algorithms.statistics.models.nlsmodel¶
Module: algorithms.statistics.models.nlsmodel
¶
Inheritance diagram for nipy.algorithms.statistics.models.nlsmodel
:

Non-linear least squares model
NLSModel
¶
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class
nipy.algorithms.statistics.models.nlsmodel.
NLSModel
(Y, design, f, grad, theta, niter=10)¶ Bases:
nipy.algorithms.statistics.models.model.Model
,nipy.externals.six.Iterator
Class representing a simple nonlinear least squares model.
Methods
SSE
()Sum of squares error. fit
()Fit a model to data. getZ
()Set Z into self getomega
()Set omega into self initialize
()Initialize (possibly re-initialize) a Model instance. next
()predict
([design])Get predicted values for design or self.design
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__init__
(Y, design, f, grad, theta, niter=10)¶ Initialize non-linear model instance
Parameters: Y : ndarray
the data in the NLS model
design : ndarray
the design matrix, X
f : callable
the map between the (linear parameters (in the design matrix) and the nonlinear parameters (theta)) and the predicted data. f accepts the design matrix and the parameters (theta) as input, and returns the predicted data at that design.
grad : callable
the gradient of f, this should be a function of an nxp design matrix X and qx1 vector theta that returns an nxq matrix df_i/dtheta_j where:
\[f_i(theta) = f(X[i], theta)\]is the nonlinear response function for the i-th instance in the model.
theta : array
parameters
niter : int
number of iterations
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SSE
()¶ Sum of squares error.
Returns: sse: float :
sum of squared residuals
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fit
()¶ Fit a model to data.
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getZ
()¶ Set Z into self
Returns: None :
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getomega
()¶ Set omega into self
Returns: None :
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initialize
()¶ Initialize (possibly re-initialize) a Model instance.
For instance, the design matrix of a linear model may change and some things must be recomputed.
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next
()¶
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predict
(design=None)¶ Get predicted values for design or
self.design
Parameters: design : None or array, optional
design at which to predict data. If None (the default) then use the initial
self.design
Returns: y_predicted : array
predicted data at given (or initial) design
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