF": true,
        "RESPONSE_TIME": 0
      }
    ]
  }
}

Prediction, Derivatives and Integrals

The polyfit function returns a function that can be used with the predict function.

In the example below the x-axis is included for clarity. The polyfit function returns a function for the fitted curve. The predict function is then used to predict a value along the curve, in this case the prediction is made for the x value of 5.

let(x=array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14),
    y=array(0, 1, 2, 3, 4, 5.7, 6, 7, 6, 5, 5, 3, 2, 1, 0),
    curve=polyfit(x, y, 5),
    p=predict(curve, 5))

When this expression is sent to the /stream handler it responds with:

{
  "result-set": {
    "docs": [
      {
        "p": 5.439695598519129
      },
      {
        "EOF": true,
        "RESPONSE_TIME": 0
      }
    ]
  }
}

The derivative and integrate functions can be used to compute the derivative and integrals for the fitted curve. The example below demonstrates how to compute a derivative for the fitted curve.

let(x=array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14),
    y=array(0, 1, 2, 3, 4, 5.7, 6, 7, 6, 5, 5, 3, 2, 1, 0),
    curve=polyfit(x, y, 5),
    d=derivative(curve))

When this expression is sent to the /stream handler it responds with:

{
  "result-set": {
    "docs": [
      {
        "d": [
          0.3198918573686361,
          0.9261492094077225,
          1.2374272373653175,
          1.30051359631081,
          1.1628032287629813,
          0.8722983646900058,
          0.47760852150945,
          0.02795050408827482,
          -0.42685159525716865,
          -0.8363663967611356,
          -1.1495552332084857,
          -1.3147721499346892,
          -1.2797639048258267,
          -0.9916699683185771,
          -0.3970225234002308
        ]
      },
      {
        "EOF": true,
        "RESPONSE_TIME": 0
      }
    ]
  }
}

Harmonic Curve Fitting

The harmonicFit function (or harmfit, for short) fits a smooth line through control points of a sine wave. The harmfit function is passed x- and y-axes and fits a smooth curve to the data. If a single array is provided it is treated as the y-axis and a sequence is generated for the x-axis.

The example below shows harmfit fitting a single oscillation of a sine wave. The harmfit function returns the smoothed values at each control point. The return value is also a model which can be used by the predict, derivative and integrate functions.

There are also three helper functions that can be used to retrieve the estimated parameters of the fitted model:

  • getAmplitude: Returns the amplitude of the sine wave.

  • getAngularFrequency: Returns the angular frequency of the sine wave.

  • getPhase: Returns the phase of the sine wave.

Note
The harmfit function works best when run on a single oscillation rather than a long sequence of oscillations. This is particularly true if the sine wave has noise. After the curve has been fit it can be extrapolated to any point in time in the past or future.

In the example below the harmfit function fits control points, provided as x and y axes, and then the angular frequency, phase and amplitude are retrieved from the fitted model.

let(echo="freq, phase, amp",
    x=array(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19),
    y=array(-0.7441113653915925,-0.8997532112139415, -0.9853140681578838, -0.9941296760805463,
            -0.9255133950087844, -0.7848096869247675, -0.5829778403072583, -0.33573836075915076,
            -0.06234851460699166, 0.215897602691855, 0.47732764497752245, 0.701579055431586,
             0.8711850882773975, 0.9729352782968976, 0.9989043923858761, 0.9470697190130273,
             0.8214686154479715, 0.631884041542757, 0.39308257356494, 0.12366424851680227),
    model=harmfit(x, y),
    freq=getAngularFrequency(model),
    phase=getPhase(model),
    amp=getAmplitude(model))
{
  "result-set": {
    "docs": [
      {
        "freq": 0.28,
        "phase": 2.4100000000000006,
        "amp": 0.9999999999999999
      },
      {
        "EOF": true,
        "RESPONSE_TIME": 0
      }
    ]
  }
}

Interpolation and Extrapolation

The harmfit function returns a fitted model of the sine wave that can used by the predict function to interpolate or extrapolate the sine wave.

The example below uses the fitted model to extrapolate the sine wave beyond the control points to the x-axis points 20, 21, 22, 23.

let(x=array(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19),
    y=array(-0.7441113653915925,-0.8997532112139415, -0.9853140681578838, -0.9941296760805463,
            -0.9255133950087844, -0.7848096869247675, -0.5829778403072583, -0.33573836075915076,
            -0.06234851460699166, 0.215897602691855, 0.47732764497752245, 0.701579055431586,
             0.8711850882773975, 0.9729352782968976, 0.9989043923858761, 0.9470697190130273,
             0.8214686154479715, 0.631884041542757, 0.39308257356494, 0.12366424851680227),
    model=harmfit(x, y),
    extrapolation=predict(model, array(20, 21, 22, 23)))
{
  "result-set": {
    "docs": [
      {
        "extrapolation": [
          -0.1553861764415666,
          -0.42233370833176975,
          -0.656386037906838,
          -0.8393130343914845
        ]
      },
      {
        "EOF": true,
        "RESPONSE_TIME": 0
      }
    ]
  }
}

Gaussian Curve Fitting

The gaussfit function fits a smooth curve through a Gaussian peak. This is shown in the example below.

let(x=array(0,1,2,3,4,5,6,7,8,9, 10),
    y=array(4,55,1200,3028,12000,18422,13328,6426,1696,239,20),
    f=gaussfit(x, y))

When this expression is sent to the /stream handler it responds with:

{
  "result-set": {
    "docs": [
      {
        "f": [
          2.81764431935644,
          61.157417979413424,
          684.2328985468831,
          3945.9411154167447,
          11729.758936952656,
          17972.951897338007,
          14195.201949425435,
          5779.03836032222,
          1212.7224502169634,
          131.17742331530349,
          7.3138931735866946
        ]
      },
      {
        "EOF": true,
        "RESPONSE_TIME": 0
      }
    ]
  }
}

Like the polyfit function, the gaussfit function returns a function that can be used directly by the predict, derivative and integrate functions.

The example below demonstrates how to compute an integral for a fitted Gaussian curve.

let(x=array(0,1,2,3,4,5,6,7,8,9, 10),
    y=array(4,55,1200,3028,12000,18422,13328,6426,1696,239,20),
    f=gaussfit(x, y),
    i=integrate(f, 0, 5))

When this expression is sent to the /stream handler it responds with:

{
  "result-set": {
    "docs": [
      {
        "i": 25261.666789766092
      },
      {
        "EOF": true,
        "RESPONSE_TIME": 3
      }
    ]
  }
}