# algorithms.statistics.models.nlsmodel¶

## Module: algorithms.statistics.models.nlsmodel¶

Inheritance diagram for nipy.algorithms.statistics.models.nlsmodel: Non-linear least squares model

## NLSModel¶

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.

__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
SSE()

Sum of squares error.

Returns: sse: float sum of squared residuals
getZ()

Set Z into self

Returns: None
getomega()

Set omega into self

Returns: None
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 y_predicted : array predicted data at given (or initial) design