List of fit() Examples

Examples of fit()

ca.nengo.math.CurveFitter.fit()
@param x Example x points @param y Example y points (must be same length as x) @return A Function that approximates the mapping Y=f(X) exemplified by x and y.
ca.nengo.math.impl.LinearCurveFitter.fit()
Note that inputs x must be sorted from lowest to highest. @see ca.nengo.math.CurveFitter#fit(float[],float[])
com.nr.model.Fitlin.fit()
com.nr.model.Fitmrq.fit()
com.nr.model.Fitsvd.fit()
jaolho.data.lma.LMA.fit()
The default fit. If used after calling fit(lambda, minDeltaChi2, maxIterations), uses those values. The stop condition is fetched from this.stop(). Override this.stop() if you want to use another stop condition.
org.apache.commons.math.optimization.fitting.CurveFitter.fit()
Fit a curve.
This method compute the coefficients of the curve that best fit the sample of observed points previously given through calls to the {@link #addObservedPoint(WeightedObservedPoint) addObservedPoint} method.
@param f parametric function to fit @param initialGuess first guess of the function parameters @return fitted parameters @exception FunctionEvaluationException if the objective function throws one during the search @exception OptimizationException if the algorithm failed to converge @exception IllegalArgumentException if the start point dimension is wrong
org.apache.commons.math.optimization.fitting.PolynomialFitter.fit()
Get the polynomial fitting the weighted (x, y) points. @return polynomial function best fitting the observed points @exception OptimizationException if the algorithm failed to converge
org.encog.ml.hmm.distributions.StateDistribution.fit()
Fit this distribution to the specified data set. @param set The data set to fit to.
weka.classifiers.functions.pace.ChisqMixture.fit()
weka.classifiers.functions.pace.NormalMixture.fit()

Examples of ca.nengo.math.CurveFitter.fit()

    for (int i=0; i<values[0].length; i++) {
      values[0][i] = -9 + i * 2;
      values[1][i] = target.map(new float[]{values[0][i]});
    }
    
    Function fitted = lcf.fit(values[0], values[1]);
    
//    Plotter.plot(target, -10, 0.1f, 10, "target");
//    Plotter.plot(fitted, -10, 0.5f, 10, "fitted");
    
    float targetVal = 0f;

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Examples of ca.nengo.math.CurveFitter.fit()

      countSpikes(myCurrents[i], dt, myTransientTime);
      rates[i] = countSpikes(myCurrents[i], dt, simTime) / simTime;
    }


    CurveFitter cf = new LinearCurveFitter();
    Function result = cf.fit(myCurrents, rates);
    myConstantRateFunction = result;
    myConstantRateFunctionOK = true;
    myMode = mode;
  }

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Examples of ca.nengo.math.CurveFitter.fit()

      reset(false);
//      Plotter.plot(output, "simulation "+i);
      rt[i] = getRefreactoryTime(Vm[i]);
    }


    Function result = fitter.fit(current, Vm);
//    Plotter.plot(result, 0, .1f, 60, "current -> Vm");
//    Plotter.plot(current, rt, "current -> rt");
    return result;
  }

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Examples of ca.nengo.math.impl.LinearCurveFitter.fit()

    for (int i=0; i<values[0].length; i++) {
      values[0][i] = -9 + i * 2;
      values[1][i] = target.map(new float[]{values[0][i]});
    }
    
    Function fitted = lcf.fit(values[0], values[1]);
    
//    Plotter.plot(target, -10, 0.1f, 10, "target");
//    Plotter.plot(fitted, -10, 0.5f, 10, "fitted");
    
    float targetVal = 0f;

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Examples of ca.nengo.math.impl.LinearCurveFitter.fit()

      countSpikes(myCurrents[i], dt, myTransientTime);
      rates[i] = countSpikes(myCurrents[i], dt, simTime) / simTime;
    }


    CurveFitter cf = new LinearCurveFitter();
    Function result = cf.fit(myCurrents, rates);
    myConstantRateFunction = result;
    myConstantRateFunctionOK = true;
    myMode = mode;
  }

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Examples of ca.nengo.math.impl.LinearCurveFitter.fit()

      reset(false);
//      Plotter.plot(output, "simulation "+i);
      rt[i] = getRefreactoryTime(Vm[i]);
    }


    Function result = fitter.fit(current, Vm);
//    Plotter.plot(result, 0, .1f, 60, "current -> Vm");
//    Plotter.plot(current, rt, "current -> rt");
    return result;
  }

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Examples of ca.nengo.math.impl.LinearCurveFitter.fit()


        //make the interpolated distortion functions (note these are normalized to radius 1; we map this online in case radius changes) ...
        LinearCurveFitter cf = new LinearCurveFitter();
        myInterpFunctions = new Function[myOrigin.getDimensions()];
        for (int i = 0; i < myInterpFunctions.length; i++) {
            myInterpFunctions[i] = cf.fit(r, difference[i]);
        }
    }


    private float[][] setNoise(DecodedOrigin origin) throws SimulationException {
        int nSamples = 500;

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Examples of com.nr.model.Fitlin.fit()

        
      }
    }


    Fitlin mylinfit = new Fitlin(x,y,sig,this);
    mylinfit.fit();
    sbeps=TOL;
    for (j=0;j<ma;j++) {
//      System.out.printf(mysvdfit.a[j] << " %f\n", mylinfit.a[j] << " ";
//      System.out.printf(sqrt(mysvdfit.covar[j][j]) << " %f\n", sqrt(mylinfit.covar[j][j]));
      localflag = abs(mysvdfit.a[j]-mylinfit.a[j]) > sbeps;

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Examples of com.nr.model.Fitlin.fit()

      y[i] += ndev.dev();
      sig[i]=stdev;
    }


    Fitlin myfit = new Fitlin(x,y,sig,this);
    myfit.fit();


    for (j=0;j<ma;j++) {
      localflag = abs(myfit.a[j]-amp[j]) > 2.0*sqrt(myfit.covar[j][j]);
      globalflag = globalflag || localflag;
      if (localflag) {

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Examples of com.nr.model.Fitlin.fit()

//    System.out.printf(endl;


    // Now check results of restricting fit parameters 1 and 3
    myfit.hold(1,amp[1]);
    myfit.hold(3,amp[3]);
    myfit.fit();


    localflag = (myfit.a[1] != amp[1]);
    globalflag = globalflag || localflag;
    if (localflag) {
      fail("*** Fitlin: A held parameter does not have its assigned value");

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