Package pylearn :: Package algorithms :: Module linear_regression
[hide private]

Module linear_regression

source code

Implementation of linear regression, with or without L2 regularization. This is one of the simplest example of learner, and illustrates the use of theano.

Classes [hide private]
LinearRegression
Implement linear regression, with or without L2 regularization (the former is called Ridge Regression and the latter Ordinary Least Squares).
LinearPredictorEquations
LinearRegressionEquations
LinearPredictor
A linear predictor has parameters theta (a bias vector and a weight matrix) it can use to make a linear prediction (according to the LinearPredictorEquations).
OnlineLinearRegression
Training can proceed sequentially (with multiple calls to update with different disjoint subsets of the training sets).
Functions [hide private]
 
linear_predictor(inputs, params, *otherargs) source code

Imports: OfflineLearningAlgorithm, OnlineLearningAlgorithm, T, prepend_1_to_each_row, as_scalar, AutoName, theano, numpy