Mercurial > pylearn
changeset 1504:bf5c0f797161
Fix test.
author | Frederic Bastien <nouiz@nouiz.org> |
---|---|
date | Mon, 12 Sep 2011 10:48:33 -0400 |
parents | 1ee532a6f33b |
children | 723e2d761985 |
files | pylearn/algorithms/kernel_regression.py pylearn/algorithms/linear_regression.py |
diffstat | 2 files changed, 5 insertions(+), 5 deletions(-) [+] |
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--- a/pylearn/algorithms/kernel_regression.py Mon Sep 12 10:24:24 2011 -0400 +++ b/pylearn/algorithms/kernel_regression.py Mon Sep 12 10:48:33 2011 -0400 @@ -2,7 +2,7 @@ Implementation of kernel regression: """ -from pylearn.old_dataset.learner import OfflineLearningAlgorithm +#from pylearn.old_dataset.learner import OfflineLearningAlgorithm from theano import tensor as T from theano.tensor.nnet import prepend_1_to_each_row from theano.scalar import as_scalar @@ -15,7 +15,7 @@ # map a N-vector to a Nx1 matrix col_vector = theano.tensor.DimShuffle((False,),[0,'x']) -class KernelRegression(OfflineLearningAlgorithm): +class KernelRegression():#OfflineLearningAlgorithm): """ Implementation of kernel regression: * the data are n (x_t,y_t) pairs and we want to estimate E[y|x]
--- a/pylearn/algorithms/linear_regression.py Mon Sep 12 10:24:24 2011 -0400 +++ b/pylearn/algorithms/linear_regression.py Mon Sep 12 10:48:33 2011 -0400 @@ -4,7 +4,7 @@ the use of theano. """ -from pylearn.old_dataset.learner import OfflineLearningAlgorithm,OnlineLearningAlgorithm +#from pylearn.old_dataset.learner import OfflineLearningAlgorithm,OnlineLearningAlgorithm from theano import tensor as T from theano.tensor.nnet import prepend_1_to_each_row from theano.scalar import as_scalar @@ -12,7 +12,7 @@ import theano import numpy -class LinearRegression(OfflineLearningAlgorithm): +class LinearRegression():#OfflineLearningAlgorithm): """ Implement linear regression, with or without L2 regularization (the former is called Ridge Regression and the latter Ordinary Least Squares). @@ -186,7 +186,7 @@ return p.compute_outputs(inputs) #TODO : an online version -class OnlineLinearRegression(OnlineLearningAlgorithm): +class OnlineLinearRegression():#OnlineLearningAlgorithm): """ Training can proceed sequentially (with multiple calls to update with different disjoint subsets of the training sets). After each call to