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UnivariateContinuousDistributionFit Method (Double, Double, IFittingOptions)
Fits the underlying distribution to a given set of observations.

Namespace:  Accord.Statistics.Distributions.Univariate
Assembly:  Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0
Syntax
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public virtual void Fit(
	double[] observations,
	double[] weights,
	IFittingOptions options
)
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Parameters

observations
Type: SystemDouble
The array of observations to fit the model against. The array elements can be either of type double (for univariate data) or type double[] (for multivariate data).
weights
Type: SystemDouble
The weight vector containing the weight for each of the samples.
options
Type: Accord.Statistics.Distributions.FittingIFittingOptions
Optional arguments which may be used during fitting, such as regularization constants and additional parameters.
Remarks
Although both double[] and double[][] arrays are supported, providing a double[] for a multivariate distribution or a double[][] for a univariate distribution may have a negative impact in performance.
Examples

The following example shows how to fit a NormalDistribution using the Fit(Double, Double) method. However, any other kind of distribution could be fit in the exactly same way. Please consider the code below as an example only:

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// Let's say we have a UnivariateContinuousDistribution that we have
// built somehow, either using a constructor for a common distribution
// or that we have received as the output of a method we have called:
UnivariateContinuousDistribution dist = new NormalDistribution();

// Let's say we have a set of observations, and some optional weights:
double[] observations = { 0.12, 2, 0.52 };

// Note: the weights are optional. You do not need to have different weights 
// for the different observations you would like to fit, but we will use them 
// as an example to show that it is also possible to specify them if we would 
// like to, but we could also set them to null in case we do not need them:
double[] weights = { 0.25, 0.25, 0.50 }; // could also be null

// Now, we can finally fit the distribution to the observations that we have:
dist.Fit(observations, weights); // changes 'dist' to become the dist we need

// Now we can verify that the distribution has been updated:
double mean = dist.Mean;                // should be 0.79
double var = dist.Variance;             // should be 0.82352
double stdDev = dist.StandardDeviation; // should be 0.90748002732842559

If you would like futher examples, please take a look at the documentation page for the distribution you would like to fit for more details and configuration options you would like or need to control.

See Also