Classes and interfaces

GaussNewtonRegression

Gauss-Newton non-linear regression.

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Classes and interfaces

ExponentialDecayRegression

f( x ) = a * ( 1 - exp( b * x ) ) Regression for the decaying exponential function.

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ExponentialDecayRegression2

f( x ) = a * ( 1 - exp( b * x ) ) + c Regression for the decaying exponential function with offset.

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ExponentialDecayRegression3

f( x ) = a * ( 1 - exp( b * x + c ) ) + d Regression for the decaying exponential function with two offsets.

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ExponentialRegression

f( x ) = a * exp( b * x ) Regression for the basic exponential function.

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ExponentialRegression2

f( x ) = a * exp( b * x ) + c Regression for the exponential function with an offset.

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ExponentialRegression3

f( x ) = a * exp( b * x + c ) + d Regression for the exponential function with two offsets.

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Classes and interfaces

ExponentiationRegression

f( x ) = a * x^b Regression for the basic exponentiation function.

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ExponentiationRegression2

f( x ) = a * x^b + c Regression for the basic exponentiation function.

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FixedPowerRegression

f( x ) = a * n^( b * x ) Regression for the basic exponentiation function.

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FixedPowerRegression2

f( x ) = a * n^( b * x ) + c Regression for the basic exponentiation function.

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PowerRegression

f( x ) = a * b^( x ) Regression for the basic exponentiation function.

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PowerRegression2

f( x ) = a * b^( c * x ) Regression for the basic exponentiation function.

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PowerRegression3

f( x ) = a * b^( c * x + d ) Regression for the basic exponentiation function.

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PowerRegression4

f( x ) = a * b^( c * x + d ) + g Regression for the basic exponentiation function.

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Classes and interfaces

DampedSineRegression

f( x ) = a * exp( b * x ) * sin( c * x + d ) Regression for the dampened sine function.

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DampedSineRegression2

f( x ) = a * exp( b * x ) * ( cos( c * x ) + sin( c * x ) ) Regression for the full dampened sine function.

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DampedSineRegression3

f( x ) = a * exp( b * x ) * ( cos( c * x + d ) + sin( c * x + d ) ) Regression for the full dampened sine function with phase.

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